Gas leakage early warning method and device of Internet of Things and medium

Abstract

The invention discloses a gas leakage early warning method and device for the Internet of Things and a medium, and relates to the field of the Internet of Things, and the method comprises the steps: collecting the gas concentration data and environmental parameters of a monitoring region in real time through a sensing layer device disposed in the monitoring region; the environment parameters comprise environment temperature and environment humidity; performing real-time compensation on the gas concentration data based on the environmental parameters to obtain a corrected gas concentration value so as to eliminate environmental interference; comparing the corrected gas concentration value with the early warning threshold sequence based on the high-low sequence of the early warning levels; and when the corrected gas concentration value exceeds any early warning threshold value in the early warning threshold value sequence, determining a corresponding current early warning level, and triggering and executing the current early warning level and all early warning responses lower than the current early warning level. And during risk upgrading, multi-level response measures including remote alarm and equipment linkage are automatically executed, so that the timeliness of leakage discovery and the initiative of disposal are remarkably improved.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) technology, specifically to an IoT-based method, device, and medium for early warning of gas leaks. Background Technology

[0002] With the acceleration of urbanization and the optimization of energy structure, the scale of urban gas use is constantly expanding, becoming an indispensable part of urban production and life. However, gas leaks frequently cause explosions, poisonings, and other safety accidents, resulting not only in serious property losses but also posing a huge threat to the lives of the people. Therefore, building an efficient and reliable gas leak alarm and real-time monitoring system to achieve comprehensive safety control over the gas usage process has become a common need and an important issue in the field of gas safety management.

[0003] Traditional gas alarm devices mostly rely on local sensor detection and audible / visual alarm mechanisms, which play a certain role in daily monitoring. However, due to their relatively simple system functions and limited information transmission range, they are unable to cope with complex and ever-changing real-world application scenarios. Especially in unattended areas or areas with strong environmental interference, existing technologies cannot achieve comprehensive and accurate leak identification and timely handling, thus creating potential safety hazards.

[0004] Specifically, traditional alarm systems generally suffer from three shortcomings: First, their monitoring coverage is small, typically supporting only single-point detection, making it difficult to achieve regional linkage and multi-parameter fusion judgment, and they are easily affected by environmental factors such as temperature and humidity; second, alarm information transmission is delayed, mainly relying on on-site audible and visual prompts, lacking remote notification and multi-terminal collaboration mechanisms, resulting in low response efficiency; and third, alarm systems operate in isolation, failing to form automatic linkages with actuators such as valve control and ventilation equipment, and also making it difficult to conduct in-depth analysis of historical data and risk prediction, thus limiting the improvement of safety management levels. Summary of the Invention

[0005] To address the aforementioned issues, this application proposes a gas leak early warning method for the Internet of Things (IoT), comprising: The gas concentration data and environmental parameters of the monitoring area are collected in real time by sensing layer devices deployed in the monitoring area; the environmental parameters include ambient temperature and ambient humidity. The gas concentration data is compensated in real time based on the environmental parameters to obtain a corrected gas concentration value, thereby eliminating environmental interference. Based on the order of warning levels, the corrected gas concentration value and the warning threshold sequence are compared; When the corrected gas concentration value exceeds any of the warning thresholds in the warning threshold sequence, the corresponding current warning level is determined, and the current warning level and all warning responses below the current warning level are triggered and executed.

[0006] On the other hand, this application also proposes an Internet of Things (IoT) gas leak early warning device, comprising: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform, for example, an IoT gas leak early warning method described in the above example.

[0007] On the other hand, this application also proposes a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as: a gas leak early warning method for the Internet of Things as described in the above example.

[0008] The gas leak early warning method proposed in this application for the Internet of Things can bring the following benefits: By integrating multiple sensors and compensating for environmental parameters in real time, the limitations of traditional single-point detection, which is susceptible to environmental interference, are effectively overcome, enabling more accurate and stable monitoring of gas concentration. Furthermore, a sequential early warning mechanism from low to high concentrations ensures that attention is triggered at the initial stage of anomalies, and automatically executes multi-level response measures, including remote alarms and equipment linkage, when the risk escalates, significantly improving the timeliness of leak detection and the proactiveness of handling.

