An environment monitoring and early warning method based on single photon imaging

Abstract

The application provides an environment monitoring and early warning method based on single-photon imaging, and relates to the technical field of laser radar, comprising the following steps: collecting air pollutant characteristic parameters by scanning and monitoring a monitoring area based on a single-photon imaging technology with high repetition frequency pulses; constructing a pollutant risk index mapping function to obtain a pollutant risk index; collecting environment-related characteristic data and performing normalization processing on the environment-related characteristic data; establishing an environment sensitive factor generating function to calculate an environment sensitive factor, correcting the generated pollutant risk index by using the environment sensitive factor, and obtaining a comprehensive risk index; and establishing a risk comparison and early warning decision mechanism based on a risk threshold interval and the comprehensive risk index. The application realizes dynamic quantification of environmental vulnerability and diffusion and dilution capacity, so that the comprehensive risk index can adaptively change with meteorological conditions and spatiotemporal allocation of social activities; and the sensitivity, reliability and actual emergency guidance efficiency of the early warning system are significantly improved as a whole.

Description

Technical Field

[0001] This invention relates to the field of lidar technology, specifically to an environmental monitoring and early warning method based on single-photon imaging. Background Technology

[0002] Single-photon imaging-based environmental monitoring and early warning methods are active remote sensing technologies that utilize lidar systems to scan a monitoring area with high-repetition-rate pulses. By recording the single-photon counts and flight times of atmospheric backscattered echo signals, characteristic parameters of air pollutants such as aerosols, ozone, and nitrogen dioxide are retrieved, and these parameters are used to assess air quality and potential risks. In this type of monitoring and early warning method, the introduction of environmental factors to dynamically correct the basic pollution risk makes the early warning results more closely reflect the actual risk level, effectively reducing false alarm and false negative rates. It also provides a more scientific basis for tiered response measures. Therefore, the integration of environmental factors is a key element in achieving precise and scenario-adaptive early warnings.

[0003] In the prior art, Chinese patent publication number CN 204116603U discloses a technology comprising: a housing and a laser emission subsystem, a photoelectric detection subsystem, and a numerical control acquisition subsystem disposed within the housing. The laser emission subsystem includes a laser source and a transmitting optical antenna; the photoelectric detection subsystem includes a receiving optical antenna, a spatial filter, a narrowband filter, a polarization module, and two single-photon detectors; the numerical control acquisition subsystem includes a multi-channel photon counter, an embedded board, a data storage unit, and a data transmission unit. The embedded board is used to control data acquisition, data storage, and data transmission; the multi-channel photon counter is connected to the single-photon detectors and the embedded board. This solution achieves independent single-unit operation, integrated data storage, and transmission, thereby enabling online monitoring of aerosols.

[0004] However, among the aforementioned existing technologies, conventional single-photon imaging environmental monitoring and early warning technologies rely solely on simple threshold comparisons using single or limited types of lidar inversion parameters. They lack the ability to comprehensively characterize the synergistic effects of multiple pollutants and extreme mutations of single factors, resulting in inaccurate risk quantification in complex pollution scenarios and delayed response to sudden high-concentration events. Furthermore, conventional solutions neglect the dynamic influence of environmental background conditions, resulting in fixed and singular early warning results that cannot adaptively adjust to changes in meteorological conditions or the spatiotemporal changes of social activities. Consequently, they are prone to false alarms in open, uninhabited areas or under strong wind diffusion conditions, while potentially underestimating the actual exposure risk in densely populated urban centers with stable and inverted temperatures.

[0005] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0006] The purpose of this invention is to provide an environmental monitoring and early warning method based on single-photon imaging to solve the problems mentioned in the background art. This invention achieves dynamic quantification of environmental vulnerability and diffusion / dilution capabilities, enabling the comprehensive risk index to adaptively change with meteorological conditions and the timing and combination of social activities. This overcomes the problems of high false alarm rates and poor scene adaptability caused by fixed weights or neglect of environmental background differences in conventional technologies; and significantly improves the sensitivity, reliability, and actual emergency guidance effectiveness of the early warning system as a whole.

