Environmental quality monitoring method and system

Abstract

The present invention belongs to the technical field of environmental monitoring, and specifically relates to an environmental quality monitoring method and system. The method performs standardized processing on three types of environmental data collected, namely, air, water quality and noise, to obtain a time series of multidimensional environmental data; performs sequence decomposition on the time series of the multidimensional environmental data, and decomposes each parameter sequence into a trend item, a periodic item and a random item; constructs an environmental quality comprehensive index calculation model, and gives quantitative analysis results of environmental quality monitoring based on the parameter sequence decomposition data; the present invention not only solves the key problems existing in the existing environmental monitoring technology, but also shows obvious advantages in terms of accuracy, response speed and practicality, and provides effective technical support for environmental monitoring and early warning.

Description

Technical Field

[0001] The present invention belongs to the technical field of environmental monitoring, and in particular relates to an environmental quality monitoring method and system. Background Art

[0002] Under the current technical background, different environmental parameters (air, water quality, noise, etc.) have different dimensions and change characteristics, and there are complex interactions between these parameters; traditional methods usually process each parameter independently, which makes it difficult to fully reflect the overall status of environmental quality. In addition, the changes in environmental parameters not only include trend changes and periodic changes, but also have random disturbance characteristics. Existing methods cannot adapt to the needs of dynamic changes in environmental parameters over time, which leads to inaccuracy in environmental quality assessment and early warning.

[0003] In summary, the existing environmental monitoring and early warning technologies have many shortcomings in dealing with multidimensional heterogeneity, dynamic complexity, spatiotemporal correlation and early warning lag. A new technical solution is urgently needed to solve these problems in order to improve the accuracy of environmental monitoring and the timeliness of early warning. Summary of the invention

[0004] The purpose of the present invention is to provide an environmental quality monitoring method and system that can effectively solve the problems of multi-dimensional heterogeneity, dynamic complexity, spatiotemporal correlation and early warning lag of environmental data.

[0005] In a first aspect, the present invention provides an environmental quality monitoring method, the method comprising the following steps:

[0006] Step S1, standardize the collected three types of environmental data: air, water quality and noise, to obtain a time series of multi-dimensional environmental data.

[0007] The time series of the multidimensional environmental data includes 15 dimensional parameters in total, among which the air environment data includes six dimensional parameters: PM2.5, PM10 particle concentration and SO 2 、NO 2 , O 3 , CO pollutant concentration; water quality environmental data include six dimensional parameters: pH value, dissolved oxygen, turbidity, conductivity, total phosphorus, and total nitrogen; noise environmental data include three dimensional parameters: equivalent sound level, maximum sound level and background noise.

[0008] The collected data is standardized by the formula: Processing is performed to eliminate the dimensional differences between different parameters, where For the The standardized values ​​of the dimension parameters, No. The original value of the dimension parameter, and For the The minimum and maximum standard limits for each dimension parameter.

[0009] Step S2, performing sequence decomposition on the time series of the multi-dimensional environmental data, and decomposing each parameter sequence into a trend item, a period item and a random item.

[0010] Using the wavelet decomposition method, the standardized environmental quality parameters are decomposed into:

[0011] ,in, For trend items, For periodic items, is a random item.

[0012] The trend item reflects the overall development direction of the environmental parameters, the periodic item represents the periodic changes in the data, and the random item is the short-term fluctuation without regularity in the data.

[0013] Step S3, constructing an environmental quality comprehensive index calculation model, and providing quantitative analysis results of environmental quality monitoring based on parameter sequence decomposition data.

[0014] Constructing environmental quality benchmark values ​​based on the trend items ; Match the periodic item with the periodic characteristics of historical data to obtain the periodic adjustment coefficient ; Based on the distribution characteristics of random items, establish a dynamic fluctuation range .

[0015] Construct a calculation model for the comprehensive index of environmental quality:

[0016] ,in, for Quantitative analysis results of the comprehensive index of environmental quality at all times.

