CN113868926A - Method for constructing spatial distribution model of water quality parameters of culture pond - Google Patents

Method for constructing spatial distribution model of water quality parameters of culture pond Download PDF

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CN113868926A
CN113868926A CN202111201494.6A CN202111201494A CN113868926A CN 113868926 A CN113868926 A CN 113868926A CN 202111201494 A CN202111201494 A CN 202111201494A CN 113868926 A CN113868926 A CN 113868926A
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沈明杰
史兵
朱可
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Abstract

The invention relates to the technical field of aquaculture, in particular to a method for constructing a spatial distribution model of water quality parameters of a culture pond, which comprises the following steps: s1, acquiring water quality parameter information of the culture pond, and acquiring water quality parameters including temperature, pH value and DO of specified coordinates of the culture pond by using a wireless sensor network; s2, optimizing the water quality parameters through a Kalman filtering algorithm, and improving the algorithm by introducing correction factors so as to improve the prediction precision, wherein the optimization aims at eliminating the error of the measuring instrument; and S3, establishing a water quality parameter spatial distribution model. The invention realizes the informatization and intelligent management of aquaculture by constructing a sensing network for monitoring the water quality parameters of aquaculture and combining the improved Kalman filtering optimization and the linear interpolation four-dimensional model fitting to estimate the water quality parameters.

Description

Method for constructing spatial distribution model of water quality parameters of culture pond
Technical Field
The invention relates to the technical field of aquaculture, in particular to a method for constructing a spatial distribution model of water quality parameters of a culture pond.
Background
In recent decades, aquaculture has become one of the main development projects of the aquaculture industry, the existing water quality testing method mainly depends on manual operation and equipment experience to perform testing, a great deal of energy and time are consumed, and the defects of long monitoring period and limited monitoring range exist. The water quality on-line monitoring system adopting the field bus technology has good real-time performance, but also has the problems of difficult wiring, inconvenient maintenance and expansion, pipeline corrosion and the like.
At present, people in Huangjianqing et al have researched and designed a wireless sensor network monitoring system for dissolved oxygen, pH value and temperature parameters constructed by adopting an MSP430F149 single chip microcomputer, and people in Liujiangfeng et al have proposed an aquaculture detection system based on a ZigBee network. However, the above methods mostly adopt a water quality automatic detector for monitoring, and have the disadvantages of high equipment cost and being not beneficial to water quality monitoring of large water areas; moreover, experts and scholars at home and abroad have less research on the aspects of estimation of water quality monitoring and spatial distribution calculation.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: by constructing a sensing network for monitoring aquaculture water quality parameters and combining Kalman filtering optimization and linear interpolation value four-dimensional model fitting, water quality parameters are estimated, and the informatization and intelligent management of aquaculture is realized.
The technical scheme adopted by the invention is as follows: a method for constructing a spatial distribution model of water quality parameters of a culture pond comprises the following steps:
s1, acquiring water quality parameter information of the culture pond, and acquiring water quality parameters including temperature, pH value and DO of specified coordinates of the culture pond by using a wireless sensor network;
s2, optimizing the water quality parameters through a Kalman filtering algorithm, introducing correction factors to preprocess the optimized water quality parameters, and the purpose of preprocessing the optimized water quality parameters through optimizing the water quality parameters and introducing the correction factors is to eliminate errors of a measuring instrument;
the process of optimizing the water quality parameters by the Kalman filtering algorithm is as follows:
s21, time update equation:
the state variable is calculated a priori, and the formula is as follows:
Figure BDA0003305111060000021
in the formula, A is a state transition parameter, and the correlation coefficient is 1; w is process noise, and the process noise follows normal distribution with the mean value of 0 and the variance of Q, and Q is given according to the variance of the error of the actually measured water quality parameter value;
Figure BDA0003305111060000022
the water quality parameter estimated value of the kth iteration step is obtained;
Figure BDA0003305111060000023
optimizing the water quality parameter of the k-1 iteration step;
the prior estimation error covariance is as follows:
Pk-＝APk-1AT+Q (2)
in the formula, Pk-The estimated covariance of the kth iteration step; pk-1An optimized covariance for the k-1 iteration step; the initial estimated covariance is 2;
s22, state update equation:
calculating Kalman gain, and the formula is as follows:
Figure BDA0003305111060000024
