A sample analysis method based on environmental monitoring services and environmental pollution control

Abstract

The application relates to the technical field of environmental pollution prevention, and discloses a sample analysis method based on environmental monitoring services and environmental pollution treatment, which comprises the following steps: step one, environmental medium selection and pollutant determination; step two, data collection and fusion; step three, dynamic data adjustment and analysis; step four, determination of detection items; step five, separate collection; and step six, result analysis. The method helps to improve the representativeness and accuracy of sampling, reduces errors caused by a small number of samples, uses a machine learning algorithm to accurately evaluate the pollution level, and achieves the beneficial effects of solving the sampling errors caused by a small number of samples and not causing data homogenization.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of environmental pollution prevention, in particular to a sample analysis method based on environmental monitoring services and environmental pollution control. BACKGROUND

[0002] In China, environmental investigation and monitoring mainly rely on scientific research projects of universities and scientific research institutions, as well as routine monitoring of national monitoring stations. The differences in the above research results are mainly due to the uneven distribution of pollutants in the environment medium, which leads to a certain sampling error between different research investigations. That is, the investigation results with the same investigation range and purpose only reflect the local data characteristics of the pollution state, and increasing the investigation frequency and the number of investigation samples is an effective way to reduce the sampling error. The strategy of collecting data with the same investigation range and the same investigation purpose is also a strategy to increase the investigation frequency and the number of investigation samples. This strategy is also helpful to improve the accuracy of the judgment of the environmental pollution concentration level.

[0003] After searching, a diagnostic method for the concentration level of environmental medium pollutants is found in CN 110781225 B. The method includes the following steps: S1 determines the environmental medium; S2 selects the pollution to be diagnosed; S3 analyzes and diagnoses the concentration of the pollution in the environmental medium; S4 conclusion: analyze and summarize the results of the concentration level of the pollution in the environmental medium; wherein, S3 specifically includes the following operations: S3.1 set the data acquisition range; S3.2 determine the data collection rules; S3.3 data sorting, merging and calculation; S3.4 data sampling; S3.5 pollution level evaluation. This method collects data with the same investigation range and the same investigation purpose, indirectly increases the investigation frequency and the number of investigation samples, avoids the existence of sampling error caused by the small number of investigation samples, improves the accuracy of the analysis of the environmental pollution concentration level, and is helpful for the managers to formulate pollution prevention strategies.

[0004] Although the foregoing technical solution uses multiple data with the same range and the same purpose to supplement the number of investigation samples to reduce the sampling error, since the data are all the same range and the same purpose, it is easy to cause data homogenization and singleness. SUMMARY

[0005] The technical problem solved is:

[0006] In view of the deficiencies of the prior art, the present application provides a sample analysis method based on environmental monitoring services and environmental pollution control, which has the advantages of solving the sampling error caused by the small number of samples, without causing data homogenization, detecting more detailed samples, and avoiding a large amount of repeated sample data, etc., and solves the above technical problems.

[0007] Technical solution:

[0008] To achieve the above object, the present application provides the following technical solution: A sample analysis method based on environmental monitoring services and environmental pollution control, comprising the following steps:

[0009] Step one, environmental medium selection and pollutant determination: determine the environmental medium and select the pollutants to be diagnosed;

[0010] Step two, data collection and fusion: set the data acquisition range, in addition to the conventional monitoring area, different time periods and weather conditions should also be considered, develop a dynamic sampling plan, including sampling rules under different time, place and weather conditions, use sensor network and remote monitoring technology to collect data in real time, use multi-source data fusion technology to integrate sample data from different sources;

[0011] Step three, dynamic data adjustment and analysis: use dynamic sampling technology, adjust the sample collection strategy according to the real-time data changes and the preliminary results of pollutant concentration, combine multi-dimensional data: time, place and weather conditions, use machine learning algorithm to accurately evaluate the pollution level, dynamically adjust the sample collection and analysis strategy according to the data performance under different conditions;

[0012] Step four, determine the detection project: the data project with a large gap between the sample data after dynamic data adjustment and analysis and the existing data is marked separately, and the collection sample corresponding to the marked project is determined;

[0013] Step five, separate collection: separately collect the collection sample determined in step four, separately collect the sample from step two for further analysis, and if the secondary detection results are consistent, proceed to the next step;

[0014] Step six, result analysis: analyze and summarize the results of the pollutant concentration level in the environmental medium, and develop more accurate pollution prevention and control strategies based on the results of dynamic data collection and analysis.

