Gas safety detection method and device based on environmental parameters, equipment and medium

Abstract

The application relates to the technical field of gas safety detection based on environmental parameters, and discloses a gas safety detection method, device, equipment and medium based on environmental parameters, wherein the method comprises the following steps: acquiring environmental parameter data at each time point, extracting the environmental parameter data to form a first parameter data set and calculating a second parameter data set; inputting the second parameter data set into a preset gas safety detector to obtain a parameter label; and judging whether a gas leakage hidden danger exists in the gas use equipment according to the parameter label. The application has the beneficial effect that potential leakage hidden dangers can be identified in advance by continuously monitoring environmental parameters and intelligently analyzing the changes, the response time is shortened, the detection accuracy and real-time performance are improved, intelligent gas safety management is realized, and the safety hidden danger and economic loss are reduced.

Description

Technical Field

[0001] This invention relates to the field of gas safety detection technology based on environmental parameters, and in particular to a gas safety detection method, apparatus, equipment and medium based on environmental parameters. Background Technology

[0002] With the acceleration of urbanization, natural gas, as an important energy source, has been widely used in daily life. However, frequent gas leaks have caused serious safety hazards and economic losses. Traditional gas leak detection technology mainly relies on sensors to monitor gas concentration in real time. Once the concentration exceeds a set threshold, an alarm is triggered and emergency measures are taken. However, this method has several obvious shortcomings.

[0003] First, in the early stages of a gas leak, changes in gas concentration may not reach the alarm threshold, making it difficult to detect potential hazards in time. For example, during a gradual gas leak, sensors may only detect trace amounts of gas, failing to trigger an alarm and thus missing the optimal response window. Second, environmental factors such as changes in temperature and humidity can also affect sensor sensitivity, further increasing the risk of missed detections. Furthermore, most existing monitoring systems rely on static threshold limits, lacking intelligent analysis and response to abnormal data changes. Summary of the Invention

[0004] Therefore, it is necessary to propose a gas safety detection method, device, equipment, and medium based on environmental parameters to address the existing gas safety detection problems based on environmental parameters.

[0005] A gas safety detection method based on environmental parameters, the method comprising:

[0006] By using preset sensors, multiple environmental parameters are acquired by the gas-using equipment at preset intervals to obtain environmental parameter data at each time point.

[0007] Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0008] The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data.

[0009] The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels;

[0010] The gas-using equipment is assessed for potential gas leaks based on the parameter labels.

[0011] Furthermore, before the step of inputting the second parameter dataset into a preset gas safety detector to obtain the parameter labels of the second parameter data, the method further includes:

[0012] Obtain multiple sets of first parameter training datasets that are similar to the first parameter dataset; wherein, at least one set of first parameter training datasets is a parameter training dataset before the gas leak occurred;

[0013] The calculation process is performed on the first parameter training dataset to obtain the second parameter training dataset corresponding to each group of first parameter training datasets;

[0014] Detect whether the training dataset for the second parameter is the dataset corresponding to the period before the gas leak occurred;

[0015] Based on the detection results, parameter training labels are assigned to each of the second parameter training datasets; wherein, the parameter training labels are either "there is a gas leak hazard" or "there is no gas leak hazard".

[0016] Furthermore, the environmental parameter data includes temperature and humidity data and methane concentration data; after the step of obtaining multiple environmental parameters at preset intervals through preset sensors to acquire environmental parameter data at each time point, the method further includes:

[0017] The methane concentration threshold is obtained based on the temperature and humidity data.

[0018] Determine whether the methane concentration exceeds the methane concentration threshold;

[0019] If the methane concentration exceeds the methane concentration threshold, it is determined that the gas-using equipment has a potential gas leak hazard.

[0020] If the methane concentration does not exceed the methane concentration threshold, then the condition for performing the step of extracting environmental parameter data from the preset number of time points closest to the current time point to form the first parameter dataset is met.

[0021] Furthermore, after the step of determining that the gas-using equipment has a potential gas leak hazard if the methane concentration exceeds the methane concentration threshold, the method further includes:

[0022] The corresponding alarm response level is obtained based on the methane concentration;

[0023] The corresponding processing method shall be executed according to the alarm response level.

[0024] Furthermore, the step of executing the corresponding processing method according to the alarm response level includes:

[0025] Ambient noise and ambient brightness are collected using a pre-set acquisition device;

[0026] The brightness of the alarm indicator light is adjusted according to the alarm response level and the ambient brightness, and the volume of the buzzer is adjusted according to the alarm response level and the ambient noise.