in the formula, KkKalman gain for the kth iteration step; h is a measurement transformation parameter matrix with determinant value of 1; r is the variance of the measurement noise;
and (3) carrying out posterior estimation filtering optimization, wherein the formula is as follows:
Figure BDA0003305111060000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003305111060000032
optimizing the water quality parameter of the kth iteration step; z is a radical ofkMeasured water quality parameters are obtained;
updating error covariance
Pk＝(I-KkH)Pk- (5)
In the formula, I is a matrix with a diagonal of 1;
the prediction processes of the Kalman filtering algorithm composed of the formulas (1) to (5) are formulas (1) to (2), and the initial estimated water quality parameters of temperature, pH value and DO are respectively 9.5 ℃, 6.8 and 6mg.L-1Preliminary prediction of prior water quality parameters is realized; then, realizing an updating process according to the measured values, wherein the updating process is expressed by the formulas (3) to (5), and obtaining an posterior estimated optimized value and an updated measurement error covariance; finally, substituting the posteriori estimated optimized value and the updated measurement error covariance into equations (1) to (5) to predict and update the value of the next moment;
s23, introducing a correction factor, and pretreating the optimized water quality parameters;
preprocessing the data before constructing the four-dimensional model, screening abnormal data at each moment of the optimized water quality parameters in the step S22 for eliminating the self measurement error of the instrument and the influence of external factors, and setting the normal temperature interval to be 9-10 ℃, the pH to be 6.8-7 and the DO to be 5.85-6.15mg.L-1The exceeding interval part is abnormal data; calculating the average value of the abnormal data of the optimized water quality parameters, dividing the abnormal data by the average value of the screened normal data to obtain a correction factor, correcting the predicted value by using the correction factor to obtain a final result, and screening the average value of the data:
Figure BDA0003305111060000033
in the formula, s is the number of screened data; a isiThe screened data are obtained; e.g. of the typekThe average value of abnormal data at the k-th moment is screened out.
Mean of data left after screening:
Figure BDA0003305111060000041
wherein n represents the total number of data; biRepresenting the data left after screening; r iskIndicating a normal data mean at time k. Correction factor:
Figure BDA0003305111060000042
in the formula gkA correction factor representing a k-th time;
and finally, multiplying the correction factor by the data at each optimized moment respectively to obtain a final optimization result.
S3, establishing a water quality parameter spatial distribution thinking model;
the method comprises the following steps of utilizing MATLAB to calculate linear interpolation values, and constructing a water quality parameter spatial distribution four-dimensional model, wherein the specific process comprises the following steps:
s31, constructing a culture pond model with X-axis length, Y-axis width and Z-axis depth in MATLAB software;
s32, inserting the water quality parameters optimized in the step S2 into a plurality of position points of X-axis direction coordinates, Y-axis direction coordinates and Z-axis direction coordinates, and establishing model meshing;
s33, performing data fitting by using an MATLAB scatter interpolation function, and calling a griddata function to scatter linear interpolation values of the known four-dimensional parameters along X, Y, Z vectors according to the specified step distance;
and S34, calling a slice function to draw slices for the three-dimensional data, drawing a plurality of slice planes which are orthogonal to the specific axis, and designating all slice water quality parameters as a matrix of a defined curved surface to obtain a four-dimensional space model.
The invention has the beneficial effects that:
1. establishing a water quality parameter four-dimensional model by establishing a water quality parameter sensing network and utilizing Kalman filtering optimization and linear interpolation values, estimating water quality parameters by fitting, and comparing actual measurement with a simulation value, wherein the average relative deviation of spatial distribution models of temperature, pH value and DO and an actual measurement value is 3.18%, 2.89% and 1.59%; the model has accurate prediction result, thereby constructing a rapid and effective method for detecting the environmental information of the culture pond.
Drawings
FIG. 1 is a flow chart of a method for constructing a spatial distribution model of water quality parameters of a culture pond according to the invention;
FIG. 2 is a plan view of the arrangement of sensor nodes for acquiring water quality parameters of a culture pond according to the invention;
FIG. 3 is a temperature optimum simulation test established by the MATLAB platform of the present invention;
FIG. 4 is a pH optimum simulation test established by the MATLAB platform of the present invention;
FIG. 5 is a DO optimum simulation test established by the MATLAB platform of the present invention;
FIG. 6 is a MATLAB platform simulation temperature four-dimensional model of the present invention;
FIG. 7 is a MATLAB platform simulation pH four-dimensional model of the present invention;
FIG. 8 is a MATLAB platform simulation DO four-dimensional model of the present invention.
