Gas concentration compensation method and gas concentration compensation system

Abstract

The invention provides a gas concentration compensation method and a gas concentration compensation system, and relates to the technical field of gas detection. The method comprises the following steps: acquiring to-be-compensated gas concentration and current environmental parameters; the current environment parameters comprise a current temperature value, a current pressure value and a current gas flow value; according to a preset standard parameter, carrying out standardization processing on the current environment parameter to obtain an environment parameter change value; the environmental parameter change value comprises a temperature change value, a pressure change value and a gas flow change value; and compensating the to-be-compensated gas concentration based on the environmental parameter change value by using the gas concentration compensation model to obtain the compensated gas concentration. According to the gas concentration compensation method and device, the standardized environmental parameters and the to-be-compensated gas concentration are input into the pre-trained gas concentration compensation model for gas concentration compensation, and the accuracy of the compensation result is improved.

Description

Technical Field

[0001] The present application relates to the field of gas detection technology, and in particular to a gas concentration compensation method and a gas concentration compensation system. Background Art

[0002] Non-dispersive infrared (NDIR) technology is a method based on gas absorption theory used to measure gas concentration. Its core principle is to exploit the selective absorption of infrared light of specific wavelengths by gas molecules. This technology is widely used in environmental monitoring, industrial process control, medical diagnostics, and other fields.

[0003] However, existing non-dispersive infrared technology is susceptible to environmental influences, necessitating the use of compensation coefficients to correct the results. However, determining these compensation coefficients typically requires extensive experimental data, and the effectiveness of these compensation coefficients is limited by the complexity of existing physical models and environmental variations, making them difficult to adapt to changing environmental conditions and resulting in limited accuracy. Summary of the Invention

[0004] The purpose of the embodiments of the present application is to provide a gas concentration compensation method and a gas concentration compensation system, which are used to input the standardized environmental parameters and the gas concentration to be compensated into a pre-trained gas concentration compensation model for gas concentration compensation, thereby improving the accuracy of the compensation results.

[0005] In the first aspect, an embodiment of the present application provides a gas concentration compensation method, which includes: obtaining the gas concentration to be compensated and the current environmental parameters; the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value; standardizing the current environmental parameters according to preset standard parameters to obtain environmental parameter change values; the environmental parameter change values include temperature change values, pressure change values and gas flow change values; using a gas concentration compensation model, compensating the gas concentration to be compensated based on the environmental parameter change values to obtain the compensated gas concentration.

[0006] In the embodiments of the present application, the data collected by the sensor used to collect environmental data may contain errors due to differences in usage environments. Therefore, the collected current environmental parameters are standardized to reduce the impact of usage environment differences on the collected data. On this basis, the acquired gas concentration data to be compensated and the environmental parameter change value are input into the gas concentration compensation model. The gas concentration compensation model then compensates the gas concentration to be compensated based on the environmental parameter change value, thereby improving the accuracy of the compensation result.

[0007] In some embodiments, after obtaining the compensated gas concentration, the method further includes: determining whether a model update condition is met; if the model update condition is met, calculating the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment; if the error is greater than a preset error threshold, obtaining the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values; or calculating the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment, if the error is greater than the preset error threshold, determining whether the model update condition is met; if the model update condition is met, obtaining the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values.

[0008] In the embodiment of the present application, taking into account that the gas concentration compensation model may have phenomena such as decreased generalization ability, overfitting and data drift after long-term use, therefore, during the use of the model, if the model update conditions are met, the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment is calculated. When the error is greater than the preset error threshold, historical data is obtained to train and update the gas concentration compensation model, thereby improving the universality and accuracy of the model and improving the accuracy of the compensation results.

[0009] In some embodiments, the current environmental parameters are standardized according to preset standard parameters to obtain environmental parameter change values, including: calculating the difference between the current environmental parameters and the preset standard parameters, and determining the environmental parameter change value according to the difference.

[0010] The embodiment of the present application standardizes the environmental parameters by calculating the difference between the current environmental parameters and the preset standard parameters, reducing the impact of the use environment differences on the collected data, improving the accuracy of the environmental data input to the gas concentration compensation model, and thus improving the accuracy of the compensation results.

[0011] In some embodiments, before obtaining the concentration of the gas to be compensated and the current environmental parameters, the method also includes: receiving a voltage signal generated by the infrared detector; the voltage signal is generated by converting the light intensity signal of the infrared light after the infrared light detected by the infrared detector interacts with the gas to be compensated; using the Lambert-Beer law, the initial gas concentration of the gas to be compensated is calculated based on the voltage signal; and the initial gas concentration is calibrated based on preset standard parameters to obtain the concentration of the gas to be compensated.

[0012] The embodiment of the present application improves the accuracy of data input into the gas concentration compensation model by calibrating the concentration of the gas to be compensated, thereby improving the accuracy of the compensation result.

[0013] In some embodiments, the gas concentration compensation model is trained in the following manner: collecting different sample gas concentration data and sample environment data under different environmental conditions; the sample environment data includes sample temperature data, sample pressure data and sample gas flow data; using the sample gas concentration data and sample environment data to update the weights and biases of the initial gas concentration compensation model to obtain the gas concentration compensation model.

[0014] The embodiment of the present application trains the initial gas concentration compensation model by collecting different sample gas concentration data and sample environment data under different environmental conditions to update the weights and biases of the initial gas concentration compensation model, thereby improving the generalization ability and accuracy of the gas concentration compensation model.

