User-end safety management system and method based on gas sensors

Abstract

This invention relates to the field of safety management technology, and in particular to a user-end safety management system and method based on gas sensors. The technical idea of ​​this invention is to upgrade traditional periodic maintenance to on-demand prediction. By quantitatively analyzing multi-dimensional risks such as dust, chemical substances, temperature and gas usage fluctuations, it can accurately assess the performance degradation trend of sensors. The benefits are that it can not only provide proactive warnings before failures occur and effectively prevent safety accidents, but also dynamically adjust the maintenance cycle according to individual risk levels, significantly improving maintenance efficiency.

Description

Technical Field

[0001] This invention relates to the field of safety management technology, and in particular to a user-end safety management system and method based on gas sensors. Background Technology

[0002] Currently, gas safety primarily relies on real-time monitoring using various gas sensors. However, existing technologies generally employ a fixed-cycle maintenance strategy, meaning that sensors are replaced or calibrated at uniform time intervals regardless of their actual operating conditions. This "one-size-fits-all" approach has significant drawbacks: firstly, in clean and stable environments, sensor performance may not show significant degradation, leading to high maintenance costs and wasted resources due to premature replacement; secondly, in complex conditions such as severe dust pollution, strong chemical corrosion, or drastic fluctuations in user gas usage, sensor performance may deteriorate rapidly, and the fixed maintenance cycle cannot promptly detect this risk, easily leading to safety accidents due to sensor malfunction or decreased accuracy, creating a hidden danger of "insufficient maintenance." Therefore, traditional static maintenance strategies struggle to balance safety and economy, failing to meet the increasingly sophisticated safety management needs. An intelligent technological solution capable of dynamically assessing sensor health and enabling predictive maintenance is urgently needed.

[0003] Therefore, the applicant proposes a user-end safety management system and method based on gas sensors. Summary of the Invention

[0004] To overcome the defects and shortcomings of existing technologies, this invention provides a user-end safety management system and method based on gas sensors. This application accurately assesses the performance degradation trend of sensors by quantitatively analyzing multi-dimensional risks such as dust, chemical substances, temperature and gas fluctuations. The advantages are that it can not only provide proactive warnings before failures occur and effectively prevent safety accidents, but also dynamically adjust the maintenance cycle according to the individual risk level, significantly improving maintenance efficiency.

[0005] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a user-end safety management method based on a gas sensor, comprising the following steps: S100: Obtain information on the composition of combustible gas, and simultaneously obtain user habit data and sensor data from the corresponding user module. S200: Analyze the influence of composition on combustible gas by using the composition of combustible gas and the sensor data of the corresponding user terminal module. S300: Obtain the user habit data, component impact analysis results and sensor data of the corresponding user terminal module to perform safety anomaly impact analysis. S400: Based on the analysis of the impact of security anomalies and the setting of maintenance cycles, the actual maintenance cycle of the user terminal is predicted. S500: Perform client maintenance based on the predicted actual client maintenance cycle.

[0006] In one implementation of the present invention, step S100 includes the following specific contents: Step 110: Obtain the composition of the combustible gas transmitted through the corresponding transmission pipeline through the gas department's acquisition terminal, including the concentration of the main components and the concentration of impurity components. Step 120: Obtain the usage habits of the corresponding user terminal by using logs, including the fluctuation of gas consumption at various times of the day, the average daily gas consumption duration, and the daily frequency of valve opening and closing. Step 130: Obtain the measurement range data and measurement sensitivity data of the gas sensor; Step 140: Store the acquired data in the corresponding storage components.

