CN117575136A - Water quality monitoring system and method - Google Patents
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Abstract
The invention provides a water quality monitoring system, which comprises a data acquisition module, a data preprocessing module, a data transmission module, a water quality monitoring module, a water quality prediction module and a water quality evaluation module, wherein the data acquisition module is used for acquiring data of a water quality of a user; the data acquisition module is used for acquiring concentration data of the water quality parameters; the data preprocessing module is used for removing and optimizing the missing errors of the concentration data of the water quality parameters; the data transmission module is used for transmitting the concentration data of the pretreated water quality parameters based on the LORA wireless communication network; the water quality prediction module is used for predicting the water quality change trend; the water quality evaluation module is used for evaluating the water quality pollution degree; the water quality monitoring module is used for displaying data. The reliability and the accuracy of the data can be ensured by preprocessing the acquired data, the data can be transmitted remotely by adopting a LORA wireless communication network, and the prediction result can be more accurate by predicting the variation trend of the water quality parameters by adopting an LM-BP neural network prediction model.
Description
Technical Field
The invention relates to the technical field of water quality monitoring, in particular to a water quality monitoring system and a water quality monitoring method.
Background
At present, a plurality of sensors are generally used for acquiring different water quality parameter indexes in remote water quality monitoring, but the data receiving end has no unified data interface, the incompatibility condition is common, the data noise is large in the data transmission process, the external interference has certain influence on the data acquisition and transmission process, and the precision and the referenceability of the data are greatly influenced by a large amount of noise in a short interval. The problem faced by the short-distance wireless communication, dense node distribution and the like is also urgent to be improved.
The above problems are to be solved.
Disclosure of Invention
The present invention is directed to overcoming at least one of the above-mentioned drawbacks of the prior art, and providing a water quality monitoring system, the system including a data acquisition module, a data preprocessing module, a data transmission module, a water quality monitoring module, a water quality prediction module, and a water quality evaluation module; the data acquisition module is used for acquiring concentration data of water quality parameters; the data preprocessing module is used for eliminating and optimizing the concentration data of the water quality parameters by the missing errors; the data transmission module is used for transmitting the concentration data of the pretreated water quality parameters to the water quality monitoring module, the water quality prediction module and the water quality evaluation module based on the LORA wireless communication network; the water quality prediction module is used for predicting the change trend of the water quality parameters based on the received concentration data of the pretreated water quality parameters; the water quality evaluation module is used for evaluating the pollution degree of the pretreated water quality parameters based on the received concentration data of the pretreated water quality parameters; the water quality monitoring module is used for displaying the received concentration data of the pretreated water quality parameters, the predicted data generated by the water quality predicting module and the evaluation data generated by the water quality evaluating module.
Further, the concentration data of the water quality parameter comprises one or a combination of chemical oxygen demand concentration, ammonia nitrogen concentration, total nitrogen concentration and total phosphorus concentration.
Further, the data acquisition module comprises a sensor module, an A/D functional module and a data reading module; at least one sensor is integrated in the sensor module and used for acquiring water quality parameter concentration data corresponding to the at least one sensor; the A/D functional module is used for carrying out signal conditioning and digital-to-analog conversion on at least one accessed sensor; the data reading module is used for reading the type, the parameters and the conversion mode of the sensor based on the electronic data table.
Furthermore, the data reading module is further used for inputting the converted signals as an interface and acquiring data according to a format described by the electronic data sheet.
Further, the data preprocessing module comprises a missing data eliminating module and a data optimizing module; the missing data eliminating module is used for eliminating abnormal values of the concentration data of the water quality parameters based on a grid Luo Beisi algorithm; the data optimization module is used for optimizing the concentration data of the water quality parameters based on a Kalman filtering algorithm.
Further, the water quality prediction module is used for predicting the change trend of the concentration of the water quality parameter based on the LM-BP neural network prediction model.
Further, the water quality prediction module comprises a prediction model construction module, a prediction model initialization module and a prediction model training module; the prediction model construction module is used for constructing an LM-BP neural network prediction model; the prediction model initialization module is used for initializing the LM-BP neural network prediction model; the prediction model training module is used for training an LM-BP neural network prediction model.