[0015] In some embodiments, the initial gas concentration compensation model includes an input layer, a hidden layer and an output layer; a first initial weight and a first initial bias are included between the input layer and the hidden layer, and a second initial weight and a second initial bias are included between the hidden layer and the output layer; the weights and biases of the initial gas concentration compensation model are updated using sample gas concentration data and sample environmental data, including: calculating the error term of the hidden layer based on a preset activation function of the hidden layer, the second initial weight and the error term of the output layer; updating the first initial weight based on the error term of the hidden layer and the sample gas concentration data and the sample environmental data; updating the first initial bias based on the error term of the hidden layer; obtaining the output of the hidden layer based on the sample gas concentration data and the sample environmental data; updating the second initial weight based on the error term of the output layer and the output of the hidden layer; and updating the second initial bias based on the error term of the output layer.

[0016] During the training process of the gas concentration compensation model, the embodiment of the present application updates the weights and biases between each layer through the error terms and input data between each layer, thereby improving the accuracy of the weights and biases and thus improving the accuracy of the gas concentration compensation model.

[0017] In the second aspect, an embodiment of the present application provides a gas concentration compensation device, which includes: an acquisition module for obtaining the gas concentration to be compensated and the current environmental parameters; the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value; a processing module for standardizing the current environmental parameters according to preset standard parameters to obtain environmental parameter change values; the environmental parameter change values include temperature change values, pressure change values and gas flow change values; a compensation module for using a gas concentration compensation model to compensate the gas concentration to be compensated based on the environmental parameter change values to obtain the compensated gas concentration.

[0018] In a third aspect, an embodiment of the present application provides an electronic device comprising: a processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method steps of any one embodiment of the first aspect can be executed.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, comprising: computer program instructions are stored on the computer-readable storage medium, and when the computer program instructions are executed by a processor, the method steps of any embodiment of the first aspect are executed.

[0020] In a fifth aspect, an embodiment of the present application provides a computer program product, comprising: computer program instructions, which, when executed by a processor, execute the method steps of any one of the embodiments of the first aspect.

[0021] In the sixth aspect, an embodiment of the present application provides a gas concentration compensation system, which includes an infrared light source module, a gas pool module, an infrared detector module and a microprocessor; the gas pool module includes a gas pool sensor; the gas pool sensor includes a temperature sensor, a pressure sensor and a gas flow sensor; the infrared light source module is used to emit infrared light to the gas pool module, so that the infrared light interacts with the gas to be compensated in the gas pool, and then passes through the gas pool module and is incident on the infrared detector module; the infrared detector module is used to detect changes in the light intensity of the infrared light, generate a voltage signal, and send the voltage signal to the microprocessor; the gas pool sensor is used to collect environmental parameters in the gas pool, and send the environmental parameters to the microprocessor; the microprocessor is used to execute the method steps of any one embodiment of the first aspect.

[0022] Other features and advantages of the present application will be described in the subsequent description, and in part will become apparent from the description, or will be understood by practicing the embodiments of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the following is a brief introduction to the drawings required for use in the embodiments of the present application. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without creative work.

[0024] Figure 1 A schematic diagram of a gas concentration compensation system provided in an embodiment of the present application;

[0025] Figure 2 A flow chart of a gas concentration compensation method provided in an embodiment of the present application;

[0026] Figure 3 A schematic diagram of the structure of an initial gas concentration compensation model provided in an embodiment of the present application;

[0027] Figure 4 A schematic diagram of another gas concentration compensation system provided in an embodiment of the present application;

[0028] Figure 5 A schematic structural diagram of a gas concentration compensation device provided in an embodiment of the present application;

[0029] Figure 6 A schematic diagram of the electronic device structure provided in an embodiment of the present application. DETAILED DESCRIPTION

[0030] The following embodiments of the technical solution of the present application will be described in detail with reference to the accompanying drawings. The following embodiments are only used to more clearly illustrate the technical solution of the present application and are therefore only examples and are not intended to limit the scope of protection of the present application.

[0031] It should be noted that all technical and scientific terms used herein have the same meanings as those commonly understood by technicians in the technical field to which this application belongs; the terms used herein are only for the purpose of describing specific embodiments and are not intended to limit this application; the terms "including" and "having" in the specification and claims of this application and the above-mentioned figure descriptions and any variations thereof are intended to cover non-exclusive inclusions.

[0032] In the description of the embodiments of this application, the technical terms "first" and "second" are used only to distinguish different objects and should not be understood to indicate or imply relative importance or implicitly specify the quantity, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, the meaning of "plurality" is more than two, unless otherwise clearly and specifically defined.

[0033] In the description of the embodiments of this application, the term "and / or" is simply a description of the association relationship between associated objects, indicating that three relationships can exist. For example, A and / or B can represent the following three situations: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0034] When measuring gas concentration using a non-dispersive infrared sensor, factors such as ambient temperature, air pressure, and airflow can significantly affect the measurement results, leading to deviations in the measured values. Therefore, the sensor output must be compensated for temperature, pressure, and flow to obtain accurate gas concentrations.

[0035] Traditional compensation methods are based on the Lambert-Beer Law and empirical formulas. However, these methods usually require a large amount of experimental data to determine the compensation coefficient. Moreover, the compensation effect is limited by the complexity of existing physical models and environmental changes, making it difficult to adapt to changing environmental conditions, resulting in low compensation accuracy.

[0036] To solve the above problems, the present application provides a gas concentration compensation method and a gas concentration compensation system, which are used to input the standardized environmental parameters and the gas concentration to be compensated into a pre-trained gas concentration compensation model for gas concentration compensation, thereby improving the accuracy of the compensation results.

[0037] Figure 1 A schematic diagram of a gas concentration compensation system provided in an embodiment of the present application is shown in FIG. Figure 1 As shown, the gas concentration compensation system 10 includes an infrared light source module 101 , a gas pool module 102 , an infrared detector module 103 and a microprocessor 104 .

