Vehicle carbon emission online analyzing and monitoring system based on cloud computing

Abstract

The invention relates to the technical field of vehicle carbon emission monitoring, in particular to a vehicle carbon emission online analyzing and monitoring system based on cloud computing. The system comprises a spectrogram obtaining unit used for obtaining a spectrogram of exhaust gas discharged by a current vehicle; the abnormal degree calculation unit is used for obtaining the abnormal degree of the target wavelength; the target wavelength screening unit is used for screening the target wavelength to obtain an expanded noise reduction wavelength; the noise reduction degree calculation unit is used for obtaining the noise degree of the expanded noise reduction wavelength; the noise degree correction unit is used for correcting the noise degree to obtain a corrected noise degree; the carbon emission monitoring unit is used for determining the size of an average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength and carrying out noise reduction to obtain a spectrogram after noise reduction; and monitoring the carbon emission of the current vehicle according to the spectrogram after noise reduction. According to the invention, the noise reduction effect of the spectrogram can be improved, so that the monitoring quality of vehicle carbon emission is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle carbon emission monitoring, and in particular to a vehicle carbon emission online analysis and monitoring system based on cloud computing. Background Art

[0002] The remote sensing detection device uses the different absorption characteristics of different pollutants emitted by automobile exhaust to light waves of different frequencies. It sends ultraviolet light and infrared light to the optical reflector on the opposite side of the road, and then receives the reflected light to monitor the change in light intensity. The absorption light intensity corresponding to the transmitted light intensity of different wavelengths is calculated to draw a spectrum, and then the corresponding gas concentration is analyzed to analyze the carbon emissions of the automobile. Due to various environmental interferences during data collection, the spectrum obtained by drawing the collected data contains a variety of noises. The sliding average method is conventionally used for denoising, but in the moving average filter, although the fixed window will better maintain the characteristics of some absorption peaks, it will cause some noises to be incompletely denoised, masking the true spectral characteristics, and thus affecting the monitoring effect of vehicle exhaust emissions. Summary of the invention

[0003] In order to solve the above technical problems, the purpose of the present invention is to provide a vehicle carbon emission online analysis and monitoring system based on cloud computing. The technical solutions adopted are as follows: An embodiment of the present invention provides a vehicle carbon emission online analysis and monitoring system based on cloud computing, which mainly includes: A spectrum acquisition unit, used to acquire the spectrum of the current vehicle exhaust gas; The abnormality degree calculation unit is used to set a sliding window, slide on the spectrum graph with a set step length, and the first wavelength in the sliding window is the target wavelength; construct a line graph according to the data in the sliding window; and obtain the abnormality degree of the target wavelength using the standard deviation of the data in the sliding window and the line graph; A target wavelength screening unit is used to establish an expansion window for each target wavelength, calculate the abnormality of the target wavelength within the expansion window and normalize it to obtain a screening abnormality degree, and use the screening abnormality degree to screen the target wavelength to obtain an expanded noise reduction wavelength; A noise reduction degree calculation unit is used to obtain an abnormal curve by fitting the abnormal degree of each target wavelength; and obtain the noise degree based on the abnormal curve and the abnormal degree of the expanded noise reduction wavelength; A noise level correction unit is used to calculate the noise level of the wavelengths that are the same as the target wavelengths on the spectrum graphs of other vehicles and to obtain a noise curve by fitting; the noise level is corrected according to the noise level of the expanded noise reduction wavelength and the noise curve to obtain a corrected noise level; The carbon emission monitoring unit is used to determine the size of the average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength and perform noise reduction to obtain a noise-reduced spectrum; and monitor the carbon emissions of the current vehicle based on the noise-reduced spectrum.

[0004] Preferably, the abnormality degree of the target wavelength is obtained by using the standard deviation and line graph of the data in the sliding window, including: Obtain the turning point where the line in the line graph is about to drop, and record it as the falling turning point; calculate the standard deviation of the data in the sliding window and the standard deviation of the data after removing the maximum and minimum values ​​in the sliding window, and obtain the absolute value of the difference between the two standard deviations, and record it as the amplitude fluctuation degree; add the amplitude fluctuation degree and the number of falling turning points in the line graph corresponding to the sliding window to obtain the abnormality degree of the target wavelength.