[0015] Preferably, the detailed steps of step one are as follows:

[0016] S1.1, collect background information, review historical monitoring data and literature, understand the environmental conditions and pollution history of the area, conduct field investigation, and understand the current situation of the environmental medium and possible pollution sources;

[0017] S1.2, select representative points for monitoring according to the characteristics of the environmental medium, including near the pollution source and different sections, design the monitoring point layout scheme to ensure coverage of different areas and pollution sources;

[0018] S1.3, according to the nature of the pollutants, the pollutants are divided into gas pollutants including sulfur dioxide and nitrogen oxides, water pollutants including heavy metals and organic matter, soil pollutants including pesticide residues and heavy metals, and the main sources of different pollutants are understood, including industrial emissions, traffic emissions and agricultural activities;

[0019] S1.4, select the pollutants that have greater impact on the environment and human health, analyze the historical monitoring data of the region, and select the pollutants with higher concentration or significant changes in historical data.

[0020] Preferably, the environmental medium expression determined in step one is:

[0021] Feature vector: set the feature vector of each environmental medium to ;

[0022] ;

[0023] wherein is the value of the environmental medium on the first feature, and is the number of features;

[0024] Feature weight: ;

[0025] Evaluation function: ;

[0026] Selection criteria: .

[0027] Preferably, the expression of the pollutants to be diagnosed in step one is selected as:

[0028] Feature vector: ;

[0029] wherein is the value of the pollutant on the first feature, and is the number of features;

[0030] Feature weight: ;

[0031] Select the key pollutants:

[0032] Evaluation function: ;

[0033] Selection criteria: .

[0034] Preferably, the data acquisition range expression set in step two is:

[0035] Determining monitoring region: Spatial extent: Set monitoring region , denoted as a multi-dimensional region:

[0036] ;

[0037] Temporal extent: Set monitoring time , denoted as a time interval: ;

[0038] Weather condition: Define weather condition as a combination of multiple meteorological variables:

[0039] ;

[0040] wherein denotes the meteorological variable, including temperature, humidity, and wind speed;

[0041] Dynamic sampling plan: ;

[0042] wherein is a sampling rule function, representing the sampling plan under specific conditions.

[0043] Preferably, the real-time data collection in step two is expressed as:

[0044] Sensor data: Set number of sensors arranged within the region, wherein from 1 to The reading of the sensor at time is: ;

[0045] Real-time data, define the real-time data set as the collection of data from all sensors at time : ;

[0046] Multi-source data fusion: Use data fusion model Fusion to integrate data from different sources, set multiple data sources The fusion process is represented as:

[0047] ;

[0048] Set the weight of each data source , the fusion result is: .

[0049] Preferably, the expression of step three is:

[0050] Real-time data: data at time : ;

[0051] Pollutant concentration, pollutant concentration at time ;

[0052] wherein denotes the th measurement of the th pollutant at time , is the measured quantity;

[0053] Adjust sampling strategy according to real-time data pollutant concentration , set adjustment strategy function :

[0054] ;

[0055] Multi-dimensional data analysis: define multi-dimensional data fusion model for combinations of time , location and meteorological conditions : ;

[0056] Prediction model, establish pollutant level prediction model using machine learning algorithm , model based on multi-dimensional data : ,

[0057] wherein is the prediction function of the linear regression algorithm;

[0058] Model optimization, optimize model parameters to minimize prediction error :

[0059] ;

[0060] wherein, is the loss function: ;

[0061] Dynamic adjustment of sampling and analysis strategy according to prediction results of machine learning model and actual data performance, set dynamic adjustment function :

[0062] .