[0027] Furthermore, the step of executing the corresponding processing method according to the alarm response level includes:

[0028] Determine whether the alarm response level has reached the preset level;

[0029] If the preset level is reached, multiple notification parties will be identified;

[0030] The notification information for each of the notifying parties is set according to the type of each party and in conjunction with the methane concentration.

[0031] The notification information is sent to the corresponding notifying party.

[0032] Furthermore, after the step of determining whether the gas-using equipment has a potential gas leak hazard based on the parameter label, the method further includes:

[0033] If the gas-using equipment is determined to have a potential gas leak based on the parameter label, then multiple historical gas leak data points with the same parameter label are obtained.

[0034] Each of the aforementioned historical gas leak data is sent to the designated terminal of the maintenance personnel.

[0035] A gas safety detection device based on environmental parameters, the device comprising:

[0036] The acquisition module is used to acquire multiple environmental parameters from the gas-using equipment at preset intervals through preset sensors, and obtain environmental parameter data at each time point.

[0037] The component module is used to extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0038] The processing module is used to perform calculations on the first parameter dataset to obtain a second parameter dataset; wherein, the calculations are performed by subtracting the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and forming the second parameter dataset based on each of the environmental parameter change data.

[0039] The input module is used to input the second parameter dataset into a preset gas safety detector to obtain the parameter labels of the second parameter data; wherein, the preset gas safety detector is a big data model, which is trained by multiple second parameter training datasets and corresponding parameter training labels;

[0040] The judgment module is used to determine whether there is a potential gas leak in the gas-using equipment based on the parameter labels.

[0041] An electronic device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps:

[0042] By using preset sensors, multiple environmental parameters are acquired by the gas-using equipment at preset intervals to obtain environmental parameter data at each time point.

[0043] Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0044] The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data.

[0045] The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels;

[0046] The gas-using equipment is assessed for potential gas leaks based on the parameter labels.

[0047] A computer-readable storage medium storing a computer program, which, when executed by a processor, causes the processor to perform the following steps:

[0048] By using preset sensors, multiple environmental parameters are acquired by the gas-using equipment at preset intervals to obtain environmental parameter data at each time point.

[0049] Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0050] The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data.

[0051] The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels;

[0052] The gas-using equipment is assessed for potential gas leaks based on the parameter labels.

[0053] The beneficial effects of this invention are: by continuously monitoring environmental parameters and intelligently analyzing their changes, potential leakage hazards can be identified in advance, and warnings can be issued even when the gas concentration has not yet reached the alarm threshold, thereby shortening the response time. This not only improves the detection accuracy and real-time performance, but also realizes intelligent gas safety management, reducing safety hazards and economic losses. Attached Figure Description

[0054] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0055] in:

[0056] Figure 1 This is an application environment diagram of a gas safety detection method based on environmental parameters in one embodiment;

[0057] Figure 2 This is a flowchart of a gas safety detection method based on environmental parameters in one embodiment;

[0058] Figure 3 This is a structural block diagram of a gas safety detection device based on environmental parameters in one embodiment;

[0059] Figure 4 This is a structural block diagram of an electronic device in one embodiment. Detailed Implementation

[0060] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0061] Figure 1 This is an environmental diagram illustrating a gas safety detection application based on environmental parameters in one embodiment. (Refer to...) Figure 1 This environmental parameter-based gas safety detection method is applied to an environmental parameter-based gas safety detection system. The system includes a terminal 110 and a server 120. The terminal 110 and server 120 are connected via a network. The terminal 110 can be a desktop terminal or a mobile terminal; a mobile terminal can be at least one of a mobile phone, tablet, or laptop. The server 120 can be a standalone server or a server cluster consisting of multiple servers. The terminal 110 is used to collect environmental parameter data, and the server 120 is used to determine whether there is a potential gas leak in the gas-using equipment.

[0062] like Figure 2 As shown, in one embodiment, a gas safety detection method based on environmental parameters is provided. This method can be applied to both terminals and servers; this embodiment illustrates its application to a terminal. The gas safety detection method based on environmental parameters specifically includes the following steps:

[0063] S1: The gas-using equipment acquires multiple environmental parameters at preset intervals through preset sensors, thus obtaining environmental parameter data at each time point;

[0064] S2: Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0065] S3: Perform calculation processing on the first parameter dataset to obtain a second parameter dataset; wherein, the calculation processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and form the second parameter dataset based on each of the environmental parameter change data;

[0066] S4: Input the second parameter dataset into the preset gas safety detector to obtain the parameter labels of the second parameter data; wherein, the preset gas safety detector is a big data model, trained by multiple second parameter training datasets and corresponding parameter training labels;

[0067] S5: Determine whether there is a potential gas leak in the gas-using equipment based on the parameter labels.

