Engine carbon emission sensor state detection system and method

Abstract

The invention discloses a method for detecting the state of an engine carbon emission sensor, relates to the field of engine detection, and solves the technical problem that follow-up repair and maintenance decisions are affected by inaccurate and misjudged modes of manually reading and judging data provided by a carbon emission sensor state detection system in the prior art. The detection method comprises the following steps: collecting the real-time concentration of carbon dioxide in exhaust gas of an engine; processing the real-time carbon dioxide concentration through a wavelet threshold noise reduction method to generate a carbon dioxide concentration measurement value after noise reduction; transmitting the carbon dioxide concentration measurement value after noise reduction into a cloud platform database; and abnormal data are screened out from the carbon dioxide concentration measurement values after noise reduction in the cloud platform database, and abnormal alarm is carried out. According to the invention, the influence of noise on the carbon dioxide concentration signal is reduced, so that the data is more accurate and reliable, and the overall detection precision is further improved.

Description

Technical Field

[0001] The present invention relates to the field of engine detection, and more specifically, to a system and method for detecting the state of an engine carbon emission sensor. Background Art

[0002] With the continuous acceleration of urbanization, greenhouse gas emissions are gradually increasing. Environmental protection is a key topic that people are currently paying attention to. Carbon emissions will have a great impact on the climate environment and are a factor that leads to the intensification of the greenhouse effect.

[0003] As an important part of modern environmental monitoring, the carbon emission sensor status detection system has been widely used and developed in recent years. The carbon emission sensor status detection system has been widely used in many fields such as electricity, chemical industry, construction, transportation, etc., providing strong technical support for the carbon emission management and energy conservation and emission reduction of enterprises. With the global attention to carbon emission management and the continuous advancement of technology, the market size of the carbon emission sensor status detection system continues to grow. The data provided by the current carbon emission sensor status detection system needs to be interpreted and judged by professionals. If the interpretation is inaccurate or the judgment is wrong, it may lead to misjudgment of the carbon emission status of the engine, thus affecting subsequent repair and maintenance decisions. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide an engine carbon emission sensor status detection system and method in response to the deficiencies in the prior art, so as to solve the technical problem that the existing manual interpretation and judgment of the data provided by the carbon emission sensor status detection system is inaccurate and misjudgments affect subsequent repair and maintenance decisions.

[0005] The present invention provides a method for detecting the state of an engine carbon emission sensor, the detection method comprising:

[0006] Step 1: Collect the real-time concentration of carbon dioxide in the engine exhaust gas;

[0007] Step 2: Processing the real-time concentration of carbon dioxide by wavelet threshold denoising method to generate a denoised carbon dioxide concentration measurement value;

[0008] Step 3: Transmitting the noise-reduced carbon dioxide concentration measurement value into the cloud platform database;

[0009] Step 4: Screen out abnormal data from the noise-reduced carbon dioxide concentration measurement values ​​in the cloud platform database and issue an abnormal alarm.

[0010] As a further improvement, in step 2, the method for generating the noise-reduced carbon dioxide concentration measurement value is:

[0011] The real-time concentration of carbon dioxide is subjected to wavelet exchange to obtain wavelet coefficients at different scales, the wavelet coefficients at different scales are subjected to threshold quantization to obtain quantized wavelet coefficients, and the quantized wavelet coefficients are subjected to inverse wavelet transform to obtain a noise-reduced carbon dioxide concentration measurement value.

[0012] Furthermore, the method for threshold quantization of the wavelet coefficients at different scales is to determine a unified threshold for each scale according to noise estimation, and perform threshold quantization on the wavelet coefficients at different scales according to the unified threshold to obtain quantized wavelet coefficients.

[0013] Furthermore, the threshold function expression for obtaining the quantized wavelet coefficients is:

[0014]

[0015] in, is the quantized wavelet coefficient, is a unified threshold, α is an adjustment parameter, when α approaches 0, the threshold function is a soft threshold function, when α approaches infinity, the threshold function is a hard threshold function, ω j，k are the wavelet coefficients at different scales.

[0016] Furthermore, the expression for obtaining the wavelet coefficients at different scales is:

[0017]

[0018] Among them, ω j，k are the wavelet coefficients at different scales, S(n) is the discrete signal of the real-time concentration of carbon dioxide, j is the decomposition scale, N is the number of discrete signals, and k represents the position.

