CN-120538591-B - Online comprehensive environment monitoring method and system

CN120538591BCN 120538591 BCN120538591 BCN 120538591BCN-120538591-B

Abstract

The invention provides an environment online comprehensive monitoring method and system, which comprises the steps of collecting noise, dust and water quality data in real time, preprocessing each data, removing abnormal, interference and impurity data, carrying out smoothing, normalization and standardization processing, judging whether the change degree of the data at the later moment exceeds a set value or not based on the preprocessing data at the former moment, respectively carrying out frequency spectrum, granularity and component analysis on the noise, dust and water quality data if the change degree exceeds the set value, extracting characteristic information to form a matrix, adopting a corresponding model to analyze the characteristic information matrix according to analysis results to obtain analysis results, and adopting an abnormal analysis model to analyze if the analysis results are abnormal to obtain abnormal data, generating early warning information and sending the early warning information to a management platform. The invention can realize comprehensive, dynamic and accurate monitoring of noise, dust, water quality and other environmental elements, and improves the efficiency and accuracy of environmental monitoring.

Inventors

SHAN DANNA
LIANG JUN
ZENG QIUYU
YU HE
GONG LEI
YANG SINAN
WANG SHUAI
WU FADONG
LIU PENGJU

Assignees

浙江省环保集团生态环保研究院有限公司

Dates

Publication Date: 20260508
Application Date: 20250522

Claims (10)

