A remote detection system for exhaust gas emissions during the treatment of kitchen waste

Abstract

The present invention provides a remote detection system for waste gas emissions during the treatment of kitchen waste, which relates to the technical field of data processing. The method includes: a calculation module, configured to calculate the pollutants at monitoring point A and the pollutants at monitoring point B according to the correction parameter, the standard data frame, the chemical reaction conversion rate at monitoring point A, and the removal rate of the incinerator purification device at monitoring point B, so as to generate a pollutant correction concentration matrix; and an early warning module, configured to perform early warning according to the pollutant correction concentration matrix, the induced draft fan flow rate, the flue gas flow rate, and a preset emission threshold. The present invention compensates for the concentration deviation caused during the waste gas transmission process in real time through the spatial correction coefficient, improving the reliability of the data.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a remote detection system for waste gas emissions during the treatment of kitchen waste. Background Art

[0002] During the treatment of kitchen waste, the accurate monitoring and early warning of waste gas emissions are key links to ensure environmental safety. Traditional technical solutions generally use single-point fixed monitoring devices (such as spectrometers and electrochemical sensors) to detect the concentration at the outlet of the incinerator or the ventilation opening of the workshop, and trigger an alarm based on a preset threshold. However, such methods have some defects:

[0003] For example, there are elbows and variable diameter structures in the pipeline between the pretreatment workshop and the incinerator flue. Computational fluid dynamics simulations show that the concentration of pollutants decays by 12% - 18% due to the turbulent effect during transmission. However, the traditional system does not consider this spatial correction and directly uses the concentration value at the monitoring point as the basis for calculating the total emissions, resulting in an overestimation of the actual total emissions by about 15%. The temperature gradient (300°C - 850°C) in the incinerator flue affects the spectral absorption characteristics, and the traditional spectrometer does not perform dynamic temperature compensation, resulting in an NO x concentration measurement error of ±10%. Summary of the Invention

[0004] The technical problem to be solved by the present invention is to provide a remote detection system for waste gas emissions during the treatment of kitchen waste, which can compensate for the concentration deviation caused during the waste gas transmission process in real time through a spatial correction coefficient, thereby improving the reliability of the data.

[0005] To solve the above technical problem, the technical solution of the present invention is as follows:

[0006] A remote detection system for waste gas emissions during the treatment of kitchen waste, comprising:

[0007] A detection module, configured to collect the spectral signals of the waste gas at monitoring point A and monitoring point B in real time, analyze the spectral absorption signals of the characteristic pollutants, and generate a first concentration matrix and a second concentration matrix corresponding to monitoring point A and monitoring point B respectively; [[ID=z8]]

[0008] A processing module, configured to multiply the first concentration matrix by the flow rate of the induced draft fan at monitoring point A at each moment to obtain a first mass matrix; multiply the second concentration matrix by the flow rate of the incinerator flue at monitoring point B at each moment to obtain a second mass matrix; splice the first mass matrix and the second mass matrix to form a combined matrix; and preprocess the combined matrix to obtain a standard data frame;

[0009] A correction module for analyzing the physical environment between monitoring point A and monitoring point B, generating a spatial correction coefficient based on the principles of fluid mechanics according to the physical environment; estimating the theoretical total amount of pollutants between monitoring point A and monitoring point B according to the design parameters and operating principles of the incinerator flue; calculating a correction factor according to the measured total amount and the theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame; obtaining a correction parameter according to the spatial correction coefficient and the correction factor.

[0010] A calculation module for calculating the pollutants at monitoring point A and monitoring point B according to the correction parameter, the standard data frame, the chemical reaction conversion rate at monitoring point A and the removal rate of the purification device of the incinerator at monitoring point B to generate a pollutant correction concentration matrix.

[0011] An early warning module for giving an early warning according to the pollutant correction concentration matrix, the induced draft fan flow rate, the flue flow rate and a preset emission threshold.

[0012] The above solution of the present invention has at least the following beneficial effects:

[0013] The processing module couples and calculates the concentration matrix with the real-time flow data (induced draft fan flow rate, flue flow rate) moment by moment, generates a mass matrix and splices it into a joint matrix, breaking through the limitation of traditional single-point concentration detection. Compared with the solution that only monitors the concentration value, this system can accurately reflect the actual total pollutant emissions (such as mass flow rate), avoiding misjudgment caused by flow fluctuations (for example, when the induced draft fan flow rate suddenly increases, the traditional system misjudges the emission reduction only through the decrease in concentration, while this system captures the total amount change in real time through the mass matrix).

[0014] The detection module simultaneously collects the spectral signals of the pretreatment area (monitoring point A) and the incinerator flue (monitoring point B), constructs a dual-node monitoring network, and realizes the full-process tracking from the source of waste gas generation (front-end workshop) to the end emission (incineration flue), solving the problem that traditional single-point monitoring cannot cover complex transmission paths.

[0015] The correction module analyzes the physical environment between monitoring points (such as pipeline structure, temperature gradient, turbulence effect) based on the principles of fluid mechanics, generates a spatial correction coefficient, and compensates in real time for the concentration deviation caused by diffusion and attenuation during the waste gas transmission process. For example, for the concentration attenuation caused by the eddy current at the pipeline elbow, the measured error of the traditional system reaches 15%-20%, while the error of this system can be controlled within 5% after correction, improving the reliability of the data.

[0016] The theoretical total amount of pollutants is estimated by incinerator design parameters (such as furnace volume and residence time) and operating principles. This is then compared with the measured total amount to generate a correction factor, forming a closed-loop mechanism of "theoretical modeling - field verification - dynamic correction." Compared to traditional static models that rely on periodic manual calibration, this system can respond in real time to operating fluctuations (such as a decrease in purification device removal efficiency or changes in chemical reaction conversion rates), avoiding long-term monitoring deviations caused by equipment aging or sudden changes in operating conditions.

[0017] The calculation module integrates the chemical reaction conversion rate at monitoring point A (such as the generation rate of volatile organic compounds in the pretreatment stage) and the removal rate of the purification device at monitoring point B (such as real-time changes in activated carbon adsorption efficiency), and combines the correction parameters to perform a secondary calibration of the pollutant concentration matrix, so that the system can adaptively handle process fluctuations (such as changes in incinerator load and degradation of purification equipment performance). The error between the corrected concentration matrix and the actual emissions is less than 8%.

[0018] Based on a revised concentration matrix and real-time flow parameters, the early warning module uses a mass flow threshold (e.g., total particulate matter emissions per hour) instead of a traditional single concentration threshold, avoiding early warning delays or misjudgments caused by air volume fluctuations. For example, when the flow in an incinerator flue is uneven (with a 30% difference in velocity between the edge and the center), the traditional system's total volume calculation error can reach 15%-20%. However, this system, through moment-by-moment flow coupling and spatial correction, can shorten early warning response time to less than one minute and increase early warning accuracy to over 95%. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 This is a schematic diagram of a remote detection system for waste gas emissions during food waste treatment provided by an embodiment of the present invention.

[0020] Figure 2 The present invention provides a flow chart of a method for remotely detecting waste gas emissions during the treatment of food waste.

[0021] Explanation of the numbers in the figure: 11, detection module; 12, processing module; 13, correction module; 14, calculation module; 15, early warning module. DETAILED DESCRIPTION

[0022] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0023] like Figure 1As shown in the figure, an embodiment of the present invention provides a remote detection system for waste gas emissions during the treatment of kitchen waste, including:

[0024] A detection module 11 for real-time collecting the spectral signals of the waste gas at monitoring point A and monitoring point B, analyzing the spectral absorption signals of characteristic pollutants, and respectively generating a first concentration matrix and a second concentration matrix corresponding to monitoring point A and monitoring point B; Monitoring point A is the tail gas emission outlet of the pretreatment area in the kitchen waste treatment workshop; Monitoring point B is the flue gas outlet of the incinerator.

[0025] A processing module 12 for multiplying the first concentration matrix by the flow rate of the induced draft fan at monitoring point A at each moment to obtain a first mass matrix; multiplying the second concentration matrix by the flue gas flow rate at monitoring point B at each moment to obtain a second mass matrix; splicing the first mass matrix and the second mass matrix to form a combined matrix; and preprocessing the combined matrix to obtain a standard data frame.

[0026] A correction module 13 for analyzing the physical environment between monitoring point A and monitoring point B, generating a spatial correction coefficient based on the principle of fluid mechanics according to the physical environment; estimating the theoretical total amount of pollutants between monitoring point A and monitoring point B according to the design parameters and operating principles of the incinerator flue; calculating a correction factor according to the measured total amount and the theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame; and obtaining a correction parameter according to the spatial correction coefficient and the correction factor.

[0027] A calculation module 14 for calculating the pollutants at monitoring point A and monitoring point B according to the correction parameter, the standard data frame, the chemical reaction conversion rate at monitoring point A, and the removal rate of the incinerator purification device at monitoring point B, so as to generate a pollutant correction concentration matrix.

[0028] An early warning module 15 for giving an early warning according to the pollutant correction concentration matrix, the induced draft fan flow rate, the flue gas flow rate, and a preset emission threshold.

[0029] In the embodiment of the present invention, the spectral signals of the tail gas emission outlet of the pretreatment area (monitoring point A) and the flue gas outlet of the incinerator (monitoring point B) are collected in real time, and the spectral absorption signals of characteristic pollutants (such as VOCs, particulate matter, SO2, NO x etc.) are analyzed to generate the first / second concentration matrix. Breaking through the limitation of traditional single-point monitoring (only measuring the end flue), simultaneously capturing the concentration data of the waste gas generation source (volatile pollutants in the pretreatment workshop) and the end emission (pollutants after incineration), forming a "source - transmission - end" full-link monitoring network, and avoiding the regulatory blind spots in the intermediate links (such as pipeline leakage and transmission attenuation).

[0030] By comparing the concentration change trends at monitoring points A / B, unorganized emissions that were not collected during the pretreatment stage can be quickly identified (e.g., a sudden increase in the concentration at monitoring point A due to a pipeline break while there is no change at monitoring point B), which cannot be achieved by traditional single-point monitoring for such anomaly localization.

[0031] Spectral signal analysis techniques (such as Fourier transform infrared spectroscopy and differential optical absorption spectroscopy) can distinguish the absorption characteristics of multi-component pollutants in real time. Compared with traditional electrochemical sensors (which are vulnerable to humidity and temperature interference), the detection accuracy is improved by more than 30%. In particular, the recognition ability for low-concentration pollutants (such as ppb-level VOCs) is significantly enhanced, dynamically generating a concentration matrix (time series data) to support concentration fluctuation analysis with second-level resolution, solving the problem of missed detection of transient over-standard caused by traditional minute-level sampling (such as the instantaneous concentration peak when the induced draft fan starts and stops).

[0032] Traditional systems only monitor concentration (such as mg / m 3 ), without combining flow rate (m 3 / h), resulting in misjudgment of the total amount when the air volume suddenly changes (e.g., when the flow rate increases by 20% but the concentration decreases by 10%, the traditional system misjudges a reduction in emissions, while this system accurately captures an 8% increase in the total amount through a mass matrix (concentration × flow rate)); performing coupled calculations at each moment to achieve second-level synchronous mass flow tracking (such as calculating the pollutant emission mass once per second), compared with traditional minute-level average calculations, the response speed to sudden operating conditions (such as a sudden change in the incinerator load) is increased by more than 60 times. Pretreatment eliminates data asynchronous errors (such as differences in the sampling frequencies of different sensors), and through timestamp alignment and format unification, ensures the parameter matching accuracy of subsequent calculation modules, avoiding correction deviations caused by data asynchronization in traditional systems (such as calculation errors in the total amount caused by time misalignment between flow rate and concentration data); through computational fluid dynamics (CFD) or empirical formulas, quantifying the effects of pipe elbows, diameter changes, and temperature gradients on pollutant transmission (such as concentration attenuation caused by turbulence and the influence of temperature changes on the spectral absorption coefficient), generating spatial correction coefficients (such as concentration attenuation compensation factors and spectral wavelength offset correction values), reducing the 15%-20% monitoring errors caused by the physical environment in traditional systems to within 5%.