[0007] To achieve the above objectives, the present invention provides the following technical solution: An environmental monitoring and early warning method based on single-photon imaging includes the following steps: S1: Establish a lidar system and collect air pollutant characteristic parameters by scanning the monitoring area with high repetition rate pulses based on single-photon imaging technology. The air pollutant characteristic parameters include single-photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value, and nitrogen dioxide differential absorption column concentration. S2: Preprocess the air pollutant feature parameters collected in S1, including denoising, background subtraction, multi-pulse accumulation and superposition, and normalization operations, to form a standardized air pollutant feature vector, construct a pollutant risk index mapping function, input the air pollutant feature vector into the mapping function, and generate the pollutant risk index. S3: Collect environmental-related feature data, including regional population density, near-surface wind speed, atmospheric stability level, and distance to the nearest sensitive target, and perform normalization processing on the environmental-related feature data; S4: Establish an environmental sensitivity factor generation function, input the normalized environmental-related characteristic data into the environmental sensitivity factor generation function, calculate the environmental sensitivity factor, use the environmental sensitivity factor to correct the generated pollutant risk index, and obtain the comprehensive risk index; S5: Set risk classification thresholds, construct risk threshold ranges based on risk classification thresholds, establish a risk comparison and early warning decision-making mechanism based on risk threshold ranges and comprehensive risk index, and generate early warning results and corresponding decision-making schemes through the risk comparison and early warning decision-making mechanism.

[0008] Further, in S1, the lidar system uses high-repetition-rate pulsed laser to illuminate the monitoring area point by point or scan, records the arrival time and photon count of the atmospheric backscattered echo signal, and obtains the single-photon count rate characterizing the aerosol concentration; combined with the aerosol extinction inversion model, the single-photon count rate is converted into the inversion value of the near-ground fine particulate matter mass concentration; by alternately emitting differential absorption laser pulses with wavelengths located at and near the ozone absorption peak, single-photon echo signals at the corresponding wavelengths are collected, and the ozone concentration inversion value is obtained according to the differential absorption spectroscopy principle; a pair of characteristic differential absorption spectral lines of nitrogen dioxide is selected, and the difference in single-photon echoes at different wavelengths is obtained by using high-repetition-rate pulses, and the nitrogen dioxide differential absorption column concentration is obtained by inverse calculation of the differential absorption column concentration.

[0009] Furthermore, when preprocessing the air pollutant characteristic parameters, the four types of original collected data—single photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value, and nitrogen dioxide differential absorption column concentration—are first subjected to noise reduction processing to remove abnormal peaks and random fluctuations caused by detector dark counts and background light noise. The pure atmospheric backscattering and absorption signals are separated, and then multi-pulse accumulation and superposition are performed on the denoised and background-subtracted signals. The four types of parameters after superposition are then normalized to unify the dimensions and value ranges, resulting in a standardized air pollutant feature vector.

[0010] Furthermore, the pollutant risk index mapping function is: in: For pollutant risk index; This refers to the single-photon count rate. This represents the inversion value of near-surface fine particulate matter mass concentration; This is the ozone concentration inversion value; The concentration of nitrogen dioxide in the differential absorption column; The normalized deviation of the dominant risk factor; The index term of the dominant risk factor is used to multiply and correct the underlying risk.

[0011] Furthermore, in the index term of the dominant risk factor, the maximum value among the four air pollutant feature vectors is extracted as the dominant risk factor. When the maximum value of each element in any air pollutant feature vector increases significantly, the index term of the dominant risk factor increases sharply. This is used to simulate the characteristic that in reality, exceeding the standard of a single air pollutant feature vector can dominate and drastically worsen the pollutant risk index. The formula for calculating the normalized deviation of the dominant risk factor is as follows: Among them, when When at least one air pollutant characteristic vector has a normalized value exceeding the normalized median level of 0.5, the... The magnitude of the value directly reflects the exceedance range; The sign of the value determines whether the index term of the dominant risk factor amplifies or diminishes it.

[0012] Furthermore, in S3, the normalization processing of environmentally relevant characteristic data includes: for regional population density data, range normalization processing is performed based on the maximum population carrying capacity reference value set for the monitoring area, mapping the original density to the [0,1] interval; for near-surface wind speed data, ratio normalization processing is performed using the local near-surface annual average wind speed as the benchmark wind speed; for atmospheric stability level data, corresponding discrete normalized values ​​are assigned according to the Pasquale stability classification from strongly unstable to stable, converting the level description into a continuous numerical dimensionless parameter; for the nearest sensitive target distance data, reverse normalization processing is performed based on the preset maximum influence distance, so that the closer the target is, the closer its normalized value is to 1, reflecting the inverse relationship between distance attenuation and risk.