[0017] Furthermore, the environmental quality benchmark value is constructed based on the trend item The following steps are involved:

[0018] Step S21, calculating the trend contribution of each dimensional parameter; the trend contribution of each dimensional parameter The expression is: ,

[0019] Step S22: Determine the dynamic contribution coefficient of each dimensional parameter , ;

[0020] Step S23, the environmental quality benchmark value is the dynamic contribution coefficient of each dimensional parameter and trend items The product and .

[0021] Furthermore, the periodic item is matched with the periodic characteristics of historical data to obtain a periodic adjustment coefficient The method is:

[0022] For periodic items Perform Fourier transform, ,in, represents the representation of periodic terms in the frequency domain, represents a complex exponential function, where is an imaginary unit, is the angular frequency, Represents a time variable; identifies the main period of data change, ,in, For the The period length of the main period, For the The frequency corresponding to the local maximum of the power spectral density.

[0023] Build cycle adjustment factor, ,in, For the The amplitude of the periodic component, For the The phase angle of the main period component.

[0024] Furthermore, the dynamic fluctuation range is established based on the distribution characteristics of the random items. The method is:

[0025] Compute the local standard deviation of a random term , , is the local standard deviation of the random term, reflecting the intensity of fluctuation, For the The random item value at the moment, is the local mean of the random terms, is the number of samples used when calculating the standard deviation;

[0026] Calculate the rate of change of a random term , ,in, represents the time derivative of the random term.

[0027] Establish a dynamic fluctuation range, ,in, The adjustment coefficient is calibrated based on historical data. The calibration method follows the principle of ensuring the dynamic fluctuation range. Can cover 95% of historical fluctuations.

[0028] Furthermore, the environmental quality monitoring method also includes: feedback adjustment of the sampling frequencies of three types of environmental data: air, water quality and noise, based on the quantitative analysis results of the comprehensive environmental quality index.

[0029] The expression of the characteristic value of environmental quality change is: ,in, is the characteristic value of environmental quality change, representing the relative change rate of the environmental quality index. for Quantitative analysis results of the comprehensive index of environmental quality at all times.

[0030] Determine the sampling frequency according to the environmental quality change characteristic value, ,in, is the base sampling frequency, is the frequency adjustment coefficient, and its value is between 0.1 and 0.5.

[0031] Furthermore, the environmental quality monitoring method also includes: correcting the quantitative analysis result of the comprehensive environmental quality index according to the spatial autocorrelation function of each monitoring point.

[0032] The expression of the spatial autocorrelation function of the monitoring point is: ,in, For monitoring point and The distance between is the feature distance.

[0033] Spatial incidence matrix: , the incidence matrix Elements The expression is:

[0034] ,in, For monitoring point and In time The correlation coefficient of .

[0035] Then the spatially corrected environmental quality index is: ,in, is the spatial weight coefficient, is the trace of the incidence matrix.

[0036] In the first aspect, based on the same inventive concept, the present invention provides an environmental quality monitoring system for executing the method described in the first aspect; the system includes: a data acquisition and processing module, a sequence decomposition module and an environmental quality comprehensive index calculation module, and the three modules are connected in sequence.

[0037] Furthermore, the data acquisition and processing module is used to collect three types of environmental data: air, water quality and noise; and to perform standardization processing on the collected environmental data to obtain a time series of multi-dimensional environmental data.

[0038] Furthermore, the sequence decomposition module is used to decompose the standardized environmental quality parameters into trend items, periodic items and random items using a wavelet decomposition method.

[0039] Furthermore, the environmental quality comprehensive index calculation module is used to construct an environmental quality baseline value based on the trend item, match the periodic item with the periodic characteristics of historical data, obtain the periodic adjustment coefficient, establish a dynamic fluctuation range based on the distribution characteristics of the random item, construct an environmental quality comprehensive index calculation model and calculate the quantitative results.

[0040] Furthermore, the environmental quality comprehensive index calculation module includes:

[0041] Trend contribution calculation unit, used to calculate the trend contribution of each dimension parameter The expression is: .

[0042] Dynamic contribution coefficient calculation unit, used to determine the dynamic contribution coefficient of each dimension parameter , whose expression is: .

[0043] Baseline value calculation unit, used to calculate environmental quality baseline value .