[0007] In one implementation of the present invention, the component influence analysis in S200 includes the following specific steps: Step 210: Obtain the composition of the transmitted combustible gas. Perform physical pollution anomaly analysis based on the particulate matter content, average size, and moisture content in the combustible gas, where size refers to diameter. Dust and moisture in the gas will form physical adhesion or blockage when flowing through the sensor gas path or protective cover, slowing down the gas diffusion rate and causing sensor response delay and decreased sensitivity. Physical pollution anomaly analysis includes the following specific steps: Obtain the particulate matter content and average size in combustible gas; obtain particulate matter content anomaly by dividing the particulate matter content in combustible gas by the safe particulate matter content; obtain particulate matter size anomaly by dividing the average particulate matter size in combustible gas by the safe particulate matter size; obtain particulate matter anomaly by multiplying particulate matter content anomaly and particulate matter size anomaly. The moisture content of the combustible gas is obtained by dividing the safe moisture content by the moisture content; The analysis results of physical pollution anomalies are obtained by weighted summation of particulate matter anomalies and moisture anomalies; the physical pollution risk causing the problem can be identified before the sensor performance shows a significant decline; the weighted summation reflects the superimposed effect of particulate matter and moisture, and the excess of either will significantly increase the pollution risk, and the calculation method is reasonable; Step 220: Obtain the content of silicides, sulfides, and halogen compounds in the combustible gas, and perform chemical inhibition analysis based on the content of silicides, sulfides, and halogen compounds in the combustible gas; silicides, sulfides, and halogen compounds in the gas can undergo irreversible chemical reactions with the sensitive materials of the sensor (such as the platinum wire of the catalytic combustion sensor and the electrolyte of the electrochemical sensor), leading to permanent sensor failure or sensitivity decay; The specific steps of chemical inhibition analysis are as follows: Obtain the contents of silicides, sulfides, and halogen compounds in the corresponding combustible gas, calculate the standard deviations of the contents of silicides, sulfides, and halogen compounds from their corresponding safe ranges, and then sum the standard deviations of each compound by weight to obtain the chemical inhibition analysis results. Step 230: Obtain the combustion temperature of the combustible gas. Divide the combustion temperature of the combustible gas by the corresponding safe ambient temperature of the sensor to obtain the temperature anomaly result. The high temperature generated during the combustion process will affect the working state of the sensor; for example, high temperature may cause the sensor baseline to drift. Step 240: The results of the physical pollution anomaly analysis, chemical inhibition analysis, and temperature anomaly analysis are weighted and summed to obtain the component influence analysis results.

[0008] In one implementation of the present invention, the safety anomaly impact analysis in S300 specifically includes the following: Step 310: Obtain the average fluctuation of gas consumption at each time of the day. Obtain the average value of the change in gas consumption at each time of the day and the gas consumption at the next time. Set it as the gas consumption fluctuation value, which is the absolute value of the difference between the gas consumption at the previous time and the gas consumption at the next time. The average result within the usage log period is obtained by dividing the gas consumption fluctuation value by the range of the sensor for measuring the gas speed. Step 320: Multiply the fluctuation anomaly by the fluctuation influence coefficient to obtain the fluctuation influence analysis result, and then perform a weighted summation of the fluctuation influence analysis result and the component influence analysis result to obtain the gas transmission influence result; Step 330: Divide the average daily gas consumption by the safe gas consumption to obtain the abnormal gas consumption. Multiply the obtained abnormal gas consumption by the gas transmission impact result to obtain the safety anomaly impact analysis result.

[0009] In one implementation of the present invention, the prediction of the actual maintenance cycle of the user terminal in step S400 includes the following specific content: Step 410: Obtain the security anomaly impact analysis results for the corresponding user and the setting and maintenance cycle for the corresponding sensor; Step 420: Obtain the safety anomaly by dividing the safety anomaly impact analysis result by the set safety anomaly impact analysis threshold. Obtain the actual maintenance cycle by dividing the set maintenance cycle of the corresponding sensor by the safety anomaly. Send the actual maintenance cycle to the management terminal, and the management terminal performs maintenance according to the actual maintenance cycle.

[0010] Secondly, the present invention also provides a user-end safety management system based on a gas sensor, including: The data acquisition module acquires information on the composition of combustible gases, as well as user habit data and sensor data from the corresponding user terminal module. The component impact analysis module analyzes the component impact based on the composition of combustible gas and the sensor data from the corresponding user terminal module. The safety anomaly impact analysis module acquires user habit data, component impact analysis results, and sensor data from the corresponding user terminal module to perform safety anomaly impact analysis. The maintenance cycle prediction module predicts the actual maintenance cycle for the user terminal based on the analysis of the impact of security anomalies and the set maintenance cycle. The maintenance module performs maintenance on the user terminal based on the predicted actual maintenance cycle.

