CN111414694A - A sewage monitoring system based on FCM and BP algorithm and its establishment method - Google Patents

Abstract

The invention discloses a sewage monitoring system based on FCM and BP algorithm and a building method thereof, wherein the system comprises a data acquisition module, a water quality monitoring management platform and a PC control end, water quality data information is acquired by the data acquisition module, the water quality monitoring management platform processes data and is displayed by the PC control end, and meanwhile, the PC control end sends an acquisition command to the data acquisition module to form a closed cycle to monitor sewage on line in real time; in the data processing process, the water pollution source is analyzed through an FCM and BP neural network algorithm, and the obtained sample is high in quality, high in convergence speed and low in sample dependence; meanwhile, the position and the influence range of the pollution source can be quickly and accurately determined through a particle swarm optimization algorithm, and the method has the advantages of high processing precision and high speed; meanwhile, the sewage monitoring system has the advantages of reducing the pollution of sewage to the environment and improving the resource utilization efficiency when in use.

Description

A sewage monitoring system based on FCM and BP algorithm and its establishment method

technical field

The invention relates to the technical field of sewage treatment monitoring, in particular to a sewage monitoring system based on FCM and BP algorithms and a method for establishing the same.

Background technique

With the development of my country's economy and technology, there are many factories, and the discharge of sewage has caused a serious impact on the environment. At present, the discharge of sewage in my country shows that there are many types of sewage, inconsistent discharge locations, extensive management of sewage discharge supervision and poor economic and technological progress. Reasonable questions are mainly manifested in:

(1) The detection technology is still simple mathematical calculation or manual review, which cannot meet the judgment and management of the real environment by the environmental supervision department;

(2) The realization of pollution source data analysis technology is based on simple mathematical calculation or manual review, and there is still a lack of in-depth research on scientific and reliable automatic analysis and diagnosis methods;

(3) In the past sewage monitoring, the form of manual monitoring and recording is usually adopted, and the monitoring has large randomness and poor real-time performance. The existence of these problems brings difficulties to the accuracy of sewage detection;

With the development of automatic monitoring technology, the system of using wired technology to realize sewage monitoring has been put into use on a large scale in developed countries such as the United States and Japan, but there are still disadvantages such as troublesome wiring and high cost; in my country, sewage monitoring methods have been changed from The traditional sampling at fixed time and fixed point and laboratory offline analysis have developed into online monitoring. The offline analysis and measurement cycle of laboratory is long, the operation is complicated, the experimental requirements are strict, and it cannot meet the needs of real-time monitoring. Moreover, the traditional online sewage monitoring system has a complicated pretreatment process. , The detection equipment is bulky, the detection period is long, and the continuous automatic detection cannot be realized;

Therefore, in the sewage online monitoring and management technology, there is an urgent need for an online automatic monitoring system with high data collection accuracy, accurate data analysis and diagnosis, as well as a small monitoring system and high cost performance.

SUMMARY OF THE INVENTION

In view of the above existing problems, the present invention aims to provide a sewage monitoring system based on FCM and BP algorithm and its establishment method. The swarm optimization algorithm can quickly determine the intrusion position, start time and intrusion speed of water pollutants. The built-in processor based on the pollution source analysis module calculated by the FCM and BP hybrid algorithm is designed, which has the advantages of high processing accuracy and fast speed; Through the sewage monitoring system, the rapid response to water pollution facilitates timely decision-making and response, and has the characteristics of reducing sewage pollution to the environment and improving resource utilization efficiency.

In order to achieve the above object, the technical scheme adopted in the present invention is as follows:

A method for establishing a sewage monitoring system based on FCM and BP algorithm, the method comprises the following steps:

Step 1: Use the FCM and BP hybrid algorithm to analyze the collected water pollution data, and use the particle swarm optimization algorithm to determine the intrusion position, start time and intrusion speed of water pollutants, and design the pollution source analysis based on the FCM and BP hybrid algorithm. The built-in processor of the module is programmed using the MATLAB programming tool;

Step 2: After designing the built-in processor of the pollution source analysis module based on the FCM and BP hybrid algorithm operation, use the object-oriented design idea, based on the C/S structure system and the MVC mode H-layer framework to build a water quality monitoring and management platform;

Step 3: On the basis of the water quality monitoring and management platform, use Nessler's reagent spectrophotometry to detect water quality parameters, and combine PLC, configuration software, and 4G mobile communication technology to build the hardware architecture of the Internet of Things-based water quality monitoring system;

Step 4: On the basis of the hardware architecture of the water quality monitoring system based on the Internet of Things, using the design ideas and implementation methods based on water quality sensors, microcontrollers and wireless modules, as well as the establishment of communication protocols between various parts, the Internet of Things for sewage monitoring can be realized. IoT-based sewage monitoring system with system information collection and transmission functions.

Further, the specific process of utilizing FCM and BP hybrid algorithm to analyze water pollution described in step 1 includes:

S1. Use the FCM (fuzzy clustering) algorithm to analyze the pollution source monitoring data, and use the membership function to determine the correlation characteristics between the data points, and divide and cluster the data:

S2. Use the BP neural network algorithm to correct the abnormal class values in the data clustering divided by S1, and obtain the processed data samples;

S3. Take the data processed by S2 as monitoring data, and substitute it into the particle swarm optimization algorithm, use the particle swarm optimization algorithm to establish a reverse tracking model of pollution sources, and compare the actual monitoring value and the simulated value of each monitoring point during the simulation optimization period. The sum of the squares of the differences is used as the objective function of the model, and then the particle swarm optimization algorithm is used as the optimization solution tool to quickly determine the location of pollution sources and the scope of influence. The function is:

where X is the optimal solution of the pollution source, y _i (t) is the simulated concentration of pollutants at the water quality monitoring point i at time t; y′ _i (t) is the measured concentration of pollutants at the water quality monitoring point i at time t; m is the water quality The number of monitoring points; t is the simulation duration.

Further, the process of using the FCM (fuzzy clustering) algorithm described in step S1 to analyze and divide and cluster the pollution source monitoring data includes:

(1) Raw data preprocessing: Data preprocessing includes imputing missing data, removing noise data, grouping by month, and observing the variation range of monthly data after grouping to determine whether it is necessary to Monthly anomaly detection process;

(2) After the data preprocessing is completed, use the FCM algorithm to cluster the preprocessed data, and use the months to be detected as the input source of the FCM algorithm for clustering. The specific process of clustering is as follows:

a. First set the number of categories C, 2≤C≤N, N is the total number of data samples, give the iteration stop threshold ε, initialize the prototype pattern P ^(b) of the cluster, and give the iteration counter b=0;

k如果

则有：b. Calculate the partition matrix U ^(b) : for

k if

Then there are:

k如果

则有：for

k if

Then there are:

且对j≠r,

And for j≠r,

c. Iterate for the above two cases, and obtain the prototype matrix P ^b+1 after iterative update is:

则计算结束同时输出分割后的矩阵U与聚类原型P；否则令b＝b+1，转步骤b再次进行迭代；d. Judge and output the segmented matrix U and the cluster prototype P: if

Then the calculation is finished and the divided matrix U and the cluster prototype P are output at the same time; otherwise, let b=b+1, and go to step b to iterate again;

where ||.|| is the appropriate matrix norm;

(3) After the data clustering is completed, analyze the clustering results, and divide the data into normal and abnormal values according to the characteristics of various types of data, and complete the abnormal detection of pollution source monitoring data.

