An intelligent data management system and method for a gas sensor

Abstract

The application discloses an intelligent data management system and method for a gas sensor, relates to the technical field of performance analysis, and utilizes a target MEMS sensor manufactured by using a prepared sensitive material and a gas sensor; the target MEMS sensor is utilized to detect H2S gas; the linear slope is utilized to calculate the minimum detection limit of the target MEMS sensor; the sensitivity when detecting different gases is calculated; the response time and the response value change of the target MEMS sensor are collected, a response-recovery curve diagram is generated, and the shortest recovery time of the target MEMS sensor is calculated; the stability of the target MEMS sensor is calculated; a performance evaluation model of the MEMS sensor is constructed, four kinds of data of the target MEMS sensor calculated are input into the performance evaluation model, and the comprehensive performance of the target MEMS sensor is calculated.

Description

Technical Field

[0001] This invention relates to the field of performance analysis technology, specifically to an intelligent data management system and method for gas sensors. Background Technology

[0002] The development of MEMS hydrogen sulfide gas sensor technology can effectively meet the needs of industrial, environmental, and medical fields for H2S detection. In the future, with the further development of new materials, microfabrication technologies, and the Internet of Things, MEMS gas sensors will evolve towards intelligence, integration, and multifunctionality, providing better solutions for H2S detection. At the same time, their low cost and high efficiency will drive their widespread application in the civilian sector.

[0003] However, there are currently no reports on the use of SnO2 materials for gas sensing. In this work, SnO2 nanostructures were synthesized via a hydrothermal method, and then Ni was doped into them using a high-temperature solid-state method to obtain NiO. x -SnO2 nanostructures. Hierarchical NiO2 multilayer stacks were fabricated using spin coating. x A specialized MEMS gas sensor was fabricated by coating SnO2 onto a micro-hot plate. By observing the changes in the current-time curve of an electrochemical workstation, the target MEMS sensor was demonstrated to be suitable for long-term monitoring of low-concentration H2S in urban air quality monitoring and wastewater treatment processes, providing accurate pollution assessment data. Summary of the Invention

[0004] The purpose of this invention is to provide an intelligent data management system and method for gas sensors to solve the problems raised in the prior art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: A smart data management method for a gas sensor, the method comprising the following steps: S100. A sensitive material is prepared by chemically reacting tin source and nickel catalyst powder to form a gas sensor that attaches to the gas sensor when measuring H2S gas. The prepared sensitive material and the initial MEMS sensor are used to manufacture the target MEMS sensor. The prepared target MEMS sensor is used to detect H2S gas. Furthermore, the specific steps for detecting H2S gas using the fabricated target MEMS sensor are as follows: S101: Dissolve 3.148g of tin source SnCl4·5H2O in 80ml of ethanol. Then add 0.592mol / L of alkaline reagent NH3·H2O to the solvent and stir magnetically for 30 minutes to form a sol. After stirring and aging, the sol transforms into a gel. After drying the gel, calcination is performed until SnO2 powder is obtained. 1mmol of nickel catalyst powder is mixed with SnO2 and ground for 12 hours. After sieving through a 300-mesh sieve, the mixed powder is transferred to an alumina corundum crucible and sintered at 900℃ for 4 hours to obtain NiO. x -SnO2 nanomaterials; NiO x -SnO2 nanomaterials as sensitive materials; S102. The prepared sensitive material is mixed with deionized water at a weight ratio of 4:1 to form a paste. The mixture is then dropped onto a micro-hot plate using a micropipette. After drying at room temperature, the mixture is placed in a vacuum drying oven for aging treatment. The aging temperature is stabilized at 400℃ for 2 days. The sensing element is then heat-treated at 500℃ for 2 hours to remove bound water and generate a dense oxide film on the surface of the gas sensor, thus obtaining the initial MEMS sensor. The sensitive material is then spin-coated onto the initial MEMS sensor to obtain the target MEMS sensor. S103. After fabricating the MEMS sensor, wait for it to cool down, then use a CNC power supply to power the electrodes and provide the reaction temperature to the micro-hot plate. Use a gas sensing analysis system to collect data, place the target MEMS sensor in a fume hood, and inject the target gas H2S mixed with dry air into the chamber until the gas contacts the sensitive material. Test the current-time curve. The response time and recovery time are defined as the time required for the target MEMS sensor to reach 90% of the total current change under adsorption and desorption conditions, respectively. The sensor operates at a temperature of 400℃.