Further, the water quality evaluation module is also used for evaluating the water quality pollution degree based on the comprehensive pollution index formula; the comprehensive pollution index formula isP i ＝C i /S i The method comprises the steps of carrying out a first treatment on the surface of the Wherein n represents the number of items of the water quality parameter index, P i Pollution index C representing the i-th water quality parameter index i Mean value of concentration of the index of the ith water quality parameter is represented by S i And (5) representing the water quality standard value of the i-th water quality parameter index.
Further, the water quality monitoring module comprises a data management module and an alarm module; the data management module is used for displaying and storing the received data in the form of a chart; the alarm module is used for alarming when the water quality acquisition equipment fails; and/or alarming when the detected water quality parameter index exceeds the standard.
In another aspect, the present invention provides a water quality monitoring method, the method comprising: collecting concentration data of water quality parameters; preprocessing the concentration data of the water quality parameters based on a lattice Luo Beisi algorithm and a Kalman filtering algorithm; transmitting the concentration data of the pretreated water quality parameters based on a LORA wireless communication network; predicting the change trend of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters; evaluating the pollution degree of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters; and displaying the concentration data, the water quality prediction data and the water quality evaluation data of the pretreated water quality parameters in real time.
In yet another aspect, the present invention provides a computer readable storage medium having one or more instructions stored therein for causing the computer to perform the water quality monitoring method described above.
In yet another aspect, the present invention provides an electronic device, including: a memory and a processor; at least one program instruction is stored in the memory; the processor is configured to implement the water quality monitoring method by loading and executing the at least one program instruction.
The beneficial effects of the invention are as follows: the invention provides a water quality monitoring system, which comprises a data acquisition module, a data preprocessing module, a data transmission module, a water quality monitoring module, a water quality prediction module and a water quality evaluation module, wherein the data acquisition module is used for acquiring data of a water quality monitoring system; the data acquisition module is used for acquiring concentration data of water quality parameters; the data preprocessing module is used for eliminating and optimizing the concentration data of the water quality parameters by the missing errors; the data transmission module is used for transmitting the concentration data of the pretreated water quality parameters to the water quality monitoring module, the water quality prediction module and the water quality evaluation module based on the LORA wireless communication network; the water quality prediction module is used for predicting the change trend of the water quality parameters based on the received concentration data of the pretreated water quality parameters; the water quality evaluation module is used for evaluating the pollution degree of the pretreated water quality parameters based on the received concentration data of the pretreated water quality parameters; the water quality monitoring module is used for displaying the received concentration data of the pretreated water quality parameters, the predicted data generated by the water quality predicting module and the evaluation data generated by the water quality evaluating module. The reliability and the accuracy of the data can be ensured by preprocessing the acquired data, the data can be transmitted remotely by adopting a LORA wireless communication network, and the prediction result can be more accurate by predicting the variation trend of the water quality parameters by adopting an LM-BP neural network prediction model.
Drawings
The invention is further described below with reference to the drawings and examples.
FIG. 1 is a schematic diagram of a water quality monitoring system according to an embodiment of the present invention.
Fig. 2 is a flowchart of a water quality monitoring method according to an embodiment of the present invention.
Fig. 3 is a partial block diagram of an electronic device provided by an embodiment of the invention.
Detailed Description
Before discussing exemplary embodiments in more detail, it should be mentioned that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although a flowchart depicts operations as a sequential process, many of the operations can be performed in parallel, concurrently, or at the same time. Furthermore, the order of the operations may be rearranged. The process may be terminated when its operations are completed, but may have additional steps not included in the figures. The processes may correspond to methods, functions, procedures, subroutines, and the like.
It will be understood that, although the terms "first," "second," etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another element. For example, a first element could be termed a second element, and, similarly, a second element could be termed a first element, without departing from the scope of example embodiments. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
The following appearing technical terms will now be explained for the convenience of subsequent understanding:
the BP network (Back Propagation) is a multi-layer feedforward network trained according to an error Back Propagation algorithm, and is one of the most widely applied neural network models at present. The BP network can learn and store a large number of input-output pattern mappings without revealing beforehand mathematical equations describing such mappings.
Example 1
Referring to FIG. 1, a schematic diagram of a water quality monitoring system is shown.
As an example, the system includes a data acquisition module 110, a data preprocessing module 120, a data transmission module 130, a water quality monitoring module 140, a water quality prediction module 150, and a water quality evaluation module 160.