[0009] Furthermore, through continuous learning and self-optimization of historical handling data, not only has the early prediction of potential risks been achieved, but the systematization and intelligence level of gas safety management has also been fundamentally improved, realizing the transformation from post-event emergency response to pre-event prevention and building a closed-loop control system from perception, decision-making to execution. Attached Figure Description

[0010] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a flowchart illustrating a gas leak early warning method for the Internet of Things (IoT) according to an embodiment of this application. Figure 2 This is a schematic diagram of a gas leak early warning device for the Internet of Things (IoT) according to an embodiment of this application. Detailed Implementation

[0011] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below in conjunction with specific embodiments and corresponding drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of them. Based on the embodiments in this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0012] The technical solutions provided by the various embodiments of this application are described in detail below with reference to the accompanying drawings.

[0013] like Figure 1 As shown in the figure, this application provides a gas leak early warning method for the Internet of Things, including: S101. Real-time collection of gas concentration data and environmental parameters of the monitoring area is achieved through sensing layer devices deployed in the monitoring area; the environmental parameters include ambient temperature and ambient humidity.

[0014] Specifically, raw data is synchronously collected at a preset sampling frequency by sensing layer devices deployed in the monitoring area. The sensing layer devices include at least: a gas concentration sensor, used to collect raw data on the concentration of combustible gases in the environment. Temperature sensor, used to collect ambient temperature. Humidity sensor, used to collect ambient humidity. .

[0015] The raw data set collected by the perception layer device The data is sent to the locally deployed edge gateway. The edge gateway then retrieves the data set through its data receiving interface.

[0016] In this embodiment, the sensing layer includes a gas concentration sensor, an environmental auxiliary sensor, a local alarm module, and an actuator. The gas concentration sensor uses electrochemical or infrared principles to detect the concentration of combustible gas in the environment and has a temperature compensation function to reduce the influence of ambient temperature on the detection results. The environmental auxiliary sensor includes a temperature and humidity sensor and a gas flow rate sensor, used to correct the gas concentration detection data and assist in determining the cause of the leak. The local alarm module integrates an audible and visual alarm and a display screen to display the current gas concentration, temperature, humidity, and equipment status in real time, triggering a local alarm upon leak. The actuator includes an electric gas valve and a ventilation equipment controller, used to automatically cut off the gas supply and activate ventilation upon leak.

[0017] S102. Based on the environmental parameters, the gas concentration data is compensated in real time to obtain a corrected gas concentration value, so as to eliminate environmental interference.

[0018] Specifically, real-time compensation is performed on the gas concentration data based on environmental parameters. Ambient temperature, humidity, and gas concentration data are input into a preset compensation model. The model then calls pre-stored temperature and humidity correction coefficients. Based on the ambient temperature, temperature correction coefficient, ambient humidity, and humidity correction coefficient, a comprehensive compensation factor is calculated. This comprehensive compensation factor is then used to compensate the gas concentration data, resulting in a corrected gas concentration value.

[0019] Specifically, pre-configured compensation parameters are read from non-volatile memory, including a temperature correction coefficient corresponding to the current gas concentration sensor model. Humidity correction factor corresponding to the current gas concentration sensor model. The preset reference temperature value corresponding to the ambient temperature. The preset baseline humidity value corresponding to the ambient humidity. .

[0020] Calculate the difference between environmental parameters and a preset baseline. Specifically, this is done using the formula... The collected ambient temperature Compared with the reference temperature value Subtract them and calculate the first difference. ; through formula Collect ambient humidity Compared with the reference humidity value Subtract them and calculate the second difference. .

[0021] Based on the above differences and correction coefficients, a synthesis operation is performed to calculate the comprehensive compensation factor. The composition operation is based on the following formula: Among them, temperature compensation item Used to correct sensor zero-point drift and sensitivity changes caused by temperature variations; humidity compensation item. Used to correct the effect of ambient humidity on the output signal of catalytic combustion or electrochemical sensors. A combined compensation factor consisting of two superimposed factors. The combined interference effects of environmental temperature and humidity were comprehensively quantified.

[0022] Perform the core compensation calculation to output the corrected gas concentration value. Specifically, the value 1 is compared with the comprehensive compensation factor. Add them together to get the compensation multiplier. This is used to convert the compensation factor into a scaling factor that can be directly multiplied. The collected raw gas concentration data... With the compensation multiplier Multiplication, that is: .