[0068] As described in step S1 above, multiple environmental parameters are acquired by the gas-using equipment at preset intervals using preset sensors, resulting in environmental parameter data at various time points. The environment surrounding the gas-using equipment is monitored by the preset sensors, collecting multiple environmental parameters. These parameters may include temperature, humidity, air pressure, oxygen concentration, methane concentration, and carbon monoxide concentration. The sensors continuously record this data at preset time intervals (e.g., every few minutes or seconds), forming a series of environmental parameter data. Changes in these environmental parameters are closely related to gas leaks. Under normal circumstances, the environmental parameters of the gas-using equipment should remain within a certain range. Abnormal fluctuations in these parameters may be an early sign of a gas leak.

[0069] As described in step S2 above, environmental parameter data from a preset number of time points closest to the current time point are extracted to form the first parameter dataset. Based on the current time point, environmental parameter data from several (preset number) time points closest to that time are selected to ensure the real-time nature and relevance of the dataset. By selecting data within a compact time range, the interference of external environmental fluctuations on data analysis can be effectively reduced, allowing focus on short-term trends. These obtained data are then combined to form the first parameter dataset.

[0070] As described in step S3 above, the first parameter dataset is processed to obtain the second parameter dataset. Specifically, by performing differential calculations on the data values ​​of each environmental parameter in the first parameter dataset—that is, subtracting the parameter data from the previous time point from the parameter data at the current time point—the change data of each environmental parameter is obtained. This allows for a direct visualization of the dynamic changes of the parameters over time, and these changes can reflect potential anomalies. For example, if the gas concentration suddenly increases but does not reach a threshold, and the temperature change is not significant, this may indicate a gas leak.

[0071] As described in step S4 above, the second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data. The generated second parameter dataset is then input into the preset gas safety detector to obtain parameter labels for each set of second parameter data. This gas safety detector, based on a big data model, learns from a large training dataset and its corresponding labels, enabling it to identify the relationship between different environmental changes and gas leak risks. It utilizes machine learning algorithms for pattern recognition and anomaly detection. By analyzing the second parameter dataset, the detector can determine whether the current environmental parameters meet normal safety standards. When data changes significantly exceed preset limits, the detector can identify potential leak risks, thereby improving the accuracy of the detection response. The gas safety detector can be one of the machine learning models, such as a support vector machine model, a decision tree model, or a deep learning model.

[0072] As described in step S5 above, the presence of a potential gas leak in the gas-using equipment is determined based on the parameter tags. The gas safety detector uses parameter tags to assess the potential for leaks in the gas-using equipment. When a detected parameter tag points to a potential leak risk, corresponding alarms and emergency measures can be triggered, such as notifying relevant personnel to conduct an inspection or automatically shutting off the gas valve. This process not only demonstrates the importance of intelligent monitoring technology but also emphasizes the role of early warning mechanisms in ensuring safety. Timely judgment and response can effectively reduce safety accidents such as fires and explosions caused by gas leaks, protecting life and property.

[0073] In one embodiment, before step S4 of inputting the second parameter dataset into a preset gas safety detector to obtain the parameter labels of the second parameter data, the method further includes:

[0074] S301: Obtain multiple sets of first parameter training datasets that are similar to the first parameter dataset; wherein, at least one set of parameter training datasets is a parameter training dataset before the gas leak occurs.

[0075] S302: Perform the calculation process on the first parameter training dataset to obtain the second parameter training dataset corresponding to each group of first parameter training datasets;

[0076] S303: Detect whether the training dataset for the second parameter is the dataset corresponding to the period before the gas leak occurred;

[0077] S304: Assign parameter training labels to each of the second parameter training datasets based on the detection results; wherein, the parameter training labels are either "there is a gas leak hazard" or "there is no gas leak hazard".

[0078] As described in steps S301-S304 above, multiple sets of first parameter training datasets similar to the first parameter dataset are obtained. These training datasets typically come from historical monitoring data and can represent normal operation and potential abnormal states under various environmental conditions. Specifically, similar first parameter training datasets can be generated using the same model of gas-using equipment, or they can be historical data from that gas-using equipment. Specifically, the parameter data for each model of gas-using equipment is stored in a background database and can be retrieved from the system later. These training datasets must include not only parameter changes under normal conditions but also various scenarios prior to gas leaks. Identifying the environmental parameter change characteristics before a gas leak occurs provides accurate sample data for subsequent model training. Having multiple sets of first parameter training datasets, especially those containing pre-leakage features, helps the system better understand the performance of gas-using equipment under different environmental conditions. Simultaneously, it greatly improves the model's generalization ability, enabling the trained model to effectively handle real-time monitoring data in different scenarios, thereby improving the accuracy and sensitivity of leak detection. After obtaining multiple sets of first parameter training datasets, these datasets need to be processed to generate corresponding second parameter training datasets. Specifically, a difference calculation will be performed on each environmental parameter in each first parameter training dataset. That is, the parameter data at the current time point will be subtracted from the parameter data at the previous time point to generate a set of data representing changes in environmental parameters. This extracts dynamic features from the training data, enabling the model to better understand and identify how environmental parameters change over time and determine whether these changes are related to potential gas leaks. The generated second parameter training dataset will be tested to determine whether these datasets correspond to features "before the existence of a gas leak." Specifically, historical information can be used to determine whether the second parameter training dataset corresponds to the dataset before the existence of a gas leak, and then the corresponding parameter training labels can be manually input. The labels are mainly divided into "potential gas leak exists" or "potential gas leak does not exist." By accurately labeling these training data, the model can learn features related to gas leaks, thereby more efficiently identifying potential risks in practical applications.

