CN111693667A - Water quality detection system and method based on gated recursive array - Google Patents
Water quality detection system and method based on gated recursive array Download PDFInfo
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Abstract
The invention provides a detection method based on gated recursive array water quality detection, which comprises the following steps: s1, obtaining water quality parameters including PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; acquiring environmental indexes including atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed; s2, normalizing the acquired water quality parameter values and environmental indexes and selecting characteristics to obtain preprocessed data; s3, selecting a neural network based on a gated recursive array as a prediction model, and setting a loss function and an optimization iteration method; s4, inputting the preprocessed data into a prediction model to perform model training to obtain a prediction result; and S5, comparing the prediction result with the set five-level water quality classification to obtain a classification result. The invention can more effectively utilize the water quality attribute to carry out prediction classification.
Description
Technical Field
The invention relates to the technical field of water quality safety, in particular to a water quality detection system and method based on a gated recursive array.
Background
With the continuous development of the industrial and scientific and technological level in China, industrial sewage, domestic sewage and other wastes enter drinking water sources such as lakes, reservoirs, rivers, seas, lakes and seas, so that the pollution is more serious than the self-purification capacity of a water body, the physical, chemical, biological and other characteristics of the water body are changed, the utilization value of water is influenced, the human health and the ecological environment are even harmed, and the gradual deterioration and the irreparable situation are finally reached. Therefore, the development of a real-time online, effective and rapid water quality monitoring system has important significance for environmental management and treatment of polluted water sources.
In recent years, the country pays more and more attention to environmental protection, and more related researchers put forward new technologies and new methods. These techniques include electrochemical analysis, chromatographic separation, biosensor and spectroscopic analysis. The data of various indexes are acquired by the methods, and the traditional water quality classification methods such as a single factor evaluation method, an index evaluation method and the like are time-consuming and labor-consuming, and the efficiency is extremely low. Researchers also put forward methods such as logistic regression and neural network to establish models so as to obtain water quality data and predict water quality categories. However, the methods have some disadvantages, such as the lack of correlation of model establishment, and the adopted model does not have a time sequence function capable of considering water quality data, so that the accuracy of water quality prediction work is reduced, and results cannot be obtained timely and effectively.
Therefore, in order to develop an effective and rapid water quality monitoring system and improve the accuracy of model prediction, we need to pay attention to more environmental attributes and introduce the time-series characteristics thereof.
Disclosure of Invention
Aiming at the problems that the correlation of model establishment is deficient and the adopted model does not have the time sequence function of considering water quality data, the invention provides a water quality detection system and method based on a gated recursive array, which can more effectively utilize the water quality attribute to carry out prediction classification.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a detection method based on gated recursive array water quality detection comprises the following steps:
s1, obtaining water quality parameters including PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; acquiring environmental indexes including atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed;
s2, normalizing the acquired water quality parameter values and environmental indexes and selecting characteristics to obtain preprocessed data;
s3, selecting a neural network based on a gated recursive array as a prediction model, and setting a loss function and an optimization iteration method;
s4, inputting the preprocessed data into a prediction model to perform model training to obtain a prediction result;
and S5, comparing the prediction result with the set five-level water quality classification to obtain a classification result.
And the water quality grade prediction containing the time sequence attribute is obtained, and the prediction accuracy is higher. This is because the gated recursive array has the characteristic of memory, so that the characteristics of the sequentiality such as temperature, chemical reaction and the like in the water quality monitoring are fully considered. And compared with other neural networks, the recursive array is gated by fewer gating units, so that the parameters are fewer and the complexity is low.
