Atmospheric mercury online analyzer data quality control method, system, equipment and medium

Abstract

The invention discloses a data quality control method, system and equipment of an atmospheric mercury online analyzer and a medium, relates to the technical field of environmental monitoring, mainly aims at the problems of missing detection or mistaken elimination of abnormal values, lack of real-time signal characteristic analysis and insufficient sampling state quality control of a traditional quality control method, and improves the data precision of the atmospheric mercury online analyzer. The method comprises the following steps: determining a concentration value of a target substance in an air sample gas based on a sampling mode and sampling data composition of a Zeeman effect mercury detector, and determining second quality control data based on measurement results of gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector; analyzing the atmospheric mercury detection signal based on signal stability to obtain third quality control data; performing data elimination and minute-to-hour data conversion on the third quality control data to obtain standard hour data; determining fourth quality control data by using the standard hour data of the target time period; and performing optimization processing on the fourth quality control data to obtain final quality control data.

Description

Technical Field

[0001] The present application relates to the field of environmental monitoring technology, and in particular to a data quality control method, system, equipment and medium for an atmospheric mercury online analyzer. Background Art

[0002] Atmospheric mercury pollution has become an important environmental issue of global concern, and is of great significance to ecological environmental protection, pollution prevention and control, and public health assessment. As an important means to study the dynamic changes of mercury pollution, the accuracy and reliability of atmospheric mercury concentration monitoring directly affect the scientific nature of pollution assessment and the effectiveness of relevant policy formulation. However, with the expansion of the atmospheric mercury monitoring network and the increase in demand, the quality control of monitoring data faces the following problems: 1. Environmental interference: changes in environmental conditions such as gas flow fluctuations, temperature and humidity changes, and air pressure fluctuations will significantly affect the measurement accuracy; 2. Equipment drift: During long-term operation, the drift and stability of the equipment may lead to monitoring data deviations; 3. Complexity of sampling state: During the sampling process of sample gas, the quality of the sample gas is difficult to guarantee due to the deviation of parameters such as gas flow, humidity, and temperature from the ideal state; 4. Data anomalies are difficult to identify: Traditional quality control methods are mainly based on fixed thresholds, and it is difficult to identify abnormal data caused by equipment failures or dynamic changes.

[0003] Existing online atmospheric mercury analyzers (such as RA-915AM from LUMEX of Russia, Hg-CEMS from Thermo Fisher Scientific of the United States, Mercury Instruments of Germany, and QFG series mercury analyzers from Qifeng Optoelectronics of China) use cold vapor atomic absorption spectroscopy (CVAAS) and Zeeman effect background correction technology to split the spectrum of mercury through a strong magnetic field, reduce background interference and achieve high-precision monitoring. However, the following technical shortcomings limit its application effect: 1. Fixed threshold rejection limitations: Traditional quality control methods cannot flexibly adapt to dynamic environments, which easily leads to missed detection or erroneous rejection of abnormal values; 2. Lack of real-time signal feature analysis: Failure to fully utilize the stability of the signal (such as the ratio of background light to absorbed light) for quality control; 3. Insufficient sampling state quality control: During the sample gas collection process, the deviation of key parameters such as gas flow, temperature and humidity is not effectively controlled, resulting in a decrease in data quality. Therefore, a high-precision data quality control method for online atmospheric mercury analyzers is urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a data quality control method, system, equipment and medium for an atmospheric mercury online analyzer, which can improve the accuracy of data quality control of an atmospheric mercury online analyzer.

[0005] To achieve the above objectives, this application provides the following solutions:

[0006] In a first aspect, the present application provides a data quality control method for an atmospheric mercury online analyzer, comprising:

[0007] Based on the sampling method and sampling data composition of atmospheric mercury by the Zeeman effect mercury detector, the concentration value of the target substance in the air sample is measured to obtain the first quality control data;

[0008] Determine the second quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector according to the first quality control data;

[0009] The atmospheric mercury detection signal of the second quality control data is analyzed based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light;

[0010] Eliminate the third quality control data and convert the minute-to-hour data to obtain standard hour data;

[0011] Determine fourth quality control data using standard hourly data for the target time period;

[0012] The fourth quality control data is optimized by using an AI module to obtain final quality control data.

[0013] In a second aspect, the present application provides an atmospheric mercury online analyzer data quality control system, comprising:

[0014] A gas control module is used to measure the concentration value of the target substance in the air sample based on the sampling method and sampling data composition of the atmospheric mercury by the Zeeman effect mercury detector to obtain the first quality control data;

[0015] A parameter quality control module, for determining second quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector according to the first quality control data;

[0016] A signal stability elimination module is used to analyze the atmospheric mercury detection signal of the second quality control data based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light;

[0017] A minute-to-hour conversion module, used for performing data elimination and minute-to-hour data conversion on the third quality control data to obtain standard hour data;

[0018] A climate rejection module, for determining fourth quality control data using standard hourly data for a target time period;

[0019] The AI ​​optimization module is used to optimize the fourth quality control data using the AI ​​module to obtain final quality control data.

[0020] In a third aspect, the present application provides a computer device, including: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the above-mentioned atmospheric mercury online analyzer data quality control method.

[0021] In a fourth aspect, the present application provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned atmospheric mercury online analyzer data quality control method.

[0022] According to the specific embodiments provided in this application, this application discloses the following technical effects:

[0023] The present application provides a data quality control method, system, equipment and medium for an atmospheric mercury online analyzer. The method measures the multi-dimensional parameter results of a Zeeman effect mercury detector and eliminates abnormal data of the measurement results of the multi-dimensional parameters in the first quality control data. The atmospheric mercury detection signal is analyzed based on signal stability, and unstable signal data in the second quality control data is eliminated. By dual control of the sampling state and signal characteristics, combined with statistical analysis of long time series data (such as time variability, climate extreme value screening, etc.), dynamic quality control from seconds to minutes is achieved, the accuracy and reliability of the monitoring data are effectively improved, and the problem of insufficient data stability of the prior art in a variety of complex environments is solved. Solid data support is provided for the atmospheric mercury monitoring network, and the method has good scientific value and broad application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 This is an application environment diagram of a data quality control method for an atmospheric mercury online analyzer in one embodiment of the present application;

[0026] Figure 2 A schematic diagram of a flow chart of a data quality control method for an atmospheric mercury online analyzer provided in one embodiment of the present application;

[0027] Figure 3 A detailed flowchart of the steps for multi-dimensional parameter screening;

[0028] Figure 4 A schematic diagram of the functional modules of a data quality control system for an atmospheric mercury online analyzer provided in one embodiment of the present application;

[0029] Figure 5 A schematic diagram of the structure of an electronic device provided in one embodiment of the present application;