[0006] The target MEMS sensor made of novel sensitive materials has a good effect on the detection of H2S gas, which greatly increases the efficiency of H2S gas detection; and it is low in cost, small in size and simple in structure, and has good application prospects for H2S gas detection.

[0007] S200. When using the target MEMS sensor to detect H2S gas, the gas sensing and analysis system collects the response time and response value of the target MEMS sensor when detecting H2S gas of different concentrations, plots the response time and response value response curves, and fits the response curves to calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. Furthermore, the specific steps for calculating the minimum detection limit of the target MEMS sensor using a linear slope are as follows: S201, using NiO x-SnO2 nanomaterials provide H2S gas of different concentrations in a stepwise growth at an operating temperature of 400℃, with the growth rate being P. When H2S gas of different concentrations is detected using a target MEMS sensor, the response value and response time of the target MEMS sensor at the corresponding concentration are collected, and the response time is used as the abscissa and the response value as the ordinate to plot a response curve. The graph shows that as the concentration of the test gas increases, the response value of the MEMS gas sensor coated with sensitive material to hydrogen sulfide also increases. S202. The plotted response curve is processed using a linear regression algorithm. The response values ​​of the target MEMS sensor at different concentrations are fitted to obtain the linear regression equation. In the equation, Res represents the response value of the target MEMS sensor, Rt represents the response time, a represents the slope of the linear regression equation, and b represents the intercept of the linear regression equation. The slope of the linear regression equation is taken as the optimal sensitivity of the target MEMS sensor, and the linearity of the linear regression equation is calculated using the following formula: ; In the formula, R 2 This indicates the linearity of the calculated linear regression equation. This represents the true response value for the i-th concentration. This represents the calculated response value for the i-th concentration. This represents the average of n concentration response values, where n represents the number of different concentrations of H2S gas detected by the special MEMS; i ranges from 1 to n. S203. Calculate the standard deviation Q using the n concentration response values ​​in the response curve graph, and then calculate the minimum detection limit (LOD) of the target MEMS sensor using the following formula: ; In the formula, LOD represents the lowest detection limit, Q represents the standard deviation of the response value, and a represents the slope of the linear regression equation.

[0008] S300: Use the target MEMS sensor to detect different gases, collect the response time and response value during detection, calculate the sensitivity when detecting different gases, and determine whether the target MEMS sensor is selective for H2S gas. Furthermore, the specific steps to determine whether the target MEMS sensor being fabricated is selective for H2S gas are as follows: S301. Detect different gases using a target MEMS sensor. Let the types of gases be... , Let m represent the first, second, third, ..., m gases being detected, where m is a positive integer. The response time and response value for detecting different gases are collected, and the sensitivity of the target MEMS sensor to different gases is calculated using the response time and response value. The formula is: ; In the formula, This represents the calculated sensitivity of the target MEMS sensor to the r-th gas. This represents the response value of the target MEMS sensor to the detection of the r-th gas. This represents the response time of the target MEMS sensor to the detection of the r-th gas; r belongs to 1 to m; S302. After calculating the sensitivity of the target MEMS sensor to different gases, judge all sensitivities and extract the gas with the highest sensitivity. When the gas corresponding to the highest sensitivity is H2S gas, determine that the target MEMS sensor selectivity is H2S gas.

[0009] S400: Use a gas sensing and analysis system to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. Furthermore, the specific steps for calculating the shortest recovery time of the target MEMS sensor are as follows: S401. Use a gas sensing analysis system to collect the response time and response value changes of the target MEMS sensor, plot a curve of response value versus response time, extract the peak value and the trough value to the right of the peak value from the curve, and calculate the recovery time of the target MEMS sensor using the following formula: In the formula, Ht represents the calculated recovery time of the target MEMS sensor. This indicates the response time corresponding to the trough value to the right of the peak. This indicates the response time corresponding to the peak value; S402. Repeat the calculation of the recovery time of the target MEMS sensor each time, and extract the minimum value as the shortest recovery time of the target MEMS sensor.

[0010] S500. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. Two repeatable detections are performed, one for short period and one for long period, and the stability of the target MEMS sensor is calculated. Furthermore, the specific steps for calculating the stability of the target MEMS sensor are as follows: S501. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. First, short-cycle acquisition is performed in seconds. The acquisition interval is h seconds to obtain the sensor output value. The standard deviation of short-cycle acquisition is calculated. The calculated standard deviation is used as the short-term stability Wd of the target MEMS sensor. S502. Long-term data acquisition is performed on a daily basis, with an acquisition interval of H days to obtain the sensor output value. The standard deviation of the long-term acquisition is calculated, and this standard deviation is used as the long-term stability (WL) of the target MEMS sensor. The short-term stability and long-term stability are combined to generate the overall stability using the following formula: ; In the formula, W represents the stability of the target MEMS sensor.