Further, the specific process of modifying the abnormal class value in the data clustering of the BP neural network algorithm described in step S2 includes:

(1) Find out the abnormal class value in the data detected in step 1, and use the data sequence before the abnormal class value as the input sample of the BP neural network;

(2) Then the data sequence before the abnormal class value is substituted into the BP neural network algorithm, and the nonlinear fitting energy of the BP neural network algorithm is used to predict the data of the abnormal class value position;

(3) The abnormal value in the original data is replaced by the data predicted by the BP neural network algorithm, that is, the correction of the abnormal value is completed;

(4) When there are multiple abnormal data in the data set, use the BP neural network algorithm to correct one by one, that is, after the correction of the first abnormal value is completed, replace the abnormal value in the original data sequence, and use the new data The sequence is used as the input sample for the BP neural network algorithm to predict the next abnormal value to correct the second abnormal data.

Further, the specific process of using the particle swarm optimization algorithm to establish the pollution source reverse tracking model described in step S3 includes:

(1) First, use the particle swarm optimization algorithm to calculate the relationship between water conservancy information, pipe network attributes and the monitoring data corrected in step 2, analyze the change law of pollutant concentration in the pipe network with time and space, and design model parameters. Establish a hydraulic water quality model and ensure that the model can better match the actual hydraulic water quality in the pipeline network. The specific process of using the particle swarm optimization algorithm to analyze the changes of pollutant concentration in the pipeline network with time and space is as follows:

a. First, initialize a group of particles in the feasible solution space of the pollution source. The characteristics of each particle are represented by three indicators: position, velocity and fitness value. The particle position represents the potential solution information of a pollution source, and the particle velocity represents the variation range of the individual position. , the fitness value indicates the quality of the potential solution of the pollution source represented by the particle position, and the model objective function is used as the fitness function;

b. When the particle moves in the solution space, the individual position is updated by tracking the historical optimal solution position of the potential pollution source of a single particle and the historical optimal solution position of the potential pollution source in the group. Each time the particle updates its position, it means that the potential solution of the pollution source is updated once. The fitness value of the particle at this time is compared with the previous optimal fitness value of the individual and the optimal fitness value of the group, and the optimal solution position of the particle's potential pollution source and the optimal solution position of the group's potential pollution source are updated. Optimal solution:

First define X=(X ₁ ,X ₂ ,...,X _n ) ^T

X _i =(x _i1 ,x _i2 ,...,x _i(3m-2) ,x _i(3m-1) ,x _i(3m) ) ^T

V=(V ₁ ,V ₂ ,...,V _n ) ^T

v _i =(v _i1 ,v _i2 ,...,v _i(3m-2) ,v _i(3m-1) ,v _i(3m) ) ^T

Among them: X is a matrix composed of n particle positions in the population, representing a matrix containing n potential solutions of pollution sources; X _i is the position of the ith particle, that is, a potential solution of the pollution source, where x _i1 represents the node number of the pollution source, _xi2 represents the start time _of pollutants intrusion _{, xi3} represents _the intrusion speed of pollutants; The information of the mth pollution source; V is the matrix composed of the velocity of n particles in the population; vi is the velocity of the _ith particle; among them, v _i1 represents the change speed of the node number of the pollution source, and v _i2 represents the change speed of the time when the pollutant starts to invade , v _i3 represents the change rate of the intrusion rate of pollutants; v _i(3m-2) , v _i(3m-1) , v _i(3m) represent the information change of the mth pollution source when there are multiple pollution sources in the pipe network speed;

(2) According to the hydraulic water quality model, the simulation of the pollution sources in the pipeline network is constructed, and the pollution source reverse tracking model is established. The model objective function is embedded with the EPANET toolbox as a simulation engine, and the particle swarm optimization algorithm is used to solve the intrusion position, intrusion start time and intrusion speed of pollutants.

Further, the specific process of using the group optimization algorithm to solve the intrusion position, intrusion start time and intrusion speed of the pollutants described in step S3(2) includes:

a. Set the algorithm parameters

In order to prevent the blind search of particles, the potential solution X _i of the pollution source and the particle velocity v _i are limited to a certain space, namely [X _min , X _max ], [v _min , v _max ]; set the upper limit of the number of iterations N and the preset precision ε, when the number of iterations reaches the upper limit q or the fitness value f(x)<ε, the optimization calculation ends;

b. Initialization and initial extreme value of particle population

Randomly generate the particle population matrix X and velocity matrix V containing the potential solution information of multiple pollution sources, and use the model objective function as the fitness function to calculate the fitness value of each initial particle; find the potential pollution source of each particle according to the fitness value. the optimal solution P _i and the optimal solution P _g of all particles in the population;

c. Iterative optimization

和位置

如下：The particle updates its own speed and position through the individual extreme value position (the optimal solution of individual pollution sources) P _i and the group extreme value position (the optimal solution of the group pollution source) P _g . speed

and location

as follows:

In the formula, ω is the inertia weight, which reflects the ability of the particle to inherit the previous speed; c ₁ , c ₂ are the acceleration factors, which are non-negative constants; r ₁ , r ₂ are random numbers distributed in the [0,1] interval ; Update the optimal solution of particle potential pollution sources, the optimal solution of group potential pollution sources and the corresponding fitness value; iterate repeatedly until the number of iterations reaches the upper limit or the fitness value reaches the preset accuracy, the iteration ends and the optimal solution of the potential pollution sources in the group is output .

Further, the specific process of constructing the water quality monitoring and management platform described in step 2 is as follows:

S1. Use the object-oriented method to establish user management module, data acquisition module, data query module and other functional units respectively, and then use the object-oriented technology developed by the system to establish the model layer, view layer and control layer respectively, forming the software of the water quality monitoring management platform The control part; the model layer is realized by JavaBean technology, the view layer is realized by JSP technology, and the control layer is realized by Servlet technology;

S2. The WEB-based B/S structure connects the software control part of the built water quality monitoring and management platform with the Web server, so that the management data of the water quality monitoring and management platform is uploaded to the network control terminal through the Web server; wherein the B/S structure is Browser/server structure, the client only needs to use the browser to send a request to the web server, and the web server processes it, and then the processing result can be returned to the client;

S3. Design the model layer, view layer and control layer of the water quality monitoring management platform using the design technology based on the MVC model H layer framework, and obtain a water quality monitoring management platform that can monitor water quality online and in real time over time.