[0030] Figure 6 A schematic diagram of the structure of a computer device provided in one embodiment of the present application. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0032] The main methods for detecting mercury include physical and chemical methods and spectroscopic methods. Among them, the main methods of the frequently used spectroscopic methods include differential absorption spectroscopy, cold atomic absorption method-gold amalgam technology, atomic fluorescence spectroscopy-gold amalgam technology and Zeeman modulation atomic absorption method. The concentration of mercury in ambient air is extremely low. When using the amalgam method for mercury monitoring, carrier gas is required. The system is complex and the equipment is expensive. It has high requirements for maintenance personnel and is difficult to meet the needs of real-time online monitoring of ambient air mercury in large-scale locations in my country. The use of Zeeman effect background correction mercury monitoring technology does not require the use of amalgam technology, and does not require complex pre-treatment of mercury in the gas. It can eliminate water vapor and SO in the ambient air with high precision. 2 、NO x The instrument system is simple to install, easy to use and maintain, and can achieve high-sensitivity and high-accuracy measurement of mercury, which has a very good advantage in the continuous monitoring of trace mercury in the atmosphere.

[0033] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, the present application is further described in detail below with reference to the accompanying drawings and specific implementation methods.

[0034] The atmospheric mercury online analyzer data quality control method provided in the embodiment of the present application can be applied to Figure 1In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be set up separately, integrated on the server 104, or placed on the cloud or other servers. The terminal 102 can send a data quality control request to the server 104. After receiving the data quality control request, the server 104 measures the concentration value of the target substance in the air sample based on the sampling method and sampling data composition of the atmospheric mercury by the Zeeman effect mercury detector, obtains the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector, and removes abnormal data. The atmospheric mercury detection signal is analyzed based on signal stability, and the unstable signal data is removed to obtain the third quality control data. The third quality control data is subjected to data removal and minute-to-hour data conversion to obtain standard hourly data, and the standard hourly data of the target time period is used to remove data that does not meet the climate extreme threshold, and the fourth quality control data is optimized by using the AI ​​module to obtain the final quality control data. The server 104 can feedback the final quality control data obtained for the data quality control request to the terminal 102. In addition, in some embodiments, the atmospheric mercury online analyzer data quality control method can also be implemented by the server 104 or the terminal 102 alone, such as the terminal 102 can directly perform data quality control processing.

[0035] The terminal 102 may be, but is not limited to, various desktop computers, laptop computers, smart phones, tablet computers, IoT devices, and portable wearable devices. The IoT devices may be smart speakers, smart TVs, smart air conditioners, smart vehicle-mounted devices, etc. The portable wearable devices may be smart watches, smart bracelets, head-mounted devices, etc. The server 104 may be implemented as an independent server or a server cluster consisting of multiple servers, or may be a cloud server.

[0036] In an exemplary embodiment, Figure 2 As shown, a data quality control method for an atmospheric mercury online analyzer is provided. The method is executed by a computer device, and can be executed by a computer device such as a terminal or a server alone, or by a terminal and a server together. In the embodiment of the present application, the method is applied to Figure 1 The server 104 in the example is used as an example to illustrate the method, which includes the following steps 201 to 206. Among them:

[0037] Step 201, based on the sampling method and sampling data composition of atmospheric mercury by the Zeeman effect mercury detector, the concentration value of the target substance in the air sample is measured to obtain the first quality control data.

[0038] Step 202: Determine second quality control data based on the first quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector.

[0039] Step 203, analyzing the atmospheric mercury detection signal of the second quality control data based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light.

[0040] Step 204, performing data elimination and minute-to-hour data conversion on the third quality control data to obtain standard hourly data.

[0041] Step 205: Determine fourth quality control data using the standard hourly data of the target time period.

[0042] Step 206: Utilize the AI ​​module to optimize the fourth quality control data to obtain final quality control data.

[0043] The implementation of the above steps 201 to 206 focuses on the problems of missed detection or erroneous elimination of abnormal values ​​in traditional quality control methods, lack of real-time signal feature analysis, and insufficient quality control of sampling status, thereby improving the accuracy of atmospheric mercury online analyzer data. In view of the original data of mercury concentration in the atmosphere obtained by online monitoring using equipment such as the QFG series mercury analyzer, the collected mercury concentration data is processed using a data quality control algorithm, thereby effectively eliminating errors affected by environmental factors and equipment drift, and ensuring the accuracy and reliability of the monitoring results. By real-time monitoring of key parameters such as gas flow (L), gas temperature (℃), and gas humidity (RH%); combined with background light and absorption light signals for screening, under the Zeeman effect, the signal intensity of background light and absorption light conforms to certain physical laws. If the laws of background light and absorption light signals are abnormal, it may indicate data anomalies or system failures; volatility data can be detected through statistical analysis (such as standard deviation, trend analysis, etc.) and eliminated according to the set threshold. Through these methods, the combined dynamic data quality control mechanism of cold atomic absorption spectrometry and Zeeman effect background correction technology can not only achieve high-precision mercury concentration measurement, but also screen and eliminate non-compliant data through real-time data monitoring and background signal correction, thereby ensuring high accuracy and stability of atmospheric mercury concentration data under different environments and operating conditions.

[0044] This application proposes a comprehensive quality control method, which includes two core modules: quality control based on sampling status and quality control based on signal stability.

[0045] Quality control module based on sampling status: 1. Real-time monitoring of gas flow (L), gas temperature (℃), gas humidity (RH%) and other parameters to ensure stable sampling conditions. By determining whether these parameters (i.e. gas flow, gas temperature, gas humidity) meet the preset range (such as flow 1.8L~2.2L, temperature 24.9℃~25.1℃, humidity 19.9%~20.1%), sampling data under abnormal conditions are eliminated. 2. Dynamically adjust the sampling parameter control range to adapt to complex environmental conditions and ensure the quality of sample gas.

[0046] Quality control module based on signal stability: 1. Combine the physical properties of background light (σ linear polarized light) and absorbed light (π linear polarized light) under the Zeeman effect to calculate the intensity ratio of the two in real time Ensure that it complies with certain physical laws. If the ratio deviates from the set range (such as Δk), it is marked as abnormal data and removed. 2. Use algorithms such as rolling window filtering and adaptive fluctuation range update to dynamically adjust the abnormal rejection threshold to adapt to rapidly changing environmental conditions.

[0047] In another exemplary embodiment of the present application, the above step 201 may include the following steps 301 to 308.

[0048] Step 301: Sampling method and data composition of Zeeman effect mercury detector. The sampling method includes standard gas and air sample gas, wherein the standard gas includes working standard gas (W) and target standard gas (T), and the air sample gas includes high-level sample gas (H) and low-level sample gas (L).