[0011] S600: Collect and calculate the weights of the minimum detection limit, sensitivity, shortest response time, and stability of the initial MEMS sensor, which is a MEMS sensor composed of MEMS micro-heaters; Construct a performance evaluation model for the target MEMS sensor, input the four types of data of the target MEMS sensor into the performance evaluation model, and calculate the comprehensive performance of the target MEMS sensor.

[0012] The specific steps to determine whether the performance of the target MEMS sensor is qualified are as follows: S601. Extract four performance data points from u initial MEMS sensors, and standardize these four performance data points using the deviation normalization formula to unify them into the same format; the four performance data points include minimum detection limit, sensitivity, shortest recovery time, and stability; calculate the weights of the four performance data points using the following formula: ; In the formula, E g e represents the weight of the g-th performance data in the calculation. g The information entropy represents the g-th type of performance data; A comprehensive performance model is constructed using the weights of the four performance data types, with the following formula: ; In the formula, P represents the overall performance of the sensor, D represents the polynomial for each performance data, and Z represents the calculated real-time performance data for each data point. max Z represents the maximum value of each performance data point in the initial MEMS sensor data collected. min Z represents the minimum value of each performance data in the initial MEMS sensor data; Z includes four performance data values: minimum detection limit, sensitivity, shortest recovery time, and stability. S602, calculate the four performance data of the target MEMS sensor: LOD, MI r Ht and W are input into the comprehensive performance model to calculate the comprehensive performance P of the target MEMS sensor.

[0013] An intelligent data management system for gas sensors includes a data collection module, a gas detection device, a performance classification calculation module, a performance model construction module, and a comprehensive performance calculation module. The data collection module is used to collect the initial performance data of the MEMS sensor; The gas detection device includes a digitally controlled power supply, an electrochemical workstation, a MEMS sensor chip, and a gas chamber; one end of the MEMS sensor chip is connected to the digitally controlled power supply, and the other end is connected to the electrochemical workstation for real-time monitoring of electrical signals; The performance classification calculation module includes a minimum detection limit unit, a sensitivity unit, a recovery time unit, and a stability unit; The minimum detection limit unit is used to fit the response curve and calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. The sensitivity unit is used to detect different gases using the target MEMS sensor, and to collect the response time and response value during detection to calculate the sensitivity when detecting different gases. The recovery time unit is used to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. The stability unit is used to collect the sensor output value at each detection, perform two repeatable detections with short and long cycles respectively, and calculate the stability of the target MEMS sensor.

[0014] The performance model building module includes a weight calculation unit and a model building unit; The weight calculation unit is used to collect four types of performance data of the initial MEMS sensor, and calculate the weight of each type of performance data after standardization using the deviation standardization formula. The model building unit generates a comprehensive performance model using the weights of each performance data point calculated.

[0015] Compared with the prior art, the beneficial effects of the present invention are: 1. The target MEMS sensor made of novel sensitive materials in this invention has a good effect on the detection of H2S gas, which greatly increases the efficiency of H2S gas detection; and it has low manufacturing cost, small size and simple structure, and has good application prospects for H2S gas detection.

[0016] 2. This invention constructs a comprehensive performance model by calculating four types of performance data, which can quickly calculate and judge the working performance of the sensor, preventing the sensor performance from deteriorating and reducing work efficiency; and it can also judge whether the sensor is abnormal based on changes in sensor performance. Attached Figure Description

[0017] Figure 1 This is a module distribution diagram of an intelligent data management system for gas sensors according to the present invention; Figure 2 The image shows the response curves of the target MEMS sensor to different concentrations of H2S in the intelligent data management method for gas sensors according to the present invention. Figure 3 Linear regression curves of H2S at different concentrations for the target MEMS sensor in the intelligent data management method for gas sensors of the present invention. Figure 4 The target MEMS sensor of the present invention is a selective graph of H2S, Cl2, Air and SF6 for a smart data management method for a gas sensor. Figure 5 The image shows the response-recovery curve of the target MEMS sensor for gas detection in the intelligent data management method for gas sensors according to the present invention. Figure 6 This is a short-term stability curve of the target MEMS sensor for gas detection, as per the intelligent data management method for gas sensors of the present invention. Figure 7 This is a graph showing the long-term stability of the target MEMS sensor for gas detection in the intelligent data management method for gas sensors according to the present invention. Detailed Implementation

[0018] 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.