[0038] The gas pool module 102 includes a gas pool sensor; the gas pool sensor includes a temperature sensor, a pressure sensor and a gas flow sensor.

[0039] The infrared light source module 101 is used to emit infrared light to the gas pool module 102, so that the infrared light interacts with the gas to be compensated in the gas pool, passes through the gas pool module 102 and is incident on the infrared detector module 103; the infrared detector module 103 is used to detect the change in light intensity of the infrared light, generate a voltage signal, and send the voltage signal to the microprocessor 104; the gas pool sensor is used to collect environmental parameters in the gas pool and send the environmental parameters to the microprocessor 104; the microprocessor 104 is used to execute the steps of the gas concentration compensation method.

[0040] In a specific implementation, the infrared light source module 101 is an infrared modulated light source, which is used to emit infrared light of a specific wavelength. The infrared light source module can be an infrared laser, an infrared LED, or a MEMS infrared light source.

[0041] The infrared detector module 103 is a device that can detect infrared radiation and convert it into an electrical signal. The infrared detector module can be a thermal detector or a photon detector.

[0042] The infrared detector module 103 detects the change in the light intensity of the infrared light, generates a voltage signal, and sends the absorbance of the voltage signal to the microprocessor 104, so that the microprocessor 104 calculates the absorbance based on the voltage signal through the Lambert-Beer law, and calculates the initial gas concentration of the gas to be compensated based on the absorbance.

[0043] The gas to be compensated refers to the gas that needs to be tested for concentration and the detected concentration needs to be compensated.

[0044] The gas to be compensated can be common gases such as carbon monoxide, carbon dioxide, hydrogen, ammonia, sulfur dioxide, and oxygen.

[0045] The gas pool module 102 has a gas inlet and a gas outlet for the flow of the gas to be compensated.

[0046] The gas cell sensor is used to detect the environmental parameters of the gas to be compensated.

[0047] Exemplarily, the temperature sensor, the pressure sensor, and the gas flow sensor respectively detect environmental parameters such as the temperature, pressure, and gas flow of the gas to be compensated in the gas pool module.

[0048] It should be noted that the gas pool sensor may also include a humidity sensor, an electromagnetic interference sensor, a light intensity sensor, etc. Therefore, the environmental parameter may also include at least one of a humidity value, an electromagnetic interference value, and a light intensity value.

[0049] The microprocessor 104 may be an electronic device, a computer-readable storage medium, a server, or an embedded single-chip microcomputer.

[0050] The electronic device may be a smart phone, a tablet computer, a computer, a personal digital assistant (PDA), etc.; the server may be an application server or a web server.

[0051] The embedded MCU can be a low-power ARM Cortex-M series MCU to enable long-term stable operation of the device. ARM Cortex-M series MCUs can use the Arm Cortex Microcontroller Software Interface Standard-Neural Networks (CMSIS-NN) to accelerate network model calculations and improve the inference speed of the gas concentration compensation model. ARM Cortex-M series MCUs can also use deep learning frameworks such as TensorFlow Lite, allowing trained deep learning models to be converted into a format suitable for running in embedded systems and providing an efficient inference engine.

[0052] Figure 2A flow chart of a gas concentration compensation method provided in an embodiment of the present application. To facilitate understanding of the technical solution provided in an embodiment of the present application, the following describes the application scenario of the workpiece defect recognition method provided in an embodiment of the present application, taking a server as an example of the execution subject.

[0053] like Figure 2 As shown, the method includes:

[0054] Step S101 , obtaining the concentration of the gas to be compensated and the current environmental parameters; the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value.

[0055] Step S102 , standardizing the current environmental parameters according to preset standard parameters to obtain environmental parameter change values; the environmental parameter change values include temperature change values, pressure change values, and gas flow change values.

[0056] Step S103 : using a gas concentration compensation model, compensating the concentration of the gas to be compensated based on the change value of the environmental parameter to obtain a compensated gas concentration.

[0057] During the specific implementation process, the current environmental parameters are collected through the gas pool sensor, and a voltage signal is generated through the infrared detector module. The voltage signal is generated by converting the absorbance intensity signal of the infrared light detected by the infrared detector after the infrared light interacts with the gas to be compensated.

[0058] The server receives the current environmental parameters sent by the gas pool sensor and the voltage signal generated by the infrared detector, calculates the absorbance of the gas to be compensated based on the voltage signal, and determines the gas concentration of the gas to be compensated based on the absorbance, thereby obtaining the gas concentration to be compensated.

[0059] In order to reduce the impact of differences in the operating environment on the data collected by the gas sensor, the current environmental parameters are standardized according to preset standard parameters to obtain the environmental parameter change values.

[0060] In one embodiment, a preset standard parameter is used as a fixed value, such as a temperature parameter of T0 = 25°C, a pressure parameter of P0 = 1013.25 hPa, and a gas flow parameter of Q0 = 0.8 L / min. The difference between the current environmental parameter and the preset standard parameter is calculated, and the environmental parameter change value is determined based on the difference.

[0061] In an optional implementation, the mean of historical environmental parameters is calculated as the preset standard parameter, and then the difference between the current environmental parameter and the preset standard parameter is calculated, and the environmental parameter change value is determined according to the difference.

[0062] After obtaining the environmental parameter change value, the environmental parameter change value and the gas concentration to be compensated are input into a pre-trained gas concentration compensation model, so that the gas concentration compensation model compensates the gas concentration to be compensated based on the environmental parameter change value to obtain the compensated gas concentration.

