Intelligent Environmental Pollution Assessment and Monitoring Platform Based on Industrial Big Data

Abstract

This invention discloses an intelligent environmental pollution assessment and monitoring platform based on industrial big data, belonging to the field of data management technology. It analyzes the attenuation characteristics of echo signals through an attenuation anomaly feature acquisition module, calculates the impact of atmospheric refraction on the laser propagation path through an angle offset feature acquisition module, evaluates the pollutant measurement accuracy using a data prediction model through a measurement accuracy calculation module, and dynamically adjusts the laser power and emission angle in a measurement error correction module to improve measurement accuracy. This technology can effectively reduce the interference of meteorological conditions on lidar measurements, improve the accuracy and stability of pollutant concentration monitoring, and provide more reliable data support for environmental pollution monitoring.

Description

Technical Field

[0001] This invention relates to the field of data management technology, and more specifically to an intelligent environmental pollution assessment and monitoring platform based on industrial big data. Background Technology

[0002] Intelligent environmental pollution assessment and monitoring based on industrial big data refers to the real-time collection, storage, analysis, and prediction of pollution data generated during industrial production processes using the Internet of Things, sensor technology, and big data analytics to achieve precise monitoring and intelligent assessment of environmental pollution. This technology, through AI algorithms, machine learning, and cloud computing, can identify pollution sources, predict pollution trends, and provide scientific decision support for environmental regulatory departments and enterprises, thereby improving pollution control efficiency. Currently, environmental pollution assessment and monitoring technologies include remote sensing monitoring, online monitoring systems, and pollution source emission modeling and analysis. For example, lidar monitoring, widely used in air pollution monitoring, can scan pollutants in the air with laser beams to obtain real-time concentration distributions of pollutants such as PM2.5, NO2, and SO2.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, when a laser beam passes through the air, it undergoes Mie scattering with suspended particulate matter (such as PM2.5) and is partially absorbed by certain gaseous pollutants (such as NO2 and SO2). By selecting different wavelengths, different pollutants can be distinguished and detected. However, atmospheric environmental factors (such as temperature gradients, atmospheric turbulence, and air pressure changes) can affect the propagation path of the laser in the air, causing beam deflection (refraction), which in turn affects the accuracy of pollutant measurement. Under different meteorological conditions, such as strong winds, precipitation, or temperature changes, monitoring data from the same pollution source may fluctuate significantly, affecting the consistency and accuracy of measurements. For example, strong winds may accelerate pollutant dispersion, causing the measured pollutant concentration to decrease, while the actual emissions may not have decreased, leading to misinterpretation of monitoring results. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent environmental pollution assessment and monitoring platform based on industrial big data to address the shortcomings of the prior art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent environmental pollution assessment and monitoring platform based on industrial big data, including an attenuation anomaly feature acquisition module, an angle offset feature acquisition module, a measurement accuracy calculation module, and a measurement error correction module;

[0007] Attenuation Anomaly Acquisition Module: Determines the laser wavelength and emits a laser beam into the atmosphere by the lidar system. The lidar-based detector receives the echo signal after it has been scattered and absorbed by pollutants. The detector converts the optical signal into an electrical signal and records the attenuation anomaly characteristics of the laser echo signal.

[0008] Angle offset feature acquisition module: Multiple temperature sensors are installed around the lidar site at different heights. The temperature at each height is recorded and the temperature change rate per unit height is calculated. The refractive index gradient is calculated using the atmospheric refraction formula. The angle offset feature of the laser propagation path is obtained based on the refractive index gradient.

[0009] Measurement accuracy calculation module: Input the attenuation anomaly characteristics of the acquired laser echo signal and the angular offset characteristics of the laser propagation path into the pre-built data prediction model, and determine the measurement accuracy of pollutants based on the model calculation results;

[0010] Measurement error correction module: Based on the measurement accuracy of pollutants, the measurement results of pollutants are divided into accurate measurement results and inaccurate measurement results. For inaccurate measurement results, the measurement error of pollutants is reduced by adjusting the laser power and emission angle.

[0011] Preferably, in the attenuation anomaly feature acquisition module, the lidar detector receives the echo signal after it has been affected by contaminants and measures its power P. receive The time delay of the echo signal is recorded to calculate the spatial location of the contaminant. The detector converts the received optical signal into an electrical signal and calculates the attenuation of the laser signal according to Beer-Lambert's law. Where: α is the attenuation coefficient, d is the laser propagation distance, and P emit The transmission power is set; a normal attenuation threshold range is set, and if the actual attenuation value exceeds the range, it is determined that there is an abnormal attenuation phenomenon.

