Method and apparatus for detecting fly ash carbon content based on pulsed discharge plasma spectroscopy

Abstract

This invention relates to the field of real-time fly ash detection, and particularly to a method and apparatus for detecting the carbon content of fly ash based on pulsed discharge plasma spectroscopy. The method includes: setting up collection points; performing detection at the collection points and selecting several collection points as detection points based on the detection results; performing staggered detection of particles at the detection points within an alternating cycle to obtain the detection results at each detection point; identifying abnormal points among the detection points and correcting the detection results for these abnormal points; and determining the carbon content of the fly ash based on the detection results of all detection points. This invention utilizes a laser to excite particles in the fly ash into plasma at the detection points, and detects the carbon content by acquiring and analyzing the characteristic spectrum of carbon and extracting the characteristic light intensity of carbon. This invention, through reasonable selection of detection points, makes the final data more reasonable, avoids data distortion, and solves the problem of inaccurate detection results.

Description

Technical Field

[0001] This invention relates to the field of real-time fly ash detection, and in particular to a method and apparatus for detecting the carbon content of fly ash based on pulsed discharge plasma spectroscopy. Background Technology

[0002] Thermal power generation is a technology that uses thermal equipment to burn fossil fuels to heat water in a boiler, and the steam drives a turbine to generate electricity. However, during the combustion of fossil fuels, impurities and residues are not completely burned, becoming fine particulate matter, which is fly ash. The carbon content of fly ash directly reflects the combustion efficiency of fossil fuels in the furnace. Quickly measuring the carbon content of fly ash helps guide operators to correctly adjust the fineness of fossil fuels.

[0003] Currently, one method for detecting fly ash particulate matter content is pulsed discharge plasma spectroscopy. This method typically involves inserting a measuring gun into a fixed position in the flue pipe, releasing a high-voltage pulse through the discharge electrode in the measuring gun to break down the fly ash in the flue gas, generating plasma, collecting the spectral signal emitted by the plasma, and converting the spectral signal into an electrical signal to obtain the corresponding fly ash carbon content measurement result.

[0004] Even if the measuring gun is moved, the fixed position limits the number of fly ash detection points and results in poor sample representativeness. Although fly ash moves randomly in the flue, the carbon content of fly ash at different locations on the same plane will vary due to differences in ventilation and combustion conditions. If the detection point is fixed, it can easily lead to data distortion and inaccurate test results. Summary of the Invention

[0005] Therefore, it is necessary to provide a method and apparatus for detecting the carbon content of fly ash based on pulsed discharge plasma spectroscopy to address the above-mentioned problems.

[0006] The present invention is implemented as follows: a method for detecting the carbon content of fly ash based on pulsed discharge plasma spectroscopy, the method comprising:

[0007] S101, Several collection points are evenly set up within the detection area;

[0008] S102, For each collection point, the light intensity average deviation value of several types of particles is determined at the collection point according to the first preset parameters of several types of particles.

[0009] S103, determine the proportion coefficient of several kinds of particles according to the first preset parameters of several kinds of particles, and select several collection points as detection points according to the proportion coefficient of several kinds of particles and the average light intensity deviation of several kinds of particles at the collection points.

[0010] S104, For each detection point, an alternating cycle is set according to several sets of second preset parameters for several types of particles. Within one alternating cycle, the particles corresponding to the second preset parameters are detected in an alternating manner at the detection point to obtain the detection result of the detection point.

[0011] S105, Based on the detection results of all detection points, determine the abnormal points among the detection points;

[0012] S106, For each abnormal point, set a new collection point near the abnormal point, and correct the detection result of the abnormal point by using the detection result of the new collection point;

[0013] S107, Determine the carbon content of fly ash based on the test results of all test points;

[0014] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0015] In one embodiment, the present invention provides a fly ash carbon content detection device based on pulsed discharge plasma spectroscopy, the fly ash carbon content detection device based on pulsed discharge plasma spectroscopy comprising:

[0016] The sampling point setting module is used to evenly set up several sampling points within the detection area;

[0017] The sampling point detection module is used to detect and determine the average light intensity deviation of several types of particles at each sampling point based on the first preset parameters of several types of particles.

[0018] The detection point screening module is used to determine the proportion coefficient of several types of particles based on the first preset parameters of several types of particles, and select several collection points as detection points based on the proportion coefficient of several types of particles and the average light intensity deviation of several types of particles at the collection points.

[0019] The detection point detection module is used to set an interleaved cycle for each detection point based on several sets of second preset parameters for several types of particles. Within one interleaved cycle, the particles corresponding to the second preset parameters are interleaved at the detection point to obtain the detection result of the detection point.

[0020] The anomaly detection module is used to identify anomalies among the detection points based on the detection results of all detection points.

[0021] The outlier correction module is used to set up new sampling points near each outlier point and correct the detection results of the outlier point based on the detection results of the new sampling points.

[0022] The carbon content determination module is used to determine the carbon content of fly ash based on the test results of all detection points.

[0023] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0024] The fly ash carbon content detection method based on pulsed discharge plasma spectroscopy provided in this invention sets up several collection points, and uses a first preset parameter of several kinds of particles to detect at the collection points to screen out detection points. At the detection points, a second preset parameter of several kinds of particles is used to cross-detect the corresponding particles to obtain the detection results of the detection points. Based on the detection results of the detection points, abnormal points in the detection points are identified and their detection results are corrected. Finally, the carbon content of fly ash is determined based on the detection results of all detection points. This approach involves using different second preset parameters for staggered detection at different particle detection points. By employing different delays and gate widths for different particles, the light intensity detected at each detection point can be accurately obtained. This allows for the accurate acquisition of the light intensity detected at the detection point for carbon particles within a group of particles, thus yielding the detection result for that point. The detection points are selected through optimized screening of collection points, ensuring minimal data fluctuations. Furthermore, the detection results of outliers within the detection points are corrected. Finally, the carbon content of fly ash is obtained by combining the detection results from all detection points. This method moves away from limiting fly ash detection points to fixed locations, allowing for the rational selection of detection points to obtain more accurate data, thereby avoiding data distortion and solving the problem of inaccurate detection results. Attached Figure Description

[0025] Figure 1 This is a flowchart of a fly ash carbon content detection method based on pulsed discharge plasma spectroscopy in one embodiment;

[0026] Figure 2 An environmental application diagram of a fly ash carbon content detection method based on pulsed discharge plasma spectroscopy in one embodiment;

[0027] Figure 3 This is a horizontal screenshot of a fly ash carbon content detection method based on pulsed discharge plasma spectroscopy in one embodiment;

[0028] Figure 4 This is a structural block diagram of a fly ash carbon content detection device based on pulsed discharge plasma spectroscopy in one embodiment.