Preferably, the data acquisition module 110 is configured to acquire concentration data of a water quality parameter. Wherein, the concentration data of the water quality parameter comprises one or a combination of chemical oxygen demand concentration, ammonia nitrogen concentration, total nitrogen concentration and total phosphorus concentration.
Preferably, the data acquisition module 110 includes a sensor module 1101, an a/D function module 1102, and a data reading module 1103; at least one sensor is integrated in the sensor module 1101, and is configured to acquire water quality parameter concentration data corresponding to the at least one sensor; the a/D functional module 1102 is configured to perform signal conditioning and digital-to-analog conversion on at least one accessed sensor; the data reading module 1103 is configured to read the sensor type, the parameter and the conversion mode based on a spreadsheet. Specifically, at present, the problem of incompatibility after different sensor buses are transmitted exists in the field of water quality factor monitoring, in the process of charging and actual use, the cost is high, the addition of sensors or the removal of sensors which do not need to monitor factors are inconvenient, and therefore, the multi-interface board of the data acquisition module is provided, and the corresponding data acquisition module can be conveniently added or reduced according to actual needs. More specifically, the MSP430F149 single-chip microcomputer is utilized to make the interface of the data acquisition interface generalized to the plug and play sensor interface in accordance with the IEEE1451.2 protocol of the intelligent sensor interface module standard. The IEEE1451.2 standard specifies that the accessed sensor is first subjected to signal conditioning, digital-to-analog conversion by an a/D or D/a functional module, the sensor type, parameters, conversion modes and the like are read via a data table TEDS, the converted signal is used as an STIM interface input, data is acquired according to a format described by the TEDS, and then the data is accessed to a transmission network according to a certain network protocol by a connecting network adapter NCAP. Therefore, the interface compatibility is improved, and the aim of self-adapting to the sensor is fulfilled.
Preferably, the data preprocessing module 120 is configured to reject and optimize the concentration data of the water quality parameter. The data preprocessing module 120 comprises a missing data eliminating module 1201 and a data optimizing module 1202; the missing data eliminating module 1201 is configured to eliminate abnormal values of the concentration data of the water quality parameter based on a lattice Luo Beisi algorithm; the data optimization module 1202 is configured to optimize concentration data of the water quality parameter based on a kalman filter algorithm. Specifically, the accuracy of the acquired data is reduced due to the influence of factors such as noise and the like brought by the surrounding environment of the data acquisition unit and the transmission line in the data acquisition, so that the reliability and the accuracy of the signals can be effectively improved by preprocessing the original data.
More specifically, the data is culled by the lattice Luo Beisi algorithm, wherein the lattice Luo Beisi algorithm is well established in the prior art and will not be described in detail herein. Because the grid Luo Beisi algorithm has no requirement on the data volume, the method still has a good processing effect in the case of smaller data volume, namely the method can still keep the rationality of the data after eliminating the error value, and is easier to realize by utilizing computer programming. Further, the data is optimized by a Kalman filtering algorithm. Since the kalman filter algorithm is already very stated in the prior art, it is not described in detail here.
Preferably, the data transmission module 130 is configured to transmit the concentration data of the pretreated water quality parameter to a water quality monitoring module, a water quality prediction module and a water quality evaluation module based on the LORA wireless communication network. In particular, although short-distance wireless technologies such as zigbee and WiFi have certain advantages in terms of ad hoc networks, the problem of small coverage area is caused by limited transmission distance, and the latter is limited by high cost of operators, which is not beneficial to large-scale use and deployment. Therefore, the long-distance transmission and the subsequent low maintenance cost can be realized by adopting the LoRa wireless communication network.
Preferably, the water quality prediction module 150 is configured to predict a trend of variation of the received pretreated water quality parameter based on its concentration data. Specifically, the water quality prediction module 150 is configured to predict a trend of a change in concentration of a water quality parameter based on an LM-BP neural network prediction model. The water quality prediction module 150 comprises a prediction model construction module 1501, a prediction model initialization module 1502 and a prediction model training module 1503; the prediction model construction module 1501 is used for constructing an LM-BP neural network prediction model; the prediction model initialization module 1502 is configured to initialize the LM-BP neural network prediction model; the prediction model training module 1503 is used for training the LM-BP neural network prediction model. The concentration value of each water quality index parameter can be predicted by the LM-BP neural network prediction model, and the concentration change trend of the water quality parameter in a future period can be known by prediction.