[0049] Step 302: Set the measurement sequence. Set a measurement sequence of standard gas and air sample gas every 7×24 hours. By alternately measuring the standard gas and air sample gas, the stability and calibration of the equipment under different gas concentration conditions can be ensured, and the reliability and accuracy of the measurement results can be ensured. The measurement sequence can be W, H, L, …, T, …, H, L; where “…” means a combination of H and L for 10 minutes each, repeated continuously.

[0050] Step 303: Sampling time allocation and data processing. Considering the dynamic variability and volatility of mercury concentration, this application adopts a more flexible data processing method. For each 10-minute sampling time, the first 5 minutes are used to remove impurities in the gas sample, and the data of the last 5 minutes are used to calculate the average concentration value of the target substance (i.e., mercury).

[0051] Step 304: Abnormal data removal. In order to better adapt to the fluctuation of concentration, the minute-level data within the last 5 minutes of the air sample is removed, which can more accurately respond to the dynamic changes of mercury concentration. There are two removal methods, namely dynamic fluctuation range removal and instantaneous mutation removal:

[0052] (1) Dynamic fluctuation range elimination: Through the rolling window filtering method, the fluctuation range is dynamically adjusted according to the changing trend of real-time data, and the data beyond the fluctuation range is eliminated. Adaptive fluctuation range update: In the minute-level data within the last 5 minutes of collecting air samples, if the data exceeds the set dynamic fluctuation range (based on the calculation of mean±3σ as the upper and lower limits of the real-time data trend, where mean represents the mean of the data and σ represents the standard deviation, that is, the dynamic fluctuation range is [mean-3σ, mean+3σ]), it is adaptively eliminated and the fluctuation range of the data is updated.

[0053] (2) Instantaneous mutation elimination: If the data changes in a very short period of time and exceeds the set maximum change threshold, the data is considered abnormal and eliminated. That is, if the data change value in the set time exceeds the set maximum change threshold, the data is considered abnormal and eliminated. The set time is a very short period of time.

[0054] Step 305: Determine the response value of the previous and next working standard gas. Determine the closest working standard gas within 7×24 hours before each sampling data, and calculate the response value of the working standard gas, which is recorded as B W1 Find the working standard gas within 7×24 hours after each sampling data, and calculate the response value of the working standard gas, recorded as B W2 .

[0055] Step 306: Read the nominal concentration of the standard gas. Read the nominal concentration value of the working standard gas, denoted as C w .

[0056] Step 307: Calculate the concentration of the target substance using the first calculation formula. When the calibration gas cylinder is replaced, the calculation formula for the concentration of the target substance is as follows:

[0057]

[0058] Among them, C x is the concentration value of the target substance in the current sample gas; B x is the response value of the target substance in the current sample gas; C w is the nominal concentration value of the working standard gas; It is the weighted average of the working standard gas response values ​​before and after a certain sampling. The calculation formula is as follows:

[0059]

[0060] in, It is the response value of the working standard gas before a certain sampling data; is the response value of the working standard gas after a certain sampling data; W 1 and w 2are weight factors related to the response values ​​of the front working standard gas and the back working standard gas, respectively, which are adjusted according to the stability of the sampling period. For example, w can be given according to the stability of the sampling period. 1 and w 2 Assign different values ​​to ensure that more attention is paid to the data in this time period when the working standard gas changes less.

[0061] Step 308: If the calibration gas bottle is replaced, the concentration value of the target substance in the current sample gas is calculated using the third calculation formula. When the calibration gas bottle is replaced, the calculation formula for the concentration value of the target substance (ie, the third calculation formula) is as follows:

[0062]

[0063] In the formula, It is the nominal concentration value of the newly replaced standard gas.

[0064] In another exemplary embodiment of the present application, the above step 202 specifically includes: filtering the first quality control data based on the screening range of gas flow to obtain gas flow screening data; filtering the first quality control data based on the screening range of gas temperature to obtain gas temperature screening data; filtering the first quality control data based on the screening range of gas humidity to obtain gas humidity screening data; the gas flow screening data, gas temperature screening data and gas humidity screening data constitute the second quality control data. Figure 3 As shown, the multi-dimensional parameter screening process of step 202 may include the following steps 401 to 403.

[0065] Step 401: The gas flow of the Zeeman effect mercury detector that does not satisfy the third calculation formula is screened and eliminated.

[0066] Step 402: The gas temperatures of the Zeeman effect mercury detectors that do not satisfy the fourth calculation formula are screened and eliminated.

[0067] Step 403: The gas humidity of the Zeeman effect mercury detector that does not satisfy the fifth calculation formula is screened and then eliminated. The data after the above steps 401 to 403 are the second quality control data.

[0068] Assume that the sampling setting flow rate is 2L, the temperature is controlled at 25℃, and the humidity is controlled at 20%.

[0069] The fourth calculation formula is:

[0070] 1.8L≤A≤2.2L (4);

[0071] Where A is the gas flow rate.

[0072] The fifth calculation formula is:

[0073] 24.9℃≤B≤25.1℃ (5);

[0074] Where, B is the gas temperature;

[0075] The sixth calculation formula is:

[0076] 19.9%≤C≤20.1% (6);

[0077] Where C is the gas humidity.

[0078] This application determines the measurement results of the gas flow, gas temperature, gas humidity, etc. of the Zeeman effect mercury detector. According to the normal operation of the Zeeman effect mercury detector, a certain normal value range of each parameter is specified. If a parameter exceeds the range, it is regarded as abnormal and the data is eliminated.

[0079] Under the Zeeman effect, the intensity of background light (σ linear polarized light) and absorbed light (π linear polarized light) should conform to certain physical laws. Mercury atoms hardly absorb background light (σ linear polarized light), while the intensity of absorbed light (π linear polarized light) is proportional to the mercury concentration. Ideally, the ratio between the two should be constant. Then the above step 203 specifically includes: obtaining the actual intensity ratio of background light and absorbed light; calculating the difference value based on the actual intensity ratio and the theoretical value; when the difference value is greater than the set tolerance range, it is unstable signal data, and the unstable signal data in the second quality control data is eliminated, and the data with a difference value not greater than the set tolerance range is determined as the third quality control data. Step 203 can specifically include the following steps 501 to 505.

[0080] Step 501: Obtain the intensity ratio of background light to absorbed light. In the operation of the Zeeman effect mercury detector, the intensity ratio of background light (σ linear polarized light) to absorbed light (π linear polarized light) is To determine the stability of the data and eliminate data caused by signal anomalies or system failures. bg is the background light intensity, I abs is the intensity of absorbed light. Ideally, the intensity ratio k of background light to absorbed light should be a constant and proportional to the concentration of mercury. Therefore, by measuring the intensity values ​​of background light and absorbed light, the actual intensity ratio kmeasured is calculated.

