Gas leakage safety monitoring method, system, device and medium based on internet of things

Abstract

The application discloses a gas leakage safety monitoring method, system, device and medium based on Internet of Things, comprising the following steps: acquiring an environment image corresponding to a monitoring area; wherein the monitoring area is an area with a gas leakage risk; identifying whether an abnormal state feature exists in the environment image; if the abnormal state feature is identified, receiving a power-on signal for powering on a target gas alarm; wherein the target gas alarm is the closest gas alarm to an abnormal point of the monitoring area; and acquiring gas leakage data through the target gas alarm, which has the advantages of ensuring the gas monitoring effect, reducing the loss degree of the gas alarm, and prolonging the service life of the gas alarm.

Description

Technical Field

[0001] This application relates to the field of gas leak monitoring technology, and in particular to methods, systems, devices and media for gas leak safety monitoring based on the Internet of Things. Background Technology

[0002] Currently, IoT gas meters can be linked with gas alarms. When a gas leak occurs, the IoT gas meter can promptly shut off the built-in motor valve, cut off the gas supply, and simultaneously upload the gas leak alarm information to the server.

[0003] A gas alarm, also known as a gas leak detector, has a core component that is a gas-sensitive sensor. Installed in areas where gas leaks may occur, the detector is triggered when the concentration of gas in the air exceeds a set value, emitting an audible and visual alarm signal. Currently, to ensure real-time monitoring of gas leaks, gas alarms are always powered on. However, gas alarms have a limited lifespan, generally less than five years. When a gas alarm reaches the end of its lifespan and fails to be replaced by the user for an extended period, it becomes ineffective, compromising safety. Furthermore, existing gas leak detection methods require the alarm to be constantly powered on, leading to high wear and tear and hindering the extension of its lifespan. Summary of the Invention

[0004] The main purpose of this application is to provide a gas leak safety monitoring method, system, device and medium based on the Internet of Things, which aims to solve the technical problem of high wear and tear on gas alarms caused by existing gas leak monitoring methods.

[0005] To achieve the above objectives, this application provides an Internet of Things-based gas leak safety monitoring method, comprising the following steps:

[0006] Acquire environmental images corresponding to the monitoring area; the monitoring area is an area with a risk of gas leakage.

[0007] Identify whether there are abnormal state features in environmental images;

[0008] If abnormal state characteristics are detected, a power-on signal is received to power on the target gas alarm; wherein, the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area;

[0009] Gas leak data is obtained through the target gas alarm.

[0010] Optionally, if abnormal state characteristics are detected, a power-on signal is received to energize the target gas alarm, including:

[0011] If abnormal state characteristics are identified, obtain the audio data corresponding to the monitoring area;

[0012] Identify whether fault frequency characteristics exist in the audio data; where fault frequency characteristics are the sound characteristics corresponding to a gas leak.

[0013] If a fault frequency characteristic exists, receive a power-on signal to power on the target gas alarm;

[0014] If no fault frequency characteristics are found, return to the step of obtaining the environmental image corresponding to the monitoring area.

[0015] Optionally, after acquiring gas leak data through the target gas alarm, the method further includes:

[0016] Determine whether gas leak data has been acquired within a preset time period;

[0017] If gas leak data is obtained within a preset time, an alarm message will be sent;

[0018] If no gas leak data is obtained within the preset time, a power-off signal will be received to de-energize the target gas alarm.

[0019] Optionally, the environmental images include leak source images and leak scene images; wherein, the leak source images include gas pipeline images, gas pipe and gas equipment connection images, gas valve images and gas meter images, and the leak scene images include smoke images and flame images.

[0020] Optionally, identifying whether there are abnormal state features in the environmental image includes:

[0021] Extract the image of the leakage source from the environmental image;

[0022] The leak source image is compared with a preset database to identify whether there are first abnormal state features in the leak source image; wherein, the first abnormal state features include at least one of the following: gas pipeline damage or rust, gas pipe and gas equipment joint damage or rust, gas valve damage or rust, and gas meter damage.