[0063] ​​Preferably, the expression of the fourth step is:

[0064] ;

[0065] represents the first adjusted sample data;

[0066] represents the first existing data;

[0067] represents the threshold value of the gap, used to determine whether the data difference is significant;

[0068] represents the set of data items marked with larger gaps.

[0069] Preferably, the pollutant concentration level in the sixth step is , wherein represents the average value of the pollutant concentration level in the environmental medium defined by different measuring points or time points is:

[0070] ;

[0071] , wherein is the pollutant concentration of the first point; is the total number of measuring points; is the average value of the pollutant concentration level.

[0072] Preferably, the standard deviation in the environmental medium defined in the sixth step is is:

[0073] ;

[0074] , wherein: is the standard deviation of the pollutant concentration level.

[0075] Compared with the prior art, the present application provides a sample analysis method based on environmental monitoring services and environmental pollution treatment, which has the following beneficial effects:

[0076] 1、The present application adjusts the sampling strategy according to the real-time data changes and the preliminary results of the pollutant concentration by setting a dynamic sampling plan. This helps to improve the representativeness and accuracy of sampling, reduce errors caused by a small number of samples, and use machine learning algorithms to accurately evaluate the pollution level. According to the multi-dimensional data including time, location and weather conditions, the machine learning model can identify patterns and trends in the data, thereby reducing errors caused by a small number of samples. By analyzing multi-dimensional data such as time, location, and weather conditions, a more comprehensive understanding of pollution can be achieved. In this way, even if the number of samples is small, the sampling error can be reduced by integrating data from multiple aspects, while avoiding data homogenization, achieving the beneficial effects of solving the sampling error caused by a small number of samples without causing data homogenization.

[0077] 2、The present application divides the sampling area into several layers according to different characteristics: geographical location and pollution source, and then samples in each layer. This ensures that data at different levels is fully covered and reduces duplication. Sampling is performed at different time periods to capture the dynamic characteristics of pollutant changes. Even with limited sample size, the diversity and detail of the data can be increased by time differences. Data from different data sources, such as sensor data and monitoring station data, can be integrated to increase the diversity and detail of the data. Different sources of data usually have different characteristics, which can reduce duplication. The anomaly detection algorithm in machine learning is used to identify abnormal data points and repeated data, thereby improving the uniqueness and detail of the data. This achieves the beneficial effects of detecting more detailed samples and avoiding a large amount of repeated sample data. BRIEF DESCRIPTION OF DRAWINGS

[0078] Figure 1 The present application is a schematic diagram of the structure. DETAILED DESCRIPTION

[0079] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0080] Please refer to Figure 1 A sample analysis method based on environmental monitoring services and environmental pollution control, comprising the following steps:

[0081] Step 1, environmental medium selection and pollutant determination: determine the environmental medium and select the pollutant to be diagnosed;

[0082] Step two, data collection and integration: Set the data acquisition range, in addition to the regular monitoring area, also consider different time periods and weather conditions, develop a dynamic sampling plan, including sampling rules at different times, places and weather conditions, use sensor networks and remote monitoring technology to collect data in real time, use multi-source data fusion technology to integrate sample data from different sources;

[0083] Step three, dynamic data adjustment and analysis: Use dynamic sampling techniques to adjust the sample collection strategy based on real-time data changes and preliminary results of pollutant concentration, combine multi-dimensional data: time, location and weather conditions, use machine learning algorithms to accurately evaluate pollution levels, and dynamically adjust sample collection and analysis strategies based on data performance under different conditions;

[0084] Step four, determine the detection project: The data project with a large gap between the sample data after dynamic data adjustment and analysis and the existing data is marked separately, and the collection sample corresponding to the marked project is determined;

[0085] Step five, separate collection: The collection sample determined in step four is collected separately, and the separately collected sample continues to be analyzed from step two, and the second detection result is consistent, then it goes to the next step;

[0086] Step six, result analysis: Analyze and summarize the results of pollutant concentration levels in the environment medium, and develop more accurate pollution prevention and control strategies based on dynamic data collection and analysis results.