[0081] Step 502: Calculate the difference between the actual intensity ratio and the theoretical value. Determine whether the data is abnormal data based on the seventh calculation formula. If the difference exceeds the set tolerance range Δk, the data is considered to be abnormal data. The seventh calculation formula is as follows:

[0082] |kmeasured-k|≥Δk (7);

[0083] Among them, kmeasured is the actual intensity ratio of background light to absorbed light; k is the theoretical value; |kmeasured-k| is the difference between the actual intensity ratio and the theoretical value; Δk is the set tolerance range.

[0084] Step 503: Determine data stability. If the result calculated by the seventh calculation formula meets the unstable condition (i.e. |kmeasured-k|≥Δk), the current signal data is considered unstable and may be affected by external interference, instrument failure or other reasons. If the difference value exceeds the tolerance range Δk, the current data is determined to be abnormal data and needs to be eliminated.

[0085] Step 504: Eliminate unstable signal data. Data that is determined to be abnormal in step 502 and step 503 is eliminated to ensure the accuracy of subsequent calculations and analysis. This elimination operation can be automatically completed through a data cleaning program to avoid the influence of unstable data on the overall results.

[0086] Step 505: Keep stable signal data. If the calculated difference value is less than the tolerance range Δk, the data is considered stable and can be used for subsequent analysis and calculation.

[0087] Step 204 specifically includes: performing data elimination on the third quality control data to form 10-minute basic data; performing abnormal marking according to the 10-minute basic data and eliminating abnormal data to obtain 10-minute analytical data; performing hourly data extraction according to the 10-minute analytical data and eliminating abnormal standard deviation data to form standard hourly data. In this embodiment, step 204 may include the following steps 601 to 612. Step 601 is the process of forming 10-minute basic data, steps 602 to 607 are the process of forming 10-minute analytical data, and steps 608 to 612 are the process of forming standard hourly data.

[0088] Step 601: When it is determined that the atmospheric mercury concentration does not satisfy the eighth calculation formula, the data is discarded and the remaining data is used as the 10-minute basic data. The eighth calculation formula is:

[0089] 0.1ng / m 3 ≤XCHg≤20ng / m 3 (8);

[0090] Where XCHg is the concentration of atmospheric mercury.

[0091] Step 602: Fill in and remove duplicates in the time series of missing data. Fill in and remove duplicates in the time series data of atmospheric mercury to ensure the integrity and accuracy of the data and avoid missing or duplicate data affecting subsequent analysis and calculations.

[0092] Step 603: Classify the stations, specifically including background stations and regional stations. The observation stations are classified into two categories: background stations and regional stations, and different processing and analysis are performed.

[0093] Step 604: Determine the monitoring data of the background station. If the monitoring data of the background station does not satisfy the ninth calculation formula, it is considered that there is a deviation. The atmospheric mercury data at the corresponding time of the target standard gas and 3 hours before and after should be marked as abnormal, and the abnormal data should be eliminated.

[0094] In order to better reflect the changing trend of atmospheric mercury concentration and the response of the equipment, the relative change rate or cumulative change of the concentration change should be considered. The relative change rate can be introduced to describe these changes. It is assumed that the change of atmospheric mercury concentration is not linear, but has a certain dynamic response. The ninth calculation formula is:

[0095]

[0096] Where F is the atmospheric mercury concentration at the current moment; F 0 is the atmospheric mercury concentration at the moment before the current moment; δ is the set change rate threshold, which can be a preset value, which can be determined according to the equipment and the actual sampling frequency, and its value is between 1% and 5%.

[0097] Step 605: The monitoring data of the regional station is judged. If the monitoring data of the regional station does not satisfy the tenth calculation formula, it is considered that there is a deviation. The atmospheric mercury data at the time corresponding to the target standard gas and 3 hours before and after should be marked as abnormal, and the abnormal data should be eliminated; considering the response lag of the equipment itself and the natural fluctuation of the atmospheric mercury concentration of the regional station over time, the cumulative change of the concentration can be included in the formula to better reflect the stability of the equipment response. The tenth calculation formula is:

[0098]

[0099] Among them: G and G 0 It is the actual monitored atmospheric mercury concentration at the current moment and the concentration at the moment before the current moment; γ is the set relative change threshold, which is between 0.5% and 2%, and the specific value can be determined according to the actual sampling situation.

[0100] Step 606: First, remove the invalid data of the Zeeman effect mercury detector failure, and remove the high-level abnormal data of the stratified period, where the high-level abnormal data of the stratified period is specifically the collected data corresponding to the first 5 minutes of each 10-minute sampling time. Zeeman effect mercury detector failures include intake pipe failures, shutdown maintenance, etc.

[0101] Step 607: All 10-minute data after removing abnormal data is used as 10-minute analysis data, and the remaining 10-minute data is 10-minute valid data, which can be used for subsequent analysis and calculation.

[0102] Step 608: Calculate hourly data. Based on the valid data at the 10-minute level, if the valid data in 1 hour reaches 50% or more, the average concentration of the target substance in that hour can be calculated to form standard hourly data. The valid data in 1 hour reaching 50% or more means that 50% or more of the data in 1 hour is not abnormal data.

[0103] Step 609: Calculate the hourly variation rate, which is the difference between two adjacent hourly data. Specifically, calculate the average concentration value of the target substance at all times within each hour, use it as the hourly data, and record the number of samples and the highest and lowest values ​​in all hourly data.

[0104] Step 610: If the difference between the current hourly data and the previous hourly data exceeds a threshold, the current hourly data is marked and removed. The current hourly data is the average concentration value of the target substance at all times in the current hour.

[0105] Step 611: If the average concentration value for the previous hour is empty, the average concentration value for the current hour is also marked and removed.

[0106] Step 612: The remaining data after deletion is used as standard hourly data. The standard deviation in the hourly data is screened, and abnormal data with standard deviation exceeding the set range is eliminated to ensure the stability and accuracy of the data. For the 10-minute data in each hour, the 3-times standard deviation method is used for quality control, and the 10-minute data exceeding the mean ± 3σ range is eliminated, and the corresponding hourly data is eliminated to obtain standard hourly data.

[0107] The above step 205 specifically includes: for each observation station of atmospheric mercury, determining the maximum concentration value and the minimum concentration value corresponding to each station in the target time period according to the standard hourly data of the target time period; when the new standard hourly data is not within the climate extreme range, eliminating the data to obtain the fourth quality control data; the upper limit value and the lower limit value of the climate extreme range are the maximum concentration value and the minimum concentration value of the target time period, respectively.