Detailed Description
The invention will be further described with reference to the accompanying drawings and examples, which are simplified schematic drawings and illustrate only the basic structure of the invention in a schematic manner, and therefore only show the structures relevant to the invention.
As shown in fig. 1, a method for constructing a spatial distribution model of water quality parameters of a culture pond includes the following steps:
s1, acquiring water quality parameter information of the culture pond, and acquiring water quality parameters including temperature, pH value and DO of specified coordinates of the culture pond by using a wireless sensor network;
FIG. 2 is a plan view of the wireless sensor arrangement of the present invention, with a DS18B20 sensor for temperature collection; the pH value adopts an E-201-C type pH value composite electrode, and the measurement range is 0-14; response time is less than two minutes; DO uses YDC100 electrodes; 6 collection points are arranged in an established culture pond, the X axis of the culture pond is 100m, the Y axis of the culture pond is 60m, the Z axis of the culture pond is 2m deep, 18 points are arranged at 15m, 50m and 85m of X axis direction coordinates, 15m and 45m of Y axis direction coordinates and 0.1m, 1m and 1.9m of Z axis direction coordinates, wireless sensors are arranged, and specific numerical values collected at a certain moment are shown in a table 1:
table 1 test data statistics
Figure BDA0003305111060000061
As shown in table 1, the temperature, pH, and DO before optimization are actually acquired sensor values;
s2, optimizing the water quality parameters through a Kalman filtering algorithm, and introducing correction factors to preprocess the optimized water quality parameters, wherein the optimization aims to eliminate the error of the measuring instrument, and the Kalman filtering algorithm comprises the following processes:
s21, time update equation:
the state variable is calculated a priori, and the formula is as follows:
Figure BDA0003305111060000062
wherein A is a state transition matrix with a correlation coefficient of 1; w is process noise, which follows a normal distribution with mean 0 and variance Q0.001;
Figure BDA0003305111060000071
the water quality parameter estimated value of the kth iteration step is obtained;
Figure BDA0003305111060000072
optimizing the water quality parameter of the k-1 iteration step;
the prior estimation error covariance is as follows:
Pk-＝APk-1AT+Q (2)
in the formula, Pk-an estimated covariance for the kth iteration step; pk-1An optimized covariance for the k-1 iteration step; the initial estimated covariance is 2;
s22, state update equation:
calculating Kalman gain, and the formula is as follows:
Figure BDA0003305111060000073
in the formula, KkKalman gain for the kth iteration step; h is a measurement transformation parameter matrix with determinant value of 1; r is the variance of the measurement noise;
and (3) carrying out posterior estimation filtering optimization, wherein the formula is as follows:
Figure BDA0003305111060000074
in the formula (I), the compound is shown in the specification,
Figure BDA0003305111060000075
optimizing the water quality parameter of the kth iteration step; z is a radical ofkMeasured water quality parameters are obtained;
updating error covariance
Pk＝(I-KkH)Pk- (5)
In the formula, I is a matrix with a diagonal of 1;
the prediction processes of the Kalman filtering algorithm composed of the formulas (1) to (5) are formulas (1) to (2), and the initial estimated water quality parameters of temperature, pH value and DO are respectively 9.5 ℃, 6.8 and 6mg.L-1Preliminary prediction of prior water quality parameters is realized; then, realizing an updating process according to the measured values, wherein the updating process is expressed by the formulas (3) to (5), and obtaining an posterior estimated optimized value and an updated measurement error covariance; finally, substituting the posteriori estimated optimized value and the updated measurement error covariance into equations (1) to (5) to predict and update the value of the next moment;
as shown in table 1, the optimized temperature, pH value and DO are obtained at 18 points at a certain time by using a kalman filter algorithm;
in order to eliminate the error between the measuring instrument and the environment, fig. 3-5 show that the variation of the time variation of the minute measurement value of the measuring instrument is large and the deviation from the actual condition is large according to the actual measurement values of the temperature, the pH value and the DO collected by the minute particle size sensor and the estimated value obtained by the kalman filter algorithm in 100 minutes, and the fluctuation of the estimated value obtained by the kalman filter algorithm is small and is close to the parameter value of the actual environment;
s23 improvement of Kalman filtering algorithm
Preprocessing data before constructing a four-dimensional model, and screening abnormal data at each moment due to the influence of the measurement error of an instrument and external factors; and (3) performing mean value calculation on the screened abnormal data, dividing the mean value of the screened abnormal data and the mean value of the screened normal data to obtain a correction factor, and finally correcting the predicted value by using the correction factor to obtain a final result, wherein the mean value of the screened data has a mean value calculation formula (6):
Figure BDA0003305111060000081
in the formula, s is the number of screened data; a isiThe screened data are obtained; e.g. of the typekScreening out the average value of abnormal data at the kth moment;
mean of data left after screening:
Figure BDA0003305111060000082
wherein n represents the total number of data; biRepresenting the data left after screening; r iskIndicating a normal data mean at time k. Correction factor:
Figure BDA0003305111060000083
in the formula gkA correction factor representing a k-th time;
and finally, multiplying the correction factor by the data at each optimized moment respectively to obtain a final optimization result.