[0033] When the treatment plant modifies the pipeline (such as adding dust removal equipment resulting in a change in the flow field), the system can automatically recalculate the spatial correction coefficient without manual recalibration; based on the incinerator design parameters (such as throughput and excess air coefficient) and chemical reaction equations (such as C x H y O zCombustion generates CO2 and H2O), estimate the total theoretical pollutant generation, compare it with the measured total to generate a correction factor (such as measured total / theoretical total), form a closed loop of "modeling prediction - measured verification - error correction", and avoid the long-term deviation caused by the traditional system relying on fixed emission factors (such as empirical values) (such as the change in the actual emission factor after the incinerator ages is not recognized).

[0034] Combine the spatial correction coefficient (physical transport impact) with the correction factor (process theory deviation) to form a comprehensive correction parameter, compensate for both spatial transport error and process operation error at the same time, and the correction accuracy is improved by 40% compared with the single correction method.

[0035] The chemical reaction conversion rate at monitoring point A (such as the rate of NH3 generated by food waste fermentation changing with temperature) is connected to the calculation module in real time to correct the concentration prediction error caused by fluctuations in reaction conditions (such as increased volatilization due to high temperature in summer) in the pretreatment stage, and avoid the source emission estimation deviation caused by the traditional system ignoring the changes in the front-end process. Through data such as the differential pressure sensor of the purification device and on-line penetration rate detection, the removal rate is dynamically obtained (such as automatically updating the parameters when the removal rate of the activated carbon adsorption tower drops by 1% per hour). Compared with the traditional system relying on manual regular detection (once a week), the correction delay is shortened from 48 hours to the minute level, ensuring the real-time accuracy of the end-of-pipe emission calculation.

[0036] Comprehensively consider multi-dimensional factors such as spatial transport (correction module), process reaction (conversion rate), and purification efficiency (removal rate), and perform secondary calibration on the original concentration matrix, so that the coincidence degree between the finally generated pollutant corrected concentration matrix and the actual emission exceeds 92%, which is higher than the 70% coincidence degree of the traditional system that only considers concentration or a single correction parameter.

[0037] Use the pollutant emission mass per hour (such as kg / h) as the core warning indicator to replace the traditional single concentration threshold (such as mg / m 3), eliminating the interference of air volume fluctuations on the warning results. For example, when the flue gas flow is uneven (high central flow velocity and low edge flow velocity) resulting in deviation in single-point concentration detection, the total mass calculated by the full-section flow coupling calculation of this system can still accurately reflect the actual emissions, and the warning accuracy rate is increased from 75% of the traditional system to over 95%. When the mass emissions at monitoring point A continue to increase while there is no significant change at monitoring point B, the system can identify that the collection efficiency of the pretreatment workshop has decreased (such as air leakage in the pipeline), and trigger equipment maintenance warnings accordingly. Such fault location cannot be achieved by traditional single-point monitoring. The second-level data processing ability shortens the warning response time to within 10 seconds (the traditional system requires 2 - 5 minutes), and can achieve real-time interception especially for sudden conditions in the incinerator (such as a sudden increase in CO concentration due to incomplete combustion). Combining the corrected concentration matrix with the distribution of sensitive points around the factory area (such as the distance to residential areas), it supports dynamic adjustment of warning thresholds (such as increasing sensitivity at night), realizing refined supervision and avoiding "over-warning" or "lagged warning" caused by traditional fixed thresholds.

[0038] In another preferred embodiment of the present invention, the spectral signals of the waste gas at monitoring point A and monitoring point B are collected in real time, and the spectral absorption signals of the characteristic pollutants are analyzed to generate a first concentration matrix and a second concentration matrix corresponding to monitoring point A and monitoring point B respectively, including:

[0039] The original spectral sequences of monitoring point A and monitoring point B are obtained respectively, and are denoted as the first spectral data stream and the second spectral data stream. Each data stream is composed of spectral curves with continuous timestamps, specifically including: Spectral collection devices (such as Fourier transform infrared spectrometers) are respectively deployed at monitoring point A (the tail gas discharge port of the pretreatment area) and monitoring point B (the flue gas outlet of the incinerator), and the spectral signals of the waste gas are continuously collected at a fixed time interval (such as once per second); Each spectral signal contains a complete light intensity distribution curve from the ultraviolet to the infrared band (such as a wavelength range of 200nm - 2000nm), and each spectral curve is marked with a collection timestamp (accurate to milliseconds) to form the first spectral data stream (monitoring point A) and the second spectral data stream (monitoring point B); For example: At 10:00:00.000, monitoring point A collects a light intensity curve containing 2000 wavelength points, and the corresponding timestamp is stored in the data stream.

[0040] The first spectral data stream and the second spectral data stream are respectively subjected to smoothing processing, baseline correction, and wavelength calibration to obtain the first spectral curve and the second spectral curve, specifically including:

[0041] For the light intensity value sequence at each wavelength point (such as 3.2μm, 7.8μm, etc.) in the original spectral data stream (each time point corresponds to a light intensity value), with the current time point as the center, select the light intensity values of its first 5 time points, the current time point, and the next 5 time points (a total of 11 time points, and the boundary points are calculated according to the actually existing time points), and calculate the average value as the smoothed light intensity value at the current time point.

[0042] For example: at the 10th time point (the middle point), take the average of the light intensity values from the 5th to the 15th time points to obtain the smoothed light intensity; at the 1st time point, only take the average of the light intensity values from the 1st to the 6th time points (take the available points when the first 5 do not exist).

[0043] Repeat this operation for the light intensity sequences of all wavelength points to obtain the smoothed light intensity sequence for each wavelength point, and combine them into a preliminarily smoothed spectral curve (the noise is reduced and the signal jitter is weakened).

[0044] According to the average width of the characteristic absorption peak (for example, through previous experiments, it is known that the absorption peak of typical pollutants occupies a width of about 5 wavelength points on the spectral curve), select a rectangular window with a larger width as the structural element (for example, set the width to 15 wavelength points to ensure that it covers multiple absorption peak periods and only retains the low-frequency drift trend). Use the above structural element to perform "erosion" processing on each wavelength point of the preliminarily smoothed spectral curve, and take the minimum light intensity value within the structural element window as the output value of the current point. This step will "flatten" the peaks (i.e., the characteristic absorption signals) in the spectral curve and only retain the baseline drift trend that may be lower than or equal to the surrounding values (such as the overall light intensity decrease trend caused by light source aging).

[0045] Perform "dilation" processing on each wavelength point of the eroded curve, and take the maximum light intensity value within the structural element window as the output value of the current point. This step will restore the compressed curve after erosion to a shape close to the original low-frequency trend, and finally obtain a smooth baseline trend curve (only reflecting the overall drift of light intensity over time, such as a slow decrease or increase, and ignoring the high-frequency characteristic absorption fluctuations).

[0046] According to the low-frequency drift characteristics of the preliminary smoothed spectral curve (for example, the light intensity attenuation caused by light source aging is approximately a quadratic curve, and the drift caused by temperature change is approximately a cubic curve), select an appropriate polynomial degree (such as 3 or 5). Take the light intensity values at all time points in the preliminarily smoothed spectral curve as fitting data, and calculate the polynomial coefficients by the least squares method to minimize the sum of the squares of the errors between the polynomial curve and the spectral curve in the low-frequency part (the overall trend after removing the high-frequency absorption signal). For example, if the spectral curve shows a gradually decreasing parabolic trend within 100 time points, the fitted quadratic polynomial curve will approximate this trend, ignoring the local fluctuations caused by pollutant absorption in the middle (such as a suddenly decreasing absorption peak); according to the fitted polynomial coefficients, calculate the baseline light intensity value corresponding to each time point to form a continuous baseline trend curve (for example, represented by y = ax 3 + bx 2 + cx + d, where x is the time point and y is the baseline light intensity), in the polynomial equation y = ax 3 + bx 2 + cx + d, this is a cubic polynomial equation, and the parameters a, b, c, and d each have specific meanings, which are as follows:

[0047] The constant term d represents the intercept of the curve on the y-axis. When x = 0, y = d. In the context of spectral curve fitting, this means that at the starting moment (time point x = 0), the light intensity value corresponding to the spectral curve is d, and this value may be related to factors such as the light intensity in the initial state of the instrument and the ambient background light intensity.

[0048] Example: Suppose that when starting to collect spectral data, even without pollutant absorption, the light intensity detected by the instrument is 500 units, then d may be equal to 500.

[0049] The coefficient c of the first-order term reflects the linear change trend of the light intensity with time. If c is positive, it indicates that the light intensity increases linearly with time; if c is negative, the light intensity decreases linearly with time; if c = 0, it means that there is no linear change trend in the light intensity. In the spectral curve, this linear change may be caused by factors such as the slow preheating of the instrument and slight ambient light changes.

[0050] Example: If c = 2, then for every increase of one unit in time x, the light intensity y will increase by 2 units on the original basis.

[0051] The quadratic coefficient b determines the quadratic trend of the light intensity change, that is, the acceleration situation of the light intensity change. If b is not 0, the change of the light intensity is not linear but presents a parabolic shape. When b > 0, the parabola opens upward, meaning that the growth rate of the light intensity will gradually increase or the attenuation rate will gradually slow down; when b < 0, the parabola opens downward, indicating that the growth rate of the light intensity gradually slows down or the attenuation rate gradually accelerates. In the spectral curve fitting, this may be related to factors such as the change of the light source aging rate and the non-linear influence of the environmental temperature.

[0052] Example: Suppose b = -0.1, which indicates that as time goes by, the change rate of the light intensity will gradually slow down. Maybe it is because the attenuation rate of the light intensity caused by the light source aging is gradually decreasing.

[0053] The cubic coefficient a represents the cubic trend of the light intensity change, that is, the further correction of the quadratic trend. The positive or negative and the magnitude of a will affect the bending degree and direction of the curve.

[0054] Example: If a = 0.01, it will cause the curve to have a more complex bend on the basis of the quadratic trend, so as to more accurately fit the actual change of the spectral curve.

[0055] When using the least squares method to perform polynomial fitting on the low-frequency drift part of the spectral curve, the purpose is to find the most suitable values of a, b, c, and d, so that the fitted cubic polynomial curve can be as close as possible to the low-frequency drift trend of the spectral curve, and then accurately estimate the baseline.

[0056] Subtract the light intensity value of the corresponding time point on the baseline trend curve from the light intensity value of each time point in the preliminarily smoothed spectral curve. For example:

[0057] At the 10th time point, the preliminarily smoothed light intensity is 850, and the light intensity of the baseline trend is 800 (reflecting the attenuation caused by the light source aging). Then the corrected light intensity is 850 - 800 = 50 (only retaining the light intensity change caused by the pollutant absorption and eliminating the overall attenuation trend). Finally, the spectral after baseline correction is obtained, and its light intensity value only reflects the absorption signal of the characteristic pollutant (such as the light intensity fluctuation caused by the change of the VOCs concentration in the waste gas), excluding the low-frequency drift interference caused by factors such as the light source aging and the environmental temperature change.

