CN108425405B - Networked water source monitoring and graded water supply device and monitoring system thereof - Google Patents
Networked water source monitoring and graded water supply device and monitoring system thereof Download PDFInfo
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- 238000012544 monitoring process Methods 0.000 title claims abstract description 81
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- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
- E03B11/00—Arrangements or adaptations of tanks for water supply
- E03B11/02—Arrangements or adaptations of tanks for water supply for domestic or like local water supply
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B01—PHYSICAL OR CHEMICAL PROCESSES OR APPARATUS IN GENERAL
- B01D—SEPARATION
- B01D35/00—Filtering devices having features not specifically covered by groups B01D24/00 - B01D33/00, or for applications not specifically covered by groups B01D24/00 - B01D33/00; Auxiliary devices for filtration; Filter housing constructions
- B01D35/14—Safety devices specially adapted for filtration; Devices for indicating clogging
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F9/00—Multistage treatment of water, waste water or sewage
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- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
- E03B7/00—Water main or service pipe systems
- E03B7/04—Domestic or like local pipe systems
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- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
- E03B7/00—Water main or service pipe systems
- E03B7/07—Arrangement of devices, e.g. filters, flow controls, measuring devices, siphons or valves, in the pipe systems
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- E—FIXED CONSTRUCTIONS
- E03—WATER SUPPLY; SEWERAGE
- E03B—INSTALLATIONS OR METHODS FOR OBTAINING, COLLECTING, OR DISTRIBUTING WATER
- E03B7/00—Water main or service pipe systems
- E03B7/07—Arrangement of devices, e.g. filters, flow controls, measuring devices, siphons or valves, in the pipe systems
- E03B7/074—Arrangement of water treatment devices
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/18—Water
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/001—Processes for the treatment of water whereby the filtration technique is of importance
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
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- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/28—Treatment of water, waste water, or sewage by sorption
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F1/00—Treatment of water, waste water, or sewage
- C02F1/44—Treatment of water, waste water, or sewage by dialysis, osmosis or reverse osmosis
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- C—CHEMISTRY; METALLURGY
- C02—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F—TREATMENT OF WATER, WASTE WATER, SEWAGE, OR SLUDGE
- C02F2301/00—General aspects of water treatment
- C02F2301/08—Multistage treatments, e.g. repetition of the same process step under different conditions
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract
The invention discloses a networked water source monitoring and grading water supply device, which belongs to the technical field of water purification devices and comprises an integral frame, wherein a water inlet pipe is connected into the integral frame and is communicated with the water source monitoring device, a first electromagnetic reversing valve is arranged on the end face of the water source monitoring device, a water outlet of the first electromagnetic reversing valve is communicated with a first water outlet pipe, a second electromagnetic reversing valve is arranged at the bottom of the water source monitoring device, and a reservoir is arranged below the water outlet of the second electromagnetic reversing valve. The invention also discloses a monitoring system of the device. The invention is suitable for being arranged at the outlet of the household water inlet bus water meter, can monitor and purify various parameters of water factors of household water supply in real time, and can carry out grading or simultaneous water supply according to water quality conditions required by different occasions.
Description
Technical Field
The invention belongs to the technical field of water purifying devices, and particularly relates to a networked water source monitoring and graded water supply device and a monitoring system thereof.
Background
Water is the source of life, and it plays an important role in our life, and it is one of the most important material resources essential for human survival and development.
With the development of modern industry, water source pollution has become a focus of attention for human beings, and various water factor parameters of water sources drunk by people are also more and more concerned.
In terms of water supply departments, the water supply departments need to know the water quality conditions delivered by all users in real time, and the water quality conditions of drinking water of all users need to be managed in a centralized way and analyzed in big data; in the case of families, people are increasingly concerned about the water quality of water sources drunk by themselves, and meanwhile, at different places, the water quality requirements required by the people are different, for example, the requirements of household clothes washing on water quality parameters are relatively low, and the requirements of the water quality parameters for drinking are relatively high.
Disclosure of Invention
The invention aims to: the invention aims to provide a networked water source monitoring and graded water supply device for purifying the quality of household water; the invention further aims to provide a monitoring system of the networked water source monitoring and graded water supply device, which is used for carrying out graded water supply and the like according to water quality requirements required by different places, and simultaneously, the intelligent decoupling control system for the water flow of the first water outlet pipe and the second water outlet pipe is used for regulating and controlling the water flow of the first water outlet pipe and the second water outlet pipe.
