Meteorological monitoring method and system based on atmospheric circle layer

Abstract

The invention discloses a meteorological monitoring method and system based on an atmospheric circle layer, and relates to the technical field of meteorological monitoring, accurate pollutant concentration data are obtained by correcting reading deviation generated by organic matter attachment on the surface of a probe, and the method comprises the following steps: determining a clean environment reference, and determining the concentration of pollutants in the clean environment; the actual response value of the probe under the clean environment reference is used as the reference response value of the probe; comparing the benchmark response value with a reference benchmark value of the probe to generate a correction factor of the probe; applying the correction factor to a subsequent response value of the probe to obtain corrected pollutant concentration data; based on the corrected pollutant concentration data, when it is monitored that the pollutant concentration data exceeds a preset early warning threshold value, early warning is triggered, and decision support information is provided. The defect that in the prior art, the reading is continuously lower than the actual level due to the fact that organic matter is attached to the surface of a probe in long-term operation can be overcome, and the reliability of monitoring data received by the environment quality evaluation model is improved.

Description

Technical Field

[0001] This application relates to the field of meteorological monitoring technology, and in particular to a meteorological monitoring method and system based on the atmosphere. Background Technology

[0002] In the field of urban environmental resource management, meteorological monitoring systems deploy multi-dimensional sensor networks, combining satellite remote sensing and ground monitoring station information to conduct real-time assessments of urban environmental quality. The core component of this system is the pollutant monitoring probe, whose chemical reaction layer is responsible for adsorbing and quantifying specific pollutants in the atmosphere. However, when the probe operates for extended periods in microenvironments rich in biological materials (such as pollen and microbial metabolites) (e.g., urban greenbelts, areas surrounding ecological parks), organic matter gradually adheres to the surface of its chemical reaction layer. This leads to a gradual and systematic decrease in sensitivity to target pollutants (such as nitrogen oxides and volatile organic compounds), resulting in reported pollutant concentrations consistently lower than actual levels.

[0003] Such "underestimation" biases caused by organic matter attachment change slowly, and their amplitude is usually within the reasonable fluctuation range preset by the system. They are difficult to identify by existing outlier removal mechanisms based on thresholds or mutation detection. The system mistakenly treats the biased readings as normal data, resulting in distorted data received by the environmental quality assessment model. This data fails to reflect the true pollution situation and cannot accurately reflect the actual pollution conditions, thus failing to provide accurate decision support during critical periods when early warning is needed. Summary of the Invention

[0004] This application proposes a meteorological monitoring method and system based on the atmospheric sphere, aiming to solve the technical problem that in existing meteorological monitoring systems, the readings of pollutant monitoring probes are consistently lower than the actual levels due to organic matter adhering to their surfaces during long-term operation, and the existing outlier removal mechanisms cannot identify this deviation, resulting in environmental quality assessment models receiving erroneous data and failing to accurately reflect the true pollution situation.

[0005] In a first aspect, this application provides a method for obtaining accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface, the method comprising the following steps: A clean environment baseline is established, and the actual response value of the probe under the clean environment baseline is used as the baseline response value of the probe. The reference response value is compared with the reference value of the probe to generate the correction factor of the probe; The correction factor is applied to the subsequent response value of the probe to obtain the corrected pollutant concentration data; Based on the corrected pollutant concentration data, an early warning is triggered when the pollutant concentration data exceeds the preset warning threshold, and decision support information is provided.

[0006] According to some embodiments of this application, the step of confirming a clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe includes: By monitoring meteorological data, when an atmospheric purification event that meets the preset purification conditions is identified, the environment after the atmospheric purification event ends and after a preset settling time is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

[0007] According to some embodiments of this application, the atmospheric purification event includes a continuous heavy rainfall event or a clean air mass transit event. The preset purification conditions for the continuous heavy rainfall event are that the continuous rainfall exceeds 5 mm per hour and lasts for more than 3 hours. The preset purification conditions for the clean air mass transit event are that the wind speed continuously exceeds 10 m per second, lasts for more than 6 hours, and the wind direction is from a non-industrial area.

[0008] According to some embodiments of this application, the step of comparing the reference response value with the reference reference value of the probe to generate the correction factor of the probe includes: When a new air purification event is identified, a new baseline response value is obtained as the current response value, and the baseline response value obtained from the previous air purification event is used as the reference baseline value. Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

[0009] According to some embodiments of this application, the step of confirming a clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe includes: When the preset triggering conditions are met, clean gas is sprayed into the sampling port of the probe based on the clean airflow generator deployed next to the probe, so as to form a local zero-pollution environment around the sampling port of the probe; the preset triggering conditions are reaching the preset periodic diagnostic time, or receiving an alarm for abnormal fluctuation of probe data issued by the environmental quality assessment system. The aforementioned local zero-pollution environment is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

[0010] According to some embodiments of this application, the step of obtaining the reference response value of the probe based on the real-time response value of the probe under the clean environment reference includes: During the injection of clean gas by the clean airflow generator, the real-time response value of the probe is continuously acquired at a preset sampling frequency to obtain a time-series signal of the real-time response value; The time series signal is decomposed into multiple signal components that characterize the changes in the real-time response value with different time scales by performing multi-time-scale signal decomposition on the time series signal. From the multiple signal components, identify the trend component that characterizes the slow changing trend of the real-time response value; Based on the trend components, the reference response value of the probe is determined.

[0011] According to some embodiments of this application, the step of determining the reference response value of the probe based on the trend component includes: Calculate the standard deviation of the trend component during the injection of clean gas by the clean airflow generator, and compare the standard deviation with a preset judgment threshold; If the standard deviation is higher than or equal to the judgment threshold, the clean airflow generator is restarted to identify the new trend component, calculate the standard deviation of the new trend component, and compare the standard deviation with the judgment threshold. If the standard deviation is lower than the judgment threshold, the trend component is used as the confirmation component. Based on the confirmed components, the reference response value of the probe is determined.

[0012] According to some embodiments of this application, the step of determining the reference response value of the probe based on the confirmed components includes: The average or median of the confirmed components is calculated during the injection of the clean airflow generator to obtain the calculation result; The calculation result is used as the reference response value of the probe.