[0058] Introduce a standard gas with a known characteristic absorption wavelength (such as methane, whose theoretical characteristic peak is at 3.3 μm) into the spectral acquisition device, collect its spectral curve, and in the spectrum after baseline correction, find the central wavelength position of the actual absorption peak (such as determining the peak position by finding the minimum point of light intensity or the extreme point of the second derivative). Compare the actual absorption peak position with the theoretical wavelength and calculate the offset (for example, the theoretical wavelength is 3.3 μm, the measured value is 3.31 μm, and the offset is +0.01 μm). For each wavelength value at each wavelength point in the spectrum after baseline correction, uniformly subtract the offset (or adjust according to the offset ratio, such as translating the entire wavelength axis) so that the characteristic peak of the standard gas returns to the theoretical wavelength position. For example: subtract 0.01 μm from the wavelength values of all wavelength points, correct the original 3.31 μm to 3.3 μm, and the original 7.82 μm to 7.81 μm, ensuring that the characteristic wavelengths for subsequent analysis are consistent with the theory, and finally obtain the calibrated first spectral curve (monitoring point A) and the second spectral curve (monitoring point B).

[0059] Pre - establish a characteristic absorption wavelength library for target pollutants, specifically including: for typical pollutants in the exhaust gas from food waste treatment (such as ammonia, hydrogen sulfide, VOCs, and the characteristic wavelength of particulate matter scattering), through literature research and standard gas experiments, pre - determine the characteristic absorption wavelength of each pollutant (for example, ammonia has a strong absorption peak at 9.6 μm, and hydrogen sulfide is at 7.8 μm), and organize it into a characteristic absorption wavelength library; for example: the library contains 10 pollutants, each corresponding to 2 - 3 characteristic wavelengths, forming a set of 25 wavelength points (such as 3.2 μm, 3.4 μm, 7.8 μm, etc.).

[0060] For the first spectral curve and the second spectral curve, within the wavelength range corresponding to the characteristic wavelength library, calculate the ratio of the actual light intensity to the light intensity of pure air to obtain the absorbance values at each characteristic wavelength, specifically including: for the obtained first spectral curve and the second spectral curve, within the wavelength range specified by the characteristic absorption wavelength library in the third step (such as only extracting the light intensity values of the 25 wavelength points included in the library), extract the actual light intensity value (denoted as I) at each time stamp one by one. At the same time, pre - collect the spectral curve of the same device under the condition of pure air (without pollutants) as the reference spectrum, and extract the light intensity value (denoted as I0) at the same characteristic wavelength points; for each characteristic wavelength point, calculate the ratio of the actual light intensity to the light intensity of pure air (I / I0), and then obtain the absorbance value through logarithmic conversion (the absorbance reflects the degree of light absorption by pollutants, the smaller the ratio, the greater the absorbance, and the higher the pollutant concentration). For example: at a certain moment at monitoring point A at the wavelength of 3.2 μm, the actual light intensity is 500, the light intensity of pure air is 1000, the ratio is 0.5, and the absorbance corresponds to the absorption intensity of a specific pollutant.

[0061] According to the absorbance values at each characteristic wavelength and the pre-established pollutant concentration-absorbance calibration model, calculate the pollutant concentration vectors at each moment for monitoring point A and monitoring point B, specifically including:

[0062] For each target pollutant (such as VOCs, ammonia, hydrogen sulfide, etc.), pre-configure multiple standard gas samples with different concentrations (for example, set 5 concentration gradients covering the range from 0 to the highest monitoring concentration, such as 0 mg / m 3 、25 mg / m 3 、50 mg / m 3 、75 mg / m 3 、100 mg / m 3 ). Each group of standard gases needs to ensure uniform and stable concentration, and record its accurate nominal concentration value (provided by the standard gas supplier or calibrated by high-precision equipment).

[0063] Collect the spectral data of the standard gas and calculate the absorbance:

[0064] Under the same experimental conditions (such as constant temperature, air pressure, avoiding external interference), sequentially introduce standard gases of each concentration into the monitoring equipment and collect the spectral curves at each concentration. For each characteristic wavelength point, first collect the light intensity value of pure air (i.e., at 0 concentration) as the reference value ,then collect the actual light intensity value of the standard gas at this concentration ,According to the definition of absorbance (absorbance = negative logarithm of transmittance, transmittance = ), calculate the absorbance value at this wavelength under the current concentration (for example, for a standard gas of a certain concentration at 3.2 μm ,then the absorbance is ), collect each group of standard gases of each concentration multiple times (such as 3 times), and take the average absorbance value to reduce random errors.

[0065] Establish the mapping relationship between absorbance and concentration:

[0066] For the pollutant corresponding to each characteristic wavelength (such as 3.2 μm corresponding to VOCs), correspond the absorbance values of standard gases of different concentrations at this wavelength to the nominal concentration one by one to form a set of "absorbance-concentration" data pairs (for example, for VOCs at 3.2 μm: absorbance 0.05 corresponds to 25 mg / m 3 ,absorbance 0.1 corresponds to 50 mg / m 3 ,absorbance 0.15 corresponds to 75 mg / m 3 etc.). By analyzing the distribution law of the data pairs (such as observing whether it shows a linear relationship), determine the mapping rule between the two (for example, for every 0.05 increase in absorbance, the corresponding concentration increases by 25 mg / m 3), and finally form a calibration model for the pollutant at the corresponding wavelength (that is, clarify the rule of "how to uniquely determine the concentration value through the absorbance value", such as absorbance 0.2 corresponding to 100 mg / m 3 ).

[0067] Introduce a set of standard gas concentrations that did not participate in the modeling (such as the intermediate concentration of 40 mg / m 3 ) into the device, collect their absorbance values, calculate the predicted concentration through the calibration model, and compare it with the actual nominal concentration. The error needs to be controlled within the allowable range (such as not exceeding ±5%). If the error exceeds the limit, re-check the standard gas configuration, data collection process or adjust the mapping rule until the model accuracy meets the standard.

[0068] Structurally store the "absorbance-concentration" mapping relationships of each pollutant at each characteristic wavelength (such as the absorbance-concentration comparison table of VOCs corresponding to 3.2 μm, the comparison table of ammonia corresponding to 4.6 μm, etc.) to form a calibration model library. When processing real-time monitoring data later, the corresponding calibration model can be quickly retrieved according to the characteristic wavelength to convert the absorbance into the pollutant concentration.

[0069] Arrange the concentration vectors at each moment vertically in chronological order to form a two-dimensional concentration matrix. Each row of the two-dimensional concentration matrix corresponds to a time point, and each column corresponds to a pollutant. Specifically, it includes: arranging the pollutant concentration vectors corresponding to each obtained timestamp (such as [50, 10, 20] corresponding to 10:00:00, [55, 12, 18] corresponding to 10:00:01) vertically in chronological order. Each row of the matrix represents a time point, and each column represents a pollutant, and finally form a two-dimensional table (such as 1000 rows × 10 columns, representing the concentration data of 1000 time points and 10 pollutants), that is, the first concentration matrix (monitoring point A) and the second concentration matrix (monitoring point B). For example: the first row is the concentration of each pollutant at the first time point, the second row is the concentration at the second time point, and so on, forming a dynamic concentration sequence that changes over time.

[0070] In the embodiment of the present invention, the spectral curves of continuous timestamps completely record the transient changes of the waste gas components (such as the sudden increase in concentration when the induced draft fan starts and stops, and the spectral absorption peak drift caused by the load fluctuation of the incinerator). Compared with the traditional minute-level discrete sampling, more than 95% of the instantaneous abnormal signals (such as the exceeding standard peak value lasting for 2 seconds) can be captured, avoiding missed detection of sudden pollution events. The unified timestamp mechanism ensures the strict synchronization of the spectral data of monitoring point A (pretreatment area) and B (incinerator flue gas duct), providing an accurate time reference for subsequent cross-node data fusion (such as analyzing the migration law of pollutants from the source to the end), and solving the correlation analysis error caused by clock asynchronization in traditional multi-device monitoring (such as unable to accurately match the concentration and flow fluctuations when the time misalignment exceeds 30 seconds).

[0071] Smoothing processing reduces the influence of environmental noise (such as instrument circuit noise, vibration interference) on spectral signals, reducing the absorbance calculation error from ±5% to ±1.5%. Especially when detecting low-concentration pollutants (such as 10 ppb-level VOCs), the signal-to-noise ratio is increased by more than 3 times, avoiding false positive / false negative results caused by noise in traditional unprocessed spectra. Baseline correction eliminates long-term drifts (such as light intensity attenuation caused by light source aging, baseline shift caused by environmental temperature changes), ensuring the comparability of spectral data at different times. A practical measurement case shows that the baseline of the spectrum without baseline correction drifts by up to 8% after 8 hours of operation, resulting in concentration misjudgment, while this step can control the drift within 1%. Wavelength calibration solves the problem of wavelength drift in spectrometers (such as the deviation of the grating diffraction angle caused by temperature changes, with a typical drift of 0.05 nm / °C). Through real-time calibration of the characteristic absorption peaks of the built-in standard gases (such as methane and carbon dioxide standard gases), the wavelength positioning accuracy reaches ±0.01 nm, ensuring the accuracy of absorbance calculation at characteristic wavelengths and avoiding misidentification of pollutants caused by wavelength deviation (such as misjudging the absorption peak of NO2 as that of SO2).

[0072] The characteristic wavelength library enables the simultaneous detection of multiple components for typical pollutants in the exhaust gas of food waste treatment (such as fatty acids, ammonia, hydrogen sulfide, dioxin precursors, etc.). More than 10 pollutants can be analyzed from a single spectral curve, increasing the detection dimension by more than 5 times compared with traditional single sensors (such as only measuring particulate matter or SO2), avoiding the problems of high cost and difficult maintenance caused by stacking multiple devices. Absorbance calculation excludes the interference of background gases (such as N2 and O2), only focusing on the absorption signals of characteristic pollutants. For example, in a pretreatment workshop with a humidity as high as 90%, the detection error of VOCs by traditional sensors due to water vapor interference reaches 20%. However, this step reduces the humidity influence to less than 3% by selecting characteristic wavelengths without water vapor absorption (such as the 3.3 - 3.5 μm band). Following the Lambert-Beer law, a linear relationship between absorbance and concentration is established. Combining with the subsequent calibration model, the concentration inversion error is reduced from ±15% of the traditional empirical formula to within ±5%, especially suitable for the precise quantification of low-concentration pollutants (such as 0 - 100 mg / m 3 Linearity R within the range 2 > 0.995).

[0073] The calibration model considers the spectral overlap effect of multiple components (such as the overlap of the absorption peaks of NO and NO2 in the visible spectral region). By using chemometric methods (such as PLS) to analyze the mixed spectrum, independent quantification of coexisting pollutants is achieved, improving the accuracy by 40% compared with traditional single-wavelength detection (assuming no cross-absorption). For example, when the concentration ratio of NO to NO2 is 1:1, the error of the traditional method reaches 30%, while the error of this model is < 8%.

[0074] Dynamically update the calibration model (e.g., automatically import the latest calibration data weekly) to adapt to the performance drift of the instrument after long-term operation (such as the attenuation of detector sensitivity), avoid the error accumulation of the traditional fixed model over time (the typical annual error increase is 10%-15%), and ensure the long-term stable operation accuracy of the system.

[0075] The two-dimensional concentration matrix integrates the time series and multi-pollutant dimensions, providing a standardized input for the subsequent processing module (such as multiplying with the flow data at each moment to generate a mass matrix), and supporting spatio-temporal correlation analysis (such as cross-verifying the VOCs concentration matrix at monitoring point A and the removal rate matrix at monitoring point B to locate the failure period of the purification device).

[0076] In another preferred embodiment of the present invention, multiplying the first concentration matrix by the flow rate of the induced draft fan at monitoring point A collected at each moment to obtain the first mass matrix, including:

[0077] Collect the flow rate data of the induced draft fan at monitoring point A. The flow rate data is a sequence that changes over time, and there is a corresponding induced draft fan flow rate value at each time point. Specifically, it includes: real-time collecting the flow rate data of the induced draft fan through a flow sensor (such as a vortex flowmeter, a pitot tube flowmeter) installed at monitoring point A, and recording a flow rate value at each time point (strictly synchronized with the time stamp of the first concentration matrix) to form a one-dimensional sequence (for example: the flow rate at the 1st second is 2000 m 3 / h, the flow rate at the 2nd second is 2050 m 3 / h, and so on), denoted as the flow rate time series. For example: the flow rate data is stored as [Q1, Q2, Q3,..., Qn], where Qn represents the induced draft fan flow rate corresponding to the nth row (the nth time point) of the first concentration matrix.

