Online air water-soluble ion monitoring data analysis platform

Abstract

The invention discloses an online air water-soluble ion monitoring data analysis platform, which belongs to the technical field of air water-soluble ion monitoring, and is characterized in that an area analysis module is used for analyzing a target monitoring area, identifying each monitoring station in the target monitoring area, and generating a dynamic monitoring map according to each monitoring station; identifying an unmonitored area in real time according to the dynamic monitoring graph, determining a corresponding supplementary monitoring area according to the unmonitored area, analyzing the supplementary monitoring area to obtain a supplementary monitoring scheme of the supplementary monitoring area, and performing monitoring arrangement according to the supplementary monitoring scheme; the monitoring module is used for monitoring a target monitoring area in real time to obtain area monitoring data of the target monitoring area; the data analysis module is used for analyzing and processing the regional monitoring data and comprises an atlas display unit and an early warning unit; the map display unit is used for analyzing the regional monitoring data and generating a data analysis map; and the early warning unit is used for performing real-time early warning analysis according to the regional monitoring data.

Description

Technical Field

[0001] This invention belongs to the field of air water-soluble ion monitoring technology, specifically an online air water-soluble ion monitoring data analysis platform. Background Technology

[0002] With the increasing severity of air pollution, air quality monitoring and control have become one of the core tasks in the global environmental protection field. Among them, water-soluble ions in the air (such as sulfates, nitrates, ammonium salts, chlorides, etc.) are the main components of fine particulate matter (PM2.5), and their concentration levels directly reflect the combined contribution of pollution sources such as coal combustion, industrial emissions, vehicle exhaust, agricultural activities, and secondary aerosol generation.

[0003] Traditional air quality monitoring primarily relies on offline sampling and laboratory analysis, such as collecting particulate matter through filters and then using ion chromatography to determine water-soluble ionic components. However, these methods suffer from drawbacks such as low temporal resolution (typically 24-hour averages), significant data lag, long sampling cycles, and high labor costs. They struggle to capture the dynamic changes of pollution events in real time and cannot meet the demands for rapid response to sudden pollution incidents. Furthermore, offline monitoring sampling points are usually fixed and sparse, failing to cover the spatial heterogeneity of large areas. This limits the accuracy of pollution source analysis and transmission path tracing. Moreover, monitoring stations are typically concentrated in urban or industrial areas, leaving data gaps in rural and mountainous regions, resulting in insufficient spatial coverage.

[0004] In order to solve the above problems, the present invention provides an online air water-soluble ion monitoring and data analysis platform. Summary of the Invention

[0005] To address the problems of the above solutions, this invention provides an online air water-soluble ion monitoring and data analysis platform.

[0006] The objective of this invention can be achieved through the following technical solutions: An online air water-soluble ion monitoring and data analysis platform includes a regional analysis module, a monitoring module, and a data analysis module; The area analysis module is used to analyze the target monitoring area, identify each monitoring station within the target monitoring area in real time, and generate a dynamic monitoring map based on each monitoring station. The dynamic monitoring map includes the location of each monitoring station and the monitoring range corresponding to each monitoring station. Unmonitored areas are identified in real time based on the dynamic monitoring map. Corresponding supplementary monitoring areas are determined based on the unmonitored areas. The supplementary monitoring areas are analyzed to obtain supplementary monitoring plans for the supplementary monitoring areas. Monitoring is then carried out according to the supplementary monitoring plans.

[0007] Furthermore, dynamic monitoring maps are generated based on each monitoring station, including: Identify various dynamic range influence items that affect the monitoring range of monitoring stations; acquire historical monitoring data of each monitoring station; perform feature identification on the historical monitoring data based on each range influence item to obtain various range feature sets of each monitoring station; identify the monitoring range of the range feature sets based on the historical monitoring data; merge the range feature sets based on the monitoring range to obtain several merged feature sets; and associate the merged feature sets with the corresponding monitoring ranges. An initial information map is generated based on the location of each monitoring station. The monitoring stations are monitored in real time according to the range influence item to obtain the corresponding range feature set. The corresponding monitoring range is matched in real time according to the range feature set. The monitoring range is marked on the initial information map. The current initial information map is marked as a dynamic monitoring map.