[0005] Preferably, the noise level is obtained based on the abnormal curve and the abnormal degree of expanding the noise reduction wavelength, including: The fitting anomaly degree corresponding to each expanded noise reduction wavelength is obtained on the anomaly curve; the absolute value of the difference between the fitting anomaly degree and the anomaly degree corresponding to the expanded noise reduction wavelength is normalized to obtain a weight coefficient; the weight coefficient is multiplied by the anomaly degree of the expanded noise reduction wavelength to obtain a multiplication result, and the sum of the multiplication result and the anomaly degree of the expanded noise reduction wavelength is the noise degree of the expanded noise reduction wavelength.

[0006] Preferably, calculating the abnormality degree of the target wavelength within the expanded window and normalizing it to obtain the screening abnormality degree includes: The peak point of the data in the expanded window is obtained, a line graph is constructed using the peak point in the expanded window, and the number of downward turning points in the line graph is obtained; the abnormality degree of the target wavelength is calculated in the expanded window according to the number of downward turning points in the line graph and the data in the expanded window, and the abnormality degree is normalized to obtain the screening abnormality degree.

[0007] Preferably, the noise level is corrected according to the noise level of the expanded noise reduction wavelength and the noise curve to obtain the corrected noise level, including: The fitting noise degree of the expanded noise reduction wavelength is obtained on the noise curve, and then the absolute value of the difference between the noise degree of the expanded noise reduction wavelength and the fitting noise degree is obtained and normalized to obtain the deviation degree of the noise degree of the expanded noise reduction wavelength; the noise degree is corrected using the deviation degree of the noise degree of the expanded noise reduction wavelength to obtain the corrected noise degree.

[0008] Preferably, the noise level is corrected by using the deviation degree of the noise level of the expanded noise reduction wavelength to obtain the corrected noise level, including: The deviation degree of the noise level of the expanded noise reduction wavelength is added to the preset value, and the product of the added result and the noise level of the expanded noise reduction wavelength is the corrected noise level of the expanded noise reduction wavelength.

[0009] Preferably, determining the size of the average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength comprises: Set the size of the initial average noise reduction window and the adaptive change threshold; multiply the adaptive change threshold by the corrected noise level of the expanded noise reduction wavelength and round it to get the expansion coefficient; the sum of the expansion coefficient and the size of the initial average noise reduction window is the size of the average noise reduction window of the expanded noise reduction wavelength.

[0010] Preferably, obtaining the denoised spectrum includes: The data corresponding to wavelengths other than the expanded noise reduction wavelength in the spectrum of the current vehicle are denoised using the initial average noise reduction window, and the data corresponding to the expanded noise reduction wavelength are denoised using the average noise reduction window corresponding to each expanded noise reduction wavelength to obtain a denoised spectrum.

[0011] Preferably, the spectrum diagram comprises: The horizontal axis of the spectrum is wavelength, and the vertical axis is absorption intensity.

[0012] The embodiment of the present invention has at least the following beneficial effects: after obtaining the spectrum of the exhaust gas emitted by the current vehicle, the present invention uses a sliding window to slide on the spectrum, analyzes the data in the sliding window, and obtains the abnormality of the target wavelength, which is convenient for the subsequent calculation of the noise level; further, an expansion window for each target wavelength is established, and the abnormality of the target wavelength is calculated according to the characteristics of the data in the expansion window and normalized to obtain the screening abnormality, and then the target wavelength in the spectrum is screened based on the calculated screening abnormality, so that the wavelength that needs to expand the window when using sliding average noise reduction can be accurately obtained, so that the noise reduction is more accurate and the accuracy of vehicle carbon emissions is improved; then the noise level of the expanded noise reduction wavelength is obtained, and the noise level of the expanded noise reduction wavelength is corrected according to the characteristics of the noise level of the wavelength same as each target wavelength on the spectrum of other vehicles to obtain the corrected noise level, and the size of the window when the expanded noise reduction wavelength is used for average noise reduction is determined according to the corrected noise level, so as to improve the noise reduction effect on the spectrum and make the monitoring of vehicle carbon emissions more accurate. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0014] Figure 1A system block diagram of a vehicle carbon emission online analysis and monitoring system based on cloud computing provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0015] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the specific implementation method, structure, features and effects of a vehicle carbon emission online analysis and monitoring system based on cloud computing proposed by the present invention are described in detail below in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0016] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0017] The specific scheme of a vehicle carbon emission online analysis and monitoring system based on cloud computing provided by the present invention is described in detail below with reference to the accompanying drawings.