[0044] Furthermore, the system also includes a sampling frequency adjustment module.

[0045] The sampling frequency adjustment module is connected to the environmental quality comprehensive index calculation module and the data acquisition processing module respectively, and is used to dynamically adjust the data acquisition frequency based on the quantitative analysis result of the environmental quality comprehensive index.

[0046] The sampling frequency adjustment module comprises:

[0047] Change characteristic value calculation unit, used to calculate the environmental quality change characteristic value .

[0048] Frequency adjustment unit, used to determine the sampling frequency according to the characteristic value of environmental quality change ,in is the frequency adjustment coefficient, and its value is between 0.1 and 0.5.

[0049] Furthermore, the system also includes:

[0050] The data storage module is used to store historical monitoring data and calculation results.

[0051] The human-computer interaction module is used to display the environmental quality monitoring results and system operation status.

[0052] The beneficial effects of the present invention are as follows:

[0053] The present invention standardizes and decomposes environmental data into sequences, adopts a dynamic weight adjustment mechanism and adaptive fluctuation range estimation, and can adapt to the needs of dynamic changes in environmental parameters over time, avoiding the lag and inaccuracy caused by fixed weights; combined with the adaptive adjustment of the sampling frequency, it can respond to abnormal changes in environmental quality and emergencies in a timely manner; it not only solves the key problems existing in the existing environmental monitoring technology, but also shows obvious advantages in terms of accuracy, response speed and practicality, providing effective technical support for environmental monitoring and early warning, and has good application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 A flow chart of an environmental quality monitoring method of the present invention;

[0055] Figure 2 It is a schematic diagram of the partial composition of an environmental quality monitoring system of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention is described clearly and completely below. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0057] It should be noted that environmental monitoring data have significant time series correlation and spatial correlation. However, traditional methods often ignore these temporal and spatial correlation characteristics, thereby reducing monitoring accuracy. For example, the data between monitoring points have spatial mutual influence, and the data at different time points also have temporal continuity. Therefore, when conducting environmental quality assessment and early warning, these related information are not fully utilized, affecting the accuracy and reliability of the monitoring results. At the same time, the existing environmental early warning mechanisms generally adopt fixed thresholds and sampling frequencies, lack adaptive capabilities, and are difficult to respond to abnormal changes and emergencies in environmental quality in a timely manner, resulting in delayed early warning.

[0058] Example 1

[0059] like Figure 1 FIG. 1 is a flow chart of an environmental quality monitoring method of the present invention, and the method comprises the following steps:

[0060] Step S1, standardizing the collected environmental data of three categories, namely, air, water quality and noise, to obtain a time series of multi-dimensional environmental data;

[0061] The time series of the multidimensional environmental data includes 15 dimensional parameters in total, among which the air environment data includes six dimensional parameters: PM2.5, PM10 particle concentration and SO 2 、NO 2 , O 3 , CO pollutant concentration; water quality environmental data includes six dimensional parameters: pH value, dissolved oxygen, turbidity, conductivity, total phosphorus, and total nitrogen; noise environmental data includes three dimensional parameters: equivalent sound level, maximum sound level, and background noise;

[0062] The collected data is standardized by the formula: Processing is performed to eliminate the dimensional differences between different parameters, where For the The standardized values ​​of the dimension parameters, No. The original value of the dimension parameter, and For the The minimum and maximum standard limits for each dimension parameter; these are reference values ​​used for data normalization.

[0063] Specifically: (Minimum Standard Limit) indicates the minimum value or lower limit allowed for the environmental parameter. For some parameters, this represents the best environmental conditions; for example, for pollutants (such as PM2.5, SO 2 wait), Close to 0, it means the air is in the cleanest state, so it can be directly set to 0; (Maximum Standard Limit) indicates the maximum value or upper limit allowed for the environmental parameter, representing the warning line or the worst allowable value of environmental quality; for example: for PM2.5, the regulations 75μg / m³, exceeding this value is considered pollution; assuming that for PM2.5: = 0 μg / m³ (ideal state), = 75 μg / m³ (warning line), if the PM2.5 concentration is measured at a certain moment = 30 μg / m³,

[0064] The standardized value is: = 0.4, this standardized value 0.4 means that the current pollution level is at 40% of the maximum permissible range.