[0078] Starting from the first row of the first concentration matrix, the first row represents the concentrations of various pollutants at the first time point; multiply each element in the first row, that is, the concentration of each pollutant, by the induced draft fan flow rate value corresponding to the time point to obtain the mass flow rate values of various pollutants at the first time point, and form a new row vector, specifically including:

[0079] Extract the data of the first row of the first concentration matrix (corresponding to the first time point), which contains the concentration values of multiple pollutants, such as [C11, C12, C13,..., C1m] (C1m represents the concentration of the mth pollutant at the first time point, unit mg / m 3 ); extract the flow rate value Q1 at the first time point from the flow rate time series in the first step (unit m 3 / h); multiply each pollutant concentration value in this row by Q1 to obtain the mass flow rate values of each pollutant (unit mg / h):

[0080] The first pollutant: C11×Q1; the second pollutant: C12×Q1; the mth pollutant: C1m×Q1; arrange the calculation results in the order of the original pollutants to form a new one-dimensional array [M11, M12, M13, …, M1m], which is the mass flow row vector at the first time point (M1j = C1j×Q1).

[0081] And so on, multiply each row of the first concentration matrix until all rows are processed. Arrange all the obtained row vectors in order to form the first mass matrix. The first mass matrix is two-dimensional, with each row corresponding to a time point and each column corresponding to a pollutant. The elements in the matrix represent the mass flow of the pollutant at the corresponding time point, specifically including:

[0082] Starting from the second row of the first concentration matrix (corresponding to the second time point), repeat the following operations until the last row: extract the concentration data [Ci1, Ci2, …, Cim] of the ith row and the corresponding ith flow value Qi; multiply each concentration value by Qi according to the above method to obtain the mass flow values [Mi1, Mi2, …, Mim] at the ith time point; arrange the row vectors generated at each time point in chronological order (i.e., the row order of the first concentration matrix) in sequence to ensure that the ith row vector corresponds to the ith row of the matrix.

[0083] Stack all the generated row vectors vertically in chronological order to form a two-dimensional matrix. The number of rows of this matrix is the same as that of the first concentration matrix (equal to the total number of time points), and the number of columns is the same as the number of pollutant types. The ith row of the matrix corresponds to the ith time point, and the jth column corresponds to the jth pollutant. The element Mij represents the mass flow of the jth pollutant at the ith time point (unit: mg / h). For example: if the first concentration matrix has 1000 rows (1000 time points) and 5 columns (5 pollutants), then the first mass matrix is also 1000 rows × 5 columns, and each row is obtained by multiplying the concentration at the corresponding time point by the flow rate.

[0084] In the embodiment of the present invention, the concentration of the pollutant (unit volume content, such as mg / m 3 )only reflects the relative content of the pollutant in the local gas, and combined with the flow rate of the induced draft fan (gas volume per unit time, such as m 3After (h), the obtained mass flow rate (the mass of pollutants per unit time, such as mg / h) can directly reflect the absolute mass of pollutants actually emitted or transported per unit time. Each row of the first mass matrix corresponds to the mass flow rate of pollutants at a time point, and completely retains the coupling relationship between the concentration and the flow rate changing with time (such as when the rotational speed of the induced draft fan fluctuates, the impact of the flow rate change on the actual emission mass). By analyzing the change trend of each column (pollutant type) in the matrix over time, the time period with a high pollution load can be accurately located (such as when the flow rate suddenly increases, the mass flow rate of a certain pollutant surges). If only the concentration is concerned, misjudgments may occur, such as "the total emission is higher when the concentration is low but the flow rate is large" or "the total emission may not be prominent when the concentration is high but the flow rate is small". The mass flow rate, through the product of the concentration and the flow rate, directly reflects the result of the combined action of the two, avoiding the one-sidedness of a single indicator. For example: At monitoring point A, the concentration of a certain pollutant at time point 1 is 50 mg / m 3 and the flow rate is 1000 m 3 / h, and the mass flow rate is 50000 mg / h; at time point 2, the concentration drops to 40 mg / m 3 but the flow rate rises to 1500 m 3 / h, and the mass flow rate is even higher (60000 mg / h). Such changes can be visually identified through the mass matrix, avoiding decision-making biases caused by only looking at the concentration.

[0085] In another preferred embodiment of the present invention, the second concentration matrix is multiplied by the flow rate of the incinerator flue at monitoring point B at each moment to obtain a second mass matrix, including:

[0086] Obtain the flow rate data of the incinerator flue at monitoring point B. The flow rate data of the incinerator flue is a sequence that changes with time, and there is a corresponding flue flow rate value at each time point. Specifically, it includes: through the flow rate monitoring equipment (such as vortex flow meters, thermal mass flow meters) installed at monitoring point B (the outlet of the incinerator flue), the flue flow rate data is collected in real time. A flow rate value is recorded at each time point (strictly aligned with the time stamp of the second concentration matrix), forming a one-dimensional sequence (for example: the flow rate at the 1st second is 15000 m 3 / h, the flow rate at the 2nd second is 15200 m 3 / h, and so on), denoted as the flue flow rate time series. This sequence corresponds one-to-one with the time points of the second concentration matrix to ensure the time synchronization of the concentration and the flow rate during subsequent calculations (such as the concentration data at the i-th time point corresponds to the i-th flow rate value).

[0087] Starting from the first row of the second concentration matrix, multiply each element in the first row, i.e., the concentration of each pollutant, by the incinerator flue gas flow value corresponding to the first time point to obtain the mass flow values of various pollutants at the first time point, forming a row vector. Specifically, extract the data of the first row of the second concentration matrix (corresponding to the first time point), which contains the concentration values of various pollutants, such as [C'11, C'12, C'13, …, C'1m] (C1j' represents the concentration of the jth pollutant at the first time point, with the unit mg / m 3 ) ; extract the flow value Q1' of the first time point from the flue gas flow time series in the first step (with the unit m 3 / h). Multiply each pollutant concentration value in this row by Q1' to obtain the mass flow values of each pollutant (with the unit mg / h):

[0088] For the 1st pollutant: C11' × Q1';

[0089] For the 2nd pollutant: C12' × Q1';

[0090] For the mth pollutant: C1m' × Q1';

[0091] Arrange the calculation results in the original pollutant order to form the mass flow row vector [M'11, M'12, M'13, …, M'1m] at the first time point, where each element represents the mass flow of the corresponding pollutant at this time point.

[0092] Perform the same operation on the second row of the second concentration matrix, multiply its elements by the flue gas flow value at the second time point to obtain the mass flow row vector at the second time point. Specifically, starting from the second row of the second concentration matrix (corresponding to the second time point), process each row in chronological order:

[0093] Extract the concentration data [C'i1, C'i2, …, C'im] of the ith row and the corresponding ith flue gas flow value Qi', repeat the above operation, multiply each concentration value by Qi' to obtain the mass flow values [M'i1, M'i2, …, M'im] at the ith time point, ensuring that each generated row vector is strictly arranged in chronological order (i.e., the ith row vector corresponds to the ith time point, which is exactly the same as the row order of the second concentration matrix).

[0094] And so on, after processing all rows of the second concentration matrix, arrange the obtained row vectors in order to obtain the second mass matrix. The second mass matrix is two-dimensional, each row represents a time point, each column represents a pollutant, and the element is the mass flow of the pollutant at the time point. Specifically, it includes:

[0095] Stack all the generated row vectors vertically in chronological order to form a two-dimensional matrix, whose structure is the same as that of the second concentration matrix:

[0096] Number of rows: equal to the total number of time points (the same as the number of rows in the second concentration matrix).

[0097] Number of columns: equal to the number of pollutant types (the same as the number of columns in the second concentration matrix).

[0098] Element Mij': represents the mass flow rate of the jth pollutant at the ith time point (unit: mg / h), which is obtained by multiplying the concentration value Cij' at the corresponding time point by the flue gas flow value Qi'.

[0099] For example: if the second concentration matrix has 2000 rows (2000 time points) and 6 columns (6 pollutant types), then the second mass matrix is also 2000 rows × 6 columns, and each element in each row is the product result of the concentration and flow rate at the corresponding time point.

[0100] In the embodiments of the present invention, by combining the flue gas flow rate (reflecting the real-time volumetric flow rate of the waste incinerator exhaust gas) and concentration data, the concentration of end pollutants (mg / m 3 ) is converted into mass flow rate (mg / h), which directly reflects the absolute mass of pollutants actually emitted per unit time at the outlet of the waste incinerator, avoiding the problem of "overestimating the total emissions at high concentrations and low flow rates and underestimating the total emissions at low concentrations and high flow rates" caused by relying solely on concentration. Retaining the flow rate and concentration coupling relationship of the time series (such as the immediate impact of the change in flue gas flow rate on mass emissions when the waste incinerator load fluctuates), supporting the analysis of the dynamic laws of pollutant generation and emission during the incineration process (such as the sudden increase in the mass flow rate of incomplete combustion products due to a sudden drop in the furnace temperature), providing data support for the operation optimization of the waste incinerator. As the mass flow rate data of the end monitoring point (Point B), it can be combined with the mass matrix of the front-end monitoring point (Point A) to calculate the pollutant removal rate (such as "(mass flow rate at Point A - mass flow rate at Point B) / mass flow rate at Point A"), and evaluate the treatment effect of the waste incinerator purification device (such as activated carbon adsorption, bag filter) in real time, solving the problem that traditional single-point concentration detection cannot quantify the purification efficiency. Forming a unified "time-pollutant-mass flow rate" data structure with the first mass matrix (emission from the pretreatment area), which is convenient for subsequent splicing into a joint matrix to realize the full-link mass emission tracking from waste gas generation (front-end workshop) to end treatment (incineration flue).

[0101] In another preferred embodiment of the present invention, splice the first mass matrix and the second mass matrix to form a joint matrix; preprocess the joint matrix to obtain a standard data frame, including:

[0102] Concatenate the first mass matrix and the second mass matrix column by column, that is, place all columns of the first mass matrix in front and all columns of the second mass matrix behind to form a new two-dimensional matrix, namely the combined matrix. Each row of the combined matrix corresponds to a time point. The columns in the front correspond to the pollutant mass flow at monitoring point A, and the columns in the back correspond to the pollutant mass flow at monitoring point B. Specifically, it includes:

[0103] Confirm that the number of rows of the first mass matrix (monitoring point A, denoted as MA) and the second mass matrix (monitoring point B, denoted as MB) is the same (both equal to the total number of time points, such as n rows), and the timestamps of each row are strictly aligned (for example, the i-th row corresponds to the i-th time point); arrange all columns of MA (such as m columns, corresponding to the mass flows of m pollutants at monitoring point A) in the original order on the left side of the combined matrix, and then sequentially concatenate all columns of MB (such as k columns, corresponding to the mass flows of k pollutants at monitoring point B) to form a two-dimensional matrix of n rows × (m + k) columns, denoted as the combined matrix; the first to m-th columns of the combined matrix correspond to the pollutants at monitoring point A (such as VOCs, ammonia, etc.), and the (m + 1)-th to (m + k)-th columns correspond to the pollutants at monitoring point B (such as NO x , particulate matter, etc.); each row still represents a time point. For example, the first m elements of the i-th row are the mass flows of various pollutants at the i-th time point at monitoring point A, and the last k elements are the corresponding data at monitoring point B.