[0063] In the embodiments of the present application, the data collected by the sensor used to collect environmental data may contain errors due to differences in usage environments. Therefore, the collected current environmental parameters are standardized to reduce the impact of usage environment differences on the collected data. On this basis, the acquired gas concentration data to be compensated and the environmental parameter change value are input into the gas concentration compensation model. The gas concentration compensation model then compensates the gas concentration to be compensated based on the environmental parameter change value, thereby improving the accuracy of the compensation result.

[0064] In some embodiments, after obtaining the compensated gas concentration, the method further includes: determining whether a model update condition is met; if the model update condition is met, calculating the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment; if the error is greater than a preset error threshold, obtaining the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values; or calculating the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment, if the error is greater than the preset error threshold, determining whether the model update condition is met; if the model update condition is met, obtaining the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values.

[0065] During the specific implementation process, considering that the gas concentration compensation model will suffer from decreased generalization ability, overfitting, and data drift after long-term use, it is necessary to dynamically update the gas concentration compensation model during its use to improve the universality and accuracy of the gas concentration compensation model.

[0066] The embodiment of the present application proposes an online learning mechanism, which includes model update conditions and error calculation.

[0067] The model update condition indicates whether the online learning mechanism is triggered. The error calculation indicates whether online learning is required.

[0068] The model update conditions include:

[0069] Regular updates: For example, the online learning mechanism is triggered every fixed period (e.g., 7 days). Alternatively, the online learning mechanism is triggered when the amount of environmental data collected by the gas pool sensor reaches a certain amount (e.g., 10,000 pieces).

[0070] Adaptive update: When the voltage signal detected by the infrared detector has long-term drift (for example, in the past 2000 measurements, more than 90% of the errors are greater than the voltage threshold), the online learning mechanism is triggered.

[0071] Manual update: Users can manually trigger the online learning mechanism through remote commands, which is suitable for calibration needs in special scenarios.

[0072] Error calculation is to determine whether online learning is required by calculating the difference between the compensated gas concentration value and the actual value of the gas to be compensated measured under standard conditions:

[0073]

[0074] Where e represents the error between the compensated gas concentration value and the true value of the gas to be compensated measured under standard environment, C true It represents the true value of the gas to be compensated measured under standard environment, and C represents the gas concentration value after compensation.

[0075] If the difference between the compensated gas concentration value and the actual value of the gas to be compensated measured under standard conditions is greater than the preset error threshold, online learning is performed:

[0076] |e|>e threshold

[0077] Among them, e threshold represents the preset error threshold, e represents the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment, and |.| represents the absolute value symbol.

[0078] For example, the preset error threshold is 5%, |e|=6%, that is, when the error exceeds 5%, online learning is started.

[0079] The specific process of online learning is: obtaining the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values and historical gas flow change values.

[0080] The historical gas concentration to be compensated and the historical environmental parameter change values refer to input data for gas concentration compensation using a gas concentration compensation model within a preset time period in the past.

[0081] The preset time period is a pre-set value. For example, the preset time period is the length of time between the current update and the previous update. Alternatively, the preset time period is the historical data of the past 7 days or the past 5 days. The specific setting can be based on actual circumstances.

[0082] In a specific implementation process, it is possible to first determine whether the trigger condition is met and then calculate the error, so that the error is calculated only when the trigger condition is met, thereby reducing unnecessary calculation overhead.

[0083] Therefore, one implementation method is: determine whether the model update conditions are met; if the model update conditions are met, calculate the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment; if the error is greater than a preset error threshold, obtain the historical concentration of the gas to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values.

[0084] During the specific implementation process, the error may be calculated first and then whether the trigger condition is met may be determined, so that the error is calculated immediately after each measurement, and the performance of the model can be understood in real time.

[0085] Therefore, an optional implementation method is: calculate the error between the compensated gas concentration value and the actual value of the gas to be compensated measured under a standard environment. If the error is greater than a preset error threshold, determine whether the model update condition is met; if the model update condition is met, obtain the historical concentration of the gas to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values and historical gas flow change values.

[0086] The embodiment of the present application can dynamically adjust the compensation model according to new environmental conditions through real-time dynamic compensation, thereby improving the universality and accuracy of the model, so that more accurate gas concentration values can be obtained under different environmental conditions, thereby improving the accuracy of the compensation results.

[0087] In some embodiments, the current environmental parameters are standardized according to preset standard parameters to obtain environmental parameter change values, including: calculating the difference between the current environmental parameters and the preset standard parameters, and determining the environmental parameter change value according to the difference.

[0088] In actual environments, sensors that detect environmental parameters will have errors under different usage environments. Therefore, in order to reduce the errors in data collected by sensors in different environments, the embodiment of the present application calculates the difference between the current environmental parameters and the preset standard parameters, and determines the change value of the environmental parameters based on the difference to standardize the environmental parameters.

[0089] For example, if the current environmental parameters include the current temperature value T, the current pressure value P, and the current gas flow value Q, the preset standard parameter of the temperature value is T0=25°C, the preset standard parameter of the pressure value is P0=1013.25hPa, and the preset standard parameter of the gas flow value is Q0=0.8L / min.

[0090] The temperature change value is T-T0, the pressure change value is P-P0, and the gas flow change value is Q-Q0.

[0091] The embodiment of the present application standardizes the environmental parameters by calculating the difference between the current environmental parameters and the preset standard parameters, reducing the impact of the use environment differences on the collected data, improving the accuracy of the environmental data input to the gas concentration compensation model, and thus improving the accuracy of the compensation results.