[0019] Furthermore, the method for screening the denoised carbon dioxide concentration measurement values ​​and theoretical concentrations in the cloud platform database to select abnormal data is: normalizing and standardizing the denoised carbon dioxide concentration measurement values ​​in the cloud platform database to obtain comparable data, selecting a standard time format to record the comparable data to obtain comparable data at the same time point, screening the comparable data at the same time point to obtain reasonable values, calculating all the reasonable values ​​through a stream processing framework to obtain an average absolute error, comparing the average absolute error with the accuracy range of the carbon emission sensor itself, and when the average absolute error is greater than the maximum value of the accuracy range of the carbon emission sensor itself or the average absolute error is less than the minimum value of the accuracy range of the carbon emission sensor itself, the reasonable value is regarded as abnormal data and an abnormal alarm is issued.

[0020] Furthermore, the expression of mean absolute error obtained by calculating all the reasonable values ​​through the stream processing framework is:

[0021]

[0022] Where MAE is the mean absolute error, n is the total number of reasonable values, and y i is the actual value after noise reduction, is the model measurement, is the absolute error for each plausible value.

[0023] Furthermore, the method for screening the comparable data at the same time point to obtain reasonable values ​​is to set a data threshold, compare the comparable data at the same time point with the data threshold, and when the comparable data at the same time point is greater than the data threshold, regard the comparable data at the same time point as an outlier and remove the outlier; when the comparable data at the same time point is less than or equal to the numerical threshold, regard the comparable data at the same time point as a reasonable value.

[0024] A system using the above-mentioned engine carbon emission sensor state detection method, the system comprising:

[0025] Data acquisition module, used to collect the real-time concentration of carbon dioxide in engine exhaust gas;

[0026] A data processing module, used for processing the real-time concentration of carbon dioxide to generate a noise-reduced carbon dioxide concentration measurement value;

[0027] A data transmission module, used to transmit the noise-reduced carbon dioxide concentration measurement value to a cloud platform database;

[0028] The data analysis module is used to screen the noise-reduced carbon dioxide concentration measurement values ​​in the cloud platform database to select abnormal values ​​and issue abnormal alarms.

[0029] As a further improvement, the data acquisition module includes a carbon dioxide concentration sensor, an antenna, a main processor, a power supply and an indicator light, and the main processor is electrically connected to the carbon dioxide concentration sensor, the antenna, the power supply and the indicator light respectively.

[0030] Beneficial Effects

[0031] The advantages of the present invention are:

[0032] 1. The present invention provides a method for detecting the state of an engine carbon emission sensor, which collects the real-time concentration of carbon dioxide in the engine exhaust gas, processes the real-time concentration of carbon dioxide by a wavelet threshold denoising method to generate a denoised carbon dioxide concentration measurement value, transmits the denoised carbon dioxide concentration measurement value to a cloud platform database, screens out abnormal data from the denoised carbon dioxide concentration measurement value in the cloud platform database and issues an abnormal alarm, and can measure the carbon dioxide concentration in the exhaust gas of a non-road engine in real time and continuously, thereby providing accurate carbon emission data. Compared with traditional methods, this technology significantly improves the real-time and accuracy of data acquisition, provides reliable data support for subsequent carbon emission management, effectively improves the quality of transmission signals, reduces the influence of noise on carbon dioxide concentration signals, thereby making data more accurate and reliable, and further improving the overall detection accuracy.

[0033] 2. The present invention provides an engine carbon emission sensor status detection system including a data acquisition module, a data processing module, a data transmission module, and a data analysis module. The detection system further improves the accuracy and availability of the data and provides a convenient monitoring method for operation and maintenance personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 It is a structural schematic diagram of the engine carbon emission sensor state detection system of the present invention;

[0035] Figure 2 A flow chart for generating noise-reduced carbon dioxide concentration measurements for the present invention;

[0036] Figure 3 It is a structural schematic diagram of the data acquisition module of the present invention.

[0037] Among them: 1-main processor, 2-antenna, 3-indicator light, 4-power supply, 5-carbon dioxide concentration sensor. DETAILED DESCRIPTION

[0038] The present invention will be further described below in conjunction with the embodiments, but it does not constitute any limitation to the present invention. Any limited number of modifications made by anyone within the scope of the claims of the present invention are still within the scope of the claims of the present invention.

[0039] See also Figure 1-Figure 3 , an engine carbon emission sensor state detection method and system of the present invention, the detection method and system are:

[0040] Step 1: Collect the real-time concentration of carbon dioxide in the engine exhaust gas through the data acquisition module.

[0041] The data acquisition module includes a carbon emission data acquisition module and a carbon dioxide concentration module in the engine carbon emission sensor simulation device. The carbon emission data acquisition module uses infrared gas sensing technology to measure the carbon dioxide concentration in the exhaust gas in real time, and then obtains real-time and continuous carbon dioxide real-time concentration. The engine carbon emission sensor simulation device can set the carbon emission sensor constant normal carbon dioxide concentration (theoretical carbon dioxide concentration) when the engine is working under the same working conditions, where the sensor measurement and simulation data correspond to the emissions at the same time or under the same working conditions.