[0013] According to some embodiments of this application, the step of comparing the reference response value with the reference reference value of the probe to generate the correction factor of the probe includes: The factory calibration value or the preset theoretical zero point value of the probe is used as the reference benchmark value; Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

[0014] Secondly, this application also provides an atmospheric meteorological monitoring system that obtains accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface. The system includes: The reference data acquisition module is used to confirm the clean environment reference and use the actual response value of the probe under the clean environment reference as the reference response value of the probe. A correction factor generation module is used to compare the reference response value with the reference reference value of the probe and generate a correction factor for the probe. The data correction acquisition module is used to apply the correction factor to the subsequent response value of the probe to obtain the corrected pollutant concentration data; The early warning triggering and decision support module is used to trigger an early warning when the pollutant concentration data exceeds the preset early warning threshold based on the corrected pollutant concentration data, and to provide decision support information.

[0015] The technical solution according to the embodiments of this application has at least the following beneficial effects: By confirming the clean environment benchmark and obtaining the benchmark response value of the probe, this application can accurately quantify the systematic deviation of the probe caused by the adhesion of organic matter. By comparing the benchmark response value with the reference benchmark value, a correction factor is generated and applied to subsequent real-time monitoring data, thereby effectively compensating for the probe reading deviation. This proactive correction mechanism based on changes in the probe's own performance enables the system to obtain more accurate pollutant concentration data, avoiding the blind spot of "underestimation" deviation in traditional methods. This compensates for the deficiency in the prior art where the probe reading is continuously lower than the actual level due to the adhesion of organic matter on the probe surface during long-term operation, improving the reliability of the monitoring data received by the environmental quality assessment model. Therefore, this application can more realistically reflect the environmental quality status and provide accurate early warning and decision support information.

[0016] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0017] The accompanying drawings are used to provide a further understanding of the technical solutions of this application and constitute a part of the specification. They are used together with the embodiments of this application to explain the technical solutions of this application and do not constitute a limitation on the technical solutions of this application.

[0018] Figure 1 This is a flowchart illustrating a meteorological monitoring method based on the atmosphere, provided as an embodiment of this application.

[0019] Figure 2 This is a schematic diagram of the architecture of a meteorological monitoring system based on the atmosphere, provided as an embodiment of this application. Detailed Implementation

[0020] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0021] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0022] In the field of urban environmental resource management, meteorological monitoring systems aim to assess urban environmental quality in real time by deploying multi-dimensional sensor networks and combining satellite remote sensing with information from ground monitoring stations. Pollutant monitoring probes are a core component, with their chemical reaction layers responsible for adsorbing and quantifying specific pollutants in the atmosphere. However, during long-term operation, especially in microenvironments rich in biological materials, organic matter gradually adheres to the surface of these probes' chemical reaction layers, leading to a systematic decrease in sensitivity to target pollutants and resulting in reported pollutant concentrations consistently lower than actual levels. This slow and continuous adhesion of organic matter directly reduces the effective contact area between the chemical reaction layer and the target pollutants. Consequently, these affected probes experience a gradual and systematic decrease in sensitivity to specific gaseous pollutants (such as nitrogen oxides and volatile organic compounds). This phenomenon creates a subtle "underestimation" bias, where the pollutant concentrations reported by the probes are consistently lower than the true environmental levels. Currently, within meteorological monitoring systems, outlier removal rules for raw sensor readings are primarily based on threshold judgments or mutation detection mechanisms. These rules aim to identify transient failures, drastic fluctuations, or extreme outliers during data acquisition. However, the gradual and persistent "underestimation" bias caused by organic matter adhesion changes slowly and is usually within the system's preset "reasonable" fluctuation range. This means that this slow decrease in sensitivity fails to trigger the existing outlier removal logic. The system therefore misjudges these persistently low, biased readings as normal data and continues to transmit and process them, resulting in the environmental quality assessment model receiving input that has been quietly "filtered out" of true pollution information.

[0023] In this regard, such as Figure 1As shown, this application discloses a meteorological monitoring method based on the atmosphere, which obtains accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface. The method includes the following steps: S110, confirm the clean environment benchmark, and use the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe; S120 compares the reference response value with the probe's reference value to generate the probe's correction factor; S130, apply the correction factor to the subsequent response value of the probe to obtain the corrected pollutant concentration data; S140, based on the corrected pollutant concentration data, triggers an early warning when the pollutant concentration data exceeds the preset early warning threshold, and provides decision support information.

[0024] To better understand the atmospheric-based meteorological monitoring method proposed in this application, it is necessary to explain some key terms and implementation environments involved.

[0025] Among them, "probe" refers to sensor equipment used to detect and quantify specific pollutants in the atmosphere (such as nitrogen oxides, volatile organic compounds, etc.), and its core is a sensitive element with a chemical reaction layer.

[0026] "Organic matter adhesion" refers to the deposition of airborne biological organic matter (such as pollen, microbial metabolites, plant debris, etc.) on the surface of the probe's chemical reaction layer, forming a thin film that affects the probe's ability to adsorb and respond to target pollutants.

[0027] "Reading bias" refers to the systematic difference between the pollutant concentration value output by the probe and the actual concentration value in the actual environment due to the adhesion of organic matter. It usually manifests as an underestimation of the actual concentration.

[0028] "Clean environment baseline" refers to an environmental state where the concentration of pollutants is known or confirmed to be extremely low or zero, which can be used as a reference point for probe performance calibration.

[0029] "Reference response value" refers to the actual output reading of the probe in a clean environment with zero or very low pollution, reflecting the inherent response characteristics of the probe in an interference-free state.

[0030] The "reference value" can be the probe's response value at the time of manufacture or during the last calibration in a clean environment, used to compare with the current reference response value to quantify changes in probe performance.

[0031] The "correction factor" is a correction parameter calculated based on the difference between the baseline response value and the reference baseline value, used to compensate for reading deviations caused by the adhesion of organic matter to the probe.

[0032] "Subsequent response value" refers to the original pollutant concentration data collected in real time by the probe during normal monitoring.

[0033] "Corrected pollutant concentration data" refers to data that has been corrected by a correction factor, making it closer to the actual pollutant concentration.

[0034] The "early warning threshold" is a preset upper limit for pollutant concentration. Once the early warning threshold is exceeded, it indicates that there may be a risk to environmental quality and an early warning needs to be triggered.

[0035] "Decision support information" refers to the relevant data, analysis reports, or suggestions provided by the system to managers after an early warning is triggered, in order to assist them in making corresponding environmental governance or emergency response decisions.