[0029] Figure 5This is a block diagram of the internal structure of a computer device in one embodiment. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0031] It is understood that the terms "first," "second," etc., used in this invention may be used to describe several elements, but unless otherwise specified, these elements are not limited by these terms. These terms are only used to distinguish one element from another. For example, without departing from the scope of this invention, a first script may be referred to as a second script, and similarly, a second script may be referred to as a first script.

[0032] like Figure 1 As shown, in one embodiment, a method for detecting the carbon content of fly ash based on pulsed discharge plasma spectroscopy is proposed, which may specifically include the following steps:

[0033] S101, Several collection points are evenly set up within the detection area;

[0034] S102, For each collection point, the light intensity average deviation value of several types of particles is determined at the collection point according to the first preset parameters of several types of particles.

[0035] S103, determine the proportion coefficient of several kinds of particles according to the first preset parameters of several kinds of particles, and select several collection points as detection points according to the proportion coefficient of several kinds of particles and the average light intensity deviation of several kinds of particles at the collection points.

[0036] S104, For each detection point, an alternating cycle is set according to several sets of second preset parameters for several types of particles. Within one alternating cycle, the particles corresponding to the second preset parameters are detected in an alternating manner at the detection point to obtain the detection result of the detection point.

[0037] S105, Based on the detection results of all detection points, determine the abnormal points among the detection points;

[0038] S106, For each abnormal point, set a new collection point near the abnormal point, and correct the detection result of the abnormal point by using the detection result of the new collection point;

[0039] S107, Determine the carbon content of fly ash based on the test results of all test points;

[0040] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0041] In this embodiment, as Figure 2 , Figure 3 As shown, the fly ash carbon content detection based on pulsed discharge plasma spectroscopy includes: computer equipment, spectral analysis equipment, pulsed discharge laser equipment, galvanometer adjustment equipment, acquisition equipment, and protective equipment. This invention applies to computer equipment. In the galvanometer adjustment equipment, the galvanometer can be adjusted so that the laser from the pulsed discharge laser equipment falls on any acquisition point within the detection area. The detection area is a plane perpendicular to the height of the flue pipe, where fly ash moves from bottom to top. The acquisition point here is essentially a small acquisition area. Typically, a pulsed discharge laser equipment is used to generate laser light through pulsed discharge at a set frequency. The laser light passes through the galvanometer in the galvanometer adjustment equipment, creating a laser region within the detection area, thus generating plasma within the laser region. When fly ash particles pass through the plasma region, they are rapidly excited, emitting spectral signals that are then acquired by the acquisition equipment. This laser region is the acquisition point. Figure 3 At this angle, the acquisition device is usually obscured by the pulsed discharge laser device and the galvanometer adjustment device. The dashed box in the figure is only used to show the connection between the acquisition device and the spectral analysis device.

[0042] In this embodiment, generally speaking, there are three types of particles: carbon particles, metal particles, and water-containing particles.

[0043] In this embodiment, each type of particle has a corresponding set of first preset parameters. If there are three types of particles, then one sampling point needs to be detected three times. The first preset parameters include two parameters: time delay and gate width, so they are represented by the number of sets.

[0044] In this embodiment, the proportional coefficient is only used to select detection points. Since the sampling points are evenly distributed in the detection area, although the fly ash moves around during its ascent, it is generally biased towards the top of the combustion material. Therefore, the detection results between sampling points are very different, and the data from the sampling points located directly above the combustion material are more accurate. Thus, it is necessary to select detection points from the sampling points.

[0045] In this embodiment, in S104, a particle can have multiple sets of second preset parameters. The second preset parameters include two parameters: time delay and gate width, and are therefore represented by the number of sets.

[0046] In this embodiment, both delay and gate width are time. An interleaving period is set according to several sets of second preset parameters for several types of particles. This interleaving period is a time period.

[0047] In this embodiment, the anomaly may not exist. If it does not exist, then S106 does not need to be executed.

[0048] In this embodiment, the new acquisition points are only used to correct the detection results of anomalies. These new acquisition points must be distinguished from the old ones, and they are unrelated to the detection points. The detection points in S107 do not include these new acquisition points. It can be understood that after correcting the detection results of anomalies, all new acquisition points are removed.

[0049] In this embodiment, in S107, the final carbon content of fly ash determined by the present invention is a single value. Of course, the carbon content of fly ash in different regions can be determined by dividing the detection points into regions and using the detection results of the detection points in different regions to monitor the combustion status of different regions.

[0050] In this embodiment, carbon particles include elemental carbon and carbon-containing particles, i.e., carbon elements. Metal particles include elemental metals and metal-containing particles. Water-containing particles include particles containing hydrogen and oxygen elements. This is because the detected spectrum is actually the spectrum of a certain element, and the particles here refer to the relevant element. Since water is composed of hydrogen and oxygen elements, the detected elements are hydrogen and oxygen elements.

