CN-122017166-A - Water quality eutrophication dynamic monitoring method based on multi-source sensor data fusion

CN122017166ACN 122017166 ACN122017166 ACN 122017166ACN-122017166-A

Abstract

The invention discloses a dynamic monitoring method for water quality eutrophication based on multi-source sensor data fusion, which relates to the technical field of water quality monitoring and comprises the steps of obtaining at least one test point, analyzing and obtaining a first value of a reference index at a target test point and a second value of a reference index at a non-target test point, screening to form at least one characteristic set, forming at least one characteristic point, forming a compensation function of the reference index, obtaining characteristic speed, setting sampling times and sampling interval duration at the characteristic point, collecting and obtaining an actual value of the reference index, obtaining a comprehensive value of the reference index in a water area to be tested, and evaluating water quality eutrophication by using the comprehensive value and the actual value. The first numerical value and the second numerical value are obtained through analysis, the characteristic points are formed, the sampling times and the sampling interval duration are set, the sensors are set as few as possible while the overall monitoring precision is ensured, and the monitoring stability of the monitoring position is ensured.

Inventors

MA RUILIN
YUAN BO
SUN JINXI
ZHANG HANG
HE YANG
CUI CHEN
LI LU

Assignees

河南省水文水资源测报中心

Dates

Publication Date: 20260512
Application Date: 20251212

Claims (10)