More specifically, an m×l×n three-layer neural BP network with a single hidden layer is constructed, i.e., the input layer has m nodes, the hidden layer has l nodes, and the output layer has n nodes. Before training the data set using the BP neural network, a model needs to be initialized, including determination of network topology and super parameters, arrangement of the data set and normalization of the data. The number of nodes of an input layer is set to be 3, the number of hidden layers is determined to be 1, a logistic function is selected as an excitation function, a network weight threshold is initialized, the initialization value is between intervals (0 and 1), the maximum iteration number is set to be 1000, and the weight and the threshold are adjusted by a base Levenberg-Marquardt (LM) algorithm, so that a training function is selected as a training function.
Because the variables of the input and output of the neural network reflect different parameters, the units of the data values are different, the selected indexes do not have unified dimension and dimension units, the range span of various data is relatively large, some data ranges can be particularly large, and some data ranges can be particularly small. Some situations of large errors may occur during the network training, which results in slow convergence of the neural network and long training time. In order to improve the training efficiency and the prediction precision of the neural network, each parameter index is not influenced by the unit dimension, the prediction function of the BP artificial neural network is fully exerted, and the original data needs to be normalized before training. The normalization formula is:wherein x is i ′ And x i For the ith vector element before and after normalization, x max And x min Is the maximum and minimum of the corresponding vector.
Preferably, the training of the water quality prediction model comprises: step 1: initializing a network. And determining the input layer number, the hidden layer number and the output layer number. The network training stage comprises 72 groups of samples, each group of samples comprises 3 input nodes and 1 output node, the water quality prediction model can be regarded as a mapping from 3 independent variables to 1 dependent variable, the number of hidden layer nodes is l, the value of l is between 3 and 12, and the connection weight of each layer is initialized and assigned.
Step 2: implicit layer output computation. The input of the i-th node of the input layer in the P-th sample (p=1, 2 … …) is recorded asThe connection weight of the input layer and the hidden layer is omega ij J=1, 2 … … l; the threshold value of the j-th node of the hidden layer is a j J=1, 2 … … l; the excitation function of the hidden layer is f (logistic function), and the hidden layer output is set to H p The output of the j-th node of the hidden layer is:
step 3: and outputting layer result calculation. The input of the output layer being the output of the hidden layer, i.el; the connection weight of the hidden layer and the output layer is omega jk K=1,; the threshold value of the kth node of the output layer is b k K=1; the excitation function of the hidden layer is f, and the output result of the kth node in the P-th sample of the neural network is +.>
Step 4: the error is calculated.For the actual output of the kth neuron of the output layer in the P-th sample (p=1, 2 … … 72), the +.>Representing the corresponding desired output, the error signal in p samples +.>Can be expressed as:
the purpose of the continuous updating of the network weights is to base the error signal e k The target function of the BP neural network is minimum, namely the difference between the predicted value and the actual measured value of the BP neural network is minimum, and the target function can select a mean square error function. Error function E of the p-th sample p The method comprises the following steps:
the total error of the system is->
Step 5: and updating the weight. Weights and thresholds in the neural network are adjusted based on the L-M algorithm.
Step 6: judging whether iteration of the algorithm is finished, namely judging whether the error reaches a preset value of an expected error epsilon, and stopping training if E is less than or equal to epsilon; if not, re-entering the step 2 to carry out the cyclic operation until the error reaches the standard of the expected error.
That is, based on the trained LM-BP neural network prediction model, the prediction data corresponding to the obtained concentration data of the pretreated water quality parameters can be directly generated after the concentration data is input.
Preferably, the water quality evaluation module is used for evaluating the pollution degree of the pretreated water quality parameters based on the received concentration data of the pretreated water quality parameters. The water quality evaluation module is also used for evaluating the water quality pollution degree based on the comprehensive pollution index formula; the comprehensive pollution index formula isP i ＝C i /S i The method comprises the steps of carrying out a first treatment on the surface of the Wherein n represents a water quality parameterNumber of target items, P i Pollution index C representing the i-th water quality parameter index i Mean value of concentration of the index of the ith water quality parameter is represented by S i And (5) representing the water quality standard value of the i-th water quality parameter index.
Preferably, the water quality monitoring module 140 is configured to display the concentration data of the received pretreated water quality parameter, the predicted data generated by the water quality prediction module, and the evaluation data generated by the water quality evaluation module. The water quality monitoring module comprises a data management module 1401 and an alarm module 1402; the data management module is used for displaying and storing the received data in the form of a chart; the alarm module is used for alarming when the water quality acquisition equipment fails; and/or alarming when the detected water quality parameter index exceeds the standard.