[0042] Among them, infrared gas sensing technology is based on the specific absorption characteristics of gas molecules to infrared light. Different types of gas molecules absorb infrared light within a specific wavelength range, a phenomenon known as "infrared absorption". The sensor emits infrared light, and when the light beam passes through the air containing the target gas, the gas molecules absorb light of a wavelength that matches their characteristics. The sensor measures the change in light intensity passing through the gas layer and then infers the concentration of the gas. The infrared gas sensor is installed in the exhaust pipe or exhaust port of the engine. When the engine is started and running, the exhaust gas passes through the optical path of the sensor, and the light emitted by the infrared light source passes through the gas. Gas molecules absorb a certain amount of infrared light at a specific wavelength depending on their chemical composition. The sensor's detection unit then compares the intensity of light received through the gas with the intensity of light not passing through the gas, and infers the concentration of the gas based on the change in the absorbed light intensity.

[0043] The data collection module for carbon emissions is shown in the figure below. Figure 3 shown.

[0044] The data acquisition module of carbon emission includes a main processor 1, a carbon dioxide concentration sensor 5, an indicator light 3, an antenna 2 and a power supply 4. The main processor 1 is electrically connected to the carbon dioxide concentration sensor 5, the antenna 2, the power supply 4 and the indicator light 3 respectively.

[0045] The carbon dioxide concentration sensor 5 is used to measure the carbon dioxide concentration in the engine exhaust gas in real time, and transmit the measured carbon dioxide concentration signal to the main processor 1, which is responsible for coordinating and controlling the operation of the entire device. The main processor 1 receives data from the carbon dioxide concentration sensor 5 and performs preliminary processing and analysis. The main processor 1 is also responsible for transmitting data to the cloud platform through the antenna 2, and communicating and coordinating with the power supply and indicator light. The indicator light 3 is used to display the working status of the device, such as whether it is operating normally. Through the different flashing modes or colors of the indicator light 3, the operator can intuitively understand the working status of the device, and thus perform corresponding operations or maintenance.

[0046] Step 2: Process the real-time concentration of carbon dioxide through the data processing module to generate a noise-reduced carbon dioxide concentration measurement value.

[0047] Wavelet exchange is performed on the real-time concentration of carbon dioxide to obtain wavelet coefficients at different scales, threshold quantization is performed on the wavelet coefficients at different scales to obtain quantized wavelet coefficients, and inverse wavelet transform is performed on the quantized wavelet coefficients to obtain the denoised carbon dioxide concentration measurement value.

[0048] The method for threshold quantization of wavelet coefficients at different scales is to determine a unified threshold for each scale according to noise estimation, and to threshold quantize the wavelet coefficients at different scales according to the unified threshold to obtain quantized wavelet coefficients.

[0049] The process of generating the noise-reduced CO2 concentration measurement value is as follows: the actual CO2 concentration S(k) and the theoretical CO2 concentration S are collected through the data acquisition module. sim(k) , perform wavelet transform on the actual carbon dioxide concentration S(k) to obtain the wavelet coefficients ω at different scales j，k , for each scale j, determine the uniform threshold λ for each scale according to the noise estimation j . For each wavelet coefficient at scale j, j，k Perform threshold quantization to obtain the quantized wavelet coefficients The quantized wavelet coefficients Perform inverse wavelet transform to obtain the denoised carbon dioxide concentration measurement value.

[0050] The carbon dioxide concentration collected by the carbon emission sensor is different from the theoretical concentration collected by the simulation device, which will lead to a deviation in the carbon dioxide concentration entering the system due to noise. This problem can be solved by using the improved wavelet threshold denoising method.

[0051] The flow chart of the wavelet threshold denoising method is as follows: Figure 2 .

[0052] The improved wavelet threshold denoising technology can greatly improve the quality of the transmitted signal. Considering only the impact of noise on the transmission of carbon dioxide concentration signal, it can be considered as a noisy one-dimensional signal model:

[0053] S(k)=f(k)+ε*e(k), k=0, 1...n-1;

[0054] Among them, f(k) is the useful signal, s(k) is the noisy signal, e(k) is the noise, and ε is the standard deviation of the noise coefficient. The purpose of wavelet transform is to suppress e(k) to restore f(k). For the one-dimensional signal S(k), it must first be discretely sampled to obtain an N-point discrete signal S(n), whose wavelet transform is:

[0055]

[0056] Among them, ω j，k is the wavelet coefficient at different scales, S(n) is the discrete signal of the real-time concentration of carbon dioxide, j is the decomposition scale, N is the number of discrete signals, k represents the position, and the wavelet transform belongs to linear transform. The wavelet coefficient is obtained by performing discrete transform on the noisy signal. It still consists of two parts: the wavelet coefficients corresponding to the real signal and the wavelet coefficients corresponding to the noise signal.