[0013] Furthermore, the formula used to calculate the environmental sensitivity factor is as follows: in: Environmentally sensitive factors; The normalized regional population density has a value range of [0,1]. The closer the regional population density is to 1, the higher the degree of population concentration in the current monitoring area. The normalized atmospheric stability level is assigned an increasing continuous value according to the Pasquale classification, ranging from strongly unstable to stable. The distance to the nearest sensitive target is normalized, and the sensitive targets include hospitals, schools, and nursing homes; This represents the normalized near-surface wind speed.

[0014] Furthermore, in the formula upon which the environmental sensitivity factor is calculated, The term is a negative exponential function acting on the wind speed ratio, used to make the reduction of wind speed sensitivity exhibit a nonlinear accelerating effect; when "When" indicates when the current wind speed exceeds the average level for the same period in previous years. The number of items decreased sharply, significantly reducing environmental sensitivity factors; when When approaching zero, The value approaches 1, and environmental sensitivity is entirely determined by the characteristics of receptor and diffusion stability.

[0015] Furthermore, in step S4, the generated pollutant risk index is corrected using environmental sensitivity factors according to the following formula: in: It is a comprehensive risk index used to characterize the final risk quantification indicator after correction for environmental sensitivity factors.

[0016] Furthermore, the process for setting the risk classification threshold is as follows: A coupled database of historical pollutant characteristic parameters and environmental sensitivity factors is constructed; retrospective analysis is performed on pollution processes and sensitive events in the monitoring area over the years to obtain a comprehensive risk index. As a statistical measure, by combining the receptor health impact concentration limits corresponding to different warning levels with the quantiles of historical event severity, the critical value that optimizes the warning accuracy is determined, and then the thresholds are defined accordingly. , and The value of is used as the risk classification threshold, and the logic of the risk comparison and early warning decision-making mechanism is as follows: when If no warning is issued, the normal monitoring frequency should be maintained, and no intervention measures are required. when When a yellow alert is issued, the frequency of lidar scanning and environmental data collection is increased to 1.5 times the normal frequency; and attention is paid to the air quality trends around densely populated areas and sensitive targets. when When an orange alert is issued, the main pollutant source area in the comprehensive risk index is identified, and surrounding emission sources are inspected. when When a red alert is issued, an emergency response is activated, and the pollution spread path and the risk of exposure to sensitive receptors are simulated in real time.

[0017] Compared with the prior art, the beneficial effects of the present invention are: This invention constructs a pollutant risk index mapping function that simultaneously reflects the synergistic cumulative effect of multiple pollutants and the extreme dominance of anomalous mutations in a single pollutant. This avoids the shortcomings of conventional methods that smooth peak signals through simple weighting or averaging, leading to missed reports or delayed responses. Furthermore, this invention independently constructs environmental sensitivity factors, comprehensively considering regional population density, atmospheric stability levels, distance to the nearest sensitive target, and the nonlinear attenuation effect of wind speed. This enables dynamic quantification of environmental vulnerability and diffusion / dilution capabilities, and uses this to perform a product-based correction of the pollutant risk index. This allows the comprehensive risk index to adaptively change with meteorological conditions and the timing and arrangement of social activities, overcoming the problems of high false alarm rates and poor scenario adaptability caused by fixed weights or neglect of environmental background differences in conventional technologies. Overall, this significantly improves the sensitivity, reliability, and actual emergency guidance effectiveness of the early warning system, achieving a comprehensive effect that conventional single-photon imaging early warning schemes cannot achieve, including composite pollution identification, adaptive correction of environmental sensitivity, and graded precise response. Attached Figure Description

[0018] Figure 1 This is a flowchart of an environmental monitoring and early warning method based on single-photon imaging according to the present invention. Detailed Implementation

[0019] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to specific embodiments.

[0020] It should be noted that, unless otherwise defined, the technical or scientific terms used in this invention should have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0021] Example: Please see Figure 1 The present invention provides the following technical solutions: An environmental monitoring and early warning method based on single-photon imaging includes the following steps: S1: Establish a lidar system based on the single-photon detection principle, which can be considered a quantum lidar system. It uses quantum photon imaging technology as its core detection method, employing high-repetition-rate pulsed lasers to perform point-by-point or continuous scanning of the monitoring area, thereby efficiently collecting characteristic parameters of air pollutants. The collected parameters specifically include single-photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value, and nitrogen dioxide differential absorption column concentration. These parameters reflect the pollution status of aerosols and trace gases within the monitoring area from different perspectives.