[0019] Example: Figures 1-7 As shown, the present invention provides a technical solution. A smart data management method for a gas sensor, the method comprising the following steps: S100. A sensitive material is prepared by chemically reacting tin source and nickel catalyst powder to form a gas sensor that attaches to the gas sensor when measuring H2S gas. The prepared sensitive material and the initial MEMS sensor are used to manufacture the target MEMS sensor. The prepared target MEMS sensor is used to detect H2S gas. The specific steps for detecting H2S gas using the fabricated target MEMS sensor are as follows: S101: Dissolve 3.148g of tin source SnCl4·5H2O in 80ml of ethanol. Then add 0.592mol / L of alkaline reagent NH3·H2O to the solvent and stir magnetically for 30 minutes to form a sol. After stirring and aging, the sol transforms into a gel. After drying the gel, calcination is performed until SnO2 powder is obtained. 1mmol of nickel catalyst powder is mixed with SnO2 and ground for 12 hours. After sieving through a 300-mesh sieve, the mixed powder is transferred to an alumina corundum crucible and sintered at 900℃ for 4 hours to obtain NiO. x -SnO2 nanomaterials; NiO x -SnO2 nanomaterials as sensitive materials; S102. The prepared sensitive material is mixed with deionized water at a weight ratio of 4:1 to form a paste. The mixture is then dropped onto a micro-hot plate using a micropipette. After drying at room temperature, the mixture is placed in a vacuum drying oven for aging treatment. The aging temperature is stabilized at 400℃ for 2 days. The sensing element is then heat-treated at 500℃ for 2 hours to remove bound water and generate a dense oxide film on the surface of the gas sensor, thus obtaining the initial MEMS sensor. The sensitive material is then spin-coated onto the initial MEMS sensor to obtain the target MEMS sensor. S103. After fabricating the MEMS sensor, wait for it to cool down, then use a CNC power supply to power the electrodes and provide the reaction temperature to the micro-hot plate. Use a gas sensing analysis system to collect data, place the target MEMS sensor in a fume hood, and inject the target gas H2S mixed with dry air into the chamber until the gas contacts the sensitive material. Test the current-time curve. The response time and recovery time are defined as the time required for the target MEMS sensor to reach 90% of the total current change under adsorption and desorption conditions, respectively. The sensor operates at a temperature of 400℃.

[0020] The target MEMS sensor made of novel sensitive materials has a good effect on the detection of H2S gas, which greatly increases the efficiency of H2S gas detection; and it is low in cost, small in size and simple in structure, and has good application prospects for H2S gas detection.

[0021] S200. When using the target MEMS sensor to detect H2S gas, the gas sensing and analysis system collects the response time and response value of the target MEMS sensor when detecting H2S gas of different concentrations, plots the response time and response value response curves, and fits the response curves to calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. The specific steps for calculating the minimum detection limit of the target MEMS sensor using a linear slope are as follows: S201, using NiO x -SnO2 nanomaterials provide H2S gas of different concentrations in a stepwise growth at an operating temperature of 400℃, with the growth rate being P. When H2S gas of different concentrations is detected using a target MEMS sensor, the response value and response time of the target MEMS sensor at the corresponding concentration are collected, and the response time is used as the abscissa and the response value as the ordinate to plot a response curve. The graph shows that as the concentration of the test gas increases, the response value of the MEMS gas sensor coated with sensitive material to hydrogen sulfide also increases. S202. The plotted response curve is processed using a linear regression algorithm. The response values ​​of the target MEMS sensor at different concentrations are fitted to obtain the linear regression equation. In the equation, Res represents the response value of the target MEMS sensor, Rt represents the response time, a represents the slope of the linear regression equation, and b represents the intercept of the linear regression equation. The slope of the linear regression equation is taken as the optimal sensitivity of the target MEMS sensor, and the linearity of the linear regression equation is calculated using the following formula: ; In the formula, R 2 This indicates the linearity of the calculated linear regression equation. This represents the true response value for the i-th concentration. This represents the calculated response value for the i-th concentration. This represents the average of n concentration response values, where n represents the number of different concentrations of H2S gas detected by the special MEMS; i ranges from 1 to n. S203. Calculate the standard deviation Q using the n concentration response values ​​in the response curve graph, and then calculate the minimum detection limit (LOD) of the target MEMS sensor using the following formula: ; In the formula, LOD represents the lowest detection limit, Q represents the standard deviation of the response value, and a represents the slope of the linear regression equation.

