VOCS governance data intelligent management method and system

Abstract

The invention relates to the technical field of environmental data processing, in particular to a VOCS treatment data intelligent management method and system, and the method comprises the following steps: based on VOCS real-time concentration data, collecting a VOCS component proportion, an emission temperature and an air flow rate, analyzing a VOCS concentration range, recognizing a change trend under an emission condition, and obtaining an emission characteristic concentration reference interval. According to the method, through collection of concentration data of volatile organic compounds and extraction of emission characteristics, pollutant change trends under different emission conditions are identified, the influence of environmental variables on the emission characteristics is clear, the analysis capability of emission rules is improved, the deviation condition of monitoring data is accurately judged, data points with relatively low credibility are screened, and the method is suitable for large-scale popularization and application. Abnormal data greatly influenced by disturbance is eliminated, data quality stability is improved, storage parameters and frequency are optimized through data matching analysis, efficient and accurate data storage is ensured, low-value data is screened, redundant occupation is reduced, storage efficiency is improved, and reasonable resource allocation is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of environmental data processing, and particularly to an intelligent management method and system for VOCS treatment data. Background Art

[0002] The technical field of environmental data processing includes multiple aspects such as the collection, analysis, storage, and management of environmental monitoring data. The core content of this technical field includes using sensor devices to obtain pollutant data in environmental media such as air, water, and soil, transmitting the monitoring information to the data processing system through data transmission technology, using specific data processing methods to clean, store, and classify the original data, and combining calculation models for pollution source analysis and trend prediction. This technical field also involves compliance assessment methods based on environmental standards, setting thresholds for different pollutants and giving real-time warnings, and at the same time combining visualization technology to display the environmental pollution situation, providing data support for environmental governance decisions.

[0003] Among them, the intelligent management method for VOCS treatment data refers to a technical means for efficiently processing the data involved in the treatment process of volatile organic compounds. This method covers the collection and analysis of VOCS emission data, uses high-precision monitoring equipment to collect industrial emission and environmental concentration data, classifies the data according to pollution characteristics, uses data screening methods to eliminate outliers, improves data accuracy through dynamic correction, and constructs an emission characteristic model based on statistical analysis methods to identify emission trends. This method also includes data verification of the implementation of treatment measures through rule matching and pattern recognition, and manages historical data with a standardized storage structure to support long-term monitoring and analysis.

[0004] The existing technology has problems in the ability to evaluate the credibility of data during the pollutant monitoring and treatment process. Due to many environmental disturbance factors, it is difficult to accurately judge the deviation of the original data, resulting in ineffective elimination of abnormal data and affecting the stability of monitoring data. In terms of data storage, the existing solutions fail to effectively match the treatment working conditions, resulting in insufficient accuracy of data storage and difficult dynamic adjustment of storage parameters according to treatment requirements, increasing the complexity of data management and affecting the effectiveness of long-term monitoring and analysis. The existing technology has limited ability to screen the value of data, unable to accurately identify the treatment contributions of the stored data, resulting in the storage space being occupied by low-value data and reducing the utilization efficiency of data storage and computing resources. Summary of the Invention

[0005] To solve the problem of insufficient ability to evaluate the credibility of data in the process of pollutant monitoring and treatment in the existing technology. Due to many environmental disturbance factors, it is difficult to accurately judge the deviation of the original data, resulting in ineffective elimination of abnormal data and affecting the stability of monitoring data. In terms of data storage, the existing solutions fail to effectively match the treatment conditions, resulting in insufficient accuracy of data storage. It is difficult to dynamically adjust the storage parameters according to the treatment requirements, increasing the complexity of data management and affecting the effectiveness of long-term monitoring and analysis. The existing technology has limited ability to screen the value of data and cannot accurately identify the treatment contribution of the stored data, resulting in the occupation of storage space by low-value data and reducing the utilization efficiency of data storage and computing resources. The embodiments of the present invention provide an intelligent management method and system for VOCS treatment data. The technical solutions are as follows:

[0006] On the one hand, an intelligent management method for VOCS treatment data is provided, and the method includes:

[0007] S1: Based on the real-time concentration data of VOCS, collect the VOCS component ratio, emission temperature, and air flow rate, analyze the VOCS concentration range, identify the change trend under the emission conditions, and obtain the reference interval of the emission characteristic concentration;

[0008] S2: Based on the reference interval of the emission characteristic concentration, extract the disturbance variables of the real-time concentration data of VOCS, wind speed, temperature, humidity, and atmospheric pressure, analyze the short-term volatility of the disturbance variables, analyze the deviation of the VOCS concentration through the original emission monitoring data, and judge whether the deviation rate of the disturbance variables exceeds the stable interval to obtain the VOCS data credibility evaluation value;

[0009] S3: According to the VOCS data credibility evaluation value, screen the VOCS data points with low credibility, judge the degree of deviation of the data from the environmental impact reference value, screen the data points with large environmental disturbance impact, and generate a VOCS abnormal data elimination set;

[0010] S4: Call the VOCS abnormal data elimination set, analyze the matching degree between the VOCS concentration data and the treatment condition data, set the data storage frequency, and obtain the VOCS data storage framework;

[0011] S5: Based on the VOCS data storage framework, analyze the treatment optimization contribution of the stored data, screen the data with low contribution, adjust the storage version, and merge the duplicate data to obtain the improved data storage efficiency of VOCS treatment.

[0012] As a further solution of the present invention, the reference interval of the emission characteristic concentration includes the VOCS concentration range, the changing trend of the emission conditions, and the reference value of the emission characteristic concentration. The VOCS data credibility evaluation value includes the concentration deviation degree, the disturbance variable volatility, and the disturbance variable deviation rate. The VOCS abnormal data rejection set includes low-credibility data points, environmental impact deviation data points, and environmental disturbance abnormal data points. The VOCS data storage framework includes the data matching degree, storage parameters, and storage frequency. The VOCS treatment data storage efficiency includes the optimization contribution, storage version, and duplicate data merging.