[0051] The fly ash carbon content detection method based on pulsed discharge plasma spectroscopy provided in this invention sets up several collection points, and uses a first preset parameter of several kinds of particles to detect at the collection points to screen out detection points. At the detection points, a second preset parameter of several kinds of particles is used to cross-detect the corresponding particles to obtain the detection results of the detection points. Based on the detection results of the detection points, abnormal points in the detection points are identified and their detection results are corrected. Finally, the carbon content of fly ash is determined based on the detection results of all detection points. This approach involves using different second preset parameters for staggered detection at different particle detection points. By employing different delays and gate widths for different particles, the light intensity detected at each detection point can be accurately obtained. This allows for the accurate acquisition of the light intensity detected at the detection point for carbon particles within a group of particles, thus yielding the detection result for that point. The detection points are selected through optimized screening of collection points, ensuring minimal data fluctuations. Furthermore, the detection results of outliers within the detection points are corrected. Finally, the carbon content of fly ash is obtained by combining the detection results from all detection points. This method moves away from limiting fly ash detection points to fixed locations, allowing for the rational selection of detection points to obtain more accurate data, thereby avoiding data distortion and solving the problem of inaccurate detection results.

[0052] In one embodiment, the step of detecting and determining the average light intensity deviation of several types of particles at each collection point based on a first preset parameter of several types of particles at that collection point includes:

[0053] For each particle, the first preset parameters of the particle are determined based on the particle's optimal delay and optimal gate width;

[0054] For each collection point, the first preset parameters of several types of particles are used to detect the light intensity of several types of particles at the collection point.

[0055] Depend on The average light intensity at the sampling point was obtained;

[0056] Based on the average light intensity of all collection points, outliers in the average light intensity of all collection points are determined, and the collection points corresponding to the outliers are removed to filter the collection points.

[0057] For the selected collection points, by The average light intensity of several types of particles was obtained at all collection points.

[0058] Depend on The mean light intensity deviation values ​​of several types of particles at the collection point were obtained respectively;

[0059] Where i is the index of the particle type, N is the number of particle types, and I i Let I be the light intensity of the i-th particle at that sampling point, j be the sampling point number, and n be the number of sampling points. ij Let I be the light intensity of the i-th particle at the j-th sampling point. ai Let be the average light intensity of the i-th particle at all sampling points.

[0060] In this embodiment, the optimal delay and optimal gate width of the particles can be obtained from existing data. For example, the optimal delay of metal particles is 150 ns and the optimal gate width is 500 ns, the optimal delay of carbon particles is 220 ns and the optimal gate width is 1800 ns, and the optimal delay of carbon particles is 300 ns and the optimal gate width is 1500 ns.

[0061] In this embodiment, the delay is the time interval from the start of the pulse discharge to the start of signal acquisition by the spectrometer, which can be simply understood as the start time of acquisition; the gate width is the duration of signal acquisition by the spectrometer, which can be understood as the continuous acquisition time. The purpose of the delay is to avoid unwanted signals (such as early interfering emission) and target the emission period of the target particle. For example, if the particle emits the strongest light 1-5 μs (microseconds, one millionth of a second) after discharge, a delay of 2 μs can just capture the peak signal; if the delay is set to 10 μs, the signal has already attenuated, and at this time the acquired signal is mainly from other particles, and the detection will be distorted. The purpose of the gate width is to ensure that most of the effective signal of the target particle is collected, while avoiding the mixing of subsequent interference signals. For example, if the particle emits light for 10 μs, a gate width of 10 μs can just collect its complete signal; if the gate width is set to 30 μs, the emission of other particles after 10 μs will be collected. This "extra light" is interference and will blur the signal.

[0062] In this embodiment, for example, if the carbon particles are detected at the sampling point using a first preset parameter, it means that the spectral signal is collected continuously for 1800 ns after 220 ns from the start of the pulse discharge. This is also the current common practice, but the resulting detection results are affected by other particles, and the data may be inaccurate. However, here the main purpose is to obtain the average light intensity deviation value to obtain a priority value, so this method can be used to detect the light intensity first.

[0063] In this embodiment, since there are three types of particles, N is 3.

[0064] In this embodiment, for example, if the carbon particle's serial number is 1, then I1 is the light intensity of the carbon particle at that collection point.

[0065] In this embodiment, for example, if the carbon particle's serial number is 1, then I 1j Let I be the light intensity of the carbon particle at the j-th sampling point. a1 This represents the average light intensity of carbon particles across all sampling points.

[0066] In one embodiment, determining the proportion coefficients of several types of particles based on a first preset parameter of several types of particles includes:

[0067] Obtain the signal-to-noise ratio of several types of particles;

[0068] The proportion coefficients of several types of particles are determined based on the ratios between the signal-to-noise ratios of several types of particles.

[0069] For each type of particle, the detection time period for that particle is determined based on the optimal delay and optimal gate width in the first preset parameters of that particle;

[0070] Set the index of the carbon particle type to 1;

[0071] For carbon particles, the detection time period t is determined based on the detection time periods of all particles, specifically the time period that overlaps only with the detection time period of any other particle type, or the time period that does not overlap with the detection time period of any other particle type. 1i Based on the detection time periods of all particles, the time period t during which the detection time period of carbon particles overlaps only with the detection time periods of m types of particles is determined. mJ ,Depend on The percentage of carbon particles detected was obtained;

[0072] For each type of particle other than carbon particles, determine the time period T during which the detection time period of that particle does not overlap with the detection time period of the carbon particles, based on the detection time periods of all particles. 1i The time period T that overlaps with the detection time of carbon particles 2i ,Depend on The detection percentage of this particle was obtained;

[0073] The proportion coefficients of several types of particles are determined based on the ratios between the detection proportions of several types of particles, and the proportion coefficients of several types of particles are normalized so that the sum of the proportion coefficients of several types of particles is 1.

[0074] Where i is the sequence number of the particle type, N is the number of particle types, m starts counting from 2, m is the number of particles whose detection time overlaps with the detection time of carbon particles, and K i denoted as the proportion coefficient of the i-th type of particle, and J is the sequence number of the types of particles whose detection time period overlaps with the detection time period of carbon particles.