[0108] The target time period can be 3 years. Specifically, the sliding window method is used for dynamic updating. According to the standard hourly data of the past three years, the 3-year maximum and 3-year minimum values ​​of atmospheric mercury at each station are analyzed one by one. After each calculation to obtain new standard hourly data, the standard hourly data is screened using a dynamic tolerance interval (for example, ±10%). If the atmospheric mercury at the current station is not between the 3-year maximum and 3-year minimum values, an outlier correction (such as smoothing) is performed to eliminate data that do not meet the climate extreme threshold, retain valid data and record them as the fourth quality control data. Based on the standard hourly data of the past three years, the influence of abnormally high / low values ​​is eliminated one by one. According to the changes in the time series, data that can represent the climate extremes is found, and two decimal places are retained to obtain the threshold of the climate extremes, and data that do not meet the threshold of the climate extremes are eliminated.

[0109] The above step 206 specifically includes: setting an initial fluctuation range for each monitoring point based on historical data; using a rolling window filter to smooth each data point in the fourth quality control data to obtain rolling window filtered data; performing fluctuation trend analysis on the rolling window filtered data to determine the trend analysis result of the current data point; according to the trend analysis result, adaptively adjusting the fluctuation range of the monitoring point to determine the adaptive fluctuation range; optimizing the fourth quality control data based on the adaptive fluctuation range to determine the final quality control data. Specifically, the following steps 701 to 708 may be included:

[0110] Step 701: Initialize the dynamic fluctuation range data and standardize the fourth quality control data (e.g., standardize the measurement data of different units into a unified unit). Initialize the dynamic fluctuation range and set the initial fluctuation range for each monitoring point based on historical data (e.g., the maximum value, minimum value and standard deviation in the past three years). The calculation formula for the initial fluctuation range is as follows:

[0111]

[0112] Among them, X i is the historical data point, μ is the mean, N is the number of data points, and σ is the standard deviation. Dynamic fluctuation range: Fluctuation range = [μ-3σ, μ+3σ], this formula is used to preliminarily determine the normal fluctuation range of the data.

[0113] Step 702: Rolling window filtering. Use a rolling window filter to smooth each data point in the fourth quality control data to reduce noise and extract trends. Weighted rolling average filtering can be used for smoothing, and the formula is as follows:

[0114]

[0115] Among them, y t is the filtered value, x t-iis the ti-th data point in the window, w i is the weight of the ith data point in the window, and w is the size of the window. Filter weight: An adaptive weight function can be designed based on the data change trend, such as using an exponential weighting method:

[0116] w i =α (w-i) (13);

[0117] Among them, α is a decay factor less than 1, indicating the decay of weight over time.

[0118] Step 703: Detect data fluctuation trend. Perform fluctuation trend analysis on the data after rolling window filtering to determine whether the current data point meets the expected trend and obtain trend analysis results. The trend detection algorithm can use the least squares method, and the formula is as follows:

[0119]

[0120] Where a is the slope, b is the intercept, and y is the i is the actual observed value of the ith data point, x i is the input data of the ith data point, and N is the number of data points.

[0121] Step 704: Adaptive fluctuation range adjustment. According to the trend analysis results, the fluctuation range is automatically adjusted to make it more flexible to adapt to the current monitoring conditions. The fluctuation range is adaptively adjusted according to the current trend correction standard deviation. The current trend correction standard deviation σ new The calculation formula is as follows:

[0122] σ new =λ·σ prev +(1-λ)·newdeviation (15);

[0123] Where λ is the smoothing factor, σ prev is the standard deviation of the previous moment, and newdeviation is the new deviation value. The adaptive fluctuation range is expressed as follows:

[0124] newrange=[μ-3σ new ,μ+3σ new ] (16);

[0125] Among them, newrange represents the adaptive fluctuation range, and μ is the mean of historical data. The fluctuation range will be adaptively adjusted according to the dynamic changes of data.

[0126] Step 705: Data verification. Verify whether the data conforms to the historical trend and whether it exceeds the reasonable range to obtain the data verification result. Use Z-Score to detect whether it is an outlier:

[0127]

[0128] Among them, Z is the Z-Score test result of the data, X is the current data point, μ is the mean of the historical data, and σ is the standard deviation of the historical data. If |Z|>3, the data is considered to be an outlier and needs to be removed or marked as an outlier.

[0129] Step 706: Data correction and screening. Correct the data according to the verification results and remove abnormal data that does not meet the requirements. Data interpolation (such as linear interpolation or spline interpolation) can be used for data correction:

[0130]

[0131] Among them, x 0 and x 1 is a known data point, y 0 and t 1 The known data points x are 0 and x 1 The corresponding time, t is the time point to be interpolated, x new is the interpolated data point. If it is detected that some data points deviate significantly from the normal range, the average of the previous and next data or other alternative strategies can be used to correct them.

[0132] Step 707: Real-time feedback and optimization. Through the feedback mechanism, the AI ​​module continuously optimizes the data processing rules. For example, the Q-learning algorithm is used to optimize the data processing strategy according to different monitoring conditions:

[0133]

[0134] Among them, Q(s,a) is the state-action value function, α is the learning rate, γ is the discount factor, r is the reward, s is the current state, s' is the next state after executing action a, and a' is one of all possible actions in the next state s', especially the action that maximizes the Q value.

[0135] When updating the Q value, the agent performs action a based on the current state s, obtains reward r, and moves to the next state s'. Then, the current state-action value function value Q(s,a) is updated based on the maximum Q value of the next state s'. Here It refers to the Q value corresponding to the action that can obtain the maximum Q value among all possible actions a' in the next state s'.

[0136] Step 708: Output optimized data. The output data includes the final value after rolling window filtering, trend verification, dynamic fluctuation range adjustment and abnormal data removal. By introducing rolling window filtering, adaptive fluctuation range adjustment, Z-score detection, least squares method, RANSAC and reinforcement learning, the AI ​​module can effectively optimize the quality control process of atmospheric mercury data. The combination of these technologies can realize dynamic and real-time data processing to ensure the accuracy and reliability of data under complex climate and environmental conditions.