1. An on-line comprehensive environmental monitoring method is characterized by comprising the following steps: collecting noise data acquired by a noise sensor in real time Dust data acquired by dust monitor Water quality data acquired by water quality monitoring equipment ; For noise data Preprocessing, removing abnormal noise value, and smoothing to obtain preprocessed noise data For dust data Preprocessing, removing data influenced by interference factors, and normalizing to obtain preprocessed dust data For water quality data Pretreating, removing impurity data, and performing standardization treatment to obtain pretreated water quality data ; Preprocessing noise data based on previous time instant Judging the preprocessing noise data at the later moment Whether the degree of change is greater than a set noise degree value Preprocessing dust data based on previous time Judging the pretreatment dust data at the later moment Whether the change degree is larger than the set dust raising degree value Pretreatment water quality data based on the previous time Judging the pretreated water quality data at the later moment Whether the change degree is larger than the set water quality degree value ; If the noise data change degree is greater than the set noise degree value For the noise data at the next moment Performing spectral analysis to determine the frequency distribution type of noise And extracting the noise data at the later moment by adopting a noise characteristic extraction model To form a noise characteristic information matrix If the change degree of the dust data is larger than the set dust degree value Then to the dust data at the later moment Performing particle size analysis to determine particle size distribution type of raised dust And extracting dust data at the later moment by adopting a dust characteristic extraction model To form a dust characteristic information matrix If the change degree of the water quality data is larger than the set water quality degree value For the water quality data at the later time Performing component analysis to determine the component type of water quality And extracting water quality data at the later moment by adopting a water quality characteristic extraction model To form a water quality characteristic information matrix ; According to the frequency distribution type of noise Noise characteristic information matrix by adopting corresponding information analysis units in noise analysis model Analyzing to obtain noise analysis result According to the particle size distribution type of the dust The corresponding information analysis unit in the dust analysis model is adopted to carry out dust characteristic information matrix Analyzing to obtain dust analysis result According to the type of the components of the water The corresponding information analysis unit in the water quality analysis model is adopted to carry out the water quality characteristic information matrix Analyzing to obtain water quality analysis result ; As a result of the noise analysis When abnormality information exists, a set time period from the beginning of the subsequent time is acquired Abnormal noise data of (a) And uses noise anomaly analysis model to make anomaly noise data Analyzing to obtain noise abnormality analysis result When dust analysis results When abnormality information exists, a set time period from the beginning of the subsequent time is acquired Abnormal dust emission data of (2) Abnormal dust data is obtained by adopting a dust abnormality analysis model Analyzing to obtain dust abnormality analysis result When the water quality analysis results When abnormality information exists, a set time period from the beginning of the subsequent time is acquired Abnormal water quality data of (a) And abnormal water quality data is obtained by adopting a water quality abnormal analysis model Analyzing to obtain water quality abnormality analysis result ; Based on the noise anomaly analysis result Generating noise warning information According to the result of dust abnormality analysis Generating dust emission early warning information According to the water quality abnormality analysis result Generating water quality early warning information And sending various early warning information to the related management platform.
2. The method for on-line integrated environmental monitoring according to claim 1, wherein the pre-processing noise data based on the previous time is Judging the preprocessing noise data at the later moment Whether the degree of change is greater than a set noise degree value Comprising: storing the noise data according to the time sequence; Acquiring noise data value at previous moment And a noise data value at a later time ; Calculating a noise data value at a later time Noise data value from previous moment Degree of change between Degree of change value The calculation formula of (2) is as follows: Wherein, the Representing the latter instant of preprocessing noise data The first of (3) A data point is provided for each of the data points, Representing pre-processed noise data from a previous time The first of (3) A data point is provided for each of the data points, Total number of data points; Judging the change degree value Whether or not it is greater than a set noise level value ; Preprocessing dust data based on previous time Judging the pretreatment dust data at the later moment Whether the change degree is larger than the set dust raising degree value Comprising: Storing dust data according to a time sequence; acquiring dust data value at previous moment And the dust data value at the later moment ; Calculating the dust data value at the later moment Dust data value at the previous moment Degree of change between Degree of change value The calculation formula of (2) is as follows: Wherein, the Representing the latter moment of preprocessing dust data The first of (3) A data point is provided for each of the data points, Representing pre-processed dust data from a previous time The first of (3) A data point is provided for each of the data points, Total number of data points; Judging the change degree value Whether or not is greater than a set dust raising degree value ; Pretreatment water quality data based on previous time Judging the pretreated water quality data at the later moment Whether the change degree is larger than the set water quality degree value Comprising: Storing the water quality data according to the time sequence; acquiring water quality data value at previous moment And water quality data value at the later time ; Calculating the water quality data value at the later moment And the water quality data value at the previous moment Degree of change between Degree of change value The calculation formula of (2) is as follows: Wherein, the Representing the pretreatment water quality data at the later moment The first of (3) A data point is provided for each of the data points, Pre-treating water quality data for the latter moment Is used for the average value of (a), Pre-treating water quality data for the latter moment Standard deviation of (2); Representing pretreatment water quality data at a previous time The first of (3) A data point is provided for each of the data points, Pretreatment of water quality data for the previous moment Is used for the average value of (a), Pretreatment of water quality data for the previous moment Is set in the standard deviation of (2), Total number of data points; Judging the change degree value Whether or not it is greater than a set water quality degree value 。
3. The method for on-line integrated environmental monitoring of claim 2, wherein said noise data for a subsequent time is based on said noise data Performing spectral analysis to determine the frequency distribution type of noise Comprising: will noise data at the later moment Conversion to an initial spectral data set by a predetermined spectral converter ; Initial spectral data set using a spectral analysis model constructed based on historical noise data Analyzing to obtain frequency distribution type ; The dust raising data at the later moment Performing particle size analysis to determine particle size distribution type of raised dust Comprising: dust raising data at the later moment Conversion to an initial particle size dataset by a preset particle size converter ; The initial granularity data set is subjected to granularity analysis model constructed based on historical dust data Analyzing to obtain particle size distribution type The water quality data at the later moment Performing component analysis to determine the component type of water quality Comprising: Water quality data at the later moment Converted into an initial component data set by a preset component converter ; Initial component data set by adopting component analysis model constructed based on historical water quality data Analyzing to obtain component types 。
4. The method for on-line comprehensive environmental monitoring according to claim 2, wherein the extracting process of the noise feature extraction model comprises: will noise data at the later moment Dividing into a plurality of frequency segment data according to a frequency range ; According to the frequency distribution type From the data of each frequency segment The frequency distribution type which accords with the corresponding frequency distribution type is screened out Obtaining a plurality of key analysis frequency band data ; Extracting each key analysis frequency band data by adopting a noise characteristic extractor To form a plurality of noise characteristic information matrixes The extraction process of the dust feature extraction model comprises the following steps: dust raising data at the later moment Dividing into a plurality of granularity segment data according to granularity range ; According to the particle size distribution type From each particle size segment data Screening out the particle size distribution type corresponding to the particle size distribution type Obtaining a plurality of key analysis granularity section data ; Extracting data of each key analysis granularity section by adopting dust-raising characteristic extractor To form a plurality of dust characteristic information matrixes ; The extraction process of the water quality characteristic extraction model comprises the following steps: Water quality data at the later moment Dividing into a plurality of component data by component category ; According to the type of the components From the component data The corresponding component types are screened out Obtaining a plurality of key analysis component data ; Extracting each key analysis component data by adopting a water quality characteristic extractor To form a plurality of water quality characteristic information matrixes 。