[0075] In this embodiment, the signal-to-noise ratio (SNR) refers to the ratio of the target signal intensity to the noise intensity. Here, the target signal intensity refers to light intensity. Based on the SNR ratios of several types of particles, the proportion coefficients of each particle type are determined. According to existing data, the ratio of carbon particles:metal particles:water-containing particles is approximately 4:5:3. Therefore, the proportion coefficients of carbon particles, metal particles, and water-containing particles can be directly set to 4, 5, and 3, respectively.

[0076] In this embodiment, the index of the carbon particle type is set to 1, so the i corresponding to the carbon particle is 1.

[0077] In this embodiment, for carbon particles, the detection time period t of the carbon particles is determined based on the detection time periods of all particles, specifically the time period that overlaps only with the detection time period of any one type of particle or the time period that does not overlap with the detection time period of any one type of particle. 1i Since i corresponds to 1 for carbon particles, t 11 t represents the time period during which the detection time of carbon particles does not overlap with the detection time period of any other particle type.12 This is the time period during which the detection time of carbon particles overlaps with the detection time period of particles with i = 2, and so on.

[0078] In this embodiment, since there are 3 types of particles among several types, and m is counted starting from 2, essentially, the detection time period t of carbon particles is determined based on the detection time periods of all particles, and the detection time periods of m types of particles overlap only with the detection time periods of m types of particles. mJ This involves determining the overlap (t) between the detection time of carbon particles and the detection time of the other two types of particles, based on the detection time of all particles. 2J And the value of J will only be 1. Assuming there are 4 types of particles among several types, the value of m can be 2 or 3. When the value of m is 2, the detection time period of carbon particles overlaps with the detection time periods of 2 types of particles, which has 3 types. In this case, the value of J is 1, 2, or 3.

[0079] In this embodiment, since there are 3 types of particles among the several types of particles, therefore The superscript is 2; The superscript is 1; The superscript is 3. Assume there are more than 3 types of particles among the various types, and the number of superscripts for each accumulation is also fixed, because when t is determined... mJ You'll know when the time comes.

[0080] In this embodiment, since the measured content is carbon, for each type of particle other than carbon particles, it is only necessary to compare it with the carbon particles. Therefore, it is only necessary to determine T. 1i and T 2i The carbon particle has the index 1, so K1 is the carbon particle proportion coefficient.

[0081] In one embodiment, the step of selecting several collection points as detection points based on the proportion coefficients of several types of particles and the average light intensity deviation values ​​of several types of particles at the collection points includes:

[0082] For each collection point, by Obtain the priority value of this collection point;

[0083] The collection points are sorted according to their priority values ​​from smallest to largest.

[0084] Select the first preset number of collection points that are ranked first and record them as detection points;

[0085] Where i is the index of the particle type, N is the number of particle types, j is the index of the collection point, and k i e is the proportion coefficient of the i-th type of particle. ij Let be the mean deviation of the light intensity of the i-th particle at the j-th sampling point.

[0086] In this embodiment, the first preset number can be set to half the number of collection points.

[0087] In this embodiment, the sampling points may fluctuate at different locations due to factors such as fly ash flow rate and ambient temperature during detection. It is necessary to select more reasonable sampling points based on priority values ​​so that the final fly ash carbon content can be more stable.

[0088] In one embodiment, setting an alternating period based on several sets of second preset parameters for several types of particles includes:

[0089] For carbon particles among several types of particles, obtain the optimal delay and optimal gate width for carbon particles;

[0090] The optimal delay and optimal gate width of the carbon particles are simultaneously increased / decreased by a preset value to obtain new delay and gate width of the carbon particles, and the second preset parameters of the carbon particles are determined based on the new delay and gate width.

[0091] The first preset parameter of several types of particles is determined as the second preset parameter;

[0092] The carbon particles are sorted according to several sets of second preset parameters to obtain a detection sequence;

[0093] A new detection sequence is obtained by inserting a set of second preset parameters for particles other than carbon particles into every two adjacent sets of second preset parameters in the detection sequence.

[0094] An interleaved cycle is set according to the sorting of the second preset parameter in the detection sequence.

[0095] In this embodiment, the preset value can be set to 20ns.

[0096] In this embodiment, carbon, metals, and water-containing particles are detected alternately, and different delays and gate widths are set for carbon detection to broaden the detection channel and improve the detection coverage. Different target particles have different optimal delays, but these optimal delays are not fixed and vary under different flue gas conditions. By dynamically moving the detector, more accurate detection results can be obtained.

[0097] In this embodiment, the arrangement of particles in the staggered cycle is carbon particle - another type of particle - carbon particle - yet another type of particle - carbon particle.

[0098] In one embodiment, the step of performing staggered detection on particles corresponding to the second preset parameters at the detection point within a staggered cycle to obtain the detection result at that detection point includes:

[0099] According to the order of the second preset parameters within an interleaved cycle, the particles corresponding to the second preset parameters are interleaved at the detection point, and the light intensity and signal-to-noise ratio obtained in each detection are recorded.

[0100] Determine whether the signal-to-noise ratio of each detection is within the preset range of the signal-to-noise ratio of the particle corresponding to the second preset parameter. If yes, obtain the light intensity obtained by detection using the second preset parameter corresponding to the carbon particle. If no, repeat the interleaved detection of the particle corresponding to the second preset parameter at the detection point in the next interleaved cycle.

[0101] Determine whether the light intensity obtained by detecting the second preset parameters corresponding to the carbon particles is within the preset range of the average light intensity obtained by detecting the second preset parameters corresponding to the carbon particles. If so, record the light intensity obtained by detecting the second preset parameters corresponding to the carbon particles as the detection result of the detection point. If not, repeat the cross-detection of the particles corresponding to the second preset parameters at the detection point in the next cross-detection cycle.

[0102] In this embodiment, the purpose is to detect the carbon content of fly ash, so the detection of carbon particles is necessary. Other particles are considered to ensure the accuracy of the carbon particle detection result. The carbon particle detection result is valid only if all detection results within the alternating cycle are within their preset range, and all three carbon particle detection results are within the preset range of the average value.