[0079] In one embodiment, the environmental parameter data includes temperature and humidity data and methane concentration data; after step S1, which involves acquiring multiple environmental parameters from the gas-using equipment at preset intervals using preset sensors to obtain environmental parameter data at each time point, the method further includes:

[0080] S201: Obtain the methane concentration threshold based on the temperature and humidity data;

[0081] S202: Determine whether the methane concentration exceeds the methane concentration threshold;

[0082] S203: If the methane concentration exceeds the methane concentration threshold, it is determined that the gas-using equipment has a potential gas leak hazard;

[0083] S204: If the methane concentration does not exceed the methane concentration threshold, then it is determined that the conditions for performing the step of extracting environmental parameter data from the preset number of time points closest to the current time point to form the first parameter dataset are met.

[0084] As described in steps S201-S203 above, the methane concentration threshold is determined based on the collected temperature and humidity data. Methane, a common fuel gas, is affected by environmental factors such as temperature and humidity, thus requiring dynamic adjustment of the threshold to improve leak detection accuracy. First, the system analyzes the normal range of methane concentration under different temperature and humidity conditions based on a model of the relationship between historical data and environmental conditions. For example, in high-temperature and high-humidity environments, the gas diffusion rate may be affected, thus changing the methane detection threshold. Through statistical analysis, the system can extract the influencing factors of various environmental parameters on methane concentration and then calculate a set of methane concentration thresholds adapted to the current environmental parameters. The key to this approach is ensuring that the threshold setting reflects the current actual situation based on real-time environmental data, rather than using a static threshold setting. If the methane concentration value exceeds the set threshold, the system will mark the state as "abnormal," providing a basis for subsequent early warnings. When a leak hazard is confirmed, the system will activate the early warning mechanism, issue an alarm, and notify relevant personnel to take necessary emergency measures. At this time, the system may automatically shut off the gas valve to avoid potential fire or explosion risks caused by equipment failure or human error. In addition, the system should record detailed data on the leak, including the time of occurrence, environmental parameters, methane concentration, etc., to facilitate subsequent analysis and safety investigation.

[0085] As described in step S204 above, if the methane concentration does not exceed the methane concentration threshold, it is determined that the condition for step S2, which involves extracting environmental parameter data from the preset number of time points closest to the current time point to form the first parameter dataset, is met. This indicates that the methane concentration has not reached the methane concentration threshold and will not directly trigger an alarm. Therefore, steps S2-S5 can be continued to analyze whether there is a potential leak based on parameter changes.

[0086] In one specific embodiment, an SHT35 temperature and humidity sensor (accuracy ±0.3℃, ±2%RH) can be integrated into the gas alarm, uploading data to the platform every 5 minutes. A base threshold for methane concentration is set, with a methane alarm threshold of 5% LEL (national standard requirement). When the temperature is >30℃, methane diffusion accelerates due to increased temperature, so the threshold is lowered to 4% LEL; when the temperature is <10℃, methane tends to accumulate due to decreased temperature, so the threshold is lowered to 3.5% LEL. When the humidity is >80%, sensor sensitivity decreases due to increased humidity, so the threshold is raised to 5.5% LEL. When the humidity is <30%, the effect of humidity on sensor sensitivity is minimal, and the threshold can be restored to the base threshold.

[0087] The comprehensive calculation formula is T adjusted =T base ×(1+0.02×(H-50))-0.1×(Temp-25); where, T base 5% LEL, H is humidity (%), Temp is temperature (°C), T adjusted To ultimately set the methane concentration threshold, 50 represents the reference humidity value (in %) and 25 represents the reference temperature value (in °C), based on standard environmental conditions. During execution, the threshold can be updated at preset intervals, such as every hour. If a temperature > 50 °C is detected (potentially indicating a fire), the formula calculation can be ignored, and a Level 1 response can be directly triggered. When the humidity sensor malfunctions, the system switches to a fixed threshold mode (setting the methane alarm threshold to 5% LEL) and sends a maintenance notification.