[0092] In some embodiments, before obtaining the concentration of the gas to be compensated and the current environmental parameters, the method also includes: receiving a voltage signal generated by the infrared detector; the voltage signal is generated by converting the light intensity signal of the infrared light after the infrared light detected by the infrared detector interacts with the gas to be compensated; using the Lambert-Beer law, the initial gas concentration of the gas to be compensated is calculated based on the voltage signal; and the initial gas concentration is calibrated based on preset standard parameters to obtain the concentration of the gas to be compensated.

[0093] Lambert-Beer's law is as follows:

[0094]

[0095] Where A is the absorbance, which indicates the intensity of infrared light absorption by the gas; T is the transmittance, which is the ratio of the outgoing light intensity I to the incident light intensity I0. The outgoing light intensity I and the incident light intensity I0 are determined by the reference voltage and measurement voltage generated by the infrared detector, reflecting the remaining intensity ratio of the infrared light after passing through the gas cell and is an indicator of the degree of light absorption by the gas; K is the molar absorptivity, which is related to the properties of the gas to be compensated and the wavelength of the incident light; c is the gas concentration, measured in moles per liter (mol / L); and L is the optical path length of the gas cell, measured in centimeters (cm).

[0096] In practice, the voltage signal generated by the infrared detector can be used to determine the transmittance, which, based on the Lambert-Beer law, can be used to determine the absorbance. Because the optical path length L and molar absorptivity K of the gas cell are known, the initial gas concentration can be calculated based on the absorbance.

[0097] Since the initial gas concentration calculated according to the Lambert-Beer law is the gas concentration under standard conditions, in actual applications, the influence of environmental parameters on its measurement results needs to be considered. Therefore, the initial gas concentration needs to be calibrated based on preset standard parameters to obtain the gas concentration to be compensated.

[0098] Among them, the preset standard parameters can be found in the above embodiment and will not be repeated here.

[0099] The calibration may be performed using a nonlinear fitting method, a segmented calibration method, or other calibration methods, which are not specifically limited in the present embodiment.

[0100] For example, if the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value, the relationship between the temperature value, the pressure value, the gas flow value and the initial gas concentration is established by nonlinear fitting: Among them, c compensated Indicates the concentration of the gas to be compensated, c indicates the initial gas concentration, T0 indicates the standard temperature value, T indicates the current temperature value, P0 indicates the standard pressure value, P indicates the current pressure value, Q0 indicates the standard flow value, and Q indicates the current gas flow value.

[0101] For another example: If the data distribution is uneven or there are multiple linear regions, a segmented calibration method can be used:

[0102] Data segmentation: Segment the collected data according to different ranges of temperature, pressure or flow.

[0103] Segment fitting: In each segment, linear or nonlinear fitting is performed to establish the relationship between the concentration and environmental factors in that segment.

[0104] Segmented calibration: According to the actual measured environmental factors, select the corresponding segmented model for calibration calculation.

[0105] Finally, the result of each segment calibration is the final gas concentration to be compensated.

[0106] The embodiment of the present application improves the accuracy of data input into the gas concentration compensation model by calibrating the concentration of the gas to be compensated, thereby improving the accuracy of the compensation result.

[0107] In some embodiments, the gas concentration compensation model is trained in the following manner: collecting different sample gas concentration data and sample environment data under different environmental conditions; the sample environment data includes sample temperature data, sample pressure data and sample gas flow data; using the sample gas concentration data and sample environment data to update the weights and biases of the initial gas concentration compensation model to obtain the gas concentration compensation model.

[0108] In a specific implementation process, different concentrations and / or different types of gases are filled into the gas pool to obtain different gas concentration data as sample gas concentration data.

[0109] Environmental data under different environments are collected as sample environmental data through the gas pool sensor.

[0110] Exemplarily, the gas cell sensor includes a temperature sensor, a pressure sensor, and a gas flow sensor. The temperature sensor is a high-precision temperature sensor (resolution ≤ 0.1°C, error ≤ ±0.2°C), the pressure sensor is a high-precision pressure sensor (resolution ≤ 0.5hPa, error ≤ ±1hPa), and the gas flow sensor is a high-precision flow meter (resolution ≤ 0.01L / min, error ≤ ±1%).

[0111] Environmental data in different environments are collected as sample environmental data through the following test environment control requirements:

[0112] (1) Temperature environment:

[0113] A. Use adjustable air pressure control system to ensure accurate pressure control with an error of ≤±5hPa.

[0114] B. Set multiple test temperature points (such as 0℃, 10℃, 25℃, 40℃, 60℃) to simulate different temperature conditions.

[0115] (2) Pressure environment:

[0116] A. Use a high-precision constant temperature box or temperature control room to ensure that temperature changes are controlled and avoid external interference.

[0117] B. Set different pressure points (such as 800hPa, 900hPa, 1013.25hPa, 1100hPa) to cover different altitude environments.

[0118] (3) Flow control:

[0119] A. Use a flow stabilizing device to ensure stable gas flow and avoid the influence of flow fluctuation on measurement results.

[0120] B. Set different flow rates (such as 0.5 L / min, 0.8 L / min, 1.2 L / min) to ensure that the model can adapt to different gas flow rate scenarios.

[0121] The gas concentration, temperature, pressure, gas flow rate and timestamp collected in each test are stored as a data record.

[0122] In order to enhance the generalization ability of the gas concentration compensation model, at least a preset number of valid data, such as 1000 groups, are collected under each set of experimental conditions.

[0123] The collected data is denoised and normalized. Denoising uses a denoising filter algorithm (such as mean filter, median filter) to remove outliers and prevent errors from affecting the training effect.