[0022] For example: using NiO x-SnO2 nanomaterials were tested at an operating temperature of 400℃ for a wide range of hydrogen sulfide gas concentrations of 20, 40, 60, 80, and 100 ppm. The response curves of the sensor to different concentrations of hydrogen sulfide gas are shown below. Figure 2 As shown, it can be observed that the response value of the MEMS gas sensor coated with sensitive material to hydrogen sulfide increases with the increase of the test gas concentration. The optimal sensitivity of the MEMS gas sensor to H2S is 0.00970 μA / ppm, indicating a linearity of 0.99191.

[0023] Linear regression curve as shown Figure 3 As shown, the lowest detection limit (LOD) of the gas sensor is calculated. The calculated minimum detection limit of the MEMS gas sensor is 1.8680 ppm.

[0024] S300: Use the target MEMS sensor to detect different gases, collect the response time and response value during detection, calculate the sensitivity when detecting different gases, and determine whether the target MEMS sensor is selective for H2S gas. The specific steps to determine whether the target MEMS sensor being fabricated is selective for H2S gas are as follows: S301. Detect different gases using a target MEMS sensor. Let the types of gases be... , Let m represent the first, second, third, ..., m gases being detected, where m is a positive integer. The response time and response value for detecting different gases are collected, and the sensitivity of the target MEMS sensor to different gases is calculated using the response time and response value. The formula is: ; In the formula, This represents the calculated sensitivity of the target MEMS sensor to the r-th gas. This represents the response value of the target MEMS sensor to the detection of the r-th gas. This represents the response time of the target MEMS sensor to the detection of the r-th gas; r belongs to 1 to m; S302. After calculating the sensitivity of the target MEMS sensor to different gases, judge all sensitivities and extract the gas with the highest sensitivity. When the gas corresponding to the highest sensitivity is H2S gas, determine that the target MEMS sensor selectivity is H2S gas.

[0025] To investigate the cross-sensitivity of coated MEMS sensors to gases, equal concentrations of H2S, Cl, Air, and SF6 gases were prepared and introduced into the gas chamber. Figure 4The gas selectivity plot of the target MEMS sensor reveals that it exhibits low sensitivity to Cl, Air, and SF6 gases at the same concentration, specifically NiO. x -SnO2 has specific selectivity for H2S, that is, it has high selectivity for hydrogen sulfide gas.

[0026] S400: Use a gas sensing and analysis system to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. The specific steps for calculating the shortest recovery time of the target MEMS sensor are as follows: S401. Use a gas sensing analysis system to collect the response time and response value changes of the target MEMS sensor, plot a curve of response value versus response time, extract the peak value and the trough value to the right of the peak value from the curve, and calculate the recovery time of the target MEMS sensor using the following formula: In the formula, Ht represents the calculated recovery time of the target MEMS sensor. This indicates the response time corresponding to the trough value to the right of the peak. This indicates the response time corresponding to the peak value; S402. Repeat the calculation of the recovery time of the target MEMS sensor each time, and extract the minimum value as the shortest recovery time of the target MEMS sensor.

[0027] Figure 5 The response-recovery curve of this sensor was measured at 1-second intervals, with random time points within the response time. The sensor's response-recovery time should also be measured repeatedly to eliminate errors introduced by manual calculation. The response time is approximately 45 seconds, and the recovery time is approximately 55 seconds. The response is fast, and the response-recovery time is significantly improved compared to existing MEMS sensors.

[0028] S500. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. Two repeatable detections are performed, one for short period and one for long period, and the stability of the target MEMS sensor is calculated. The specific steps for calculating the stability of the target MEMS sensor are as follows: S501. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. First, short-cycle acquisition is performed in seconds. The acquisition interval is h seconds to obtain the sensor output value. The standard deviation of short-cycle acquisition is calculated. The calculated standard deviation is used as the short-term stability Wd of the target MEMS sensor. S502. Long-term data acquisition is performed on a daily basis, with an acquisition interval of H days to obtain the sensor output value. The standard deviation of the long-term acquisition is calculated, and this standard deviation is used as the long-term stability (WL) of the target MEMS sensor. The short-term stability and long-term stability are combined to generate the overall stability using the following formula: ; In the formula, W represents the stability of the target MEMS sensor.