[0065] With reference to the national environmental quality standards, this embodiment provides the minimum and maximum standard limits of each dimensional parameter, see Table 1. The standards for air quality parameters mainly refer to the "Ambient Air Quality Standard" (GB 3095-2012), the standards for water quality parameters mainly refer to the "Surface Water Environmental Quality Standard" (GB 3838-2002), and the standards for noise parameters mainly refer to the "Acoustic Environment Quality Standard" (GB 3096-2008); for special parameters such as pH value, the optimal value is in the middle range, and for parameters such as dissolved oxygen, there are minimum requirements, but they should not be too high.

[0066] Table 1: Minimum and maximum standard limits for dimensional parameters

[0067]

[0068] Step S2, performing sequence decomposition on the time series of the multidimensional environmental data, decomposing each parameter sequence into a trend item, a period item and a random item;

[0069] Using the wavelet decomposition method, the standardized environmental quality parameters are decomposed into:

[0070] ,in, For trend items, For periodic items, is a random item;

[0071] The trend item

[0072] Step S3, constructing a calculation model for the comprehensive index of environmental quality, and providing quantitative analysis results of environmental quality monitoring based on parameter sequence decomposition data;

[0073] Constructing environmental quality benchmark values ​​based on the trend items ; Match the periodic item with the periodic characteristics of historical data to obtain the periodic adjustment coefficient ; Based on the distribution characteristics of random items, establish a dynamic fluctuation range ;

[0074] Construct a calculation model for the comprehensive index of environmental quality:

[0075] ,in, It is the quantitative analysis result of the comprehensive index of environmental quality; the environmental quality benchmark value B(t) reflects the basic level of environmental quality, the periodic adjustment coefficient α(t) is used to correct the impact of periodic fluctuations, and the dynamic fluctuation range D(t) describes the uncertainty range of environmental quality; this multi-level comprehensive evaluation model takes into account both the overall level of environmental quality and the time evolution characteristics, and can objectively reflect the dynamic change process of environmental quality.

[0076] Constructing environmental quality benchmark values ​​based on the trend items The following steps are involved:

[0077] Step S21, calculating the trend contribution of each dimensional parameter; the trend contribution of each dimensional parameter The expression is: ,

[0078] Step S22: Determine the dynamic contribution coefficient of each dimensional parameter , ;

[0079] Step S23, the environmental quality benchmark value is the dynamic contribution coefficient of each dimensional parameter and trend items The product and .

[0080] Match the periodic term with the periodic characteristics of historical data to obtain the periodic adjustment coefficient The method is:

[0081] For periodic items Perform Fourier transform, ,in, represents the representation of periodic terms in the frequency domain, represents a complex exponential function, where is an imaginary unit, is the angular frequency, Represents a time variable; identifies the main period of data change, ,in, For the The period length of the main period, For the The frequency corresponding to the local maximum of the power spectral density.

[0082] Build cycle adjustment factor, ,in, For the The amplitude of the periodic component, For the The phase angle of the main period component.

[0083] Establish a dynamic fluctuation range based on the distribution characteristics of random items The method is:

[0084] Compute the local standard deviation of a random term , , is the local standard deviation of the random term, reflecting the intensity of fluctuation, For the The random item value at the moment, is the local mean of the random terms, is the number of samples used when calculating the standard deviation;

[0085] Calculate the rate of change of a random term , ,in, represents the time derivative of the random term.

[0086] Establish a dynamic fluctuation range, ,in, The adjustment coefficient is calibrated based on historical data. The calibration method follows the principle of ensuring the dynamic fluctuation range. Can cover 95% of historical fluctuations.

[0087] The environmental quality monitoring method also includes: feedback adjustment of the sampling frequency of three types of environmental data: air, water quality and noise, based on the quantitative analysis results of the comprehensive environmental quality index; identifying and quantifying periodic changes in environmental data, thereby periodically adjusting the sampling frequency.