[0022] During system operation, the lidar illuminates the target area with a high-repetition-rate pulsed laser beam, accurately recording the arrival time of the atmospheric backscattered echo signal to the detector and the photon count value in each detection cycle. This process is achieved by using the high-sensitivity single-photon detection and time-correlated photon counting method in quantum photon imaging technology. By jointly analyzing the echo time and the number of photons, the single-photon count rate, which characterizes the aerosol scattering ability, can be obtained. This count rate is directly related to the concentration level of particulate matter in the atmosphere.

[0023] By combining an aerosol extinction inversion model, the measured single-photon count rate is converted into a mass concentration inversion value of near-surface fine particulate matter, thereby quantitatively assessing the degree of near-surface particulate pollution. For ozone concentration detection, the system employs differential absorption laser pulse technology. It alternately emits a pair of laser pulses with wavelengths near and outside the ozone strong absorption peak, respectively, and acquires the single-photon echo signals corresponding to these two wavelengths. Based on the principle of differential absorption spectroscopy, the difference between the two wavelength echo signals is used to invert and calculate the ozone concentration inversion value, effectively eliminating background interference and improving detection sensitivity. For nitrogen dioxide detection, a pair of differential absorption spectral lines with distinct gas characteristics is selected. High-repetition-rate pulses are used to acquire the difference signals of single-photon echoes under different wavelength excitations. Then, the differential absorption column concentration inversion method is used to finally obtain the nitrogen dioxide differential absorption column concentration, thus characterizing the total column distribution characteristics of nitrogen dioxide along the monitoring path. The entire acquisition process fully utilizes the high sensitivity and time resolution advantages of single-photon imaging, ensuring the accuracy and reliability of simultaneous acquisition of multiple parameters.

[0024] S2: Preprocessing of the air pollutant characteristic parameters collected in S1. This step aims to remove various interferences and noises from the raw data, improve signal quality, and lay a reliable data foundation for subsequent risk quantification. The preprocessing process includes denoising, background subtraction, multi-pulse accumulation and superposition, and normalization operations, ultimately forming a standardized air pollutant feature vector. Based on this vector, a pollutant risk index mapping function is constructed. Inputting the feature vector into this function generates the pollutant risk index.

[0025] In the specific implementation of preprocessing, denoising processing is first performed on four types of raw acquired data: single-photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value, and nitrogen dioxide differential absorption column concentration. The denoising process employs appropriate filtering or threshold discrimination methods to effectively eliminate abnormal spikes and random fluctuations caused by detector dark counts, ambient background light noise, and circuit stray signals, thereby restoring the intrinsic variation trend of the data.

[0026] Then, non-target components such as Rayleigh scattering of atmospheric molecules, surface reflection, and background radiation are removed from the denoised signal, thus separating the pure, effective signal that truly reflects aerosol backscattering and differential absorption of pollutants. After completing these two steps, multi-pulse accumulation and superposition are performed on the retained effective signal. By aligning and averaging the results of multiple high-repetition-rate pulse detections in time, the signal-to-noise ratio of the weak echo signal can be significantly enhanced, while further suppressing residual random noise, making the extraction of characteristic parameters more stable and reliable.

[0027] After overlay processing, the four types of parameters are normalized one by one. Based on the physical magnitude and distribution range of each parameter, this embodiment selects benchmark ratio normalization to map data from different units and scales to the same numerical range, eliminating dimensional differences and ensuring comparability and balance of various parameters in subsequent calculations. After the above series of processing steps, a standardized air pollutant feature vector is obtained. This feature vector is used as input and imported into a pre-constructed pollutant risk index mapping function. This mapping function is fitted based on a large amount of historical monitoring data and pollution evolution patterns, and can comprehensively reflect the risk level under the synergistic effect of multiple pollutants. After function calculation, the pollutant risk index of the current monitoring area can be output, providing an accurate initial quantitative indicator for subsequent environmental sensitivity factor correction.