[0088] In one embodiment, after step S203, which states that if the methane concentration exceeds the methane concentration threshold, a gas-using device is deemed to have a potential gas leak, the method further includes:

[0089] S2041: Obtain the corresponding alarm response level based on the methane concentration;

[0090] S2042: Execute the corresponding processing method according to the alarm response level.

[0091] As described in steps S2041-S2042 above, when the methane concentration is detected to exceed a preset threshold, the system will determine the corresponding alarm response level based on the current methane concentration level. Different methane concentration levels typically correspond to different levels of risk, therefore a clear grading system needs to be pre-defined to reflect this. Based on the determined alarm response level, corresponding processing methods will be implemented. For example, for a low-level alarm, the system may recommend routine checks and monitoring to ensure there are no signs of further deterioration; for a medium-level alarm, the system may activate the automatic venting system to reduce gas pressure and begin notifying maintenance personnel for on-site inspection; for a high-level alarm, the system may trigger a comprehensive emergency response, including automatically shutting off gas valves, initiating emergency notifications, and even notifying the fire department for on-site handling.

[0092] In one specific embodiment, four response levels can be set. Specifically, Level 4 response: triggered when methane concentration is less than 3% LEL, only data is recorded, and a log is generated and stored in the database. Level 3 response: triggered when methane concentration is between 3% and 5% LEL, sending a "minor anomaly detected" notification to the user's APP and issuing a shut-off command to the gas shut-off valve (user confirmation required). Level 2 response: triggered when methane concentration is between 5% and 10% LEL, automatically shutting off the valve, simultaneously pushing SMS and APP alarms (including leak location and concentration value), and sending a warning to the property management system. Level 1 response: triggered when methane concentration is greater than 10% LEL, urgently shutting off the main gas valve, triggering the audible and visual alarm, and simultaneously pushing information to the user, the gas company's emergency repair platform (generating a work order number), and the community emergency center (activating evacuation broadcast). The correspondence between each response and methane concentration is pre-set, and the corresponding level of response is executed after the actual methane concentration is obtained.

[0093] In one embodiment, step S2042, which involves executing the corresponding processing method based on the alarm response level, includes:

[0094] S20421: Collect ambient noise and ambient brightness using a preset acquisition device;

[0095] S20422: Adjust the brightness of the alarm indicator light according to the alarm response level and the ambient brightness, and adjust the volume of the buzzer according to the alarm response level and the ambient noise.

[0096] As described in steps S20421-S20422 above, noise and brightness data of the current environment will be collected using a preset acquisition device. The purpose of this process is to dynamically adjust the alarm response, thereby improving the effectiveness of the alarm system and the user experience. Ambient noise and brightness can affect people's attention and reaction to alarm signals. For example, in a noisy environment, loud sounds may be drowned out, while in a brightly lit environment, low-brightness indicator lights may be difficult to notice, and high-brightness lights may cause glare to the user. Ambient noise is typically collected using acoustic sensors, which assess the current noise level by monitoring the audio data of the surrounding environment in real time. Ambient brightness can be obtained using a photosensitive sensor, which can detect changes in ambient light intensity. Based on the previously determined alarm response level and the real-time collected ambient brightness and noise data, the brightness of the alarm indicator light and the volume of the buzzer are adjusted.

[0097] Specifically, when the ambient light is low, such as at night or in a dimly lit room, the system can lower the brightness of the alarm indicator light to ensure that it does not cause glare. Conversely, if the ambient light conditions are good, the brightness of the indicator light can be increased to make it clearly visible. Simultaneously, the buzzer volume can be appropriately increased in noisy environments to ensure the alarm sound effectively penetrates background noise and is noticed by the user promptly. In quiet environments, excessively high volume may cause unnecessary interference, so the volume can be reduced. In some embodiments, specific parameter settings can be configured according to actual conditions, and user-defined settings are also supported.

[0098] In one embodiment, step S2042, which involves executing the corresponding processing method based on the alarm response level, includes:

[0099] S21421: Determine whether the alarm response level has reached a preset level;

[0100] S21422: If the preset level is reached, then multiple notification parties are identified;

[0101] S21423: Based on the type of each notifying party and in conjunction with the methane concentration, set the notification information for each notifying party;

[0102] S21424: Send the notification information to the corresponding notifying party.