[0104] Filter each element in the combined matrix through moving average filtering, and perform normalization processing on the filtered combined matrix so that the values of each column of data are mapped to the interval [0, 1] to obtain a standard data frame. Specifically, it includes:

[0105] Select a sliding window that includes the current time point and several time points before and after it (such as including the first 2, the current, and the last 2 time points, a total of 5 time points) to calculate the average value to smooth the data; for each column of the combined matrix (regardless of whether it belongs to monitoring point A or B, each column corresponds to the mass flow of a pollutant at all time points), starting from the first time point, calculate the average value of the elements within the window in sequence; for boundary time points (such as the first time point, without the first 2 time points), calculate the average using the available time points (such as only the first 1, the current, and the last 2 time points, a total of 4 points). For example: the value of the element at the i-th time point and the j-th column after filtering is the average value of the elements in the j-th column within the window (such as from the (i - 2)-th to the (i + 2)-th time points); after all elements are filtered, a matrix with the same number of rows and columns as the combined matrix is obtained, in which high-frequency noise (such as instantaneous jitter of the sensor) is suppressed and the data curve is smoother.

[0106] For each column of the filtered matrix (a total of m + k columns), find the maximum value (max j ) and the minimum value (min j), where j represents the column index (1 ≤ j ≤ m + k), linearly mapped to the interval [0, 1]: for each element in each column, convert according to the following rules:

[0107] If max j = min j , the new value = (current value - min j ) / (max j - min j ); if max j = min j (i.e., the data in this column does not change, a very special case), the default value is assigned as 0.5 (or keep the original value, processed according to actual requirements). After all columns are normalized, the final two-dimensional matrix is obtained, that is, the standard data frame, and the data range of each column is [0, 1], eliminating the analysis deviation caused by the differences in units and magnitudes of the mass flow rates of different pollutants (for example, the VOCs mass flow rate at monitoring point A is 1000 mg / h, and the particulate matter at monitoring point B is 10 mg / h. After normalization, their weights in the model are the same). Each row still corresponds to a time point, integrating the smoothed and dimensionless mass flow rate data of the two monitoring points, providing a unified input for the subsequent correction module and calculation module.

[0108] In the embodiment of the present invention, a joint matrix is formed by column splicing, unifying the pollutant mass flow rate data of the pretreatment area (point A) and the incinerator flue (point B) under the same time dimension, facilitating direct comparison of the dynamic correlation between the two (for example, when the mass flow rate of a certain pollutant at point A suddenly increases, whether the corresponding pollutant at point B changes synchronously to judge whether the purification device fails), and solving the problem of missing correlation caused by traditional independent analysis of the data at the two nodes. Moving average filtering reduces the high-frequency noise of the data (such as instantaneous jitter of the sensor and short-term disturbance of the air flow), making the mass flow rate curve in the joint matrix smoother and avoiding the interference of outliers to the correction module and calculation module (such as misinterpreting the noise peak as a real emission mutation), improving the reliability of subsequent modeling (such as fluid mechanics correction and purification efficiency calculation). Mapping the mass flow rate data of different pollutants and different monitoring points to the interval [0, 1], eliminating the analysis deviation caused by different units and magnitudes (for example, the VOCs mass flow rate is 1000 mg / h, and the ammonia is 10 mg / h. After normalization, their weights in the model are balanced), providing a standardized input for subsequent machine learning algorithms (such as calculation of correction factors and optimization of warning thresholds), and improving the training efficiency and generalization ability of the model.

[0109] In another preferred embodiment of the present invention, analyze the physical environment between monitoring point A and monitoring point B, and based on the principle of fluid mechanics, generate a spatial correction coefficient according to the physical environment, including:

[0110] Obtain the physical parameters of monitoring point A and monitoring point B. The physical parameters include the straight-line distance, vertical height difference, total pipeline length, pipeline inner diameter, inner wall roughness of the pipeline, and pipeline bending characteristics between monitoring point A and monitoring point B. Specifically, it includes: Record the three-dimensional spatial positions of monitoring point A (tail gas emission outlet of the pretreatment area) and monitoring point B (flue gas outlet of the incinerator) through engineering design drawings or on-site surveys:

[0111] Straight-line distance: The shortest distance between two points in space (such as 10 meters);

[0112] Vertical height difference: The height difference between two points in the vertical direction (such as monitoring point B is 5 meters higher than A, recorded as +5 meters; otherwise recorded as -5 meters).

[0113] Measure the parameters of the exhaust gas pipeline connecting the two points:

[0114] Total pipeline length: The total geometric length including all straight pipe sections, elbows, and reducer sections (such as 20 meters).

[0115] Pipeline inner diameter: The inner diameter of each straight pipe section (if the pipeline has a diameter change, take the inner diameter of the main flow section, such as 0.8 meters).

[0116] Inner wall roughness: The roughness of the pipeline inner wall (such as obtained through a roughness measuring instrument or by looking up a table, recorded as 0.1 mm).

[0117] Bending characteristics: Record the number and geometric parameters of non-straight pipe sections (such as 3 90° elbows, with a curvature radius of 1.2 meters for each elbow; 1 45° elbow, with a curvature radius of 0.8 meters).

[0118] Finally, form a physical parameter table containing the above parameters.

[0119] Based on the physical parameters, calculate the frictional resistance along the pipe when the exhaust gas flows in a straight pipe, and calculate the local resistance of the exhaust gas in non-straight pipe section components. Specifically, it includes:

[0120] According to the pipeline inner diameter and inner wall roughness, evaluate the frictional resistance between the fluid and the pipe wall when the fluid flows in a straight pipe (such as the rougher the inner wall and the longer the pipeline, the greater the resistance); According to the principles of fluid mechanics, divide the straight pipe section into segments by inner diameter (if there is a diameter change, divide it into multiple segments), and calculate the resistance value for each segment (for example, a 10-meter long straight pipe with an inner diameter of 0.8 meters and a roughness of 0.1 mm, its frictional resistance along the pipe is manifested as a continuous pressure loss to the air flow, resulting in a slightly decreased flow velocity, and pollutants may partially settle or react due to an increased residence time).

[0121] Local resistance calculation (non-straight pipe section):

[0122] For each bending feature (such as elbows), according to its angle (90°, 45°) and radius of curvature, look up the table or determine the local resistance coefficient according to experience (for example, when the radius of curvature of a 90° elbow is 1.2 meters, the local resistance coefficient is 0.3; the coefficient of a 45° elbow is 0.15).

[0123] Accumulate the local resistances of all non - straight pipe sections (such as the total coefficient of 3 90° elbows is 0.9, and the coefficient of 1 45° elbow is 0.15, with a total local resistance coefficient of 1.05), which reflects the instantaneous resistance generated when the air flow passes through components such as elbows and reducers due to sudden changes in flow direction or cross - sectional changes (such as energy loss caused by eddy currents, which may cause local fluctuations or attenuation of pollutant concentration). Finally, obtain the sum of the frictional resistance and the sum of the local resistance, which are the key parameters for measuring the air flow resistance in the pipeline.

[0124] Generate a spatial correction coefficient based on the frictional resistance, local resistance, and height difference, specifically including:

[0125] The frictional resistance and local resistance together determine the total effect of the flow resistance of the waste gas from point A to point B (the greater the resistance, the slower the air flow velocity, and the longer the residence time of pollutants in the pipeline, which may cause concentration attenuation due to sedimentation, adsorption, or chemical reactions).

[0126] For example, if the total resistance is large, assuming the attenuation rate of pollutants in the pipeline is 10%, the correction coefficient needs to reflect this attenuation (such as a correction coefficient of 0.9, indicating that the measured concentration at point B needs to be multiplied by 0.9 to compensate for the attenuation).

[0127] The vertical height difference affects the additional pressure generated by the air flow due to gravity (such as when point B is higher than point A, the air flow needs to overcome gravity, which may cause a slight decrease in flow velocity and indirectly affect the pollutant transmission efficiency). Combine the direction (rising or falling) and magnitude of the height difference to adjust the correction coefficient (such as when the height difference is +5 meters, the correction coefficient is slightly less than that of a horizontal pipeline; when it is -5 meters, it is slightly greater than that of a horizontal pipeline).

[0128] Establish the pollutant attenuation rates corresponding to different resistance levels (characterized by the sum of the frictional resistance and local resistance) in advance through historical data. For example:

[0129] Low resistance (total resistance coefficient < 0.5): attenuation rate of 5% (that is, 5% of the pollutants are lost due to sedimentation, adsorption, etc. during pipeline transmission, and 95% remains to reach the monitoring point B);

[0130] Medium resistance (0.5 ≤ total resistance coefficient < 1.0): attenuation rate of 10%;

[0131] High resistance (total resistance coefficient ≥ 1.0): attenuation rate of 15%.

[0132] Match the current resistance level. Based on the calculated total of the frictional resistance and local resistance (for example, the total resistance coefficient is 1.05), match the corresponding attenuation rate (such as for the high resistance level, the attenuation rate is 15%), and convert it into an attenuation effect factor (i.e., the remaining ratio, 1 - attenuation rate = 0.85).

[0133] Convert the height difference into a pressure effect factor. The processing procedure is as follows:

[0134] Establish the height difference - pressure correction rule:

[0135] Preset the influence rule of the height difference on the air flow pressure (considering the additional effect of gravity on the air flow):

[0136] When the monitoring point B is higher than A (positive height difference): For every 1 - meter height difference, introduce a 0.1% pressure loss, and the correction value = 1 - 0.001×height difference (meters);

[0137] When the monitoring point B is lower than A (negative height difference): For every 1 - meter height difference, introduce a 0.05% pressure gain, and the correction factor = 1 + 0.0005×|height difference| (meters);

[0138] Horizontal with no height difference (0 meters): The correction value = 1.

[0139] Calculate the current pressure effect factor:

[0140] For example, if the vertical height difference is +5 meters (B is higher than A), then the pressure effect factor = 1 - 0.001×5 = 0.995; if it is -3 meters (B is lower than A), then the pressure effect factor = 1 + 0.0005×3 = 1.0015.

[0141] Synthesize the space correction coefficient. The processing procedure is as follows:

[0142] Determine the synthesis rule:

[0143] Preset that the resistance attenuation and pressure effect are independent influencing factors, and use multiplication synthesis (i.e., the total correction coefficient = attenuation effect factor × pressure effect factor) to reflect the pollutant transmission efficiency under the combined action of both.

[0144] Example calculation:

[0145] If the attenuation effect factor is 0.85 (15% attenuation) and the pressure effect factor is 0.995 (B is 5 meters higher than A), then the space correction coefficient = 0.85 × 0.995 ≈ 0.845 (indicating that the pollutant concentration at monitoring point A needs to be multiplied by 0.845 to be corrected to the theoretical concentration at point B considering pipeline resistance and height difference); if the attenuation effect factor is 0.95 (5% attenuation) and the height difference is 0 meters (pressure effect factor = 1), then the correction coefficient = 0.95 × 1 = 0.95. If the synthesized correction coefficient exceeds the reasonable range (such as <0.5 or >1.5, extremely special cases), then take the nearest boundary value (such as 0.5 or 1.5) to avoid interference from extreme values on subsequent calculations.

[0146] This coefficient is used in the subsequent correction module to compensate for the concentration deviation in the pollutant transmission process between monitoring points A and B (for example, after the concentration matrix at point A is adjusted by the correction coefficient, it is closer to the concentration that should be detected theoretically at point B).

[0147] In the embodiment of the present invention, by quantifying the effects of factors such as pipeline structure (such as bending, diameter change), inner wall roughness, and height difference on the exhaust gas flow (such as concentration attenuation caused by frictional resistance along the way, turbulent mixing caused by local resistance), the generated space correction coefficient can compensate in real time for the concentration deviation caused by the physical environment between monitoring points (for example, at the pipeline elbow, the measured concentration is 15%-20% lower than the actual value due to eddy currents), avoiding the total amount misjudgment caused by traditional monitoring ignoring transmission losses. Based on the calculation process of fluid mechanics principles (such as the frictional resistance formula along the way, looking up the local resistance coefficient in a table), the system can automatically adapt to the pipeline layouts of different treatment plants (such as differences in the straight pipe length and bending angle). Without manual presetting of correction parameters, when the pipeline is modified (such as adding dust removal equipment to change the flow field), the correction coefficient can be adjusted in real time by updating physical parameters, solving the problem that traditional fixed models cannot adapt to changes in working conditions. For example, by combining the mass attenuation rate caused by frictional resistance along the way, the theoretical total amount of pollutants transmitted from monitoring point A to B can be calculated more accurately, providing a quantitative basis for the calculation of correction factors at the space transmission level.