Preferably, the set five-level water quality classification specifically includes:
acquiring water quality parameter values in continuous time, and simultaneously acquiring environmental indexes and inputting the environmental indexes into a computer for recording; at present, the national surface water environment quality standard is divided into five types of I, II, III, IV and V according to the surface water area environment function and the protection target, and 16 types of water quality monitoring data collected at the time t are set as follows:
Xt＝(x1，x2，......，x16)
x is abovetIs a water quality characteristic array at the time t, xnIs the nth data index, and the water quality classification result at the time t is as follows:
Yt＝y y∈(I，II，III，IV，V)。
preferably, the step S2 specifically includes:
normalizing the collected data, and subjecting the X totNormalized to [0, 1 ] data]To obtain:
Xt＝(x1，x2，......，x16)xn∈[0，1]
selecting k characteristic attributes with the maximum correlation with the predicted water quality classification result through a characteristic elimination and cross validation algorithm to obtain:
Xt＝(x1，x2，......，xk)xk∈[0，1]。
the method mainly adopts a recursive feature elimination and cross validation algorithm, and the algorithm has the main idea that a model is repeatedly constructed, then the best feature is selected and determined according to the coefficient proportion of the feature, the selected feature is put into a set, and then the rest features are repeatedly constructed until all the features are traversed. The data needs to be normalized before the model is built, because without normalization, the result of the model built based on the data is not stable enough.
Preferably, the step S3 specifically includes: a neural network is designed based on a gating recursive array, and the gating recursive array has two gates: the updating gate is used for controlling the degree of the state information at the previous moment brought into the current state in the current state, and the larger the value of the updating gate is, the more the state information at the previous moment is brought; how much information is written to the current candidate set before reset gate controls the previous state
The smaller the reset gate, the less information of the previous state is written; the process of processing data, i.e. the forward propagation process of the gated recursive array, is:
u＝σ(Wu[c<t-1>，Xt]+bu)
in the above formulauTo update the value of the gate, σ is the activation function, WuTo update the weight of the door, c<t-1>Is the state value at the previous moment, buTo update the door weight value;
r＝σ(Wr[c<t-1>，Xt]+br)
in the above formularTo reset the gate value, σ is the activation function, WrTo reset the weight of the gate, c<t-1>Is the state value at the previous moment, brA reset gate bias value;
in the above formula
Is a candidate state value at the current moment, tanh is an activation function, WcIs a weight value, bcIs the current operation bias value;
in the above formula c<t>Is the current state value;
the function of the sigma activation function is to compress the state value between (0, 1), and is a sigmoid activation function, and the formula is as follows:
k is a parameter of the incoming activation function.
Preferably, the loss function to be optimized in step S3 is:
J(θ)＝E[Loss(f(Xt；θ)，Y)]
theta is an internal parameter of the neural network, and J (theta) is a loss function.
Preferably, the loss function adopts SGD optimization algorithm or Adam optimization algorithm,
the SGD optimization algorithm specifically includes:
input learning rate ξkInitial parameter θ, gradient
When the stopping condition is satisfied, collecting a sample containing m samples { X ] from the training sett (1)，Xt (2)，...，Xt (m)Small lot of where data x(i)Corresponding to object y(i)；
Calculating a gradient estimate:
updating parameters:
the Adam optimization algorithm specifically comprises:
input global learning rate ξk(default 0.001), exponential decay Rate of moment estimation, ρ1、ρ2Within the interval [0, 1) (default 0.9 and 0.999), small constants for numerical stability (default 10)-7)，
Inputting an initial parameter theta, initializing a first-order moment and a second-order moment variable s as 0, r as 0, initializing a time step t as 0, and collecting m samples { x in a training set when a stop condition is not met(1)，x(2)，...，x(m)Where the data x(i)Corresponding to object y(i)，
Calculating a gradient estimate:
t←t+1
updating biased first moment estimates: s ← ρ1s+(1-p1)g
Updating the biased second moment estimation: r ← ρ2r+(1-ρ2)ge g。
During the training period of the neural network, the method can be practically equivalent to the process of reducing the loss function, the initial stage of the development of the neural network algorithm is generally to select a gradient descent algorithm, output data is obtained through batch input data, the difference between the output data and a target function is calculated to obtain the loss function value, and the method for reducing the loss function value is to change parameters in the network. The final objective is to have the input correspond to the output and the calculated loss function value reach the lowest point, equivalent to an output close to the ideal value. The optimization algorithm is optimized in the aspects of loss function convergence speed, gradient descent speed and self-adaptive descent. So that the loss function approaches the global optimum as much as possible.