[0023] It should be noted that if environmental conditions are harsh, It may be a positive value, thus allowing for an appropriate increase in the reading to compensate for the decrease in sensor sensitivity; if environmental conditions are better than the reference, It may be a negative value, so the reading should be appropriately reduced to correct for any possible positive drift.

[0024] Finally, the edge gateway will correct the gas concentration value. With corresponding environmental parameters They are packaged together and uploaded to the cloud platform layer via a wireless communication module for use in subsequent early warning judgment logic.

[0025] In this embodiment, the transport layer includes an edge gateway and a communication network. The edge gateway is deployed in the monitoring area, such as a residential building unit or a commercial building floor. It communicates with the sensing layer device through an RS485 / TTL interface to preprocess the collected data, such as data filtering and outlier removal. It also uploads the data to the platform layer through a LoRa / NB-IoT / 5G module and supports receiving control commands issued by the platform layer. The communication network preferentially adopts NB-IoT low-power wide-area network, which is suitable for low-speed, wide-coverage scenarios.

[0026] S103. Based on the order of warning levels, compare the corrected gas concentration value with the warning threshold sequence.

[0027] Specifically, when the intelligent analysis module at the platform layer starts the monitoring cycle, it first performs an initialization operation, setting the current warning level of the system to the safe level, so as to ensure that each judgment starts from a definite, warning-free initial state.

[0028] The system retrieves multiple preset warning level thresholds from the system configuration database, performs a sorting operation, and constructs a warning threshold sequence in ascending order of value. In a typical embodiment, the sequence includes at least: a first warning threshold with a lower value; and a second warning threshold with a higher value.

[0029] The corrected gas concentration value is compared with the first threshold in the sequence, i.e., the first warning threshold. If the concentration value is determined not to exceed the first warning threshold, the current warning level is maintained at the safe level, and the current judgment cycle ends; if the concentration value is determined to exceed the first warning threshold, an update operation is performed to update the current warning level from the safe level to the first warning level.

[0030] With the current warning level updated to the first warning level, the corrected gas concentration value is then compared with the next threshold in the sequence, namely the second warning threshold. If the concentration value is determined not to exceed the second warning threshold, the current warning level remains at the first warning level, and the current judgment cycle ends; if the concentration value is determined to exceed the second warning threshold, the update operation is performed again to update the current warning level from the first warning level to the second warning level.

[0031] After the judgment process is completed, the final current warning level will be output as the judgment result for this monitoring cycle to trigger the corresponding response process.

[0032] In this embodiment, the intelligent analysis and early warning module: based on machine learning algorithms, such as the LSTM sequential prediction model, performs trend analysis on gas concentration and flow data, sets three-level early warning thresholds, and generates a risk heat map by combining historical leakage data to achieve regional risk prediction.

[0033] S104. When the corrected gas concentration value exceeds any of the warning thresholds in the warning threshold sequence, determine the corresponding current warning level, trigger and execute the current warning level and all warning responses below the current warning level.

[0034] Specifically, once the intelligent analysis module determines the current alert level, it immediately initiates a query operation to the response strategy library. The core logic of this query is to obtain the set of response operations corresponding to all alert levels that are not higher than the current alert level.

[0035] If the current warning level is the first warning level, the query result will only contain the set of response operations corresponding to the first warning level; if the current warning level is the second warning level, the query result will contain the set of response operations corresponding to both the first and second warning levels.

[0036] The platform-level command issuance module performs an encapsulation operation based on the retrieved set of response operations. This operation assembles all abstract operations in the set, such as sending notifications or closing valves, into a specific sequence of control commands that can be parsed by downlink devices. This sequence ensures that all necessary response actions are included in an orderly manner.

[0037] The instruction issuing module sends the encapsulated control instruction sequence to the corresponding execution unit via the communication network. For information alarm instructions, the execution unit provides a message push service, and its execution action is to send alarm information to preset user terminals and / or management terminals; for equipment control instructions, the execution unit is an edge gateway, and its execution action is to drive the electric gas valve to close and / or start the ventilation equipment.