[0011] Thirdly, the present invention provides an electronic device comprising: a processor and a memory, wherein the memory stores a computer program that can be called by the processor, and the processor executes a user-end safety management method based on a gas sensor by calling the computer program stored in the memory.

[0012] Fourthly, the present invention provides a computer-readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a user-end safety management method based on a gas sensor.

[0013] Compared with the prior art, the present invention has the following advantages and beneficial effects: The technical approach is to upgrade traditional periodic maintenance to on-demand prediction. By quantitatively analyzing multi-dimensional risks such as dust, chemicals, temperature, and gas fluctuations, the sensor performance degradation trend can be accurately assessed. The benefits are that it can not only provide proactive warnings before failures occur and effectively prevent safety accidents, but also dynamically adjust the maintenance cycle according to the individual risk level, significantly improving maintenance efficiency. Attached Figure Description

[0014] Other features, objects, and advantages of the invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a schematic diagram of the overall process structure of an embodiment of the method of the present invention; Figure 2 This is a schematic diagram of the process structure of step S200 in the embodiment of the method of the present invention; Figure 3 This is a schematic diagram of the characteristic structure for component influence analysis in an embodiment of the method of the present invention; Figure 4 This is a schematic diagram of the module composition structure of an embodiment of the system of the present invention. Detailed Implementation

[0015] The technical solution of the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present invention and the specific features in the embodiments are detailed descriptions of the technical solution of the present invention, rather than limitations thereof. In the absence of conflict, the embodiments of the present invention and the technical features in the embodiments can be combined with each other.