[0008] Furthermore, the range feature set is merged based on the monitoring range, including: Step SA1: Establish the combined valuation model. The expression for the combined valuation model is: ; In the formula: (q, p) represents the input data, q and p represent the monitoring ranges of the two range feature sets to be merged and analyzed, respectively. p indicates that the two monitoring ranges can be considered equal; the output data is the combined evaluation value HP(q, p), and the combined evaluation value is 1 or 0; Step SA2: Analyze the monitoring range between the two corresponding range feature sets using the merged evaluation model to obtain the merged evaluation value between the corresponding range feature sets; The two range feature sets with a combined evaluation value of 1 are merged to obtain a combined feature set, and the monitoring range of the combined feature set is set; and the two range feature sets with a combined evaluation value of 0 are marked. Step SA3: Repeat step SA2 until the combined evaluation value between each range feature set is not 1, and obtain several combined feature sets. During the iteration of step SA2, the combined feature sets are treated as new range feature sets and participate in the iterative analysis.

[0009] Furthermore, corresponding supplementary monitoring areas are determined based on the unmonitored areas, including: Acquire air features at various locations within the unmonitored area, merge locations with air feature similarity not less than threshold X1 and adjacent locations to obtain several unit regions; determine the unit features of each unit region; Each unit area is monitored and analyzed, and unit areas that do not meet the monitoring requirements are removed; the remaining unit areas are integrated and marked as supplementary monitoring areas.

[0010] Furthermore, monitoring and analysis are conducted on each unit area, including: A monitoring and screening model is established, and its expression is as follows: ; In the formula: s represents the cell feature of the corresponding cell region, and the output data is the cell filter value HK(s), where the cell filter value is 1 or 0; By analyzing the unit characteristics of each unit region through the monitoring and screening model, the unit screening value of the corresponding unit region is obtained; When the unit screening value is 1, the assessment indicates that the monitoring requirements are met. When the unit screening value is 0, the assessment does not meet the monitoring requirements.

[0011] Furthermore, the supplementary monitoring areas were analyzed, including: Identify each unit region corresponding to the supplementary monitoring area, obtain the unit characteristics of each unit region, determine the various types of air water-soluble ions that need to be monitored in the unit region based on the unit characteristics, and analyze the monitoring type combinations for various types of air water-soluble ions. Obtain the user's monitoring accuracy requirements for each unit area, and filter the monitoring type combinations for the unit area according to the monitoring accuracy requirements, eliminating monitoring type combinations that do not meet the monitoring accuracy requirements; The monitoring cost of the remaining monitoring type combinations is estimated according to the minimum standard of monitoring accuracy requirements. The monitoring type combinations corresponding to the unit area are prioritized in order of increasing monitoring cost to obtain the priority monitoring sequence of the unit area. Supplementary monitoring plans are determined based on the priority monitoring sequence for each unit area.

[0012] The monitoring module is used to monitor the target monitoring area in real time, obtain the regional monitoring data of the target monitoring area, and preprocess the regional monitoring data; the preprocessed regional monitoring data is then sent to the data analysis module.

[0013] The data analysis module is used to analyze and process regional monitoring data, including a map display unit and an early warning unit; The graph display unit is used to analyze regional monitoring data and generate data analysis graphs; the data analysis graphs include spatiotemporal heat maps, ion percentage rose diagrams, and trend prediction curves; The early warning unit is used to perform real-time early warning analysis based on regional monitoring data and obtain corresponding early warning analysis results, including monitoring normal and monitoring abnormal. When the early warning analysis result indicates that the monitoring is normal, no corresponding action will be taken; When the early warning analysis result indicates an anomaly, the corresponding cause of the anomaly is determined, and corresponding prevention and control recommendations are generated based on the cause of the anomaly.