[0137] The present application also provides an application scenario, which applies the above-mentioned atmospheric mercury online analyzer data quality control method. Specifically: the atmospheric mercury online analyzer data quality control method provided in this embodiment can be applied in the atmospheric mercury data quality control scenario. The atmospheric mercury data quality control scenario includes a data production link and an atmospheric mercury data quality control link; the monitoring data of the Zeeman effect mercury detector enters the atmospheric mercury data quality control link from the data production link, and the corresponding final quality control data is obtained through human-computer collaboration. The atmospheric mercury online analyzer data quality control method provided in this embodiment belongs to the atmospheric mercury data quality control link. Specifically, in the process of the atmospheric mercury data quality control link for the monitoring data of the Zeeman effect mercury detector, the concentration value of the target substance in the air sample can be measured based on the sampling method and sampling data composition of the atmospheric mercury by the Zeeman effect mercury detector, and the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector can be obtained, and the abnormal data can be eliminated. The atmospheric mercury detection signal is analyzed based on the signal stability, and the unstable signal data is eliminated to obtain the third quality control data. The third quality control data is subjected to data elimination and minute-to-hour data conversion to obtain standard hourly data. The standard hourly data of the target time period is used to eliminate data that does not meet the climate extreme threshold, and the fourth quality control data is optimized using the AI ​​module to obtain the final quality control data.

[0138] Based on the same inventive concept, the embodiment of the present application also provides an atmospheric mercury online analyzer data quality control system for implementing the atmospheric mercury online analyzer data quality control method involved above. The implementation scheme for solving the problem provided by the system is similar to the implementation scheme recorded in the above method, so the specific limitations in one or more atmospheric mercury online analyzer data quality control system embodiments provided below can refer to the limitations of the atmospheric mercury online analyzer data quality control method above, and will not be repeated here.

[0139] In an exemplary embodiment, Figure 4 As shown, a data quality control system for an atmospheric mercury online analyzer is provided, comprising:

[0140] The gas control module 801 is used to measure the concentration value of the target substance in the air sample based on the sampling method and sampling data composition of atmospheric mercury by the Zeeman effect mercury detector to obtain the first quality control data; the sampling method includes standard gas and air sample gas, wherein the standard gas includes working standard gas and target standard gas, and the air sample gas includes high-layer sample gas and low-layer sample gas.

[0141] Function: Set the sampling method and sampling data composition of the Zeeman effect mercury detector, and be responsible for adjusting the concentration value of the target substance in the sample gas to ensure the validity of the data. Specific functions: Set the sampling order of the standard gas and the air sample gas to ensure the accuracy and reliability of the measurement. Control the sampling frequency and alternating order of the standard gas (working standard gas and target standard gas) and the sample gas (high-level sample gas and low-level sample gas) to ensure the stability of the equipment under different gas concentration conditions. Optimize the injection time and the time setting for removing impurities to improve the accuracy of data collection.

[0142] The parameter quality control module 802 is used to determine the second quality control data according to the first quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector.

[0143] Function: Real-time monitoring and quality control of key parameters of Zeeman effect mercury detector (such as gas flow, gas temperature, gas humidity), and elimination of abnormal data. Specific functions: Real-time measurement of gas flow, temperature, and humidity, and judgment of whether they meet the set standard range. When the gas flow is not within the set range (such as 1.8L≤A≤2.2L), the gas temperature is not within the control range (such as 24.9℃≤B≤25.1℃), and the gas humidity is not within the specified range (such as 19.9%≤C≤20.1%), the corresponding data is eliminated. Ensure the stability and consistency of all input data to avoid data deviations caused by instrument failure or environmental changes.

[0144] The signal stability elimination module 803 is used to analyze the atmospheric mercury detection signal of the second quality control data based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light.

[0145] Function: By analyzing the signal intensity ratio of background light (σ linear polarized light) and absorbed light (π linear polarized light), the stability of the data is judged, and data caused by signal abnormality or system failure is eliminated. Specific function: Set the theoretical ratio k, calculate the measured k value in real time, and compare it with the theoretical value. If the measured ratio kmeasured differs from the theoretical value by more than the preset tolerance range (such as Δk), the data is judged as abnormal data and eliminated. Avoid data distortion caused by factors such as light source fluctuations, instrument failures or environmental interference.

[0146] The minute-to-hour conversion module is used to perform data elimination and minute-to-hour data conversion on the third quality control data to obtain standard hour data. The minute-to-hour conversion module is divided into an extreme value control module 804, a deviation check module 805 and a time check module 806.

[0147] Extreme value control module 804: performs extreme value control on the collected mercury concentration data to form 10-minute basic data and remove abnormal data that exceeds the predetermined concentration range. Specific functions: According to the actual concentration range (such as 0.1ng / m 3 ≤XCHg≤20ng / m 3 ), and remove the data that does not meet the range. Dynamically screen the concentration data and filter out the extreme values ​​that do not meet the regulations to ensure data quality.

[0148] Deviation check module 805: Function: Mark anomalies based on 10-minute basic data, remove abnormal data, and generate 10-minute analysis data. Specific function: For the data of background stations and regional stations, perform change trend analysis according to the set relative change rate threshold (such as δ=1%-5%). If the change exceeds the range, it is marked as abnormal. Time series completion and deduplication processing are performed on missing data to ensure the completeness of the analysis data. When the standard gas data does not meet expectations, the atmospheric mercury data of the previous and subsequent time periods are marked as abnormal and eliminated.

[0149] Time check module 806: Function: Extract hourly data based on 10-minute analysis data, remove abnormal standard deviation data, and form standard hourly data. Specific function: Count the 10-minute valid data. If the valid data in an hour is greater than or equal to 50%, calculate the average concentration value of the hour. Compare the data difference between two adjacent hours. If the difference exceeds the threshold, mark the hourly data as abnormal and remove it. If the concentration value of the previous hour is empty, the data of the current hour will also be marked and removed.

[0150] The climate rejection module 807 is used to determine the fourth quality control data using the standard hourly data of the target time period.

[0151] Function: Perform climate extreme threshold analysis based on the standard hourly data of the past three years, remove data that does not meet the climate extreme value, and ensure long-term data consistency. Specific function: Analyze the maximum and minimum values ​​of each station in the past three years one by one, and check whether the current atmospheric mercury concentration exceeds this range. If the current data exceeds the maximum or minimum value in the past three years, it is considered abnormal and the data is removed.

[0152] The AI ​​optimization module 808 is used to optimize the fourth quality control data using the AI ​​module to obtain final quality control data.

[0153] Function: Use artificial intelligence (AI) technology to dynamically optimize the data processing process and improve the accuracy and efficiency of data quality control. Specific functions: 1) Rolling window filter optimization: Use machine learning algorithms to analyze the fluctuation trend of real-time data and intelligently adjust the filtering parameters of the rolling window. The system will adaptively adjust the window size and filter threshold based on the fluctuation pattern of historical data, so as to more accurately remove outliers. 2) Dynamic threshold adjustment: The AI ​​model dynamically adjusts the thresholds of various types of data, such as gas concentration, flow, temperature, etc., by comparing historical data with real-time data, so that data processing is more in line with actual changes. 3) Anomaly detection and marking: Through deep learning or clustering algorithms, potential abnormal data is identified and intelligently marked in combination with background data. The AI ​​module can handle complex nonlinear relationships and detect abnormal data that is difficult to identify by traditional methods in a timely manner. 4) Adaptive fluctuation range update: According to data trends, the AI ​​module will dynamically adjust the fluctuation range to better adapt to different climate and environmental conditions, and accurately remove abnormal data whose fluctuations exceed a reasonable range. 5) Forecast and trend analysis: Trend prediction can be performed based on historical data to provide forward-looking decision support for data verification and anomaly removal.