The technical scheme is as follows: in order to achieve the above purpose, the invention adopts the following technical scheme:
the networked water source monitoring and grading water supply device comprises an integral frame, wherein a water inlet pipe is connected into the integral frame and is communicated with the water source monitoring device, a first electromagnetic reversing valve is arranged on the end face of the water source monitoring device, a water outlet of the first electromagnetic reversing valve is communicated with a first water outlet pipe, a second electromagnetic reversing valve is arranged at the bottom of the water source monitoring device, and a water reservoir is arranged below the water outlet of the second electromagnetic reversing valve; the water inlet end of the first water outlet pipe is provided with a first loop water pressure sensor, and the water inlet end of the second water outlet pipe is provided with a second loop water pressure sensor.
A first baffle plate and a second baffle plate are arranged in the reservoir in a mutually perpendicular mode, the first baffle plate and the second baffle plate are fixed in a staggered mode through grooves, a rotating shaft is arranged at the staggered fixed position, and the rotating shaft penetrates through the joint of the first baffle plate and the second baffle plate; a stepping motor is arranged below the reservoir and drives the first baffle and the second baffle through a rotating shaft.
A filter screen is arranged above the inner wall of the reservoir; the filter screen include the filter cassette, set up hollow fiber milipore filter UF filter core from bottom to top in the filter cassette, granule fine wash coconut shell active carbon filter core and PP cotton filter core.
The system is a drinking water source monitoring system based on a ZigBee network, and comprises a terminal detection node, a data aggregation processing node, a web server and a data monitoring processor, wherein the terminal detection node consists of monitoring equipment, and the monitoring equipment comprises a monitoring water quality sensor, a singlechip and a wireless communication module; the data aggregation processing node and the web server are realized by constructing a Linux system by raspberry pie and running a program of a Django framework and a MySQL database; the monitoring devices collect data information of water quality parameters and communicate with the web server through a ZigBee composition network; the data monitoring processor consists of mobile portable equipment, and a user monitors, controls and processes and analyzes data in real time through a browser of a computer or a mobile phone.
Two paths of intelligent decoupling controllers for water flow are embedded in the singlechip, and the singlechip controls the water flow of the first water outlet pipe and the second water outlet pipe by adjusting the opening of the first electromagnetic reversing valve and the opening of the second electromagnetic reversing valve; the two-path water flow intelligent decoupling controller comprises a first loop fuzzy least square support vector machine prediction controller, a first loop PID real-time controller, a first loop GM water pressure prediction model, a second loop fuzzy least square support vector machine prediction controller, a second loop PID real-time controller, a second loop GM water pressure prediction model and an RBF neural network inverse decoupling controller; the first loop fuzzy least square support vector machine prediction controller and the first loop PID real-time controller are connected in parallel to be used as a water flow composite controller of the first loop, the second loop fuzzy least square support vector machine prediction controller and the second loop PID real-time controller are connected in parallel to be used as a water flow composite controller of the second loop, and the outputs of the water flow composite controller of the first loop and the water flow composite controller of the second loop are respectively used as inputs of an RBF neural network inverse decoupling controller; the output of the first loop water pressure sensor is used as the input of a first loop GM water pressure prediction model, and the output of the first loop GM water pressure prediction model is used as the feedback quantity of a first loop fuzzy least square support vector machine prediction controller to form the prediction control of the first loop water supply flow; the output of the second loop water pressure sensor is used as the input of a second loop GM water pressure prediction model, and the output of the second loop GM water pressure prediction model is used as the feedback quantity of a second loop fuzzy least square support vector machine prediction controller to form the prediction control of the second loop water supply flow.
The RBF neural network inverse decoupling controller consists of an integral first loop, an integral second loop and an RBF neural network, wherein the RBF neural network consists of T, H, I, J, K and L six input nodes, ten intermediate nodes and P, Q two output nodes, and the P, Q two output nodes are respectively used as input control amounts of a first electromagnetic reversing valve and a second electromagnetic reversing valve.