A water quality detection system based on a gated recursive array is suitable for the detection method based on the gated recursive array, and comprises
The data acquisition module is used for acquiring the water quality parameter value and the environmental index and transmitting the water quality parameter value and the environmental index to the database module;
the database module is used for storing the water quality parameter values and the data of the environmental indexes;
the data processing module calls the data of the water quality parameter values and the environmental indexes in the database, carries out pretreatment to obtain pretreatment data, and transmits the pretreatment data to the gated recursive array model;
the gated recursive array model is provided with a pre-established training model, receives the preprocessed data input by the data processing module, and inputs the preprocessed data into the training model for training to obtain a predicted result;
and the computer water quality monitoring platform is provided with a five-level water quality classification early warning threshold value, receives a predicted result, obtains a classification result according to the predicted result and carries out early warning.
Preferably, the system is further provided with an alarm module, and the computer water quality monitoring platform is electrically connected with the alarm module. When the water quality grade belongs to an abnormal range, the alarm module is controlled to give an alarm
Preferably, the data acquisition module comprises a broad spectrum water quality analysis unit, a temperature sensor, a humidity sensor, a pressure gauge and an anemometer, the broad spectrum water quality analysis unit acquires a water quality parameter value through a broad spectrum water quality analysis technology, and the temperature sensor, the humidity sensor, the pressure gauge and the anemometer acquire an environmental index.
Preferably, the water quality parameter values comprise PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; the environmental indexes comprise atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed.
The invention has the following beneficial effects: the deep neural network based on the gated recursive array is used as a prediction model, the gated recursive array has a memory function, and influences of attribute time sequence changes are brought into factors influencing results during training, such as water temperature, atmospheric pressure, chemical reactions generated by substances in water and the like, so that the gated recursive array has certain time-delay influence factors. A model with higher characteristic correlation and more accurate prediction is obtained through training, and the method has important significance for timely and accurately predicting the water quality grade and making an emergency response; obtaining the water quality grade prediction containing the time sequence attribute, wherein the prediction accuracy is higher; the recursive array is gated with fewer gating cells, and therefore with fewer parameters and less complexity, than other neural networks.
Drawings
FIG. 1 is a system configuration diagram of the present embodiment;
FIG. 2 is a block diagram of the input-output framework of the gated recursive array in the present embodiment;
FIG. 3 is a diagram of the internal framework of the gated recursive array in the present embodiment;
wherein: s01, a data acquisition module S02, a database module S03, a data processing module S04, a gated recursive array model S05, a computer water quality monitoring platform S06 and an alarm module.
Detailed Description
Example (b):
the embodiment provides a detection method based on gated recursive array water quality detection, which comprises the following steps:
s1, obtaining water quality parameters including PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; acquiring environmental indexes including atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed;
the setting of the five-level water quality classification specifically comprises the following steps:
acquiring water quality parameter values in continuous time, and simultaneously acquiring environmental indexes and inputting the environmental indexes into a computer for recording; at present, the national surface water environment quality standard is divided into five types of I, II, III, IV and V according to the surface water area environment function and the protection target, and 16 types of water quality monitoring data collected at the time t are set as follows:
Xt＝(x1，x2，......，x16)
x is abovetIs a water quality characteristic array at the time t, xnIs the nth data index, and the water quality classification result at the time t is as follows:
Yt＝y y∈(I，II，III，IV，V)。
s2, normalizing the acquired water quality parameter values and environmental indexes and selecting characteristics to obtain preprocessed data; step S2 specifically includes:
normalizing the collected data to obtain XtNormalized to [0, 1 ] data]To obtain:
Xt＝(x1，x2，......，x16)xn∈[0，1]
selecting k characteristic attributes with the maximum correlation with the predicted water quality classification result through a characteristic elimination and cross validation algorithm to obtain:
Xt＝(x1，x2，......，xk)xk∈[0，1]。
the method mainly adopts a recursive feature elimination and cross validation algorithm, and the algorithm has the main idea that a model is repeatedly constructed, then the best feature is selected and determined according to the coefficient proportion of the feature, the selected feature is put into a set, and then the rest features are repeatedly constructed until all the features are traversed. The data needs to be normalized before the model is built, because without normalization, the result of the model built based on the data is not stable enough.
S3, selecting a neural network based on a gated recursive array as a prediction model, and setting a loss function and an optimization iteration method;
referring to fig. 2, the input and output of the gated recursive array includes input data, a state value of a previous time, a state value of a current time, and output data.