1. The water quality eutrophication dynamic monitoring method based on the multi-source sensor data fusion is characterized by comprising the following steps: Generating at least one reference index for detecting water quality eutrophication, acquiring a sensor for measuring the reference index, and measuring by using the sensor; Uniformly dividing a water area to be measured to obtain at least one test point, selecting one test point as a target test point, and taking the rest test points as non-target test points; Analyzing to obtain a first value of a reference index at a target test point and a second value of a reference index at a non-target test point based on data processing, and screening to form at least one feature set based on the second value, wherein the feature set is formed by the test points; Forming at least one feature point according to the feature set; Forming a compensation function of a reference index according to the measurement result at the characteristic point, identifying the flow of the water body at the characteristic point to obtain a characteristic speed, and setting the sampling times and the sampling interval duration at the characteristic point based on the characteristic speed; And acquiring an actual value of the reference index at the characteristic point, synthesizing the actual value of the reference index by using a compensation function to obtain a comprehensive value of the reference index in the water area to be detected, and evaluating the water quality eutrophication by using the comprehensive value and the actual value.
2. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 1, wherein the generating at least one reference index for water quality eutrophication detection comprises the steps of: The at least one baseline indicator is phosphorus content, nitrogen content, chlorophyll a content, transparency, permanganate index, dissolved oxygen, and algae density, respectively.
3. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 2, wherein the analyzing to obtain the first value of the reference index at the target test point and the second value of the reference index at the non-target test point comprises the following steps: Setting at least one characteristic moment, setting at least one acquisition day, wherein the acquisition day is a continuous working day, the number of the characteristic moments is equal to the number of the non-target test points, and randomly establishing a one-to-one correspondence between the characteristic moments and the non-target test points; Randomly setting and selecting a numerical value as a first numerical value of a reference index at a target test point; measuring a reference index at a target test point at the characteristic moment of the day of acquisition to obtain a reference value, measuring a reference index at a non-target test point corresponding to the characteristic moment at the characteristic moment of the day of acquisition to obtain a flowing value, and matching the reference value obtained at the same characteristic moment with the flowing value; Dividing the first value by the reference value to obtain an adjustment proportion of the reference value, and multiplying the adjustment proportion of the reference value by the corresponding flow value to obtain a second value of the reference index at the non-target test point; And summarizing the second values acquired in the same acquisition day to obtain a second value set.
4. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 3, wherein the step of screening to form at least one feature set based on the second value comprises the steps of: The method comprises the steps of obtaining a sample water area in advance, counting the number of days with pollution in one year in the sample water area based on historical data of sample water area monitoring, and dividing the number of days with pollution by 365 to obtain pollution probability; taking the maximum value of the second values in all the second value sets as a reference value, subtracting the pollution probability from 1 to obtain a retention proportion, and multiplying the reference value by the retention proportion to obtain a screening critical value; Taking non-target test points corresponding to the second values exceeding the screening critical value in the second value set as the preparation points, and summarizing the preparation points in the second value set to obtain a preparation point set; based on experience data, acquiring a measurement allowable error, taking an average value of second values in a second value set to obtain a preset value, and taking a subset of the second value set as a set to be verified; taking an average value of the second numerical values in the set to be verified to obtain a value to be verified, and if the difference between the value to be verified and the preset value is smaller than the measurement allowable error, generating the set to be verified of the value to be verified as a preliminary set; The preliminary set including the preliminary point set is used as a preliminary set, and the preliminary set with the smallest number of elements is used as a feature set.
5. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 4, wherein the forming at least one feature point according to the feature set comprises the following steps: and taking the union set of at least one feature set to obtain an overall set, and taking the test points in the overall set as feature points.
6. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 5, wherein the forming of the compensation function of the reference index according to the measurement result at the characteristic point comprises the following steps: And taking an average value of second values corresponding to the feature points in the second value set to obtain feature values, matching and fitting the feature values with preset values to obtain a compensation function of the reference index, wherein the feature values are independent variables, and the preset values are dependent variables.
7. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 6, wherein the step of identifying the flow of the water body at the characteristic point to obtain the characteristic speed comprises the steps of: carrying out contour recognition on an object at the characteristic points at the current moment to obtain at least one first object contour, taking the first object contour with the largest contour as a first target contour, and acquiring the position of the center of the first target contour as a first position; after the preset time, carrying out contour recognition on the object at the characteristic points to obtain at least one second object contour, taking the second object contour with the largest contour as a second target contour, and acquiring the position of the center of the second target contour as a second position, wherein the preset time is set based on experience; The distance between the second position and the first position is divided by the preset time to obtain the characteristic speed.
8. The method for dynamically monitoring water quality eutrophication based on multi-source sensor data fusion according to claim 7, wherein the setting of the sampling times and sampling interval durations at the characteristic points based on the characteristic speed comprises the steps of: Acquiring a minimum value of the flow speed of the water body at the characteristic point as a target value based on the historical data; taking a water surface area at the characteristic points as a characteristic area, uniformly taking at least one identification point at the edge of the characteristic area to form at least one identification point combination, wherein the identification point combination consists of two identification points; taking the distance of the identification points in the identification point combination as a parameter value of the identification point combination and taking the maximum value of the parameter value as a characteristic distance; Dividing the characteristic distance by the target value to obtain first residence time, setting the sampling frequency corresponding to the target value as 2, and setting the sampling interval duration corresponding to the target value as the first residence time; dividing the characteristic speed by the target value to obtain an amplification factor, if the amplification factor is an integer, enabling the amplification factor to be equal to the amplification factor, otherwise enabling the amplification factor to be equal to the integral of the amplification factor and 1; the sampling times at the feature point is 2 times of magnification, and the first residence time is divided by the magnification to obtain the sampling interval duration at the feature point.
9. The method for dynamically monitoring the eutrophication of water based on the data fusion of the multisource sensors according to claim 8, wherein the step of acquiring the actual values of the reference indexes at the characteristic points comprises the following steps: At least one collection is carried out on the value of the reference index at the characteristic point to obtain at least one collection value, the collection times are equal to the sampling times, the collection interval is equal to the sampling interval duration, and the average value of the at least one collection value is taken to obtain the actual value of the reference index.
10. The method for dynamically monitoring the eutrophication of water based on the data fusion of the multisource sensors according to claim 9, wherein the step of integrating the actual values of the reference indicators to obtain the integrated values of the reference indicators in the water area to be measured comprises the following steps: And (3) taking an average value of the actual values of the reference indexes at all the characteristic points to obtain an actual average value, and substituting the actual average value into a compensation function of the reference indexes to obtain the comprehensive value of the reference indexes in the water area to be measured.