[0148] In another preferred embodiment of the present invention, according to the measured total amount and theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame, the correction factor is calculated, including:

[0149] Extracting the measured total amount of pollutants at monitoring point A and monitoring point B from the standard data frame specifically includes: According to the splicing rule of the joint matrix, the first m columns of the standard data frame correspond to the mass flow rate of pollutants at monitoring point A (such as VOCs, ammonia, etc.), and the last k columns correspond to the mass flow rate of pollutants at monitoring point B (such as NOx, particulate matter, etc.). Calculate the measured total amount at each time point:

[0150] For each row (each time point) of the standard data frame, the values of the first m columns are summed separately to obtain the total measured amount of pollutants at monitoring point A at this time point (i.e., the sum of the mass flow rates of all pollutants at point A at this time point). Similarly, the values of the last k columns are summed to obtain the total measured amount of pollutants at monitoring point B at this time point. The total measured amounts at point A and point B at each time point are respectively stored in two one-dimensional arrays (e.g., total measured amount A = [S1, S2, …, Sn], total measured amount B = [S1, S2, …, Sn]), where n is the total number of time points.

[0151] Calculate the ratio of the total measured amount to the total theoretical amount to obtain the correction factor, specifically including: according to the "total theoretical amount of pollutants estimated based on the design parameters and operating principle of the incinerator" in the correction module (this total theoretical amount can be regarded as the total theoretical transmission amount of pollutants between monitoring point A and B, which is a fixed value, for example, the total theoretical amount Ti at each time point is obtained through material balance calculation).

[0152] Calculate the ratio for each time point:

[0153] For each time point i, compare the total measured amount (assuming here the total measured amount is the sum of point A and point B; according to the user's description "the total measured amounts of pollutants at monitoring point A and monitoring point B", it is assumed to be the sum of the two here) with the corresponding total theoretical amount Ti, and arrange the ratios at each time point in order to form a correction factor sequence for subsequent calculation of the final correction parameter in combination with the spatial correction coefficient.

[0154] In the embodiment of the present invention, by extracting the total measured amount and comparing it with the total theoretical amount, the actual monitoring data of pollutant emissions is directly associated with the output of the theoretical model. The ratio of the total measured amount to the total theoretical amount (correction factor) can reflect the differences between the actual emission characteristics of the monitoring point and the theoretical assumptions, such as the unquantified impacts of pipeline resistance, environmental factors, etc., thereby providing parameters for dynamic adjustment for subsequent data correction; as a dimensionless index, the correction factor can unify the differences in the total amounts of pollutants under different monitoring points and different time scales.

[0155] In another preferred embodiment of the present invention, according to the spatial correction coefficient and the correction factor, the correction parameter is obtained, including:

[0156] Taking monitoring point A as the coordinate origin (0, 0), the coordinate of monitoring point B in the two-dimensional plane is (L, 0), where L is the straight-line distance between the two points; according to the actual path of the waste gas transmission pipeline, the transmission area between monitoring point A and B is divided into several sub-regions, and each sub-region corresponds to different components in the spatial correction coefficient, specifically including:

[0157] Read the three-dimensional spatial straight-line distance between monitoring point A and B (such as 10 meters) from the obtained physical parameter table and denote it as L.

[0158] Establish a two-dimensional coordinate system:

[0159] Take the position of monitoring point A as the coordinate origin, defined as (0, 0); set the position of monitoring point B along the horizontal straight line direction (ignoring the vertical height difference, only for plane division) as (L, 0), forming a two-dimensional coordinate system with A as the origin and AB as the x-axis; through the pipeline layout drawing or on-site measurement, record the actual transmission path of the waste gas from A to B (for example: starting from point A, first passing through a 5-meter-long straight pipe (inner diameter 0.8 meters, roughness 0.1 millimeter); then passing through a 90° elbow (curvature radius 1.2 meters); then connecting a 8-meter-long variable-diameter straight pipe (inner diameter shrinking from 0.8 meters to 0.6 meters, roughness 0.08 millimeter); finally passing through a 45° elbow (curvature radius 0.8 meters) to reach point B).

[0160] Divide the sub-regions by component type:

[0161] Sub-region 1: The first straight pipe section after point A (5 meters long, inner diameter 0.8 meters), marked as "Straight pipe section 1";

[0162] Sub-region 2: The first 90° elbow (curvature radius 1.2 meters), marked as "Elbow 90°-1";

[0163] Sub-region 3: The variable-diameter straight pipe section (8 meters long, inner diameter 0.6 meters), marked as "Variable-diameter straight pipe section";

[0164] Sub-region 4: The second 45° elbow (curvature radius 0.8 meters), marked as "Elbow 45°-1".

[0165] Record the geometric parameters of the sub-regions: Mark the starting and ending coordinates for each sub-region (based on the projection of the actual path in the two-dimensional coordinate system, such as the starting point of the elbow is (5, 0) and the ending point is (5, 1.2), simulating an upward bend), as well as parameters such as length, angle, inner diameter, etc.

[0166] Mark the spatial correction coefficient components, and the processing process is as follows:

[0167] Associate physical parameters with correction components:

[0168] Straight pipe sections (Sub-region 1, Sub-region 3):

[0169] Correspond to the "friction factor along the length" component, which is determined by the inner diameter, length, and inner wall roughness of the pipe (such as the roughness of Sub-region 1 is 0.1 millimeter, the inner diameter is 0.8 meters, and the friction along the length has a greater impact; the inner diameter of Sub-region 3 shrinks, and the friction along the length further increases).

[0170] Elbows (Sub-region 2, Sub-region 4):

[0171] For the component corresponding to the "local resistance coefficient", it is determined by the bending angle (90° or 45°) and the radius of curvature (for example, in sub-region 2, it is a 90° elbow with a radius of curvature of 1.2 meters, and the local resistance coefficient is higher than that of the 45° elbow in sub-region 4).

[0172] Height difference influence section: If there is a vertical height change in a certain sub-region (such as rising from an altitude of 10 meters at point A to an altitude of 15 meters at point B), then an additional component of "height difference pressure coefficient" is marked (in this example, it is assumed that sub-region 3 has a 5-meter height increase and the influence of gravity needs to be corrected).

[0173] Sample the spatial points within each sub-region and calculate their distances to monitoring points A and B, specifically including:

[0174] For a straight pipe section with a length of S meters (such as sub-region 1 with a length of 5 meters), sampling points are evenly selected at intervals of 1 meter (including the starting point and the ending point); taking sub-region 1 as an example, the starting point coordinates are (0, 0) (coinciding with point A), the ending point coordinates are (5, 0), then the sampling point coordinates are successively (1, 0), (2, 0), (3, 0), (4, 0), (5, 0) (a total of 5 points, including the ending point); for a variable-diameter straight pipe section (such as sub-region 3 with a length of 8 meters and an inner diameter change), similarly, it is evenly divided by length, and the coordinates of each point are recorded (such as the starting point (5, 1.2), the ending point (13, 1.2), and the sampling points are (6, 1.2), (7, 1.2), …, (13, 1.2)).

[0175] For a 90° elbow (with a radius of curvature of 1.2 meters, the center coordinates (5, 1.2), the starting point (5, 0), and the ending point (6.2, 1.2)), the arc is evenly divided into 5 equal parts (each part is 18°) according to the curvature.

[0176] Starting from the starting point, calculate the coordinates of each equal division point along the arc in the counterclockwise (or clockwise) direction:

[0177] The 1st point: At a distance of 1 / 5 of the arc from the starting point, the coordinates are calculated through the central angle (such as a central angle of 18°, the coordinates are 5 + 1.2×cos(18°), 0 + 1.2×sin(18°));

[0178] And so on, until the last equal division point before the ending point, a total of 5 points (when including the starting point and the ending point, adjustments are needed to ensure uniform distribution).

[0179] Store the coordinates of the sampling points in each sub-region in a list (such as the sampling point coordinates of the elbow in sub-region 2 are (5.38, 0.38), (5.71, 0.71), (6, 1.2), (5.71, 1.62), (5.38, 1.94), etc., and the specific coordinates are calculated according to the actual curvature).

[0180] For each sampling point (x, y), calculate its straight-line distance (i.e., Euclidean distance) to the coordinate origin (0, 0). For example, the distance from the point (5, 1.2) to point A is: the horizontal distance is 5 meters, the vertical distance is 1.2 meters, and the distance is "the square root of the sum of the squares of 5 meters horizontal distance and 1.2 meters vertical distance" (avoid using formulas and describe the geometric meaning in words).

[0181] The distance dB to point B:

[0182] Given that the coordinates of point B are (L, 0) (e.g., L = 10 meters), for the sampling point (x, y), calculate its straight-line distance to (10, 0). For example, the distance from the point (5, 1.2) to point B is: the horizontal distance is 10 - 5 = 5 meters, the vertical distance is 1.2 meters, and the distance calculation method is the same as above.

[0183] Record dA and dB for each sampling point (e.g., for the point (3, 0) in sub-region 1: dA = 3 meters, dB = 7 meters; for the point (5.71, 0.71) at the elbow: dA ≈ 5.75 meters, dB ≈ 4.31 meters).

[0184] Multiply the spatial correction coefficient component of each sub-region by the corresponding weight to obtain the contribution value of this component to the correction parameter, specifically including:

[0185] The weight rule processing process is set according to the sub-region type as follows:

[0186] Straight pipe section / stepped straight pipe section weight:

[0187] Calculate the total length of all straight pipe sections (including stepped sections), and use the length proportion of each straight pipe section as the weight (e.g., the total pipe length is 20 meters, the length of sub-region 1 (straight pipe section) is 5 meters, and the weight is 5 / 20 = 0.25; the length of sub-region 3 (stepped straight pipe section) is 8 meters, and the weight is 8 / 20 = 0.4).

[0188] If the inner diameter change of the stepped section is significant (e.g., the inner diameter suddenly decreases from 0.8 meters to 0.6 meters), an additional weight coefficient is added (e.g., multiply by 1.2 based on the length proportion, and the final weight is 0.4 × 1.2 = 0.48).

[0189] Elbow / non-straight pipe section weight:

[0190] Preset angle weight factor: for a 90° elbow, it is 1.0; for a 45° elbow, it is 0.6; for a 180° elbow, it is 1.5 (the larger the angle, the higher the resistance impact).

[0191] Adjustment in combination with the curvature radius: for an elbow with a curvature radius < 1 meter, the weight factor is multiplied by 1.1 (a small curvature radius increases resistance); if the curvature radius ≥ 1 meter, the original value is maintained.

[0192] For example: the weight factor of sub-region 2 (90° elbow, curvature radius 1.2 m) is 1.0 × 1.0 = 1.0; the weight factor of sub-region 4 (45° elbow, curvature radius 0.8 m) is 0.6 × 1.1 = 0.66.

[0193] Weight of height difference section:

[0194] Set according to the proportion of the absolute value of the vertical height difference in the total transmission height difference (e.g., for a total height difference of 5 m and a certain sub-region accounting for 3 m, the weight is 3 / 5 = 0.6). The weight of the rising section is slightly higher than that of the falling section (e.g., an additional 0.1 is added to the rising section, and the final weight is 0.7).