[0103] As described in steps S21421-S21424 above, it is determined whether the current alarm response level has reached a preset trigger level. The preset alarm response level is typically categorized based on the methane concentration and potential safety risks, for example, low, medium, and high. The real-time methane concentration is compared with these levels to confirm whether the criteria for immediate action have been met. These notification recipients are pre-defined key contacts or departments, which may include family members, property management, emergency services, etc. The specific type of notification recipient varies depending on the context, device settings, and user needs.

[0104] The content of the notification information is dynamically set based on the different types of notifying parties, combined with the methane concentration and the corresponding alarm response level. The focus of this process is to ensure the accuracy and effectiveness of information delivery so that the notifying parties can correctly understand the current risk and take appropriate measures. Such notification information typically includes a hazard description, current methane concentration, potential safety risks, and recommended action steps. For technicians or safety managers, the notification information may be more detailed, including data charts or trends in environmental parameters; while for family members or ordinary users, the information may be conveyed in a simpler, more understandable way, emphasizing emergency actions, such as recommending shutting off gas valves and immediately leaving the danger zone. The generated notification information is sent to the previously identified notifying parties, which can be achieved through various communication methods such as SMS, email, push notifications from applications, and callbacks. Each notifying party can then take corresponding actions. For example, the gas company can automatically generate a repair task and assign it to the engineer closest to the leak location, while the property management can provide voice prompts for evacuation, etc., the specifics of which can be set by each notifying party.

[0105] In one embodiment, after step S5 of determining whether the gas-using equipment has a potential gas leak hazard based on the parameter label, the method further includes:

[0106] S601: If it is determined that there is a potential gas leak in the gas-using equipment based on the parameter label, then obtain multiple historical gas leak data that are the same as the parameter label;

[0107] S602: Send each of the aforementioned historical gas leak data to the designated terminal of the maintenance personnel.

[0108] As described in steps S601-S602 above, after determining that there is a potential leak in the gas-using equipment, multiple historical gas leak data points matching the current parameter label will be automatically acquired. Historical gas leak data typically includes detailed information about previously recorded leak events, such as the date, location, methane concentration at the time of the leak, environmental parameters, emergency response measures, and cause of the leak. By comparing the current equipment's parameter label with this historical data, maintenance personnel can identify potential patterns, analyze the handling methods and results in similar situations, and develop more effective maintenance and emergency measures. This type of historical data not only helps to quickly locate the root cause of the problem but also provides trend information on equipment performance and environmental impact, helping maintenance personnel make more comprehensive judgments. For example, if historical data indicates that a leak has occurred under similar environmental conditions, maintenance personnel can focus on the specific risks in the area and take appropriate preventative measures. Historical data can be sent to the maintenance personnel's smart devices, such as mobile phones, tablets, or dedicated terminals, via the network or other communication methods (such as SMS, email, mobile application push notifications, etc.).

[0109] Reference Figure 3 The present invention also provides a gas safety detection device based on environmental parameters, the device comprising:

[0110] The acquisition module 902 is used to acquire multiple environmental parameters from the gas-using equipment at preset intervals through preset sensors, and obtain environmental parameter data at each time point.

[0111] Module 904 is used to extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0112] The processing module 906 is used to perform calculation processing on the first parameter dataset to obtain a second parameter dataset; wherein, the calculation processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data;

[0113] The input module 908 is used to input the second parameter dataset into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained by multiple second parameter training datasets and corresponding parameter training labels;

[0114] The judgment module 910 is used to determine whether there is a potential gas leak in the gas-using equipment based on the parameter label.

[0115] In one embodiment, the gas safety detection device based on environmental parameters further includes:

[0116] The first parameter training dataset acquisition module is used to acquire multiple sets of first parameter training datasets that are similar to the first parameter dataset; wherein, at least one set of parameter training datasets is the parameter training dataset before the gas leak exists.

[0117] The calculation processing module is used to perform the calculation processing on the first parameter training dataset to obtain the second parameter training dataset corresponding to each group of first parameter training datasets;

[0118] The second parameter dataset detection module is used to detect whether the second parameter training dataset is the dataset corresponding to the period before the gas leak occurred.

[0119] The parameter training label assignment module is used to assign parameter training labels to each of the second parameter training datasets based on the detection results; wherein, the parameter training label is either "there is a gas leak hazard" or "there is no gas leak hazard".

[0120] In one embodiment, the environmental parameter data includes temperature and humidity data and methane concentration data; the gas safety detection device based on environmental parameters further includes:

[0121] A methane concentration threshold acquisition module is used to acquire a methane concentration threshold based on the temperature and humidity data.

[0122] A methane concentration determination module is used to determine whether the methane concentration exceeds the methane concentration threshold.

[0123] The first gas leak hazard determination module is used to determine that the gas-using equipment has a gas leak hazard if the methane concentration exceeds the methane concentration threshold.

