Multi-element cooperative processing method and system for haze environment monitoring and forecasting data

Abstract

The invention relates to the technical field of environment data processing, in particular to a multi-element cooperative processing method and system for haze environment monitoring and forecasting data. Comprising the following steps: S1, acquiring pollutant data in a CUACE mode, and performing interpolation processing on the pollutant data to obtain hourly pollutant concentration data; s2, acquiring pollutant IAQI data according to the hourly pollutant concentration data, and trimming the hourly data to obtain adjusted hourly pollutant concentration data; and S3, according to the adjusted hourly pollutant concentration data, calculating the primary pollutant and the AQI hour by hour, or calculating the primary pollutant and the AQI 24 hours by 24 hours, and adjusting the primary pollutant and the AQI by using a genetic algorithm. According to the method, primary pollutant sorting is dynamically adjusted by using the genetic algorithm, pollution characteristics under special weather conditions are adapted, and the accuracy and practicability of forecasting are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data processing, and in particular to a multi-factor collaborative processing method and system for haze environment monitoring and forecasting data. Background Art

[0002] In recent years, with the acceleration of industrialization and the increase in transportation, air pollution has gradually intensified, especially in some large cities and industrial areas, where smog weather has occurred frequently, seriously affecting environmental quality and human health. In order to solve this problem, various air quality monitoring and forecasting systems have been proposed. These systems usually rely on AQI to assess and predict the degree of air pollution.

[0003] Traditional air quality monitoring methods are usually based on fixed models and data processing techniques, such as obtaining pollutant concentration data through physical and chemical sensors and processing the data through linear interpolation or simple regression analysis. However, these traditional methods have shortcomings in the following aspects: the interpolation processing of pollutant data by traditional methods is usually too simple, and does not take into account the differences in the characteristics of different pollutants, resulting in inaccurate hourly pollutant concentration data; existing methods are mostly general models, which fail to be customized and optimized according to the distribution of regional pollution sources and meteorological conditions, resulting in poor adaptability of the system in different regions. Summary of the invention

[0004] In order to overcome the disadvantage that primary pollutants solidify in different seasons, the present invention provides a multi-factor collaborative processing method and system for haze environment monitoring and forecasting data.

[0005] The technical solution of the present invention is: a multi-factor collaborative processing method for haze environment monitoring and forecasting data, comprising the following steps:

[0006] S1: Obtain pollutant data in CUACE mode, perform interpolation processing on the pollutant data, and obtain hourly pollutant concentration data;

[0007] S2: acquiring pollutant IAQI data according to the hourly pollutant concentration data, and trimming the hourly data to obtain adjusted hourly pollutant concentration data;

[0008] S3: Calculate the primary pollutants and AQI hourly or 24-hourly based on the adjusted hourly pollutant concentration data, and adjust the primary pollutants and AQI using a genetic algorithm;

[0009] S4: After adjusting the primary pollutant index in the grid data, the grid primary pollutant index is obtained.

[0010] Preferably, the step of acquiring pollutant data in the CUACE mode and performing interpolation processing on the pollutant data to obtain hourly pollutant concentration data includes:

[0011] When the pollutant is ozone data, collect ozone concentration data for 8 times every 3 hours, recorded as (y1, y2, y8); calculate the sliding average of ozone concentration data for three consecutive times, where, for each time period, select the concentration data of the three times before and after the time period and calculate their average value; take the time period with the largest sliding average value during the 8 hours as the time period with the maximum average concentration; use the first interpolation formula according to the maximum average concentration to obtain the first time concentration assignment, and assign values ​​to the other 3-hour time concentration data except this time period according to the first time concentration assignment; assign values ​​to other time periods without data as the first time concentration assignment or the minimum value of the concentration data of each time period in the time period with the largest sliding average value; obtain the hourly pollutant concentration data of ozone, where the first interpolation formula is:

[0012]

[0013] In the formula, K1 is the concentration value assigned at the first time; is the maximum average concentration; c i is the ozone concentration data at the ith time; q is the starting time, and its range is [1, 6].