[0028] The pollutant risk index mapping function is: in: For pollutant risk index; The single-photon count rate is a direct quantification of lidar echo intensity, characterizing the total optical thickness of aerosols in the atmosphere. An increase in its value indicates an increase in the extinction coefficient, directly reflecting the column concentration accumulation of particulate matter (including dust and soot), and is related to... Positive correlation; This is the inverted value of near-surface fine particulate matter mass concentration. Under the influence of the square term, when its value is abnormally prominent, it will sharply increase the RMS value, dominating the underlying risk, and... Positive correlation; This is the ozone concentration inversion value. Ozone is a secondary photochemical pollutant, and its concentration shows a non-linear positive correlation with solar radiation and its precursors (NOx / VOCs). Positive correlation; This refers to the concentration of nitrogen dioxide in a differential absorption column. Besides its respiratory irritant properties, an increase in NO2 concentration indicates a strong primary emission, similar to... Positive correlation; The normalized deviation of the dominant risk factor; The index term of the dominant risk factor is used to multiply and correct the underlying risk.

[0029] In processing the exponential term of the dominant risk factor, the maximum value is extracted from the standardized characteristic vectors of four air pollutants, and this maximum value is determined as the dominant risk factor. When any air pollutant characteristic vector shows a significant increase, the exponential term of the dominant risk factor will increase exponentially, thus generating a strong multiplicative correction effect on the basic risk. The core purpose of this mechanism is to accurately simulate the actual evolution of pollution risk in real-world situations, where exceeding the concentration of a single pollutant can rapidly escalate the overall pollution risk. This ensures that the assessment model remains highly sensitive to extreme high values, can promptly capture pollution peaks caused by sudden local emissions or abnormal accumulation, and avoids weakening the early warning response capability to sudden deterioration trends due to multi-parameter averaging.

[0030] The formula for calculating the normalized deviation of the dominant risk factor is as follows: Among them, when When at least one air pollutant characteristic vector has a normalized value exceeding the normalized median level of 0.5, the... The magnitude of the value directly reflects the exceedance range; The sign of the value determines whether the index term of the dominant risk factor amplifies or diminishes it.

[0031] The formula identifies the primary contradiction by screening for the maximum extreme value, distinguishes between normal fluctuations and abnormal alarms by using the -0.5 baseline difference, and transforms the extreme deviation of a single factor into a nonlinear multiplicative adjustment of the overall risk by using it as an exponential power.

[0032] S3: Collect environmentally relevant characteristic data. This data is gathered through ground monitoring stations, meteorological observation equipment, geographic information systems, and population distribution statistics, specifically including regional population density, near-surface wind speed, atmospheric stability level, and distance to the nearest sensitive target. Because these environmentally relevant characteristic data have different physical units and magnitudes, they cannot be directly used in subsequent calculations of environmental sensitivity factors. Therefore, they need to be uniformly normalized to eliminate dimensional differences and make their numerical ranges comparable.

[0033] Differentiated normalization strategies are adopted for different types of feature data. For regional population density data, the maximum population carrying capacity reference value set in advance for the monitoring area is used as the upper limit. The range normalization method is used to map the original density value to the range of 0 to 1 in a linear proportion. The closer the normalized value is to 1, the higher the degree of population concentration in the current monitoring area and the larger the potential population base affected by the risk.

[0034] For near-surface wind speed data, the local historical average near-surface wind speed is used as the benchmark wind speed. Ratio normalization is adopted, that is, the current measured wind speed is divided by the benchmark wind speed, and the resulting ratio is directly used as the normalized wind speed value. This value can intuitively reflect the strength of the current wind speed relative to the average level of the years.

[0035] For atmospheric stability level data, this type of data is originally represented by the level symbols in the Pasqual stability classification, which correspond to different diffusion conditions in order from strongly unstable to stable. In order to include it in quantitative calculation, it is assigned incremental discrete normalized values ​​in order from strongly unstable to stable.

[0036] For the nearest sensitive target distance data, this indicator reflects the distance to sensitive receptors such as hospitals, schools, and nursing homes around the monitoring area. Considering the physical fact that closer targets have more direct risk transmission and a greater possibility of exposure, a reverse normalization method is adopted based on the preset maximum impact distance. That is, the closer the target, the closer its normalized value is to 1, and the farther the target, the closer its normalized value is to 0. This accurately reflects the inverse relationship between the distance attenuation effect and the degree of risk. After the above-mentioned normalization operations, all environmentally relevant characteristic data are converted into standardized values ​​with unified dimensions, providing standardized input parameters for the subsequent comprehensive calculation of environmental sensitivity factors.