[0023] Optionally, identifying whether there are abnormal state features in the environmental image also includes:

[0024] Extract the leak scene image from the environmental image;

[0025] Identify whether human figures are present in images of the leak scene;

[0026] If human facial information is present, the leaked scene images will be classified as normal images;

[0027] If no human image information is available, the leaked scene image is classified as an abnormal image with a second abnormal state feature; wherein, the second abnormal state feature is at least one of smoke and fire.

[0028] Optionally, gas leak data is obtained through the target gas alarm, including:

[0029] The gas concentration is obtained by targeting the gas alarm;

[0030] Compare the gas concentration with a preset concentration threshold;

[0031] If the gas concentration is greater than the concentration threshold, obtain the spatial volume and air velocity of the space environment where the target gas alarm is located.

[0032] Based on the gas concentration, space volume, and air velocity, the leak prediction value is obtained. The expression for the leak prediction value is as follows:

[0033] E = C / (ε1V + ε2S)

[0034] In the formula, E is the predicted leakage value, C is the gas concentration, V is the space volume, S is the air velocity, ε1 is the first conversion coefficient, and ε2 is the second conversion coefficient;

[0035] Compare the predicted leakage value with a preset standard threshold;

[0036] If the predicted leak value is greater than the standard threshold, then gas leak data is acquired.

[0037] To achieve the above objectives, this application also provides an Internet of Things-based gas leak safety monitoring system, comprising a user platform, a service platform, a management platform, a sensor network platform, and a gas alarm object platform connected in sequence, wherein the gas alarm object platform includes:

[0038] The image acquisition module is used to acquire environmental images corresponding to the monitoring area; wherein, the monitoring area is an area with a risk of gas leakage.

[0039] The feature recognition module is used to identify whether there are abnormal state features in the environmental image;

[0040] The signal receiving module is used to receive a power-on signal that powers on the target gas alarm if abnormal state characteristics are detected; wherein, the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area;

[0041] The data acquisition module is used to acquire gas leak data through the target gas alarm.

[0042] To achieve the above objectives, this application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0043] To achieve the above objectives, this application also provides a computer-readable storage medium storing a computer program, on which a processor executes the computer program to implement the above-described method.

[0044] The beneficial effects that this application can achieve are as follows:

[0045] This application acquires environmental images corresponding to the monitoring area and identifies whether there are abnormal features in the environmental images. If abnormal features are identified, it indicates that there may be a gas leak in the monitoring area. At this time, a power-on signal is received to power on the target gas alarm, thereby acquiring gas leak data through the target gas alarm. When no abnormal features are detected in the environmental images, the gas alarm remains in a de-powered state. When the gas alarm is in a de-powered state, existing monitoring equipment in the monitoring area can be used to replace the gas alarm for gas leak monitoring. Once an abnormality occurs, the gas alarm is powered on again for accurate gas leak monitoring, thus avoiding the gas alarm being constantly powered on. While ensuring effective monitoring of gas leaks, this reduces the wear and tear on the gas alarm and increases its service life. Attached Figure Description

[0046] To more clearly illustrate the technical solutions in the specific embodiments of this application or the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0047] Figure 1 This is a flowchart illustrating the IoT-based gas leak safety monitoring method in an embodiment of this application.

[0048] Figure 2 This is a schematic diagram of the overall logic flow of the IoT-based gas leak safety monitoring method in the embodiments of this application;

[0049] Figure 3 This is a schematic diagram of the framework structure of the Internet of Things system involved in the embodiments of this application.

[0050] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0051] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of this application.

[0052] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of this application are only used to explain the relative positional relationship and movement of each component in a certain specific posture. If the specific posture changes, the directional indication will also change accordingly.