[0014] Preferably, the step of acquiring pollutant data in the CUACE mode and performing interpolation processing on the pollutant data to obtain hourly pollutant concentration data includes:

[0015] When the pollutant is not ozone data, the pollutant concentration data of 8 times are collected every 3 hours, and the 24-hour average concentration of each pollutant is obtained based on the inferred pollutant concentration data. The second time concentration assignment is obtained using the second interpolation formula based on the average concentration and the pollutant concentration data. The other 16 time periods except the 3-hour time period are assigned according to the second time concentration assignment to obtain the hourly pollutant concentration data of other pollutants, wherein the second interpolation formula is:

[0016]

[0017] In the formula, K2 is the concentration value assigned for the second time period, x is the 24-hour average concentration of each pollutant, i is the i-th time period in the 3-hour period, c i is the pollutant concentration data for the ith time period, and n is the total number of times every 3 hours.

[0018] Preferably, obtaining pollutant IAQI data based on the hourly pollutant concentration data and trimming the hourly data include: back-calculating the pollutant IAQI data using a rounding method to obtain a back-calculated pollutant concentration; and comparing the back-calculated pollutant concentration with the average value of the hourly pollutant concentration data, and when the back-calculated pollutant concentration is not equal to the average value of the hourly pollutant concentration data, modifying the pollutant concentration at 20 o'clock.

[0019] Preferably, the primary pollutants and AQI are calculated hourly or 24 hours according to the adjusted hourly pollutant concentration data, and the primary pollutants and AQI are adjusted using a genetic algorithm, including: using the O3 / 1h calculation method when calculating the primary pollutants and AQI hourly, and using the O3 / 8h calculation method when calculating the primary pollutants and AQI 24 hours later, wherein, when calculating the primary pollutants and AQI 24 hours later, the O3 / 8h calculation method is not used for the first 7 times of the daily time limit, and the first 7 times or times exceeding the forecast time limit are supplemented with 9999.

[0020] Preferably, the primary pollutants and AQI are calculated hourly based on the adjusted hourly pollutant concentration data, including: taking ozone as the primary pollutant in a seasonal context; using a genetic algorithm to optimize the order of PM2.5, PM10, ozone, SO2, CO, and NO2 in a non-seasonal context, and dynamically adjusting the priority under special weather conditions based on the optimization results of the genetic algorithm.

[0021] Preferably, the order of PM2.5, PM10, ozone, SO2, CO, and NO2 is optimized using a genetic algorithm in a non-seasonal context, and the priority is dynamically adjusted under special weather conditions based on the optimization results of the genetic algorithm, including: obtaining real-time pollutant concentration data, historical AQI data, and pollution source distribution characteristics in the target area; encoding the priority of the primary pollutant in the form of a sequence as a chromosome, each chromosome containing all pollutants, and the position of the gene represents the priority of the pollutant; defining a fitness function, and optimizing the priority ranking of the primary pollutants based on the actual impact correlation of the pollutants on AQI, the public health risk index, and the pollution source distribution characteristics of the target area; initializing the population by randomly generating a number of chromosomes as the initial priority sequence; using a roulette wheel selection or tournament selection method to select chromosomes from the population according to the fitness value; using a partial mapping crossover method to generate a new generation of chromosomes; performing a mutation operation on the chromosome to randomly exchange the positions of two pollutants; and outputting the optimal priority ranking after the fitness function value converges or reaches a preset iteration number.

[0022] Preferably, the priority sorting of primary pollutants is optimized based on the correlation of the actual impact of pollutants on AQI, the public health risk index and the distribution characteristics of pollution sources in the target area, including: maximizing the correlation between the adjusted priority and the actual AQI change trend; minimizing the public health risk corresponding to the adjusted priority; maximizing the consistency between the priority and the characteristics of the pollution source in the target area; obtaining the proportion of major pollution sources in the target area, and weightedly adjusting the priority of the primary pollutants according to their impact; simulating the diffusion of pollutants in different time periods based on the meteorological conditions of the target area, and optimizing the priority sorting; analyzing the historical air quality data of the target area, and adjusting the priority according to the dominant pollutants and their impact on health risks.