[0013] As a further solution of the present invention, the steps of the reference interval of the emission characteristic concentration are specifically as follows:

[0014] S101: Based on the real-time VOCS concentration data, collect the VOCS component ratio, emission temperature, and air flow rate, extract the meteorological conditions and surrounding pollutant interference data, screen the same spatio-temporal characteristic data, remove the outliers, and calculate the distribution of the VOCS concentration under different emission conditions to obtain the VOCS concentration distribution data;

[0015] S102: Based on the VOCS concentration distribution data, calculate the change range of the VOCS concentration under different emission conditions, select the time periods with large changes in meteorological conditions, air flow rate, and emission temperature, compare the corresponding changes in the VOCS concentration, screen the influencing emission conditions, and obtain the emission condition influence coefficient;

[0016] S103: Invoke the emission condition influence coefficient, analyze the VOCS concentration trend, analyze the concentration range under different emission conditions, adjust the reference value according to the influence ratio, and extract the concentration interval in the stable emission state to obtain the reference interval of the emission characteristic concentration.

[0017] As a further solution of the present invention, the steps of the VOCS data credibility evaluation value are specifically as follows:

[0018] S201: Based on the reference interval of the emission characteristic concentration, extract the disturbance variables of wind speed, temperature, humidity, and atmospheric pressure, screen the complete data, remove the outliers, and analyze the instantaneous change range of the disturbance variables to obtain the disturbance variable fluctuation range;

[0019] S202: Based on the disturbance variable fluctuation range, calculate the short-period volatility of the disturbance variables, screen the high-frequency change time periods, extract the fluctuation rates of wind speed, temperature, humidity, and atmospheric pressure, compare the influence degrees of different disturbance variables on the VOCS concentration, determine the key disturbance factors, and obtain the disturbance variable offset;

[0020] S203: Invoke the perturbation variable offset, analyze the original emission monitoring data, identify the degree to which the VOCS concentration deviates from the reference interval, determine whether the deviation rate exceeds the stable interval, and obtain the VOCS data credibility evaluation value.

[0021] As a further solution of the present invention, the short-term volatility of the perturbation variable is calculated using the formula:

[0022]

[0023] where, σ ΔX represents the short-term volatility of the perturbation variable, X i represents the value of the perturbation variable at the i-th data point, represents the average value of the perturbation variable, n represents the total number of data points, represents the absolute deviation of each data point from the average value, α represents the weight coefficient associated with temperature fluctuation, T ΔX represents the temperature fluctuation value in the perturbation variable, P ΔX represents the atmospheric pressure fluctuation value in the perturbation variable, β represents the weight coefficient associated with humidity fluctuation, H ΔX represents the humidity fluctuation value in the perturbation variable, W ΔX represents the wind speed fluctuation value in the perturbation variable.

[0024] As a further solution of the present invention, the steps of the VOCS abnormal data elimination set are specifically as follows:

[0025] S301: Based on the VOCS data credibility evaluation value, screen the VOCS data points with low credibility, analyze the deviation degree of the VOCS data points, and obtain the VOCS data deviation value;

[0026] S302: Invoke the VOCS data deviation value, screen the data points with large deviation amplitude, analyze the influence of wind speed, temperature, humidity, and atmospheric pressure on data deviation, calculate the contribution rate of the perturbation variable, screen the data points with large environmental perturbation influence, and obtain the environmental perturbation influence data set;

[0027] S303: Invoke the environmental perturbation influence data set, eliminate the data points where the VOCS data deviates too much from the environmental influence reference value, screen the abnormal data points, and obtain the VOCS abnormal data elimination set.

[0028] As a further solution of the present invention, the steps of the VOCS data storage framework are specifically as follows:

[0029] S401: Invoke the VOCS abnormal data elimination set, screen the VOCS concentration data, match the VOCS concentration value at each time point with the treatment working condition data, classify the VOCS concentration data, eliminate the VOCS data with abnormal treatment working conditions, and obtain the VOCS treatment matching data set;

[0030] S402: Based on the VOCS treatment matching dataset, calculate the storage parameters of the VOCS concentration at the time point, including the concentration mean, the fluctuation range, and the treatment condition classification parameters, analyze the correlation between the treatment condition category and the VOCS concentration storage, adjust the storage parameters according to the storage requirements, set the VOCS data storage parameters corresponding to different treatment condition categories, and obtain the VOCS data storage parameter set;

[0031] S403: According to the VOCS data storage parameter set, set the VOCS data storage frequency, adjust the storage interval according to the VOCS concentration fluctuation of the treatment condition category, and construct the VOCS data storage framework.

[0032] As a further solution of the present invention, the storage parameters of the VOCS concentration at the time point adopt the formula:

[0033]

[0034] where C st represents the storage parameter of the VOCS concentration at the time point, C VOC,k represents the VOCS concentration at the k-th time point, C avg represents the mean value of the VOCS concentration, N represents the total number of data points, and std represents the standard deviation of the VOCS concentration.

[0035] As a further solution of the present invention, the steps to improve the data storage efficiency of the VOCS treatment are specifically as follows:

[0036] S501: Based on the VOCS data storage framework, calculate the governance optimization contribution value of the stored data, screen the datasets with low contribution, classify and store the data and mark the low-contribution data, and obtain the low-contribution storage dataset;

[0037] S502: Call the low-contribution storage dataset, adjust the storage version, analyze the duplication rate of the stored data, screen the duplicate data, set the merging standard according to the governance optimization contribution value, merge the duplicate and low-contribution data, optimize the storage version parameters, adjust the storage hierarchy structure, and set the storage data archiving rules to obtain the optimized storage version dataset;

[0038] S503: According to the optimized storage version dataset, update the storage framework, adjust the storage frequency and path, and optimize the data storage hierarchy to obtain the improved data storage efficiency of the VOCS treatment.

[0039] On the other hand, an electric vehicle status monitoring system is provided. The electric vehicle status monitoring system is used to execute the above-mentioned electric vehicle status monitoring method, and the system includes:

[0040] Based on the real-time concentration data of VOCs, the emission characteristic analysis module extracts the proportion of VOCs components, emission temperature, and air flow rate, identifies the VOCs concentration range, compares the VOCs concentration change trends under different emission conditions, screens the concentration corresponding to the stable emission state, and combines with the industrial waste gas emission control standards to analyze the change range of the emission characteristic concentration within the compliance range, so as to obtain the reference interval of the emission characteristic concentration;

[0041] Based on the reference interval of the emission characteristic concentration, the disturbance variable evaluation module extracts the real-time concentration data of VOCs, wind speed, temperature, humidity, and atmospheric pressure, compares the original emission monitoring data, and combines with the operating parameters of waste gas treatment to analyze the correlation between the disturbance variable and the operating state of the treatment equipment, and determines whether the deviation rate of the disturbance variable exceeds the stable interval, so as to obtain the evaluation value of the reliability of VOCs data;

[0042] Based on the evaluation value of the reliability of VOCs data, the abnormal data screening module screens the VOCs data points with low reliability, compares the degree of data deviation from the environmental impact reference value, extracts the data points with large environmental disturbance impact, and establishes a set of VOCs abnormal data to be excluded;

[0043] Based on the set of VOCs abnormal data to be excluded, the data storage optimization module analyzes the matching degree between the VOCs concentration data and the treatment working condition data, adjusts the VOCs data storage parameters, and establishes a VOCs data storage framework;

[0044] Based on the VOCs data storage framework, the treatment data screening module identifies the contribution of the stored data to treatment optimization, screens the data with low contribution, compares the duplicate data of the stored version, and merges the data storage entries to obtain the improved data storage efficiency of VOCs treatment.