The first integrating loop and the second integrating loop are respectively formed by serially connecting a 1 st integrator and a 2 nd integrator, the output of the water flow composite controller of the first integrating loop is the input of the 1 st integrator of the first integrating loop and the I input of the RBF neural network, the output of the 1 st integrator of the first integrating loop is the H input of the RBF neural network and the 2 nd integrator input of the first integrating loop, and the output of the 2 nd integrator of the first integrating loop is used as the T input of the RBF neural network; the output of the water flow composite controller of the second loop is the input of the 1 st integrator of the second loop and the L input of the RBF neural network, the 1 st integrator output of the second loop is the K input of the RBF neural network and the 2 nd integrator input of the second loop, and the 2 nd integrator output of the second loop is used as the J input of the RBF neural network.
The web server is a web system website developed based on Django and MySQL, manages a plurality of monitoring devices based on ZigBee at the same time, and monitors the device status of each site at the same time.
The singlechip processor is characterized in that the singlechip processor is provided with a STM32F103c8t6, and the wireless communication module is provided with a ZigBee communication module with a chip model CC2530F 256; the water quality monitoring sensor comprises a DS18B20 temperature sensor, a TDS water quality conductivity sensor, a pH value sensor and a turbidity sensor.
The web server is a PC, is connected with the ZigBee data aggregation processing node and receives data, and provides web service to realize data query and analysis; the web server runs a C++ program to receive data of temperature, TDS value, turbidity and pH value sent back by the ZigBee-based monitoring equipment and store the data into a database; the information data of each index of the water quality processed by the web server is obtained by the information data access server and displayed on the mobile equipment in real time; meanwhile, the web server collects data of each monitoring device in the terminal detection node, analyzes the data packet, sequentially classifies each data into a MySQL database, compares the stored data with a set threshold value when the data is stored into the MySQL database, and timely informs a manager and alarms if the stored data is too large or too small; the data monitoring processor and the MySQL database are realized by a UDP communication mode, and the UDP can exchange data on different platforms.
The beneficial effects are that: compared with the prior art, the networked water source monitoring and graded water supply device is suitable for being installed at the outlet of the household water inlet bus water meter, can monitor and purify various parameters of water factors of household water supply in real time, and can perform graded water supply according to water quality conditions required by different occasions.
The utility model provides a networked water source monitoring and hierarchical water supply installation's monitoring system has following advantage:
1) The invention designs a fuzzy least square support vector machine prediction controller in two paths of water flow intelligent decoupling controllers, which fuses fuzzy control and least square support vector machine technology, wherein the Fuzzy Control (FC) is an intelligent nonlinear control technology imitating human thinking, is independent of a controlled object model and has strong robustness, is widely applied, but the control rule relied on by the traditional fuzzy control lacks online self-learning capability, is not suitable for the requirement of the change of the controlled object, and seriously affects the control effect; the advent of least squares support vector machine technology provides a new approach to the design of adaptive FCs that enables optimization and fuzzy reasoning of membership functions, thereby designing a Fuzzy Support Vector Machine (FSVM) control system. The controller combines the advantages of a support vector machine and a fuzzy technology, has the advantages of the support vector machine, such as small sample learning, strong generalization capability, global optimum and the like of the least square support vector machine, and has the characteristics of no dependence on a controlled object model and strong robustness of the fuzzy technology;
2) Aiming at the characteristics of large inertia, large delay, time variation and multiple interferences of the water flow of a controlled water outlet pipe loop, a fuzzy least square support vector machine prediction controller and a PID real-time controller are designed in the two-path water flow intelligent decoupling controller to be connected in parallel to be used as a composite main regulator, so that the composite main regulator has the advantages of high convergence rate, good dynamic response, strong robustness, small overshoot, high control precision and good stability in water flow control of the water outlet pipe, meets the control requirement of the water flow variation of the controlled water outlet pipe, and realizes the intelligent control of the water flow variation of the water outlet pipe. Experiments show that the controller has good control effect and can better counteract the influence of various interferences;