[0103] In this embodiment, the preset range of the signal-to-noise ratio of the particle corresponding to the second preset parameter can be set to 80%-120% of the signal-to-noise ratio of the particle corresponding to the second preset parameter, that is, a fluctuation of 20% is allowed.

[0104] In this embodiment, the preset range of the average light intensity obtained by detecting the second preset parameter corresponding to the carbon particles is calculated first. The preset range of the average light intensity can be set to 80%-120% of the average value, that is, a fluctuation of 20% is allowed.

[0105] In this embodiment, the particles corresponding to the second preset parameters are re-detected at the detection point within the next interleaving cycle. The steps are the same as in S104, where the particles corresponding to the second preset parameters are re-detected at the detection point within one interleaving cycle to obtain the detection result for that point. This is equivalent to performing another detection at that point. After this second detection, it is still necessary to determine whether the signal-to-noise ratio of each detection is within the preset range of the signal-to-noise ratio of the particles corresponding to the second preset parameters, and whether the light intensity obtained by detecting the carbon particles using the second preset parameters is within the preset range of the average light intensity obtained by detecting the carbon particles using the second preset parameters.

[0106] In one embodiment, determining the outliers among the detection points based on the detection results of all detection points includes:

[0107] For each detection point, the average light intensity in the detection results of that detection point is recorded as the light intensity detected by that detection point in one interleaving cycle;

[0108] Determine whether the light intensity detected at the detection point in the preset number of interleaving cycles is within the preset range of the average light intensity detected at the detection point in the preset number of interleaving cycles. If not, the detection point is recorded as an abnormal point. If yes, the average light intensity detected at the detection point in the preset number of interleaving cycles is recorded as the detection data of the detection point, and the detection point is recorded as a normal point.

[0109] For each normal point, determine whether the normal point is outside the preset range of the average value of the detection data of all normal points. If so, change the normal point to an abnormal point.

[0110] In this embodiment, the light intensity in the detection result of the detection point is the light intensity obtained by detecting the second preset parameter corresponding to the carbon particle. There are multiple second preset parameters corresponding to the carbon particle, so there are also multiple light intensities. Therefore, the average value of the light intensity in the detection result of the detection point is recorded as the light intensity detected by the detection point in one interleaving cycle.

[0111] In this embodiment, the preset number of times can be set to 5.

[0112] In this embodiment, the preset range of the average light intensity detected by the detection point in a preset number of interleaving cycles requires calculating the average light intensity detected by the detection point in a preset number of interleaving cycles. The preset range of the average light intensity detected by the detection point in a preset number of interleaving cycles can be set to 80%-120% of the average value, that is, a fluctuation of 20% is allowed.

[0113] In this embodiment, the preset range of the average value of the detection data of all normal points is calculated first. The preset range of the average value of the detection data of all normal points can be set to 80%-120% of the average value, that is, a fluctuation of 20% is allowed.

[0114] In one embodiment, setting up a new sampling point near the anomaly point and correcting the detection result of the anomaly point based on the detection result of the new sampling point includes:

[0115] Get the distance to the nearest normal point that is the anomaly point;

[0116] Generate a circle with the anomaly point as the center and the distance to the nearest normal point as the radius;

[0117] Determine the intersection point of the line from the second preset number of normal points closest to the circumference of the circle to the center of the circle and the circumference of the circle, and determine the intersection point as the new sampling point;

[0118] Within an alternating cycle, the particles corresponding to the second preset parameters are detected at the new sampling point to obtain the detection results of the new sampling point.

[0119] Based on the detection results of the detection points and the detection results of the new collection points, determine whether there are any abnormal points in the new collection points. If so, cancel the setting of all detection points in the circular area. If not, determine the average value of the detection results of the new collection points as the detection result of the abnormal point in order to correct the detection result of the abnormal point.

[0120] In this embodiment, the second preset quantity can be set to any value between 3 and 5.

[0121] In this embodiment, the step of performing staggered detection on the particles corresponding to the second preset parameters at the new collection point within a staggered cycle to obtain the detection result of the new collection point is the same as step S104.

[0122] In this embodiment, determining whether there are abnormal points in the new acquisition point based on the detection results of the detection point and the detection results of the new acquisition point can be understood as identifying abnormal points in the new acquisition point. In S105, if an abnormal point is identified in the detection point, it is determined whether the light intensity detected by the detection point in the preset number of interleaving cycles is within the preset range of the average light intensity detected by the detection point in the preset number of interleaving cycles, and whether the normal point is outside the preset range of the average value of the detection data of all normal points. Similarly, using the preset range of the average value of the light intensity detected by the detection point in the preset number of interleaving cycles and the preset range of the average value of the detection data of all normal points, the data of the new acquisition point can be judged in the same way. It is not necessary to re-determine the data required for judgment based on the detection results of the new acquisition point.

[0123] In this embodiment, all detection points within the circular area are cancelled. The circle is generated with the abnormal point as its center and the distance to the nearest normal point as its radius. Therefore, there is essentially only one detection point within the circular area, which is the abnormal point. The new acquisition point itself is not considered a detection point; in other words, when the abnormal point is cancelled, the corresponding new acquisition point is also cancelled simultaneously.

[0124] In one embodiment, determining the carbon content of fly ash based on the detection results of all detection points includes:

[0125] For each detection point, the average light intensity in the detection results of that detection point is recorded as the light intensity detected by that detection point in one interleaving cycle;

[0126] The average light intensity detected at the detection point over a preset number of interleaved cycles is recorded as the detection data of that detection point;

[0127] The carbon content of fly ash is determined by referring to a table based on the average value of the test data from all test points.

[0128] In this embodiment, the carbon content of fly ash is determined by looking up a table, which is a common method.