[0124] After obtaining different sample gas concentration data and sample environment data under different environmental conditions, the weights and biases are initialized to obtain an initial gas concentration compensation model. Using the initial gas concentration compensation model, the error is calculated based on the sample gas concentration data and sample environment data, and the weights and biases are updated until the error converges to a set threshold to obtain the gas concentration compensation model.

[0125] The embodiment of the present application trains the initial gas concentration compensation model by collecting different sample gas concentration data and sample environment data under different environmental conditions to update the weights and biases of the initial gas concentration compensation model, thereby improving the generalization ability and accuracy of the gas concentration compensation model.

[0126] In some embodiments, the initial gas concentration compensation model includes an input layer, a hidden layer and an output layer; a first initial weight and a first initial bias are included between the input layer and the hidden layer, and a second initial weight and a second initial bias are included between the hidden layer and the output layer; the weights and biases of the initial gas concentration compensation model are updated using sample gas concentration data and sample environmental data, including: calculating the error term of the hidden layer based on a preset activation function of the hidden layer, the second initial weight and the error term of the output layer; updating the first initial weight based on the error term of the hidden layer and the sample gas concentration data and the sample environmental data; updating the first initial bias based on the error term of the hidden layer; obtaining the output of the hidden layer based on the sample gas concentration data and the sample environmental data; updating the second initial weight based on the error term of the output layer and the output of the hidden layer; and updating the second initial bias based on the error term of the output layer.

[0127] Figure 3 A schematic diagram of the structure of an initial gas concentration compensation model provided in an embodiment of the present application is shown in FIG. Figure 3As shown in Figure 1, the model consists of an input layer, a hidden layer, and an output layer. The input layer and hidden layer each contain four nodes. Each node in the input layer represents the gas concentration, temperature change, pressure change, and gas flow change to be compensated. Each node in the hidden layer represents a neuron, which receives input data from each node in the input layer.

[0128] based on Figure 3 The structure of the initial gas concentration compensation model shown in the figure describes the specific process of updating the weights and biases in the model as follows:

[0129] (1) Input layer: The concentration of the gas to be compensated, the temperature change value, the pressure change value and the gas flow change value are taken as input, respectively denoted as x1, x2, x3, x4, where:

[0130] x1=C0

[0131] x2=T-T0

[0132] x3=P-P0

[0133] x4=Q-Q0

[0134] Wherein, C0 represents the concentration of the gas to be compensated.

[0135] (2) Input layer to hidden layer:

[0136] Among them, x i is the value of the i-th node in the input layer, n represents the number of input layer nodes, i represents the number of input layer nodes; j represents the number of neurons in the hidden layer, y j represents the value of the jth neuron node in the hidden layer, w ij represents the weight from the i-th node in the input layer to the j-th node in the hidden layer, b j represents the bias of the jth node in the hidden layer.

[0137] (3) From hidden layer to output layer:

[0138] Among them, C is the value of the output layer. Since the number of nodes in the hidden layer is the same as the number of nodes in the input layer, n also represents the number of nodes in the hidden layer, j represents the number of neurons in the hidden layer, and y j represents the value of the jth neuron node in the hidden layer, w j5 represents the weight from the jth node in the hidden layer to the output layer, and b represents the bias of the output layer.

[0139] (4) Hidden layer error calculation: δ j =w j5 .δ.f′(y j )

[0140] Among them, δ j represents the error of the jth node in the hidden layer, w j5 represents the weight from the jth node in the hidden layer to the output layer, δ represents the error term of the output layer; f′(y j ) represents the derivative of the preset activation function of the hidden layer.

[0141] (5) Update weights and biases:

[0142] Update the weights and biases between the input layer and the hidden layer:

[0143] w ij (t+1)=w ij (t)-η.δ j .x i

[0144] b j (t+1)=b j (t)-η.δ j

[0145] Among them, w ij (t) represents the weight between the input layer and the hidden layer after the tth update, w ij (t+1) represents the weight between the input layer and the hidden layer after the (t+1)th update, η represents the learning rate, x i is the value of the i-th node in the input layer, δ j represents the error of the jth node in the hidden layer, b j (t) represents the bias between the input layer and the hidden layer after the tth update, b j (t+1) represents the bias between the input layer and the hidden layer after the t+1th update.

[0146] Update the weights and biases between the hidden layer and the output layer:

[0147] w j5 (t+1)=w j5 (t)-η.δ.y j

[0148] b(t+1)=b(t)-η.δ

[0149] Among them, w j5 represents the weight between the hidden layer and the output layer after the tth update, w j5 (t+1) represents the weight between the hidden layer and the output layer after the (t+1)th update, η represents the learning rate, δ represents the error term of the output layer, and y jrepresents the value of the jth neuron node in the hidden layer, b(t) represents the bias between the hidden layer and the output layer after the tth update, and b(t+1) represents the bias between the hidden layer and the output layer after the t+1th update.

[0150] It should be noted that Figure 3 The schematic diagram of the initial gas concentration compensation model is only an example. In actual applications, the number of input layer nodes and hidden layers can be increased based on actual conditions. For example, the number of input layer nodes can be 6 or 8, and the number of hidden layers can be 3 or 4, etc., thereby improving the model's complexity and accuracy.

[0151] During the training process of the gas concentration compensation model, the embodiment of the present application updates the weights and biases between each layer through the error terms and input data between each layer, thereby improving the accuracy of the weights and biases and thus improving the accuracy of the gas concentration compensation model.

[0152] Figure 4 A schematic diagram of another gas concentration compensation system provided for the implementation of this application is shown in FIG. Figure 4 As shown, the system includes: an infrared modulated light source, a filter, a gas pool, an infrared detector, an A / D converter, and a microprocessor; wherein the gas pool includes a gas pool sensor, and the gas pool sensor includes a temperature sensor, a pressure sensor, and a flow sensor (gas flow sensor).