[0045] The beneficial effects brought by the technical solutions provided by the embodiments of the present invention at least include:

[0046] By collecting volatile organic compound concentration data and extracting emission characteristic parameters, the variation trend of pollutants under different emission conditions is identified, the influence of different environmental variables on emission characteristics is clarified, a concentration reference interval is constructed, the analytical ability of emission rules is enhanced. Combining the short-period volatility analysis of environmental disturbance variables, the deviation of monitoring data is accurately judged, the accuracy of data credibility evaluation is improved, and the reliability of monitoring data is ensured. Screen data points with low credibility, eliminate abnormal data greatly affected by environmental disturbances based on the degree of data deviation, improve the stability of data quality, and provide a more accurate reference for the optimization of subsequent treatment measures. With the help of data matching analysis, the correlation between concentration data and treatment working condition data is verified, and the storage parameters and storage frequency are adjusted to ensure the efficiency and accuracy of data storage. Through the analysis of the contribution of storage data governance optimization, screen data points with low value, adjust the storage version, reduce the occupation of redundant data, improve data storage efficiency, and realize the reasonable allocation of storage resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a schematic diagram of the working process of the present invention;

[0048] Figure 2 It is a flowchart for obtaining the reference interval of emission characteristic concentration in the present invention;

[0049] Figure 3 It is a flowchart for obtaining the credibility evaluation value of VOCS data in the present invention;

[0050] Figure 4 It is a flowchart for obtaining the set of eliminated abnormal VOCS data in the present invention;

[0051] Figure 5 It is a flowchart for obtaining the storage framework of VOCS data in the present invention;

[0052] Figure 6 It is a flowchart for obtaining the improved data storage efficiency of VOCS treatment in the present invention;

[0053] Figure 7 It is a system flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0054] The technical solutions in the present invention will be described below with reference to the accompanying drawings.

[0055] In the embodiments of the present invention, words such as "exemplarily" and "for example" are used to give examples, illustrations or explanations. Any embodiment or design solution described as an "example" in the present invention should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of the word "example" is intended to present concepts in a specific manner. In addition, in the embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one of the two.

[0056] In the embodiments of the present invention, "image" and "picture" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same. "(of)", "corresponding", and "corresponding" can sometimes be used interchangeably. It should be noted that when the difference is not emphasized, the meanings they express are the same.

[0057] In the embodiments of the present invention, sometimes subscripts such as W1 may be written in a non-subscript form such as W1. When the difference is not emphasized, the meanings they express are the same.

[0058] To make the technical problems, technical solutions and advantages to be solved by the present invention clearer, the following will be described in detail with reference to the drawings and specific embodiments.

[0059] Please refer to Figure 1 , the embodiments of the present invention provide a method for intelligent management of VOCS governance data. The processing flow of this method may include the following steps:

[0060] S1: Based on the real-time concentration data of VOCS, collect the component ratio of VOCS, emission temperature, and gas flow rate, extract meteorological conditions and surrounding pollutant interference data, analyze the VOCS concentration range, identify the change trend under emission conditions, and obtain the reference interval of the emission characteristic concentration;

[0061] S2: Based on the reference interval of the emission characteristic concentration, extract the disturbance variables of the real-time concentration data of VOCS, wind speed, temperature, humidity, and atmospheric pressure, analyze the short-term volatility of the disturbance variables, analyze the deviation of the VOCS concentration through the original emission monitoring data, and judge whether the deviation rate of the disturbance variables exceeds the stable interval to obtain the VOCS data credibility evaluation value;

[0062] S3: According to the VOCS data credibility evaluation value, screen the VOCS data points with low credibility, judge the degree of deviation of the data from the environmental impact reference value, screen the data points with large environmental disturbance impacts, and generate a VOCS abnormal data rejection set;

[0063] S4: Call the VOCS abnormal data elimination set, analyze the matching degree between the VOCS concentration data and the treatment condition data, adjust the VOCS data storage parameters, set the data storage frequency, and obtain the VOCS data storage framework;

[0064] S5: Based on the VOCS data storage framework, analyze the contribution of the stored data to treatment optimization, screen out the data with low contribution, adjust the storage version, and merge duplicate data to obtain the improved data storage efficiency of VOCS treatment.

[0065] The emission characteristic concentration reference interval includes the VOCS concentration range, the change trend of emission conditions, and the reference value of emission characteristic concentration. The VOCS data credibility evaluation value includes the concentration deviation degree, the disturbance variable volatility, and the disturbance variable deviation rate. The VOCS abnormal data elimination set includes low-credibility data points, environmental impact deviation data points, and environmental disturbance abnormal data points. The VOCS data storage framework includes data matching degree, storage parameters, and storage frequency. The VOCS treatment data storage efficiency includes optimization contribution, storage version, and duplicate data merging.

[0066] Specifically, as Figure 2 shown, the steps of the emission characteristic concentration reference interval are specifically as follows:

[0067] S101: Based on the real-time VOCS concentration data, collect the VOCS component ratio, emission temperature, and air flow rate, extract the meteorological conditions and surrounding pollutant interference data, screen out the data with the same spatio-temporal characteristics, remove the outliers, and calculate the distribution of the VOCS concentration under different emission conditions to obtain the VOCS concentration distribution data;

[0068] The collection process includes the VOCS component ratio, emission temperature, and air flow rate. At the same time, it is also necessary to extract the associated meteorological conditions such as temperature, humidity, and wind speed and the surrounding pollutant interference data such as the concentrations of sulfur dioxide and nitrogen oxides. The next step is to screen out the data with the same spatio-temporal characteristics. This step involves comparing the timestamps and geographical location information to ensure the spatio-temporal consistency of the data. The outliers are removed by statistical methods such as box plot analysis to determine the outlier threshold and remove those points that exceed the threshold from the data set. Finally, calculate the distribution of the VOCS concentration under different emission conditions. This requires establishing a multivariate distribution model based on the meteorological conditions and emission parameters such as temperature and air flow rate. Through this model, the VOCS concentration distribution data can be obtained, and the data shows the variation and central tendency of the VOCS concentration under specific meteorological and emission conditions.