[0057] Based on the above, a threshold function is constructed:

[0058]

[0059] in, is the quantized wavelet coefficient, is a unified threshold, α is an adjustment parameter, when α approaches 0, the threshold function is a soft threshold function, when α approaches infinity, the threshold function is a hard threshold function, ω j，k are wavelet coefficients at different scales. The hard threshold function is used to process the high-frequency coefficients of each decomposition level of the denoised signal, and the soft threshold function is used to perform soft threshold processing on the high-frequency coefficients of each decomposition scale of the denoised signal. The new threshold function has better flexibility. According to different denoising purposes, the value can be adjusted to obtain better denoising effect.

[0060] Step 3: The noise-reduced carbon dioxide concentration measurement value is transmitted to the cloud platform database through the data transmission module.

[0061] The data transmission module transmits the measured value of carbon dioxide concentration after noise reduction and the carbon dioxide concentration of the simulation device (theoretical carbon dioxide concentration) to the cloud platform through the data gateway.

[0062] Cloud computing monitoring module based on cloud platform:

[0063] The cloud computing monitoring module based on the cloud platform includes a data storage module, a data analysis module and a display module.

[0064] The data storage module is used to store the real-time data uploaded by the sensor to the cloud platform through the data gateway. For data storage, InfluxDB is used, which is suitable for storing real-time data streams with timestamps.

[0065] Step 4: Use the data analysis module to filter out abnormal data from the noise-reduced carbon dioxide concentration measurement values ​​in the cloud platform database and issue an abnormal alarm.

[0066] The data analysis module performs real-time data processing on the data uploaded to the cloud platform. The noise-reduced carbon dioxide concentration measurement values ​​are cleaned. First, the units of the noise-reduced carbon dioxide concentration measurement values ​​are unified, and then the noise-reduced carbon dioxide concentration measurement value data are normalized and standardized to obtain comparable data. After that, because the comparable data are processed, the sensor may record data in different frequencies or timestamp formats. Select a standard time format and ensure that all comparable data use the same time format. Then interpolate or resample the comparable data so that they are comparable at the same time point. Finally, set a data threshold, compare the comparable data at the same time point with the data threshold. When the comparable data at the same time point is greater than the data threshold, the comparable data at the same time point is regarded as an outlier, and the outlier can be removed or replaced with a reasonable value for processing; when the comparable data at the same time point is less than or equal to the numerical threshold, the comparable data at the same time point is regarded as a reasonable value.

[0067] Use the stream processing framework to perform calculations on real-time data, using AWS Kinesis cloud platform services.

[0068] Real-time data from sensors enters the streaming computing framework. Calculate the mean absolute error:

[0069] The expression for the mean absolute error calculated by the stream processing framework for all reasonable values ​​is:

[0070]

[0071] Where MAE is the mean absolute error, n is the total number of reasonable values, and y i is the actual value after noise reduction, is the model measurement, is the absolute error for each plausible value.

[0072] Compare and calculate the size of the mean absolute error in real time. The streaming data flows in by time, so the window operation can be used to divide the data into different time windows for processing. Compare the size of the mean absolute error with the accuracy level of the carbon emission sensor itself. Compare the mean absolute error with the accuracy range of the carbon emission sensor itself. When the mean absolute error is greater than the maximum value of the accuracy range of the carbon emission sensor itself or the mean absolute error is less than the minimum value of the accuracy range of the carbon emission sensor itself, the reasonable value is regarded as abnormal data and an abnormal alarm is issued, thereby notifying the operation and maintenance personnel to check or repair.

[0073] Display module:

[0074] The display module can not only display the actual carbon emission value of the engine at every moment, but also display whether the actual carbon emission is normal, thereby achieving the purpose of monitoring.

[0075] The above is only a preferred embodiment of the present invention. It should be pointed out that for those skilled in the art, several modifications and improvements can be made without departing from the structure of the present invention, which will not affect the effect of the implementation of the present invention and the practicality of the patent.