[0036] First, a clean environment baseline needs to be established, and the probe's actual response value under this baseline should be used as the baseline response value. In one implementation, the clean environment baseline can be established through manual intervention. For example, the probe can be temporarily removed from the monitoring point and placed in a strictly controlled laboratory environment, ensured to be free of pollution or with extremely low pollutant concentrations. In this clean environment, the probe is allowed to operate stably for a period of time, and its output response value is then recorded as the baseline response value. In another implementation, a clean environment baseline can be identified by analyzing historical meteorological data. For example, a condition can be set where there are several consecutive days without rainfall and the wind speed is consistently high, with the wind direction originating from a non-industrial area; this period is then designated as the clean environment baseline. Under this clean environment baseline, the probe continuously monitors for a period of time, and its stabilized response value is used as the baseline response value.

[0037] Next, the reference response value needs to be compared with the probe's reference value to generate a correction factor for the probe. In one implementation, the zero-point response value recorded at the factory or during the most recent maintenance can be used as the reference value. Once the current reference response value is obtained, the difference between the current reference response value and the zero-point response value at the factory or maintenance time is directly calculated. This difference can be directly used as the correction factor; for example, if the current reference response value is higher than the reference value, the correction factor is negative, indicating that subsequent readings need to be corrected downwards. This method is simple and direct, but it may not reflect the gradual changes in probe performance over long-term operation. In another implementation, the probe can be calibrated periodically, and the reference response value obtained at the last calibration can be used as the reference value. When a new calibration is performed and a new reference response value is obtained, the new reference response value is compared with the previous reference value, and the difference between the two is calculated. This difference can be considered as a trend in probe performance over time, and a correction factor is generated accordingly. For example, if the new reference response value is higher than the reference value, it indicates that the probe may have a positive deviation, and the correction factor should be negative to compensate. This method can reflect the dynamic changes in probe performance, but it requires periodic calibration.

[0038] Subsequently, the correction factor is applied to the probe's subsequent response values ​​to obtain the corrected pollutant concentration data. In one implementation, the correction factor can be directly added to each raw response value acquired by the probe in real time. For example, if the probe's raw response value is X and the calculated correction factor is C, then the corrected pollutant concentration data is X + C. This method is simple to operate and suitable for situations where the correction factor is a fixed value. In another implementation, the correction factor can be a function that dynamically adjusts based on the probe's response value range or environmental conditions. For example, a preset correction function f(X, C) can be used, where X is the raw response value, C is the correction factor, and the corrected data is f(X, C). This function can be designed as a linear or non-linear relationship according to actual conditions to more accurately correct deviations within different concentration ranges. For example, when the pollutant concentration is low, the correction factor may have a smaller impact; while when the concentration is high, the correction factor may require a larger correction magnitude.

[0039] Finally, based on the corrected pollutant concentration data, an early warning is triggered when the pollutant concentration exceeds a preset warning threshold, and decision support information is provided. In one embodiment, the system can continuously monitor the corrected pollutant concentration data and compare it with a preset single fixed warning threshold. Once the corrected concentration data is higher than or equal to the threshold, the system immediately triggers an early warning, for example, through audible and visual alarms, SMS notifications, or emails. Simultaneously, the system can provide basic decision support information, such as displaying the type of pollutant currently exceeding the standard, the degree of exceedance, and the duration of exceedance. In another embodiment, multiple warning thresholds can be set, such as Level 1, Level 2, and Level 3 warnings, each corresponding to different pollution levels. When the corrected concentration data reaches the threshold at a different level, the system triggers the corresponding level of warning. Furthermore, the system can provide more detailed decision support information, such as analyzing pollution trends based on historical data, predicting future pollution patterns, recommending possible pollution sources, and providing corresponding emergency response suggestions, such as advising residents to reduce outdoor activities, activate air purification equipment, or notify relevant environmental protection departments to take measures.

[0040] This application establishes a clean environment benchmark and obtains the probe's benchmark response value. This method accurately quantifies the systematic deviation of the probe caused by organic matter adhesion. By comparing the benchmark response value with a reference benchmark value, a correction factor is generated and applied to subsequent real-time monitoring data, effectively compensating for probe reading deviations. This proactive correction mechanism, based on changes in probe performance, enables the system to obtain more accurate pollutant concentration data, avoiding the blind spot of "underestimation" bias in traditional methods. It compensates for the deficiency in existing technologies where probe readings are consistently lower than actual levels due to organic matter adhesion on the probe surface during long-term operation, improving the reliability of monitoring data received by the environmental quality assessment model. Therefore, this application can more realistically reflect the environmental quality status and provide accurate early warning and decision support information.

[0041] In one embodiment of this application, the step of confirming a clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe preferably includes: By monitoring meteorological data, when an atmospheric purification event that meets the preset purification conditions is identified, the environment after the atmospheric purification event ends and after a preset settling time is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

[0042] Meteorological monitoring data refers to various meteorological parameters in the environment acquired through deployed meteorological sensors or from external meteorological service systems, such as rainfall, wind speed, wind direction, humidity, temperature, and atmospheric pressure. Preset purification conditions refer to a series of meteorological conditions set based on experience, historical data analysis, or environmental models to effectively remove pollutants from the atmosphere. These conditions aim to ensure that pollutants in the atmosphere are significantly diluted, settled, or removed during specific meteorological events. An atmospheric purification event can be understood as a natural phenomenon where the concentration of atmospheric pollutants decreases significantly under specific meteorological conditions, such as a continuous heavy rainfall event or a clean air mass passage event. Settling time refers to the period of time required after an atmospheric purification event to ensure that residual pollutants in the environment fully settle or diffuse, allowing the environment to reach a stable and clean state. Its length can be adjusted according to local climate characteristics and pollutant types. The clean environment baseline refers to the environmental state where the concentration of atmospheric pollutants is at the natural background level or extremely low levels after the aforementioned atmospheric purification event and the preset settling time. At this point, the response value measured by the probe best represents its inherent characteristics in a pollution-free environment. The real-time response value refers to the output signal obtained by the probe in real time when measuring the concentration of pollutants in the environment under a confirmed clean environmental baseline. The baseline response value, on the other hand, refers to the stable response value exhibited by the probe under a confirmed clean environmental baseline. It represents the probe's inherent reading in a pollution-free or extremely low-pollution environment and is used for subsequent calibration.