[0014] Compared with the prior art, the beneficial effects of the present invention are: This platform utilizes online monitoring technology to achieve continuous, real-time acquisition and transmission of water-soluble ion concentrations, significantly increasing the data update frequency and enabling precise capture of the onset, peak, and dissipation processes of pollution events. It also provides comprehensive coverage of different types of areas, including urban, rural, mountainous, and industrial zones. For example, in the event of sudden industrial emissions or straw burning pollution, the platform can quickly detect abnormal fluctuations in ion concentrations, providing regulatory authorities with immediate early warning information to help them activate emergency response mechanisms as soon as possible, minimizing the scope and duration of pollution spread, thereby significantly improving the timeliness and effectiveness of air pollution control. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a block diagram illustrating the principle of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, an online air water-soluble ion monitoring and data analysis platform includes a regional analysis module, a monitoring module, and a data analysis module. The regional analysis module is used to analyze the target monitoring area, which is the area where air water-soluble ion monitoring is required. It identifies the various monitoring stations within the target monitoring area in real time, that is, the various monitoring stations that have been deployed or are planned to be deployed. Generally, these are the monitoring stations deployed in the city and the monitoring stations to be added later. A dynamic monitoring map is generated based on each monitoring station. The dynamic monitoring map includes the location of each monitoring station and the monitoring range corresponding to each monitoring station. "Dynamic" means that the monitoring range of the monitoring station changes dynamically due to external factors. Unmonitored areas are identified in real time based on the dynamic monitoring map. Corresponding supplementary monitoring areas are determined based on the unmonitored areas. The supplementary monitoring areas are analyzed to obtain supplementary monitoring plans for the supplementary monitoring areas. Monitoring is then carried out according to the supplementary monitoring plans.

[0019] In one embodiment, generating a dynamic monitoring map based on each monitoring station includes: Identify various dynamic range-affecting factors that influence the monitoring range of the monitoring stations, such as range-affecting factors mainly influenced by weather, for example, atmospheric boundary layer height changes, precipitation and cloud dynamics, and spatiotemporal heterogeneity of wind fields; Historical monitoring data from each monitoring station is acquired. Based on the influence of each range, the historical monitoring data is characterized to obtain various range feature sets for each monitoring station. The range feature set is a data set of the combination of identification features corresponding to each range feature item. Based on the historical monitoring data, the monitoring range corresponding to the corresponding range feature set is identified. Based on the monitoring range, the range feature sets are merged to obtain several merged feature sets. The merged feature sets are associated with the corresponding monitoring ranges. An initial information map is generated based on the location of each monitoring station, which can be regarded as a map marked with information about each monitoring station. The monitoring stations are monitored in real time based on the range impact factor to obtain the corresponding range feature set. The corresponding monitoring range is matched according to the range feature set. The monitoring range is marked on the initial information map. The current initial information map is marked as a dynamic monitoring map.

[0020] For monitoring stations that have not yet been deployed or for which historical monitoring data is insufficient, a representative monitoring range can be preset based on the monitoring station information, or a preset monitoring range that meets the requirements can be used as a representative.

[0021] In one embodiment, merging the range feature set according to the monitoring range includes: Step SA1: Establish the combined valuation model. The expression for the combined valuation model is: ; In the formula: (q, p) represents the input data, q and p represent the monitoring ranges of the two range feature sets to be merged and analyzed, respectively. p indicates that the two monitoring ranges can be considered equal, and is determined according to the user's merging requirements. For example, they can be absolutely equal, meaning that the two monitoring ranges are completely identical. Alternatively, an allowable deviation can be set, and the similarity between the two ranges can be used to determine their equality. If the difference in similarity is not greater than the allowable deviation, they are considered equal. The output data is the merged evaluation value HP(q, p), which is 1 or 0. Step SA2: Analyze the monitoring range between the two corresponding range feature sets using the merged evaluation model to obtain the merged evaluation value between the corresponding range feature sets; Merge the two range feature sets with a combined evaluation value of 1 to obtain a combined feature set, set the monitoring range of the combined feature set, and take the average range of each monitoring range corresponding to the combined feature set. And mark the two range feature sets with a combined evaluation value of 0, so that when a combined feature set with a combined evaluation value of 0 appears, no analysis is required; Step SA3: Repeat step SA2 until the combined evaluation value between each range feature set is not 1. During the iteration of step SA2, the combined feature set is regarded as a new range feature set and participates in the iterative analysis; several combined feature sets are obtained, and independent unmerged range feature sets are also regarded as combined feature sets.

[0022] In one embodiment, the corresponding supplementary monitoring area is determined based on the unmonitored area. This can be done using existing methods, such as manually setting up supplementary monitoring areas within the unmonitored area.