[0088] The expression of the characteristic value of environmental quality change is: ,in, is the characteristic value of environmental quality change, representing the relative change rate of the environmental quality index. for Quantitative analysis results of the comprehensive index of environmental quality at all times.

[0089] Determine the sampling frequency according to the environmental quality change characteristic value, ,in, is the base sampling frequency, is the frequency adjustment coefficient, and its value is between 0.1 and 0.5.

[0090] The environmental quality monitoring method also includes: correcting the quantitative analysis result of the environmental quality comprehensive index according to the spatial autocorrelation function of each monitoring point.

[0091] The expression of the spatial autocorrelation function of the monitoring point is: ,in, For monitoring point and The distance between is the feature distance.

[0092] Spatial incidence matrix: , the incidence matrix Elements The expression is:

[0093] ,in, For monitoring point and In time The correlation coefficient of .

[0094] Then the spatially corrected environmental quality index is: ,in, is the spatial weight coefficient, is the trace of the incidence matrix.

[0095] The following is a specific example to illustrate the practical application effect of the above environmental quality monitoring method:

[0096] Data standardization example Taking part of the monitoring data of a city monitoring point at 10:00 am on January 1, 2023 as an example: the PM2.5 concentration is 45 μg / m³, and it is standardized according to the standard limit range [0, 75] in Table 1: = 0.6, indicating that the current PM2.5 pollution level reaches 60% of the limit range; pH value is 7.5, and the equivalent sound level is 55 dB(A), which is standardized according to the standard limit range [30, 70] in Table 1 =0.625.

[0097] Analysis of sequence decomposition effect Taking the annual monitoring data of PM2.5 concentration as an example, after wavelet decomposition: trend item It shows obvious seasonal variation characteristics, with winter generally higher than summer; Captures two main cycles: 24 hours and 7 days; random items This reflects the impact of uncertain factors such as weather changes; Dynamic sampling optimization case: The sampling frequency of a monitoring point is adjusted during a period of rapid changes in air quality: Under normal conditions, the baseline sampling frequency of 60 minutes / time is adopted ( ); When the air quality change characteristic value ΔE(t)=0.3 is detected: the sampling frequency f(t) adjusted by the adjustment coefficient λ=0.2 = 60×[1+0.2×0.3] = 63.6 minutes / time; When a heavy pollution weather warning occurs: the change characteristic value ΔE(t) can reach above 0.5, and the sampling frequency can be increased to 30 minutes / time to ensure monitoring accuracy.

[0098] Spatial correlation analysis example: Take three adjacent monitoring points A, B, and C in a city as an example: the distance between points A and B is d(A, B) = 2km, and the characteristic distance =5km; spatial autocorrelation function S(A,B)=exp(-2 / 5)≈0.67; if the correlation coefficient of PM2.5 concentration at points A and B ρ(A,B,t)=0.85; then the correlation matrix element c_AB(t)=0.67×0.85≈0.57 The spatial correlation matrix constructed in this way can effectively reflect the diffusion law of pollutants.

[0099] This method has been applied in the environmental quality monitoring system of a provincial capital city for one year and has achieved the following results: 94% of abnormal environmental quality fluctuations were accurately identified, dynamic adjustment of the sampling frequency saved about 25% of equipment operating costs, spatial correlation analysis improved the accuracy of pollution source tracking to more than 85%; the accuracy of comprehensive monitoring and early warning was increased to 92%.

[0100] It can be seen from the above examples that the environmental quality monitoring method proposed in the present invention has strong practicability and adaptability, and can meet the environmental monitoring needs in different scenarios.

[0101] Example 2

[0102] like Figure 2 As shown, it is a schematic diagram of the partial composition of an environmental quality monitoring system of the present invention, which is used to execute the method described in Example 1; the system includes: a data acquisition and processing module, a sequence decomposition module and an environmental quality comprehensive index calculation module, and the three modules are connected in sequence.

[0103] The data acquisition and processing module is used to collect three types of environmental data: air, water quality and noise; and perform standardization processing on the collected environmental data to obtain a time series of multi-dimensional environmental data.

[0104] The sequence decomposition module is used to decompose the standardized environmental quality parameters into trend items, period items and random items by using a wavelet decomposition method.