[0043] This application's solution, through monitoring meteorological data, can intelligently and objectively identify atmospheric purification events where pollutants are naturally removed. When an atmospheric purification event meeting preset purification conditions is identified, it indicates that the atmospheric environment is undergoing an effective self-purification process, and the pollutant concentration will significantly decrease. After the atmospheric purification event ends, and after a preset settling time, it can be ensured that the pollutants in the atmosphere have sufficiently settled or diffused. At this point, the environment is considered to have reached a truly clean state, i.e., a clean environment benchmark. Obtaining the probe's real-time response value under this clean environment benchmark can more accurately reflect the probe's true response characteristics in pollution-free or extremely low-pollution environments, thus providing reliable benchmark data for subsequent correction factor generation. This benchmark confirmation method based on natural purification events avoids errors that may be caused by human intervention and improves the automation and reliability of benchmark acquisition.

[0044] The following is a specific example to illustrate this.

[0045] Suppose a pollutant monitoring probe is deployed in an urban area. The system continuously monitors local meteorological data, such as rainfall, wind speed, and wind direction. When the system detects a sustained heavy rainfall event, such as rainfall exceeding 5 mm per hour and lasting for more than 3 hours, it identifies it as an air purification event. After the heavy rainfall ends, the system waits for a preset settling time, such as 6 hours, to ensure that particulate matter and soluble pollutants in the air have settled sufficiently. After this settling time, the system identifies the current environment as a clean environmental baseline. At this point, the probe begins to collect real-time response values ​​and calculates the probe's baseline response value based on these real-time response values. For example, if the probe's real-time response value under the clean environmental baseline stabilizes at a certain low value, this low value is determined as the baseline response value. This baseline response value is then used to compare with the probe's reference baseline value to generate a correction factor, thereby correcting for reading deviations caused by pollutant adhesion.

[0046] In a specific embodiment of this application, the atmospheric purification event preferably includes a continuous heavy rainfall event or a clean air mass transit event. The preset purification conditions for the continuous heavy rainfall event are that the continuous rainfall exceeds 5 mm per hour and lasts for more than 3 hours. The preset purification conditions for the clean air mass transit event are that the wind speed continuously exceeds 10 meters per second, lasts for more than 6 hours, and the wind direction is from a non-industrial area.

[0047] Among them, continuous heavy rainfall events refer to natural phenomena in which rainfall reaches a certain intensity and lasts for a period of time. The preset purification conditions are set at rainfall exceeding 5 millimeters per hour and lasting for more than 3 hours, aiming to ensure that pollutants in the atmosphere can be effectively washed away and settled, thereby creating a relatively clean environment.

[0048] A clean air mass transit event refers to the phenomenon where a clean air mass originating from a non-industrial area passes through a monitored area, reducing the concentration of local pollutants through dilution and displacement. The preset purification conditions are set to a sustained wind speed exceeding 10 meters per second for more than 6 hours, with the wind direction originating from a non-industrial area, to ensure a sufficiently strong and continuous clean airflow to effectively remove pollutants from the monitored area and avoid the influence of local or nearby pollution sources.

[0049] This application's solution, by clearly defining the specific types of atmospheric purification events and their corresponding preset purification conditions, enables the system to more accurately identify truly clean environmental benchmarks. When meteorological events meeting these stringent conditions are detected, it can be assured that pollutants in the atmosphere have been effectively removed or diluted, thus providing a reliable environmental basis for obtaining subsequent probe benchmark response values. This precise identification mechanism helps avoid benchmark response value deviations caused by incomplete environmental purification.

[0050] In a more specific embodiment of this application, the step of comparing the reference response value with the reference reference value of the probe to generate the correction factor of the probe preferably includes: When a new air purification event is identified, a new baseline response value is obtained as the current response value, and the baseline response value obtained from the previous air purification event is used as the reference baseline value. Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

[0051] When the system identifies an atmospheric purification event that meets preset purification conditions by monitoring meteorological data, such as a continuous heavy rainfall event or a clean air mass passage event, and the environment after the atmospheric purification event ends and a preset settling time has elapsed, this is confirmed as the clean environment baseline. Under this clean environment baseline, the real-time response value of the probe is acquired and used as the new baseline response value. This new baseline response value is defined as the "current response value." Simultaneously, the system stores the baseline response value acquired during the previous atmospheric purification event and uses it as the "reference baseline value" in this calibration process. In this way, the reference baseline value is no longer a fixed initial value but is dynamically updated to reflect the probe response state under the previous clean environment, thus reflecting the change in probe performance between two purification events.

[0052] Calculating the difference between the current response value and the reference value involves subtracting the previously acquired reference response value from the currently acquired reference response value. This difference represents the amount of response value drift caused by factors such as organic matter adhesion between two clean environment reference verifications. For example, if the probe's response value increases in a clean environment due to contamination, the current response value will be higher than the reference value, and the difference will be positive.

[0053] Based on the difference, a correction factor for the probe is generated. This correction factor is set to a negative value, and its absolute value is equal to the difference calculated above. For example, if the difference is +X, the correction factor is -X. The purpose is that when the probe's response value in a clean environment increases due to contamination (a positive difference), an equal negative correction is applied to offset this increase, making the corrected reading closer to the true value. Conversely, if the probe's response value decreases (although this is less common due to organic matter adhesion), the correction factor will be positive to compensate for this decrease. This setting ensures that the correction factor accurately compensates for reading deviations caused by probe contamination.

[0054] This application's solution dynamically uses the baseline response value obtained from the previous air purification event as a reference value and compares it with the new baseline response value as the current response value, thereby accurately capturing the performance drift or degradation of the probe between two consecutive clean environment baseline verifications. This method avoids the cumulative errors that may result from using a long-term unchanging initial reference value, because the actual zero-point drift of the probe is a continuous and dynamic process. By calculating the difference between the current response value and the reference value, the system can quantify the actual deviation of the probe within a specific time period. Furthermore, by setting a correction factor to a negative value with its absolute value equal to this difference, it ensures that the correction factor can directly and effectively offset the positive reading deviation caused by factors such as organic matter adhesion, thereby calibrating the probe's response value back to its ideal clean environment response level.

[0055] The following is a specific example to illustrate this.