Referring to fig. 3, the internal framework of the gated recursive array includes an input/output module, a current state value module, and a previous state value module, where the operation module 1 is a multiplication operation, the operation module 2 is a difference operation with 1, the operation module 3 is an addition operation, the activation function 1 is a sigma activation function, and the activation function 2 is a tanh activation function.
Step S3 specifically includes: a neural network is designed based on a gating recursive array, and the gating recursive array has two gates: the updating gate is used for controlling the degree of the state information at the previous moment brought into the current state in the current state, and the larger the value of the updating gate is, the more the state information at the previous moment is brought; how much information is written to the current candidate set before reset gate controls the previous state
The smaller the reset gate, the less information of the previous state is written; the process of processing data, i.e. the forward propagation process of the gated recursive array, is:
u＝σ(Wu[c<t-1>，Xt]+bu)
in the above formulauTo update the value of the gate, σ is the activation function, WuTo update the weight of the door, c<t-1>Is the state value at the previous moment, buTo update the door weight value;
r＝σ(Wr[c<t-1>，Xt]+br)
in the above formularTo reset the gate value, σ is the activation function, WrTo reset the weight of the gate, c<t-1>Is the state value at the previous moment, brA reset gate bias value;
in the above formula
Is a candidate state value at the current moment, tanh is an activation function, WcIs a weight value, bcIs the current operation bias value;
in the above formula c<t>Is the current state value;
the function of the sigma activation function is to compress the state value between (0, 1), and is a sigmoid activation function, and the formula is as follows:
k is a parameter of the incoming activation function.
The loss function to be optimized in step S3 is:
J(θ)＝E[Loss(f(Xt；θ)，Y)]
theta is an internal parameter of the neural network, and J (theta) is a loss function.
The loss function adopts an SGD optimization algorithm or an Adam optimization algorithm,
the SGD optimization algorithm specifically includes:
input learning rate ξkInitial parameter θ, gradient
When the stopping condition is satisfied, collecting a sample containing m samples { X ] from the training sett (1)，Xt (2)，...，Xt (m)Small lot of where data x(i)Corresponding to object y(i)；
Calculating a gradient estimate:
updating parameters:
the Adam optimization algorithm specifically comprises:
input global learning rate ξk(default 0.001), exponential decay Rate of moment estimation, ρ1、ρ2Within the interval [0, 1) (default 0.9 and 0.999), small constants for numerical stability (default 10)-7)，
Inputting an initial parameter theta, initializing a first-order moment and a second-order moment variable s as 0, r as 0, initializing a time step t as 0, and collecting m samples { x in a training set when a stop condition is not met(1)，x(2)，...，x(m)Where the data x(i)Corresponding to object y(i)，
Calculating a gradient estimate:
t←t+1
updating biased first moment estimates: s ← ρ1s+(1-p1)g
Updating the biased second moment estimation: r ← ρ2r+(1-ρ2)ge g。
S4, inputting the preprocessed data into a prediction model to perform model training to obtain a prediction result;
and S5, comparing the prediction result with the set five-level water quality classification to obtain a classification result.
And the water quality grade prediction containing the time sequence attribute is obtained, and the prediction accuracy is higher. This is because the gated recursive array has the characteristic of memory, so that the characteristics of the sequentiality such as temperature, chemical reaction and the like in the water quality monitoring are fully considered. And compared with other neural networks, the recursive array is gated by fewer gating units, so that the parameters are fewer and the complexity is low.
During the training period of the neural network, the method can be practically equivalent to the process of reducing the loss function, the initial stage of the development of the neural network algorithm is generally to select a gradient descent algorithm, output data is obtained through batch input data, the difference between the output data and a target function is calculated to obtain the loss function value, and the method for reducing the loss function value is to change parameters in the network. The final objective is to have the input correspond to the output and the calculated loss function value reach the lowest point, equivalent to an output close to the ideal value. The optimization algorithm is optimized in the aspects of loss function convergence speed, gradient descent speed and self-adaptive descent. So that the loss function approaches the global optimum as much as possible.