[0069] S102: Based on the VOCS concentration distribution data, calculate the change range of the VOCS concentration under different emission conditions, select the time periods with large changes in meteorological conditions, air flow rate, and emission temperature, compare the corresponding changes in the VOCS concentration, and screen out the influencing emission conditions to obtain the emission condition influence coefficient;

[0070] The calculation process includes determining the change range of VOCs concentration under differential emission conditions. The calculation can be carried out by analyzing the changes in meteorological conditions, air flow rate and emission temperature within different time periods, and selecting the time period with a large change range, which is determined by comparing the statistical data changes in different time periods, such as the comparison of average values and standard deviations. Then, the corresponding changes in VOCs concentration within the time period are compared, which involves calculating the influence intensity and direction of the change in calculation conditions on the VOCs concentration. The emission conditions that have an impact are screened by establishing a regression model. The model includes meteorological and emission parameters as independent variables and VOCs concentration as the dependent variable. In this way, the emission condition influence coefficient can be obtained, and the coefficient directly represents the influence strength of different emission conditions on the VOCs concentration.

[0071] S103: Call the emission condition influence coefficient, analyze the trend of VOCs concentration, analyze the concentration range under differential emission conditions, adjust the reference value according to the influence ratio, extract the concentration interval under the stable emission state, and obtain the reference interval of the emission characteristic concentration;

[0072] The analysis process involves time series analysis of the trend of VOCs concentration. According to the time series data, the concentration range under differential emission conditions is analyzed, which includes calculating the minimum value, maximum value and average value of the concentration under different conditions. Adjusting the reference value according to the influence ratio is achieved by setting an adjustment coefficient, which is based on the influence coefficient obtained previously and applied to historical data, thereby adjusting the historical concentration data to reflect future state changes. Next, the concentration interval under the stable emission state is extracted. The process involves using statistical methods such as confidence intervals or probability density functions to determine the concentration interval under the stable state, and obtaining the reference interval of the emission characteristic concentration. This interval is calculated based on the adjusted reference value and actual measurement data, providing an expected VOCs concentration range for future monitoring and management.

[0073] Specifically, as Figure 3 shown, the steps for evaluating the credibility of VOCs data are specifically as follows:

[0074] S201: Based on the reference interval of the emission characteristic concentration, extract the perturbation variables of wind speed, temperature, humidity, and atmospheric pressure, screen the complete data, remove the outliers, and analyze the instantaneous change range of the perturbation variables to obtain the fluctuation range of the perturbation variables;

[0075] First, obtain the wind speed, temperature, humidity, and atmospheric pressure values within a specific period through the real-time data provided by the weather station. The data is preprocessed to filter out complete and error-free records. For outliers, such as sudden drops in temperature or rapid increases in wind speed, the median and interquartile range method is used to identify and exclude them to ensure data consistency and reliability. Subsequently, statistical methods such as standard deviation and coefficient of variation are used to accurately calculate the instantaneous fluctuation amplitude of each variable. For example, the fluctuation amplitude of wind speed can be obtained by measuring the difference between the highest and lowest wind speeds within one hour, which can accurately describe the impact of environmental variables on emission characteristics. Further analysis will evaluate the actual effect of disturbance variables based on the fluctuation data, providing data support for the formulation of subsequent control strategies and obtaining the fluctuation amplitude of disturbance variables.

[0076] S202: Based on the fluctuation amplitude of the disturbance variable, calculate the short-term volatility of the disturbance variable, screen the high-frequency change periods, extract the fluctuation rates of wind speed, temperature, humidity, and atmospheric pressure, compare the influence degrees of different disturbance variables on the VOCS concentration, determine the key disturbance factors, and obtain the offset of the disturbance variable;

[0077] The short-term volatility of the disturbance variable adopts the formula:

[0078]

[0079] where, σ ΔX represents the short-term volatility of the disturbance variable, X i represents the value of the disturbance variable at the i-th data point, represents the average value of the disturbance variable, n represents the total number of data points, represents the absolute deviation of each data point from the average value, α represents the weight coefficient associated with temperature fluctuation, T ΔX represents the temperature fluctuation value in the disturbance variable, P ΔX represents the atmospheric pressure fluctuation value in the disturbance variable, β represents the weight coefficient associated with humidity fluctuation, H ΔX represents the humidity fluctuation value in the disturbance variable, W ΔX represents the wind speed fluctuation value in the disturbance variable;

[0080] The first part of the formula is to square and sum the deviations of the data points, then calculate the average value and take the square root to obtain the fluctuation rate σ ΔX ;

[0081] Temperature and atmospheric pressure fluctuation coefficient

[0082] T ΔX represents the temperature fluctuation value in the disturbance variable, P ΔX is the atmospheric pressure fluctuation value;

[0083] Assume that through monitoring, the temperature fluctuation T ΔX has a specific value of 2°C, and the atmospheric pressure fluctuation P ΔX is 1013 hPa;

[0084] Based on the data, the temperature and atmospheric pressure fluctuation coefficients can be expressed as where α = 1.5 is a weight coefficient based on historical data, representing the influence of temperature fluctuation on the disturbance fluctuation;

[0085] In this case, so its square is (0.00197) 2 ≈ 3.88×10 -6 , and then multiplied by α = 1.5, the result of this part is 1.5×3.88×10 -6 = 5.82×10 -6 ;

[0086] The humidity and wind speed fluctuation coefficients

[0087] H ΔX represents the fluctuation value of humidity in the disturbance variable, and W ΔX is the wind speed fluctuation value;

[0088] Assume that the humidity fluctuation H ΔX is 5% (the change range of relative humidity), and the wind speed fluctuation W ΔX is 15 m / s;

[0089] The coefficient β = 1.2 for this part is the influence weight of humidity and wind speed on the disturbance fluctuation obtained from related meteorological research;

[0090] According to the above data, calculate the ratio of humidity and wind speed fluctuations as and then multiply by β = 1.2, the value of this part is 1.2×0.333 = 0.4;

[0091] Calculate the fluctuation rate:

[0092] Substitute the results of the above parts into the original formula and calculate to get:

[0093]

[0094] Assume that n = 100 data points are collected during this period, and the calculated result of the sum of squared wind speed fluctuation deviations is 2.4 (obtained from data monitoring), then:

[0095]

[0096] After calculation, σ ΔX ≈ 0.651, and this value represents the comprehensive fluctuation rate of wind speed, temperature, humidity, and atmospheric pressure;

[0097] The result shows that the comprehensive influence of the fluctuation rates of perturbation variables (such as wind speed, temperature, humidity, atmospheric pressure) over a certain period is 0.651, indicating a relatively high degree of perturbation fluctuation and strong volatility during this period, which has a significant impact on the change of VOCS concentration.