[0018] Example: The main application scenarios of the present invention are: by monitoring, the spectral data collected by the remote sensing detection equipment is obtained, and a spectrum is drawn from the obtained data. By analyzing the characteristics of the spectrum, the noise level of each wavelength is obtained. According to the noise level of each wavelength obtained, the size of the window is adaptively adjusted, and sliding average denoising is implemented, and then the carbon emissions of the vehicle are analyzed according to the denoised spectrum, so as to achieve the purpose of monitoring vehicle carbon emissions.

[0019] See also Figure 1 , which shows a system block diagram of a vehicle carbon emission online analysis and monitoring system based on cloud computing provided by an embodiment of the present invention, the system includes a spectrum acquisition unit, an abnormality degree calculation unit, a target wavelength screening unit, a noise reduction degree calculation unit, a noise degree correction unit and a carbon emission monitoring unit.

[0020] The spectrum acquisition unit is used to acquire the spectrum of the exhaust gas currently emitted by the vehicle.

[0021] After collecting the transmitted light intensity corresponding to different wavelengths when the vehicle is emitting exhaust gas through remote sensing detection equipment, it is necessary to convert it into absorbance A. The absorbance is the absorption intensity corresponding to different wavelengths in the spectrum. The infrared spectrum consists of wavelength on the horizontal axis and absorption intensity on the vertical axis. The calculation formula for converting the transmitted light intensity corresponding to different wavelengths into the absorption intensity is: , in, The wavelength is The absorption intensity at The wavelength is The transmitted light intensity at is the incident light intensity. From this, we can get the spectrum of the current vehicle exhaust gas.

[0022] The abnormality degree calculation unit is used to set a sliding window, slide on the spectrum graph with a set step size, and the first wavelength in the sliding window is the target wavelength; construct a line graph according to the data in the sliding window; and obtain the abnormality degree of the target wavelength using the standard deviation of the data in the sliding window and the line graph.

[0023] When using the sliding average method for denoising, the choice of window size often depends on the empirical value. When the entire spectrum is denoised according to the original empirical window, the fixed window will better maintain the characteristics of the absorption peak, but it will lead to incomplete noise removal. Therefore, it is necessary to analyze the spectral data characteristics corresponding to each wavelength to obtain the noise level of the target wavelength. According to the obtained noise level, the window size is adaptively adjusted to implement sliding average denoising.

[0024] Ideally, the spectrum of common gases emitted by vehicles shows relatively smooth peaks in some areas and more fluctuations in some areas, but the overall trend is a peak shape, which represents the absorption spectrum of each gas molecule. If the spectrum contains noise, the curve may show obvious fluctuations or jitters, and abnormal peaks or valleys may appear, making the originally smooth curve irregular and the peaks that were originally in the shape of a peak do not have a peak shape. Therefore, the degree of abnormality of the target wavelength can be obtained by analyzing the degree of fluctuation of the data in the target wavelength window and then according to the changing trend of the data with large fluctuations.

[0025] First, a sliding window is set in the spectrum graph, and the size of the sliding window is 1×n, where the empirical value of n is 5, and the implementer can adjust it according to the actual situation; the sliding window slides on the spectrum graph with a set step size, where the step size is set to 1. During the sliding process, the first wavelength in the sliding window is the target wavelength. For example, when the sliding window slides one step size, the target wavelength is the first wavelength in the second sliding window, that is, the second wavelength in the horizontal axis of the spectrum graph.

[0026] Furthermore, the spectral data in the sliding window, that is, the absorption intensity corresponding to each wavelength, is obtained. The wavelength is used as the horizontal axis and the absorption intensity is used as the vertical axis to draw a line graph corresponding to the sliding window. Without being affected by noise, the line graph Figure 1Generally, some areas show relatively smooth peaks, and some areas show more fluctuations, but the overall trend is in the shape of peaks. Under the influence of noise, the line graph of the smooth part will show obvious fluctuations or jitters, which is manifested as more turning points in the corresponding line graph. Therefore, the number of turning points of the line graph corresponding to the sliding window can represent the degree of fluctuation of the data in the sliding window of the target wavelength to a certain extent. The turning points in the sliding window include lower points and higher points, where the lower point is the turning point where the line is about to rise, recorded as the rising turning point, and the higher point is the turning point where the line is about to fall, recorded as the falling turning point; obtain the number of falling turning points in the line graph, the more the number of falling turning points, the greater the frequency of data fluctuation in the sliding window.