Further, the process of establishing the hardware architecture of the water quality monitoring system described in step 3 includes:

S1. Design the parameters of the water quality sensor according to the principle of detecting water quality parameters by Nessler reagent spectrophotometry to ensure that the water quality sensor can detect water quality information quickly and accurately;

S2. Select Siemens PLC as the control part of the controller to reduce the size of the testing equipment;

S3. Select the 4G wireless gateway as the data transmission module to shorten the information transmission cycle, thereby shortening the detection cycle;

S4. Establish an information server to realize remote transmission of data.

Further, the establishment process of the IoT-based sewage monitoring system described in step 4 includes:

S1. Select a sensor and a microcontroller, the sensors include a water quality sensor, a water level sensor and a flow sensor, wherein the water quality sensor selects DPS400A digital pH sensor, DLS400A digital residual chlorine sensor and DS18B20 digital temperature sensor, and the water level sensor selects the submerged liquid sensor Potentiometer and flow sensor use Hall flow sensor; select the matching microcontroller according to the model of the sensor, and use the microcontroller to control the water quality sensor to collect water quality data information;

S2. Select CZ80DTD analog wireless transmission device for wireless module;

S3. Select two 12V rechargeable batteries in series as the power supply unit of the sewage monitoring system;

S4. The voltage management module uses the LM2576 chip to reduce the 24V voltage to 12V, uses the LM2940 chip to reduce the 12V voltage to 5V, and uses the AMS11173.3 chip to reduce the 5V voltage to 3.3V to ensure stable output of the voltage required by each part of the system;

A sewage monitoring system based on FCM and BP algorithm, the sewage monitoring system includes a data acquisition module, a water quality monitoring management platform and a PC control terminal:

The data acquisition module includes a water quality sensor, a microcontroller and a wireless transmission unit, and the microcontroller controls the water quality sensor to collect water quality data information, and transmits it to the water quality monitoring and management platform through the wireless transmission unit;

The water quality monitoring and management platform includes a wastewater resource management module, a data editing module, a data query module, a dye source analysis module, a user management module and several communication protocol modules, and the control input end of the dye source analysis module receives the data information of the data acquisition module, It is assigned to the pollution source analysis module for analysis and processing, and the processing results are sent to the data editing module through the communication protocol module. After the data editing module processes the data, a data packet is formed, and then sent to the database of the data query module through the communication protocol module. storage and query;

The PC control terminal has built-in host computer software, and is connected to the water quality monitoring and management platform and the data acquisition module through the 4G mobile communication network. The pollution source analysis module analyzes the intrusion position, start time and intrusion speed of water pollutants. The results are displayed, and at the same time Query and call the data in the communication protocol module database as needed, and can also issue commands to the data acquisition module according to user needs to control the data acquisition module to collect data;

The beneficial effects of the present invention are: the present invention discloses a sewage monitoring system based on FCM and BP algorithm and its establishment method. Compared with the prior art, the improvements of the present invention are:

(1) The present invention establishes a sewage monitoring system based on the FCM and BP hybrid algorithm. On the basis of the previous fuzzy clustering data mining method, a method for analyzing water pollution sources is proposed. The water pollution data is analyzed, and the particle swarm optimization algorithm is used to determine the intrusion position, start time and intrusion speed of water pollutants. The correction effect of abnormal class value solves the problems that the traditional BP neural network algorithm is prone to local minimum points, slow convergence speed and strong sample dependence;

(2) The present invention proposes an online platform and mobile terminal display, which is convenient for the supervision and inspection of technicians and enterprise leaders, and even environmental monitoring personnel, solves the problem of slow feedback of existing sewage monitoring information, and has higher processing accuracy than traditional pollution source data detection technology. High and fast, providing technical support for scientific and information-based environmental supervision.

Description of drawings

FIG. 1 is a flowchart of a method for establishing a sewage monitoring system based on the FCM and BP algorithms of the present invention.

Fig. 2 is the flow chart that the BP algorithm of the present invention corrects the abnormal class value;

FIG. 3 is a flow chart of establishing a pollution source reverse tracking model by the particle swarm optimization algorithm of the present invention.

FIG. 4 is a flowchart of the establishment of the hardware architecture of the water quality monitoring system of the present invention.

FIG. 5 is a system block diagram of the sewage monitoring system based on FCM and BP algorithm of the present invention.

Detailed ways

In order to enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention are further described below with reference to the accompanying drawings and embodiments.

1-5, a sewage monitoring system based on FCM and BP algorithm and its establishment method, wherein the establishment method includes the following steps:

Step 1: Use the FCM and BP hybrid algorithm to analyze the collected water pollution data, and use the particle swarm optimization algorithm to determine the intrusion position, start time and intrusion speed of water pollutants, and design the pollution source based on the FCM and BP hybrid algorithm. The built-in processor of the parsing module is programmed using the MATLAB programming tool;

Step 2: After designing the built-in processor of the pollution source analysis module based on the FCM and BP hybrid algorithm operation, use the object-oriented design idea, based on the C/S structure system and the MVC mode H-layer framework to build a water quality monitoring and management platform;

Step 3: On the basis of the water quality monitoring and management platform, use Nessler's reagent spectrophotometry to detect water quality parameters, and combine PLC, configuration software, and 4G mobile communication technology to build the hardware architecture of the Internet of Things-based water quality monitoring system;

Step 4: On the basis of the hardware architecture of the water quality monitoring system based on the Internet of Things, using the design ideas and implementation methods based on water quality sensors, microcontrollers and wireless modules, as well as the establishment of communication protocols between various parts, the Internet of Things for sewage monitoring can be realized. IoT-based sewage monitoring system with system information collection and transmission functions.

Preferably, the specific process of analyzing the collected water pollution data using the FCM and BP hybrid algorithm described in step 1 includes:

S1. Use the FCM (fuzzy clustering) algorithm to analyze the pollution source monitoring data: that is, use the membership degree to determine the correlation characteristics between the data points to divide and cluster the data. The specific process includes:

(1) Raw data preprocessing: Data preprocessing includes imputing missing data, removing noise data, grouping by month, and observing the variation range of monthly data after grouping to determine whether it is necessary to Monthly anomaly detection process;

(2) After the data preprocessing is completed, use the FCM algorithm to cluster the preprocessed data, and use the months to be detected as the input source of the FCM algorithm for clustering. The specific process of clustering is as follows:

a. First set the number of categories C, 2≤C≤N, N is the total number of data samples, give the iteration stop threshold ε, initialize the prototype pattern P ^(b) of the cluster, and give the iteration counter b=0;

k如果

则有：b. Calculate the partition matrix U ^(b) : for

k if

Then there are:

k如果

则有：for

k if

Then there are:

且对j≠r,

And for j≠r,

c. Iterate for the above two cases, and obtain the prototype matrix P ^b+1 after iterative update is:

则计算结束同时输出分割后的矩阵U与聚类原型P；否则令b＝b+1，转步骤b再次进行迭代；d. Judge and output the segmented matrix U and the cluster prototype P: if

Then the calculation is finished and the divided matrix U and the cluster prototype P are output at the same time; otherwise, let b=b+1, and go to step b to iterate again;

where ||.|| is the appropriate matrix norm;

(3) After the data clustering is completed, analyze the clustering results, and divide the data into normal class values and abnormal class values according to the characteristics of various types of data, and complete the abnormal detection of pollution source monitoring data;

Among them, in the process of analyzing the pollution source monitoring data by using the FCM (fuzzy clustering) algorithm, the FCM (fuzzy clustering) algorithm applies the concept of fuzzy membership, optimizes the objective function, calculates each sample data and the cluster center The membership degree is used to determine which category the data belongs to, and the automatic classification of the data is realized. The process is: reduce the clustering to a nonlinear programming problem with constraints, and then convert it into an optimization problem using classical The mathematical nonlinear programming method obtains the optimal solution, and finally completes the fuzzy segmentation and classification of the data. The specific process is to first determine the target membership function, and then realize the clustering process;

The membership function represents the degree of membership of all the data object elements in question to the fuzzy set, represented by u(x), and its value range is [0, 1], where the value of u(x) represents the element The size of the degree to which x belongs to the fuzzy set s; assuming that the data set is defined as X={x ₁ , x ₂ ,...x _n }, and it is clustered into class c, and the cluster center is v, the goal of clustering is The sum of the distances from all the data to the cluster center to which it belongs is minimized; according to the objective function proposed by Bezdek's Dunn method and the weighted error square to generalize and improve the method, the expression based on the objective membership function of the clustering algorithm is obtained. The formula is:

where n and c are the number of data and the number of cluster centers respectively, and the optimal value of the target membership function is obtained by the following iterative formula:

where u _ij is the fuzzy membership degree between the data x _j and the cluster center and v _j , d _ij is the distance d _ij between the data x _j and the cluster center v _j of the i-th class, representing the sample point and the cluster similarity of centers;

条件下，使得min{J_FCM}，利用拉格朗日方法能够求得该解，并进一步得到聚类中心解；In the objective function expression, m∈[1,+∞] is the fuzzy weighting index, also known as the smoothing parameter, which is used to adjust the fuzzy degree of fuzzy clustering. The smaller the degree is, the value is used to adjust the effectiveness of the FCM division; the value of J _FCM is used to represent the compactness within a class under a certain difference definition, the smaller the J _FCM , the more compact the clustering; in order to obtain the FCM The optimal solution of the membership function of the clustering target, using the principle of clustering, under the constraints of extreme values

Under the condition of min{J _FCM }, the solution can be obtained by using the Lagrangian method, and the cluster center solution can be obtained further;

Therefore, the clustering process of the FCM algorithm is to first select the C class, select C initial cluster centers, assign each pattern to a certain class in the C class, and then continuously calculate the cluster centers, and then follow the minimum distance principle. And adjust the class of each pattern until the sum of the squares of the distances between all patterns and the center of the class to which they belong is the smallest.

S2. Use the BP (neural network) algorithm to correct the abnormal class values in the data clustering (pollution source data) divided by S1, and obtain the processed data samples. The specific process includes:

(1) Find out the abnormal class value in the data detected in step 1, and use the data sequence before the abnormal class value as the input sample of the BP neural network;

(2) Then the data sequence before the abnormal class value is substituted into the BP neural network algorithm, and the nonlinear fitting energy of the BP neural network algorithm is used to predict the data of the abnormal class value position;

(3) The abnormal value in the original data is replaced by the data predicted by the BP neural network algorithm, that is, the correction of the abnormal value is completed;

(4) When there are multiple abnormal data in the data set, use the BP neural network algorithm to correct one by one, that is, after the correction of the first abnormal value is completed, replace the abnormal value in the original data sequence, and use the new data The sequence is used as the input sample for the BP neural network algorithm to predict the next abnormal value to correct the second abnormal data;

Wherein, in the process of correcting the abnormal class values in the data clustering of the BP neural network algorithm, the abnormal class value correction algorithm based on the BP neural network algorithm utilizes the powerful nonlinear fitting ability of the BP neural network, through The training of the network realizes the correction of abnormal data; the specific process of abnormal class value correction is to use the data sequence before the abnormal class value as the input sample of the BP neural network after detecting the abnormal data, and use the BP neural network algorithm. The linear fitting can be used to predict the position of the abnormal class value, and replace the original abnormal class value with the predicted data to complete the correction of the abnormal class value; and, not only a single abnormal data will appear in the monitoring data of environmental pollution sources, There will also be multiple abnormal data. Correcting the abnormal data based on the BP neural network algorithm can realize the correction of multiple abnormal data: that is, after the correction of the first abnormal class value is completed, the abnormal data in the original data sequence is replaced. Class value, use the new data sequence as the input sample for the BP neural network algorithm to predict the next abnormal class value to correct the second abnormal data; according to this method, the correction of the abnormal class value of the monitoring data is completed in turn, and the specific correction The process is shown in Figure 2.

S3. Take the data processed by S2 as monitoring data, and substitute it into the particle swarm optimization algorithm, use the particle swarm optimization algorithm to establish a reverse tracking model of pollution sources, and compare the actual monitoring value and the simulated value of each monitoring point during the simulation optimization period. The sum of the squares of the differences is used as the objective function of the model, and then the particle swarm optimization algorithm is used as the optimization solution tool to quickly determine the location of pollution sources and the scope of influence. The function is:

where X is the optimal solution of the pollution source, y _i (t) is the simulated concentration of pollutants at the water quality monitoring point i at time t; y′ _i (t) is the measured concentration of pollutants at the water quality monitoring point i at time t; m is the water quality The number of monitoring points; t is the simulation duration;

Preferably, the specific process of using the particle swarm optimization algorithm to establish the reverse tracking model of the pollution source includes:

(1) First, use the particle swarm optimization algorithm to calculate the relationship between water conservancy information, pipe network attributes and the monitoring data corrected in step 2, analyze the change law of pollutant concentration in the pipe network with time and space, and design model parameters. Establish a hydraulic water quality model and ensure that the model can better match the actual hydraulic water quality in the pipeline network. The specific process of using the particle swarm optimization algorithm to analyze the changes of pollutant concentration in the pipeline network with time and space is as follows:

a. First, initialize a group of particles in the feasible solution space of the pollution source. The characteristics of each particle are represented by three indicators: position, velocity and fitness value. The particle position represents the potential solution information of a pollution source, and the particle velocity represents the variation range of the individual position. , the fitness value indicates the quality of the potential solution of the pollution source represented by the particle position, and the model objective function is used as the fitness function;

b. When the particle moves in the solution space, the individual position is updated by tracking the historical optimal solution position of the potential pollution source of a single particle and the historical optimal solution position of the potential pollution source in the group. Each time the particle updates its position, it means that the potential solution of the pollution source is updated once. The fitness value of the particle at this time is compared with the previous optimal fitness value of the individual and the optimal fitness value of the group, and the optimal solution position of the particle's potential pollution source and the optimal solution position of the group's potential pollution source are updated. Optimal solution:

First define X=(X ₁ ,X ₂ ,...,X _n ) ^T

X _i =(x _i1 ,x _i2 ,...,x _i(3m-2) ,x _i(3m-1) ,x _i(3m) ) ^T

V=(V ₁ ,V ₂ ,...,V _n ) ^T

v _i =(v _i1 ,v _i2 ,...,v _i(3m-2) ,v _i(3m-1) ,v _i(3m) ) ^T

Among them: X is a matrix composed of n particle positions in the population, representing a matrix containing n potential solutions of pollution sources; X _i is the position of the ith particle, that is, a potential solution of the pollution source, where x _i1 represents the node number of the pollution source, _xi2 represents the start time _of pollutants intrusion _{, xi3} represents _the intrusion speed of pollutants; The information of the mth pollution source; V is the matrix composed of the velocity of n particles in the population; vi is the velocity of the _ith particle; among them, v _i1 represents the change speed of the node number of the pollution source, and v _i2 represents the change speed of the time when the pollutant starts to invade , v _i3 represents the change rate of the intrusion rate of pollutants; v _i(3m-2) , v _i(3m-1) , v _i(3m) represent the information change of the mth pollution source when there are multiple pollution sources in the pipe network speed;

(2) According to the hydraulic water quality model, the simulation of the pollution sources in the pipeline network is constructed, and the pollution source reverse tracking model is established. The objective function of the model, the embedded EPANET toolbox is used as a simulation engine, and the particle swarm optimization algorithm is used to solve the intrusion position, intrusion start time and intrusion speed of pollutants. The specific process of intrusion speed includes:

a. Set the algorithm parameters

In order to prevent the blind search of particles, the potential solution X _i of the pollution source and the particle velocity v _i are limited to a certain space, namely [X _min , X _max ], [v _min , v _max ]; set the upper limit of the number of iterations N and the preset precision ε, when the number of iterations reaches the upper limit q or the fitness value f(x)<ε, the optimization calculation ends;

b. Initialization and initial extreme value of particle population

Randomly generate the particle population matrix X and velocity matrix V containing the potential solution information of multiple pollution sources, and use the model objective function as the fitness function to calculate the fitness value of each initial particle; find the potential pollution source of each particle according to the fitness value. the optimal solution P _i and the optimal solution P _g of all particles in the population;

c. Iterative optimization

和位置

如下：The particle updates its own speed and position through the individual extreme value position (the optimal solution of individual pollution sources) P _i and the group extreme value position (the optimal solution of the group pollution source) P _g . speed

and location

as follows:

In the formula, ω is the inertia weight, which reflects the ability of the particle to inherit the previous speed; c ₁ , c ₂ are the acceleration factors, which are non-negative constants; r ₁ , r ₂ are random numbers distributed in the [0,1] interval ; Update the optimal solution of particle potential pollution sources, the optimal solution of group potential pollution sources and the corresponding fitness value; iterate repeatedly until the number of iterations reaches the upper limit or the fitness value reaches the preset accuracy, the iteration ends and the optimal solution of the potential pollution sources in the group is output (The specific process is shown in Figure 3).

The specific process of constructing the water quality monitoring and management platform described in step 2 is as follows:

S1. Use the object-oriented method to establish user management module, data acquisition module, data query module and other functional units respectively, and then use the object-oriented technology developed by the system to establish the model layer, view layer and control layer respectively, forming the software of the water quality monitoring management platform The control part; the model layer is realized by JavaBean technology, the view layer is realized by JSP technology, and the control layer is realized by Servlet technology;

S2. WEB-based B/S structure will connect the software control part of the built water quality monitoring and management platform with the Web server, so that the management data of the water quality monitoring and management platform is uploaded to the network control terminal through the Web server; wherein the B/S structure ( browser/server), that is, the browser/server structure 1. The client only needs to use the browser to send a request to the Web server, which will be processed by the Web server, and the processing result will be returned to the client;

S3. Design the model layer, view layer and control layer of the water quality monitoring management platform using the design technology based on the MVC model H layer framework, and obtain a water quality monitoring management platform that can monitor water quality online and in real time over time.

Among them: the MVC (model view controller) programming concept H-layer framework technology divides the software into 3-layer structure, namely model layer, view layer and control layer;

(1) Model layer: The model layer mainly deals with real business operations, handles requests from the control layer accordingly, reads database data, and completes business operations;

(2) View layer: The view layer is the page where the user interacts with the system. It presents a view to the user, but does not contain business logic. It processes the user's request, transmits the form information filled in by the user, and invokes the corresponding business through the control layer. , and display the results to the user;

(3) Control layer: The control layer controls the entire business process, makes the view layer and the model layer work together, handles user requests, calls the business return method of the model layer, and displays the results through the view layer.

The process of establishing the hardware architecture of the Internet of Things-based water quality monitoring system described in step 3 includes:

S1. Design the parameters of the water quality sensor according to the principle of detecting water quality parameters by Nessler reagent spectrophotometry to ensure that the water quality sensor can detect water quality information quickly and accurately;

S2. Select Siemens PLC as the control part of the controller to reduce the size of the testing equipment;

S3. Select the 4G wireless gateway as the data transmission module to shorten the information transmission cycle, thereby shortening the detection cycle;

S4. Establish an information server to realize remote transmission of data.