[0098] S203: Invoke the offset of the perturbation variable, analyze the original emission monitoring data, identify the degree to which the VOCS concentration deviates from the reference interval, determine whether the deviation rate exceeds the stable interval, and obtain the credibility evaluation value of the VOCS data;

[0099] Firstly, it involves using the offset of the perturbation variable as an adjustment factor to correct the original emission monitoring data. By calculating the deviation between the corrected data and the reference interval, the deviation degree of the VOCS concentration is judged. For example, if the adjusted VOCS concentration exceeds 10% of the upper limit of the reference interval, it is considered that the deviation rate is too high, indicating abnormal activities of the emission source or malfunction of the monitoring equipment. Therefore, it is necessary to further analyze the deviation situation, such as by checking the environmental or operation data during the corresponding period to determine the cause of the deviation. Finally, based on the severity and frequency of the deviation degree, the credibility of the VOCS data is evaluated. This evaluation helps environmental protection agencies determine whether to take countermeasures or adjust the monitoring strategy, and obtain the credibility evaluation value of the VOCS data.

[0100] Specifically, as Figure 4 shown, the steps for the VOCS abnormal data rejection set are specifically as follows:

[0101] S301: Based on the credibility evaluation value of the VOCS data, screen out the VOCS data points with low credibility, analyze the deviation degree of the VOCS data points, and obtain the VOCS data deviation value;

[0102] Firstly, screen out the data points with low credibility from the dataset. For example, the credibility of the data point is lower than the set threshold of 0.5. Then, conduct a detailed analysis of the deviation degree of the screened data points. For example, calculate the difference between the actual measured value and the expected model output of each data point to obtain a list of deviation values. This deviation value can be determined by the ratio of the measured concentration of the data point to the predicted concentration of the model. The deviation value reflects the size of the difference between the data point and the model expectation, and can be positive or negative. A positive value indicates that the measured value is higher than the predicted value, and a negative value indicates that it is lower than the predicted value. Finally, by calculating the deviation value of each data point, obtain the deviation value distribution of the entire dataset, and obtain the VOCS data deviation value.

[0103] S302: Call the VOCS data deviation value, filter the data points with large deviation, analyze the influence of wind speed, temperature, humidity and atmospheric pressure on data deviation, calculate the contribution rate of disturbance variables, filter the data points with large environmental disturbance influence, and obtain the environmental disturbance influence data set;

[0104] By analyzing the deviation values ​​of VOCS data, data points with large deviations are screened out. For example, the absolute value of the deviation is set to be greater than 1.0 as a significant deviation. The specific impact of environmental factors such as wind speed, temperature, humidity and atmospheric pressure on data deviation is further analyzed for the data points. By constructing a multivariate regression model, the contribution rate of each environmental variable to the data deviation is calculated. The contribution rate can be measured by the absolute value of the regression coefficient of each variable. A larger coefficient value indicates a greater impact on the deviation. By comparing the contribution rates of different environmental variables, data points with large environmental disturbance effects are screened out. For example, when the contribution rate of wind speed exceeds 30%, it is considered that wind speed is the main disturbance factor, and an environmental disturbance impact data set dominated by wind speed, temperature, humidity and atmospheric pressure is obtained.

[0105] S303: calling the environmental disturbance impact data set, removing the data points whose VOCS data deviate too much from the environmental impact benchmark value, screening the abnormal data points, and obtaining the VOCS abnormal data removal set;

[0106] The environmental disturbance impact data set is used to analyze the degree to which the VOCS concentration in the data point deviates from the environmental impact benchmark value. The benchmark value can be a theoretical predicted value under the influence of multiple environmental parameters. Data points that deviate too much from the benchmark value are eliminated. For example, when the difference between the measured value of a data point and the environmental benchmark value exceeds 20%, it is considered to be abnormal data. This elimination operation is performed by comparing the percentage difference between the measured value and the benchmark value of each data point, and then the abnormal data points are screened out. The screening criterion can be the upper 95% quantile of the absolute value of the deviation value. The data points screened out in this way constitute the VOCS abnormal data elimination set. This method can effectively exclude abnormal data points caused by environmental disturbances or non-target factors from the data set.

[0107] Specifically, if Figure 5 As shown in the figure, the steps of the VOCS data storage framework are as follows:

[0108] S401: calling the VOCS abnormal data elimination set, screening the VOCS concentration data, matching the VOCS concentration value at each time point with the governance condition data, classifying the VOCS concentration data, eliminating the VOCS data with abnormal governance conditions, and obtaining the VOCS governance matching data set;

[0109] Extract the VOCS concentration data and corresponding treatment operating conditions data at the current moment from the overall data pool. The data includes environmental variables such as VOCS concentration, wind speed, temperature, humidity, etc. To ensure the accuracy and availability of the data, first, preliminarily screen the VOCS concentration data and remove data points that significantly deviate from the normal range. For example, if the VOCS concentration suddenly rises from the normal 50 ppm to 1000 ppm, then this data point is considered abnormal and removed from the data set. Match the screened VOCS concentration data with the treatment operating conditions data to ensure that each data point has corresponding operating conditions data. Conduct data classification. According to different treatment operating conditions, such as turning on or off the purification system, divide the VOCS data into several categories. The data in each category represents the VOCS concentration performance under similar operating conditions, which is completed by setting thresholds. Set the VOCS threshold to 30 ppm, and any data point exceeding this threshold will be classified into a specific category. After classification, further check the data in these categories and remove abnormal data that significantly does not match the treatment operating conditions. For example, record abnormal high-concentration VOCS data points when the purification is turned on, forming a clear and well-classified VOCS treatment matching data set. This data set will be directly used for subsequent analysis and storage operations.