[0053] In this application, unless otherwise expressly specified and limited, the terms "connection," "fixed," etc., should be interpreted broadly. For example, "fixed" can mean a fixed connection, a detachable connection, or an integral part; it can mean a mechanical connection or an electrical connection; it can mean a direct connection or an indirect connection through an intermediate medium; it can mean the internal communication of two components or the interaction between two components, unless otherwise expressly limited. Those skilled in the art can understand the specific meaning of the above terms in this application according to the specific circumstances.

[0054] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the meaning of "and / or" throughout the text includes three parallel solutions; for example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied simultaneously. Furthermore, the technical solutions of the various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0055] Example 1

[0056] Reference Figures 1-2 This embodiment provides a gas leak safety monitoring method based on the Internet of Things, including the following steps:

[0057] Step S100: Obtain an environmental image corresponding to the monitoring area; wherein, the monitoring area is an area with a risk of gas leakage;

[0058] Step S200: Identify whether there are abnormal state features in the environmental image;

[0059] Step S300: If abnormal state characteristics are detected, receive a power-on signal to power on the target gas alarm; wherein, the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area;

[0060] Step S400: Obtain gas leak data through the target gas alarm.

[0061] In this embodiment, by acquiring environmental images corresponding to the monitoring area and identifying whether there are abnormal state features in the environmental images, the abnormal state features refer to the characteristics of abnormal changes in the environment when a gas leak occurs. If abnormal state features are identified, it indicates that there may be a gas leak in the monitoring area. At this time, a power-on signal is received to power on the target gas alarm, so that the target gas alarm can further detect and obtain gas leak data. When no abnormal state features are detected in the environmental image, the gas alarm is always in a de-powered state. When the gas alarm is in a de-powered state, the existing monitoring equipment in the monitoring area can be used to replace the gas alarm for gas leak monitoring. Once an anomaly occurs, the gas alarm is powered on again for accurate gas leak monitoring, thereby avoiding the gas alarm being in a continuously powered state. In addition, the gas alarm closest to the anomaly point in the monitoring area can be matched according to the location information, thereby ensuring monitoring accuracy. Therefore, this embodiment reduces the wear and tear of the gas alarm and improves its service life while ensuring effective monitoring of gas leaks.

[0062] It should be noted that after acquiring environmental images, existing image processing algorithms (such as HOG feature algorithm, LBP feature algorithm, and Haar feature algorithm) can be used to extract abnormal state features, and the feature extraction and recognition are accurate.

[0063] As an optional implementation, in step S300, if an abnormal state characteristic is detected, receiving an energizing signal to power on the target gas alarm includes:

[0064] Step S310: If abnormal state characteristics are identified, obtain the audio data corresponding to the monitoring area;

[0065] Step S320: Identify whether there are fault frequency features in the audio data; wherein, the fault frequency features are the sound features corresponding to the occurrence of a gas leak;

[0066] Step S330: If a fault frequency characteristic exists, receive a power-on signal to power on the target gas alarm; if no fault frequency characteristic exists, return to the step of acquiring the environmental image corresponding to the monitoring area.

[0067] In this embodiment, since judging the gas leak status solely through image recognition may lead to misidentification or inaccurate identification due to other factors, resulting in the false activation of the gas alarm, this embodiment further identifies the audio data corresponding to the monitoring area after identifying the abnormal status characteristics. Then, it identifies and extracts the fault frequency characteristics corresponding to the gas leak in the audio data. Since a gas leak will emit a sound at a different frequency than the ambient sound, existing audio feature extraction algorithms can be used to compare the sound frequency and amplitude with standard values, thereby accurately identifying whether fault frequency characteristics exist. This embodiment combines image recognition and audio recognition to improve the judgment of the gas leak status, resulting in high accuracy and a significant reduction in the false judgment rate. Only when both the abnormal status characteristics and the fault frequency characteristics are met simultaneously will the power-on signal for energizing the target gas alarm be received, and the gas leak monitoring process will begin.