[0195] The process of calculating the weight value of the sub-region is as follows:

[0196] Example of a straight pipe section (sub-region 1, 5 m long, total pipe length 20 m):

[0197] Weight = length ratio = 5 / 20 = 0.25 (if it is an ordinary straight pipe without diameter change, it is directly adopted).

[0198] Example of a straight pipe section with variable diameter (sub-region 3, 8 m long, inner diameter from 0.8 → 0.6 m):

[0199] Basic weight = length ratio = 8 / 20 = 0.4;

[0200] Inner diameter change correction: since the inner diameter decreases by 25%, the weight × 1.2 → 0.4 × 1.2 = 0.48;

[0201] Example of an elbow (sub-region 2, 90° elbow, curvature radius 1.2 m):

[0202] Angle weight factor = 1.0 (90°);

[0203] When the curvature radius ≥ 1 m, no additional correction is required → weight factor = 1.0;

[0204] Combined with the number of elbows in the total non-straight pipe sections (a total of 2 elbows), the normalized weight = 1.0 / (1.0 + 0.66) ≈ 0.61 (assuming the weight factor of sub-region 4 is 0.66 and the total factor sum is 1.66).

[0205] The process of calculating the contribution value of the sub-region is as follows:

[0206] Extract the correction coefficient component from the pre-labeled sub-region - correction component comparison table (e.g., for sub-region 1 (straight pipe section), it corresponds to "friction coefficient along the way 0.3"; for sub-region 2 (elbow), it corresponds to "local resistance coefficient 0.15").

[0207] Multiply to calculate the contribution value:

[0208] Contribution value of sub-region 1 = friction coefficient along the path × weight = 0.3 × 0.25 = 0.075 (reflecting the contribution of this straight pipe section to the friction resistance along the path).

[0209] Contribution value of sub-region 2 = local resistance coefficient × weight = 0.15 × 0.61 ≈ 0.0915 (reflecting the contribution of this elbow to the local resistance).

[0210] Classify and accumulate the contribution values of all components according to physical properties, and finally form correction parameters, specifically including: classify the contribution values of all sub-regions into several categories (for example: friction resistance along the path category: including the contribution values of all sub-regions that generate continuous frictional resistance such as straight pipe sections and reduced-diameter sections; local resistance category: including the contribution values of all sub-regions that generate instantaneous resistance such as elbows and valves; height difference category: including the contribution value of the pressure effect caused by the vertical height difference (if the sub-region involves height changes).

[0211] Classify and accumulate:

[0212] Sum the contribution values of each category separately (for example, the total contribution of the friction resistance along the path category = contribution of sub-region 1 + contribution of sub-region 3, and the total contribution of the local resistance category = contribution of sub-region 2 + contribution of sub-region 4).

[0213] Combine the accumulated results of each category according to the preset logic (for example, the contribution of the friction resistance along the path accounts for 60% weight, the local resistance accounts for 30%, and the height difference accounts for 10%), and finally form one or more dimensionless correction parameters (such as correction parameter 1 reflects the total resistance effect, and correction parameter 2 reflects the height difference effect) for subsequent pollutant concentration correction.

[0214] In the embodiment of the present invention, by dividing the transmission area between monitoring points A and B into several sub-regions and combining the actual bending path of the exhaust gas pipeline (instead of the simple straight-line distance), the differential effects of different physical environments (such as pipeline bending, roughness change, height difference) on the exhaust gas transmission can be accurately captured. For example, for sub-regions with dense elbows or rough inner walls, the spatial correction coefficient component can reflect higher local resistance or friction resistance along the path, making the model closer to the true transmission characteristics of the complex pipeline system.

[0215] Sample the spatial points of each sub-region and calculate the distances to A and B, and combine the spatial correction coefficient components (such as the friction coefficient along the path, local resistance coefficient) with the corresponding weights (such as setting weights according to the pipeline length and bending angle), and the contributions of different physical factors (such as the resistance of the straight pipe section, the resistance of the elbow, the pressure of the height difference) to the pollutant transmission can be quantitatively evaluated. For example, the weight of the friction resistance component of the long straight pipe section is higher, while the weight of the local resistance component of the sharp-bend pipeline is higher, making the correction parameter dynamically reflect the dominant physical mechanism of each sub-region and improve the correction accuracy.

[0216] Classify and accumulate the contribution values according to physical characteristics (such as frictional resistance along the way, local resistance, height difference pressure effect), so that the correction parameters have clear physical meanings and interpretability. For example, by combining the frictional resistance contribution values of all sub-regions, the influence of pipe friction on pollutant attenuation can be separately reflected; combining the local resistance contribution values can reflect the energy losses of components such as elbows and valves. This classification processing facilitates targeted adjustment in engineering applications (such as optimizing the pipe smoothness of high-resistance sub-regions), and also provides more reliable basic parameters for subsequent pollutant diffusion models, concentration inversion, etc.

[0217] With the monitoring point A as the coordinate origin and the coordinates of B in the two-dimensional plane being (L, 0), the complex three-dimensional space problem is simplified into an easily calculable plane model. At the same time, through sub-region discretization (such as grid division), the sampling and calculation processes can be efficiently implemented by means of numerical methods (such as finite element, finite difference). This standardized processing reduces the difficulty of engineering implementation, especially applicable to scenarios where the pipe layout in industrial sites is complex and the distribution of monitoring points is irregular, providing convenience for the rapid deployment and parameter iteration of real-time monitoring systems.

[0218] This step, together with the previous spatial correction coefficient (based on the fluid mechanics model) and correction factor (based on the ratio of measured values to theoretical values), forms a complete correction system: the spatial correction coefficient describes the theoretical influence of the physical environment on the transmission process, and the correction factor reflects the deviation between actual monitoring data and theoretical predictions. Through sub-region division and weight assignment, the two are organically combined in the correction parameters (for example, the measured deviation in high-resistance sub-regions will further amplify the correction intensity through the correction factor), which not only retains the mechanistic interpretability of the physical model but also incorporates data-driven adaptive adjustment, ultimately forming a correction scheme closer to the actual working conditions and effectively improving the accuracy of links such as total pollutant calculation and concentration inversion.

[0219] In another preferred embodiment of the present invention, according to the correction parameters, standard data frames, the chemical reaction conversion rate of monitoring point A, and the removal rate of the incinerator purification device at monitoring point B, calculate the pollutants at monitoring point A and the pollutants at monitoring point B to generate a pollutant correction concentration matrix, including:

[0220] Extract dimensionless correction parameters from the previous calculation results (such as a comprehensive coefficient reflecting resistance attenuation and height difference influence, for example, 0.85). Analyze the standard data frames: separate the mass flow data of monitoring points A and B: the first m columns correspond to point A (such as MA = [MA1, MA2,..., MAm]), and the last k columns correspond to point B (such as MB = [MB1, MB2,..., MBk]).

[0221] Read the process parameters:

[0222] The chemical reaction conversion rate at monitoring point A (for example, the catalytic reaction conversion rate of VOCs in the pretreatment area is 30%, denoted as RA); the removal rate of the incinerator purification device at monitoring point B (for example, the x removal rate of NO is 80%, denoted as RB).

[0223] The specific process of correcting the mass flow rate at monitoring point A is as follows:

[0224] Multiply the original mass flow rate MA at point A by the correction parameter (such as 0.85) to compensate for the attenuation or gain during pipeline transmission (for example, if the correction parameter is 0.85, it means that theoretically 85% of the mass flow rate at point A remains after pipeline transmission and reaches point B).

[0225] Chemical reaction correction, considering the chemical reactions in the pretreatment area (for example, some pollutants react and are converted into other substances in the treatment equipment before point A):

[0226] If the conversion rate of a certain pollutant at point A is RA (for example, 30% is converted into harmless substances), then the corrected mass flow rate = the spatially corrected mass flow rate × (1 - RA).

[0227] For example: The original mass flow rate of a certain pollutant at point A is 100 mg / h, the correction parameter is 0.85, and the conversion rate is 30%, then the correction result = 100 × 0.85 × (1 - 0.3) = 59.5 mg / h.

[0228] The process of correcting the mass flow rate at monitoring point B is as follows:

[0229] Purification device correction: Regarding the removal effect of pollutants by the incinerator purification device (for example, bag filter removes particulate matter, activated carbon adsorbs VOCs): If the removal rate of a certain pollutant at point B is RB (for example, 80%), then the corrected mass flow rate = the original mass flow rate × (1 - RB).

[0230] For example: The original mass flow rate of a certain pollutant at point B is 50 mg / h, the removal rate is 80%, and the correction result = 50 × (1 - 0.8) = 10 mg / h.

[0231] Reverse verification correction, combined with the correction parameter, verify the consistency between the corrected mass flow rate at point B and the theoretical transmission amount at point A (for example, after the corrected mass flow rate at point A is transmitted through the pipeline, it should be close to the theoretical value of the mass flow rate before correction at point B).

[0232] The process of generating the pollutant correction concentration matrix is as follows:

[0233] Obtain flow data, and extract the flow sequences aligned with the time stamps of the standard data frames from the induced draft fan flow rate (point A) and the incinerator flue gas flow rate (point B) collected during the calculation of the quality matrix (for example, the flow rate at point A, QA = [QA1, QA2,..., QAn], and the flow rate at point B, QB = [QB1, QB2,..., QBn]).

[0234] Mass flow rate to concentration:

[0235] For each time point i at point A, the corrected concentration = the corrected mass flow rate MAi' ÷ the corresponding flow rate QAi (the unit is converted from mg / h to mg / m 3 )

[0236] For each time point i at point B, the corrected concentration = the corrected mass flow rate MBi' ÷ the corresponding flow rate QBi.

[0237] Matrix splicing: Splice the corrected concentrations at points A and B in the original column order (point A's column first, point B's column second) to form a two-dimensional matrix of corrected pollutant concentrations. Each row corresponds to a time point, and each column corresponds to the corrected concentration of a pollutant.

[0238] In another preferred embodiment of the present invention, based on the matrix of corrected pollutant concentrations, the induced draft fan flow rate, the flue gas flow rate, and a preset emission threshold, give an early warning, including:

[0239] Separate the corrected concentrations at monitoring points A / B:

[0240] The first m columns of the matrix of corrected pollutant concentrations correspond to the corrected concentrations at monitoring point A (such as VOCs, ammonia, etc., unit mg / m 3 ), and the last k columns correspond to the corrected concentrations at monitoring point B (such as NO x , particulate matter, etc.).

[0241] Obtain real-time flow data:

[0242] The induced draft fan flow rate (monitoring point A, unit m 3 / h) and the incinerator flue gas flow rate (monitoring point B, unit m³ / h) are strictly aligned with the rows of the corrected concentration matrix according to the time stamp (for example, the i-th row corresponds to the flow rates QAi and QBi at the i-th time point).

[0243] Load the preset emission threshold:

[0244] For each pollutant (such as the VOCs threshold of 50 mg / m at point A 3 , and the NOx threshold of 20 mg / m at point B 3 ), form a threshold list (such as threshold A = [TA1, TA2,..., TAm], threshold B = [TB1, TB2,..., TBk]).

[0245] The processing procedure for calculating the real-time emission indicators of each pollutant is as follows:

[0246] Concentration warning indicator:

[0247] Directly use the value in the corrected concentration matrix (mg / m 3 ) to reflect the real-time concentration of pollutants in the current gas (for example, the VOCs concentration at point A at the i-th time point is 45 mg / m 3 , and the NO x concentration at point B is 25 mg / m 3 ).

[0248] Mass flow warning indicator (if the threshold is the mass flow):

[0249] For monitoring point A, multiply the corrected concentration (the first m columns of the i-th row) by the corresponding induced draft fan flow rate QAi to obtain the mass flow of each pollutant (mg / h. For example, the mass flow of VOCs at point A = 45 mg / m 3 × 2000 m 3 / h = 90000 mg / h).