[0027] Since the fluctuation degree of the data in the sliding window is not only affected by the fluctuation frequency, but also by the fluctuation amplitude of the data, it is also necessary to analyze the fluctuation amplitude of the data in the window. If the peak in the sliding window is caused by a normal absorption peak, the change of its line graph should be relatively smooth, and the data will not fluctuate greatly. Then the standard deviation should not change significantly after removing the maximum and minimum values. If the peak in the sliding window is caused by noise, the change amplitude of its line graph should be large, and the data will fluctuate greatly. Then the standard deviation will change significantly after removing the maximum and minimum values. Therefore, the difference between the two standard deviations in the sliding window can also represent the degree of data fluctuation to a certain extent. The larger the difference, the greater the fluctuation amplitude of the data in the window.

[0028] Next, calculate the standard deviation of the data in the sliding window and the standard deviation of the data after removing the maximum and minimum values ​​in the sliding window, and obtain the absolute value of the difference between the two standard deviations, which is recorded as the amplitude fluctuation degree; add the amplitude fluctuation degree and the number of descending turning points in the line graph corresponding to the sliding window to obtain the abnormality degree of the target wavelength; the specific calculation formula is: , in, Indicates the abnormality degree of the target wavelength corresponding to the jth sliding window; Indicates the number of descending turning points in the line graph corresponding to the j-th sliding window, that is, the magnitude of the data fluctuation rate; Represents the standard deviation of the data in the jth sliding window; It represents the standard deviation of the data in the jth sliding window after removing the maximum and minimum values; Indicates the magnitude of the data fluctuation amplitude abnormality corresponding to the jth sliding window, that is, the amplitude fluctuation degree. From this, the abnormality degree of each target wavelength can be obtained, that is, the abnormality degree of each wavelength in the spectrum of the current vehicle exhaust emission.

[0029] The target wavelength screening unit is used to establish an expansion window for each target wavelength, calculate the abnormality of the target wavelength within the expansion window and normalize it to obtain the screening abnormality degree, and use the screening abnormality degree to screen the target wavelength to obtain the expanded noise reduction wavelength.

[0030] After the abnormality calculation unit obtains the abnormality of the target wavelength, since the normal absorption peak will also fluctuate greatly when not affected by noise, the overall trend of its peak point is a relatively smooth absorption peak, that is, the absorption peak composed of its peak points has a small fluctuation. After the abnormality of the target wavelength is obtained as above, further analysis is required to eliminate the misjudgment of normal fluctuations.

[0031] Furthermore, an expanded window for each target wavelength is established, wherein the first wavelength in the expanded window is the target wavelength, the peak point of the data in the expanded window is obtained, a line graph is constructed using the peak point in the expanded window, and the number of declining turning points in the line graph is obtained, and the degree of abnormality of the target wavelength is calculated and normalized in the expanded window according to the number of declining turning points in the line graph and the data in the expanded window to obtain the screening abnormality degree. It should be noted that after obtaining the number of declining turning points in the line graph corresponding to the expanded window, the method for obtaining the degree of abnormality is the same as the above-mentioned method for calculating the degree of abnormality of the target wavelength.

[0032] Then, the screening threshold is set. Preferably, in the embodiment of the present invention, the value of the screening threshold is 0.2. If the screening abnormality degree of the target wavelength is less than or equal to the screening threshold, the target wavelength is the normal noise reduction wavelength; if the screening abnormality degree of the target wavelength is greater than the screening threshold, the target wavelength is the expanded noise reduction wavelength.

[0033] In this way, the wavelengths for normal noise reduction and the wavelengths for noise reduction that require the window to be expanded can be screened out, which can avoid the problem of misjudgment of normal fluctuations of data in the sliding window.

[0034] The noise reduction degree calculation unit is used to obtain an abnormal curve by fitting the abnormal degree of each target wavelength; and obtain the noise degree based on the abnormal curve and the abnormal degree of the expanded noise reduction wavelength.

[0035] Since the noise level of the target wavelength affects the window size of the target wavelength during denoising, it is necessary to associate the abnormality level of a single target wavelength with the abnormality level of the entire spectrum, and obtain the noise level of the target wavelength based on the change in the abnormality level of the target wavelength and the overall spectrum. When the abnormality level of the target wavelength deviates greatly from the overall abnormality level, its noise level should be greater, and a larger window is needed for denoising.