[0105] The environmental quality comprehensive index calculation module is used to construct an environmental quality baseline value based on the trend item, match the periodic item with the periodic characteristics of historical data, obtain the periodic adjustment coefficient, establish a dynamic fluctuation range based on the distribution characteristics of the random item, construct an environmental quality comprehensive index calculation model and calculate the quantitative results.

[0106] The environmental quality comprehensive index calculation module includes:

[0107] Trend contribution calculation unit, used to calculate the trend contribution of each dimension parameter The expression is: .

[0108] Dynamic contribution coefficient calculation unit, used to determine the dynamic contribution coefficient of each dimension parameter , whose expression is: .

[0109] Baseline value calculation unit, used to calculate environmental quality baseline value .

[0110] The system also includes a sampling frequency adjustment module.

[0111] The sampling frequency adjustment module is connected to the environmental quality comprehensive index calculation module and the data acquisition processing module respectively, and is used to dynamically adjust the data acquisition frequency based on the quantitative analysis result of the environmental quality comprehensive index.

[0112] The sampling frequency adjustment module comprises:

[0113] Change characteristic value calculation unit, used to calculate the environmental quality change characteristic value .

[0114] Frequency adjustment unit, used to determine the sampling frequency according to the characteristic value of environmental quality change ,in is the frequency adjustment coefficient, and its value is between 0.1 and 0.5.

[0115] The system further comprises:

[0116] The data storage module is used to store historical monitoring data and calculation results.

[0117] The human-computer interaction module is used to display the environmental quality monitoring results and system operation status.

[0118] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for monitoring environmental quality, characterized in that: The method comprises the following steps: Step S1, standardizing the collected environmental data of three categories, namely, air, water quality and noise, to obtain a time series of multi-dimensional environmental data; The time series of the multidimensional environmental data includes 15 dimensional parameters in total, among which the air environment data includes six dimensional parameters: PM2.5, PM10 particle concentration and SO2, NO2, O3, CO pollutant concentration; the water quality environment data includes six dimensional parameters: pH value, dissolved oxygen, turbidity, conductivity, total phosphorus, total nitrogen; the noise environment data includes three dimensional parameters: equivalent sound level, maximum sound level and background noise; The collected data is standardized by the formula: Processing is performed to eliminate the dimensional differences between different parameters, where For the The standardized values ​​of the dimension parameters, No. The original value of the dimension parameter, and For the The minimum and maximum standard limits of the dimensional parameters; Step S2, performing sequence decomposition on the time series of the multidimensional environmental data, decomposing each parameter sequence into a trend item, a period item and a random item; Using the wavelet decomposition method, the standardized environmental quality parameters are decomposed into: ,in, For trend items, For periodic items, is a random item; The trend item reflects the overall development direction of the environmental parameters, the period item represents the periodic changes in the data, and the random item is the short-term fluctuation in the data that does not have regularity; Step S3, constructing a calculation model for the comprehensive index of environmental quality, and providing quantitative analysis results of environmental quality monitoring based on parameter sequence decomposition data; Constructing environmental quality benchmark values ​​based on the trend items ; Match the periodic item with the periodic characteristics of historical data to obtain the periodic adjustment coefficient ; Based on the distribution characteristics of random items, establish a dynamic fluctuation range ; The environmental quality benchmark value is constructed based on the trend item The following steps are involved: Step S21, calculating the trend contribution of each dimensional parameter; the trend contribution of each dimensional parameter The expression is: ; Step S22: Determine the dynamic contribution coefficient of each dimensional parameter , ; Step S23, the environmental quality benchmark value is the dynamic contribution coefficient of each dimensional parameter and trend items The product and ; The periodic item is matched with the periodic characteristics of historical data to obtain a periodic adjustment coefficient The method is: For periodic items Perform Fourier transform, ,in, represents the representation of periodic terms in the frequency domain, represents a complex exponential function, where is an imaginary unit, is the angular frequency, Represents a time variable; identifies the main period of data change, ,in, For the The period length of the main period, For the The frequency corresponding to the local maximum of the power spectral density; Build cycle adjustment factor, ,in, For the The amplitude of the periodic component, For the The phase angle of the main period component; The dynamic fluctuation range is established based on the distribution characteristics of random items The method is: Compute the local standard deviation of a random term , , is the local standard deviation of the random term, reflecting the intensity of fluctuation, For the The random item value at the moment, is the local mean of the random terms, is the number of samples used when calculating the standard deviation; Calculate the rate of change of a random term , ,in, represents the derivative of the random term with respect to time; Establish a dynamic fluctuation range, ,in, The adjustment coefficient is calibrated based on historical data. The calibration method follows the principle of ensuring the dynamic fluctuation range. Can cover 95% of historical fluctuations; Construct a calculation model for the comprehensive index of environmental quality: ,in, for Quantitative analysis results of the comprehensive index of environmental quality at all times.