The data management module is used for enabling the remote monitoring center server to convert current data into a chart form to be displayed on the human-computer interaction interface after receiving the data, and storing the data charts so as to facilitate inquiry of the historical data charts. Therefore, the human-computer interaction interface needs to be updated in real time, the human-computer interaction interface is updated according to the change of the zone bit, and when the zone bit of the displayed page changes, the page is updated; when the flag bit of the page data changes, the data displayed on the interface is updated; when the flag bit of the interface status bar changes, the display of the status bar is updated.
The alarm module is used for alarming equipment applied in the water quality monitoring system, the remote center server can monitor information such as current and voltage of the equipment in real time, and when the information is different from a reference value set by a worker in the system greatly or the server cannot receive the information, the server can send out alarm information to identify which equipment is likely to be failed.
The alarm module is used for alarming when the water quality parameter index exceeds the standard, specifically, the monitored water quality parameter is compared with the upper parameter limit set by the manager, and the alarm is given when the monitored data exceeds the upper limit. When alarming, the interface pops up an alarm window to prompt the staff, and the staff can know which monitoring station has which water quality parameter exceeding standard.
In the embodiment, the LM-BP neural network prediction model is adopted to predict the change trend of the concentration of the water quality parameter, so that the accuracy of predicting each index in the water quality monitoring process can be effectively improved, and the data guarantee can be provided for the subsequent water quality treatment by predicting the development trend of each index.
Example 2
As shown in fig. 2, a flow chart of a water quality monitoring method is shown.
As an example, the method comprises:
s210: concentration data of water quality parameters are collected.
S220: preprocessing the concentration data of the water quality parameters based on a lattice Luo Beisi algorithm and a Kalman filtering algorithm.
S230: and transmitting the concentration data of the pretreated water quality parameters based on a LORA wireless communication network.
S240: and predicting the change trend of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters.
S250: and evaluating the pollution degree of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters.
S260: and displaying the concentration data, the water quality prediction data and the water quality evaluation data of the pretreated water quality parameters in real time.
Example 3
The embodiment of the invention also provides a storage medium, wherein the storage medium is stored with a water quality monitoring method, and the water quality monitoring program realizes the steps of the water quality monitoring method when being executed by a processor. Because the storage medium adopts all the technical schemes of all the embodiments, the storage medium has at least all the beneficial effects brought by the technical schemes of the embodiments, and the description is omitted here.
Example 4
Referring to fig. 3, an embodiment of the present invention further provides an electronic device, including: a memory and a processor; at least one program instruction is stored in the memory; the processor implements the water quality monitoring method provided in embodiment 2 by loading and executing the at least one program instruction.
The memory 502 and the processor 501 are connected by a bus, which may include any number of interconnected buses and bridges, which connect together the various circuits of the one or more processors 501 and the memory 502. The bus may also connect various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art, and therefore, will not be described any further herein. The bus interface provides an interface between the bus and the transceiver. The transceiver may be one element or may be a plurality of elements, such as a plurality of receivers and transmitters, providing a means for communicating with various other apparatus over a transmission medium. The data processed by the processor 501 is transmitted over a wireless medium via an antenna, which further receives the data and transmits the data to the processor 501.
The processor 501 is responsible for managing the bus and general processing and may also provide various functions including timing, peripheral interfaces, voltage regulation, power management, and other control functions. And memory 502 may be used to store data used by processor 501 in performing operations.
The foregoing is merely an embodiment of the present invention, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application day or before the priority date of the present invention, and can know all the prior art in the field, and have the capability of applying the conventional experimental means before the date, so that a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
The foregoing is merely an embodiment of the present invention, and a specific structure and characteristics of common knowledge in the art, which are well known in the scheme, are not described herein, so that a person of ordinary skill in the art knows all the prior art in the application day or before the priority date of the present invention, and can know all the prior art in the field, and have the capability of applying the conventional experimental means before the date, so that a person of ordinary skill in the art can complete and implement the present embodiment in combination with his own capability in the light of the present application, and some typical known structures or known methods should not be an obstacle for a person of ordinary skill in the art to implement the present application. It should be noted that modifications and improvements can be made by those skilled in the art without departing from the structure of the present invention, and these should also be considered as the scope of the present invention, which does not affect the effect of the implementation of the present invention and the utility of the patent. The protection scope of the present application shall be subject to the content of the claims, and the description of the specific embodiments and the like in the specification can be used for explaining the content of the claims.