[0036] Furthermore, the least square method is used to fit the abnormality degree of each target wavelength to obtain an abnormal curve, and the noise degree is obtained according to the deviation degree between the abnormality degree of the current window and the abnormal curve, where the function expression of the abnormal curve is: , in, It represents the degree of fitting anomaly corresponding to the j-th target wavelength, that is, the vertical axis value of the j-th point on the abnormal curve; m and b are the slope and intercept of the function expression of the abnormal curve respectively; j represents the horizontal coordinate of the j-th point on the abnormal curve, that is, the independent variable.

[0037] Furthermore, the fitting abnormality degree corresponding to each enlarged noise reduction wavelength is obtained on the abnormality curve; the absolute value of the difference between the fitting abnormality degree and the abnormality degree corresponding to the enlarged noise reduction wavelength is normalized to obtain a weight coefficient; the weight coefficient is multiplied by the abnormality degree of the enlarged noise reduction wavelength to obtain a multiplication result, and the sum of the multiplication result and the abnormality degree of the enlarged noise reduction wavelength is the noise degree of the enlarged noise reduction wavelength; the specific calculation formula is: , in, is the noise level of the i-th expanded noise reduction wavelength; Indicates the degree of fitting anomaly corresponding to the i-th expanded noise reduction wavelength; It indicates the abnormal degree of the i-th expanded noise reduction wavelength, that is, the actual value; norm() indicates the normalization operation; | | indicates the absolute value operation; represents the weight coefficient. and When they are equal, there is no deviation between the abnormal degree and the fitting abnormal degree, and the noise is small, which can be expressed as the abnormal degree itself. and When they are not equal, there is a deviation between the abnormal degree and the fitting abnormal degree, and the noise is larger.

[0038] The noise level correction unit is used to calculate the noise level of the wavelength same as each target wavelength on the spectrum diagram of other vehicles and fit it to obtain a noise curve; the noise level is corrected according to the noise level of the expanded noise reduction wavelength and the noise curve to obtain a corrected noise level.

[0039] Since the noise level obtained may be deviated due to certain special factors, it is also necessary to obtain the credibility of the noise level of the expanded noise reduction wavelength based on the change characteristics of the spectral data collected in multiple sliding windows with the same wavelength. The noise level of the current expanded noise reduction wavelength obtained according to a single current spectrum graph may be deviated due to some special factors, resulting in a certain degree of unreliability in the noise level of the expanded noise reduction wavelength, so it is necessary to obtain the spectrum graphs of other multiple vehicles passing through the remote sensing detection device to obtain the noise levels corresponding to multiple same wavelengths. Because the influence of noise in the same environment on the spectrum graph drawn from the data collected by the remote sensing detection equipment is almost the same, and the difference in the position and size of each absorption peak is determined by the carbon emission level of the vehicle, the noise levels of the spectrum graphs corresponding to different motor vehicles in the same environment are almost the same; it should be noted that the noise generated by the difference in the vehicle itself is ignored in the present invention, and only the noise generated by the environment is considered.

[0040] The noise level of the wavelengths with the same target wavelengths is calculated on the spectrum graphs of other vehicles, and the noise curve is obtained by fitting according to the least squares method. The fitted noise level of the expanded noise reduction wavelength of the current vehicle is obtained on the noise curve, and then the absolute value of the difference between the noise level of the expanded noise reduction wavelength and the fitted noise level is obtained and normalized to obtain the deviation degree of the noise level of the expanded noise reduction wavelength. The specific calculation formula is: , in, Indicates the degree of deviation of the noise level of the i-th expanded noise reduction wavelength; and They represent the noise level and fitting noise level of the i-th expanded noise reduction wavelength respectively; norm() represents the normalization operation; || represents the absolute value operation. The larger the value, the greater the deviation, that is, the greater the deviation between the currently calculated noise level and the noise generated by the environment.