[0037] The formula used to calculate environmental sensitivity factors is: in: Environmentally sensitive factors; This represents the normalized regional population density, with values ​​ranging from [0,1]. A population density closer to 1 indicates a higher degree of population concentration in the monitored area. They are positively correlated, and because they are in a product, their influence is constrained by the other two terms; The normalized atmospheric stability level is assigned a continuously increasing numerical value according to the Pasquale scale, ranging from strongly unstable to stable. A positive correlation is found, and sequentially increasing normalized values ​​are assigned, which means that the more stable the atmosphere, the less likely pollutants are to disperse, the higher the cumulative concentration at ground level, and the weaker the environment's capacity to contain pollutants. The normalized distance to the nearest sensitive target, which includes hospitals, schools, and nursing homes, and... There is a positive correlation; the closer the distance, the higher the concentration of pollutants before they are diluted or transformed during transport, the shorter the arrival time, the narrower the window period for early warning response, and the significantly improved sensitivity. The normalized near-surface wind speed, and It shows a negative correlation.

[0038] when (Wind speed below the annual average): Index item Its reduction in sensitivity is relatively weak. Especially in calm conditions, this factor approaches 1, and the sensitivity factor is entirely determined by the first three factors.

[0039] when (Wind speed equals average): The exponential term is This means that the sensitivity has been reduced to 37% of its original value, indicating that the average annual wind speed can provide significant dilution capacity.

[0040] when (Wind speed above average): The exponential term drops sharply. For example, when the wind speed ratio is 2, The sensitivity was significantly suppressed; at a wind speed ratio of 3, it dropped to 0.05, almost completely suppressing the sensitivity. This simulates the rapid advection diffusion effect under strong wind conditions—pollutants are quickly blown away from the monitoring area, and the actual exposure risk is significantly reduced even in the presence of sensitive receptors and stable stratification.

[0041] S4: Establish an environmental sensitivity factor generation function, input the normalized environmental-related characteristic data into the environmental sensitivity factor generation function, calculate the environmental sensitivity factor, use the environmental sensitivity factor to correct the generated pollutant risk index, and obtain the comprehensive risk index; The risk index of the generated pollutants is corrected using environmental sensitivity factors using the following formula: in: It is a comprehensive risk index used to characterize the final risk quantification indicator after correction for environmental sensitivity factors.

[0042] It is the final quantitative indicator output by the early warning system, and its value directly drives subsequent risk classification and emergency response. For every increase of a certain percentage, If it remains unchanged, Increase proportionally.

[0043] S5: Set risk classification thresholds and construct risk threshold ranges accordingly. Then, based on the risk threshold ranges and the comprehensive risk index, establish a risk comparison and early warning decision-making mechanism. Through the comprehensive judgment of this mechanism, the early warning result and its corresponding decision plan are finally generated. The core of this process lies in reasonably defining risk thresholds that can effectively distinguish different levels of danger.

[0044] The process for setting risk classification thresholds is as follows: Long-term observation records of historical pollutant characteristic parameters and environmental sensitive factors within the monitoring area are collected and integrated to construct a coupled database encompassing multiple time periods, seasons, and meteorological backgrounds. Based on this, retrospective analysis and statistical assessment are conducted on historical air pollution processes and related sensitive events occurring within the monitoring area to determine a comprehensive risk index. As a statistical measure, by combining the receptor health impact concentration limits corresponding to different warning levels with the quantiles of historical event severity, the critical value that optimizes the warning accuracy is determined, and then the thresholds are defined accordingly. , and The value of is used as the risk classification threshold, and the logic of the risk comparison and early warning decision-making mechanism is as follows: when When no warning is issued, it indicates that the risk level of pollutants in the current monitoring area is within an acceptable range and poses little threat to the environment and public health. At this time, the system will continue to operate at the regular monitoring frequency and collect lidar data and environmental parameters according to the established cycle. No additional intervention or control measures are required. when When a yellow alert is issued, the system automatically increases the frequency of lidar scanning and the frequency of collecting various environmental data to 1.5 times the normal level to obtain more intensive observation data to track changes in risk trends; at the same time, it closely monitors the air quality trends in densely populated areas and around sensitive targets such as hospitals, schools, and nursing homes to promptly detect potential signs of deterioration. when When an orange alert is issued, pollution source tracing work needs to be carried out. The focus should be on identifying the dominant pollutant that contributes the most to the comprehensive risk index and the direction of its main source area. The scope of investigation should be narrowed down by combining wind direction, wind speed and pollution distribution information. On-site inspections and assessments should also be conducted on possible industrial emission sources, traffic emission sources or dust sources in the surrounding area. when When a red alert is issued, the emergency response mechanism is activated, relevant functional departments are organized to enter emergency status, and the transport path of pollutants is dynamically simulated based on real-time meteorological fields and diffusion models to accurately determine the movement direction and diffusion speed of polluted air masses. In addition, the potential exposure concentration and exposure duration of each receptor are assessed in conjunction with the spatial distribution of sensitive targets, and targeted public protection and emergency control plans are formulated.