5. The method for on-line integrated environmental monitoring according to claim 4, wherein the processing procedure of the noise analysis model comprises: According to the frequency distribution type Matrix each noise characteristic information Inputting the noise information into a corresponding noise information analysis unit; Each noise information analysis unit judges a noise characteristic information matrix according to the respective preset noise characteristic information base Whether the set standard of the preset noise characteristic information base is met or not, and obtaining abnormal noise frequency band data when the set standard is not met And obtaining a noise analysis result containing abnormal noise frequency band data information ; The dust analysis model comprises the following processing procedures: According to the particle size distribution type Each dust characteristic information matrix Inputting the dust information into a corresponding dust information analysis unit; each dust information analysis unit judges a dust characteristic information matrix according to respective preset dust characteristic information base Whether the set standard of the preset dust characteristic information base is met or not, and when the set standard is not met, obtaining abnormal dust particle size section data And obtaining dust analysis results containing abnormal dust particle size section data information ; The processing process of the water quality analysis model comprises the following steps: According to the type of the components Matrix each water quality characteristic information Inputting the water quality information into a corresponding water quality information analysis unit; Each water quality information analysis unit judges a water quality characteristic information matrix according to respective preset water quality characteristic information base Whether the set standard of the preset water quality characteristic information base is met or not, and when the set standard is not met, obtaining abnormal water quality component data And obtaining a water quality analysis result containing abnormal water quality component data information 。
6. The method for on-line integrated environmental monitoring according to claim 5, wherein the predetermined noise characteristic information base setting criteria comprises a noise characteristic information matrix Standard set frequency interval of each element in the system Set frequency variation interval of element and peripheral element ; The setting standard of the preset dust characteristic information base comprises a dust characteristic information matrix Standard setting granularity interval of each element in the list Set granularity change interval of element and peripheral element ; The setting standard of the preset water quality characteristic information base comprises a water quality characteristic information matrix Standard setting component content interval of each element Set component content variation interval of element and peripheral element 。
7. The method for on-line integrated environmental monitoring according to claim 5, wherein the analyzing process of the noise analysis model comprises: According to the abnormal noise frequency band data information From abnormal noise data Middle screening of data containing abnormal noise frequency band To obtain abnormal noise samples ; When abnormal noise sample The number of (2) exceeds a preset number When based on the frequency distribution type Corresponding preset noise abnormal information base pair different noise samples Performing analysis to determine various abnormal noise samples Is the first membership value of (1) A second membership value corresponding to the number of abnormal noise samples ; Using weighted average method to obtain first membership value And a second membership value Processing and calculating average membership value The calculation formula is as follows: Wherein, the And Is a weight coefficient, and ; With average membership value The corresponding grade is taken as an abnormal grade According to the frequency distribution type Abnormal grade And various unusual noise samples Formation of noise anomaly analysis results ; The dust anomaly analysis model comprises the following analysis processes: According to the data information of the abnormal dust particle size section From abnormal dust data Middle screening data containing abnormal dust particle size section To obtain abnormal dust sample ; When an abnormal dust sample is raised The number of (2) exceeds a preset number Then based on the granularity distribution type Corresponding to the preset dust abnormality information base, the corresponding dust abnormality information base is used for detecting different dust samples Analyzing and determining various abnormal dust samples Is the first membership value of (1) A second membership value corresponding to the number of the abnormal dust samples ; Using weighted average method to obtain first membership value And a second membership value Processing and calculating average membership value The calculation formula is as follows: Wherein, the And Is a weight coefficient, and ; With average membership value The corresponding grade is taken as an abnormal grade According to the particle size distribution type Abnormal grade And various samples of dust Forming dust abnormality analysis result ; The analysis process of the water quality abnormality analysis model comprises the following steps: According to the abnormal water quality component data information From abnormal water quality data The medium screening contains abnormal water quality component data To obtain abnormal water quality samples ; When abnormal water quality sample The number of (2) exceeds a preset number When based on the component type Corresponding preset water quality abnormality information base for different abnormal water quality samples Analyzing and determining various water quality samples Is the first membership value of (1) A second membership value corresponding to the number of abnormal water quality samples ; Using weighted average method to obtain first membership value And a second membership value Processing and calculating average membership value The calculation formula is as follows: Wherein, the And Is a weight coefficient, and ; With average membership value The corresponding grade is taken as an abnormal grade According to the type of the components Abnormal grade And various water quality samples Forming water quality abnormality analysis results 。
8. The method for online comprehensive monitoring of environment according to claim 7, wherein the obtaining of the abnormal noise sample The screening process of (2) comprises: based on abnormal noise frequency band data Is a characteristic information matrix of (a) Judging abnormal noise data Transformed feature information matrix Whether or not the content of the similarity exceeds the set similarity Is a feature information matrix of (1); If yes, abnormal noise data is generated Marked as abnormal noise sample ; The obtained abnormal dust sample The screening process of (2) comprises: Based on abnormal dust particle size section data Is a characteristic information matrix of (a) Judging abnormal dust data Transformed feature information matrix Whether or not the content of the similarity exceeds the set similarity Is a feature information matrix of (1); if yes, abnormal dust data is generated Marked as an abnormal dust sample ; The obtained abnormal water quality sample The screening process of (2) comprises: Based on abnormal water quality component data Is a characteristic information matrix of (a) Judging abnormal water quality data Transformed feature information matrix Whether or not the content of the similarity exceeds the set similarity Is a feature information matrix of (1); If yes, abnormal water quality data is obtained Marked as abnormal water quality sample 。
9. The on-line comprehensive environmental monitoring method according to claim 5, wherein the noise information analysis unit at least comprises a traffic noise analysis unit, an industrial noise analysis unit and a living noise analysis unit; the dust information analysis unit at least comprises a construction dust analysis unit, a road dust analysis unit and a storage yard dust analysis unit; the water quality information analysis unit at least comprises a heavy metal component analysis unit, an organic matter component analysis unit and a microorganism component analysis unit.
10. The environment online comprehensive monitoring system is characterized by comprising a data acquisition module, a data preprocessing module, an environment analysis module, an abnormality analysis module, a data storage module and an early warning module, and is used for realizing the environment online comprehensive monitoring method according to any one of claims 1-9 through mutual coordination among the modules.