[0023] Preferably, the step of adjusting the primary pollutant index in the grid point data to obtain the grid point primary pollutant index comprises:

[0024] The grid point adjustment formula is used to adjust the primary pollutant data of each grid point, where the grid point adjustment formula is:

[0025] I adjusted =I*μP;

[0026] In the formula, I adjusted is the adjusted grid point primary pollutant index; μ is the adjustment factor; P is the adjustment weight factor; I is the unadjusted grid point primary pollutant index;

[0027] The weight adjustment formula is used to obtain the adjusted weight factor, where the weight adjustment formula is:

[0028]

[0029] In the formula, E i is the primary pollutant data of the ith grid point; ∑E is the sum of the primary pollutant concentration data at all grid points; W loc is the geographic attribute weight of the grid point location; W hist The historical weight of the primary pollutant concentration data at the grid location;

[0030] The historical weight ratio formula is used to obtain the historical weight ratio of the primary pollutant concentration data at the grid location, where the historical weight ratio formula is:

[0031]

[0032] Where W hist is the historical weight of the primary pollutant concentration data at the grid location; r1 is the actual primary pollutant concentration data; r0 is the standard primary pollutant concentration data from historical experience; ρ is the adjustment factor.

[0033] Preferably, a multi-factor collaborative processing system for haze environment monitoring and forecasting data includes:

[0034] The data acquisition and preprocessing module is used to obtain pollutant data under the CUACE mode, perform interpolation processing on ozone and other pollutants respectively, and obtain hourly pollutant concentration data;

[0035] The pollutant index calculation and trimming module is used to calculate the pollutant IAQI based on the hourly pollutant concentration data, trim the hourly data, and obtain the hourly pollutant concentration data;

[0036] The primary pollutant and AQI calculation module is used to calculate the primary pollutants and AQI hourly or 24-hourly, and optimize the primary pollutants and AQI using genetic algorithms;

[0037] Dynamic adjustment and optimization module, which adjusts the priority of primary pollutants based on seasonal background and special weather conditions, and dynamically optimizes the ranking of primary pollutants;

[0038] The grid data adjustment module adjusts the primary pollutant index in the grid data to obtain the grid primary pollutant index.

[0039] The beneficial effect is that the present invention significantly improves the accuracy of pollutant concentration data by performing differentiated designs on interpolation processing of different pollutants and adopting a combination of a sliding average method and an interpolation formula.

[0040] In particular, an interpolation method based on the maximum sliding average was used for ozone concentration data to ensure the smoothness and accuracy of ozone concentration data when calculated hourly.

[0041] The system optimizes the priority ranking of primary pollutants by analyzing the distribution characteristics of pollution sources and meteorological conditions in the target area. By adjusting the priorities by weight, the system can effectively predict and respond to changes in pollutants in different regions, and has strong regional adaptability; at the same time, the system combines the historical data and real-time monitoring data of the region to achieve accurate modeling of the impact of pollutants on AQI.

[0042] The present invention takes into account the public health risk index in the pollutant priority sorting, ensuring that the health hazards of pollutants are prioritized and incorporated into system optimization; through the optimized pollutant priorities, the system can better provide decision support for air quality management and public health risk control.

[0043] The spatial resolution and accuracy of grid data are enhanced through grid adjustment formulas and multi-layer weight factors, such as geographic attributes and historical concentration weights, which helps to improve monitoring and prediction effects over large areas. The introduction of optimization methods based on target area characteristics, such as pollution source distribution and meteorological conditions, can achieve personalized adaptation for different regions and provide more detailed data support for air quality management and decision-making. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] Figure 1 This is a flow chart of a digital archive management method based on big data of the present invention;

[0045] Figure 2 This is a flow chart of a digital archive management system based on big data of the present invention. DETAILED DESCRIPTION

[0046] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0047] Embodiment 1: A multi-factor collaborative processing method for haze environment monitoring and forecasting data, such as Figure 1-Figure 2 As shown, the following steps are included:

[0048] S1: Obtain pollutant data in CUACE mode, perform interpolation processing on the pollutant data, and obtain hourly pollutant concentration data;

[0049] When the pollutant is ozone data, collect ozone concentration data for 8 times every 3 hours, recorded as (y1, y2, y8); calculate the sliding average of ozone concentration data for three consecutive times, where, for each time period, select the concentration data of the three times before and after the time period and calculate their average value; take the time period with the largest sliding average value during the 8 hours as the time period with the maximum average concentration; use the first interpolation formula according to the maximum average concentration to obtain the first time concentration assignment, and assign values ​​to the other 3-hour time concentration data except this time period according to the first time concentration assignment; assign values ​​to other time periods without data as the first time concentration assignment or the minimum value of the concentration data of each time period in the time period with the largest sliding average value; obtain the hourly pollutant concentration data of ozone, where the first interpolation formula is:

[0050]

[0051] In the formula, K1 is the concentration value assigned at the first time; is the maximum average concentration; c i is the ozone concentration data at the ith time; q is the starting time, and its range is [1, 6].