[0045] The above formulas are all dimensionless calculations. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0046] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented in software, the above embodiments can be implemented, in whole or in part, as a computer program product. Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution.

[0047] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0048] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. An environmental monitoring and early warning method based on single-photon imaging, characterized in that, Includes the following steps: S1: Establish a lidar system and collect air pollutant characteristic parameters by scanning the monitoring area with high repetition rate pulses based on single-photon imaging technology. The air pollutant characteristic parameters include single-photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value, and nitrogen dioxide differential absorption column concentration. S2: Preprocess the air pollutant feature parameters collected in S1, including denoising, background subtraction, multi-pulse accumulation and superposition, and normalization operations, to form a standardized air pollutant feature vector, construct a pollutant risk index mapping function, input the air pollutant feature vector into the mapping function, and generate the pollutant risk index. S3: Collect environmental-related feature data, including regional population density, near-surface wind speed, atmospheric stability level, and distance to the nearest sensitive target, and perform normalization processing on the environmental-related feature data; S4: Establish an environmental sensitivity factor generation function, input the normalized environmental-related characteristic data into the environmental sensitivity factor generation function, calculate the environmental sensitivity factor, use the environmental sensitivity factor to correct the generated pollutant risk index, and obtain the comprehensive risk index; S5: Set risk classification thresholds, construct risk threshold ranges based on risk classification thresholds, establish a risk comparison and early warning decision-making mechanism based on risk threshold ranges and comprehensive risk index, and generate early warning results and corresponding decision-making schemes through the risk comparison and early warning decision-making mechanism.

2. The environmental monitoring and early warning method based on single-photon imaging according to claim 1, characterized in that: In step S1, the lidar system uses high-repetition-rate pulsed laser to illuminate the monitoring area point by point or scan, recording the arrival time and photon count of atmospheric backscattered echo signals to obtain the single-photon count rate characterizing aerosol concentration. Combined with an aerosol extinction inversion model, the single-photon count rate is converted into a near-surface fine particulate matter mass concentration inversion value. By alternately emitting differential absorption laser pulses with wavelengths located at and near the ozone absorption peak, single-photon echo signals at corresponding wavelengths are acquired, and the ozone concentration inversion value is obtained based on the differential absorption spectroscopy principle. Characteristic differential absorption spectral line pairs of nitrogen dioxide are selected, and high-repetition-rate pulses are used to obtain the single-photon echo differences at different wavelengths. The nitrogen dioxide differential absorption column concentration is then calculated using differential absorption column concentration back-calculation.

3. The environmental monitoring and early warning method based on single-photon imaging according to claim 1, characterized in that: When preprocessing the characteristic parameters of air pollutants, the four types of original collected data, namely single photon count rate, near-ground fine particulate matter mass concentration inversion value, ozone concentration inversion value and nitrogen dioxide differential absorption column concentration, are first subjected to noise reduction processing to remove abnormal peaks and random fluctuations caused by detector dark count and background light noise. The pure atmospheric backscattering and absorption signals are separated, and then multi-pulse accumulation and superposition are performed on the denoised and background-subtracted signals. The four types of parameters after superposition are then normalized to unify the dimensions and value ranges, resulting in a standardized air pollutant feature vector.

4. The environmental monitoring and early warning method based on single-photon imaging according to claim 1, characterized in that: The pollutant risk index mapping function is: in: For pollutant risk index; This refers to the single-photon count rate. This represents the inversion value of near-surface fine particulate matter mass concentration; This is the ozone concentration inversion value; The concentration of nitrogen dioxide in the differential absorption column; Normalized deviation of the dominant risk factor; The index term of the dominant risk factor is used to multiply and correct the underlying risk.