[0056] Suppose a pollutant monitoring probe, after its initial deployment and the first atmospheric cleanup event, has a baseline response value of 5 units in a clean environment (e.g., corresponding to an ideal response with zero pollution). The system records this 5-unit value as the initial reference baseline. Over the following months, organic matter gradually adheres to the probe surface, causing its response value in a clean environment to gradually increase. When a second atmospheric cleanup event occurs and is identified, the system re-verifies the clean environment baseline and acquires the probe's real-time response value in that clean environment, let's say it's 7 units.

[0057] According to the scheme of this application, the 7-unit response value is identified as the "current response value," while the 5-unit response value obtained during the first air purification event is used as the "reference baseline value." The system calculates the difference between the current response value and the reference baseline value, i.e., 7 - 5 = 2 units. Based on this difference, a correction factor for the probe is generated. This correction factor is negative, and its absolute value is equal to the difference; therefore, the correction factor is -2. Subsequently, before the next air purification event occurs, all subsequent response values ​​from all probes will be corrected by subtracting 2 units.

[0058] For example, if the probe reads 10 units during routine monitoring, after calibration, the actual pollutant concentration will be 10 - 2 = 8 units. When the third air purification event occurs, the system will acquire a new baseline response value, for example, 8 units. At this time, the 7-unit response value from the second purification event will become the new reference baseline, while the 8-unit response value will become the current response value. The difference is 8 - 7 = 1 unit, and the calibration factor is updated to -1. Through this dynamic updating of the calibration factor, the system can continuously adapt to changes in probe performance, ensuring the provision of high-accuracy pollutant concentration data throughout the monitoring cycle.

[0059] In another embodiment of this application, the step of confirming a clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe preferably includes: When the preset triggering conditions are met, clean gas is sprayed into the sampling port of the probe based on the clean airflow generator deployed next to the probe, so as to form a local zero-pollution environment around the sampling port of the probe; the preset triggering conditions are reaching the preset periodic diagnostic time, or receiving an alarm for abnormal fluctuation of probe data issued by the environmental quality assessment system. The aforementioned local zero-pollution environment is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

[0060] The preset triggering condition aims to ensure that the verification process of clean environment benchmarks can be initiated promptly and as needed. Specifically, this condition can be set as a periodic diagnostic time, such as once every 24 hours, weekly, or monthly, to ensure that the probe can perform self-calibration regularly. In addition, this process can also be triggered when the environmental quality assessment system detects abnormal fluctuations in probe data, such as a sudden and significant deviation of the reading from the normal range or trend, thereby enabling immediate diagnosis and correction of the probe.

[0061] The clean gas flow generator is a device capable of generating and directionally injecting clean gas, typically deployed near the probe. The clean gas can be highly filtered air, nitrogen, or other inert gases, the purpose of which is to ensure that the gas is free of any contaminants. When the clean gas is injected into the probe's sampling port, a localized area is formed around the port where the air is replaced by the clean gas, thus creating a localized zero-contamination environment. This localized zero-contamination environment is recognized as the clean environment benchmark because it provides a known and controllable state of contamination-free condition, allowing the probe to measure its true baseline response under these conditions.

[0062] Under this clean environment baseline, the probe acquires its real-time response values. These real-time response values ​​reflect the probe's inherent response in a contaminant-free environment, eliminating the influence of external environmental pollutants. By processing these real-time response values, a baseline response value for the probe can be obtained, which will be used to generate subsequent correction factors.

[0063] This application's solution effectively addresses the dependence of traditional methods on natural atmospheric purification events for clean environment baseline verification by introducing a clean airflow generator and preset trigger conditions. When the preset trigger conditions are met, the clean airflow generator actively injects clean gas into the probe's sampling port, artificially creating a localized zero-pollution environment around the probe. This localized zero-pollution environment provides a stable, controllable, and pollution-free reference point, enabling the probe to accurately measure its baseline response within this environment. In this way, the probe's baseline response value can be acquired at any desired time, no longer limited by the occurrence of natural events, thus ensuring the timeliness and predictability of the calibration process.

[0064] The following is a specific example to illustrate this.

[0065] Suppose a pollutant monitoring probe is deployed in an urban environment. To ensure the accuracy of probe readings, the system is configured to automatically activate the clean airflow generator at 2:00 AM daily. At the preset periodic diagnostic time (2:00 AM), the clean airflow generator begins to spray highly filtered clean air into the probe's sampling port for 5 minutes. During this time, the air surrounding the probe's sampling port is replaced by clean air, creating a localized zero-pollution environment. The probe continuously collects real-time response values ​​within this localized zero-pollution environment, for example, once per second. The system records these real-time response values ​​and calculates their average as the baseline response value for the day.

[0066] Furthermore, suppose that one afternoon, the environmental quality assessment system detects abnormal fluctuations in the pollutant concentration data output by the probe—for example, a sudden 20% increase in reading without any obvious change in the pollution source. In this case, the environmental quality assessment system will immediately issue an alarm for abnormal probe data fluctuations, triggering the clean airflow generator to restart. The clean airflow generator will again inject clean gas into the probe sampling port, creating a localized zero-pollution environment and acquiring new real-time response values ​​to determine the current baseline response value. In this way, the system can promptly detect and correct reading deviations caused by factors such as organic matter adhesion, ensuring the accuracy of the monitoring data.

[0067] In a further embodiment of this application, the step of obtaining the reference response value of the probe based on the real-time response value of the probe under a clean environment reference preferably includes: During the injection of clean gas by the clean airflow generator, the real-time response value of the probe is continuously acquired at a preset sampling frequency to obtain a time-series signal of the real-time response value; The time series signal is decomposed into multiple signal components that characterize the changes in the real-time response value with different time scales by performing multi-time-scale signal decomposition on the time series signal. From the multiple signal components, identify the trend component that characterizes the slow changing trend of the real-time response value; Based on the trend components, the reference response value of the probe is determined.