Claims (10)
1. The system is characterized by comprising a data acquisition module, a data preprocessing module, a data transmission module, a water quality monitoring module, a water quality prediction module and a water quality evaluation module;
the data acquisition module is used for acquiring concentration data of water quality parameters;
the data preprocessing module is used for eliminating and optimizing the concentration data of the water quality parameters by the missing errors;
the data transmission module is used for transmitting the concentration data of the pretreated water quality parameters to the water quality monitoring module, the water quality prediction module and the water quality evaluation module based on the LORA wireless communication network;
the water quality prediction module is used for predicting the change trend of the water quality parameters based on the received concentration data of the pretreated water quality parameters;
the water quality evaluation module is used for evaluating the pollution degree of the pretreated water quality parameters based on the received concentration data of the pretreated water quality parameters;
the water quality monitoring module is used for displaying the received concentration data of the pretreated water quality parameters, the predicted data generated by the water quality predicting module and the evaluation data generated by the water quality evaluating module.
2. The water quality monitoring system of claim 1, wherein the concentration data of the water quality parameter comprises one or a combination of chemical oxygen demand concentration, ammonia nitrogen concentration, total nitrogen concentration, and total phosphorus concentration.
3. The water quality monitoring system of claim 1, wherein the data acquisition module comprises a sensor module, an a/D function module, a data reading module;
at least one sensor is integrated in the sensor module and used for acquiring water quality parameter concentration data corresponding to the at least one sensor;
the A/D functional module is used for carrying out signal conditioning and digital-to-analog conversion on at least one accessed sensor;
the data reading module is used for reading the type, the parameters and the conversion mode of the sensor based on the electronic data table.
4. The water quality monitoring system of claim 1, wherein the data reading module is further configured to input the converted signal as an interface and perform the data acquisition in a format described in the electronic data sheet.
5. The water quality monitoring system of claim 1, wherein the data preprocessing module comprises a missing data rejection module and a data optimization module;
the missing data eliminating module is used for eliminating abnormal values of the concentration data of the water quality parameters based on a grid Luo Beisi algorithm;
the data optimization module is used for optimizing the concentration data of the water quality parameters based on a Kalman filtering algorithm.
6. The water quality monitoring system of claim 1, wherein the water quality prediction module is configured to predict a trend of a concentration of the water quality parameter based on an LM-BP neural network prediction model.
7. The water quality monitoring system of claim 6, wherein the water quality prediction module comprises a prediction model construction module, a prediction model initialization module, a prediction model training module;
the prediction model construction module is used for constructing an LM-BP neural network prediction model;
the prediction model initialization module is used for initializing the LM-BP neural network prediction model;
the prediction model training module is used for training an LM-BP neural network prediction model.
8. The water quality monitoring system of claim 1, wherein the water quality evaluation module is further configured to evaluate a degree of water quality pollution based on a comprehensive pollution index formula;
the comprehensive pollution index formula is
P i ＝C i /S i ；
Wherein n represents the number of items of the water quality parameter index, P i Pollution index C representing the i-th water quality parameter index i Mean value of concentration of the index of the ith water quality parameter is represented by S i And (5) representing the water quality standard value of the i-th water quality parameter index.
9. The water quality monitoring system of claim 1, wherein the water quality monitoring module comprises a data management module and an alarm module;
the data management module is used for displaying and storing the received data in the form of a chart;
the alarm module is used for alarming when the water quality acquisition equipment fails; and/or
And alarming when the detected water quality parameter index exceeds the standard.
10. A method of monitoring water quality, the method comprising:
collecting concentration data of water quality parameters;
preprocessing the concentration data of the water quality parameters based on a lattice Luo Beisi algorithm and a Kalman filtering algorithm;
transmitting the concentration data of the pretreated water quality parameters based on a LORA wireless communication network;
predicting the change trend of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters;
evaluating the pollution degree of the pretreated water quality parameters based on the concentration data of the pretreated water quality parameters;
and displaying the concentration data, the water quality prediction data and the water quality evaluation data of the pretreated water quality parameters in real time.
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