[0110] S402: Based on the VOCS treatment matching data set, calculate the storage parameters of the VOCS concentration at the time point, including the concentration mean, fluctuation range, and treatment operating conditions classification parameters, analyze the correlation between the treatment operating conditions category and the storage of the VOCS concentration, adjust the storage parameters according to the storage requirements, set the VOCS data storage parameters corresponding to different treatment operating conditions categories, and obtain the VOCS data storage parameter set;

[0111] The storage parameters of the VOCS concentration at the time point are calculated using the formula:

[0112]

[0113] where C st represents the storage parameter of the VOCS concentration at the time point, C VOC,k represents the VOCS concentration at the kth time point, C avg represents the mean of the VOCS concentration, N represents the total number of data points, and std represents the standard deviation of the VOCS concentration;

[0114] The parameters in the formula are:

[0115] C st is the storage parameter of the VOCS concentration at the time point, indicating the variation characteristics of the VOCS concentration at a certain moment or within a certain period of time;

[0116] C VOC,k$C_{k}$ is the VOCs concentration at the $k$-th time point, which is detected in real time by environmental monitoring equipment or obtained from an air quality monitoring station;

[0117] C avg $\overline{C}$ is the mean value of all collected VOCs concentrations, and the calculation method is the sum of all concentration values divided by the total number of collected data;

[0118] $N$ is the total number of time point data, representing all the data points collected during the calculation process;

[0119] $std$ is the standard deviation of the VOCs concentration, reflecting the dispersion degree of the collected data. The calculation method is the square root of the sum of the squares of the deviations of all data from the mean value, and it is obtained through data processing software or statistical tools;

[0120] In actual operation, the required parameters are obtained from the real-time data collection of environmental monitoring equipment. The specific steps are as follows:

[0121] Collection of VOCs concentration:

[0122] Collect VOCs concentration data from multiple monitoring points (such as urban air monitoring stations). The data can be periodically detected by installed sensors. Assume that 10 time point data are collected: $C_{1}$ VOC,1 $ = 120$ ppb, $C_{2}$ VOC,2 $ = 115$ ppb, …, $C_{10}$ VOC,10 $ = 130$ ppb;

[0123] Here, the data unit is ppb (parts per billion), representing the concentration of VOCs in every billion units of air;

[0124] Calculation of the mean value:

[0125] Calculate the mean value $\overline{C}$ of the VOCs concentration avg :

[0126]

[0127] Calculation of the standard deviation:

[0128] The calculation method of the standard deviation $std$ of the VOCs concentration is:

[0129] Substitute the values:

[0130]

[0131] The calculated standard deviation is 5.42 ppb;

[0132] Calculation of the storage parameter of the VOCs concentration at the time point:

[0133] Assume for the 1st data point $C_{1}$VOC,1 Calculate based on 120 ppb:

[0134] |C VOC,1 - C avg | = |120 - 121.7| = 1.7 ppb;

[0135] Substitute the values into the formula for calculation:

[0136]

[0137] Substitute this value into the main formula:

[0138]

[0139] After performing operations on all data points, the final obtained value of C st is 5.38. This result indicates that the storage parameter value of the VOCS concentration at 10 time points is 5.38, and this value reflects the fluctuation characteristics of the VOCS concentration during the monitoring period.

[0140] S403: According to the VOCS data storage parameter set, set the VOCS data storage frequency, adjust the storage interval based on the VOCS concentration fluctuation of the treatment working condition category, and construct a VOCS data storage framework;

[0141] First, adjust the data storage interval according to the VOCS concentration fluctuation of the treatment working condition category. For example, if the VOCS concentration fluctuates greatly under a certain working condition, reduce the storage interval, adjusting from storing data every 10 minutes to every 5 minutes. This is done to more accurately capture the rapid changes in concentration and ensure the timeliness and accuracy of the data. Conversely, if the concentration fluctuation is small, the storage interval will be increased accordingly, thereby optimizing the use of storage resources and reducing unnecessary data accumulation. Use the set storage frequency parameter to construct a VOCS data storage framework. This framework is responsible for managing the collection, storage, and access of data, ensuring that all collected data meets the preset storage parameters and frequencies. This framework also supports subsequent data analysis and processing requirements, such as data mining and trend prediction, to construct a VOCS data storage framework.

[0142] Specifically, as Figure 6 shown, the steps to improve the data storage efficiency of VOCS treatment are specifically as follows:

[0143] S501: Based on the VOCS data storage framework, calculate the governance optimization contribution value of the stored data, screen out the data sets with low contributions, classify and store the data and mark the low - contribution data to obtain the low - contribution storage data sets;

[0144] By calculating the contribution value of storage data governance optimization, screening the data based on this value to identify low - contribution data sets. It is necessary to evaluate the data quality of each data set to determine its contribution value to the storage system and business objectives. When calculating the contribution value, factors such as the access frequency of the data, the usage duration of the data, and the data quality metrics can be used. The metrics are weighted and summed to obtain the total contribution value. For example, if the access frequency of a certain data set is 10 times per month, the quality score is 80 / 100, and its usage duration is 20 hours per month, its contribution value is calculated through the weighted - sum formula. During screening, data sets with contribution values lower than a certain set threshold are marked. Assuming the threshold is set to 30, data sets with contribution values lower than 30 can be identified and marked as low - contribution data sets. The data sets are classified and stored according to the level of contribution value. The low - contribution data is stored separately and specially marked. The marked low - contribution data sets can further improve the storage efficiency through subsequent optimization processes. Finally, the low - contribution data that needs further governance is screened out. Through this process, low - contribution storage data sets are finally obtained, providing a basis for subsequent optimization and adjustment.

[0145] S502: Call the low - contribution storage data set, adjust the storage version, analyze the duplication rate of the stored data, screen the duplicate data, set the merging criteria based on the governance optimization contribution value, merge the duplicate and low - contribution data, optimize the storage version parameters, adjust the storage hierarchy structure, set the storage data archiving rules, and obtain the optimized storage version data set;

[0146] First, analyze the duplication rate of the data to determine whether there is redundant data in the storage. The storage should compare each piece of data in the low - contribution data set to detect its duplication degree with the data set. For example, for two data sets A and B, if their contents are exactly the same, the duplication rate is 100%. If the similarity is 80%, it is a relatively high duplication rate. When screening the duplicate data, a duplication rate threshold can be set. Assuming it is set to 80%, that is, data with a duplication rate exceeding 80% is considered redundant data and can be considered for merging. The setting of the merging criteria is adjusted according to the governance optimization contribution value. For data with a low contribution value and a high duplication rate, they are merged. When merging, clustering or merging algorithms can be used to merge the duplicate data to reduce redundancy. During the process of optimizing the storage version, by adjusting the storage structure of the data, deleting the duplicate low - contribution data, and updating the storage version parameters, the data storage efficiency is improved. Finally, an optimized storage version data set is obtained, which can provide support for subsequent improvement of storage efficiency.