[0087] Dynamic adjustment strategy: By setting a dynamic sampling plan, adjust the sampling strategy based on real-time data changes and preliminary results of pollutant concentration. This helps improve the representativeness and accuracy of sampling and reduces errors caused by small sample size. For example, when real-time data shows that the pollutant concentration in certain areas or time periods is abnormal, increase the sampling frequency of these areas.

[0088] Multi-time, multi-location sampling: Sampling at different times and locations can provide more comprehensive data coverage, reduce errors caused by single sampling, and avoid data homogeneity.

[0089] Multi-source data fusion: Use sensor networks and remote monitoring technology to collect data in real time, and integrate data from different sources through multi-source data fusion technology. This reduces the sampling error caused by the shortcomings of a single data source, increases the diversity of data, and avoids data homogeneity.

[0090] Weighted data fusion: In the data fusion process, set weights for different data sources, adjust their influence in the fusion according to the quality and reliability of each data source, and ensure the accuracy and representativeness of the integrated data.

[0091] Machine Learning Model: Use machine learning algorithms to accurately evaluate pollution levels, and predict pollution levels based on multi-dimensional data such as time, location, and weather conditions. Machine learning models can identify patterns and trends in data, reducing errors caused by small sample sizes.

[0092] Dynamic Adjustment Strategy: Based on the prediction results of the machine learning model and the actual data performance, dynamically adjust the sampling and analysis strategy, which can effectively deal with the uncertainty caused by small sample size, and ensure the optimization of sampling strategy.

[0093] Multi-dimensional data fusion: Combine time, location, weather conditions and other multi-dimensional data for analysis, which can provide a more comprehensive understanding of pollution. In this way, even if the sample size is small, the sampling error can be reduced by integrating data from multiple aspects, while avoiding data homogenization.

[0094] Dynamic data adjustment: Based on real-time data changes, adjust the sample collection strategy and analysis method to adapt to different environmental conditions and pollution levels, which effectively reduces errors and improves data reliability.

[0095] Marking data with large gaps: By comparing dynamic data with existing data, mark data items with large gaps, and sample these items separately. This can identify and solve anomalies in the data, further reducing errors caused by small sample sizes.

[0096] Preferably, the detailed steps of step one are as follows:

[0097] S1.1, Collect background information, review historical monitoring data and literature, understand the environmental conditions and pollution history of the region, conduct field investigation, and understand the current situation of environmental media and possible pollution sources;

[0098] S1.2, Select representative points for monitoring according to the characteristics of environmental media, including near pollution sources and different sections, design monitoring point layout scheme, and ensure coverage of different areas and pollution sources;

[0099] S1.3, According to the nature of pollutants, divide them into gas pollutants including sulfur dioxide and nitrogen oxides, water pollutants including heavy metals and organic matter, and soil pollutants including pesticide residues and heavy metals, understand the main sources of different pollutants including industrial emissions, traffic emissions and agricultural activities;

[0100] S1.4, Select pollutants that have a greater impact on the environment and human health, analyze historical monitoring data in the region, and select pollutants with high concentration or significant changes in historical data.

[0101] Preferably, the expression of environmental media determined in step one is:

[0102] Feature vector: Set the feature vector of each environmental medium ;

[0103] ;

[0104] wherein is the value of the environmental medium on the th feature, is the number of features;

[0105] Feature weight: ;

[0106] Evaluation function: ;

[0107] Selection criteria: .