[0041] Therefore, it is necessary to use the deviation degree of the noise level of the expanded noise reduction wavelength to correct the acquired noise, thereby obtaining the corrected noise level. Further, the deviation degree of the noise level of the expanded noise reduction wavelength is added to the preset value, and the product of the added result and the noise level of the expanded noise reduction wavelength is the corrected noise level of the expanded noise reduction wavelength, where the preset value is 1. The specific calculation formula is: , in, Indicates the corrected noise level of the i-th expanded noise reduction wavelength; Indicates the degree of deviation of the noise level of the i-th expanded noise reduction wavelength; Indicates the noise level of the i-th expanded noise reduction wavelength. The minimum value of the corrected noise level is the originally calculated noise level itself. When the minimum value is taken, it means that the noise level does not deviate from the fitted noise level. Only the window size of the average noise reduction can be expanded according to the current noise level. When the corrected noise level takes other values, it means that there is a certain deviation between the noise level and the fitted noise level. At this time, in order to prevent the deviation from affecting the subsequent average noise reduction effect, it is necessary to further expand the window for noise reduction. In this way, the corrected noise level of each expanded noise reduction wavelength in the spectrum diagram of the current vehicle can be obtained.

[0042] The carbon emission monitoring unit is used to determine the size of the average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength and perform noise reduction to obtain a noise-reduced spectrum; and monitor the carbon emissions of the current vehicle based on the noise-reduced spectrum.

[0043] For the spectrum of the current vehicle, when performing sliding average noise reduction, it is necessary to set the size n of the initial average noise reduction window based on experience. In the embodiment of the present invention, the value is 5. It should be noted that the initial window size is different in different environments, and the implementer needs to set it according to the actual situation. In addition, after obtaining the corrected noise level for expanding the noise reduction wavelength, since the window cannot be expanded or reduced indefinitely, it is necessary to set the window adaptive change threshold z, and the empirical value of z is z=4. At this time, the size of the average noise reduction window for expanding the noise reduction wavelength can be calculated based on the corrected noise level for expanding the noise reduction wavelength and the adaptive change threshold.

[0044] The adaptive change threshold is multiplied by the corrected noise level of the expanded noise reduction wavelength and rounded to obtain the expansion coefficient; the sum of the expansion coefficient and the size of the initial average noise reduction window is the size of the average noise reduction window of the expanded noise reduction wavelength. The specific calculation formula is: , in, represents the size of the average noise reduction window of the i-th expanded noise reduction wavelength; n represents the size of the initial average noise reduction window; z represents the window adaptive change threshold; Indicates the corrected noise level of the i-th expanded noise reduction wavelength; [ ] indicates rounding off; Represents the expansion coefficient of the window corresponding to the i-th expanded noise reduction wavelength.

[0045] After obtaining the size of the average noise reduction window of the expanded noise reduction wavelength, the data corresponding to the wavelengths other than the expanded noise reduction wavelength in the spectrum of the current vehicle are denoised with the initial average noise reduction window, and the data corresponding to the expanded noise reduction wavelength are denoised with the average noise reduction window corresponding to each expanded noise reduction wavelength to obtain a denoised spectrum. When denoising, the data corresponding to the first wavelength in the window is used as the target data for denoising.

[0046] After the moving average denoising is completed, the concentration of each gas to be tested, that is, each harmful gas, can be calculated from the spectrum of the current vehicle. By comparing the measured concentration with the emission standard of the current area, it can be determined whether the carbon emissions of the current motor vehicle meet the emission standards of the monitoring area.

[0047] After completing the moving average denoising to obtain the denoised spectrum, the concentration of each gas in the vehicle's carbon emissions can be calculated using the Lambert Beer law and the absorption wavelength of each gas. By comparing the concentration of harmful gases with the emission standards of the current region, it can be determined whether the current motor vehicle emissions meet the carbon emission standards of the monitored area, thereby effectively supervising vehicles with excessive carbon emissions.

[0048] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The above is a description of a specific embodiment of this specification. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0049] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0050] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A vehicle carbon emission online analysis and monitoring system based on cloud computing, characterized in that: The system includes: A spectrum acquisition unit, used to acquire the spectrum of the current vehicle exhaust gas; The abnormality degree calculation unit is used to set a sliding window, slide on the spectrum graph with a set step length, and the first wavelength in the sliding window is the target wavelength; construct a line graph according to the data in the sliding window; and obtain the abnormality degree of the target wavelength using the standard deviation of the data in the sliding window and the line graph; A target wavelength screening unit is used to establish an expansion window for each target wavelength, calculate the abnormality of the target wavelength within the expansion window and normalize it to obtain a screening abnormality degree, and use the screening abnormality degree to screen the target wavelength to obtain an expanded noise reduction wavelength; A noise reduction degree calculation unit is used to obtain an abnormal curve by fitting the abnormal degree of each target wavelength; and obtain the noise degree based on the abnormal curve and the abnormal degree of the expanded noise reduction wavelength; A noise level correction unit is used to calculate the noise level of the wavelengths that are the same as the target wavelengths on the spectrum graphs of other vehicles and to obtain a noise curve by fitting; the noise level is corrected according to the noise level of the expanded noise reduction wavelength and the noise curve to obtain a corrected noise level; The carbon emission monitoring unit is used to determine the size of the average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength and perform noise reduction to obtain a noise-reduced spectrum; and monitor the carbon emissions of the current vehicle based on the noise-reduced spectrum.