[0068] As an optional implementation, after acquiring gas leak data through the target gas alarm, the method further includes:

[0069] Step S500: Determine whether gas leak data has been acquired within a preset time period;

[0070] Step S600: If gas leak data is acquired within a preset time, send an alarm message; if gas leak data is not acquired within a preset time, receive a power-off signal to de-energize the target gas alarm, and return to the step of acquiring the environmental image corresponding to the monitoring area.

[0071] In this embodiment, after the gas alarm monitors for gas leaks, if no gas leak data is obtained within a preset time (e.g., 30 minutes, 40 minutes, 1 hour, etc.), it proves that the previous image recognition and sound recognition were incorrect or inaccurate, resulting in the false triggering of the gas alarm. At this time, a power-off signal can be received to de-energize the target gas alarm, putting the corresponding gas alarm back into a power-off state. This automated management eliminates the need for manual management, thus providing an adaptive start-stop function for the gas alarm and facilitating intelligent management.

[0072] As an optional implementation, the environmental images include leak source images and leak scene images; wherein, the leak source images include gas pipeline images, gas pipe and gas equipment joint images, gas valve images and gas meter images, and the leak scene images include smoke images and flame images.

[0073] In this embodiment, the identified environmental images include leak source images and leak scene images. Gas leak sources are mostly concentrated at gas pipelines, gas pipe-to-gas equipment joints, gas valves, and gas meters. These leak source objects are all places where gas leaks are easily caused by damage or rust. Therefore, the key monitoring area should be concentrated in these places, and corresponding leak source images should be acquired separately to facilitate individual monitoring and improve monitoring accuracy. In addition, partial leakage may have already occurred, forming smoke or flames, causing environmental changes that may obscure the leak source and make it unidentifiable. In this case, leak scene images including smoke and flame images can be detected, and gas alarms can be triggered immediately to activate the alarm. The response is timely and can handle various gas leak scenarios.

[0074] As an optional implementation, in step S200, identifying whether there are abnormal state features in the environmental image includes:

[0075] Step S210: Extract the image of the leakage source from the environmental image;

[0076] Step S220: Compare the leak source image with a preset database to identify whether there is a first abnormal state feature in the leak source image; wherein, the first abnormal state feature includes at least one of the following: gas pipeline damage or rust, gas pipe and gas equipment joint damage or rust, gas valve damage or rust, and gas meter damage.

[0077] In this embodiment, for the identification and extraction of abnormal state features of the leak source image, the leak source image can be compared with a preset database. Since most leak sources such as gas pipelines, gas pipe and gas equipment joints, gas valves and gas meters are gas leaks caused by damage or rust, their surface state will change when damage or rust occurs. For example, the presence of damage can be determined by image contour recognition, and the degree of rust can be identified by gray value comparison, thereby realizing the identification and judgment of the first abnormal state feature.

[0078] As an optional implementation, step S200, identifying whether there are abnormal state features in the environmental image, further includes:

[0079] Step S230: Extract the leakage scene image from the environmental image;

[0080] Step S240: Identify whether there is human image information in the leaked scene image;

[0081] Step S250: If human image information exists, classify the leaked scene image as a normal image; if human image information does not exist, classify the leaked scene image as an abnormal image with a second abnormal state feature; wherein, the second abnormal state feature is at least one of smoke and fire.

[0082] In this embodiment, for the identification and extraction of abnormal state features in leak scene images, since the scene state suspected to be caused by gas leak may be man-made (such as a large amount of smoke and gas stove flame generated when cooking in a home scene, or smoke or flame generated when carrying out industrial operations in a factory area, etc.), the presence of human image information in the leak scene image is first identified. If human image information is present, it can be determined that there is no abnormal state, and the leak scene image can be classified as a normal image, that is, it will not trigger the gas alarm. If no human image information is identified, the leak scene image can be classified as an abnormal image with the second abnormal state feature. At this time, the next step of audio recognition monitoring is then carried out, thereby improving the identification and judgment of abnormal state features of leak scene images and reducing the probability of misjudgment.