By calculating the average relative errors of the temperature, the pH value and the DO of the traditional Kalman filtering algorithm to be 14.927%, 8.346% and 10.294%, and introducing correction factors, the average relative errors of the water quality parameters are 7.412%, 2.637% and 6.887%.
S3, establishing a water quality parameter spatial distribution model;
the method comprises the following steps of utilizing MATLAB to calculate linear interpolation values, and constructing a water quality parameter spatial distribution four-dimensional model, wherein the specific process comprises the following steps:
s31, constructing a culture pond model with X-axis length, Y-axis width and Z-axis depth in MATLAB software;
s32, inserting the water quality parameters optimized in the step S2 into a plurality of positions of X-axis direction coordinates, Y-axis direction coordinates and Z-axis direction coordinates, and establishing model mesh division;
s33, performing data fitting by using an MATLAB scatter interpolation function, drawing slices for the three-dimensional data, drawing one or more slice planes orthogonal to a specific axis, and designating slice parameters as scalar quantities or vectors;
s34, drawing a single slice along the curved surface, and designating all slice parameters as a matrix for defining the curved surface;
as shown in fig. 6-8, the simulation temperature, pH value and DO of each point in the culture pond model after data fitting is performed by the MATLAB scatter interpolation function;
in order to verify the reliability of the simulation data, the temperature, pH and DO comparison experiments of coordinates (15, 25, 1.9), (25, 25, 1.9), (35, 25, 1.9), (45, 25, 1.9), (65, 25, 1.9) were selected. The average relative deviation of the spatial distribution model of the temperature, the pH value and the DO from the measured value is 3.18 percent, 2.89 percent and 1.59 percent through calculation; the obtained result is basically consistent with the water quality parameter data of the measured value, which shows that the water quality parameters based on the improved Kalman filtering optimization algorithm and the linear interpolation value four-dimensional model fitting can accurately master the water quality dynamic change information of the whole culture pond through simulation,
the invention establishes a water quality parameter four-dimensional model by establishing a water quality parameter sensing network and utilizing improved Kalman filtering optimization and linear interpolation values, and realizes aquaculture informatization and intelligent management by fitting and estimating water quality parameters.
In light of the foregoing description of the preferred embodiment of the present invention, many modifications and variations will be apparent to those skilled in the art without departing from the spirit and scope of the invention. The technical scope of the present invention is not limited to the content of the specification, and must be determined according to the scope of the claims.

Claims (4)

1. A method for constructing a spatial distribution model of water quality parameters of a culture pond is characterized by comprising the following steps:
s1, acquiring water quality parameter information of the culture pond, and acquiring water quality parameters of specified coordinates of the culture pond by using a wireless sensor, wherein the water quality parameters comprise temperature, pH value and DO;
s2, optimizing the water quality parameters through a Kalman filtering algorithm, and introducing correction factors to carry out pretreatment on the optimized water quality parameters;
s3, establishing a water quality parameter spatial distribution four-dimensional model;
and (3) performing linear interpolation value calculation by using MATLAB to construct a water quality parameter spatial distribution four-dimensional model.