2. According to the cloud computing-based vehicle carbon emission online analysis and monitoring system of claim 1, it is characterized in that: The method of obtaining the abnormality degree of the target wavelength by using the standard deviation and the line graph of the data in the sliding window includes: Obtain the turning point where the line in the line graph is about to drop, and record it as the falling turning point; calculate the standard deviation of the data in the sliding window and the standard deviation of the data after removing the maximum and minimum values ​​in the sliding window, and obtain the absolute value of the difference between the two standard deviations, and record it as the amplitude fluctuation degree; add the amplitude fluctuation degree and the number of falling turning points in the line graph corresponding to the sliding window to obtain the abnormality degree of the target wavelength.

3. According to the cloud computing-based vehicle carbon emission online analysis and monitoring system of claim 1, it is characterized in that: The step of obtaining the noise level based on the abnormal curve and the abnormal degree of expanding the noise reduction wavelength includes: The fitting anomaly degree corresponding to each expanded noise reduction wavelength is obtained on the anomaly curve; the absolute value of the difference between the fitting anomaly degree and the anomaly degree corresponding to the expanded noise reduction wavelength is normalized to obtain a weight coefficient; the weight coefficient is multiplied by the anomaly degree of the expanded noise reduction wavelength to obtain a multiplication result, and the sum of the multiplication result and the anomaly degree of the expanded noise reduction wavelength is the noise degree of the expanded noise reduction wavelength.

4. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 1 is characterized in that: The step of calculating the abnormality degree of the target wavelength within the expanded window and normalizing the abnormality degree of the screening comprises: The peak point of the data in the expanded window is obtained, a line graph is constructed using the peak point in the expanded window, and the number of downward turning points in the line graph is obtained; the abnormality degree of the target wavelength is calculated in the expanded window according to the number of downward turning points in the line graph and the data in the expanded window, and the abnormality degree is normalized to obtain the screening abnormality degree.

5. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 1 is characterized in that: The step of correcting the noise level according to the noise level of the expanded noise reduction wavelength and the noise curve to obtain the corrected noise level includes: The fitting noise degree of the expanded noise reduction wavelength is obtained on the noise curve, and then the absolute value of the difference between the noise degree of the expanded noise reduction wavelength and the fitting noise degree is obtained and normalized to obtain the deviation degree of the noise degree of the expanded noise reduction wavelength; the noise degree is corrected using the deviation degree of the noise degree of the expanded noise reduction wavelength to obtain the corrected noise degree.

6. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 5 is characterized in that: The method of correcting the noise level by using the deviation degree of the noise level of the expanded noise reduction wavelength to obtain the corrected noise level includes: The deviation degree of the noise level of the expanded noise reduction wavelength is added to the preset value, and the product of the added result and the noise level of the expanded noise reduction wavelength is the corrected noise level of the expanded noise reduction wavelength.

7. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 1 is characterized in that: The method of determining the size of the average noise reduction window of the expanded noise reduction wavelength by using the corrected noise degree of the expanded noise reduction wavelength comprises: Set the size of the initial average noise reduction window and the adaptive change threshold; multiply the adaptive change threshold by the corrected noise level of the expanded noise reduction wavelength and round it to get the expansion coefficient; the sum of the expansion coefficient and the size of the initial average noise reduction window is the size of the average noise reduction window of the expanded noise reduction wavelength.

8. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 1 is characterized in that: The obtained denoised spectrum includes: The data corresponding to wavelengths other than the expanded noise reduction wavelength in the spectrum of the current vehicle are denoised using the initial average noise reduction window, and the data corresponding to the expanded noise reduction wavelength are denoised using the average noise reduction window corresponding to each expanded noise reduction wavelength to obtain a denoised spectrum.

9. The vehicle carbon emission online analysis and monitoring system based on cloud computing according to claim 1 is characterized in that: The spectrogram comprises: The horizontal axis of the spectrum is wavelength, and the vertical axis is absorption intensity.

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