[0016] Please see Figures 1 to 3 , Figure 1 This is a schematic diagram of the overall process of the user terminal safety management method based on gas sensors provided in this embodiment of the invention, which specifically includes the following steps: S100: Obtain information on the composition of combustible gas, and simultaneously obtain user habit data and sensor data from the corresponding user module. In this embodiment, S100 includes the following specific contents: Step 110: Obtain the composition of the combustible gas transmitted through the corresponding transmission pipeline via the gas department's acquisition terminal. This includes the concentration of major components (e.g., methane, propane, butane) and the concentration of impurities (e.g., hydrogen sulfide, sulfur dioxide, total sulfur, moisture, carbon dioxide, nitrogen). This step lays the data foundation for the entire prediction method. The composition of the gas, especially the impurities, is the root cause of sensor performance degradation. By obtaining accurate composition data from the source, the subsequent degradation analysis is transformed from empirical speculation to mechanism-based accurate prediction, significantly improving the scientific rigor and predictive capability of the prediction model. Step 120: Obtain the usage habits of the corresponding user by using logs, including the fluctuation of gas consumption at various times of the day, the average daily gas consumption duration, and the daily frequency of valve opening and closing; this step introduces the dynamic load characteristics of the user side and associates the maintenance cycle with the actual working conditions of the specific user; gas consumption fluctuations and frequency directly affect the physical environment such as airflow and thermal circulation in which the sensor is located, and are important stress sources that cause sensor aging and response characteristics in addition to chemical components; Step 130: Obtain measurement range and sensitivity data of the gas sensor; this step takes into account the performance differences of individual sensors. Different models or batches of sensors have different initial performance parameters (such as range and sensitivity) and different tolerances to the same external conditions. Introducing this data can make the prediction model more adaptable to the device, and the prediction results are more in line with the design limits of the specific sensor. Step 140: Store the acquired data in the corresponding storage components; S200: Analyze the influence of composition on combustible gas by using the composition of combustible gas and the sensor data of the corresponding user terminal module. In this embodiment, the component influence analysis in S200 includes the following specific steps: Step 210: Obtain the composition of the transmitted combustible gas. Perform physical pollution anomaly analysis based on the particulate matter content, average size, and moisture content in the combustible gas, where size refers to diameter. Dust and moisture in the gas will form physical adhesion or blockage when flowing through the sensor gas path or protective cover, slowing down the gas diffusion rate and causing sensor response delay and decreased sensitivity. Physical pollution anomaly analysis includes the following specific steps: Obtain the particulate matter content and average size in combustible gas; obtain particulate matter content anomaly by dividing the particulate matter content in combustible gas by the safe particulate matter content; obtain particulate matter size anomaly by dividing the average particulate matter size in combustible gas by the safe particulate matter size; obtain particulate matter anomaly by multiplying particulate matter content anomaly and particulate matter size anomaly. The moisture content of the combustible gas is obtained by dividing the safe moisture content by the moisture content; The analysis results of physical pollution anomalies are obtained by weighted summation of particulate matter anomalies and moisture anomalies. This allows for the identification of physical pollution risks leading to the problem before a significant decline in sensor performance occurs. The weighted summation reflects the combined effect of particulate matter and moisture; any excess of either will significantly increase the pollution risk, and the calculation method is reasonable. A quantitative assessment of physical blockage and pollution, a typical sensor failure mode, is performed. By calculating particulate matter content anomalies, size anomalies, and moisture anomalies separately, and then using a multiplicative weighted summation model, the composite effect of physical pollution is accurately characterized. The model multiplying particulate matter content and size scientifically reflects that the blockage risk is not only related to the total amount of pollutants but also closely related to the proportion of large-diameter particles. This step enables the solution to proactively warn of physical pollution risks before a significant delay in sensor response or a decrease in sensitivity occurs. The threshold parameters for safe particulate matter content, safe particulate matter size, and safe moisture content are obtained based on the sensor manufacturer's technical specifications and national gas quality standards. Step 220: Obtain the content of silicides, sulfides, and halogen compounds in the combustible gas, and perform chemical inhibition analysis based on the content of silicides, sulfides, and halogen compounds in the combustible gas; silicides, sulfides, and halogen compounds in the gas can undergo irreversible chemical reactions with the sensitive materials of the sensor (such as the platinum wire of the catalytic combustion sensor and the electrolyte of the electrochemical sensor), leading to permanent sensor failure or sensitivity decay; The specific steps of chemical inhibition analysis are as follows: The content of silicides, sulfides, and halogen compounds in the corresponding combustible gas was obtained. The standard deviations of the silicide, sulfide, and halogen compound contents from their respective safe ranges were calculated. The standard deviations of each compound were then weighted and summed to obtain the chemical inhibition analysis results. For the irreversible sensor failure mode of chemical poisoning, silicides, sulfides, and halogen compounds can irreversibly react with the sensor's catalytic material or electrolyte. The method of calculating the standard deviations of each compound concentration from its safe range and then weighting and summing them has the advantage of simultaneously measuring the synergistic inhibitory