3) Aiming at the characteristics of mutual influence and mutual coupling of water flows in the 2 water outlet pipes to be controlled, an RBF neural network-based inverse decoupling controller is designed in the two-path water flow intelligent decoupling controller to realize decoupling control of the water flows which are mutually influenced and mutually coupled in the two-path water outlet pipes, so that the control accuracy of the water flows in each water outlet pipe is improved, the response speed and the control accuracy are improved, and the stability of the system is improved;
4) According to the characteristics of large inertia, large delay, time variation and multiple interferences of the water flow of the controlled water outlet pipe loop, 2 loops GM (1, 1) water pressure prediction models and 2 loops fuzzy least square support vector machine prediction controllers are designed in the two-loop water flow intelligent decoupling controller to realize the predictive control of the water flow of the 2 water outlet pipes, and the control system has stronger robustness and interference resistance, and meanwhile, the method is simple and easy to realize engineering and has better practical application value;
5) Aiming at the characteristics of large inertia, large delay, time variation and multiple interferences of the water flow of a controlled water outlet pipe loop, a two-path water flow intelligent decoupling controller combines predictive control and real-time control with decoupling control, gives full play to the excellent characteristics and anti-interference performance of compound control in overcoming time lag, designs a two-path water flow accurate control system of compound control and decoupling control, realizes the organic combination of a compound control and decoupling control method of predictive control and real-time control, gives full play to the characteristics of strong interference resistance and good robustness of various control methods, and theoretical research and practical application show that the system has quick response and good interference resistance and robustness;
6) The intelligent decoupling controller for the two paths of water flow in the patent of the invention enables the fuzzy support vector machine predictive controller and the PID to control the composite main regulator in real time, and the composite main regulator and the RBF neural network inverse decoupling controller are connected in series to form a decoupling control system, thereby realizing accurate control of the water flow of the two paths of water outlet pipes. The method fully integrates the advantages of predictive control, fuzzy control, decoupling control and intelligent control; experiments on water flow control of two paths of water outlet pipes show that the intelligent controller has better control effect than conventional PID control, can adapt to the change of object parameters, has stronger robustness, anti-interference performance and self-adaptive capacity and good control quality, has better application and popularization values, and has obvious substantial progress.
In sum, this application can make things convenient for water supply department to carry out centralized management and carry out big data analysis to the drinking water quality condition of each subscriber unit, and the user that can be very big convenient carries out real-time supervision to the drinking water quality condition of oneself, and data monitoring processor and MySQL database in this system realize through UDP communication mode, and UDP can carry out data exchange at different platforms. The information data of various indexes of the water quality processed by the web server can be obtained through the access server such as 3G, 4G, wiFi and the like and displayed on the mobile equipment in real time, so that the operation requirement on users is low, and various index information of the water quality can be fed back to various users and water supply departments in high efficiency, convenience and real time.
Drawings
FIG. 1 is a diagram of an overall framework of a networked water source monitoring and staged water supply apparatus;
FIG. 2 is a diagram showing the internal structure of a networked water source monitoring and staged water supply device;
FIG. 3 is a block diagram of a stirring device;
FIG. 4 is an enlarged view of a portion of the purification apparatus;
FIG. 5 is a diagram of a two-way outlet pipe water flow intelligent decoupling control system;
FIG. 6 is a schematic diagram of a drinking water source monitoring system based on a ZigBee network;
FIG. 7 is a data acquisition flow chart;
fig. 8 is a web page function distribution diagram of a ZigBee network-based drinking water source monitoring system.
Detailed Description
The invention is further described below in conjunction with the detailed description.
As shown in fig. 1-8, the reference numerals are as follows: the system comprises an integral frame 1, a liquid crystal display 2, a first water outlet pipe 3, a second water outlet pipe 4, a water inlet pipe 5, a water source monitoring device 6, a first electromagnetic directional valve 7, a second electromagnetic directional valve 8, a filter screen 9, a stepping motor 10, a water pump 11, a water quality monitoring sensor 12, a water reservoir 13, a first baffle 14, a second baffle 15, a rotating shaft 16, a second loop water pressure sensor 17, a first loop water pressure sensor 18, a singlechip 19, a terminal detection node 20, a data aggregation processing node 21, a web server 22, a data monitoring processor 23, a wireless communication module 24, a MySQL database 25 and a Django frame 26.
The networked water source monitoring and grading water supply device comprises an integral frame 1, a water source monitoring device 6, a filter screen 9 and a stirring device; the stirring device comprises a water pump 11, a water reservoir 13, a first baffle 14, a second baffle 15 and a rotating shaft 16.