[0124] The second gas leak hazard determination module is used to determine that if the methane concentration does not exceed the methane concentration threshold, the conditions for performing the step of extracting environmental parameter data from the preset number of time points closest to the current time point to form a first parameter dataset are met.

[0125] In one embodiment, the gas safety detection device based on environmental parameters further includes:

[0126] An alarm response level acquisition module is used to acquire the corresponding alarm response level based on the methane concentration.

[0127] The processing method execution module is used to execute the corresponding processing method according to the alarm response level.

[0128] In one embodiment, the processing execution module includes:

[0129] The environmental parameter acquisition submodule is used to collect environmental noise and ambient brightness through a preset acquisition device.

[0130] The parameter adjustment submodule is used to adjust the brightness of the alarm indicator light according to the alarm response level and the ambient brightness, and to adjust the volume of the buzzer according to the alarm response level and the ambient noise.

[0131] In one embodiment, the processing execution module includes:

[0132] The alarm response level determination submodule is used to determine whether the alarm response level has reached a preset level;

[0133] The notification confirmation submodule is used to determine multiple notification recipients if a preset level is reached.

[0134] The notification information setting submodule is used to set the notification information for each of the notification parties based on the type of each notification party and the methane concentration.

[0135] The notification information sending submodule is used to send the notification information to the corresponding notification party.

[0136] In one embodiment, the gas safety detection device based on environmental parameters further includes:

[0137] The historical gas leak data acquisition module is used to acquire multiple historical gas leak data that are identical to the parameter label if it is determined that the gas-using equipment has a potential gas leak hazard based on the parameter label.

[0138] The historical gas leak data sending module is used to send the historical gas leak data to the designated terminal of the maintenance personnel.

[0139] Figure 4 An internal structural diagram of an electronic device in one embodiment is shown. This electronic device can specifically be a terminal or a server, and more specifically, a computer device. Figure 4 As shown, the electronic device includes a processor, a memory, and a network interface connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement a gas safety detection method based on environmental parameters. The internal memory may also store a computer program, which, when executed by the processor, enables the processor to implement a gas safety detection method based on environmental parameters. Those skilled in the art will understand that... Figure 4The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the electronic device to which the present application is applied. The specific electronic device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0140] In one embodiment, an electronic device is provided, including a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the following steps:

[0141] By using preset sensors, multiple environmental parameters are acquired by the gas-using equipment at preset intervals to obtain environmental parameter data at each time point.

[0142] Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0143] The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data.

[0144] The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels;

[0145] The gas-using equipment is assessed for potential gas leaks based on the parameter labels.

[0146] By continuously monitoring environmental parameters and intelligently analyzing their changes, potential leak hazards can be identified in advance. Even if the gas concentration has not yet reached the alarm threshold, an early warning can be issued, thereby shortening the response time. This not only improves the detection accuracy and real-time performance, but also realizes intelligent gas safety management, reducing safety hazards and economic losses.

[0147] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, causes the processor to perform the following steps:

[0148] By using preset sensors, multiple environmental parameters are acquired by the gas-using equipment at preset intervals to obtain environmental parameter data at each time point.

[0149] Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset;

[0150] The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data.

[0151] The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels;

[0152] The gas-using equipment is assessed for potential gas leaks based on the parameter labels.

[0153] By continuously monitoring environmental parameters and intelligently analyzing their changes, potential leak hazards can be identified in advance. Even if the gas concentration has not yet reached the alarm threshold, an early warning can be issued, thereby shortening the response time. This not only improves the detection accuracy and real-time performance, but also realizes intelligent gas safety management, reducing safety hazards and economic losses.

[0154] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and RAMbus dynamic RAM (RDRAM), etc.

[0155] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0156] The embodiments described above are merely illustrative of several implementation methods of this application, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of this patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the protection scope of this patent application should be determined by the appended claims.

Claims

1. A gas safety detection method based on environmental parameters, characterized in that, The method includes: By using preset sensors to acquire multiple environmental parameters of the gas-using equipment at preset intervals, environmental parameter data at each time point is obtained. Extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset; The first parameter dataset is processed to obtain a second parameter dataset; wherein the processing method is to subtract the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and to form the second parameter dataset based on each of the environmental parameter change data. The second parameter dataset is input into a preset gas safety detector to obtain parameter labels for the second parameter data; wherein, the preset gas safety detector is a big data model, trained using multiple second parameter training datasets and corresponding parameter training labels; Determine whether the gas-using equipment has a potential gas leak hazard based on the parameter labels; The environmental parameter data includes temperature and humidity data and methane concentration data; after the step of acquiring multiple environmental parameters at preset intervals through preset sensors to obtain environmental parameter data at each time point, the method further includes: The methane concentration threshold is obtained based on the temperature and humidity data. Determine whether the methane concentration exceeds the methane concentration threshold; If the methane concentration exceeds the methane concentration threshold, it is determined that the gas-using equipment has a potential gas leak hazard. If the methane concentration does not exceed the methane concentration threshold, then the condition for performing the step of extracting environmental parameter data from the preset number of time points closest to the current time point to form the first parameter dataset is met.