[0153] During system operation, an infrared modulated light source generates infrared light of a specific wavelength. After passing through a filter, it enters the gas cell and interacts with the gas being measured. The gas cell is equipped with an inlet and outlet for gas circulation. Temperature, pressure, and flow sensors monitor gas environment parameters in real time. An infrared detector detects changes in infrared light intensity after passing through the gas and transmits the detection signal to an A / D converter for conversion into a digital signal. The microprocessor executes the gas concentration compensation process by receiving the digital signal converted by the A / D converter and the temperature, pressure, and gas flow data collected by the temperature, pressure, and flow sensors, respectively.

[0154] This application can be widely used in environmental monitoring, industrial process control, gas analysis, automated detection and other fields, and is particularly suitable for occasions requiring high-precision gas concentration measurement, such as air quality detection, industrial waste gas emission monitoring, greenhouse gas emission monitoring, etc.

[0155] The beneficial effects of this application are as follows:

[0156] Strong adaptability: Through deep learning technology, the system can dynamically adapt to complex environmental changes and perform gas concentration compensation in real time.

[0157] High precision: By training the neural network, it can accurately capture the impact of temperature, pressure, and flow changes on gas concentration measurement, thereby improving measurement accuracy.

[0158] Real-time: The neural network model can update compensation parameters in real time to ensure accurate measurement results in different working environments.

[0159] Flexibility: This compensation method is widely applicable to the concentration measurement of multiple gases and can work stably in various industrial applications and environmental monitoring.

[0160] Figure 5 A schematic diagram of the structure of a gas concentration compensation device provided in an embodiment of the present application is shown in FIG. Figure 5 As shown, the device includes: an acquisition module 501, a processing module 502 and a compensation module 503; wherein,

[0161] The acquisition module 501 is used to obtain the concentration of the gas to be compensated and the current environmental parameters; the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value; the processing module 502 is used to standardize the current environmental parameters according to the preset standard parameters to obtain the environmental parameter change value; the environmental parameter change value includes the temperature change value, the pressure change value and the gas flow change value; the compensation module 503 is used to use the gas concentration compensation model to compensate the gas concentration to be compensated based on the environmental parameter change value to obtain the compensated gas concentration.

[0162] Based on the above embodiment, the device also includes an update module for determining whether the model update condition is met; if the model update condition is met, the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment is calculated; if the error is greater than a preset error threshold, the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period are obtained to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values; or the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment is calculated, and if the error is greater than the preset error threshold, it is determined whether the model update condition is met; if the model update condition is met, the historical gas concentration to be compensated and the historical environmental parameter change values within a preset time period are obtained to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values.

[0163] Based on the above embodiment, the processing module 502 is specifically used to calculate the difference between the current environmental parameter and the preset standard parameter, and determine the environmental parameter change value according to the difference.

[0164] Based on the above embodiment, the device also includes a calibration module for receiving a voltage signal generated by the infrared detector; the voltage signal is generated by converting the light intensity signal of the infrared light detected by the infrared detector after the infrared light interacts with the gas to be compensated; using the Lambert-Beer law, the initial gas concentration of the gas to be compensated is calculated based on the voltage signal; the initial gas concentration is calibrated based on preset standard parameters to obtain the concentration of the gas to be compensated.

[0165] Based on the above embodiment, the device also includes a model training module for collecting different sample gas concentration data and sample environment data under different environmental conditions; the sample environment data includes sample temperature data, sample pressure data and sample gas flow data; the sample gas concentration data and sample environment data are used to update the weights and biases of the initial gas concentration compensation model to obtain a gas concentration compensation model.

[0166] Based on the above embodiment, the initial gas concentration compensation model includes an input layer, a hidden layer and an output layer; the input layer and the hidden layer include a first initial weight and a first initial bias, and the hidden layer and the output layer include a second initial weight and a second initial bias; the model training module is specifically used to: calculate the error term of the hidden layer based on the preset activation function of the hidden layer, the second initial weight and the error term of the output layer; update the first initial weight based on the error term of the hidden layer and the sample gas concentration data and the sample environment data; update the first initial bias according to the error term of the hidden layer; obtain the output of the hidden layer according to the sample gas concentration data and the sample environment data; update the second initial weight according to the error term of the output layer and the output of the hidden layer; and update the second initial bias according to the error term of the output layer.

[0167] It should be understood that the device corresponds to the above-mentioned gas concentration compensation method embodiment and can perform each step involved in the above-mentioned method embodiment. The specific functions of the device can be found in the description above. To avoid repetition, a detailed description is omitted here. The device includes at least one software function module that can be stored in a memory in the form of software or firmware or embedded in the device's operating system (OS).

[0168] Figure 6 This is a schematic diagram of the electronic device structure provided in the embodiment of the present application, such as Figure 6 As shown, the electronic device includes a processor 601 (processor), a memory 602 (memory), and a bus 603; wherein the processor 601 and the memory 602 communicate with each other via the bus 603. The processor 601 is used to call program instructions in the memory 602 to execute the methods provided by the above-mentioned method embodiments.

[0169] The processor 601 can be an integrated circuit chip with signal processing capabilities. The above-mentioned processor 601 can be a general-purpose processor, including a single-chip microcomputer (Microcontroller Unit, MCU), a central processing unit (Central Processing Unit, CPU), a network processor (Network Processor, NP), etc.; it can also be a digital signal processor (Digital Signal Processor, DSP), an application-specific integrated circuit (Application Specific Integrated Circuit, ASIC), a field programmable gate array (Field Programmable Gate Array, FPGA) or other programmable logic devices, discrete gates or transistor logic devices, discrete hardware components. It can implement or execute the various methods, steps and logic block diagrams disclosed in the embodiments of the present application. The general-purpose processor can be a microprocessor or the processor can also be any conventional processor, etc.