5. The environmental monitoring and early warning method based on single-photon imaging according to claim 4, characterized in that: The dominant risk factor is defined by extracting the maximum value from the feature vectors of the four air pollutants. When the maximum value of each element in any air pollutant feature vector increases significantly, the exponential term of the dominant risk factor increases sharply. This is used to simulate the characteristic in reality that exceeding the standard of a single air pollutant feature vector can dominate and drastically worsen the pollutant risk index. The formula for calculating the normalized deviation of the dominant risk factor is as follows: Among them, when When at least one air pollutant characteristic vector has a normalized value exceeding the normalized median level of 0.5, the... The magnitude of the value directly reflects the exceedance range; The sign of the value determines whether the index term of the dominant risk factor amplifies or diminishes it.

6. The environmental monitoring and early warning method based on single-photon imaging according to claim 1, characterized in that: In step S3, the normalization processing of environmentally relevant characteristic data includes: for regional population density data, range normalization processing is performed based on the maximum population carrying capacity reference value set for the monitoring area, mapping the original density to the [0,1] interval; for near-surface wind speed data, ratio normalization processing is performed using the local near-surface annual average wind speed as the benchmark wind speed; for atmospheric stability level data, corresponding discrete normalized values ​​are assigned according to the Pasquale stability classification from strongly unstable to stable, transforming the level description into a continuous numerical dimensionless parameter; for the nearest sensitive target distance data, inverse normalization processing is performed based on the preset maximum influence distance, so that the closer the target is, the closer its normalized value is to 1, reflecting the inverse relationship between distance attenuation and risk.

7. The environmental monitoring and early warning method based on single-photon imaging according to claim 4, characterized in that: The formula used to calculate the environmental sensitivity factor is as follows: in: Environmentally sensitive factors; The normalized regional population density has a value range of [0,1]. The closer the regional population density is to 1, the higher the degree of population concentration in the current monitoring area. The normalized atmospheric stability level is assigned an increasing continuous value from strongly unstable to stable according to the Pasquale classification. The distance to the nearest sensitive target is normalized, and the sensitive targets include hospitals, schools, and nursing homes; This represents the normalized near-surface wind speed.

8. The environmental monitoring and early warning method based on single-photon imaging according to claim 7, characterized in that: The formula used to calculate the environmental sensitivity factor is as follows: The term is a negative exponential function acting on the wind speed ratio, used to make the reduction of wind speed sensitivity exhibit a nonlinear accelerating effect; when "When" indicates when the current wind speed exceeds the average level for the same period in previous years. The number of items decreased sharply, significantly reducing environmental sensitivity factors; when When approaching zero, The value approaches 1, and environmental sensitivity is entirely determined by the characteristics of receptor and diffusion stability.

9. The environmental monitoring and early warning method based on single-photon imaging according to claim 7, characterized in that: In step S4, the generated pollutant risk index is corrected using environmental sensitivity factors according to the following formula: in: It is a comprehensive risk index used to characterize the final risk quantification indicator after correction for environmental sensitivity factors.

10. The environmental monitoring and early warning method based on single-photon imaging according to claim 9, characterized in that: The process for setting risk classification thresholds is as follows: A coupled database of historical pollutant characteristic parameters and environmental sensitivity factors is constructed; retrospective analysis of pollution processes and sensitive events over the years within the monitoring area is conducted; and a comprehensive risk index is established. As a statistical measure, by combining the receptor health impact concentration limits corresponding to different warning levels with the quantiles of historical event severity, the critical value that optimizes the warning accuracy is determined, and then the thresholds are defined accordingly. , and The value of is used as the risk classification threshold, and the logic of the risk comparison and early warning decision-making mechanism is as follows: when If no warning is issued, the normal monitoring frequency should be maintained, and no intervention measures are required. when When a yellow alert is issued, the frequency of lidar scanning and environmental data collection is increased to 1.5 times the normal frequency; and attention is paid to the air quality trends around densely populated areas and sensitive targets. when When an orange alert is issued, the main pollutant source area in the comprehensive risk index is identified, and surrounding emission sources are inspected. when When a red alert is issued, an emergency response is activated, and the pollution spread path and the risk of exposure to sensitive receptors are simulated in real time.

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