[0023] In one embodiment, determining corresponding supplementary monitoring areas based on unmonitored areas includes: The study acquires air characteristics at various locations within unmonitored areas. These characteristics include the types, concentrations, proportions, and spatiotemporal dynamics of water-soluble ions in the air at those locations, reflecting the direct impact of human activities or natural processes on air quality; for example, the K+ corresponding to straw burning. + (Biomass combustion markers), Cl - (Straw contains high levels of chlorine), organic carbon (OC), etc., and the corresponding NH4 content in chemical fertilizer application. + (Main forms of fertilizer volatilization), NO3 - (Products of soil nitrification), Ca 2+ (Impurities in phosphate fertilizers), SO4 pollution from coal combustion, etc. 2- (Sulfur oxidation products from coal combustion), heavy metal ions such as As and Se (carried by coal additives), etc., are used to determine air characteristics based on the actual production and living conditions at the corresponding locations. This information is used to determine the causes, types, concentrations, and proportions of water-soluble ions produced at those locations. Locations with air characteristic similarity not less than a threshold X1 and adjacent locations are merged to obtain several unit regions. The threshold X1 is set empirically, such as 0.8 or 0.9, to reduce the amount of subsequent data analysis. It can also be set directly to 100%. Data items that do not meet the threshold X1 can be directly considered as not meeting it at a certain location. For example, if a location contains Cl... - And there is no Cl at the other position. - If the condition is not met, it is considered that the average air characteristics of each location in the unit region are used as the unit characteristics.

[0024] Each unit area is monitored and analyzed, and unit areas that do not meet the monitoring requirements are removed; the remaining unit areas are integrated and marked as supplementary monitoring areas.

[0025] In one embodiment, each unit region is monitored and analyzed to determine whether monitoring is necessary. If the influence of water-soluble ions generated is less than a preset standard based on the unit characteristics, it is considered as not meeting the monitoring requirements and is removed. Specifically, monitoring requirements are set according to user needs, and then the unit characteristics of the unit region are analyzed based on the monitoring requirements to achieve the screening of unit regions.

[0026] Specifically, existing methods can be used for screening. For example, a corresponding intelligent screening model can be established based on machine learning and deep learning algorithms. The intelligent screening model can be used to analyze the unit characteristics of the unit region to obtain the screening results.

[0027] In one embodiment, monitoring and analysis of each unit area includes: A monitoring and screening model is established, and the corresponding training set is labeled with relevant historical air characteristics for training. The expression of the monitoring and screening model is: ; In the formula: s represents the cell feature of the corresponding cell region, and the output data is the cell filter value HK(s), where the cell filter value is 1 or 0; By analyzing the unit characteristics of each unit region through the monitoring and screening model, the unit screening value of the corresponding unit region is obtained; Remove cell regions with a filter value of 0.

[0028] In one embodiment, the analysis of the supplementary monitoring area includes: Identify the individual unit regions corresponding to the supplementary monitoring area, obtain the unit characteristics of each unit region, and determine the types of air water-soluble ions that need to be monitored in that unit region based on the unit characteristics. Analyze the possible combinations of monitoring types to achieve monitoring of all types of air water-soluble ions. For example, if there are 7 types of air water-soluble ions, but only 5 need to be monitored to infer the other unmonitored air water-soluble ion types, then this is considered a monitoring type combination. For example, NH3 can be used to predict NH4. + (Ammonium salts in PM2.5), SO2 estimated SO4 2- (Secondary sulfate), NOx forecast NO3 - (Secondary nitrates), Cl - Predict K + (Biomass combustion markers), PM2.5 mass concentration prediction (Ca) 2+ / Mg 2+(Soil dust), etc. Based on this, the monitoring data of various air water-soluble ion species corresponding to the unit area can be obtained, and the monitoring species combination can be obtained. Various data prediction and inference relationships can be preset, and then rapid analysis can be carried out.

[0029] The process involves obtaining the user's monitoring accuracy requirements for each unit area. Since the monitoring accuracy varies depending on the combination of different monitoring types, and the monitoring requirements for airborne water-soluble ions also differ across areas, users need to preset their corresponding monitoring accuracy requirements. Based on these requirements, the monitoring type combinations are screened, eliminating those that do not meet the accuracy requirements. The monitoring accuracy can be determined using historical or simulated data for each combination, followed by comparison and screening. The monitoring cost of the remaining combinations is estimated according to the minimum required accuracy. The monitoring type combinations for each unit area are then prioritized in ascending order of monitoring cost to obtain a priority monitoring sequence for each unit area. Supplementary monitoring plans are determined based on the priority monitoring sequence for each unit area.