[0052] It should be noted that data collection: During the monitoring process, ozone concentration data is recorded once every 3 hours, for a total of 8 times, recorded as (y1, y2, ..., y8). These 8 times represent the discontinuous sampling results within 24 hours; for each period, three consecutive times of ozone concentration data are selected to calculate the sliding average, the formula is: The sliding average calculated by (i=1, 2, ..., 6) reflects the trend of ozone concentration change in each period. In the formula, y i is the ith time period, AVG is the sliding average; from the 6 sliding averages, find the maximum value, which corresponds to the time period with the highest ozone concentration; this value will be used as the core parameter of the interpolation processing to reflect the peak period of ozone pollution; the first time concentration value is calculated by combining the maximum sliding average with the corresponding three time ozone concentration values ​​using the first interpolation formula; for the time concentration values ​​other than the time period corresponding to the maximum sliding average, assign values ​​according to the following rules: if there is no data for the time period, directly assign it to K1; or select the minimum value in the maximum sliding average period for assignment; finally generate hourly ozone concentration data within 24 hours, covering all time periods, providing a complete concentration change trend for subsequent analysis.

[0053] When the pollutant is not ozone data, the pollutant concentration data of 8 times are collected every 3 hours, and the 24-hour average concentration of each pollutant is obtained based on the inferred pollutant concentration data. The second time concentration assignment is obtained using the second interpolation formula based on the average concentration and the pollutant concentration data. The other 16 time periods except the 3-hour time period are assigned according to the second time concentration assignment to obtain the hourly pollutant concentration data of other pollutants, wherein the second interpolation formula is:

[0054]

[0055] In the formula, K2 is the concentration value assigned for the second time period, x is the 24-hour average concentration of each pollutant, i is the i-th time period in the 3-hour period, c i is the pollutant concentration data for the ith time period, and n is the total number of times every 3 hours.

[0056] It should be noted that the 24-hour average concentration x is calculated using 8 hourly data, the 24-hour average concentration is combined with the 8 3-hourly data, and the second interpolation formula is used to calculate the concentrations of the other 16 hourly times except the 3-hourly data. The original concentration value of the 3-hourly sampling time is retained, and the other 16 unsampled time times are uniformly assigned a value of K2 to complete the hourly data.

[0057] S2: acquiring pollutant IAQI data according to the hourly pollutant concentration data, and trimming the hourly data to obtain adjusted hourly pollutant concentration data;

[0058] The pollutant IAQI data is back-calculated using the rounding method to obtain the back-calculated pollutant concentration; the back-calculated pollutant concentration is compared with the average value of the hourly pollutant concentration data. When the back-calculated pollutant concentration is not equal to the average value of the hourly pollutant concentration data, the pollutant concentration at 20 o'clock is modified.

[0059] It should be noted that the IAQI value of the pollutant is obtained by using the hourly pollutant concentration data and the calculation formula of the pollutant air quality index. The calculation of IAQI complies with relevant national standards. The IAQI value is rounded off and reversed to re-estimate the pollutant concentration value, which is called the reversed concentration. The reversed pollutant concentration value is compared with the average value of the hourly pollutant concentration data. When the reversed pollutant concentration is not equal to the average value of the hourly concentration, the pollutant concentration value at the 20th hour is adjusted to make the data more consistent with the actual distribution trend of the pollutant concentration.

[0060] S3: Calculate the primary pollutants and AQI hourly or 24-hourly based on the adjusted hourly pollutant concentration data, and adjust the primary pollutants and AQI using a genetic algorithm;

[0061] When calculating the primary pollutants and AQI on an hourly basis, the O3 / 1h calculation method is adopted. When calculating the primary pollutants and AQI on a 24-hour basis, the O3 / 8h calculation method is adopted. When calculating the primary pollutants and AQI on a 24-hour basis, the O3 / 8h calculation method is not used for the first 7 hours of the day, and 9999 is used to supplement the first 7 hours or hours that exceed the forecast time limit.