[0170] The memory 602 can be implemented by any type of volatile or non-volatile memory device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0171] An embodiment of the present application provides a computer program product, including: computer program instructions, which, when executed by a processor, execute the methods provided by the above-mentioned method embodiments.

[0172] An embodiment of the present application provides a computer-readable storage medium, including: computer program instructions stored on the computer-readable storage medium, and the computer program instructions execute the methods provided by the above-mentioned method embodiments when executed by a processor.

[0173] Computer-readable storage media can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk or optical disk.

[0174] In the embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interface, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0175] In addition, the units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0176] Furthermore, the functional modules in each embodiment of the present application can be integrated together to form an independent part, or each module can exist independently, or two or more modules can be integrated to form an independent part.

[0177] The above description is merely an embodiment of the present application and is not intended to limit the scope of protection of the present application. For those skilled in the art, various modifications and variations of the present application are possible. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A gas concentration compensation method, characterized in that: The method comprises: Acquire the concentration of the gas to be compensated and the current environmental parameters; the current environmental parameters include the current temperature value, the current pressure value and the current gas flow value; Standardize the current environmental parameters according to preset standard parameters to obtain environmental parameter change values; the environmental parameter change values include temperature change values, pressure change values and gas flow change values; The gas concentration to be compensated is compensated based on the environmental parameter change value using a gas concentration compensation model to obtain a compensated gas concentration.

2. The method according to claim 1, characterized in that After obtaining the compensated gas concentration, the method further includes: Determine whether a model update condition is met; if the model update condition is met, calculate the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment; if the error is greater than a preset error threshold, obtain historical concentrations of the gas to be compensated and historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model; the historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values; or Calculate the error between the compensated gas concentration value and the true value of the gas to be compensated measured under a standard environment. If the error is greater than a preset error threshold, determine whether the model update condition is met. If the model update condition is met, obtain the historical concentration of the gas to be compensated and the historical environmental parameter change values within a preset time period to train and update the gas concentration compensation model to obtain an updated gas concentration compensation model. The historical environmental parameter change values include historical temperature change values, historical pressure change values, and historical gas flow change values.

3. The method according to claim 1, characterized in that The step of normalizing the current environmental parameters according to the preset standard parameters to obtain the environmental parameter change value includes: The difference between the current environmental parameter and the preset standard parameter is calculated, and the environmental parameter change value is determined according to the difference.

4. The method according to claim 1, wherein Before obtaining the concentration of the gas to be compensated and the current environmental parameters, the method further includes: Receive a voltage signal generated by an infrared detector; the voltage signal is generated by converting a light intensity signal of the infrared light detected by the infrared detector after the infrared light interacts with the gas to be compensated; Calculating the initial gas concentration of the gas to be compensated based on the voltage signal using the Beer-Lambert law; The initial gas concentration is calibrated based on the preset standard parameters to obtain the gas concentration to be compensated.

5. The method according to any one of claims 1 to 4, characterized in that: The gas concentration compensation model is trained in the following way: Collect different sample gas concentration data and sample environment data under different environmental conditions; the sample environment data includes sample temperature data, sample pressure data and sample gas flow data; The weights and biases of the initial gas concentration compensation model are updated using the sample gas concentration data and the sample environment data to obtain a gas concentration compensation model.

6. The method according to claim 5, characterized in that The initial gas concentration compensation model includes an input layer, a hidden layer, and an output layer; a first initial weight and a first initial bias are included between the input layer and the hidden layer, and a second initial weight and a second initial bias are included between the hidden layer and the output layer; The updating of the weights and biases of the initial gas concentration compensation model using the sample gas concentration data and the sample environment data includes: Calculating an error term of the hidden layer according to a preset activation function of the hidden layer, the second initial weight, and an error term of the output layer; updating the first initial weight based on the error term of the hidden layer, the sample gas concentration data, and the sample environment data; and updating the first initial bias according to the error term of the hidden layer; The output of the hidden layer is obtained according to the sample gas concentration data and the sample environment data; the second initial weight is updated according to the error term of the output layer and the output of the hidden layer; and the second initial bias is updated according to the error term of the output layer.

7. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores machine-readable instructions executable by the processor, and when the machine-readable instructions are executed by the processor, the method according to any one of claims 1 to 6 is performed.

8. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer program instructions, and the computer program instructions are executed by a processor to perform the method according to any one of claims 1 to 6.

9. A computer program product, characterized in that include: Computer program instructions, which, when executed by a processor, perform the method according to any one of claims 1 to 6.

10. A gas concentration compensation system, characterized in that: The system includes an infrared light source module, a gas pool module, an infrared detector module and a microprocessor; the gas pool module includes a gas pool sensor; the gas pool sensor includes a temperature sensor, a pressure sensor and a gas flow sensor; The infrared light source module is used to emit infrared light to the gas pool module, so that the infrared light interacts with the gas to be compensated in the gas pool and then passes through the gas pool module and is incident on the infrared detector module; The infrared detector module is used to detect the change in the light intensity of the infrared light, generate a voltage signal, and send the voltage signal to the microprocessor; The gas pool sensor is used to collect environmental parameters in the gas pool and send the environmental parameters to the microprocessor; The microprocessor is configured to execute the method according to any one of claims 1 to 6.

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