[0083] It should be noted that multiple images of the leakage scene can be acquired within a certain preset time period. If more than a preset number of images of the leakage scene do not contain human figures, then the leakage scene images will be classified as abnormal images with the characteristics of the second abnormal state. This avoids the situation where the target person has only left temporarily, thereby further improving the accuracy of monitoring.

[0084] As an optional implementation, in step S400, gas leak data is acquired through the target gas alarm, including:

[0085] Step S410: Obtain the gas concentration through the target gas alarm;

[0086] Step S420: Compare the gas concentration with a preset concentration threshold;

[0087] Step S430: If the gas concentration is greater than the concentration threshold, obtain the spatial volume and air velocity of the space environment where the target gas alarm is located;

[0088] Step S440: Based on the gas concentration, space volume, and air velocity, obtain the leak prediction value. The expression for the leak prediction value is as follows:

[0089] E = C / (ε1V + ε2S)

[0090] In the formula, E is the predicted leakage value, C is the gas concentration, V is the space volume, S is the air velocity, ε1 is the first conversion coefficient, and ε2 is the second conversion coefficient;

[0091] Step S450: Compare the predicted leakage value with a preset standard threshold;

[0092] Step S460: If the predicted leakage value is greater than the standard threshold, then obtain the gas leakage data.

[0093] In this embodiment, after entering the gas alarm monitoring stage, the gas concentration obtained by the gas alarm is first compared with a preset concentration threshold. If it is less than the concentration threshold, it can be identified as no gas leak. If it is greater than the concentration threshold, further detection is performed to obtain the spatial volume and air velocity of the environment where the gas alarm is located. The leak prediction value E is calculated according to the above expression. The larger the spatial volume and / or the greater the air velocity, the less likely a gas leak accident will occur. If there is no ventilation, the air velocity is close to 0. Therefore, the spatial volume and air velocity are negatively correlated with the leak prediction value E, while the gas concentration is positively correlated with the leak prediction value E. The spatial volume and air velocity are measurement parameters of different units. Therefore, the first conversion coefficient ε1 and the second conversion coefficient ε2 are assigned to the parameters respectively. The transformation allows the two parameters to be superimposed. Therefore, a predictive leak value E with guiding significance can be calculated based on three representative parameters: gas concentration, space volume, and air velocity. The predicted leak value E is compared with a preset standard threshold. If the predicted leak value E is greater than the standard threshold, the currently obtained gas concentration can be used as gas leak data to trigger the sending of an alarm. If the predicted leak value E is less than or equal to the standard threshold, the process returns to the step of obtaining the environmental image corresponding to the monitoring area. This embodiment judges the predicted leak value E based on a comprehensive assessment of gas concentration, space volume, and air velocity, thereby avoiding misjudgment due to high gas concentrations around the gas alarm caused by wind direction or other environmental factors. Compared with the existing method that judges based solely on concentration thresholds, this embodiment can improve the accuracy of monitoring gas leak status.

[0094] Example 2

[0095] Reference Figures 1-3 Based on the same inventive concept as the aforementioned embodiments, this embodiment also provides an Internet of Things-based gas leak safety monitoring system, including a user platform, a service platform, a management platform, a sensor network platform, and a gas alarm object platform connected in sequence. The gas alarm object platform includes:

[0096] The image acquisition module is used to acquire environmental images corresponding to the monitoring area; wherein, the monitoring area is an area with a risk of gas leakage.

[0097] The feature recognition module is used to identify whether there are abnormal state features in the environmental image;

[0098] The signal receiving module is used to receive a power-on signal that powers on the target gas alarm if abnormal state characteristics are detected; wherein, the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area;

[0099] The data acquisition module is used to acquire gas leak data through the target gas alarm.

[0100] The explanations and examples of each module in the device of this embodiment can be referred to the methods of the foregoing embodiments, and will not be repeated here.