[0012] Preferably, after analyzing the attenuation anomaly characteristics of the acquired laser echo signal, an echo signal attenuation anomaly index is generated. The method for obtaining the echo signal attenuation anomaly index is as follows:

[0013] Let the received power of the echo signal be P. receive (t), where t is time, calculate the normalized decay signal: Where: P emit For transmission power, P receive Let S(t) be the received power at time t, and S(t) represent the attenuated signal. Empirical Mode Decomposition (EMD) is performed on the attenuated signal S(t), decomposing it into multiple IMF components and a residual term, expressed as: Where n is the number of IMFs obtained from the decomposition, rn(t) is the residual trend term, and the energy of each IMF is calculated as follows: E i =Σ t IMFi 2 (t); where: E i Let i be the energy of the i-th IMF component, and IMFi(t) be the i-th IMF signal; define the energy ratio R of the high-frequency IMF component. HF The expression is: Where: k is the number of high-frequency IMF components. The echo signal attenuation anomaly index (EAI) is calculated as follows: EAI = -log(1-R) HF ).

[0014] Preferably, the angle offset feature acquisition module includes: installing multiple temperature sensors around the lidar site at different heights, recording temperature data at each height at regular intervals, and denoting the temperature at height hi as Ti to form a dataset; calculating the temperature gradient between adjacent heights: Where Thigh and Tlow represent the temperatures at high and low locations, and hhigh and hlow represent the corresponding altitudes; calculate the average temperature gradient at multiple measurement points, record the calculated temperature gradient values, and calculate the refractive index gradient based on the atmospheric refraction formula. The expression is: Where: r is the refractive sensitivity coefficient. For temperature gradient.

[0015] Preferably, a laser propagation angle shift index is generated after analyzing the angular shift characteristics of the laser propagation path. The method for obtaining the laser propagation angle shift index is as follows: calculating the temperature gradient. The laser propagation angle shift θ(t) is calculated using the atmospheric refraction formula, and the expression is: Where r is the refractive sensitivity coefficient and d is the laser propagation path length. The transient changes of the angular offset θ(t) at different scales are analyzed using wavelet transform to extract high-frequency anomaly features. The wavelet transform formula is: WT(θ)(a,b)=∫θ(t)ψ a,b (t)dt; where: ψ a,b (t) is the wavelet basis function, a is the control frequency scale, b is the control time offset, and WT(θ)(a,b) represents the wavelet transform result of the angle offset signal θ(t) under different control frequency scales a and control time offsets b;

[0016] To quantify the transient shift in laser propagation angle, wavelet energy at different frequency scales is calculated. The wavelet energy E(a) for each control frequency scale a is calculated, expressed as: E(a) = Σ b |WT(θ)(a,b)|2 Where: E(a) represents the wavelet energy corresponding to control frequency scale a, WT(θ)(a,b) are the wavelet transform coefficients of the angle offset signal. By summing all control time offsets b, the total energy under control frequency scale a is obtained, and the proportion of the total energy in the high-frequency part is calculated, that is, the laser propagation angle offset index DFC is calculated, and the expression is: Where: A is the set of all scales, H represents the high-frequency scale range.

[0017] Preferably, the measurement accuracy calculation module specifically includes: normalizing the echo signal attenuation anomaly index and the laser propagation angle offset index so that they are both between [0,1], and calculating the measurement accuracy value of pollutants based on the normalized echo signal attenuation anomaly index and the laser propagation angle offset index.

[0018] Preferably, the measurement error correction module compares the obtained measurement accuracy value of the pollutant with a preset measurement accuracy standard threshold based on historical data. If the measurement accuracy value of the pollutant is greater than or equal to the preset measurement accuracy standard threshold, it indicates that the measurement accuracy of the pollutant is high, and the measurement result of the pollutant is classified as an accurate measurement result. If the measurement accuracy value of the pollutant is less than the preset measurement accuracy standard threshold, it indicates that the measurement accuracy of the pollutant is low, and the measurement result of the pollutant is classified as an inaccurate measurement result.