[0068] During the injection of clean gas by the clean airflow generator, the real-time response value of the probe is continuously acquired at a preset sampling frequency. This preset sampling frequency can be set according to the probe's response characteristics and the required data accuracy, such as once per second or once per minute. Through continuous acquisition, a time-series signal containing the probe's response variation pattern in a local zero-pollution environment can be obtained. This time-series signal reflects the probe's continuous response data within a specific time period. Multi-time-scale signal decomposition of the time-series signal can be understood as using signal processing techniques to decompose the original real-time response value time-series signal into multiple signal components that vary at different time scales. For example, wavelet decomposition, empirical mode decomposition (EMD), or variational mode decomposition (VMD) methods can be used. These methods can separate information at different scales in the signal, such as high-frequency noise, mid-frequency fluctuations, and low-frequency trends, so that each signal component characterizes the variation features of the real-time response value at a specific time scale. The purpose is to separate noise and instantaneous fluctuations in the signal from the probe's true, slowly changing baseline response trend. In practical applications, identifying the trend component representing the slow change trend of the real-time response value from multiple signal components refers to selecting or extracting the component representing the long-term, stable change of the probe response from the multiple signal components obtained through decomposition. Typically, the trend component corresponds to the low-frequency components in the signal decomposition result or the last one or several in the intrinsic mode functions (IMF). This trend component can effectively filter out high-frequency noise and short-term fluctuations, more accurately reflecting the probe's true baseline response in a clean environment. Therefore, based on the trend component, the probe's baseline response value can be determined. This means that instead of directly using the original real-time response value or its simple average, the more stable and accurate trend component obtained after signal decomposition and trend extraction is used to calculate the baseline response value. For example, the average or median of this trend component during the entire clean gas injection period can be calculated as the probe's baseline response value.

[0069] This application's solution continuously acquires the probe's real-time response value at a preset sampling frequency during the injection of clean gas by a clean gas flow generator, thereby obtaining a time-series signal containing the dynamic changes in the probe's response. Given that this time-series signal may contain noise and transient fluctuations, this application further employs multi-timescale signal decomposition technology to process it. This decomposition can separate the original signal into multiple signal components that vary at different time scales, effectively separating high-frequency noise, mid-frequency disturbances, and the probe's true, slowly changing reference response trend. By identifying the trend component characterizing the slowly changing trend of the real-time response value from these signal components, irrelevant interference information can be filtered out, resulting in a smoother and more stable signal. Finally, the probe's reference response value is determined based on this purified trend component, ensuring that the obtained reference response value more accurately reflects the probe's true zero-point drift in a clean environment, avoiding errors caused by transient fluctuations or noise.

[0070] The following is a specific example to illustrate this.

[0071] Suppose that after a pollutant monitoring probe has been deployed in the field for a long period, its surface may be covered with organic matter, causing reading drift. To calibrate the probe, the system activates a clean gas flow generator next to the probe at a preset periodic diagnostic time, injecting clean gas into the probe's sampling port for 10 minutes to create a local zero-pollution environment. During this period, the probe continuously acquires real-time response values ​​at a sampling frequency of 10 times per second, generating a time-series signal containing 6000 data points. Subsequently, wavelet decomposition is performed on this time-series signal, decomposing it into wavelet coefficients of multiple different scales. By reconstructing the low-frequency wavelet coefficients, the trend component characterizing the slow change trend of the probe's response can be extracted. For example, if the original real-time response value fluctuates slightly at the beginning of the clean gas injection but then tends to stabilize, wavelet decomposition can effectively filter out these initial fluctuations and high-frequency noise, resulting in a smooth trend curve. Further, the average value of this trend component during the clean gas injection period is calculated. Assume the calculated average value is 0.05 ppm. This 0.05 ppm is determined as the current baseline response value of the probe. Compared to simply averaging the original 6000 real-time response values ​​(which may be too high or too low due to initial fluctuations), the baseline response value obtained based on the trend component is more stable and accurate, and can more realistically reflect the actual response of the probe in a zero-pollution environment, thus providing a more accurate basis for the subsequent generation of correction factors.

[0072] In a further embodiment of this application, the step of determining the reference response value of the probe based on the trend component preferably includes: Calculate the standard deviation of the trend component during the injection of clean gas by the clean airflow generator, and compare the standard deviation with a preset judgment threshold; If the standard deviation is higher than or equal to the judgment threshold, the clean airflow generator is restarted to identify the new trend component, calculate the standard deviation of the new trend component, and compare the standard deviation with the judgment threshold. If the standard deviation is lower than the judgment threshold, the trend component is used as the confirmation component. Based on the confirmed components, the reference response value of the probe is determined.

[0073] During the injection of clean gas by the clean airflow generator, the probe continuously acquires real-time response values. After multi-timescale signal decomposition, a trend component characterizing the slow changing trend of the real-time response values ​​is obtained. To evaluate the stability of this trend component, its standard deviation during the injection of clean gas by the clean airflow generator needs to be calculated. The standard deviation is a statistic that measures the degree of data dispersion; the smaller the value, the smaller the data fluctuation and the higher the stability. A preset judgment threshold is used to define the acceptable fluctuation range of the trend component. This threshold can be set according to the probe type, measurement accuracy requirements, and actual application scenarios; for example, it can be set as a percentage of the probe measurement error or an empirical value.

[0074] If the calculated standard deviation is higher than or equal to the preset judgment threshold, it indicates that the currently identified trend component fluctuates significantly and lacks stability, making it unsuitable as a basis for determining the baseline response value. In this case, to obtain a more stable trend component, the clean airflow generator will be restarted. The purpose of restarting is to re-establish a local zero-pollution environment and allow the probe to re-acquire data, thereby identifying new trend components. Subsequently, the standard deviation of the new trend component is recalculated and compared with the judgment threshold until a trend component that meets the stability requirements is obtained.

[0075] If the calculated standard deviation is lower than a preset judgment threshold, the currently identified trend component is considered to have sufficient stability and can be confirmed as a confirmed component. This confirmed component represents the true and stable baseline state of the probe response value in a local zero-contamination environment. Finally, based on this confirmed component, the probe's baseline response value can be accurately determined.

[0076] This application's solution effectively addresses the potential volatility issue of trend components in the aforementioned solutions by introducing a quantitative evaluation mechanism for the stability of trend components. Specifically, by calculating the standard deviation of the trend component and comparing it with a preset judgment threshold, the stability of the trend component can be objectively determined. When the volatility of the trend component exceeds an acceptable range, the system will actively restart the clean airflow generator, forcing the probe to re-measure and re-identify the trend component until a sufficiently stable trend component is obtained. This iterative and verification mechanism ensures that the trend component ultimately used to determine the baseline response value is highly stable and reliable, thereby avoiding deviations in the baseline response value caused by trend component instability.

[0077] The following is a specific example to illustrate this.