[0101] It should be noted that the gas leak safety monitoring system in this embodiment can be connected to an Internet of Things (IoT) system. The IoT system includes a user platform, a service platform, a management platform, a sensor network platform, and a gas alarm object platform that interact sequentially, thus forming a standard five-platform IoT structure. The user platform includes user terminals such as individual users, government users, and regulatory users. The physical entities of the user platform include various user terminals, such as mobile phones, computers, and dedicated terminals. Through integration with user information system software, user-end services are provided.

[0102] A service platform is a functional platform that enables service communication. In some embodiments, the service platform may include servers for gas services, operation services, and security services.

[0103] The management platform is a functional platform for the operation and management of the Internet of Things (IoT) system. It includes an equipment management sub-platform, a business management sub-platform, and a data center module. The data center module handles the interaction and processing of equipment data. The equipment management sub-platform can further include modules for equipment operation status monitoring and management, fault data monitoring and management, equipment parameter management, and equipment lifecycle management. These modules manage and monitor various indicators of the smart water meter. The business management sub-platform includes modules for revenue management, business operator management, application management, message management, dispatch management, sales difference management, operation analysis management, and comprehensive business management. Through the collaborative efforts of these modules, the interaction and processing of various business data can be achieved.

[0104] The sensor network platform is a functional platform for realizing sensor communication. The sensor network platform includes a device management module and a data transmission management module. The device management module includes a network management module, a command management module, and a device status management module. The data transmission management module includes a data protocol management module, a data parsing module, a data classification module, a data transmission monitoring module, and a data transmission security module.

[0105] The gas alarm object platform is a functional platform for realizing sensing and control. In some embodiments, the gas alarm object platform may include multiple gas alarms, each corresponding to an image acquisition module, a feature recognition module, a signal receiving module, and a data acquisition module.

[0106] In some embodiments, the gas alarm platform includes not only the modules described above but also an MCU control module. Therefore, through the synergistic effect of these functional modules, an interactive IoT five-platform structure is achieved, providing a framework for an IoT-based gas leak safety monitoring system.

[0107] Example 3

[0108] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer device, which includes a memory and a processor. The memory stores a computer program, and the processor executes the computer program to implement the above-described method.

[0109] Example 4

[0110] Based on the same inventive concept as the foregoing embodiments, this embodiment provides a computer-readable storage medium storing a computer program, and a processor executes the computer program to implement the above-described method.

[0111] The above are merely preferred embodiments of this application and do not limit the patent scope of this application. Any equivalent structural or procedural transformations made using the content of this application's specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of this application.

Claims

1. A gas leak safety monitoring method based on the Internet of Things, characterized in that, Includes the following steps: Acquire environmental images corresponding to the monitoring area; wherein, the monitoring area is an area with a risk of gas leakage, and the environmental images include images of the leakage source and images of the leakage scene, and the images of the leakage scene include smoke images and flame images; Identifying whether there are abnormal state features in the environmental image includes: extracting the leak scene image from the environmental image; identifying whether there is human image information in the leak scene image; if the human image information exists, classifying the leak scene image as a normal image; if the human image information does not exist, classifying the leak scene image as an abnormal image with a second abnormal state feature; wherein, the second abnormal state feature is at least one of smoke and fire. If the abnormal state characteristics are detected, a power-on signal is received to power on the target gas alarm; wherein, the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area; Obtaining gas leak data through the target gas alarm includes: obtaining gas concentration through the target gas alarm; comparing the gas concentration with a preset concentration threshold; if the gas concentration is greater than the concentration threshold, obtaining the spatial volume and air velocity of the environment where the target gas alarm is located; and obtaining a leak prediction value based on the gas concentration, the spatial volume, and the air velocity, wherein the expression for the leak prediction value is as follows: E = C / (ε1V + ε2S) In the formula, E is the predicted leakage value, C is the gas concentration, V is the space volume, S is the air velocity, ε1 is the first conversion coefficient, and ε2 is the second conversion coefficient; The predicted leakage value is compared with a preset standard threshold; if the predicted leakage value is greater than the standard threshold, gas leakage data is obtained.