[0019] Preferably, the transmit power P emit With received power P receive The relationship is represented as: P receive =P emit ·e -zd Where: z is the attenuation coefficient, and d is the laser propagation path length;

[0020] To reduce the echo signal attenuation anomaly index (EAI), the new transmit power P is adjusted. emit,new :P emit,new =P emit ×(1+β1·EAI norm Where: β1 is the power adjustment coefficient, EAI norm The normalized echo signal attenuation anomaly index; if EAI norm A value greater than 0.5 indicates abnormally severe signal attenuation; therefore, the transmit power P should be increased. emit To enhance the echo signal; if EAI norm If the value is less than or equal to 0.5, maintain the original power.

[0021] To compensate for the angular shift, the new launch angle θ is adjusted. emit,new The expression is: θ emit,new =θ emit -β2·DFCnorm ·θ(t); where: β2 is the angle adjustment coefficient, DFC norm The normalized laser propagation angle offset index; if DFC norm If the value is greater than 0.5, it indicates a serious angular deviation, so the emission angle θ should be adjusted. emit Compensation for refraction effects; if DFC norm If the angle is less than or equal to 0.5, the original emission angle is maintained.

[0022] After adjusting the laser power and emission angle, the measurement accuracy value of the pollutants is recalculated. If it is greater than or equal to the preset measurement accuracy standard threshold, the adjustment is effective and the measurement accuracy is improved; otherwise, the parameters are optimized.

[0023] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0024] This invention provides an intelligent environmental pollution assessment and monitoring platform based on industrial big data. Addressing the measurement errors caused by atmospheric environmental factors (such as temperature gradients, atmospheric turbulence, and air pressure changes) in traditional lidar monitoring, it proposes a complete optimization scheme. The platform analyzes echo signal attenuation anomalies through an attenuation anomaly acquisition module, calculates the impact of atmospheric refraction on the laser propagation path through an angle offset feature acquisition module, comprehensively evaluates the measurement accuracy of pollutants using a data prediction model, and adjusts the laser power and emission angle based on the measurement accuracy value in the measurement error correction module, thereby reducing measurement errors. This invention accurately extracts echo signal attenuation and angle offset features using advanced algorithms such as Beer-Lambert's law, empirical mode decomposition, and wavelet transform. Combined with normalization calculation and dynamic adjustment mechanisms, it effectively improves the accuracy and stability of pollutant concentration monitoring. This technology can adaptively optimize under different meteorological conditions, ensuring the reliability of monitoring data and providing strong technical support for environmental pollution monitoring and precise governance. Attached Figure Description

[0025] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0026] Figure 1 This is a module diagram of an intelligent environmental pollution assessment and monitoring platform based on industrial big data. Detailed Implementation

[0027] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] For examples, please refer to Figure 1 As shown in the figure, the intelligent environmental pollution assessment and monitoring platform based on industrial big data described in this embodiment includes an attenuation anomaly feature acquisition module, an angle offset feature acquisition module, a measurement accuracy calculation module, and a measurement error correction module;

[0029] Attenuation Anomaly Acquisition Module: Determines the laser wavelength and emits a laser beam into the atmosphere by the lidar system. The lidar-based detector receives the echo signal after it has been scattered and absorbed by pollutants. The detector converts the optical signal into an electrical signal and records the attenuation anomaly characteristics of the laser echo signal.

[0030] Angle offset feature acquisition module: Multiple temperature sensors are installed around the lidar site at different heights. The temperature at each height is recorded and the temperature change rate per unit height is calculated. The refractive index gradient is calculated using the atmospheric refraction formula. The angle offset feature of the laser propagation path is obtained based on the refractive index gradient.

[0031] Measurement accuracy calculation module: Input the attenuation anomaly characteristics of the acquired laser echo signal and the angular offset characteristics of the laser propagation path into the pre-built data prediction model, and determine the measurement accuracy of pollutants based on the model calculation results;

[0032] Measurement error correction module: Based on the measurement accuracy of pollutants, the measurement results of pollutants are divided into accurate measurement results and inaccurate measurement results. For inaccurate measurement results, the measurement error of pollutants is reduced by adjusting the laser power and emission angle.

[0033] The attenuation anomaly characteristic acquisition module specifically includes: selecting a suitable laser wavelength (such as ultraviolet, visible, or infrared) based on the optical properties of the target pollutant; emitting a pulsed or continuous-wave laser beam into the atmosphere from the lidar system to ensure the beam interacts with the pollutants in the air; and recording the initial power P of the emitted laser. emit As a benchmark reference value.