[0030] Ideally, all unit areas would use the highest priority monitoring type combination, and then the monitoring equipment and monitoring stations would be determined based on the monitoring type combination. However, due to the impact of the monitoring range and to avoid resource waste, smaller unit areas need to be monitored collaboratively with adjacent unit areas. In this case, the lowest-cost monitoring method analyzed based on the priority monitoring sequence of the corresponding unit area may not be the highest priority for a particular unit area, but rather the highest priority of the combination. Moreover, it is related to the actual monitoring method collected subsequently. For example, the current common method of installing monitoring equipment on passenger vehicles to achieve dynamic monitoring requires determining a monitoring scheme that can monitor all unit areas at the lowest cost based on the unit areas that this method can monitor.

[0031] The specific supplementary monitoring plan needs to be determined based on the actual situation. The standard is to select the monitoring plan with the lowest monitoring cost, given the known priority monitoring needs of each unit area.

[0032] In one embodiment, monitoring is arranged according to a supplementary monitoring plan, and corresponding sensors are arranged for the air water-soluble ions that need to be monitored according to the supplementary monitoring plan.

[0033] The monitoring module is used to monitor the target monitoring area in real time, obtain the corresponding regional monitoring data, and preprocess the regional monitoring data, namely, to remove invalid and outlier values, correct, and standardize the data, as well as to estimate and extrapolate the data according to the corresponding monitoring type combination, so as to obtain complete monitoring data of various air water-soluble ions. At the same time, the regional monitoring data also includes meteorological data, geographic information data and other related data required for subsequent air water-soluble ion analysis.

[0034] The data analysis module is used to analyze and process regional monitoring data, including a map display unit and an early warning unit; The graph display unit is used to analyze regional monitoring data and generate spatiotemporal heat maps, ion percentage rose diagrams, trend prediction curves, and other data analysis graphs that meet user needs; it generates data analysis graphs based on existing air water-soluble ion monitoring data analysis methods.

[0035] The early warning unit is used to perform real-time early warning analysis based on regional monitoring data, obtain corresponding early warning analysis results, and when the early warning analysis result indicates a monitoring anomaly, determine the corresponding cause of the anomaly, achieve causal tracing, and then generate corresponding prevention and control suggestions based on the cause of the anomaly; and generate early warning, causal tracing, and prevention and control suggestions based on existing technologies.

[0036] In one embodiment, the data analysis module may also include other functional units that perform analysis based on regional monitoring data.

[0037] The above formulas are all numerical calculations after removing dimensions. The formulas are obtained by software simulation based on a large amount of data and are closest to the real situation. The preset parameters and preset thresholds in the formulas are set by those skilled in the art according to the actual situation or obtained by simulation based on a large amount of data.

[0038] The above embodiments are only used to illustrate the technical methods of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical methods of the present invention without departing from the spirit and scope of the technical methods of the present invention.

Claims

1. An online air water-soluble ion monitoring data analysis platform, comprising a data analysis module, wherein the data analysis module is used to analyze and process regional monitoring data, including a spectrum display unit and an early warning unit; The graph display unit is used to analyze regional monitoring data and generate data analysis graphs; the data analysis graphs include spatiotemporal heat maps, ion percentage rose diagrams, and trend prediction curves; The early warning unit is used to perform real-time early warning analysis based on regional monitoring data and obtain corresponding early warning analysis results, including monitoring normal and monitoring abnormal. When the early warning analysis result indicates that the monitoring is normal, no corresponding action will be taken; When the early warning analysis result indicates a monitoring anomaly, the corresponding cause of the anomaly is determined, and corresponding prevention and control suggestions are generated based on the cause of the anomaly. The feature is that it also includes a regional analysis module and a monitoring module. The area analysis module is used to analyze the target monitoring area, identify each monitoring station within the target monitoring area in real time, and generate a dynamic monitoring map based on each monitoring station. The dynamic monitoring map includes the location of each monitoring station and the monitoring range corresponding to each monitoring station. Unmonitored areas are identified in real time based on the dynamic monitoring map. Corresponding supplementary monitoring areas are determined based on the unmonitored areas. The supplementary monitoring areas are analyzed to obtain supplementary monitoring plans for the supplementary monitoring areas. Monitoring is then carried out according to the supplementary monitoring plans. The monitoring module is used to monitor the target monitoring area in real time, obtain the regional monitoring data of the target monitoring area, and preprocess the regional monitoring data; the preprocessed regional monitoring data is then sent to the data analysis module.