[0062] It should be noted that when calculating hourly, the primary pollutants and air quality index for each hour are determined based on the concentration of each pollutant and the corresponding air quality sub-index; the methods for calculating the primary pollutants include: O3 (ozone) 1-hour average, called O3 / 1h; when calculating 24 hours, the daily primary pollutants and AQI are calculated based on the 8-hour sliding mean of ozone (O3 / 8h); for the first 7 time periods of the day, O3 / 8h is not used for calculation, but 9999 is used to supplement these time periods or time periods beyond the forecast time period to reduce the impact on the accuracy of the 24-hour sliding mean; a genetic algorithm is used to adjust the sorting order of the primary pollutants, where the primary pollutant is the pollutant with the largest IAQI value in that time period.

[0063] Acquire real-time pollutant concentration data, historical AQI data and pollution source distribution characteristics of the target area; encode the priority of the primary pollutant in the form of a sequence as a chromosome, each chromosome contains all pollutants, and the position of the gene represents the priority of the pollutant; define a fitness function, and optimize the priority ranking of the primary pollutants based on the actual impact correlation of pollutants on AQI, the public health risk index and the pollution source distribution characteristics of the target area; initialize the population by randomly generating a number of chromosomes as the initial priority sequence; use roulette wheel selection or tournament selection method to select chromosomes from the population according to the fitness value; use partial mapping crossover method to generate a new generation of chromosomes; perform mutation operations on the chromosomes to randomly exchange the positions of two pollutants; output the optimal priority ranking after the fitness function value converges or reaches the preset iteration number.

[0064] It should be noted that the priority of ozone in a seasonal context: In certain seasons (May-September), due to high temperatures and strong solar radiation, ozone concentrations are usually high, which has a significant impact on air quality. Therefore, in such a seasonal context, ozone is directly set as the primary pollutant without further adjustment; dynamic adjustment in a non-seasonal context: In other seasons (such as autumn or winter), ozone contributes little to AQI, and the original fixed ranking order is PM2.5>PM10>ozone>other pollutants, where the other pollutants include SO2, CO, and NO2, and the priority of each pollutant needs to be optimized through a genetic algorithm; optimize the order of pollutants: including PM2.5, PM10, ozone and other pollutants, comprehensively consider the actual impact of each pollutant, and adjust its priority order according to the optimization results; dynamic adjustment of special weather conditions: under special weather conditions such as sandstorms, heavy rains or haze, the priority of the primary pollutants is dynamically adjusted according to weather characteristics, for example, PM10 is given priority in sandstorm weather.

[0065] In a seasonal context, ozone is taken as the primary pollutant; in a non-seasonal context, a genetic algorithm is used to optimize the order of PM2.5, PM10, ozone, SO2, CO, and NO2, and the priority is dynamically adjusted under special weather conditions based on the optimization results of the genetic algorithm.

[0066] It should be noted that the real-time pollutant concentration data (such as PM2.5, PM10, ozone), historical data of AQI, and the distribution characteristics of pollution sources in the target area (such as industrial emissions, motor vehicle exhaust, natural sources) are obtained to encode chromosomes: the pollutant priority is encoded as a chromosome, where each chromosome represents a pollutant priority order, and the position of the gene represents the priority of the pollutant. For example, chromosomes PM2.5, PM10, ozone, and other pollutants indicate that PM2.5 has the highest priority and other pollutants have the lowest priority. The fitness function is defined to comprehensively consider the actual impact of pollutants on AQI, the public health risk index (HR index), and the distribution characteristics of pollution sources in the target area; population initialization: randomly generate a number of chromosomes to form an initial population, and each chromosome is a pollutant priority sequence; selection operation: using roulette selection or tournament selection methods, according to the fitness value of the chromosome, select chromosomes with high fitness to enter the next generation; using the partial mapping crossover (PMX) method, the gene sequences of two chromosomes are crossed to generate a new A generation of chromosomes, for example: Chromosome 1: PM2.5, ozone, PM10, others; Chromosome 2: ozone, PM10, others, PM2.5; Crossover results: PM2.5, PM10, others, ozone; Mutation operation: Randomly exchange two gene positions in a chromosome, for example: mutate PM2.5, PM10, ozone, others to: PM2.5, ozone, PM10, others; Iteration and convergence: Repeat selection, crossover and mutation operations until the fitness value converges or reaches the preset number of iterations, and output the optimal chromosome; Dynamic adjustment: Under special weather conditions (such as sandstorms, heavy rains), dynamically adjust the priority of primary pollutants based on the optimization results.