[0154] Through a series of modular designs, a data quality control system for atmospheric mercury online analyzers suitable for different structures is realized. The system can achieve closed-loop, reliable and efficient execution through collection, analysis and control.

[0155] This application has the following beneficial effects:

[0156] 1. Refined data quality control system: This application significantly improves the accuracy and real-time performance of data processing by expanding data quality control from the traditional hourly level to the second and minute levels. Based on the statistical analysis of long-sequence data, the system can accurately identify the thresholds of key indicators such as temporal variability and climate extremes to ensure comprehensive control of data quality.

[0157] 2. Comprehensive diagnosis of multi-dimensional information: This application scheme realizes comprehensive diagnosis and identification of erroneous data by combining multi-dimensional information such as equipment parameters (such as gas flow, temperature, humidity, etc.), climate state changes, outlier abnormal data, and station historical records. This diagnostic system can not only timely detect abnormal data caused by factors such as equipment failure and climate fluctuations, but also intelligently optimize and correct data based on factors such as equipment health status and environmental changes.

[0158] 3. Dynamic optimization and intelligent adjustment: By introducing AI optimization modules and adaptive threshold adjustment mechanisms, this application can dynamically adjust data processing parameters according to the changing trends of real-time data and the working status of the equipment, ensuring that data quality can still be accurately controlled under rapidly changing environmental conditions, avoiding data deviations caused by environmental fluctuations or changes in equipment status.

[0159] 4. Comprehensive abnormal data elimination and accurate analysis output: In terms of data elimination, this application eliminates abnormal data through multi-stage screening strategies (such as based on signal stability, climate extreme value control, relative change rate, etc.) to ensure that the final output analysis data has a high degree of accuracy and reliability. These processing methods provide a solid data foundation for subsequent data analysis and decision-making.

[0160] In an exemplary embodiment, an electronic device is provided, which may be a smart phone, a computer, a camera, a Bluetooth device, etc., and its internal structure may be as follows: Figure 5 shown.

[0161] Electronic device 900: The hardware body of the system, supporting connection with external devices (such as detectors, sensors, etc.). The electronic device can be a smart phone, a computer, a camera, a Bluetooth device, etc., with a highly modular design to support future expansion.

[0162] Processor 901: The core module of the system, responsible for loading and executing computer program instructions stored in the memory, completing data sampling and quality control. Function: Set the sampling method and data composition. Eliminate abnormal data and support adaptive fluctuation range adjustment. Form 10-minute basic data and further extract hourly data. Perform climate extreme value screening to optimize data quality. Dynamically adjust parameters in real time to ensure data accuracy and robustness. Support AI model operation (such as dynamic threshold adjustment and rolling window filtering).

[0163] Memory 902: used to store computer programs, historical data and dynamic parameters. Function: Store three years of climate extreme data for dynamic parameter calculation. Save real-time monitoring data and historical records. Classified storage (by time series, abnormal marking, etc.). Support data backup and fast retrieval.

[0164] Communication module 903: Provides wired (such as USB) and wireless (such as Bluetooth, Wi-Fi) communication functions. Function: Data exchange between devices, receiving sensor data or uploading processing results. Upload processing results to the cloud in real time. Support remote program updates and real-time abnormal alarm push. Data transmission encryption to ensure data security.

[0165] Peripheral interface module 904: supports connection to external sensors (such as pressure, humidity, temperature, etc.) and detection instruments. Function: Receive sensor data from external devices. Support camera modules to monitor environmental changes in real time. Expandable interface to support new sensor devices in the future.

[0166] Display module 905: provides data display function. Function: real-time display of current data and analysis results. Supports scrolling trend graph display. Highlights abnormal data and analysis results.

[0167] Input module 906: used for user interaction (keyboard, mouse, touch screen, etc.). Function: Provides an operation interface to allow users to adjust parameters and filter conditions. Supports voice input and quick gesture operation. Provides data export function to facilitate user analysis and archiving.

[0168] In this embodiment, the processor 801 in the electronic device 800 will load the instructions corresponding to the processes of one or more computer programs into the memory 802 according to the following steps, and the processor 801 will run the computer program stored in the memory 802 to realize various functions, such as: setting the sampling mode and sampling data composition of the Zeeman effect mercury detector to obtain the concentration value of the target substance in the sample gas; judging the Zeeman effect mercury detector result of the Zeeman effect mercury detector and eliminating abnormal data; eliminating data of atmospheric mercury parameters to form 10-minute basic data; marking abnormalities based on the 10-minute basic data and eliminating abnormal data as 10-minute analysis data; extracting hourly data based on the 10-minute analysis data, and eliminating abnormal standard deviation data to form standard hourly data; and eliminating data that does not meet the threshold of climate extremes based on the standard hourly data of the past three years.

[0169] Core module: responsible for the core calculation of mercury concentration data processing, including data quality control and dynamic adjustment. Including the core processing logic and signal operation module of the Zeeman effect mercury detector.

[0170] Signal processing module: responsible for processing background light and absorption light signals, performing signal stability detection, and screening out effective signals. It includes sub-functions such as signal correction and signal fluctuation calculation.

[0171] AI Optimization Module: Performs rolling window filtering and adaptive threshold adjustment. Dynamically adjusts the data exclusion range and optimizes data quality control.

[0172] Gas control module: controls gas flow, temperature and humidity. Ensures that the sample gas quality meets the measurement standards.

[0173] Data storage module: used to store raw data, analysis data after quality control and historical climate data. Supports storage of 3 years of extreme climate data for subsequent testing.

[0174] Data output module: provides final data output and supports real-time monitoring and historical analysis reports.

[0175] In an exemplary embodiment, a computer device is provided. The computer device may be a server or a terminal. The internal structure diagram thereof may be as follows: Figure 6As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data quality control processing data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a data quality control method for an atmospheric mercury online analyzer is implemented.

[0176] Those skilled in the art will understand that Figure 6 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0177] In an exemplary embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above-mentioned method embodiments when executing the computer program.

[0178] In an exemplary embodiment, a computer-readable storage medium is provided, storing a computer program, and when the computer program is executed by a processor, the steps in the above method embodiments are implemented.