Wherein, the establishment process of the hardware architecture of the water quality monitoring system based on the Internet of Things is the construction process of the water quality monitoring system of the Internet of Things, and the water quality monitoring system of the Internet of Things includes a mobile terminal, a server, a wireless gateway, a controller and a water quality sensor; The overall structure design process of the hardware architecture of the water quality monitoring system based on the Internet of Things is shown in Figure 4. After the water quality sensor detects the water quality information, it sends it to the controller. The controller connects to the wireless gateway through communication, and then uploads the water quality information to the server. Stored in the database, the display terminal accesses the server through the Internet, and then obtains the water quality and water quantity parameter information in the database, and then judges whether the measured water quality meets the standard according to the urban sewage recycling standard, and finally the specific parameter information and judgment results. Display on the terminal, so as to realize remote monitoring of water quality;

The hardware architecture of the IoT-based water quality monitoring system established in step 3 includes:

(1) Sensing layer: It consists of a control module, a peristaltic pump, a multi-way solenoid valve, a flow meter, a detection device and a stepping motor. The stepping motor controls the peristaltic pump to pump and discharge liquid, and the controller collects the sensors of the sensing layer. The received water quality parameters are sent to the main controller, and the main controller operates the water pump and solenoid valve of the execution layer to realize automatic control of water inlet and outlet according to the water quality parameters and the water level information of the detection pool; The secondary utilization water is uploaded to the server in real time, and its function is to cooperate with the water quality testing device to extract testing samples and testing agents, and to discharge the waste liquid after testing;

(2) The transmission layer is connected by a mobile communication network and is composed of WIFI and 4G networks and DTU transmission modules. The main controller of this layer uploads the water quality and water quantity information collected by the sensing layer to the server and stores it in the database by connecting the WIFI wireless module. , the display terminal then accesses the data on the server through the Internet, and displays the real-time water quality parameters;

(3) The application layer is composed of the data center and the remote monitoring center, which is realized by the Internet network. The application layer realizes the real-time monitoring of water quality through the water quality monitoring platform based on the FCM and BP algorithm through the real-time data fed back by the data center and the remote monitoring center. The particle swarm algorithm is used to realize the reverse tracking of the pollution source when the water is polluted, and the structure is fed back through the display terminal;

The working principle of the hardware architecture of the water quality monitoring system based on the Internet of Things is: when the water quality sensor detects the water quality information and sends it to the controller, the controller connects to the wireless gateway through communication, and then uploads the water quality information to the server and stores it in the database. , the display terminal accesses the server through the Internet, then obtains the water quality and water quantity parameter information in the database, and then judges whether the measured water quality meets the standard according to the urban sewage recycling standard, and finally displays the specific parameter information and judgment results on the terminal. , so as to realize remote monitoring of water quality.

The establishment process of the IoT-based sewage monitoring system described in step 4 includes:

S1. Select a sensor and a microcontroller, the sensors include a water quality sensor, a water level sensor and a flow sensor, wherein the water quality sensor selects DPS400A digital pH sensor, DLS400A digital residual chlorine sensor and DS18B20 digital temperature sensor, and the water level sensor selects the submerged liquid sensor Potentiometer and flow sensor use Hall flow sensor; select the matching microcontroller according to the model of the sensor, and use the microcontroller to control the water quality sensor to collect water quality data information;

S2. Select CZ80DTD analog wireless transmission device for wireless module;

S3. Select two 12V rechargeable batteries in series as the power supply unit of the sewage monitoring system;

S4. The voltage management module uses the LM2576 chip to reduce the 24V voltage to 12V, uses the LM2940 chip to reduce the 12V voltage to 5V, and uses the AMS11173.3 chip to reduce the 5V voltage to 3.3V to ensure stable output of the voltage required by each part of the system;

Among them: DPS-600A digital PH sensor, working voltage DC24V, PH theoretical measurement range is 0-14, corresponding analog voltage output is 0-5V; DLS>600A digital residual chlorine sensor, working voltage DC24V, theoretical measurement range is 0 -20.00mg/L, the corresponding analog voltage output is 0-5V; the DS18B20 digital temperature sensor probe is a stainless steel waterproof package, the working voltage is DC5V, and the single bus interface is used to connect with the slave controller, only one line is needed to realize the slave controller. Two-way communication between the controller and DS18B20; the working voltage of the submersible level gauge is DC24V, the measurement range is 0-50m, and the corresponding analog voltage output is 0-5V; the working voltage of the Hall flow sensor is DC5, and the output form It is a pulse signal with a high level of 4.8V;

The working principle of the sewage monitoring system based on the Internet of Things is as follows: the hardware circuit of the application layer controls the water pump and the solenoid valve according to the command transmitted by the main controller to complete the automatic control of water inlet and outlet, and the main controller connects the WIFI wireless module to the water quality and water quantity information. Upload to the server; since the water pump and solenoid valve are both driven by DC24V, a 24V drive circuit composed of ULN2003 and PC817 is designed. The corresponding I/O port of the main controller generates a low-level signal, which makes the PC817 optocoupler circuit conductive. After the high-voltage ULN2003 inverter is reversed, the 5V relay is output by OUT4 to pull in, and finally drives the 24V water pump and The purpose of the solenoid valve; when the water level sensor detects that the water level in the detection tank reaches a low water level, it drives the water inlet pump to drive the treated water into the detection tank, and when the water level reaches a high water level, the water inlet is stopped. At this time, parameter uploading and water quality compliance judgment are started. If it is detected that the water quality does not meet the standard, the solenoid valve will be opened to discharge the water into the sewer; otherwise, the water pump will be driven to discharge the water for secondary use. When the water level reaches the low water level again, the next round of pumping starts, so as to realize the automatic control of water inlet and outlet.

A sewage monitoring system based on FCM and BP algorithm is established by the above-mentioned establishment method (as shown in Figure 5). The sewage monitoring system includes a data acquisition module, a water quality monitoring management platform and a PC control terminal:

The data acquisition module includes a water quality sensor, a microcontroller and a wireless transmission unit, and the microcontroller controls the water quality sensor to collect water quality data information, and transmits it to the water quality monitoring and management platform through the wireless transmission unit;

The water quality monitoring and management platform includes a wastewater resource management module, a data editing module, a data query module, a dye source analysis module, a user management module and several communication protocol modules, and the control input end of the dye source analysis module receives the data information of the data acquisition module, It is assigned to the pollution source analysis module for analysis and processing, and the processing results are sent to the data editing module through the communication protocol module. After the data editing module processes the data, a data packet is formed, and then sent to the database of the data query module through the communication protocol module. storage and query;

The PC control terminal has built-in host computer software, and is connected to the water quality monitoring and management platform and the data acquisition module through the 4G mobile communication network. The pollution source analysis module analyzes the intrusion position, start time and intrusion speed of water pollutants. The results are displayed, and at the same time The data in the communication protocol module database can be queried and called as needed, and commands can also be issued to the data acquisition module according to user needs to control the data acquisition module to collect data.

The foregoing has shown and described the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above-mentioned embodiments, and the descriptions in the above-mentioned embodiments and the description are only to illustrate the principle of the present invention. Without departing from the spirit and scope of the present invention, the present invention will have Various changes and modifications fall within the scope of the claimed invention. The claimed scope of the present invention is defined by the appended claims and their equivalents.