[0129] In this embodiment, firstly, the acquisition device collects the spectrum. The spectrum is essentially a two-dimensional distribution map of wavelength and light intensity (the horizontal axis represents wavelength, and the vertical axis represents light intensity), including signals from all excited primitives (carbon, silicon, iron, etc.) in the fly ash. Then, the entire spectrum is scanned using a spectral analysis device, and based on the known characteristic wavelengths of carbon, the "signal peak" of carbon is located in the spectrum. This process also eliminates background noise. Finally, the light intensity of the carbon characteristic is determined using the peak height method or peak area method; this is the detection data for the detection point. Light intensity is not equal to carbon content. There are two ways to determine carbon content based on light intensity. One is to select an element as an internal standard element, such as silicon. Since the silicon content in the same batch of fly ash is relatively stable, and silicon is the main component of fly ash (high content, easy to excite), the characteristic light intensity of silicon is often used as an internal standard signal in industrial testing. By calculating the ratio of "carbon characteristic light intensity / silicon characteristic light intensity" (this ratio can offset the influence of system fluctuations on carbon light intensity), a standard curve is plotted using the "carbon / silicon light intensity ratio". This standard curve is the lookup table in the lookup table method. At this time, knowing the silicon content and the carbon / silicon light intensity ratio, the carbon content can be obtained. The other way is to determine it by the ratio to the total light intensity. By using the first preset parameters of carbon particles for detection multiple times in advance, the ratio of "carbon characteristic light intensity / total light intensity of the detection" is calculated, and the carbon content corresponding to the total light intensity of the detection is formed into a lookup table. In this way, the carbon content can also be obtained when the ratio of "carbon characteristic light intensity / total light intensity of the detection" is known.

[0130] like Figure 4 As shown, in one embodiment, a fly ash carbon content detection device based on pulsed discharge plasma spectroscopy is provided, which may specifically include:

[0131] The sampling point setting module is used to evenly set up several sampling points within the detection area;

[0132] The sampling point detection module is used to detect and determine the average light intensity deviation of several types of particles at each sampling point based on the first preset parameters of several types of particles.

[0133] The detection point screening module is used to determine the proportion coefficient of several types of particles based on the first preset parameters of several types of particles, and select several collection points as detection points based on the proportion coefficient of several types of particles and the average light intensity deviation of several types of particles at the collection points.

[0134] The detection point detection module is used to set an interleaved cycle for each detection point based on several sets of second preset parameters for several types of particles. Within one interleaved cycle, the particles corresponding to the second preset parameters are interleaved at the detection point to obtain the detection result of the detection point.

[0135] The anomaly detection module is used to identify anomalies among the detection points based on the detection results of all detection points.

[0136] The outlier correction module is used to set up new sampling points near each outlier point and correct the detection results of the outlier point based on the detection results of the new sampling points.

[0137] The carbon content determination module is used to determine the carbon content of fly ash based on the test results of all detection points.

[0138] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0139] In this embodiment, the modules of the fly ash carbon content detection device based on pulsed discharge plasma spectroscopy are modularized from the method of this invention. For a detailed explanation of each module, please refer to the corresponding content in the method section of this invention. This embodiment will not be repeated here.

[0140] Figure 5 An internal structural diagram of a computer device in one embodiment is shown. Figure 5 As shown, the computer device includes a processor, memory, network interface, input device, and display screen connected via a system bus. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system and may also store a computer program. When executed by the processor, this computer program enables the processor to implement the fly ash carbon content detection method based on pulsed discharge plasma spectroscopy provided in this embodiment of the invention. The internal memory may also store a computer program. When executed by the processor, this computer program enables the processor to implement the fly ash carbon content detection method based on pulsed discharge plasma spectroscopy provided in this embodiment of the invention. The display screen of the computer device can be a liquid crystal display screen or an e-ink display screen. The input device of the computer device can be a touch layer covering the display screen, or buttons, a trackball, or a touchpad provided on the casing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0141] Those skilled in the art will understand that Figure 5 The structure shown is merely a block diagram of a portion of the structure related to the present invention and does not constitute a limitation on the computer device to which the present invention is applied. A specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0142] In one embodiment, the fly ash carbon content detection device based on pulsed discharge plasma spectroscopy provided in this invention can be implemented as a computer program, which can be configured as follows: Figure 5The computer device shown is running the program. The computer device's memory can store the various program modules that make up the fly ash carbon content detection device based on pulsed discharge plasma spectroscopy, for example, Figure 4 The diagram shows a sampling point setting module, a sampling point detection module, a detection point screening module, a detection point detection module, an anomaly point identification module, an anomaly point correction module, and a carbon content determination module. The computer program, comprised of these modules, enables the processor to execute the steps in the fly ash carbon content detection method based on pulsed discharge plasma spectroscopy, as described in the various embodiments of the present invention.

[0143] For example, Figure 5 The computer device shown can be used as follows Figure 4 The fly ash carbon content detection device based on pulsed discharge plasma spectroscopy shown in the diagram executes step S101 via the sampling point setting module; step S102 via the sampling point detection module; step S103 via the detection point screening module; step S104 via the detection point detection module; step S105 via the anomaly point determination module; step S106 via the anomaly point correction module; and step S107 via the carbon content determination module.

[0144] In one embodiment, a computer device is provided, the computer device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, performs the following steps:

[0145] S101, Several collection points are evenly set up within the detection area;

[0146] S102, For each collection point, the light intensity average deviation value of several types of particles is determined at the collection point according to the first preset parameters of several types of particles.

[0147] S103, determine the proportion coefficient of several kinds of particles according to the first preset parameters of several kinds of particles, and select several collection points as detection points according to the proportion coefficient of several kinds of particles and the average light intensity deviation of several kinds of particles at the collection points.

[0148] S104, For each detection point, an alternating cycle is set according to several sets of second preset parameters for several types of particles. Within one alternating cycle, the particles corresponding to the second preset parameters are detected in an alternating manner at the detection point to obtain the detection result of the detection point.

[0149] S105, Based on the detection results of all detection points, determine the abnormal points among the detection points;

[0150] S106, For each abnormal point, set a new collection point near the abnormal point, and correct the detection result of the abnormal point by using the detection result of the new collection point;

[0151] S107, Determine the carbon content of fly ash based on the test results of all test points;

[0152] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0153] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, causes the processor to perform the following steps:

[0154] S101, Several collection points are evenly set up within the detection area;

[0155] S102, For each collection point, the light intensity average deviation value of several types of particles is determined at the collection point according to the first preset parameters of several types of particles.