[0034] When laser light propagates through the air, it is scattered and absorbed by pollutants (such as PM2.5, NO2, and SO2), resulting in some energy loss. The detector of a lidar system receives the echo signal after it has passed through the pollutants and measures its power P.receive The time delay of the echo signal is recorded to calculate the spatial location of the contaminant.

[0035] The detector converts the received optical signal into an electrical signal and transmits it to the data processing unit. Environmental noise is removed through signal filtering and noise reduction algorithms to ensure data reliability. The power variation curve of the echo signal is recorded to extract the attenuation characteristics of the optical signal.

[0036] Calculate the attenuation of the laser signal based on Beer-Lambert's law: Where α is the attenuation coefficient and d is the laser propagation distance; a normal attenuation threshold range is set, and if the actual attenuation value exceeds this range, an abnormal attenuation phenomenon is determined. Historical data is used to analyze possible causes of abnormal attenuation, such as high humidity, strong turbulence, or interference from unknown pollutants.

[0037] After analyzing the attenuation anomaly characteristics of the acquired laser echo signal, an echo signal attenuation anomaly index is generated. The method for obtaining the echo signal attenuation anomaly index is as follows:

[0038] Let the received power of the echo signal be P. receive (t), where t is time, calculate the normalized decay signal: Where: P emit For transmission power, P receive S(t) represents the received power at time t, and S(t) represents the relative attenuation signal.

[0039] Empirical Mode Decomposition (EMD) is performed on the decaying signal S(t). EMD decomposes S(t) into multiple intrinsic mode functions (IMFs) through an iterative method, satisfying the following conditions:

[0040] The IMF exhibits local symmetry, and the number of extrema is close to the number of zero intersections. The IMF is arranged from high-frequency components to low-frequency components, reflecting different time-frequency characteristics in the signal.

[0041] Identify all local maxima and minima of S(t). Fit the maxima and minima using spline interpolation to obtain the upper envelope U(t) and lower envelope L(t). Calculate the mean m(t) of the upper and lower envelopes. Calculate the first-layer IMF component h1(t): h1(t) = S(t) - m(t);

[0042] If h1(t) satisfies the IMF condition, then it is set as IMFi(t); otherwise, the iteration continues until the IMF condition is satisfied. The IMF is then removed and the decomposition is repeated. The new residual signal r1(t) is calculated, expressed as: r1(t) = S(t) - IMF1(t); S(t) is decomposed into multiple IMF components and a residual term. Where n is the number of IMFs obtained from the decomposition, and rn(t) is the residual trend term. The echo signal attenuation anomaly index can be calculated based on the energy ratio of the IMF components. The energy of each IMF is calculated using the expression: E i =Σ t IMFi 2 (t); where: E i Let Ri be the energy of the i-th IMF component, and IMfi(t) be the i-th IMF signal. Since anomalous signals usually contain higher frequency components, the energy ratio Ri of the high-frequency IMF components is defined as... HF The expression is: Where: k is the number of high-frequency IMF components (usually the first 3-5 IMFs), and the echo signal attenuation anomaly index (EAI) is calculated as: EAI = -log(1-R HF If R HF The larger the value, the more obvious the high-frequency anomaly; therefore, the larger the EAI, the higher the degree of attenuation anomaly.

[0043] The angle offset feature acquisition module includes: installing multiple temperature sensors around the lidar site, set at different heights (e.g., 1m, 10m, 50m) to ensure uniform sensor distribution and accurately reflect the temperature gradient.

[0044] Temperature data at various altitudes are recorded periodically. Let the temperature at altitude hi be Ti, forming a dataset: (h1,T1),(h2,T2),…,(hn,Tn). A filtering algorithm (such as the moving average method) is used to smooth the collected data, remove random errors, and improve data stability.

[0045] Calculate the temperature gradient (rate of temperature change per unit height) between adjacent heights: Where Thigh and Tlow are the temperatures at high and low positions, and hhigh and hlow are the corresponding heights; calculate the average temperature gradient at multiple measurement points, record the calculated temperature gradient values, and use them as the basis for subsequent calculations of the refractive index gradient.