effect of multiple chemical toxins; the standard deviations can effectively characterize... The weighting coefficient reflects the degree to which the current concentration deviates from the safe range, and thus the strength of the toxicity of different toxins to specific types of sensors (for example, silicon compounds are highly toxic to catalytic combustion sensors). This makes the analysis results more targeted. For each target compound, the safe range is mainly determined based on the application guidelines published by international authoritative sensor industry associations and the compatibility gas list provided by the manufacturer. Weighting coefficient: The weighting coefficient of each compound is calibrated through laboratory toxicity experiments. The specific method is as follows: the same batch of sensors is exposed to different types and concentrations of toxins, and their sensitivity decay rate is monitored. The faster the decay, the greater the weight is given to the toxin. Step 230: Obtain the combustion temperature of the combustible gas. Divide the combustion temperature of the combustible gas by the corresponding safe ambient temperature of the sensor to obtain the temperature anomaly result. The high temperature generated during the combustion process will affect the working state of the sensor; for example, high temperature may cause the sensor baseline to drift. Step 240: The obtained physical pollution anomaly analysis results, chemical inhibition analysis results, and temperature anomaly results are weighted and summed to obtain the component influence analysis results. This step realizes a multi-factor integrated assessment of the three major failure mechanisms: physical, chemical, and thermal. The weighted summation model can output a comprehensive component influence index based on the relative importance of different types of influencing factors, providing a unified and quantitative input for subsequent overall maintenance decisions. The weights here reflect the contribution of the three types of influences to the overall lifespan of the sensor, and can be determined by principal component analysis combined with historical failure data. For example, if historical data shows that chemical poisoning is the main cause of sensor failure in this area, then the weight of chemical inhibition analysis should be the highest. S300: Obtain the user habit data, component impact analysis results and sensor data of the corresponding user terminal module to perform safety anomaly impact analysis. In this embodiment, the safety anomaly impact analysis in S300 specifically includes the following: Step 310: Obtain the average fluctuation of gas consumption at each time of day. The average value of the change in gas consumption between each time of day and the subsequent time is set as the gas consumption fluctuation value. This value is the absolute value of the difference between the gas consumption at the previous time and the gas consumption at the subsequent time, averaged over the usage log period. The fluctuation anomaly is obtained by dividing the gas consumption fluctuation value by the sensor's gas velocity measurement range. This step transforms the dynamic fluctuation characteristics of user gas consumption into a quantitative indicator of its impact on the sensor. Severe flow fluctuations can cause airflow impact and thermal cycling stress on the sensor, accelerating mechanical and material fatigue. Normalization is achieved by using the ratio of the gas consumption fluctuation value to the sensor's range, making this indicator applicable to sensors with different ranges and possessing universality. The sensor's gas velocity measurement range is an inherent parameter of the sensor, taken from the technical specifications. Step 320: Multiply the fluctuation anomaly by the fluctuation influence coefficient to obtain the fluctuation influence analysis result. Then, perform a weighted summation of the fluctuation influence analysis result and the composition influence analysis result to obtain the gas transmission influence result. This step is the first integration of environmental factors and usage factors. Even if the gas composition is clean, frequent and drastic fluctuations will still consume the sensor's lifespan. Conversely, stable gas usage habits can mitigate the impact of poor gas composition to some extent. This integration more comprehensively reflects the sensor's true working state. Step 330: Divide the average daily gas consumption by the safe gas consumption to obtain the abnormal gas consumption. Multiply the obtained abnormal gas consumption with the gas transmission impact result to obtain the safety anomaly impact analysis result. It reflects the coupling relationship between "total gas consumption" (i.e., cumulative exposure) and "unit gas quality" (i.e., the severity of composition and fluctuation). For example, high-pollution gas (high transmission impact result) and large gas consumption (high gas consumption anomaly) have a combined impact that is not simply additive but multiplicative. This is consistent with the understanding of accelerated equipment wear under severe working conditions in engineering practice. S400: Based on the analysis of the impact of security anomalies and the setting of maintenance cycles, the actual maintenance cycle of the user terminal is predicted. In this embodiment, S400 predicts the actual maintenance cycle of the user terminal, including the following specific details: Step 410: Obtain the security anomaly impact analysis results for the corresponding user and the setting and maintenance cycle for the corresponding sensor; Step 420: Obtain the safety anomaly by dividing the safety anomaly impact analysis result by the set safety anomaly impact analysis threshold. Obtain the actual maintenance cycle by dividing the set maintenance cycle of the corresponding sensor by the safety anomaly. Send the actual maintenance cycle to the management terminal, and the management terminal performs maintenance according to the actual maintenance cycle. Set maintenance cycle: This is the baseline maintenance cycle recommended by the manufacturer under standard laboratory conditions (clean gas, standard flow rate); Safety anomaly impact analysis threshold is obtained through experiments: This threshold is usually set to 1, which means that when the comprehensive impact index is equal to 1, it indicates that the actual operating conditions are comparable to the laboratory baseline operating conditions. At this time, the actual maintenance cycle is equal to the set cycle. This setting makes the model have a clear benchmark. S500: Perform client maintenance based on the predicted actual client maintenance cycle.