[0078] Suppose that when a pollutant monitoring probe reaches its periodic diagnostic time, a clean gas flow generator is activated, injecting clean gas into the probe's sampling port. The probe continuously acquires real-time response values ​​in a locally zero-pollution environment, and after multi-timescale signal decomposition, identifies a first trend component. At this point, the system calculates the standard deviation of this first trend component during the injection period, for example, 0.08. If the preset judgment threshold is 0.05, since 0.08 is higher than 0.05, the system determines that the first trend component is not stable enough. Therefore, the clean gas flow generator is activated again, injecting clean gas again, and the probe acquires data again and identifies a second trend component. The system recalculates the standard deviation of the second trend component, for example, 0.03. Since 0.03 is lower than 0.05, the system confirms that the second trend component is a stable confirmed component. Finally, based on this confirmed component, for example, by calculating its average value, the probe's baseline response value is obtained, which will be used for subsequent correction factor generation.

[0079] In a specific embodiment of this application, the step of determining the reference response value of the probe based on the confirmed components includes: The average or median of the confirmed components is calculated during the injection of the clean airflow generator to obtain the calculation result; The calculation result is used as the reference response value of the probe.

[0080] The "confirmed component" refers to the trend component characterized by the slow change trend of the real-time response value, identified through multi-timescale signal decomposition of the probe during the injection of clean gas by the clean gas generator. This trend component's standard deviation is below a preset threshold, indicating high stability. "Clean gas generator injection period" refers to the entire time period during which the clean gas generator injects clean gas into the probe's sampling port to create a localized zero-contamination environment around the port. During this period, the environment in which the probe is located is confirmed as the clean environment baseline. "Average or median" refers to the statistical calculation of the confirmed component's values ​​throughout the injection period. The average reflects the overall level of the data sequence, while the median effectively avoids the influence of outliers, providing a more robust measure of central tendency. "Calculation result" refers to the value obtained by calculating the average or median. "Baseline response value" refers to the probe's ideal response value under the clean environment baseline, used for subsequent correction of reading deviations caused by organic matter adhesion.

[0081] The proposed solution effectively and accurately determines the probe's baseline response value by calculating and confirming the average or median of the components during the clean gas flow generator's injection. Specifically, during the injection of clean gas by the clean gas flow generator, the probe is placed in a locally zero-contamination environment, where the probe's response value should theoretically approach zero or a stable low value. However, due to factors such as organic matter adhering to the probe surface, the response value may drift. By performing multi-timescale signal decomposition on the real-time response value and identifying slowly changing trend components, environmental noise and rapid fluctuations can be effectively filtered out. Furthermore, by using standard deviation determination, the selected trend component (i.e., the confirmatory component) is ensured to have high stability, excluding components still affected by residual fluctuations. Based on this, calculating the average or median of this stable confirmatory component further smooths the data, eliminates minor random errors, and thus obtains a value that highly represents the probe's true response level in a clean environment. Choosing the average or median ensures that the determination of the baseline response value reflects the overall trend while effectively resisting the influence of possible individual measurement noise or slight fluctuations, ensuring the accuracy and robustness of the baseline response value.

[0082] In a more specific embodiment of this application, the step of comparing the reference response value with the reference reference value of the probe to generate the correction factor of the probe preferably includes: The factory calibration value or the preset theoretical zero point value of the probe is used as the reference benchmark value; Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

[0083] The factory calibration value refers to the stable response value measured by the probe in a strictly controlled laboratory environment before it leaves the factory, exposed to standard zero gas (i.e., clean air free of the target contaminant). The factory calibration value is pre-stored in the probe's memory or the system's configuration file and represents the probe's baseline performance in a brand-new, uncontaminated state. The zero-point theoretical value is a preset theoretical value, usually 0, which characterizes the response output that an ideal probe should have in an absolutely clean environment.

[0084] If the probe's performance degrades due to long-term operation and the adhesion of organic matter on its surface, its response in a clean environment will deviate from its original zero-point state. In this case, the current reference response value will be greater than the factory calibration value or the preset theoretical zero-point value, and the calculated difference will be positive.

[0085] Based on the difference, a correction factor for the probe is generated. This correction factor is set to a negative value, and its absolute value is equal to the difference calculated above. The purpose is to directly offset any overestimation or underestimation of the probe reading caused by factors such as the adhesion of organic matter to the probe surface, thereby making the corrected data closer to the true value.

[0086] This application utilizes a clean environment benchmark created by a clean airflow generator and compares the benchmark response value of the probe with the factory calibration value or the preset zero-point theoretical value of the probe to calculate the correction factor of the probe. This allows the reading deviation caused by the adhesion of organic matter to be effectively identified and offset, ensuring the accuracy of subsequent pollutant concentration data.

[0087] like Figure 2 As shown, this application also discloses an atmospheric meteorological monitoring system 200, which obtains accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface. The system includes: The reference data acquisition module 210 is used to confirm the clean environment reference and use the actual response value of the probe under the clean environment reference as the reference response value of the probe. The correction factor generation module 220 is used to compare the reference response value with the reference reference value of the probe and generate the correction factor of the probe. The data correction acquisition module 230 is used to apply the correction factor to the subsequent response value of the probe to obtain the corrected pollutant concentration data; The early warning triggering and decision support module 240 is used to trigger an early warning based on the corrected pollutant concentration data when the pollutant concentration data exceeds the preset early warning threshold, and to provide decision support information.

[0088] The system proposed in this application includes a baseline data acquisition module, a correction factor generation module, a data correction acquisition module, and an early warning triggering and decision support module. These modules can be implemented by hardware, software, or a combination thereof, and work together to complete meteorological monitoring tasks.

[0089] The reference data acquisition module 210 can be a hardware unit, such as containing a microcontroller and memory, for executing preset logic to identify a clean environment and record the probe response. As one implementation, the reference data acquisition module 210 can be configured to periodically trigger the probe to perform self-calibration under specific conditions, such as at night or under specific weather conditions. In another implementation, the reference data acquisition module 210 can receive instructions from an external system to initiate the reference data acquisition process when needed.

[0090] The correction factor generation module 220 can be a processing unit, such as a digital signal processor or an embedded processor, responsible for executing algorithms for comparing and calculating correction factors. As one implementation, the correction factor generation module 220 can be programmed to simply calculate the difference between the current baseline response value and a pre-stored reference baseline value. In another implementation, the correction factor generation module 220 can employ more complex statistical analysis methods, such as regression analysis, to generate more refined correction factors.