[0046] Calculate the refractive index gradient (rate of change of refractive index with altitude) based on the atmospheric refraction formula. The expression is: Where r is the refractive sensitivity coefficient, which depends on the wavelength and meteorological conditions. The temperature gradient is used. The trend of refractive index variation throughout the monitoring area is calculated, and the refractive index distribution curve is plotted to analyze the refractive characteristics under different weather conditions.

[0047] After analyzing the angular offset characteristics of the laser propagation path, a laser propagation angle offset index is generated. The method for obtaining the laser propagation angle offset index is as follows:

[0048] Multiple temperature sensors are installed around the lidar site to acquire temperature data at different altitudes and calculate the temperature gradient. The laser propagation angle shift θ(t) is calculated using the atmospheric refraction formula, and the expression is: Where r is the refractive sensitivity coefficient and d is the laser propagation path length, the transient changes of the angle offset θ(t) at different scales are analyzed by wavelet transform to extract high-frequency anomaly features. The wavelet transform formula is: WT(θ)(a,b)=∫θ(t)ψ a,b (t)dt; where: ψ a,b WT(θ)(a,b) represents the wavelet transform result of the angle offset signal θ(t) under different control frequency scales a and control time offsets b.

[0049] Choose an appropriate wavelet basis function ψ(t) (such as Morlet wavelet or Daubechies wavelet), calculate the WT coefficients WT(θ)(a,b) at different control frequency scales a, form a wavelet coefficient matrix, identify the high-frequency part (smaller a value), and use it to detect burst migration.

[0050] To quantify the transient shift of the laser propagation angle, wavelet energy at different frequency scales is calculated. The wavelet energy E(a) for each control frequency scale a is calculated, expressed as: E(a) = ∑ b |WT(θ)(a,b)| 2 Where: E(a) represents the wavelet energy corresponding to control frequency scale a, WT(θ)(a,b) are the wavelet transform coefficients of the angle offset signal. By summing all control time offsets b, the total energy under control frequency scale a is obtained, and the proportion of the total energy in the high-frequency part is calculated, that is, the laser propagation angle offset index DFC is calculated, and the expression is: Where: A is the set of all scales, H represents the high-frequency scale range (usually the first 3-5 smallest a values ​​are selected), and DFC reflects the proportion of high-frequency energy in the total energy. The larger the value, the stronger the transient shift.

[0051] Measurement accuracy calculation module: The attenuation anomaly characteristics of the acquired laser echo signal and the angular offset characteristics of the laser propagation path are input into the pre-built data prediction model. The measurement accuracy of pollutants is determined based on the model calculation results. Specifically, the attenuation anomaly index of the echo signal and the angular offset index of the laser propagation path are normalized so that they are both between [0,1]. The measurement accuracy value of pollutants is calculated based on the normalized attenuation anomaly index of the echo signal and the angular offset index of the laser propagation path.

[0052] For example, the present invention can use the following formula to calculate the measurement accuracy value of pollutants, the calculation expression being: In the formula, MP is the measurement accuracy value of pollutants, EAI is the echo signal attenuation anomaly index, DFC is the laser propagation angle offset index, and f1 and f2 are the weighting coefficients of the echo signal attenuation anomaly index and the laser propagation angle offset index (which can be optimized based on experimental experience or machine learning), and both f1 and f2 are greater than 0.

[0053] Measurement error correction module: Based on the measurement accuracy of pollutants, the measurement results of pollutants are divided into accurate measurement results and inaccurate measurement results. For inaccurate measurement results, the measurement error of pollutants is reduced by adjusting the laser power and emission angle.

[0054] The obtained measurement accuracy value of pollutants is compared with the measurement accuracy standard threshold preset based on historical data. If the measurement accuracy value of pollutants is greater than or equal to the preset measurement accuracy standard threshold, it indicates that the measurement accuracy of pollutants is high, and the measurement result of pollutants is classified as an accurate measurement result. If the measurement accuracy value of pollutants is less than the preset measurement accuracy standard threshold, it indicates that the measurement accuracy of pollutants is low, and the measurement result of pollutants is classified as an inaccurate measurement result.