[0017] The advantages of the above embodiments are: by quantitatively analyzing multi-dimensional risks such as dust, chemicals, temperature and gas fluctuations, the performance degradation trend of sensors can be accurately assessed. This not only provides proactive warnings before failures occur and effectively prevents safety accidents, but also allows for dynamic adjustment of maintenance cycles based on individual risk levels, significantly improving maintenance efficiency.

[0018] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a user-end safety management system based on a gas sensor provided in an embodiment of the present invention, including: The data acquisition module acquires information on the composition of combustible gases, as well as user habit data and sensor data from the corresponding user terminal module. The component impact analysis module analyzes the component impact based on the composition of combustible gas and the sensor data from the corresponding user terminal module. The safety anomaly impact analysis module acquires user habit data, component impact analysis results, and sensor data from the corresponding user terminal module to perform safety anomaly impact analysis. The maintenance cycle prediction module predicts the actual maintenance cycle for the user terminal based on the analysis of the impact of security anomalies and the set maintenance cycle. The maintenance module performs maintenance on the user terminal based on the predicted actual maintenance cycle.

[0019] The steps for implementing the corresponding functions of each parameter and each unit module in the gas sensor-based user terminal safety management system of the present invention can be referred to the parameters and steps in the embodiments of the gas sensor-based user terminal safety management method described above, and will not be repeated here.

[0020] Embodiments of the present invention also provide an electronic device, including a memory, a processor, and a communication bus; the memory and the processor are connected via the communication bus. The memory stores a user terminal safety management method based on a gas sensor, which can be loaded by the processor 320 and executed as provided in the above embodiments.

[0021] The memory can be used to store instructions, programs, code, code sets, or instruction sets. The memory 310 may include a program storage area and a data storage area. The program storage area may store instructions for implementing an operating system, instructions for at least one function, and instructions for implementing the gas sensor-based user terminal safety management method provided in the above embodiments. The data storage area may store data involved in the gas sensor-based user terminal safety management method provided in the above embodiments.

[0022] The processor may include one or more processing cores. The processor executes instructions, programs, code sets, or instruction sets stored in memory, and calls data stored in memory to perform various functions and process data according to the present invention. The processor may be at least one of the following: Application-Specific Integrated Circuit (ASIC), Digital Signal Processor (DSP), Digital Signal Processing Device (DSPD), Programmable Logic Device (PLD), Field Programmable Gate Array (FPGA), Central Processing Unit (CPU), controller, microcontroller, and microprocessor. It is understood that, for different devices, the electronic devices used to implement the functions of the processor 320 described above may also be other types, and the embodiments of the present invention do not specifically limit this.

[0023] A communication bus may include a pathway for transmitting information between the aforementioned components. The communication bus 330 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Communication buses can be categorized as address buses, data buses, control buses, etc.

[0024] This invention provides a computer-readable storage medium storing a computer program that can be loaded by a processor and executed as described in the above embodiments, representing a user terminal safety management method based on a gas sensor.

[0025] In this embodiment of the invention, the computer-readable storage medium can be a tangible device that holds and stores instructions used by an instruction execution device. The computer-readable storage medium can be, but is not limited to, an electrical storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any combination thereof. Specifically, the computer-readable storage medium can be a portable computer disk, a hard disk, a USB flash drive, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), spoofing random access memory (SRAM), portable compact disc read-only memory (CD-ROM), digital multifunction disc (DVD), memory stick, floppy disk, optical disk, magnetic disk, mechanical encoding device, or any combination thereof.

[0026] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0027] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of this application is not limited to the technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the foregoing concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions claimed in this invention.

Claims

1. A user-end safety management method based on gas sensors, characterized in that, Includes the following steps: S100: Obtain information on the composition of combustible gas, and simultaneously obtain user habit data and sensor data from the corresponding user module. S200: Analyze the influence of composition on combustible gas by using the composition of combustible gas and the sensor data of the corresponding user terminal module. S300: Obtain the user habit data, component impact analysis results and sensor data of the corresponding user terminal module to perform safety anomaly impact analysis. S400: Based on the analysis of the impact of security anomalies and the setting of maintenance cycles, the actual maintenance cycle of the user terminal is predicted. S500: Perform client maintenance based on the predicted actual client maintenance cycle.

2. The user-end safety management method based on a gas sensor according to claim 1, characterized in that, S100 includes the following specific contents: Step 110: Obtain the composition of the combustible gas transmitted through the corresponding transmission pipeline through the gas department's acquisition terminal, including the concentration of the main components and the concentration of impurity components. Step 120: Obtain the usage habits of the corresponding user terminal by using logs, including the fluctuation of gas consumption at various times of the day, the average daily gas consumption duration, and the daily frequency of valve opening and closing. Step 130: Obtain the measurement range data and measurement sensitivity data of the gas sensor; Step 140: Store the acquired data in the corresponding storage components.