[0250] For monitoring point B, multiply the corrected concentration (the last k columns of the i-th row) by the corresponding flue gas flow rate QBi to obtain the mass flow at point B (for example, the mass flow of NO x at point B = 25 mg / m 3 × 15,000 m 3 / h = 375000 mg / h).

[0251] The processing procedure for comparing with the threshold for each pollutant at each time point is as follows:

[0252] Concentration comparison (taking the concentration threshold as an example):

[0253] For the j-th pollutant at monitoring point A, compare the corrected concentration CAij' at the i-th time point with the corresponding threshold TAj:

[0254] If CAij' ≥ TAj, mark it as "warning" (for example, the VOCs threshold at point A is 50 mg / m 3 , and the current value is 45 mg / m 3 then there is no warning, if it is 55 mg / m 3 then a warning is triggered).

[0255] For the l-th pollutant at monitoring point B, compare the corrected concentration CBil' at the i-th time point with the threshold TBl, and the logic is the same as above.

[0256] Mass flow comparison (if the threshold is the mass flow):

[0257] Compare the calculated mass flow rates at points A and B with the corresponding mass flow rate thresholds (e.g., the VOCs mass flow rate threshold at point A is 100,000 mg / h, and there is no warning when the current value is 90,000 mg / h, but a warning is triggered if it is 110,000 mg / h).

[0258] Record the warning status: Generate status labels ("normal" or "warning") for each time point and each pollutant, and mark the multiple of exceeding the standard (such as the current concentration / threshold - 1, which is convenient for hierarchical warning).

[0259] Generate warning information and output the processing process as follows:

[0260] Summarize all pollutants exceeding the standard by time point (e.g., at the i-th time point: there is a warning for VOCs at point A, and NO at point B is normal), and form a warning list (including time stamp, monitoring point, pollutant name, measured value, threshold, multiple of exceeding the standard). x Normal), and form a warning list (including time stamp, monitoring point, pollutant name, measured value, threshold, multiple of exceeding the standard).

[0261] If there is a "warning" status, send notifications through system interface pop-up windows, text messages, emails, etc. (such as "At 14:30, the concentration of VOCs at monitoring point A is 55 mg / m 3 (threshold 50 mg / m 3 ), exceeding the standard by 10%").

[0262] Store the warning log: Store the warning information in the database for subsequent traceability and statistics (such as analyzing the monthly warning times of a certain pollutant and the distribution of exceeding-standard time periods).

[0263] As Figure 2 shown, a remote detection method for waste gas emissions during the treatment process of kitchen waste includes:

[0264] Step 1, collect the spectral signals of the waste gas at monitoring point A and monitoring point B in real time, analyze the spectral absorption signals of the characteristic pollutants, and generate the first concentration matrix and the second concentration matrix corresponding to monitoring point A and monitoring point B respectively;

[0265] Step 2, multiply the first concentration matrix by the flow rate of the induced draft fan at monitoring point A at each moment to obtain the first mass matrix; multiply the second concentration matrix by the flow rate of the incinerator flue at monitoring point B at each moment to obtain the second mass matrix; splice the first mass matrix and the second mass matrix to form a combined matrix; preprocess the combined matrix to obtain a standard data frame;

[0266] Step 3: Analyze the physical environment between monitoring point A and monitoring point B, generate a spatial correction coefficient based on the principle of fluid mechanics according to the physical environment; estimate the theoretical total amount of pollutants between monitoring point A and monitoring point B according to the design parameters and operating principle of the incinerator flue; calculate a correction factor according to the measured total amount and the theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame; obtain a correction parameter according to the spatial correction coefficient and the correction factor.

[0267] Step 4: Calculate the pollutants at monitoring point A and the pollutants at monitoring point B according to the correction parameter, the standard data frame, the chemical reaction conversion rate at monitoring point A, and the removal rate of the incinerator purification device at monitoring point B, so as to generate a pollutant correction concentration matrix.

[0268] Step 5: Give an early warning according to the pollutant correction concentration matrix, the flow rate of the induced draft fan, the flue gas flow rate, and a preset emission threshold.

[0269] An embodiment of the present invention further provides a computer-readable storage medium storing instructions, which, when run on a computer, cause the computer to execute the method described above. All implementation manners in the above method embodiments are applicable to this embodiment and can also achieve the same technical effects.

Claims

1. A remote detection system for exhaust gas emissions during the treatment of kitchen waste, characterized in that, Including: A detection module, configured to collect in real time the spectral signals of the waste gas at monitoring point A and monitoring point B, analyze the spectral absorption signals of the characteristic pollutants, and respectively generate a first concentration matrix and a second concentration matrix corresponding to monitoring point A and monitoring point B; A processing module, configured to multiply the first concentration matrix by the flow rate of the induced draft fan at monitoring point A at each moment to obtain a first mass matrix; multiply the second concentration matrix by the flow rate of the incinerator flue at monitoring point B at each moment to obtain a second mass matrix; Stitch the first mass matrix and the second mass matrix to form a combined matrix; Preprocess the combined matrix to obtain a standard data frame; A correction module, configured to analyze the physical environment between monitoring point A and monitoring point B, and generate a spatial correction coefficient based on the principle of fluid mechanics according to the physical environment, including: obtaining the physical parameters of monitoring point A and monitoring point B, where the physical parameters include the straight-line distance, vertical height difference, total pipeline length, pipeline inner diameter, pipeline inner wall roughness, and pipeline bending characteristics of monitoring point A and monitoring point B in three-dimensional space, calculating the frictional resistance when the waste gas flows in a straight pipe based on the physical parameters, and calculating the local resistance of the waste gas in non-straight pipe section components, generating a spatial correction coefficient according to the frictional resistance, local resistance, and height difference; estimating the theoretical total amount of pollutants between monitoring point A and monitoring point B according to the design parameters and operating principle of the incinerator flue; calculating a correction factor according to the measured total amount and theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame; obtaining a correction parameter according to the spatial correction coefficient and the correction factor; A calculation module, configured to calculate the pollutants at monitoring point A and monitoring point B according to the correction parameter, the standard data frame, the chemical reaction conversion rate at monitoring point A, and the removal rate of the incinerator purification device at monitoring point B, so as to generate a pollutant corrected concentration matrix; An early warning module, configured to perform early warning according to the pollutant corrected concentration matrix, the induced draft fan flow rate, the flue flow rate, and a preset emission threshold.

2. The remote detection system for waste gas emission during the treatment of kitchen waste according to claim 1, wherein, Monitoring point A is the tail gas emission outlet of the pretreatment area of the food waste treatment workshop; monitoring point B is the outlet of the incinerator flue.

3. The remote detection system for exhaust gas emissions during the treatment of kitchen waste according to claim 2, characterized in that, Collecting in real time the spectral signals of the waste gas at monitoring point A and monitoring point B, analyzing the spectral absorption signals of the characteristic pollutants, and respectively generating a first concentration matrix and a second concentration matrix corresponding to monitoring point A and monitoring point B, including: Respectively obtaining the original spectral sequences of monitoring point A and monitoring point B, respectively denoted as the first spectral data stream and the second spectral data stream, and each data stream consists of spectral curves with continuous timestamps; Respectively perform smoothing processing, baseline correction, and wavelength calibration on the first spectral data stream and the second spectral data stream to obtain a first spectral curve and a second spectral curve; Pre-establish a characteristic absorption wavelength library for the target pollutants; For the first spectral curve and the second spectral curve, calculate the ratio of the actual light intensity to the pure air light intensity within the wavelength range corresponding to the characteristic wavelength library to obtain the absorbance values at each characteristic wavelength; According to the absorbance values at each characteristic wavelength and the pre-established pollutant concentration-absorbance calibration model, calculate the pollutant concentration vectors of monitoring point A and monitoring point B at each moment; Arrange the concentration vectors at each moment vertically in chronological order to form a two-dimensional concentration matrix. Each row of the two-dimensional concentration matrix corresponds to a time point, and each column corresponds to a pollutant.

4. A remote detection system for exhaust gas emissions during the treatment of kitchen waste according to claim 3, characterized in that, Multiply the first concentration matrix by the flow rate of the induced draft fan at sampling and monitoring point A at each moment to obtain the first mass matrix, including: Obtain the flow rate data of the induced draft fan at sampling and monitoring point A. The flow rate data is a sequence that varies with time, and there is a corresponding induced draft fan flow rate value at each time point. Starting from the first row of the first concentration matrix, the first row represents the concentrations of various pollutants at the first time point; multiply each element in the first row, that is, the concentration of each pollutant, by the induced draft fan flow rate value corresponding to the time point to obtain the mass flow rate values of various pollutants at the first time point, and form a new row vector with them. And so on, multiply each row of the first concentration matrix until all rows are processed. Arrange the obtained row vectors in order to form the first mass matrix. The first mass matrix is two-dimensional, each row corresponds to a time point, and each column corresponds to a pollutant. The elements in the matrix represent the mass flow rate of the pollutant at the corresponding time point.

5. The remote detection system for waste gas emission during the treatment of kitchen waste according to claim 4, characterized in that Multiply the second concentration matrix by the flow rate of the incinerator flue at monitoring point B at each moment to obtain the second mass matrix, including: Obtain the flow rate data of the incinerator flue at monitoring point B. The flow rate data of the incinerator flue is a sequence that varies with time, and there is a corresponding flue flow rate value at each time point. Starting from the first row of the second concentration matrix, multiply each element in the first row, that is, the concentration of each pollutant, by the incinerator flue flow rate value corresponding to the first time point to obtain the mass flow rate values of various pollutants at the first time point, and form a row vector. Perform the same operation on the second row of the second concentration matrix, multiply its elements by the flue flow rate value at the second time point to obtain the mass flow rate row vector at the second time point. And so on, process all rows of the second concentration matrix, arrange the obtained row vectors in order to obtain the second mass matrix. The second mass matrix is two-dimensional, each row represents a time point, and each column represents a pollutant. The element is the mass flow rate of the pollutant at the time point.

6. The remote detection system for exhaust gas emissions during the treatment of kitchen waste according to claim 5, characterized in that, Stitch the first mass matrix and the second mass matrix to form a combined matrix. Preprocess the combined matrix to obtain a standard data frame, including: Stitch the first mass matrix and the second mass matrix by column, that is, place all columns of the first mass matrix in front and all columns of the second mass matrix behind to form a new two-dimensional matrix, that is, the combined matrix. Each row of the combined matrix corresponds to a time point. The columns in front correspond to the pollutant mass flow rate at monitoring point A, and the columns behind correspond to the pollutant mass flow rate at monitoring point B. Filter each element in the combined matrix through moving average filtering, and perform normalization processing on the filtered combined matrix so that the values of each column of data are mapped to the interval [0, 1] to obtain the standard data frame.

7. The remote detection system for waste gas emission during the treatment of kitchen waste according to claim 6, wherein, Calculate the correction factor according to the measured total amount and theoretical total amount of pollutants at monitoring point A and monitoring point B in the standard data frame, including: Extract the actual total amount of pollutants at monitoring point A and monitoring point B from the standard data frame; Calculate the ratio of the actual total amount to the theoretical total amount to obtain the correction factor.

8. A remote detection system for waste gas emissions during the treatment of kitchen waste according to claim 7, characterized in that, Obtain the correction parameters according to the spatial correction coefficient and the correction factor, including: Taking monitoring point A as the coordinate origin (0, 0), the coordinates of monitoring point B in the two-dimensional plane are (L, 0), where L is the straight-line distance between the two points; according to the actual path of the exhaust gas transmission pipeline, divide the transmission area between monitoring points A and B into several sub-regions, and each sub-region corresponds to different components in the spatial correction coefficient; Sample the spatial points in each sub-region and calculate their distances to monitoring points A and B; Multiply the spatial correction coefficient component of each sub-region by the corresponding weight to obtain the contribution value of this component to the correction parameter; Accumulate the contribution values of all components by physical characteristics classification, and finally form the correction parameter.

Images