[0055] Since the attenuation of the echo signal is mainly caused by atmospheric absorption and scattering, the transmitted power P emit With received power P receive The relationship can be represented as: P receive =P emit ·e -zd Where: z is the attenuation coefficient (related to atmospheric absorption and scattering), and d is the laser propagation path length;

[0056] To reduce the echo signal attenuation anomaly index (EAI), the new transmit power P is adjusted. emit,new :P emit,new =P emit ×(1+β1·EAI norm ); where: β1 is the power adjustment coefficient (which can be optimized through experiments or machine learning), EAI norm The normalized echo signal attenuation anomaly index (range [0,1]); if EAI norm A value greater than 0.5 indicates abnormally severe signal attenuation; therefore, the transmit power P should be increased. emit To enhance the echo signal. If EAI norm If the value is less than or equal to 0.5, the original power will be maintained to avoid increasing energy consumption.

[0057] Atmospheric refraction can cause laser path deviation, leading to measurement errors in the target area.

[0058] To compensate for the angular shift, the new launch angle θ is adjusted. emit,new The expression is: θ emit,new =θ emit -β2·DFC norm ·θ(t); where: β2 is the angle adjustment coefficient (optimized based on experiments or machine learning), DFC norm The normalized laser propagation angle offset index (range [0,1]); if DFC norm If the value is greater than 0.5, it indicates a serious angular deviation, so the emission angle θ should be adjusted. emit Compensation for refraction effects; if DFC norm If the angle is less than or equal to 0.5, maintain the original emission angle to avoid unnecessary adjustments.

[0059] After adjusting the laser power and emission angle, the measurement accuracy value of the pollutants is recalculated. If it is greater than or equal to the preset measurement accuracy standard threshold, the adjustment is effective and the measurement accuracy is improved; otherwise, the parameters are optimized.

[0060] It should be noted that the measurement accuracy standard threshold is an important parameter used to determine whether the pollutant measurement results are accurate, and it is usually obtained through historical data analysis, experimental testing and statistical optimization.

[0061] The above is only a specific implementation method of the present application, but the scope of protection of the present application is not limited thereto. Any technician familiar with this technical field can easily think of changes or replacements within the technical scope disclosed in this application, which should be covered by the scope of protection of the present application.

Claims

1. An intelligent environmental pollution assessment and monitoring platform based on industrial big data, characterized in that: It includes an attenuation anomaly feature acquisition module, an angle offset feature acquisition module, a measurement accuracy calculation module, and a measurement error correction module; Attenuation Anomaly Acquisition Module: Determines the laser wavelength and emits a laser beam into the atmosphere by the lidar system. The lidar-based detector receives the echo signal after it has been scattered and absorbed by pollutants. The detector converts the optical signal into an electrical signal and records the attenuation anomaly characteristics of the laser echo signal. Angle offset feature acquisition module: Multiple temperature sensors are installed around the lidar site at different heights. The temperature at each height is recorded and the temperature change rate per unit height is calculated. The refractive index gradient is calculated using the atmospheric refraction formula. The angle offset feature of the laser propagation path is obtained based on the refractive index gradient. Measurement accuracy calculation module: Input the attenuation anomaly characteristics of the acquired laser echo signal and the angular offset characteristics of the laser propagation path into the pre-built data prediction model, and determine the measurement accuracy of pollutants based on the model calculation results; Measurement error correction module: Based on the measurement accuracy of pollutants, the measurement results of pollutants are divided into accurate measurement results and inaccurate measurement results. For inaccurate measurement results, the measurement error of pollutants is reduced by adjusting the laser power and emission angle. In the attenuation anomaly feature acquisition module, the lidar detector receives the echo signal after it has been affected by contaminants and measures its power. The time delay of the echo signal is recorded to calculate the spatial location of the contaminant. The detector converts the received optical signal into an electrical signal and calculates the attenuation of the laser signal according to Beer-Lambert's law. Where: α is the attenuation coefficient, and d is the distance the laser propagates. The transmission power is set; a normal attenuation threshold range is set, and if the actual attenuation value exceeds the range, it is determined that there is an abnormal attenuation phenomenon. After analyzing the attenuation anomaly characteristics of the acquired laser echo signal, an echo signal attenuation anomaly index is generated. The method for obtaining the echo signal attenuation anomaly index is as follows: Let the received power of the echo signal be... Where t is time, calculate the normalized decay signal: ;in: For transmission power, Let S(t) be the received power at time t, and S(t) represent the attenuated signal. Empirical Mode Decomposition (EMD) is performed on the attenuated signal S(t), decomposing S(t) into multiple IMF components and a residual term, expressed as: Where n is the number of IMFs obtained from the decomposition, rn(t) is the residual trend term, and the energy of each IMF is calculated as follows: ;in: Let i be the energy of the i-th IMF component, and IMFi(t) be the i-th IMF signal; define the energy ratio of the high-frequency IMF component. The expression is: Where: k is the number of high-frequency IMF components, and the echo signal attenuation anomaly index (EAI) is calculated as follows: ; The angle offset feature acquisition module includes: installing multiple temperature sensors around the lidar site at different heights, recording temperature data at each height at regular intervals, and defining the temperature at height hi as Ti to form a dataset; calculating the temperature gradient between adjacent heights. Where: Thigh and Tlow are the temperatures at high and low locations, and hhigh and hlow are the corresponding altitudes; calculate the average temperature gradient at multiple measurement points, record the calculated temperature gradient values, and calculate the refractive index gradient based on the atmospheric refraction formula. The expression is: Where: r is the refractive sensitivity coefficient, For temperature gradient; After analyzing the angular offset characteristics of the laser propagation path, a laser propagation angular offset index is generated. The method for obtaining the laser propagation angular offset index is as follows: calculating the temperature gradient. The atmospheric refraction formula was used to calculate the laser propagation angle shift. The expression is: Where: r is the refractive sensitivity coefficient, d is the laser propagation path length, and the transient changes of the angle offset θ(t) at different scales are analyzed by wavelet transform to extract high-frequency anomaly features. The wavelet transform formula is: ;in: Let be the wavelet basis function, a be the control frequency scale, and b be the control time offset. The wavelet transform results of the representative angle offset signal θ(t) under different control frequency scales a and control time offsets b; To quantify the transient shift in laser propagation angle, wavelet energy at different frequency scales is calculated, specifically the wavelet energy at each control frequency scale 'a'. The expression is: ;in: Let WT(θ)(a,b) represent the wavelet energy corresponding to the control frequency scale a, and let WT(θ)(a,b) be the wavelet transform coefficients of the angle offset signal. By summing all control time offsets b, the total energy at control frequency scale a is obtained, and the proportion of the total energy in the high-frequency component is calculated, i.e., the laser propagation angle offset index is calculated. The expression is: Where: A is the set of all scales, H⊂A, and H is the range of high-frequency scales.