[0179] In another exemplary embodiment, a computer-readable storage medium is provided, on which computer program instructions are stored, and when the computer program instructions are executed by a processor, the method as described in any one of the first aspects of the embodiments of the present application is implemented. These program instructions include but are not limited to the implementation of steps such as data acquisition, data preprocessing, abnormal data detection, quality control data analysis, and AI optimization module operation.

[0180] The computer program instructions include: 1. Data acquisition instructions: used to control the sampling method of the Zeeman effect mercury detector, collect different types of gas samples and record the corresponding concentration values. 2. Data preprocessing instructions: including steps such as signal stability detection, data cleaning, filtering and normalization processing to ensure that the data meets the quality standards before further analysis. 3. Abnormal data removal instructions: used to detect abnormal values, such as abnormal judgments based on physical parameters such as gas flow, gas temperature, humidity, etc., and eliminate data according to set thresholds. 4. AI optimization instructions: dynamically adjust thresholds, filter parameters, etc. based on real-time monitoring data and historical data to ensure that the system can adapt to working environments under different climates and equipment conditions. 5. Storage medium: The storage medium can be any common computer storage medium, such as a hard disk, SSD, USB flash drive or cloud storage.

[0181] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0182] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM may be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM).

[0183] The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. The non-relational database may include a distributed database based on blockchain, etc., but is not limited thereto. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but is not limited thereto.

[0184] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0185] This article uses specific examples to illustrate the principles and implementation methods of this application. The description of the above embodiments is only used to help understand the method and core ideas of this application; at the same time, for those skilled in the art, according to the ideas of this application, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting this application.

Claims

1. A data quality control method for an atmospheric mercury online analyzer, characterized in that: The atmospheric mercury online analyzer data quality control method comprises: Based on the sampling method and sampling data composition of atmospheric mercury by the Zeeman effect mercury detector, the concentration value of the target substance in the air sample is measured to obtain the first quality control data; Determine the second quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector according to the first quality control data; The atmospheric mercury detection signal of the second quality control data is analyzed based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light; Eliminate the third quality control data and convert the minute-to-hour data to obtain standard hour data; Determine fourth quality control data using standard hourly data for the target time period; The fourth quality control data is optimized by using an AI module to obtain final quality control data.

2. The data quality control method of the atmospheric mercury online analyzer according to claim 1, characterized in that: When the standard gas cylinder is not replaced, the concentration value of the target substance is calculated as follows: When the standard gas cylinder is replaced, the concentration value of the target substance is calculated as follows: Among them, C x is the concentration value of the target substance in the current sample gas; B x is the response value of the target substance in the current sample gas; C w is the nominal concentration value of the working standard gas; It is the weighted average of the working standard gas response values ​​before and after sampling; It is the nominal concentration value of the newly replaced standard gas.

3. The data quality control method of the atmospheric mercury online analyzer according to claim 1, characterized in that: Based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector, the second quality control data is determined according to the first quality control data, specifically including: The first quality control data is screened based on the screening range of the gas flow rate to obtain gas flow rate screening data; Screening the first quality control data based on the screening range of the gas temperature to obtain gas temperature screening data; The first quality control data is screened based on the screening range of gas humidity to obtain gas humidity screening data; the gas flow screening data, the gas temperature screening data and the gas humidity screening data constitute the second quality control data.

4. The atmospheric mercury online analyzer data quality control method according to claim 1, characterized in that: The atmospheric mercury detection signal of the second quality control data is analyzed based on signal stability to obtain third quality control data, specifically including: Obtain the actual intensity ratio of background light and absorbed light; Calculate the difference value according to the actual intensity ratio and the theoretical value; When the difference value is greater than the set tolerance range, it is unstable signal data. The unstable signal data in the second quality control data is eliminated, and the data with a difference value not greater than the set tolerance range is determined as the third quality control data.

5. The atmospheric mercury online analyzer data quality control method according to claim 1, characterized in that: The third quality control data is subjected to data elimination and minute-to-hour data conversion to obtain standard hourly data, including: Eliminate the third quality control data to form 10-minute basic data; Anomalies are marked according to the 10-minute basic data, and abnormal data is eliminated to obtain 10-minute analysis data; Hourly data is extracted based on the 10-minute analysis data, and abnormal standard deviation data is eliminated to form standard hourly data.

6. The data quality control method of atmospheric mercury online analyzer according to claim 1, characterized in that: The fourth quality control data is determined using the standard hourly data of the target time period, including: For each atmospheric mercury observation station, determine the maximum concentration value and the minimum concentration value of the target time period corresponding to each station based on the standard hourly data of the target time period; When the new standard hourly data is not within the climate extreme range, the data is discarded to obtain the fourth quality control data; the upper limit and lower limit of the climate extreme range are the maximum concentration in the target time period and the minimum concentration in the target time period, respectively.

7. The data quality control method of atmospheric mercury online analyzer according to claim 1, characterized in that: The fourth quality control data is optimized by using the AI ​​module to obtain the final quality control data, specifically including: Set the initial fluctuation range for each monitoring point based on historical data; Using a rolling window filter to smooth each data point in the fourth quality control data to obtain rolling window filtered data; Perform fluctuation trend analysis on the data after rolling window filtering to determine the trend analysis result of the current data point; According to the trend analysis results, the fluctuation range of the monitoring points is adaptively adjusted to determine the adaptive fluctuation range; The fourth quality control data is optimized based on the adaptive fluctuation range to determine final quality control data.

8. An atmospheric mercury online analyzer data quality control system, characterized in that: The atmospheric mercury online analyzer data quality control system comprises: A gas control module is used to measure the concentration value of the target substance in the air sample based on the sampling method and sampling data composition of the atmospheric mercury by the Zeeman effect mercury detector to obtain the first quality control data; A parameter quality control module, for determining second quality control data based on the measurement results of the gas flow, gas temperature and gas humidity of the Zeeman effect mercury detector according to the first quality control data; A signal stability elimination module is used to analyze the atmospheric mercury detection signal of the second quality control data based on signal stability to obtain third quality control data; the signal stability is characterized by background light and absorption light; A minute-to-hour conversion module, used for performing data elimination and minute-to-hour data conversion on the third quality control data to obtain standard hour data; A climate rejection module, for determining fourth quality control data using standard hourly data for a target time period; The AI ​​optimization module is used to optimize the fourth quality control data using the AI ​​module to obtain final quality control data.

9. A computer device comprising: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the data quality control method for an atmospheric mercury online analyzer according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the data quality control method of an atmospheric mercury online analyzer according to any one of claims 1 to 7 is implemented.

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