[0067] Maximize the correlation between the adjusted priorities and the actual AQI change trend; minimize the public health risks corresponding to the adjusted priorities; maximize the consistency between the priorities and the characteristics of the pollution sources in the target area; obtain the proportion of the main pollution sources in the target area, and make weighted adjustments to the priorities of the primary pollutants according to their impact; simulate the diffusion of pollutants in different time periods based on the meteorological conditions of the target area, and optimize the priority sorting; analyze the historical air quality data of the target area, and adjust the priorities according to the dominant pollutants and their impact on health risks.

[0068] It should be noted that the correlation between the adjusted priority and the actual AQI change trend is maximized: by optimizing the ranking of the primary pollutants, the adjusted priority can more accurately reflect the change trend of AQI; minimize public health risks: according to the degree of harm to health caused by different pollutants, give priority to pollutants with high health risk index; maximize the matching degree with the characteristics of pollution sources in the target area: by analyzing the proportion of major pollution sources in the target area, high-impact pollutants are given a higher priority. According to the impact of each pollutant on AQI, the health risk index and the matching degree with the characteristics of the pollution source, the priority is dynamically adjusted through a weighted method.

[0069] S4: After adjusting the primary pollutant index in the grid data, the grid primary pollutant index is obtained.

[0070] The grid point adjustment formula is used to adjust the primary pollutant data of each grid point, where the grid point adjustment formula is:

[0071] I adjusted =I*μP;

[0072] In the formula, I adjusted is the adjusted grid point primary pollutant index; μ is the adjustment factor; P is the adjustment weight factor; I is the unadjusted grid point primary pollutant index;

[0073] The weight adjustment formula is used to obtain the adjusted weight factor, where the weight adjustment formula is:

[0074]

[0075] In the formula, E i is the primary pollutant data of the ith grid point; ∑E is the sum of the primary pollutant concentration data at all grid points; W loc is the geographic attribute weight of the grid point location; W hist The historical weight of the primary pollutant concentration data at the grid location;

[0076] The historical weight ratio formula is used to obtain the historical weight ratio of the primary pollutant concentration data at the grid location, where the historical weight ratio formula is:

[0077]

[0078] Where W hist is the historical weight of the primary pollutant concentration data at the grid location; r1 is the actual primary pollutant concentration data; r0 is the standard primary pollutant concentration data from historical experience; ρ is the adjustment factor.

[0079] It should be noted that the data of primary pollutants at the grid points are first collected and initialized, and the grid data of the target area are obtained from the monitoring network, including: the primary pollutant data of each grid point, the sum of the primary pollutant concentration data of all grid points in the area, the location attributes of each grid point and its geographical weight, and the historical primary pollutant concentration weight of each grid point; the primary pollutant index of each grid point is adjusted by using the grid adjustment formula to accurately reflect the actual pollution status of the grid point, while ensuring the coordination of data between grid points; the weight adjustment formula is used to calculate the adjustment weight factor, and the importance of each grid point data is comprehensively weighed through the proportion of pollutant concentration, geographical location attributes and historical pollution characteristics; the historical proportion weight formula is used to quantify the impact of historical pollution data on the current grid index adjustment to ensure that historical characteristics are fully reflected in the adjustment process.

[0080] Embodiment 2: Based on Embodiment 1, a multi-factor collaborative processing system for haze environment monitoring and forecasting data includes:

[0081] The data acquisition and preprocessing module is used to obtain pollutant data under the CUACE mode, perform interpolation processing on ozone and other pollutants respectively, and obtain hourly pollutant concentration data;

[0082] The pollutant index calculation and trimming module is used to calculate the pollutant IAQI based on the hourly pollutant concentration data, trim the hourly data, and obtain the hourly pollutant concentration data;

[0083] The primary pollutant and AQI calculation module is used to calculate the primary pollutants and AQI hourly or 24-hourly, and optimize the primary pollutants and AQI using genetic algorithms;

[0084] Dynamic adjustment and optimization module, which adjusts the priority of primary pollutants based on seasonal background and special weather conditions, and dynamically optimizes the ranking of primary pollutants;

[0085] The grid data adjustment module adjusts the primary pollutant index in the grid data to obtain the grid primary pollutant index.