2. The intelligent environmental pollution assessment and monitoring platform based on industrial big data according to claim 1, characterized in that: The measurement accuracy calculation module specifically includes: normalizing the echo signal attenuation anomaly index and the laser propagation angle offset index so that they are both between [0,1], and calculating the measurement accuracy value of pollutants based on the normalized echo signal attenuation anomaly index and laser propagation angle offset index.

3. The intelligent environmental pollution assessment and monitoring platform based on industrial big data according to claim 2, characterized in that: The measurement error correction module compares the obtained measurement accuracy value of the pollutant with a pre-set measurement accuracy standard threshold based on historical data. If the measurement accuracy value of the pollutant is greater than or equal to the pre-set measurement accuracy standard threshold, it indicates that the measurement accuracy of the pollutant is high, and the measurement result of the pollutant is classified as an accurate measurement result. If the measurement accuracy value of the pollutant is less than the pre-set measurement accuracy standard threshold, it indicates that the measurement accuracy of the pollutant is low, and the measurement result of the pollutant is classified as an inaccurate measurement result.

4. The intelligent environmental pollution assessment and monitoring platform based on industrial big data according to claim 3, characterized in that: Transmit power With received power The relationship is represented as: Where: z is the attenuation coefficient, and d is the laser propagation path length; To reduce the echo signal attenuation anomaly index (EAI), adjust the new transmit power. : ;in: This is the power adjustment coefficient. The normalized echo signal attenuation anomaly index; if A value greater than 0.5 indicates abnormally severe signal attenuation, in which case the transmission power should be increased. To enhance the echo signal; if If the value is less than or equal to 0.5, maintain the original power. To compensate for the angular deviation, a new launch angle was adjusted. The expression is: ;in: This is the angle adjustment coefficient. The normalized laser propagation angle offset index; if If the value is greater than 0.5, it indicates a serious angular deviation, and the launch angle should be adjusted. Compensation for refraction effects; if If the angle is less than or equal to 0.5, the original emission angle is maintained. After adjusting the laser power and emission angle, the measurement accuracy value of the pollutants is recalculated. If it is greater than or equal to the preset measurement accuracy standard threshold, the adjustment is effective and the measurement accuracy is improved; otherwise, the parameters are optimized.

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