[0038] After the instruction is issued, a recording operation is automatically performed, storing the handling process data of this warning event in a distributed database. The recorded data includes at least: the warning trigger timestamp, the final determined current warning level, the sequence of executed control instructions, and the time point when the gas concentration returns to a safe range.

[0039] The platform-level intelligent analysis module periodically invokes the data analysis model to perform statistical analysis on the stored disposal process data. Based on the analysis results, the model generates optimization suggestions and performs adjustments accordingly, optimizing and updating the warning thresholds or correction coefficients in the compensation model to achieve self-evolution of the system's warning accuracy.

[0040] In this embodiment, the instruction issuing module receives application layer operation instructions or automatically generates control instructions based on the warning level and sends them to the actuator through the transmission layer; the device management module monitors the online status of the sensing layer and transmission layer devices in real time and supports device fault diagnosis and remote firmware upgrades.

[0041] For example, a gas concentration sensor and a temperature and humidity sensor are installed near the gas meter in the residential kitchen. An electric valve and a flow sensor are installed at the gas pipeline entrance. One edge gateway is deployed in each building, using NB-IoT communication. The platform layer is deployed on the gas company's cloud server, and users install a mobile app. When a user's kitchen gas hose leaks due to aging, and the gas concentration sensor detects the concentration rising from 5% LEL to 35% LEL, the edge gateway preprocesses the data and uploads it to the platform layer. The platform layer determines it as a level two warning, triggering a local audible and visual alarm and simultaneously pushing a warning notification to the user's app. After checking the app and confirming the leak, the user issues a valve-closing command through the app. The edge gateway receives the command and controls the electric valve to close, and the leak concentration gradually decreases. The management terminal generates a maintenance work order, and maintenance personnel arrive on-site within 30 minutes to replace the hose, record the handling data, and update the risk database.

[0042] This application effectively overcomes the limitations of traditional single-point detection, which is susceptible to environmental interference, by using multi-sensor fusion and real-time environmental parameter compensation, achieving more accurate and stable monitoring of gas concentration. Furthermore, it employs a sequential early warning mechanism from low to high concentration to ensure that attention is triggered at the initial stage of anomalies, and automatically executes multi-level response measures, including remote alarms and equipment linkage, when the risk escalates, significantly improving the timeliness of leak detection and the proactiveness of handling.

[0043] Furthermore, through continuous learning and self-optimization of historical handling data, not only has the early prediction of potential risks been achieved, but the systematization and intelligence level of gas safety management has also been fundamentally improved, realizing the transformation from post-event emergency response to pre-event prevention and building a closed-loop control system from perception, decision-making to execution.

[0044] like Figure 2 As shown in the figure, this application also proposes an Internet of Things (IoT) gas leak early warning device, including: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform an IoT gas leak early warning method as described in any of the above embodiments.

[0045] This application also provides a non-volatile computer storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured as: a gas leak early warning method for the Internet of Things as described in any of the above embodiments.

[0046] The various embodiments in this application are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, the device and medium embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the description of the method embodiments.

[0047] The devices and media provided in this application are one-to-one with the methods. Therefore, the devices and media also have similar beneficial technical effects as their corresponding methods. Since the beneficial technical effects of the methods have been described in detail above, the beneficial technical effects of the devices and media will not be repeated here.