3. The user-end safety management method based on a gas sensor according to claim 1, characterized in that, The component influence analysis in S200 includes the following specific steps: Step 210: Obtain the composition of the transmitted combustible gas, and perform physical pollution anomaly analysis based on the particulate matter content, average size, and moisture content in the combustible gas. Step 220: Obtain the content of silicides, sulfides and halogen compounds in the combustible gas, and perform chemical inhibition analysis based on the content of silicides, sulfides and halogen compounds in the combustible gas; Step 230: Obtain the combustion temperature of the combustible gas, and divide the combustion temperature of the combustible gas by the corresponding safe ambient temperature of the sensor to obtain the temperature anomaly result; Step 240: The results of the physical pollution anomaly analysis, chemical inhibition analysis, and temperature anomaly analysis are weighted and summed to obtain the component influence analysis results.

4. The user-end safety management method based on a gas sensor according to claim 1, characterized in that, The safety anomaly impact analysis in S300 specifically includes the following: Step 310: Obtain the average fluctuation of gas consumption at each time of the day, obtain the average value of the change in gas consumption at each time of the day and the subsequent time, set it as the gas consumption fluctuation value, and obtain the fluctuation anomaly by dividing the gas consumption fluctuation value by the range of the sensor for measuring gas speed. Step 320: Multiply the fluctuation anomaly by the fluctuation influence coefficient to obtain the fluctuation influence analysis result, and then perform a weighted summation of the fluctuation influence analysis result and the component influence analysis result to obtain the gas transmission influence result; Step 330: Divide the average daily gas consumption by the safe gas consumption to obtain the abnormal gas consumption. Multiply the obtained abnormal gas consumption by the gas transmission impact result to obtain the safety anomaly impact analysis result.

5. The user-end safety management method based on a gas sensor according to claim 1, characterized in that, The prediction of the actual maintenance cycle of the user terminal in S400 includes the following specific contents: Step 410: Obtain the security anomaly impact analysis results for the corresponding user and the setting and maintenance cycle for the corresponding sensor; Step 420: Obtain the safety anomaly by dividing the safety anomaly impact analysis result by the set safety anomaly impact analysis threshold. Obtain the actual maintenance cycle by dividing the set maintenance cycle of the corresponding sensor by the safety anomaly. Send the actual maintenance cycle to the management terminal, and the management terminal performs maintenance according to the actual maintenance cycle.

6. The user-end safety management method based on a gas sensor according to claim 2, characterized in that, The physical pollution anomaly analysis includes the following specific steps: Obtain the particulate matter content and average size in combustible gas; obtain particulate matter content anomaly by dividing the particulate matter content in combustible gas by the safe particulate matter content; obtain particulate matter size anomaly by dividing the average particulate matter size in combustible gas by the safe particulate matter size; obtain particulate matter anomaly by multiplying particulate matter content anomaly and particulate matter size anomaly. The moisture content of the combustible gas is obtained by dividing the safe moisture content by the moisture content; The results of the physical pollution anomaly analysis were obtained by weighted summation of particulate matter anomalies and moisture anomalies.

7. The user-end safety management method based on a gas sensor according to claim 2, characterized in that, The specific steps of the chemical inhibition analysis are as follows: Obtain the contents of silicides, sulfides, and halogen compounds in the corresponding combustible gas, calculate the standard deviations of the contents of silicides, sulfides, and halogen compounds from their corresponding safe ranges, and then sum the weighted standard deviations of each compound to obtain the chemical inhibition analysis results.

8. A user-end safety management system based on a gas sensor, used to implement the user-end safety management method based on a gas sensor as described in any one of claims 1-7, characterized in that, Specifically, it includes: The data acquisition module acquires information on the composition of combustible gases, as well as user habit data and sensor data from the corresponding user terminal module. The component impact analysis module analyzes the component impact based on the composition of combustible gas and the sensor data from the corresponding user terminal module. The safety anomaly impact analysis module acquires user habit data, component impact analysis results, and sensor data from the corresponding user terminal module to perform safety anomaly impact analysis. The maintenance cycle prediction module predicts the actual maintenance cycle for the user terminal based on the analysis of the impact of security anomalies and the set maintenance cycle. The maintenance module performs maintenance on the user terminal based on the predicted actual maintenance cycle.

9. An electronic device, comprising: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; characterized in that the processor executes the user terminal safety management method based on a gas sensor as described in any one of claims 1-7 by calling the computer program stored in the memory.

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