Description

Online comprehensive environment monitoring method and system Technical Field The invention relates to the technical field of environment monitoring, in particular to an environment online comprehensive monitoring method and system. Background In the field of environmental monitoring, conventional monitoring methods generally rely on manual sampling and laboratory analysis, which is time-consuming and laborious, and cannot acquire environmental data in real time, resulting in a lag in response to environmental changes. With the development of sensor technology, environmental monitoring systems based on sensor networks appear, and the systems can acquire environmental data in real time, but often have the problems of low data accuracy, difficult timely discovery and treatment of abnormal conditions and the like. For example, some monitoring systems cannot effectively distinguish noise from different sources when collecting noise data, resulting in data clutter and difficulty in accurately analyzing the cause of noise pollution. In the aspect of dust monitoring, the prior art is difficult to accurately measure the granularity distribution of dust, and can not provide effective basis for the treatment of dust pollution. The water quality monitoring also faces similar problems, and when the traditional water quality monitoring equipment analyzes water quality components, the defects of high detection limit, long response time and the like often exist, so that the requirement of quickly and accurately monitoring the water quality change cannot be met. In the process of realizing the embodiment of the invention, the inventor discovers that the prior art has at least the following problems or defects that the prior environment monitoring method can not realize comprehensive, real-time and accurate monitoring of various environment elements such as noise, dust, water quality and the like, and the data preprocessing is not perfect enough, so that abnormal data is difficult to discover and process in time, and the timely and accurate information support can not be provided for environment management and decision. Disclosure of Invention The invention provides an environment online comprehensive monitoring method and system. In a first aspect of the present invention, there is provided an on-line integrated environmental monitoring method, comprising: Collecting noise data N acquired by a noise sensor, dust data D acquired by a dust monitor and water quality data W acquired by water quality monitoring equipment in real time; Preprocessing each data, removing abnormal, interference and impurity data, smoothing, normalizing and standardizing the data, preprocessing the noise data N to obtain preprocessed noise data N p, preprocessing the dust data D to obtain preprocessed dust data D p, preprocessing the water quality data W to obtain preprocessed water quality data W p; Judging whether the change degree of the pre-processed noise data N pt at the later moment is larger than a set noise degree value epsilon N based on the pre-processed noise data N p(t-1) at the former moment, judging whether the change degree of the pre-processed dust data D pt at the later moment is larger than the set dust degree value epsilon D based on the pre-processed dust data D p(t-1) at the former moment, judging whether the change degree of the pre-processed water quality data W pt at the later moment is larger than the set water quality degree value epsilon W based on the pre-processed water quality data W p(t-1) at the former moment, and respectively carrying out frequency spectrum, granularity and component analysis on the noise, dust and water quality data if the change degree exceeds the set value, and extracting characteristic information to form a matrix; according to the analysis result, adopting a corresponding model to analyze the characteristic information matrix to obtain an analysis result; If the analysis result is abnormal, abnormal data are obtained, an abnormal analysis model is adopted for analysis, and early warning information is generated and sent to the management platform. Further, the performing the spectrum analysis on the noise data N pt at the later time to determine the frequency distribution type T N of the noise includes: Converting the noise data N pt at the next moment into an initial spectrum data set S N through a preset spectrum converter; analyzing the initial spectrum data set S N by adopting a spectrum analysis model constructed based on historical noise data to obtain a frequency distribution type T N; The particle size analysis is performed on the dust data D pt at the later moment, and the particle size distribution type T D of the dust is determined, which includes: Converting the dust raising data D pt at the later moment into an initial granularity data set S D through a preset granularity converter; Analyzing the initial granularity data set S D by adopting a granularity analysis model constructed