[0048] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0049] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0050] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0051] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0052] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0053] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0054] Computer-readable media include both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic tape, disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0055] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] The above description is merely an embodiment of this application and is not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A gas leakage pre-warning method for Internet of Things, characterized in that, The method comprises the steps of: Real-time collection of gas concentration data and environmental parameters of the monitoring area through the perception layer device deployed in the monitoring area; The environmental parameters include environmental temperature and environmental humidity; Real-time compensation of the gas concentration data based on the environmental parameters to obtain corrected gas concentration values to eliminate environmental interference; Comparing the corrected gas concentration values with a sequence of pre-warning threshold values based on the high-low order of the pre-warning levels; When the corrected gas concentration value exceeds any pre-warning threshold value in the sequence of pre-warning threshold values, the corresponding current pre-warning level is determined, and the current pre-warning level and all pre-warning responses lower than the current pre-warning level are triggered and executed. 2.The gas leakage early warning method of Internet of Things according to claim 1, characterized in that, The real-time compensation of the gas concentration data based on the environmental parameters to obtain corrected gas concentration values to eliminate environmental interference comprises the steps of: Inputting the environmental temperature, the environmental humidity and the gas concentration data into a preset compensation model; Calling pre-stored temperature correction coefficients and humidity correction coefficients through the compensation model; Calculating a comprehensive compensation factor based on the environmental temperature, the temperature correction coefficients, the environmental humidity and the humidity correction coefficients; Compensating the gas concentration data based on the comprehensive compensation factor to obtain corrected gas concentration values. 3.The gas leakage early warning method of Internet of Things according to claim 2, characterized in that, The calculation of the comprehensive compensation factor based on the environmental temperature, the temperature correction coefficients, the environmental humidity and the humidity correction coefficients comprises the steps of: Determining a preset reference temperature value corresponding to the environmental temperature and a preset reference humidity value corresponding to the environmental humidity; Calculating a first difference value between the environmental temperature and the preset reference temperature value and a second difference value between the environmental humidity and the preset reference humidity value; Synthesizing the comprehensive compensation factor based on the product of the temperature correction coefficients and the first difference value, the product of the humidity correction coefficients and the second difference value.

4. The gas leakage early warning method of the Internet of Things according to claim 3, characterized in that, The compensation of the gas concentration data based on the comprehensive compensation factor to obtain corrected gas concentration values comprises the steps of: Superimposing the comprehensive compensation factor and a preset reference compensation amount to generate a compensation multiplier; Compensating the gas concentration data based on the compensation multiplier to output a gas concentration compensation product value as the corrected gas concentration value. 5.The gas leakage early warning method of Internet of Things according to claim 1, characterized in that, The comparison of the corrected gas concentration values with a plurality of pre-warning level threshold values and the determination of the corresponding pre-warning level based on the comparison result comprises the steps of: Obtaining preset pre-warning threshold values of pre-warning levels, sorting the pre-warning threshold values based on the level high-low to construct a sequence of pre-warning threshold values; Comparing the corrected gas concentration values with the sequence of pre-warning threshold values based on the level high-low; wherein when the corrected gas concentration value exceeds a low pre-warning threshold value, the corrected gas concentration value is compared with a high pre-warning threshold value. 6.The gas leakage early warning method of Internet of Things according to claim 5, characterized in that, The sequence of pre-warning threshold values at least includes a first pre-warning threshold value and a second pre-warning threshold value of a higher level; The determination of the corresponding pre-warning level when the corrected gas concentration value exceeds any pre-warning threshold value in the sequence of pre-warning threshold values comprises the steps of: Initializing the current pre-warning level as a safe level; When the modified gas concentration value exceeds the first early warning threshold, updating the current early warning level to a first early warning level, and comparing the modified gas concentration value with the second early warning threshold; When the modified gas concentration value exceeds the second early warning threshold, updating the current early warning level to a second early warning level. 7.The gas leakage early warning method of Internet of Things according to claim 1, characterized in that, The triggering and executing the early warning level and all early warning responses lower than the early warning level specifically include: According to the current early warning level, querying a response operation set not higher than the current early warning level in a pre-defined response strategy library; Based on the response operation set, generating a control instruction sequence; The control instruction sequence is sent to a corresponding execution unit to drive the execution unit to perform an action corresponding to the response operation set. 8.The gas leakage early warning method of Internet of Things according to claim 1, characterized in that, After the triggering and executing the early warning level and all early warning responses lower than the early warning level, the method further includes: Recording and storing trigger process data of the early warning response, the trigger process data including early warning trigger time, early warning level, execution action, and time for gas concentration to recover to a safe range; Based on the trigger process data, optimizing and adjusting each early warning threshold of the early warning threshold sequence through a data analysis model.

9. A gas leakage early warning device of Internet of Things, characterized in that, Comprise: At least one processor; And The memory is in communication connection with the at least one processor; wherein The memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to execute an Internet of Things gas leakage early warning method as claimed in any one of claims 1-8.

10. A non-transitory computer storage medium storing computer-executable instructions that, when executed, cause a computer to perform: The computer executable instructions are configured to execute an Internet of Things gas leakage early warning method as claimed in any one of claims 1-8.

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