[0029] Figure 6 The repeatability of sensors with different parameters in 100 ppm H2S gas is presented. For the same sensor, the repeatability error, i.e., the relative standard deviation of sensitivity, should not exceed ±5%. When the repeatability error of a MEMS sensor meets the above standard, it indicates that the sensor has good repeatability. Long-term stability testing was conducted on the MEMS gas sensor over a period of 30 days. Every 5 days, the sensor's response value was tested in a 100 ppm hydrogen sulfide gas atmosphere. The test results are as follows: Figure 7 As shown, the response fluctuations are very small, and the response remains basically stable. This fully demonstrates that the sensor has good short-term and long-term stability.

[0030] S600: Collect and calculate the weights of the minimum detection limit, sensitivity, shortest response time, and stability of the initial MEMS sensor, which is a MEMS sensor composed of MEMS micro-heaters; Construct a performance evaluation model for the target MEMS sensor, input the four types of data of the target MEMS sensor into the performance evaluation model, and calculate the comprehensive performance of the target MEMS sensor.

[0031] The specific steps to determine whether the performance of the target MEMS sensor is qualified are as follows: S601. Extract four performance data points from u initial MEMS sensors, and standardize these four performance data points using the deviation normalization formula to unify them into the same format; the four performance data points include minimum detection limit, sensitivity, shortest recovery time, and stability; calculate the weights of the four performance data points using the following formula: ; In the formula, E g e represents the weight of the g-th performance data in the calculation. g The information entropy represents the g-th type of performance data; A comprehensive performance model is constructed using the weights of the four performance data types, with the following formula: ; In the formula, P represents the overall performance of the sensor, D represents the polynomial for each performance data, and Z represents the calculated real-time performance data for each data point. maxZ represents the maximum value of each performance data point in the initial MEMS sensor data collected. min Z represents the minimum value of each performance data in the initial MEMS sensor data; Z includes four performance data values: minimum detection limit, sensitivity, shortest recovery time, and stability. S602, calculate the four performance data of the target MEMS sensor: LOD, MI r Ht and W are input into the comprehensive performance model to calculate the comprehensive performance P of the target MEMS sensor.

[0032] An intelligent data management system for gas sensors includes a data collection module, a gas detection device, a performance classification calculation module, a performance model construction module, and a comprehensive performance calculation module. The data collection module is used to collect the initial performance data of the MEMS sensor; The gas detection device includes a digitally controlled power supply, an electrochemical workstation, a MEMS sensor chip, and a gas chamber; one end of the MEMS sensor chip is connected to the digitally controlled power supply, and the other end is connected to the electrochemical workstation for real-time monitoring of electrical signals; The performance classification calculation module includes a minimum detection limit unit, a sensitivity unit, a recovery time unit, and a stability unit; The minimum detection limit unit is used to fit the response curve and calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. The sensitivity unit is used to detect different gases using the target MEMS sensor, and to collect the response time and response value during detection to calculate the sensitivity when detecting different gases. The recovery time unit is used to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. The stability unit is used to collect the sensor output value at each detection, perform two repeatable detections with short and long cycles respectively, and calculate the stability of the target MEMS sensor.

[0033] The performance model building module includes a weight calculation unit and a model building unit; The weight calculation unit is used to collect four types of performance data of the initial MEMS sensor, and calculate the weight of each type of performance data after standardization using the deviation standardization formula. The model building unit generates a comprehensive performance model using the weights of each performance data point calculated.

[0034] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered in all respects as exemplary and non-limiting, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be included within the present invention. No reference numerals in the claims should be construed as limiting the scope of the claims.

Claims

1. A smart data management method for gas sensors, characterized in that: The method includes the following steps: S100. A sensitive material is prepared by chemically reacting a tin source and nickel catalyst powder to form a gas sensor that attaches to the gas sensor when measuring H2S gas. The prepared sensitive material and the initial MEMS sensor are used to manufacture a target MEMS sensor. The target MEMS sensor is then used to detect H2S gas. S200. When using the target MEMS sensor to detect H2S gas, the gas sensing and analysis system collects the response time and response value of the target MEMS sensor when detecting H2S gas of different concentrations, plots the response time and response value response curves, and fits the response curves to calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. S300: Use the target MEMS sensor to detect different gases, collect the response time and response value during detection, calculate the sensitivity when detecting different gases, and determine whether the target MEMS sensor is selective for H2S gas. S400: Use a gas sensing and analysis system to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. S500. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. Two repeatable detections are performed, one for short period and one for long period, and the stability of the target MEMS sensor is calculated. S600: Collect and calculate the weights of the minimum detection limit, sensitivity, shortest response time, and stability of the initial MEMS sensor, which is a MEMS sensor composed of MEMS micro-heaters; Construct a performance evaluation model for the target MEMS sensor, input the four types of data of the target MEMS sensor into the performance evaluation model, and calculate the comprehensive performance of the target MEMS sensor.

2. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps for detecting H2S gas using the fabricated target MEMS sensor in step S100 are as follows: S101. Dissolve the tin source SnCl4·5H2O in ethanol, then add the alkaline reagent NH3·H2O to the solvent and stir magnetically to form a sol. After stirring and aging, the sol transforms into a gel. After drying the gel, calcination is performed until SnO2 powder is obtained. Nickel catalyst powder is then mixed and ground with SnO2. After sieving, the mixed powder is transferred to an alumina corundum crucible for sintering to obtain NiO. x -SnO2 nanomaterials; NiO x -SnO2 nanomaterials as sensitive materials; S102. The prepared sensitive material is mixed with deionized water to form a paste. The mixture is then dropped onto a micro-hot plate using a micropipette. After drying at room temperature, the mixture is placed in a vacuum drying oven for aging treatment. The sensing element is then heat-treated to remove bound water and to form a dense oxide film on the surface of the gas sensor, thus obtaining the initial MEMS sensor. The sensitive material is then spin-coated onto the initial MEMS sensor to obtain the target MEMS sensor. S103. After the MEMS sensor is fabricated and cooled, power is supplied to the electrodes with a CNC power supply to provide the reaction temperature to the micro-hot plate. Data is collected using a gas sensing analysis system. The target MEMS sensor is placed in a fume hood, and the target gas H2S is mixed with dry air and injected into the chamber until the gas comes into contact with the sensitive material. The current-time curve is tested. The response time and recovery time are defined as the time required for the target MEMS sensor to reach 90% of the total current change under adsorption and desorption conditions, respectively.

3. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps in S200 for calculating the minimum detection limit of the target MEMS sensor using a linear slope are as follows: S201, using NiO x -SnO2 nanomaterials provide H2S gas of different concentrations in a stepwise growth at an operating temperature of 400℃, with the growth rate being P. When H2S gas of different concentrations is detected using a target MEMS sensor, the response value and response time of the target MEMS sensor at the corresponding concentration are collected, and the response time is used as the abscissa and the response value as the ordinate to plot a response curve. S202. The plotted response curve is processed using a linear regression algorithm. The response values ​​of the target MEMS sensor at different concentrations are fitted to obtain the linear regression equation. In the equation, Res represents the response value of the target MEMS sensor, Rt represents the response time, a represents the slope of the linear regression equation, and b represents the intercept of the linear regression equation. The slope of the linear regression equation is taken as the optimal sensitivity of the target MEMS sensor, and the linearity of the linear regression equation is calculated using the following formula: ； In the formula, R 2 This indicates the linearity of the calculated linear regression equation. This represents the true response value for the i-th concentration. This represents the calculated response value for the i-th concentration. This represents the average of n concentration response values, where n represents the number of different concentrations of H2S gas detected by the special MEMS; i ranges from 1 to n. S203. Calculate the standard deviation Q using the n concentration response values ​​in the response curve graph, and then calculate the minimum detection limit (LOD) of the target MEMS sensor using the following formula: ； In the formula, LOD represents the lowest detection limit, Q represents the standard deviation of the response value, and a represents the slope of the linear regression equation.

4. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps in S300 to determine whether the target MEMS sensor being fabricated is selective for H2S gas are as follows: S301. Detect different gases using a target MEMS sensor. Let the types of gases be... , Let m represent the first, second, third, ..., m gases being detected, where m is a positive integer. The response time and response value for detecting different gases are collected, and the sensitivity of the target MEMS sensor to different gases is calculated using the response time and response value. The formula is: ； In the formula, This represents the calculated sensitivity of the target MEMS sensor to the r-th gas. This represents the response value of the target MEMS sensor to the detection of the r-th gas. This represents the response time of the target MEMS sensor to the detection of the r-th gas; r belongs to 1 to m; S302. After calculating the sensitivity of the target MEMS sensor to different gases, judge all sensitivities and extract the gas with the highest sensitivity. When the gas corresponding to the highest sensitivity is H2S gas, determine that the target MEMS sensor selectivity is H2S gas.

5. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps for calculating the shortest recovery time of the target MEMS sensor in S400 are as follows: S401. Use a gas sensing analysis system to collect the response time and response value changes of the target MEMS sensor, plot a curve of response value versus response time, extract the peak value and the trough value to the right of the peak value from the curve, and calculate the recovery time of the target MEMS sensor using the following formula: In the formula, Ht represents the calculated recovery time of the target MEMS sensor. This indicates the response time corresponding to the trough value to the right of the peak. This indicates the response time corresponding to the peak value; S402. Repeat the calculation of the recovery time of the target MEMS sensor each time, and extract the minimum value as the shortest recovery time of the target MEMS sensor.

6. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps for calculating the stability of the target MEMS sensor in S500 are as follows: S501. Under the same environment, the target MEMS sensor is used to detect H2S gas of the same concentration. The sensor output value is collected for each detection. First, short-cycle acquisition is performed in seconds. The acquisition interval is h seconds to obtain the sensor output value. The standard deviation of short-cycle acquisition is calculated. The calculated standard deviation is used as the short-term stability Wd of the target MEMS sensor. S502. Long-term data acquisition is performed on a daily basis, with an acquisition interval of H days to obtain the sensor output value. The standard deviation of the long-term acquisition is calculated, and this standard deviation is used as the long-term stability (WL) of the target MEMS sensor. The short-term stability and long-term stability are combined to generate the overall stability using the following formula: ； In the formula, W represents the stability of the target MEMS sensor.

7. The intelligent data management method for a gas sensor according to claim 1, characterized in that: The specific steps in S600 to determine whether the performance of the target MEMS sensor is qualified are as follows: S601. Extract four performance data from u initial MEMS sensors and standardize these four performance data using the deviation standardization formula to unify them into the same format; the four performance data include the minimum detection limit, sensitivity, shortest recovery time and stability. The weights of the four performance data points are calculated using the following formula: ； In the formula, E g e represents the weight of the g-th performance data in the calculation. g The information entropy represents the g-th type of performance data; A comprehensive performance model is constructed using the weights of the four performance data types, with the following formula: ； In the formula, P represents the overall performance of the sensor, D represents the polynomial for each performance data, and Z represents the calculated real-time performance data for each data point. max Z represents the maximum value of each performance data point in the initial MEMS sensor data collected. min Z represents the minimum value of each performance data in the initial MEMS sensor data; Z includes four performance data values: minimum detection limit, sensitivity, shortest recovery time, and stability. S602, calculate the four performance data of the target MEMS sensor: LOD, MI r Ht and W are input into the comprehensive performance model to calculate the comprehensive performance P of the target MEMS sensor.

8. An intelligent data management system for gas sensors, characterized in that: The intelligent data management system includes a data collection module, a gas detection device, a performance classification calculation module, a performance model construction module, and a comprehensive performance calculation module. The data collection module is used to collect the initial performance data of the MEMS sensor; The gas detection device includes a digitally controlled power supply, an electrochemical workstation, a MEMS sensor chip, and a gas chamber; one end of the MEMS sensor chip is connected to the digitally controlled power supply, and the other end is connected to the electrochemical workstation for real-time monitoring of electrical signals.

9. The intelligent data management system for a gas sensor according to claim 8, characterized in that: The performance classification calculation module includes a minimum detection limit unit, a sensitivity unit, a recovery time unit, and a stability unit; The minimum detection limit unit is used to fit the response curve and calculate the linear equation; the minimum detection limit of the target MEMS sensor is calculated using the linear slope. The sensitivity unit is used to detect different gases using the target MEMS sensor, and to collect the response time and response value during detection to calculate the sensitivity when detecting different gases. The recovery time unit is used to collect the response time and response value changes of the target MEMS sensor, generate a response-recovery curve, and calculate the shortest recovery time of the target MEMS sensor. The stability unit is used to collect the sensor output value at each detection, perform two repeatable detections with short and long cycles respectively, and calculate the stability of the target MEMS sensor.

10. The intelligent data management system for a gas sensor according to claim 8, characterized in that: The performance model construction module includes a weight calculation unit and a model construction unit; The weight calculation unit is used to collect four types of performance data of the initial MEMS sensor, and calculate the weight of each type of performance data after standardization using the deviation standardization formula. The model building unit generates a comprehensive performance model using the weights of each performance data point calculated.

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