The embodiment further provides a system for detecting water quality based on gated recursive array, which is suitable for the above method for detecting water quality based on gated recursive array, and with reference to fig. 1, the method includes the steps of
The data acquisition module is used for acquiring the water quality parameter value and the environmental index and transmitting the water quality parameter value and the environmental index to the database module;
the database module is used for storing the water quality parameter values and the data of the environmental indexes;
the data processing module calls the data of the water quality parameter values and the environmental indexes in the database, carries out pretreatment to obtain pretreatment data, and transmits the pretreatment data to the gated recursive array model;
the gated recursive array model is provided with a pre-established training model, receives the preprocessed data input by the data processing module, and inputs the preprocessed data into the training model for training to obtain a predicted result;
and the computer water quality monitoring platform is provided with a five-level water quality classification early warning threshold value, receives a predicted result, obtains a classification result according to the predicted result and carries out early warning.
The system is also provided with an alarm module, and the computer water quality monitoring platform is electrically connected with the alarm module. When the water quality grade belongs to an abnormal range, the alarm module is controlled to give an alarm
The data acquisition module comprises a broad spectrum water quality analysis unit, a temperature sensor, a humidity sensor, a pressure gauge and an anemometer, wherein the broad spectrum water quality analysis unit acquires a water quality parameter value through a broad spectrum water quality analysis technology, and the temperature sensor, the humidity sensor, the pressure gauge and the anemometer acquire an environment index.
The water quality parameters comprise PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; the environmental indicators include atmospheric pressure, atmospheric humidity, atmospheric temperature, and wind speed.
The invention has the following advantages: the deep neural network based on the gated recursive array is used as a prediction model, the gated recursive array has a memory function, and influences of attribute time sequence changes are brought into factors influencing results during training, such as water temperature, atmospheric pressure, chemical reactions generated by substances in water and the like, so that the gated recursive array has certain time-delay influence factors. A model with higher characteristic correlation and more accurate prediction is obtained through training, and the method has important significance for timely and accurately predicting the water quality grade and making an emergency response; obtaining the water quality grade prediction containing the time sequence attribute, wherein the prediction accuracy is higher; the recursive array is gated with fewer gating cells, and therefore with fewer parameters and less complexity, than other neural networks.
Claims (10)
1. A detection method based on gated recursive array water quality detection is characterized by comprising the following steps:
s1, obtaining water quality parameters including PH value, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll A content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content and tin content; acquiring environmental indexes including atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed;
s2, normalizing the acquired water quality parameter values and environmental indexes and selecting characteristics to obtain preprocessed data;
s3, selecting a neural network based on a gated recursive array as a prediction model, and setting a loss function and an optimization iteration method;
s4, inputting the preprocessed data into a prediction model to perform model training to obtain a prediction result;
and S5, comparing the prediction result with the set five-level classification to obtain a classification result.
2. The gated recursive array-based water quality detection method according to claim 1, wherein the set five-level classification specifically comprises:
acquiring water quality parameter values in continuous time, and simultaneously acquiring environmental indexes and inputting the environmental indexes into a computer for recording; at present, the national surface water environment quality standard is divided into five types of I, II, III, IV and V according to the surface water area environment function and the protection target, and 16 types of water quality monitoring data collected at the time t are set as follows:
Xt＝(x1，x2，......，x16)
x is abovetIs a water quality characteristic array at the time t, xnIs the nth data index, and the water quality classification result at the time t is as follows:
Yt＝y y∈(I，II，III，IV，V)。
3. the method as claimed in claim 2, wherein the step S2 specifically comprises:
normalizing the collected data, and subjecting the X totNormalized to [0, 1 ] data]To obtain:
Xt＝(x1，x2，......，x16) xn∈[0，1]
selecting k characteristic attributes with the maximum correlation with the predicted water quality classification result through a characteristic elimination and cross validation algorithm to obtain:
Xt＝(x1，x2，.....，xk) xk∈[0，1]。
4. the method as claimed in claim 1, wherein the step S3 specifically comprises: a neural network is designed based on a gating recursive array, and the gating recursive array has two gates: the updating gate is used for controlling the degree of the state information at the previous moment brought into the current state in the current state, and the larger the value of the updating gate is, the more the state information at the previous moment is brought; how much information is written to the current candidate set before reset gate controls the previous state
The smaller the reset gate, the less information of the previous state is written; the process of processing data, i.e. the forward propagation process of the gated recursive array, is:
u＝σ(Wu[c<t-1>，Xt]+bu)
in the above formulauTo update the value of the gate, σ is the activation function, WuTo update the weight of the door, c<t-1>Is the state value at the previous moment, buTo update the door weight value;
r＝σ(Wr[c<t-1>，Xt]+br)
in the above formularTo reset the gate value, σ is the activation function, WrTo reset the weight of the gate, c<t-1>Is the state value at the previous moment, brA reset gate bias value;
in the above formula
Is a candidate state value at the current moment, tanh is an activation function, WcIs a weight value, bcIs the current operation bias value;
in the above formula c<t>Is the current state value;
the function of the sigma activation function is to compress the state value between (0, 1), and is a sigmoid activation function, and the formula is as follows:
k is a parameter of the incoming activation function.