2. The online air water-soluble ion monitoring data analysis platform according to claim 1, characterized in that, Dynamic monitoring maps are generated based on each monitoring station, including: Identify various dynamic range influence items that affect the monitoring range of monitoring stations; acquire historical monitoring data of each monitoring station; perform feature identification on the historical monitoring data based on each range influence item to obtain various range feature sets of each monitoring station; identify the monitoring range of the range feature sets based on the historical monitoring data; merge the range feature sets based on the monitoring range to obtain several merged feature sets; and associate the merged feature sets with the corresponding monitoring ranges. An initial information map is generated based on the location of each monitoring station. The monitoring stations are monitored in real time according to the range influence item to obtain the corresponding range feature set. The corresponding monitoring range is matched in real time according to the range feature set. The monitoring range is marked on the initial information map. The current initial information map is marked as a dynamic monitoring map.

3. The online air water-soluble ion monitoring data analysis platform according to claim 2, characterized in that, The range feature set is merged based on the monitoring range, including: Step SA1: Establish a combined evaluation model; Step SA2: Analyze the monitoring range between the two corresponding range feature sets using the merged evaluation model to obtain the merged evaluation value between the corresponding range feature sets, wherein the merged evaluation value is 1 or 0; The two range feature sets with a combined evaluation value of 1 are merged to obtain a combined feature set, and the monitoring range of the combined feature set is set; and the two range feature sets with a combined evaluation value of 0 are marked. Step SA3: Repeat step SA2 until the combined evaluation value between each range feature set is not 1, and obtain several combined feature sets. During the iteration of step SA2, the combined feature sets are treated as new range feature sets and participate in the iterative analysis.

4. The online air water-soluble ion monitoring data analysis platform according to claim 1, characterized in that, Based on the unmonitored areas, corresponding supplementary monitoring areas were determined, including: Acquire air features at various locations within the unmonitored area, merge locations with air feature similarity not less than threshold X1 and adjacent locations to obtain several unit regions; determine the unit features of each unit region; Each unit area is monitored and analyzed, and unit areas that do not meet the monitoring requirements are removed; the remaining unit areas are integrated and marked as supplementary monitoring areas.

5. The online air water-soluble ion monitoring data analysis platform according to claim 4, characterized in that, Monitoring and analysis of each unit area, including: A monitoring and screening model is established, and its expression is as follows: ； In the formula: s represents the cell feature of the corresponding cell region, and the output data is the cell filter value HK(s), where the cell filter value is 1 or 0; By analyzing the unit characteristics of each unit region through the monitoring and screening model, the unit screening value of the corresponding unit region is obtained; When the unit screening value is 1, the assessment indicates that the monitoring requirements are met. When the unit screening value is 0, the assessment does not meet the monitoring requirements.

6. The online air water-soluble ion monitoring data analysis platform according to claim 3, characterized in that, The expression for the combined evaluation model is: ； In the formula: (q, p) represents the input data, q and p represent the monitoring ranges of the two range feature sets to be merged and analyzed, respectively. p indicates that the two monitoring ranges can be considered equal; the output data is the combined evaluation value HP(q, p).

7. The online air water-soluble ion monitoring data analysis platform according to claim 1, characterized in that, An analysis of the supplementary monitoring areas was conducted, including: Identify each unit region corresponding to the supplementary monitoring area, obtain the unit characteristics of each unit region, determine the various types of air water-soluble ions that need to be monitored in the unit region based on the unit characteristics, and analyze the monitoring type combinations for various types of air water-soluble ions. Obtain the user's monitoring accuracy requirements for each unit area, and filter the monitoring type combinations for the unit area according to the monitoring accuracy requirements, eliminating monitoring type combinations that do not meet the monitoring accuracy requirements; The monitoring cost of the remaining monitoring type combinations is estimated according to the minimum standard of monitoring accuracy requirements. The monitoring type combinations corresponding to the unit area are prioritized in order of increasing monitoring cost to obtain the priority monitoring sequence of the unit area. Supplementary monitoring plans are determined based on the priority monitoring sequence for each unit area.

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