Claims (10)

1. A method for establishing a sewage monitoring system based on FCM and BP algorithm is characterized in that: the method comprises the following steps:

analyzing collected water pollution data by using an FCM and BP mixed algorithm, determining an intrusion position, an intrusion starting time and an intrusion speed of water pollutants by using a particle swarm optimization algorithm, designing a built-in processor of a pollution source analysis module based on the FCM and BP mixed algorithm operation, and programming by using a MAT L AB programming tool;

step two: after a built-in processor of a pollution source analysis module based on FCM and BP mixed algorithm operation is designed, a water quality monitoring management platform is constructed by using an object-oriented design idea, a C/S structure system and an MVC mode H-layer framework;

on the basis of a water quality monitoring management platform, detecting water quality parameters by adopting a nano reagent spectrophotometry, and building a hardware architecture of the water quality monitoring system based on the Internet of things by combining P L C, configuration software and a 4G mobile communication technology;

step four: on the basis of a hardware architecture of the water quality monitoring system based on the Internet of things, a sewage monitoring system based on the Internet of things is established by utilizing a design idea and an implementation method based on a water quality sensor, a microcontroller and a wireless module and a communication protocol among all parts, wherein the information acquisition and transmission functions of the sewage monitoring Internet of things system can be realized.

2. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 1, wherein the method comprises the following steps: the specific process for analyzing the water pollution by using the FCM and BP mixed algorithm comprises the following steps:

s1, analyzing pollution source monitoring data by using an FCM (fuzzy clustering) algorithm, determining correlation characteristics among data points by using a membership function, and dividing and clustering the data:

s2, correcting abnormal values in the data clusters divided in the S1 by using a BP neural network algorithm to obtain processed data samples;

s3, taking the data processed in the step S2 as monitoring data, substituting the monitoring data into a particle swarm optimization algorithm, establishing a pollution source reverse tracking model by using the particle swarm optimization algorithm, taking the square sum of the difference between the actual monitoring value and the simulation value of each monitoring point in a simulation optimization time period as a model objective function, then adopting the particle swarm optimization algorithm as an optimization solving tool to quickly determine the position and the influence range of a pollution source, designing a built-in processor of a pollution source analysis module based on the operation of the FCM and BP mixed algorithm, wherein the model objective function is as follows:

where X is the optimal solution for the source of contamination, y_i(t) is the pollutant simulated concentration of the water quality monitoring point i at the time t; y'_i(t) the actually measured pollutant concentration of the water quality monitoring point i at the time t; m is the number of water quality monitoring points; t is the analog duration.

3. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 2, wherein the method comprises the following steps: the process of analyzing and clustering the pollution source monitoring data by using the FCM algorithm in step S1 includes:

(1) preprocessing raw data: the data preprocessing comprises the steps of interpolating missing data of the monitored original data of water pollution, clearing noise data, grouping by month, observing the change range of data of each month after grouping, and judging whether an abnormal detection process needs to be carried out on the month or not;

(2) after the data preprocessing is finished, clustering the preprocessed data by using an FCM algorithm, and sequentially using months to be detected as input sources of the FCM algorithm for clustering, wherein the clustering specifically comprises the following steps:

a. firstly, setting the number C of categories, wherein C is more than or equal to 2 and less than or equal to N, N is the total amount of data samples, giving an iteration stop threshold value, and initializing a prototype pattern P of clustering^(b)Giving the iteration counter b equal to 0;

b. calculating a partition matrix U^(b): for the

If it is not

Then there are:

for the

If it is not

Then there are:

and is aligned with

c. Iterating the two conditions to obtain an iterated and updated prototype matrix P^b+1Comprises the following steps:

d. judging and outputting the segmented matrix U and the clustering prototype P: if P | |_i ^(b)-P_i ^(b+1)If the | | is less than or equal to the predetermined threshold, outputting the segmented matrix U and the clustering prototype P at the same time after the calculation is finished; otherwise, making b equal to b +1, and repeating the step b for iteration again;

where | is the appropriate matrix norm;

(3) and after the data clustering is finished, analyzing clustering results, dividing the data into normal class values and abnormal class values according to the self characteristics of various data, and finishing the abnormal detection of the pollution source monitoring data.

4. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 3, wherein the method comprises the following steps: the specific process of correcting by using the abnormal class values in the BP neural network algorithm data cluster described in step S2 includes:

(1) finding out an abnormal class value in the data detected in the step one, and taking a data sequence before the abnormal class value as an input sample of the BP neural network;

(2) then substituting the data sequence before the abnormal class value into a BP neural network algorithm, and performing data prediction on the position of the abnormal class value by utilizing the nonlinear fitting energy of the BP neural network algorithm;

(3) replacing abnormal values in the original data by the data predicted by the BP neural network algorithm, namely finishing the correction of the abnormal values;

(4) when a plurality of abnormal value data appear in the data set, the BP neural network algorithm is used for correcting one by one, namely after the first abnormal value is corrected, the abnormal value in the original data sequence is replaced, and the new data sequence is used as an input sample of the BP neural network algorithm for correcting the second abnormal data when the next abnormal value is predicted.

5. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 4, wherein the method comprises the following steps: the specific process of establishing the pollution source back tracking model by using the particle swarm optimization algorithm in the step S3 includes:

(1) firstly, calculating the relationship among water conservancy information, pipe network attributes and monitoring data corrected in the second step by using a particle swarm optimization algorithm, analyzing the change rule of the pollutant concentration in the pipe network along with time and space, designing model parameters, establishing a hydraulic water quality model, and ensuring that the model can better accord with the actual hydraulic water quality condition in the pipe network, wherein the specific process of analyzing the change of the pollutant concentration in the pipe network along with time and space by using the particle swarm optimization algorithm comprises the following steps:

a. firstly, initializing a group of particles in a feasible solution space of a pollution source, wherein the characteristics of each particle are represented by three indexes of a position, a speed and a fitness value, wherein the particle position represents potential solution information of the pollution source, the particle speed represents the variation amplitude of an individual position, the quality of the fitness value represents the quality of the potential solution of the pollution source represented by the particle position, and a model objective function is used as a fitness function;

b. when the particles move in the solution space, updating the positions of the individuals by tracking the historical optimal solution position of the single particle potential pollution source and the historical optimal solution position of the potential pollution source in the group, wherein the updating of the positions of the particles once represents that the potential solution of the pollution source is updated once, and the optimal solution position of the particle potential pollution source and the optimal solution position of the group potential pollution source are updated by comparing the fitness value of the particles at the moment with the previous optimal fitness value of the individuals and the optimal fitness value of the group, and the optimal solution position of the particle potential pollution source and the optimal solution position of the group potential pollution source are repeatedly calculated in an iterative:

first, X ═ X is defined₁,X₂,...,X_n)^T

X_i＝(x_i1,x_i2,...,x_i(3m-2),x_i(3m-1),x_i(3m))^T

V＝(V₁,V₂,...,V_n)^T

v_i＝(v_i1,v_i2,...,v_i(3m-2),v_i(3m-1),v_i(3m))^T

Wherein: x is a matrix formed by n particle positions in the population and represents a matrix containing potential solutions of n pollution sources; x_iIs the position of the ith particle, i.e., a potential solution to the contamination source, where x_i1Node number, x, representing a pollution source_i2Represents the initial time of ingress of the contaminant, x_i3Represents the rate of ingress of contaminants; x is the number of_i(3m-2),x_i(3m-1),x_i(3m)Information of the mth pollution source when a plurality of pollution sources exist in the pipe network is represented; v is a matrix composed of n particle velocities in the population; v. of_iIs the velocity of the ith particle; wherein v is_i1Representing the speed of change of the node number of the pollution source, v_i2Representing the rate of change of time of initial ingress of the contaminant, v_i3Representing the rate of change of the contaminant intrusion rate; v. of_i(3m-2),v_i(3m-1),v_i(3m)The information change speed of the mth pollution source when a plurality of pollution sources exist in the pipe network is represented;