2. The gas leak safety monitoring method based on the Internet of Things as described in claim 1, characterized in that, If the abnormal state characteristics are detected, receiving a power-on signal to power on the target gas alarm includes: If the abnormal state characteristics are identified, the audio data corresponding to the monitoring area is obtained; Identify whether fault frequency features exist in the audio data; wherein, the fault frequency features include the sound features corresponding to a gas leak; If the aforementioned fault frequency characteristics exist, receive a power-on signal to power on the target gas alarm; If the fault frequency characteristic does not exist, return to the step of obtaining the environmental image corresponding to the monitoring area.

3. The gas leak safety monitoring method based on the Internet of Things as described in claim 1 or 2, characterized in that, After acquiring gas leak data through the target gas alarm, the process further includes: Determine whether the gas leak data has been acquired within a preset time period; If the gas leak data is obtained within a preset time, an alarm message will be sent; If the gas leak data is not obtained within a preset time, a power-off signal is received to de-energize the target gas alarm.

4. The gas leak safety monitoring method based on the Internet of Things as described in claim 1, characterized in that, The leak source images include images of gas pipelines, gas pipe and gas equipment joints, gas valves, and gas meters.

5. The gas leak safety monitoring method based on the Internet of Things as described in claim 4, characterized in that, The process of identifying whether there are abnormal state features in the environmental image includes: Extract the image of the leakage source from the environmental image; The image of the leak source is compared with a preset database to identify whether there is a first abnormal state feature in the image of the leak source; wherein, the first abnormal state feature includes at least one of the following: gas pipeline damage or rust, gas pipe and gas equipment joint damage or rust, gas valve damage or rust, and gas meter damage.

6. A gas leak safety monitoring system based on the Internet of Things, characterized in that, It includes a user platform, a service platform, a management platform, a sensor network platform, and a gas alarm object platform that are connected in sequence. The gas alarm object platform includes: The image acquisition module is used to acquire environmental images corresponding to the monitoring area; wherein, the monitoring area is an area with a risk of gas leakage, and the environmental images include images of the leakage source and images of the leakage scene, and the images of the leakage scene include smoke images and flame images; The feature recognition module is used to identify whether there are abnormal state features in the environmental image, including: extracting the leakage scene image from the environmental image; identifying whether there is human image information in the leakage scene image; if the human image information exists, classifying the leakage scene image as a normal image; if the human image information does not exist, classifying the leakage scene image as an abnormal image with a second abnormal state feature; wherein, the second abnormal state feature is at least one of smoke and fire; The signal receiving module is used to receive a power-on signal that powers on the target gas alarm if the abnormal state characteristics are detected; wherein the target gas alarm is the gas alarm closest to the abnormal point in the monitoring area. The data acquisition module is used to acquire gas leak data through the target gas alarm, including: acquiring gas concentration through the target gas alarm; comparing the gas concentration with a preset concentration threshold; if the gas concentration is greater than the concentration threshold, acquiring the spatial volume and air velocity of the environment where the target gas alarm is located; and obtaining a leak prediction value based on the gas concentration, the spatial volume, and the air velocity, wherein the expression for the leak prediction value is as follows: E = C / (ε1V + ε2S) In the formula, E is the predicted leakage value, C is the gas concentration, V is the space volume, S is the air velocity, ε1 is the first conversion coefficient, and ε2 is the second conversion coefficient; The predicted leakage value is compared with a preset standard threshold; if the predicted leakage value is greater than the standard threshold, gas leakage data is obtained.

7. A computer device, characterized in that, The computer device includes a memory and a processor, wherein the memory stores a computer program and the processor executes the computer program to implement the method as described in any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the processor executes the computer program to implement the method as described in any one of claims 1-5.

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