[0108] Preferably, the expression of the selected pollutant to be diagnosed in step one is:

[0109] Feature vector: ;

[0110] wherein is the value of the pollutant on the th feature, is the number of features;

[0111] Feature weight: ;

[0112] Select the key pollutant:

[0113] Evaluation function: ;

[0114] Selection criteria: .

[0115] Preferably, the expression of the data acquisition range set in step two is:

[0116] Determine the monitoring area: Spatial range: Set the monitoring area , represented as a multi-dimensional area:

[0117] ;

[0118] Time range: Set the monitoring time , represented as a time interval: ;

[0119] Weather condition: Define the weather condition as a combination of multiple meteorological variables:

[0120] ​ ;

[0121] wherein represents the th weather variable, including temperature, humidity and wind speed;

[0122] Dynamic sampling plan: ;

[0123] wherein is the sampling rule function, representing the sampling plan under certain conditions.

[0124] Preferably, the real-time data collection expression in step two is:

[0125] Sensor data: set up sensors in the area , wherein from 1 to The reading of the sensor at time is : ;

[0126] Real-time data, define the real-time data set as the data set of all sensors at time : ;

[0127] Multi-source data fusion: use the data fusion model Fusion to integrate data from different sources, set up multiple data sources The fusion process is represented as:

[0128] ;

[0129] Set the weight of each data source , and the fusion result is: .

[0130] Preferably, the expression of step three is:

[0131] Real-time data: real-time data set at time : ;

[0132] Pollutant concentration, pollutant concentration at time Preliminary results ;

[0133] wherein represents the th measurement value of the th pollutant at time , is the measured quantity;

[0134] According to real-time data Pollutant concentration Adjust the sampling strategy, set the adjustment strategy function :

[0135] ;

[0136] Multi-dimensional data analysis: define multi-dimensional data fusion model For the combination of time , place And weather conditions : ;

[0137] Prediction model, use machine learning algorithm to establish pollution level prediction model , the model is based on multi-dimensional data : ,

[0138] Where is the prediction function of the linear regression algorithm;

[0139] Model optimization, optimize model parameters To minimize prediction error :

[0140] ;

[0141] Where, is the loss function: ;

[0142] According to the prediction results and actual data performance of the machine learning model, dynamically adjust the sampling and analysis strategy, and set the dynamic adjustment function :

[0143] .

[0144] Preferably, the fourth step expression is:

[0145] ;

[0146] Indicates the adjusted sample data of the th item;

[0147] Indicates the existing data of the th item;

[0148] Indicates the threshold of the gap, used to judge whether the data difference is significant;

[0149] The set of data items that represent the marked gaps.

[0150] Preferably, the pollutant concentration level in step six wherein represents the average value of the pollutant concentration level in the environmental medium is:

[0151] ;

[0152] wherein is the pollutant concentration at the point; is the total number of measurement points; is the average value of the pollutant concentration level.

[0153] Preferably, the standard deviation in the environmental medium in step six is:

[0154] ;

[0155] wherein: is the standard deviation of the pollutant concentration level.

[0156] Optimizing sampling design;

[0157] Stratified sampling: The sampling area is divided into several layers according to different geographical locations, pollution sources, etc., and then sampling is conducted within each layer. This ensures that data from different layers is fully covered and reduces duplication.

[0158] Random sampling: Random sampling within each stratification can reduce duplication caused by systematic bias and provide more representative samples.

[0159] Multi-dimensional data collection;

[0160] Integrated collection of different parameters: Collecting data on multiple different parameters such as pollutant concentration, weather conditions, etc. can not only obtain more detailed information, but also avoid the repetition of a single parameter.

[0161] Multi-period sampling: Sampling at different time periods to capture the dynamic characteristics of pollutant changes, so that even with limited sample size, the diversity and details of the data can be increased through time differences.

[0162] Data preprocessing and intelligent analysis;

[0163] De-duplication algorithm: Apply a de-duplication algorithm during data processing to identify and exclude duplicate data. Common de-duplication methods include similarity calculation based on data characteristics and threshold screening.