2. The gas safety detection method based on environmental parameters according to claim 1, characterized in that, Before the step of inputting the second parameter dataset into a preset gas safety detector to obtain the parameter labels of the second parameter data, the method further includes: Obtain multiple sets of first parameter training datasets that are similar to the first parameter dataset; wherein, at least one set of first parameter training datasets is the parameter training dataset before the gas leak occurred; The calculation process is performed on the first parameter training dataset to obtain the second parameter training dataset corresponding to each group of first parameter training datasets; Detect whether the training dataset for the second parameter is the dataset corresponding to the period before the gas leak occurred; Based on the detection results, parameter training labels are assigned to each of the second parameter training datasets; wherein, the parameter training labels are either "there is a gas leak hazard" or "there is no gas leak hazard".

3. The gas safety detection method based on environmental parameters according to claim 1, characterized in that, Following the step of determining that the gas-using equipment has a potential gas leak hazard if the methane concentration exceeds the methane concentration threshold, the method further includes: The corresponding alarm response level is obtained based on the methane concentration; The corresponding processing method shall be executed according to the alarm response level.

4. The gas safety detection method based on environmental parameters according to claim 3, characterized in that, The step of executing the corresponding processing method according to the alarm response level includes: Ambient noise and ambient brightness are collected using a pre-set acquisition device; The brightness of the alarm indicator light is adjusted according to the alarm response level and the ambient brightness, and the volume of the buzzer is adjusted according to the alarm response level and the ambient noise.

5. The gas safety detection method based on environmental parameters according to claim 3, characterized in that, The step of executing the corresponding processing method according to the alarm response level includes: Determine whether the alarm response level has reached the preset level; If the preset level is reached, multiple notification parties will be identified; The notification information for each of the notifying parties is set according to the type of each party and in conjunction with the methane concentration. The notification information is sent to the corresponding notifying party.

6. The gas safety detection method based on environmental parameters according to claim 1, characterized in that, After the step of determining whether the gas-using equipment has a potential gas leak hazard based on the parameter label, the method further includes: If the gas-using equipment is determined to have a potential gas leak based on the parameter label, then multiple historical gas leak data points with the same parameter label are obtained. Each of the aforementioned historical gas leak data is sent to the designated terminal of the maintenance personnel.

7. A gas safety detection device based on environmental parameters, characterized in that, The device includes: The acquisition module is used to acquire multiple environmental parameters of the gas-using equipment through preset sensors at preset intervals to obtain environmental parameter data at each time point; the environmental parameter data includes temperature and humidity data and methane concentration data; The component module is used to extract environmental parameter data from a preset number of time points closest to the current time point to form the first parameter dataset; The processing module is used to perform calculations on the first parameter dataset to obtain a second parameter dataset; wherein, the calculations are performed by subtracting the previous environmental parameter data from each environmental parameter data in the first parameter dataset to obtain the environmental parameter change data corresponding to each environmental parameter data, and forming the second parameter dataset based on each of the environmental parameter change data. The input module is used to input the second parameter dataset into a preset gas safety detector to obtain the parameter labels of the second parameter data; wherein, the preset gas safety detector is a big data model, which is trained by multiple second parameter training datasets and corresponding parameter training labels; The judgment module is used to determine whether there is a potential gas leak in the gas-using equipment based on the parameter labels; A methane concentration threshold acquisition module is used to acquire a methane concentration threshold based on the temperature and humidity data. A methane concentration determination module is used to determine whether the methane concentration exceeds the methane concentration threshold. The first gas leak hazard determination module is used to determine that the gas-using equipment has a gas leak hazard if the methane concentration exceeds the methane concentration threshold. The second gas leak hazard determination module is used to determine that if the methane concentration does not exceed the methane concentration threshold, the conditions for performing the step of extracting environmental parameter data from the preset number of time points closest to the current time point to form a first parameter dataset are met.

8. A computer-readable storage medium, characterized in that, The device contains a computer program that, when executed by a processor, causes the processor to perform the steps of the gas safety detection method based on environmental parameters as described in any one of claims 1 to 6.

9. An electronic device, characterized in that, The device includes a memory and a processor, the memory storing a computer program that, when executed by the processor, causes the processor to perform the steps of the gas safety detection method based on environmental parameters as described in any one of claims 1 to 6.

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