2. The environmental quality monitoring method according to claim 1, characterized in that: The environmental quality monitoring method further includes: feedback adjustment of the sampling frequencies of three types of environmental data, namely, air quality, water quality and noise, based on the quantitative analysis results of the comprehensive environmental quality index; The expression of the characteristic value of environmental quality change is: ,in, is the characteristic value of environmental quality change, representing the relative change rate of the environmental quality index. for Quantitative analysis results of comprehensive environmental quality index at all times; Determine the sampling frequency according to the environmental quality change characteristic value, ,in, is the base sampling frequency, is the frequency adjustment coefficient, and its value is between 0.1 and 0.

5.

3. The environmental quality monitoring method according to claim 2, characterized in that: The environmental quality monitoring method further comprises: correcting the quantitative analysis result of the environmental quality comprehensive index according to the spatial autocorrelation function of each monitoring point; The expression of the spatial autocorrelation function of the monitoring point is: ,in, For monitoring point and The distance between is the characteristic distance; Spatial incidence matrix: , the incidence matrix Elements The expression is: ,in, For monitoring point and In time The correlation coefficient of Then the spatially corrected environmental quality index is: ,in, is the spatial weight coefficient, is the trace of the incidence matrix.

4. An environmental quality monitoring system, used to execute the method according to any one of claims 1 to 3, characterized in that: The system comprises: a data acquisition and processing module, a sequence decomposition module and an environmental quality comprehensive index calculation module, the three modules are connected in sequence; The data acquisition and processing module is used to collect three types of environmental data: air, water quality and noise; standardize the collected environmental data to obtain a time series of multi-dimensional environmental data; The sequence decomposition module is used to decompose the standardized environmental quality parameters into trend items, period items and random items by using wavelet decomposition method; The environmental quality comprehensive index calculation module is used to construct an environmental quality baseline value based on the trend item, match the periodic item with the periodic characteristics of historical data, obtain the periodic adjustment coefficient, establish a dynamic fluctuation range based on the distribution characteristics of the random item, construct an environmental quality comprehensive index calculation model and calculate the quantitative results.

5. The environmental quality monitoring system according to claim 4, characterized in that: The environmental quality comprehensive index calculation module includes: Trend contribution calculation unit, used to calculate the trend contribution of each dimension parameter The expression is: ; Dynamic contribution coefficient calculation unit, used to determine the dynamic contribution coefficient of each dimension parameter , whose expression is: ; Baseline value calculation unit, used to calculate environmental quality baseline value .

6. The environmental quality monitoring system according to claim 5, characterized in that: The system also includes a sampling frequency adjustment module; The sampling frequency adjustment module is connected to the environmental quality comprehensive index calculation module and the data acquisition processing module respectively, and is used to dynamically adjust the data acquisition frequency based on the quantitative analysis result of the environmental quality comprehensive index; The sampling frequency adjustment module comprises: Change characteristic value calculation unit, used to calculate the environmental quality change characteristic value ; Frequency adjustment unit, used to determine the sampling frequency according to the characteristic value of environmental quality change ,in is the frequency adjustment coefficient, and its value is between 0.1 and 0.

5.

7. The environmental quality monitoring system according to claim 6, characterized in that: The system further comprises: Data storage module, used to store historical monitoring data and calculation results; The human-computer interaction module is used to display the environmental quality monitoring results and system operation status.

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