[0164] Feature engineering: By extracting and reducing the dimensionality of data, valuable features are extracted, thus avoiding the problem of duplication caused by data redundancy.

[0165] Data fusion and integration;

[0166] Cross-source data fusion: Fusion of data from different data sources, such as sensor data, monitoring station data, etc., to increase the diversity and details of data. Data from different sources usually has different characteristics, which can reduce duplication.

[0167] Multi-method integration: Combine multiple detection methods (such as chemical analysis, physical detection) for comprehensive evaluation, so as to obtain information from multiple angles and reduce the duplication of single method.

[0168] High-frequency dynamic monitoring;

[0169] Real-time monitoring system: Use real-time monitoring equipment for high-frequency data collection. This method captures subtle changes and ensures the details of the data, while reducing duplication through the diversity of real-time data.

[0170] Dynamic adjustment of sampling strategy: According to the changes of real-time data, dynamically adjust the sampling strategy to avoid repeated sampling under the same conditions.

[0171] Machine learning and data modeling;

[0172] Anomaly detection algorithm: Apply anomaly detection algorithms in machine learning to identify abnormal data points and duplicates, thus improving the uniqueness and details of the data.

[0173] Data augmentation: Use synthetic data generation to enrich sample data, thus increasing the diversity of data.

[0174] Although the embodiments of the present application have been shown and described, those skilled in the art will understand that various changes, modifications, replacements and variations of these embodiments can be made without departing from the principles and spirit of the present application, and the scope of the present application is defined by the appended claims and their equivalents.

Claims

1. A sample analysis method based on environmental monitoring services and environmental pollution control, characterized in that, Includes the following steps: Step 1: Selection of Environmental Media and Identification of Pollutants: Identify the environmental media and select the pollutants to be diagnosed; Step 2, Data Acquisition and Fusion: Define the data acquisition scope. In addition to the regular monitoring area, different time periods and weather conditions should also be considered. Develop a dynamic sampling plan, including sampling rules for different times, locations, and weather conditions. Use sensor networks and remote monitoring technology to collect data in real time. Use multi-source data fusion technology to integrate sample data from different sources. Step 3: Dynamic Data Adjustment and Analysis: Using dynamic sampling technology, the sample collection strategy is adjusted based on real-time data changes and preliminary results of pollutant concentrations. Combining multi-dimensional data—time, location, and meteorological conditions—machine learning algorithms are used to accurately evaluate pollution levels. Based on data performance under different conditions, the sample collection and analysis strategies are dynamically adjusted. Step 4: Determine the testing items: Data items with significant differences between the sample data after dynamic data adjustment and analysis and the existing data are marked separately, and the corresponding samples for the marked items are determined. Step 5: Individual sampling: Collect the samples determined in Step 4 individually. Analyze the individually collected samples from Step 2 onwards. If the results of the two tests are consistent, proceed to the next step. Step Six: Results Analysis: Analyze and summarize the results of pollutant concentration levels in the environmental media, and formulate more accurate pollution prevention and control strategies based on the dynamic data collection and analysis results.

2. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The detailed steps of step one are as follows: S1.1 Collect background information by reviewing historical monitoring data and literature to understand the environmental conditions and pollution history of the area, and conduct on-site investigations to understand the current status of environmental media and possible sources of pollution; S1.2 Select representative monitoring points based on the characteristics of the environmental media, including those near pollution sources and in different areas, and design a monitoring point layout plan to ensure coverage of different areas and pollution sources; S1.3 According to the nature of pollutants, they are classified into gaseous pollutants, water pollutants, and soil pollutants. The gaseous pollutants include sulfur dioxide and nitrogen oxides, the water pollutants include heavy metals and organic matter, and the soil pollutants include pesticide residues and heavy metals. The sources of different pollutants include industrial emissions, traffic emissions, and agricultural activities.

3. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The environmental medium expression determined in step one is: Feature vector: Defines the feature vector for each environmental medium E. i The eigenvectors are F i F i =[f i1 ,f i2 ,…,f ik ] Where f ij It is environmental medium E i The value at the j-th feature, where k is the number of features; Feature weights: Weighted eigenvectors Evaluation function: Score i =Score(W i ) Selection criteria:

4. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 3, characterized in that: The expression for the pollutant to be diagnosed in step one is: Eigenvector: Q j =[q j1 ,q j2 ,…,q jl ] Where q jk It is pollutant P j The value at the k-th feature, where l is the number of features; Feature weights: Weighted eigenvectors Select key pollutants: Evaluation function: Impact j =Impact(V j ) Selection criteria:

5. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The expression for setting the data acquisition range in step two is: Define the monitoring area: Spatial range: Set the monitoring area R, representing a multi-dimensional region: R={(x,y,z)∣x min ≤x≤x max ,y min ≤y≤y max ,z min ≤z≤z max } Time range: Set the monitoring time T, which is represented as the time interval: T={t∣t start ≤t≤t end }; Weather conditions: Define weather conditions W as a combination of multiple meteorological variables: W={(w1,w2,…,w m )} Where w i Let i represent the i-th meteorological variable, including temperature, humidity, and wind speed; Dynamic sampling plan: Sample(x,y,z,t,W) = {sampling points, sampling frequency}; Where Sample(x,y,z,t,W) is the sampling rule function.

6. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The expression for real-time data acquisition in step two is: Sensor data: N sensors are arranged within region R, where i ranges from 1 to N. The readings of the sensors at time t are represented by D. i (t): D i (t)={d i1 (t),d i2 (t),…,d ik (t)}; Real-time data, defined as the real-time dataset D(t) is the set of data from all sensors at time t: D(t)={D1(t),D2(t),…,D N (t)}; Multi-source data fusion: The Fusion data fusion model is used to integrate data from different sources.

7. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The expression for step three is: Real-time data: A real-time dataset D(t) at time t: D(t) = {D1(t), D2(t), ..., D...} N (T)}; Pollutant concentration, pollutant concentration C j (t) Preliminary results at time t Where d jk (t) represents the k-th measurement value of the j-th pollutant at time t, where n j It measures quantity; Based on real-time data D(t), pollutant concentration C j (t) Adjust the sampling strategy and set the adjustment strategy function Adjust(D(t), C j (t)): Adjust(D(t),C j (t))={new sampling point, new sampling frequency, adjustment rule} Multidimensional data analysis: Define a multidimensional data fusion model X as a combination of time t, location (x,y,z), and weather conditions W: X={(t,x,u,z,W)}; Predictive models, using machine learning algorithms to build pollution level prediction models. The model is based on multidimensional data X: Where f is the prediction function of the linear regression algorithm; Model optimization, optimizing model parameters To minimize prediction error in, It is the loss function: Based on the prediction results of the machine learning model and the actual data performance, the sampling and analysis strategies are dynamically adjusted, and the dynamic adjustment function DynamicAdjus is defined:

8. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: The expression for step four is: M={i||D new,i -D old,i |>Δ threshold } D new,i This represents the adjusted sample data for the i-th term; D old,i This represents the existing data for the i-th item; Δ threshold A threshold representing the difference, used to determine whether the data differences are significant; M represents the set of data items that have significant differences.

9. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: In step six, the pollutant concentration level C i Where i represents different measuring points or time points, and defines the average concentration level of pollutants in the environmental medium. for: Where C i Where is the pollutant concentration at point i; N is the total number of measurement points; It is the average level of pollutant concentration.

10. The sample analysis method based on environmental monitoring services and environmental pollution control according to claim 1, characterized in that: In step six, the standard deviation σ in the environmental medium is defined as: Where σ is the standard deviation of pollutant concentration levels.

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