[0091] The data correction acquisition module 230 can be a data processing unit, such as a field-programmable gate array (FPGA) or an application-specific integrated circuit (ASIC), used to correct the probe's raw data in real time. As one implementation, the data correction acquisition module 230 can be configured to directly add or subtract the correction factor from the real-time response value. In another implementation, the data correction acquisition module 230 can implement a lookup table or a function model to output corrected data based on the correction factor and the raw response value.

[0092] The early warning triggering and decision support module 240 is used to trigger an early warning based on corrected pollutant concentration data when the detected pollutant concentration data exceeds a preset early warning threshold, and to provide decision support information. This module can be a communication and control unit, for example, including a wireless communication module and a display interface, for sending early warning information and displaying decision support data. As one implementation, the early warning triggering and decision support module 240 can be configured to issue an alarm via a simple indicator light or buzzer when an exceedance of the threshold is detected. In another implementation, the early warning triggering and decision support module 240 can connect to a remote server to send detailed early warning reports and analysis results via a network.

[0093] It should be noted that the information interaction and execution process between the above modules are based on the same concept as the method embodiments of this application. For details on their specific functions and technical effects, please refer to the method embodiments section, which will not be repeated here.

[0094] The foregoing has provided a detailed description of the preferred embodiments of this application. However, this application is not limited to the above-described embodiments. Those skilled in the art can make various equivalent modifications or substitutions without departing from the spirit of this application. All such equivalent modifications or substitutions are included within the scope defined in this application.

Claims

1. A meteorological monitoring method based on the atmospheric sphere, which obtains accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface, characterized in that... The method includes the following steps: A clean environment baseline is established, and the actual response value of the probe under the clean environment baseline is used as the baseline response value of the probe. The reference response value is compared with the reference value of the probe to generate the correction factor of the probe; The correction factor is applied to the subsequent response value of the probe to obtain the corrected pollutant concentration data; Based on the corrected pollutant concentration data, an early warning is triggered when the pollutant concentration data exceeds the preset warning threshold, and decision support information is provided.

2. The meteorological monitoring method based on the atmosphere according to claim 1, characterized in that, The step of confirming the clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe includes: By monitoring meteorological data, when an atmospheric purification event that meets the preset purification conditions is identified, the environment after the atmospheric purification event ends and after a preset settling time is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

3. The meteorological monitoring method based on the atmosphere according to claim 2, characterized in that, The atmospheric purification events include continuous heavy rainfall events or clean air mass transit events. The preset purification conditions for continuous heavy rainfall events are that the continuous rainfall exceeds 5 mm per hour and lasts for more than 3 hours. The preset purification conditions for clean air mass transit events are that the wind speed continuously exceeds 10 meters per second, lasts for more than 6 hours, and the wind direction is from a non-industrial area.

4. The meteorological monitoring method based on the atmosphere according to claim 2, characterized in that, The step of comparing the reference response value with the reference value of the probe to generate the correction factor of the probe includes: When a new air purification event is identified, a new baseline response value is obtained as the current response value, and the baseline response value obtained from the previous air purification event is used as the reference baseline value. Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

5. The meteorological monitoring method based on the atmosphere according to claim 1, characterized in that, The step of confirming the clean environment benchmark and using the actual response value of the probe under the clean environment benchmark as the benchmark response value of the probe includes: When the preset triggering conditions are met, clean gas is sprayed into the sampling port of the probe based on the clean airflow generator deployed next to the probe, so as to form a local zero-pollution environment around the sampling port of the probe; the preset triggering conditions are reaching the preset periodic diagnostic time, or receiving an alarm for abnormal fluctuation of probe data issued by the environmental quality assessment system. The aforementioned local zero-pollution environment is identified as the clean environment benchmark. The reference response value of the probe is obtained based on the real-time response value of the probe under the clean environment benchmark.

6. The meteorological monitoring method based on the atmosphere according to claim 5, characterized in that, The step of obtaining the reference response value of the probe based on the real-time response value of the probe under the clean environment reference includes: During the injection of clean gas by the clean airflow generator, the real-time response value of the probe is continuously acquired at a preset sampling frequency to obtain a time-series signal of the real-time response value; The time series signal is decomposed into multiple signal components that characterize the changes in the real-time response value with different time scales by performing multi-time-scale signal decomposition on the time series signal. From the multiple signal components, identify the trend component that characterizes the slow changing trend of the real-time response value; Based on the trend components, the reference response value of the probe is determined.

7. A meteorological monitoring method based on the atmosphere as described in claim 6, characterized in that, The step of determining the reference response value of the probe based on the trend component includes: Calculate the standard deviation of the trend component during the injection of clean gas by the clean airflow generator, and compare the standard deviation with a preset judgment threshold; If the standard deviation is higher than or equal to the judgment threshold, the clean airflow generator is restarted to identify the new trend component, calculate the standard deviation of the new trend component, and compare the standard deviation with the judgment threshold. If the standard deviation is lower than the judgment threshold, the trend component is used as the confirmation component. Based on the confirmed components, the reference response value of the probe is determined.

8. A meteorological monitoring method based on the atmosphere according to claim 7, characterized in that, The step of determining the reference response value of the probe based on the confirmed components includes: The average or median of the confirmed components is calculated during the injection of the clean airflow generator to obtain the calculation result; The calculation result is used as the reference response value of the probe.

9. A meteorological monitoring method based on the atmosphere according to claim 5, characterized in that, The step of comparing the reference response value with the reference value of the probe to generate the correction factor of the probe includes: The factory calibration value or the preset theoretical zero point value of the probe is used as the reference benchmark value; Calculate the difference between the current response value and the reference value; Based on the difference, a correction factor for the probe is generated; the correction factor is negative, and the absolute value of the correction factor is equal to the difference.

10. A meteorological monitoring system based on the atmosphere, which obtains accurate pollutant concentration data by correcting reading deviations caused by organic matter adhering to the probe surface, characterized in that... The system includes: The reference data acquisition module is used to confirm the clean environment reference and use the actual response value of the probe under the clean environment reference as the reference response value of the probe. A correction factor generation module is used to compare the reference response value with the reference reference value of the probe and generate a correction factor for the probe. The data correction acquisition module is used to apply the correction factor to the subsequent response value of the probe to obtain the corrected pollutant concentration data; The early warning triggering and decision support module is used to trigger an early warning when the pollutant concentration data exceeds the preset early warning threshold based on the corrected pollutant concentration data, and to provide decision support information.

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