Claims

1. A method for detecting the state of an engine carbon emission sensor, characterized in that: The detection method is: Step 1: Collect the real-time concentration of carbon dioxide in the engine exhaust gas; Step 2: Processing the real-time concentration of carbon dioxide by a wavelet threshold denoising method to generate a denoised carbon dioxide concentration measurement value; Step 3: Transmitting the noise-reduced carbon dioxide concentration measurement value into the cloud platform database; Step 4: Screen the noise-reduced carbon dioxide concentration measurement values ​​in the cloud platform database to pick out abnormal data and issue an abnormal alarm.

2. The method for detecting the state of an engine carbon emission sensor according to claim 1, characterized in that: In step 2, the method for generating the noise-reduced carbon dioxide concentration measurement value is: The real-time concentration of carbon dioxide is subjected to wavelet exchange to obtain wavelet coefficients at different scales, the wavelet coefficients at different scales are subjected to threshold quantization to obtain quantized wavelet coefficients, and the quantized wavelet coefficients are subjected to inverse wavelet transform to obtain a noise-reduced carbon dioxide concentration measurement value.

3. The method for detecting the state of an engine carbon emission sensor according to claim 2, characterized in that: The method for threshold quantizing the wavelet coefficients at different scales is to determine a unified threshold for each scale according to noise estimation, and perform threshold quantization on the wavelet coefficients at different scales according to the unified threshold to obtain quantized wavelet coefficients.

4. The method for detecting the state of an engine carbon emission sensor according to claim 3, characterized in that: The threshold function expression for obtaining the quantized wavelet coefficients is: in, is the quantized wavelet coefficient, is the unified threshold, α is the adjustment parameter, when α approaches 0, the threshold function is a soft threshold function, when α approaches infinity, the threshold function is a hard threshold function, ω j，k are the wavelet coefficients at different scales.

5. The method for detecting the state of an engine carbon emission sensor according to claim 4, characterized in that: The expression for obtaining the wavelet coefficients at different scales is: Among them, ω j，k are the wavelet coefficients at different scales, S(n) is the discrete signal of the real-time concentration of carbon dioxide, j is the decomposition scale, N is the number of discrete signals, and k represents the position.

6. The method for detecting the state of an engine carbon emission sensor according to claim 1, characterized in that: The method for screening the denoised carbon dioxide concentration measurement values ​​and theoretical concentrations in the cloud platform database to select abnormal data is as follows: normalizing and standardizing the denoised carbon dioxide concentration measurement values ​​in the cloud platform database to obtain comparable data, selecting a standard time format to record the comparable data to obtain comparable data at the same time point, screening the comparable data at the same time point to obtain reasonable values, calculating all the reasonable values ​​through a stream processing framework to obtain an average absolute error, comparing the average absolute error with the accuracy range of the carbon emission sensor itself, and when the average absolute error is greater than the maximum value of the accuracy range of the carbon emission sensor itself or the average absolute error is less than the minimum value of the accuracy range of the carbon emission sensor itself, treating the reasonable value as abnormal data and issuing an abnormal alarm.

7. The method for detecting the state of an engine carbon emission sensor according to claim 6, characterized in that: The expression of the mean absolute error obtained by calculating all the reasonable values ​​through the stream processing framework is: Where MAE is the mean absolute error, n is the total number of reasonable values, and y i is the actual value after noise reduction, is the model measurement, is the absolute error for each plausible value.

8. The method for detecting the state of an engine carbon emission sensor according to claim 6, characterized in that: The method for screening the comparable data at the same time point to obtain a reasonable value is to set a data threshold, compare the comparable data at the same time point with the data threshold, and when the comparable data at the same time point is greater than the data threshold, regard the comparable data at the same time point as an outlier and remove the outlier; When the comparable data at the same time point is less than or equal to a numerical threshold, the comparable data at the same time point is taken as a reasonable value.

9. A system using the engine carbon emission sensor state detection method according to any one of claims 1 to 8, characterized in that: The system includes, Data acquisition module, used to collect the real-time concentration of carbon dioxide in engine exhaust gas; A data processing module, used for processing the real-time concentration of carbon dioxide to generate a noise-reduced carbon dioxide concentration measurement value; A data transmission module, used to transmit the noise-reduced carbon dioxide concentration measurement value to a cloud platform database; The data analysis module is used to screen the noise-reduced carbon dioxide concentration measurement values ​​in the cloud platform database to select abnormal values ​​and issue abnormal alarms for the abnormal values.

10. The engine carbon emission sensor state detection system according to claim 9, characterized in that: The data acquisition module comprises a carbon dioxide concentration sensor (5), an antenna (2), a main processor (1), a power supply (4) and an indicator light (3); the main processor (1) is electrically connected to the carbon dioxide concentration sensor (5), the antenna (2), the power supply (4) and the indicator light (3), respectively.

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