[0086] The above is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A multi-factor collaborative processing method for haze environment monitoring and forecasting data, characterized in that: The following steps are involved: S1: Obtain pollutant data in CUACE mode, perform interpolation processing on the pollutant data, and obtain hourly pollutant concentration data; S2: acquiring pollutant IAQI data according to the hourly pollutant concentration data, and trimming the hourly data to obtain adjusted hourly pollutant concentration data; S3: Calculate the primary pollutants and AQI hourly or 24-hourly based on the adjusted hourly pollutant concentration data, and adjust the primary pollutants and AQI using a genetic algorithm; S4: After adjusting the primary pollutant index in the grid data, the grid primary pollutant index is obtained.

2. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 1 is characterized in that: The step of obtaining pollutant data in the CUACE mode and performing interpolation processing on the pollutant data to obtain hourly pollutant concentration data includes: When the pollutant is ozone data, collect ozone concentration data for 8 times every 3 hours, recorded as (y1, y2, ..., y8); calculate the sliding average of three consecutive ozone concentration data, where for each time period, select the concentration data of the three times before and after the time period and calculate their average value; The time period with the maximum sliding average value during the 8-hour period is regarded as the time period with the maximum average concentration; the first interpolation formula is used according to the maximum average concentration to obtain the first time concentration assignment, and the concentration data of other 3-hour time periods except this time period are assigned according to the first time concentration assignment; the time assignment for other time periods without data is the minimum value of the first time concentration assignment or the concentration data of each time period in the time period with the maximum sliding average value; the hourly pollutant concentration data of ozone is obtained, wherein the first interpolation formula is: In the formula, K1 is the concentration value assigned at the first time; is the maximum average concentration; c i is the ozone concentration data at the ith time; q is the starting time, and its range is [1, 6].

3. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 1 is characterized in that: The step of obtaining pollutant data in the CUACE mode and performing interpolation processing on the pollutant data to obtain hourly pollutant concentration data includes: When the pollutant is not ozone data, the pollutant concentration data of 8 times are collected every 3 hours, and the 24-hour average concentration of each pollutant is obtained based on the inferred pollutant concentration data. The second time concentration assignment is obtained using the second interpolation formula based on the average concentration and the pollutant concentration data. The other 16 time periods except the 3-hour time period are assigned according to the second time concentration assignment to obtain the hourly pollutant concentration data of other pollutants, wherein the second interpolation formula is: In the formula, K2 is the concentration value assigned for the second time period, x is the 24-hour average concentration of each pollutant, i is the i-th time period in the 3-hour period, c i is the pollutant concentration data for the ith time period, and n is the total number of times every 3 hours.

4. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 1 is characterized in that: The method of obtaining pollutant IAQI data based on the hourly pollutant concentration data and trimming the hourly data includes: performing back-calculation based on the pollutant IAQI data using a rounding method to obtain a back-calculated pollutant concentration; and comparing the back-calculated pollutant concentration with the average value of the hourly pollutant concentration data, and when the back-calculated pollutant concentration is not equal to the average value of the hourly pollutant concentration data, modifying the pollutant concentration at 20 o'clock.

5. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 1 is characterized in that: The method comprises: calculating the primary pollutants and AQI hourly or 24-hourly based on the adjusted hourly pollutant concentration data, and adjusting the primary pollutants and AQI using a genetic algorithm, including: using the O3 / 1h calculation method when calculating the primary pollutants and AQI hourly, and using the O3 / 8h calculation method when calculating the primary pollutants and AQI 24 hours later, wherein, when calculating the primary pollutants and AQI 24 hours later, the O3 / 8h calculation method is not used for the first 7 times of the daily time limit, and the first 7 times or the times exceeding the forecast time limit are supplemented with 9999.

6. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 5 is characterized in that: The method calculates the primary pollutants and AQI hourly based on the adjusted hourly pollutant concentration data, including: taking ozone as the primary pollutant in a seasonal context; using a genetic algorithm to optimize the order of PM2.5, PM10, ozone, SO2, CO, and NO2 in a non-seasonal context, and dynamically adjusting the priority under special weather conditions based on the optimization results of the genetic algorithm.

7. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 6 is characterized in that: The method uses a genetic algorithm to optimize the order of PM2.5, PM10, ozone, SO2, CO, and NO2 in a non-seasonal context, and dynamically adjusts the priority under special weather conditions based on the optimization results of the genetic algorithm, including: obtaining real-time pollutant concentration data, historical AQI data, and pollution source distribution characteristics of the target area; encoding the priority of the primary pollutant in a sequence form as a chromosome, each chromosome contains all pollutants, and the position of the gene represents the priority of the pollutant; defining a fitness function, and optimizing the priority sorting of the primary pollutants based on the actual impact correlation of the pollutants on the AQI, the public health risk index, and the pollution source distribution characteristics of the target area; initializing the population, and randomly generating a number of chromosomes as the initial priority sequence; using a roulette wheel selection or tournament selection method to select chromosomes from the population according to the fitness value; using a partial mapping crossover method to generate a new generation of chromosomes; performing a mutation operation on the chromosome to randomly exchange the positions of two pollutants; and outputting the optimal priority sorting after the fitness function value converges or reaches a preset iteration number.

8. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 7 is characterized in that: The method of optimizing the priority ranking of primary pollutants based on the actual impact correlation of pollutants on AQI, the public health risk index and the distribution characteristics of pollution sources in the target area includes: maximizing the correlation between the adjusted priority and the actual AQI change trend; minimizing the public health risk corresponding to the adjusted priority; maximizing the consistency between the priority and the characteristics of the pollution source in the target area; obtaining the proportion of major pollution sources in the target area, and weightedly adjusting the priority of the primary pollutants according to their impact; simulating the diffusion of pollutants in different time periods based on the meteorological conditions of the target area, and optimizing the priority ranking; analyzing the historical air quality data of the target area, and adjusting the priority according to the dominant pollutants and their impact on health risks.

9. The multi-factor collaborative processing method for haze environment monitoring and forecasting data according to claim 1 is characterized in that: After the primary pollutant index in the grid point data is adjusted, the grid point primary pollutant index is obtained, including: The grid point adjustment formula is used to adjust the primary pollutant data of each grid point, where the grid point adjustment formula is: I adjusted =I*μP; In the formula, I adjusted is the adjusted grid point primary pollutant index; μ is the adjustment factor; P is the adjustment weight factor; I is the unadjusted grid point primary pollutant index; The weight adjustment formula is used to obtain the adjusted weight factor, where the weight adjustment formula is: In the formula, E i is the primary pollutant data of the ith grid point; ∑E is the sum of the primary pollutant concentration data at all grid points; W loc is the geographic attribute weight of the grid point location; W hist The historical weight of the primary pollutant concentration data at the grid location; The historical weight ratio formula is used to obtain the historical weight ratio of the primary pollutant concentration data at the grid location, where the historical weight ratio formula is: Where W hist is the historical weight of the primary pollutant concentration data at the grid location; r1 is the actual primary pollutant concentration data; r0 is the standard primary pollutant concentration data from historical experience; ρ is the adjustment factor.

10. A multi-factor collaborative processing system for haze environment monitoring and forecasting data, according to a multi-factor collaborative processing method for haze environment monitoring and forecasting data according to any one of claims 1 to 9, characterized in that: include: The data acquisition and preprocessing module is used to obtain pollutant data under the CUACE mode, perform interpolation processing on ozone and other pollutants respectively, and obtain hourly pollutant concentration data; The pollutant index calculation and trimming module is used to calculate the pollutant IAQI based on the hourly pollutant concentration data, trim the hourly data, and obtain the hourly pollutant concentration data; The primary pollutant and AQI calculation module is used to calculate the primary pollutants and AQI hourly or 24-hourly, and optimize the primary pollutants and AQI using genetic algorithms; Dynamic adjustment and optimization module, which adjusts the priority of primary pollutants based on seasonal background and special weather conditions, and dynamically optimizes the ranking of primary pollutants; The grid data adjustment module adjusts the primary pollutant index in the grid data to obtain the grid primary pollutant index.

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