[0147] S503: According to the optimized storage version data set, update the storage framework, adjust the storage frequency and path, optimize the data storage hierarchy, and obtain the improved data storage efficiency for VOCS governance;

[0148] The update of the storage framework requires re-planning the data storage hierarchy, reducing the frequent access to low-contribution data, and adopting a hierarchical storage strategy. For example, frequently accessed data can be stored in a low-latency storage area, while less frequently accessed data can be migrated to a low-cost storage area. When adjusting the storage frequency, frequency adjustment rules can be set according to the access pattern of the data. For example, the storage frequency of data that has not been accessed in the past six months can be set to low to reduce its access frequency. The adjustment of the storage path needs to be planned according to the storage requirements and access requirements of the data, so as to optimize the selection and use of the storage path. Finally, by setting reasonable data archiving rules for storage, data can be stored in layers according to its importance and access frequency, avoiding low-contribution data from occupying too much storage resources, and finally achieving an optimized data storage version, thereby improving the data storage efficiency.

[0149] As Figure 7 shown, the intelligent management system for VOCs treatment data, the system includes:

[0150] The emission characteristic analysis module, based on the real-time concentration data of VOCs, extracts the proportion of VOCs components, emission temperature, air flow rate, identifies the VOCs concentration range, compares the change trend of VOCs concentration under different emission conditions, screens the concentration corresponding to the stable emission state, and combines the industrial waste gas emission control standards to analyze the change range of the emission characteristic concentration within the compliance range, so as to obtain the reference interval of the emission characteristic concentration;

[0151] The disturbance variable evaluation module, based on the reference interval of the emission characteristic concentration, extracts the real-time concentration data of VOCs, wind speed, temperature, humidity, atmospheric pressure, compares the original emission monitoring data, and combines the operating parameters of the waste gas treatment to analyze the correlation between the disturbance variable and the operating state of the treatment equipment, judges whether the deviation rate of the disturbance variable exceeds the stable interval, and obtains the evaluation value of the credibility of the VOCs data;

[0152] The abnormal data screening module, based on the evaluation value of the credibility of the VOCs data, screens the VOCs data points with low credibility, compares the degree of deviation of the data from the environmental impact reference value, extracts the data points with great environmental disturbance impact, and establishes a set of VOCs abnormal data to be excluded;

[0153] The data storage optimization module, based on the set of VOCs abnormal data to be excluded, analyzes the matching degree between the VOCs concentration data and the treatment working condition data, adjusts the storage parameters of the VOCs data, and establishes a VOCs data storage framework;

[0154] The treatment data screening module, based on the VOCs data storage framework, identifies the contribution of the stored data to treatment optimization, screens the data with low contribution, compares the duplicate data of the storage version, and merges the data storage entries to obtain the improved data storage efficiency of VOCs treatment.

[0155] The above are only the specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of changes or substitutions, which should all be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the said claims.

Claims

1. An intelligent management method for VOCs governance data, characterized in that, It includes the following steps: S1: Based on the real-time concentration data of VOCs, collect the proportion of VOCs components, emission temperature, and gas flow rate, analyze the VOCs concentration range, identify the change trend under emission conditions, and obtain the baseline interval of emission characteristic concentration; S2: Based on the baseline interval of emission characteristic concentration, extract the disturbance variables of real-time VOCs concentration data, wind speed, temperature, humidity, and atmospheric pressure, analyze the short-term volatility of disturbance variables, analyze the deviation of VOCs concentration through the original emission monitoring data, and judge whether the deviation rate of disturbance variables exceeds the stable interval to obtain the VOCs data credibility evaluation value; S3: According to the VOCs data credibility evaluation value, screen out the VOCs data points with low credibility, judge the degree of deviation of the data from the environmental impact baseline value, screen out the data points with large environmental disturbance impacts, and generate a VOCs abnormal data elimination set; S4: Call the VOCs abnormal data elimination set, analyze the matching degree between the VOCs concentration data and the treatment working condition data, set the data storage frequency, and obtain the VOCs data storage framework; S5: Based on the VOCs data storage framework, analyze the contribution of the stored data to treatment optimization, screen out the data with low contribution, adjust the storage version, and merge duplicate data to obtain the improved data storage efficiency of VOCs treatment; 2. The intelligent management method for VOCS treatment data according to claim 1, wherein The baseline interval of emission characteristic concentration includes the VOCs concentration range, the change trend of emission conditions, and the baseline value of emission characteristic concentration. The VOCs data credibility evaluation value includes the concentration deviation degree, disturbance variable volatility, and disturbance variable deviation rate. The VOCs abnormal data elimination set includes low-credibility data points, environmental impact deviation data points, and environmental disturbance abnormal data points. The VOCs data storage framework includes data matching degree, storage parameters, and storage frequency. The VOCs treatment data storage efficiency includes optimization contribution, storage version, and duplicate data merging; 3. The intelligent management method for VOCs treatment data according to claim 1, wherein The steps of the baseline interval of emission characteristic concentration are specifically as follows: S101: Based on the real-time concentration data of VOCs, collect the proportion of VOCs components, emission temperature, and gas flow rate, extract the meteorological conditions and surrounding pollutant interference data, screen out the same spatio-temporal characteristic data, remove the outliers, and calculate the distribution of VOCs concentration under different emission conditions to obtain the VOCs concentration distribution data; S102: Based on the VOCs concentration distribution data, calculate the change amplitude of VOCs concentration under different emission conditions, select the time periods with large changes in meteorological conditions, gas flow rate, and emission temperature, compare the corresponding changes in VOCs concentration, and screen out the influencing emission conditions to obtain the emission condition influence coefficient; S103: Call the emission condition influence coefficient, analyze the VOCs concentration trend, analyze the concentration range under different emission conditions, adjust the baseline value according to the influence ratio, and extract the concentration interval in the stable emission state to obtain the baseline interval of emission characteristic concentration; 4. The intelligent management method for VOCs treatment data according to claim 1, wherein The steps of the VOCs data credibility evaluation value are specifically as follows: S201: Based on the reference interval of the emission characteristic concentration, extract the perturbation variables of wind speed, temperature, humidity, and atmospheric pressure, screen the complete data, remove the outliers, analyze the instantaneous change amplitude of the perturbation variables, and obtain the fluctuation amplitude of the perturbation variables. S202: Based on the fluctuation amplitude of the perturbation variables, calculate the short-term volatility of the perturbation variables, screen the high-frequency change periods, extract the fluctuation rates of wind speed, temperature, humidity, and atmospheric pressure, compare the influence degrees of different perturbation variables on the VOCS concentration, determine the key perturbation factors, and obtain the offset of the perturbation variables. S203: Invoke the offset of the perturbation variables, analyze the original emission monitoring data, identify the degree to which the VOCS concentration deviates from the reference interval, and determine whether the deviation rate exceeds the stable interval to obtain the credibility evaluation value of the VOCS data.