[0156] S103, determine the proportion coefficient of several kinds of particles according to the first preset parameters of several kinds of particles, and select several collection points as detection points according to the proportion coefficient of several kinds of particles and the average light intensity deviation of several kinds of particles at the collection points.

[0157] S104, For each detection point, an alternating cycle is set according to several sets of second preset parameters for several types of particles. Within one alternating cycle, the particles corresponding to the second preset parameters are detected in an alternating manner at the detection point to obtain the detection result of the detection point.

[0158] S105, Based on the detection results of all detection points, determine the abnormal points among the detection points;

[0159] S106, For each abnormal point, set a new collection point near the abnormal point, and correct the detection result of the abnormal point by using the detection result of the new collection point;

[0160] S107, Determine the carbon content of fly ash based on the test results of all test points;

[0161] Among them, several types of particles include carbon particles, metal particles, and water-containing particles.

[0162] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0163] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0164] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0165] The embodiments described above are merely examples of several implementations of the present invention, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention.

Claims

1. A method for fly ash carbon content detection based on pulsed discharge plasma spectroscopy, characterized in that, The fly ash carbon content detection method based on pulse discharge plasma spectrum comprises: S101, uniformly setting a plurality of collection points in a detection area; S102, for each collection point, detecting and determining the light intensity mean deviation value of the plurality of particles at the collection point according to the first preset parameters of the plurality of particles; S103, determining the proportion coefficient of the plurality of particles according to the first preset parameters of the plurality of particles, and selecting a plurality of collection points as detection points from the collection points according to the proportion coefficient of the plurality of particles and the light intensity mean deviation value of the plurality of particles at the collection points; S104, for each detection point, setting an interleaving period according to a plurality of groups of second preset parameters of the plurality of particles, and performing interleaving detection on the particles corresponding to the second preset parameters at the detection point in the interleaving period to obtain the detection result of the detection point; S105, determining the abnormal points in the detection points according to the detection results of all the detection points; S106, for each abnormal point, setting a new collection point near the abnormal point, and correcting the detection result of the abnormal point through the detection result of the new collection point; S107, determining the fly ash carbon content according to the detection results of all the detection points; The plurality of particles comprises carbon particles, metal particles and water-containing particles; The method comprises the following steps: Obtaining the signal-to-noise ratios of the plurality of particles; Determining the proportion coefficient of the plurality of particles according to the ratio between the signal-to-noise ratios of the plurality of particles; For each particle, determining the detection time period of the particle according to the optimal delay and the optimal gate width in the first preset parameters of the particle; The serial number of the type of carbon particles is set as 1; For the carbon particles, the detection time period of the carbon particles is determined according to the detection time periods of all the particles, and the detection time period of the carbon particles is only overlapped with the detection time period of any one kind of particles or the time period t that is not overlapped with the detection time period of any one kind of particles 1i The detection time period of the carbon particles is determined according to the detection time periods of all the particles, and the detection time period of the carbon particles is only overlapped with the detection time period of m kinds of particles mJ The detection proportion of the carbon particles is obtained by ​ For each particle other than the carbon particle, a time period T is determined in which the detection time period of the particle does not overlap with the detection time period of the carbon particle, according to the detection time periods of all the particles 1i and a time period T in which the detection time period overlaps with the detection time period of the carbon particle 2i , and a detection proportion of the particle is obtained Determining the proportion coefficient of the plurality of particles according to the ratio between the detection proportions of the plurality of particles, and performing normalization processing on the proportion coefficient of the plurality of particles so that the sum of the proportion coefficients of the plurality of particles is 1; where i is the serial number of the type of particle, N is the number of types of particles, m is counted from 2, m is the number of particles whose detection time period overlaps with the detection time period of the carbon particle, K i is the proportion coefficient of the i-th type of particle, and J is the serial number of the type of the number of particles whose detection time period overlaps with the detection time period of the carbon particle.

2. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, characterized in that, The method comprises the following steps: For each particle, determining the first preset parameters of the particle according to the optimal delay and the optimal gate width of the particle; For each collection point, detecting at the collection point by using the first preset parameters of the plurality of particles respectively, and obtaining the light intensity of the plurality of particles at the collection point; from obtaining the average light intensity of the collection point; Determining the outlier value in the average light intensity of all the collection points according to the average light intensity of all the collection points, and canceling the setting of the collection point corresponding to the outlier value to screen the collection points; For the screened collection points, the average light intensity of each particle at all collection points is obtained respectively. The average light intensity of each particle at all collection points is obtained respectively. obtained by respectively, the light intensity average deviation value of several kinds of particles at the collection point; where i is the serial number of the type of particle, N is the number of types of particles, I i is the light intensity of the i-th type of particle at the j-th collection point, j is the serial number of the collection point, n is the number of collection points, I ij is the light intensity of the i-th type of particle at the j-th collection point, I ai is the average light intensity of the i-th type of particle at all collection points.

3. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, The method comprises the following steps: For each collection point, the priority value of the collection point is obtained by the collection point. According to the order from small to large of the priority values, the collection points are sorted; Selecting the first preset number of collection points with high sorting as detection points; where i is the serial number of the type of particle, N is the number of types of particles, j is the serial number of the collection point, k i is the proportionality coefficient of the i-th type of particle, e ij is the average deviation of the light intensity of the i-th type of particle at the j-th collection point.

4. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, The method comprises the following steps: For the carbon particles in the plurality of particles, obtaining the optimal delay and the optimal gate width of the carbon particles; simultaneously increase / decrease the optimal delay and the optimal gate width of the carbon particles by a preset value to obtain new delay and gate width of the carbon particles and determine second preset parameters of the carbon particles according to the new delay and gate width; determine the first preset parameters of the several kinds of particles as the second preset parameters; sort the second preset parameters of the several groups of carbon particles to obtain a detection sequence; insert a group of second preset parameters of particles other than the carbon particles between every two adjacent groups of second preset parameters in the detection sequence to obtain a new detection sequence; set an interleaving period according to the sorting of the second preset parameters in the detection sequence.

5. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, the interleaved detection of the particles corresponding to the second preset parameters employed at the detection point in one interleaving period to obtain the detection result of the detection point, including: interleaved detection of the particles corresponding to the second preset parameters employed at the detection point according to the order of the second preset parameters in one interleaving period, and record the light intensity and signal-to-noise ratio obtained by each detection; determine whether the signal-to-noise ratio of each detection is within the preset range of the signal-to-noise ratio of the particles corresponding to the second preset parameters employed, if yes, obtain the light intensity obtained by detection with the second preset parameters corresponding to the carbon particles, if no, re-detect the particles corresponding to the second preset parameters employed at the detection point in the next interleaving period; determine whether the light intensity obtained by detection with the second preset parameters corresponding to the carbon particles is within the preset range of the average value of the light intensity obtained by detection with the second preset parameters corresponding to the carbon particles, if yes, record the light intensity obtained by detection with the second preset parameters corresponding to the carbon particles as the detection result of the detection point, if no, re-detect the particles corresponding to the second preset parameters employed at the detection point in the next interleaving period.

6. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, determine the abnormal point in the detection points according to the detection results of all the detection points, including: for each detection point, record the average value of the light intensity in the detection result of the detection point as the light intensity detected by the detection point in one interleaving period; determine whether the light intensity detected by the detection point in the preset number of interleaving periods is within the preset range of the average value of the light intensity detected by the detection point in the preset number of interleaving periods, if no, record the detection point as an abnormal point, if yes, record the average value of the light intensity detected by the detection point in the preset number of interleaving periods as the detection data of the detection point, and record the detection point as a normal point; for each normal point, determine whether the normal point is outside the preset range of the average value of the detection data of all normal points, if yes, change the normal point to an abnormal point.

7. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, set a new collection point near the abnormal point, and correct the detection result of the abnormal point through the detection result of the new collection point, including: obtain the distance of the normal point closest to the abnormal point; generate a circle with the abnormal point as the center and the distance of the normal point closest to the abnormal point as the radius; determine the intersection points of the straight lines from the second preset number of normal points closest to the circumference of the circle to the center of the circle and the circumference of the circle, and determine the intersection points as new collection points; The granules corresponding to the second preset parameter used are staggered detected at the new collection point in one staggered period to obtain a detection result of the new collection point; The detection result of the new collection point is determined as the detection result of the abnormal point to correct the detection result of the abnormal point.

8. The pulsed electrical discharge plasma spectroscopy-based fly ash carbon content detection method according to claim 1, wherein, The fly ash carbon content is determined according to the detection results of all the detection points, including: For each detection point, the average value of the light intensity in the detection result of the detection point is recorded as the light intensity detected by the detection point in one staggered period; The average value of the light intensity detected by the detection point in a preset number of staggered periods is recorded as the detection data of the detection point; The fly ash carbon content is determined according to the average value of the detection data of all the detection points by looking up a table.

9. A device for detecting fly ash carbon content based on pulsed discharge plasma spectroscopy, characterized by, The fly ash carbon content detection device based on pulsed discharge plasma spectrum includes: A collection point setting module for uniformly setting a plurality of collection points in a detection area; A collection point detection module for detecting a plurality of granules at each collection point according to a first preset parameter of the plurality of granules and determining a light intensity average deviation value of the plurality of granules at the collection point; A detection point screening module for determining a proportion coefficient of the plurality of granules according to the first preset parameter of the plurality of granules, selecting a plurality of collection points as detection points according to the proportion coefficient of the plurality of granules and the light intensity average deviation value of the plurality of granules at the collection points; A detection point detection module for setting one staggered period according to a plurality of second preset parameters of a plurality of granules for each detection point, staggered detecting the granules corresponding to the second preset parameter used at the detection point in one staggered period to obtain a detection result of the detection point; An abnormal point determination module for determining an abnormal point in the detection points according to the detection results of all the detection points; An abnormal point correction module for setting a new collection point near each abnormal point and correcting the detection result of the abnormal point through the detection result of the new collection point; A carbon content determination module for determining the fly ash carbon content according to the detection results of all the detection points; The plurality of granules include carbon granules, metal granules and water-containing granules; the proportion coefficient of the plurality of granules is determined according to the first preset parameter of the plurality of granules, including: The signal-to-noise ratios of the plurality of granules are obtained; The proportion coefficient of the plurality of granules is determined according to the ratio between the signal-to-noise ratios of the plurality of granules; For each granule, the detection time period of the granule is determined according to the best delay and the best gate width in the first preset parameter of the granule; The serial number of the type of carbon granules is set as 1; For the carbon particles, the detection time period of the carbon particles is determined according to the detection time periods of all the particles, and the detection time period of the carbon particles is only overlapped with the detection time period of any one kind of particles or the time period t that is not overlapped with the detection time period of any one kind of particles 1i The detection time period of the carbon particles is determined according to the detection time periods of all the particles, and the detection time period of the carbon particles is only overlapped with the detection time period of m kinds of particles mJ The detection proportion of the carbon particles is obtained by ​ For each particle other than the carbon particle, a time period T is determined in which the detection time period of the particle does not overlap with the detection time period of the carbon particle, according to the detection time periods of all the particles 1i and a time period T in which the detection time period overlaps with the detection time period of the carbon particle 2i , and a detection proportion of the particle is obtained The proportion coefficient of the plurality of granules is determined according to the ratio between the detection proportions of the plurality of granules and is normalized to make the sum of the proportion coefficients of the plurality of granules equal to 1; where i is the serial number of the type of particle, N is the number of types of particles, m is counted from 2, m is the number of particles whose detection time period overlaps with the detection time period of the carbon particle, K i is the proportion coefficient of the i-th type of particle, and J is the serial number of the type of the number of particles whose detection time period overlaps with the detection time period of the carbon particle.

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