Description

Water quality eutrophication dynamic monitoring method based on multi-source sensor data fusion Technical Field The invention relates to the technical field of water quality monitoring, in particular to a water quality eutrophication dynamic monitoring method based on multi-source sensor data fusion. Background The eutrophication of water body refers to pollution phenomenon that nutrient substances such as nitrogen, phosphorus and the like excessively enter slow-flowing water bodies such as lakes, estuaries and the like to cause abnormal proliferation of algae and plankton, so that dissolved oxygen is reduced and water quality is deteriorated. The process is slow in natural state, but industrial wastewater, domestic sewage and agricultural non-point source pollution input can accelerate the process, and water bloom or red tide is formed. Because the water body monitoring area is large and the eutrophication degree is different at different positions, the accurate monitoring result is difficult to obtain by monitoring only a single point location, and the local high-risk area is difficult to monitor, and by adopting a multipoint monitoring mode, although the monitoring precision can be improved and local high-risk areas are considered, a sensor is arranged at each position of the area needing water body monitoring, so that a large amount of money is consumed, the feasibility is low, and in addition, the monitoring result is unstable due to the flow rate of water during monitoring. Disclosure of Invention In order to solve the technical problems, the technical scheme solves the problems in the background technology by providing a water quality eutrophication dynamic monitoring method based on multi-source sensor data fusion. In order to achieve the above purpose, the invention adopts the following technical scheme: A water quality eutrophication dynamic monitoring method based on multi-source sensor data fusion comprises the following steps: Generating at least one reference index for detecting water quality eutrophication, acquiring a sensor for measuring the reference index, and measuring by using the sensor; Uniformly dividing a water area to be measured to obtain at least one test point, selecting one test point as a target test point, and taking the rest test points as non-target test points; Analyzing to obtain a first value of a reference index at a target test point and a second value of a reference index at a non-target test point based on data processing, and screening to form at least one feature set based on the second value, wherein the feature set is formed by the test points; Forming at least one feature point according to the feature set; Forming a compensation function of a reference index according to the measurement result at the characteristic point, identifying the flow of the water body at the characteristic point to obtain a characteristic speed, and setting the sampling times and the sampling interval duration at the characteristic point based on the characteristic speed; And acquiring an actual value of the reference index at the characteristic point, synthesizing the actual value of the reference index by using a compensation function to obtain a comprehensive value of the reference index in the water area to be detected, and evaluating the water quality eutrophication by using the comprehensive value and the actual value. Preferably, the generating at least one reference index for water quality eutrophication detection comprises the following steps: The at least one baseline indicator is phosphorus content, nitrogen content, chlorophyll a content, transparency, permanganate index, dissolved oxygen, and algae density, respectively. Preferably, the analyzing to obtain the first value of the reference index at the target test point and the second value of the reference index at the non-target test point includes the following steps: Setting at least one characteristic moment, setting at least one acquisition day, wherein the acquisition day is a continuous working day, the number of the characteristic moments is equal to the number of the non-target test points, and randomly establishing a one-to-one correspondence between the characteristic moments and the non-target test points; Randomly setting and selecting a numerical value as a first numerical value of a reference index at a target test point; measuring a reference index at a target test point at the characteristic moment of the day of acquisition to obtain a reference value, measuring a reference index at a non-target test point corresponding to the characteristic moment at the characteristic moment of the day of acquisition to obtain a flowing value, and matching the reference value obtained at the same characteristic moment with the flowing value; Dividing the first value by the reference value to obtain an adjustment proportion of the reference value, and multiplying the adjustment proportion of the reference value by the cor