(2) the method comprises the steps of establishing simulation of a pollution source in a pipe network according to a hydraulic water quality model, establishing a pollution source reverse tracking model, taking the square sum of the difference between the simulated pollutant concentration and the actual concentration of each water quality monitoring point in a simulated optimization time period as a model objective function, embedding an EPANET tool box as a simulation engine, and solving the invasion position, the invasion starting time and the invasion speed of pollutants by utilizing a particle swarm optimization algorithm.

6. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 5, wherein the method comprises the following steps: the specific process of solving the intrusion position, the intrusion start time and the intrusion speed of the pollutant by using the group optimization algorithm in the step S3(2) includes:

a. setting algorithm parameters

To prevent blind search of particles, the pollution source is potentially solved by X_iAnd the particle velocity v_iConfined to a certain space, i.e. [ X ]_min,X_max],[v_min,v_max](ii) a Setting an upper limit N of iteration times and preset precision, and finishing optimization calculation when the iteration times reach an upper limit q or a fitness value f (x) < f;

b. initialization and initial extrema of particle populations

Randomly generating a particle population matrix X and a speed matrix V containing potential solution information of a plurality of pollution sources, and calculating the fitness value of each initial particle by taking a model objective function as a fitness function; searching the pollution source potential optimal solution P of each particle according to the fitness value_iAnd the optimal solution P of all particles in the population_g；

c. Iterative optimization

Particle transit through individual extreme position (individual pollution source optimal solution) P_iAnd position of extremum of population (optimal solution of population pollution source) P_gUpdating the speed and position of the particle, and the speed of the ith particle in the (k + 1) th iteration

And position

The following were used:

in the formula, ω is an inertia weight, which reflects the ability of the particle to inherit the previous velocity; c. C₁,c₂Is an acceleration factor, which is a non-negative constant; r is₁,r₂Is distributed in [0,1 ]]A random number of intervals; updating the optimal solution of the particle potential pollution source, the optimal solution of the group potential pollution source and the corresponding fitness value; and repeating iteration until the iteration times reach the upper limit or the adaptability value reaches the preset precision, finishing the iteration and outputting the optimal solution of the potential pollution source in the population.

7. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 6, wherein the method comprises the following steps: the concrete process for constructing the water quality monitoring management platform comprises the following steps:

s1, respectively establishing functional units such as a user management module, a data acquisition module, a data query module and the like by using an object-oriented method, and then respectively establishing a model layer, a view layer and a control layer by using an object-oriented technology developed by a system to form a software control part of a water quality monitoring management platform; the model layer is realized by using a JavaBean technology, the view layer is realized by using a JSP technology, and the control layer is realized by using a Servlet technology;

s2, communicating a software control part of the built water quality monitoring management platform with a Web server based on a B/S structure of WEB, so that management data of the water quality monitoring management platform is uploaded to a network control end through the Web server; the B/S architecture is a browser/server structure, a client only needs to adopt a browser to send a request to a Web server, and the Web server processes the request to return a processing result to the client;

and S3, designing a model layer, a view layer and a control layer of the water quality monitoring management platform by using a design technology based on an MVC mode H-layer frame to obtain the water quality monitoring management platform capable of monitoring water quality online in real time along with time.

8. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 7, wherein the method comprises the following steps: step three, the establishment process of the hardware architecture of the water quality monitoring system comprises the following steps:

s1, designing parameters of a water quality sensor according to a principle of detecting water quality parameters by a nano reagent spectrophotometry, and ensuring that the water quality sensor can quickly and accurately detect water quality information;

s2, selecting Siemens P L C as a control part of the controller, and reducing the volume of the detection equipment;

s3, selecting a 4G wireless gateway as a data transmission module, and shortening an information transmission period so as to shorten a detection period;

and S4, establishing an information server to realize remote transmission of data.

9. The method for establishing the sewage monitoring system based on the FCM and BP algorithm according to claim 8, wherein the method comprises the following steps: step four, the establishment process of the sewage monitoring system based on the Internet of things comprises the following steps:

s1, selecting a sensor and a microcontroller, wherein the sensor comprises a water quality sensor, a water level sensor and a flow sensor, the water quality sensor selects a DPS400A digital PH sensor, a D L S400A digital chlorine residue sensor and a DS18B20 digital temperature sensor, the water level sensor selects an input liquid level meter, the flow sensor selects a Hall flow sensor, the microcontroller is selected according to the model of the sensor, and the microcontroller is used for controlling the water quality sensor to acquire data information of water quality;

s2, the wireless module selects a CZ80DTD analog quantity wireless transmission device;

s3, selecting two 12V rechargeable storage batteries connected in series as a power supply unit of the sewage monitoring system;

s4, the voltage management module adopts an L M2576 chip to reduce the voltage of 24V to 12V, adopts a L M2940 chip to reduce the voltage of 12V to 5V, and reduces the voltage of 5V to 3.3V through an AMS11173.3 chip, so that the voltage required by each part of a system circuit is ensured to be stably output.

10. The sewage monitoring system based on the FCM and BP algorithm according to claim 1, wherein the monitoring system comprises a data acquisition module, a water quality monitoring management platform and a PC control end:

the data acquisition module comprises a water quality sensor, a microcontroller and a wireless transmission unit, wherein the microcontroller controls the water quality sensor to acquire data information of water quality and transmits the data information to the water quality monitoring management platform through the wireless transmission unit;

the water quality monitoring and management platform comprises a wastewater resource management module, a data editing module, a data query module, a dye source analysis module, a user management module and a plurality of communication protocol modules, wherein the control input end of the dye source analysis module receives data information of the data acquisition module and distributes the data information to the pollution source analysis module for analysis and processing, the processed result is sent to the data editing module through the communication protocol module, and the data editing module processes the data to form a data packet which is then sent to a database of the data query module through the communication protocol module for storage and query;

PC control end embeds host computer software, is connected with water quality monitoring management platform and data acquisition module through 4G mobile communication network, and the invasion position, the time of beginning to invade and the invasion speed result of the water pollution thing that pollution source analysis module analysis obtained show, simultaneously inquire the data in the communication protocol module database and call as required, can also be according to user's needs to data acquisition module issue the order, control data acquisition module data acquisition.

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