5. The intelligent management method for VOCs treatment data according to claim 4, wherein The short-term volatility of the perturbation variables is calculated using the formula: Among them, σ ΔX represents the short - term volatility of the disturbance variable, X i represents the value of the disturbance variable at the i - th data point, represents the average value of the disturbance variable, n represents the total number of data points, represents the absolute deviation of each data point from the average value, α represents the weight coefficient associated with temperature fluctuations, T ΔX represents the temperature fluctuation value in the disturbance variable, P ΔX represents the atmospheric pressure fluctuation value in the disturbance variable, β represents the weight coefficient associated with humidity fluctuations, H ΔX represents the humidity fluctuation value in the disturbance variable, W ΔX represents the wind speed fluctuation value in the disturbance variable.

6. The intelligent management method for VOCs treatment data according to claim 1, wherein, The steps for eliminating the abnormal VOCS data set are specifically as follows: S301: Based on the credibility evaluation value of the VOCS data, screen the VOCS data points with low credibility, analyze the deviation degree of the VOCS data points, and obtain the deviation value of the VOCS data. S302: Invoke the deviation value of the VOCS data, screen the data points with large deviation amplitudes, analyze the influence of wind speed, temperature, humidity, and atmospheric pressure on the data deviation, calculate the contribution rate of the perturbation variables, screen the data points with large environmental perturbation effects, and obtain the environmental perturbation influence data set. S303: Invoke the environmental perturbation influence data set, eliminate the data points where the VOCS data deviates too much from the environmental influence reference value, screen the abnormal data points, and obtain the abnormal VOCS data elimination set.

7. The intelligent management method for VOCs treatment data according to claim 1, wherein The steps for the VOCS data storage framework are specifically as follows: S401: Invoke the abnormal VOCS data elimination set, screen the VOCS concentration data, match the VOCS concentration value at each time point with the treatment working condition data, classify the VOCS concentration data, eliminate the VOCS data with abnormal treatment working conditions, and obtain the VOCS treatment matching data set. S402: Based on the VOCS treatment matching data set, calculate the storage parameters of the VOCS concentration at the time point, including the concentration mean, fluctuation range, and treatment working condition classification parameters, analyze the correlation between the treatment working condition category and the VOCS concentration storage, adjust the storage parameters according to the storage requirements, set the VOCS data storage parameters corresponding to different treatment working condition categories, and obtain the VOCS data storage parameter set. S403: According to the VOCS data storage parameter set, set the VOCS data storage frequency, adjust the storage interval according to the VOCS concentration fluctuation of the treatment working condition category, and construct the VOCS data storage framework.

8. The intelligent management method for VOCs treatment data according to claim 7, characterized in that, The storage parameters of the VOCS concentration at the time point are calculated using the formula: Among them, C st represents the storage parameter of the VOCS concentration at the time point, C VOC,k represents the VOCS concentration at the k-th time point, C avg represents the mean value of the VOCS concentration, N represents the total number of data points, and std represents the standard deviation of the VOCS concentration.

9. The intelligent management method for VOCs treatment data according to claim 1, characterized in that, The steps for improving the data storage efficiency of the VOCS treatment are specifically as follows: S501: Based on the VOCS data storage framework, calculate the treatment optimization contribution value of the stored data, screen the data sets with low contributions, classify and mark the stored data with low contributions, and obtain the low contribution storage data set. S502: Call the low - contribution stored data set, adjust the storage version, analyze the duplication rate of the stored data, screen the duplicate data, set the merging criteria according to the governance optimization contribution value, merge the duplicate and low - contribution data, optimize the storage version parameters, adjust the storage hierarchy structure, set the storage data archiving rules, and obtain the optimized storage version data set; S503: According to the optimized storage version data set, update the storage framework, adjust the storage frequency and path, optimize the data storage hierarchy, and obtain the improved data storage efficiency for VOCS governance.

10. The intelligent management system for VOCs treatment data is characterized in that According to the intelligent management method for VOCS governance data according to any one of claims 1 - 9, the system includes: The emission characteristic analysis module, based on the real - time VOCS concentration data, extracts the VOCS component ratio, emission temperature, and air flow rate, identifies the VOCS concentration range, compares the VOCS concentration change trend under different emission conditions, screens the concentration corresponding to the stable emission state, and combines with the industrial waste gas emission control standard to analyze the change range of the emission characteristic concentration within the compliance range, and obtains the emission characteristic concentration reference interval; The perturbation variable evaluation module, based on the emission characteristic concentration reference interval, extracts the real - time VOCS concentration data, wind speed, temperature, humidity, and atmospheric pressure, compares the original emission monitoring data, combines with the waste gas treatment operation parameters, analyzes the correlation between the perturbation variable and the operation state of the treatment equipment, judges whether the deviation rate of the perturbation variable exceeds the stable interval, and obtains the VOCS data credibility evaluation value; The abnormal data screening module, based on the VOCS data credibility evaluation value, screens the VOCS data points with low credibility, compares the degree of data deviation from the environmental impact reference value, extracts the data points with large environmental perturbation impact, and establishes a VOCS abnormal data elimination set; The data storage optimization module, based on the VOCS abnormal data elimination set, analyzes the matching degree between the VOCS concentration data and the treatment working condition data, adjusts the VOCS data storage parameters, and establishes a VOCS data storage framework; The governance data screening module, based on the VOCS data storage framework, identifies the governance optimization contribution of the stored data, screens the data with low contribution, compares the duplicate data of the storage version, merges the data storage entries, and obtains the improved data storage efficiency for VOCS governance.

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