5. The method as claimed in claim 4, wherein the loss function to be trained and optimized in step S3 is:
J(θ)＝E[Loss(f(Xt；θ)，Y)]
theta is an internal parameter of the neural network, and J (theta) is a loss function.
6. The method as claimed in claim 4, wherein the loss function adopts SGD optimization algorithm or Adam optimization algorithm,
the SGD optimization algorithm specifically includes:
input learning rate ξkInitial parameter θ, gradient
When the stop condition is satisfied, the slave trainingThe training set collects samples containing m samples { Xt (1)，Xt (2)，...，Xt (m)Small lot of where data x(i)Corresponding to object y(i)；
Calculating a gradient estimate:
updating parameters:
the Adam optimization algorithm specifically comprises:
input global learning rate ξk(default 0.001), exponential decay Rate of moment estimation, ρ1、ρ2Within the interval [0, 1) (default 0.9 and 0.999), small constants for numerical stability (default 10)-7)，
Inputting an initial parameter theta, initializing a first-order moment and a second-order moment variable s as 0, r as 0, initializing a time step t as 0, and collecting m samples { x in a training set when a stop condition is not met(1)，x(2)，...，x(m)Where the data x(i)Corresponding to object y(i)，
Calculating a gradient estimate:
t←t+1
updating biased first moment estimates: s ← ρ1s+(1-p1)g
Updating the biased second moment estimation: r ← ρ2r+(1-ρ2)ge g。
7. A water quality detection system based on a gated recursive array, which is suitable for the detection method based on the gated recursive array water quality detection in claims 1 to 7, and is characterized by comprising
The data acquisition module is used for acquiring the water quality parameter value and the environmental index and transmitting the water quality parameter value and the environmental index to the database module;
the database module is used for storing the water quality parameter values and the data of the environmental indexes;
the data processing module calls the data of the water quality parameter values and the environmental indexes in the database, carries out pretreatment to obtain pretreatment data, and transmits the pretreatment data to the gated recursive array model;
the gated recursive array model is provided with a pre-established training model, receives the preprocessed data input by the data processing module, and inputs the preprocessed data into the training model for training to obtain a predicted result;
and the computer water quality monitoring platform is provided with a five-level classification early warning threshold value, receives the predicted result, obtains the classification result according to the predicted result and carries out early warning.
8. The gated recursive array based water quality detection system according to claim 7, wherein the system is further provided with an alarm module, and the computer water quality monitoring platform is electrically connected with the alarm module.
9. The gated recursive array based water quality detection system according to claim 7, wherein the data acquisition module comprises a broad spectrum water quality analysis unit, a temperature sensor, a humidity sensor, a pressure gauge and an anemometer, the broad spectrum water quality analysis unit obtains a water quality parameter value through a broad spectrum water quality analysis technology, and the temperature sensor, the humidity sensor, the pressure gauge and the anemometer obtain an environmental index.
10. The gated recursive array based water quality detection system according to claim 9, wherein the water quality parameter values include PH, conductivity, turbidity, dissolved oxygen, water temperature, chlorophyll a content, blue-green algae content, ammonia nitrogen content, total phosphorus content, copper content, lead content, tin content; the environmental indexes comprise atmospheric pressure, atmospheric humidity, atmospheric temperature and wind speed.
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