2. The method for constructing the spatial distribution model of the water quality parameters of the culture pond according to claim 1, wherein the Kalman filtering algorithm is used for optimizing the water quality parameters in the following process:
s21, time update equation:
the state variable is calculated a priori, and the formula is as follows:
Figure FDA0003305111050000011
in the formula, A is a state transition parameter, and the correlation coefficient is 1; w is process noise, which follows a normal distribution with mean 0 and variance Q;
Figure FDA0003305111050000012
the water quality parameter estimated value of the kth iteration step is obtained;
Figure FDA0003305111050000013
optimizing the water quality parameter of the k-1 iteration step;
the prior estimation error covariance is as follows:
Figure FDA0003305111050000014
in the formula (I), the compound is shown in the specification,
Figure FDA0003305111050000015
the estimated covariance of the kth iteration step; pk-1An optimized covariance for the k-1 iteration step;
s22, state update equation:
calculating Kalman gain, and the formula is as follows:
Figure FDA0003305111050000021
in the formula, KkKalman gain for the kth iteration step; h is a measurement transformation parameter matrix with determinant value of 1; r is the variance of the measurement noise;
and (3) carrying out posterior estimation filtering optimization, wherein the formula is as follows:
Figure FDA0003305111050000022
in the formula (I), the compound is shown in the specification,
Figure FDA0003305111050000023
optimizing the water quality parameter of the kth iteration step; z is a radical ofkMeasured water quality parameters are obtained;
updating error covariance
Figure FDA0003305111050000024
In the formula, I is a matrix with a diagonal of 1;
the prediction process of the Kalman filtering algorithm formed by the formulas (1) to (5) is formulas (1) to (2), and initial prediction of the prior water quality parameters is realized by assuming initial estimated water quality parameter temperature, pH value and DO; then, realizing an updating process according to the measured values, wherein the updating process is expressed by the formulas (3) to (5), and obtaining an posterior estimated optimized value and an updated measurement error covariance; finally, substituting the posteriori estimated optimized value and the updated measurement error covariance into equations (1) to (5) to predict and update the value of the next moment;
and S23, introducing a correction factor, and pretreating the optimized water quality parameters.
3. The method for constructing the spatial distribution model of the water quality parameters of the culture pond according to claim 2, wherein the step S23 is to introduce the correction factor, and the pretreatment process of the optimized water quality parameters is as follows:
screening out abnormal data at each moment for the water quality parameters optimized in the step S22; and (3) performing mean value calculation on the screened abnormal data, dividing the mean value of the screened abnormal data and the mean value of the screened normal data to obtain a correction factor, and finally correcting the predicted value by using the correction factor to obtain a final result, wherein the mean value of the screened data has a mean value calculation formula (6):
Figure FDA0003305111050000031
in the formula, s is the number of screened data; a isiThe screened data are obtained; e.g. of the typekScreening out the average value of abnormal data at the kth moment;
mean of data left after screening:
Figure FDA0003305111050000032
wherein n represents the total number of data; biRepresenting the data left after screening;rkdata mean value indicating normal at the k-th time, correction factor:
Figure FDA0003305111050000033
in the formula gkA correction factor representing a k-th time;
and finally, multiplying the correction factor by the data at each optimized moment respectively to obtain a final optimization result.
4. The method for constructing the spatial distribution model of the water quality parameters of the culture pond according to claim 1, wherein the method comprises the following steps: the construction of the water quality parameter spatial distribution four-dimensional model comprises the following steps:
s31, constructing a culture pond model with X-axis length, Y-axis width and Z-axis depth in MATLAB software;
s32, inserting the water quality parameters optimized in the step S2 into a plurality of position points of X-axis direction coordinates, Y-axis direction coordinates and Z-axis direction coordinates, and establishing model meshing;
s33, performing data fitting by using an MATLAB scatter interpolation function, and calling a griddata function to scatter linear interpolation values of the known four-dimensional parameters along X, Y, Z vectors according to the specified step distance;
and S34, calling a slice function to draw slices for the three-dimensional data, drawing a plurality of slice planes which are orthogonal to the specific axis, and designating all slice water quality parameters as a matrix of a defined curved surface to obtain a four-dimensional space model.
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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115490368A (en) * 2022-10-12 2022-12-20 平行数字科技(江苏)有限公司 Intelligent dosing system of tap water plant based on clustering integration algorithm
CN115931055A (en) * 2023-01-06 2023-04-07 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115490368A (en) * 2022-10-12 2022-12-20 平行数字科技(江苏)有限公司 Intelligent dosing system of tap water plant based on clustering integration algorithm
CN115931055A (en) * 2023-01-06 2023-04-07 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis
CN115931055B (en) * 2023-01-06 2023-06-16 长江信达软件技术（武汉）有限责任公司 Rural water supply operation diagnosis method and system based on big data analysis

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