A liquid crystal display 2 is arranged on the upper end face of the integral frame 1; the water inlet pipe 5 is connected into the integral frame 1 and is communicated with the water source monitoring device 6, a first electromagnetic directional valve 7 is arranged on the end face of the water source monitoring device 6, a water outlet of the first electromagnetic directional valve 7 is communicated with the first water outlet pipe 3, a second electromagnetic directional valve 8 is arranged at the bottom of the water source monitoring device 6, and a water reservoir 13 is arranged below the water outlet of the second electromagnetic directional valve 8. A first loop water pressure sensor 18 is arranged at the joint of the valve port of the electromagnetic directional valve 7 and the water inlet end of the water outlet pipe 3; a second loop water pressure sensor 17 is arranged at the joint of the water pump 11 and the water inlet end of the second water outlet pipe 4, the water pipe pressure of the second water outlet pipe 4 is monitored through the second loop water pressure sensor 17, and the water pipe pressure of the first water outlet pipe 3 is monitored through a first loop water pressure sensor 18.
The first baffle plate 14 and the second baffle plate 15 are arranged in the reservoir 13 in a mutually perpendicular mode, the first baffle plate 14 and the second baffle plate 15 are fixed in a staggered mode through grooves, a rotating shaft 16 is arranged at the staggered fixing position, and the rotating shaft 16 penetrates through the joint position of the first baffle plate 14 and the second baffle plate 15. A stepping motor 10 is arranged below the reservoir 13, the stepping motor 10 drives a first baffle 14 and a second baffle 15 through a rotating shaft 16, a water pump 11 is arranged on the lower bottom surface of the reservoir 13, and a water outlet of the water pump 11 is matched with the second water outlet pipe 4.
A filter screen 9 is arranged above the inner wall of the reservoir 13, and the filter screen 9 comprises a hollow fiber ultrafiltration membrane UF filter element 901, a granule fine-washing coconut shell activated carbon filter element 902, a PP cotton filter element 903 and a filter box 904; a hollow fiber ultrafiltration membrane UF filter element 901, a granular fine-washing coconut shell activated carbon filter element 902 and a PP cotton filter element 903 are arranged in the filter box 904 from bottom to top.
As shown in fig. 5, the two-path water flow intelligent decoupling controller realizes accurate decoupling control on water flow of the first water outlet pipe 3 and the second water outlet pipe 4, the two-path water flow intelligent decoupling controller is composed of a first loop fuzzy least square support vector machine prediction controller, a first loop PID real-time controller, a first loop GM water pressure prediction model, a second loop fuzzy least square support vector machine prediction controller, a second loop PID real-time controller, a second loop GM water pressure prediction model and an RBF neural network inverse decoupling controller, the first loop fuzzy least square support vector machine prediction controller and the first loop PID real-time controller are connected in parallel to serve as a water flow composite controller of the first loop, the second loop fuzzy least square support vector machine prediction controller and the second loop PID real-time controller are connected in parallel to serve as a water flow composite controller of the second loop, the outputs of the first loop complex controller and the second loop PID composite controller serve as 2 inputs of the RBF neural network inverse decoupling controller respectively, the RBF neural network inverse decoupling controller and the Q electromagnetic valve Q output of the RBF neural network inverse decoupling controller serve as a first loop GM prediction model and a first loop GM output of the first loop GM prediction model, and the second loop GM output of the second loop GM water flow composite controller serves as a first loop water flow composite controller of the second loop, and the first loop GM output of the second loop GM prediction model is used as a first water flow sensor prediction model 18; the output of the second loop hydraulic pressure sensor 17 is used as the input of a second loop GM hydraulic pressure prediction model, and the output of the second loop GM hydraulic pressure prediction model is used as the feedback quantity of the second loop fuzzy least square support vector machine prediction controller to form the prediction control of the second loop water supply flow.
The RBF neural network inverse decoupling controller consists of an integral first loop, an integral second loop and an RBF neural network, wherein the integral first loop and the integral second loop are respectively formed by serially connecting a 1 st integrator and a 2 nd integrator, the output of a water flow composite controller of the first loop is the input of the 1 st integrator of the integral first loop and the I input of the RBF neural network, the output of the 1 st integrator of the integral first loop is the H input of the RBF neural network and the 2 nd integrator input of the integral first loop, and the output of the 2 nd integrator of the integral first loop is used as the T input of the RBF neural network; the output of the water flow composite controller of the second loop is the input of the 1 st integrator of the second loop and the L input of the RBF neural network, the 1 st integrator output of the second loop is the K input of the RBF neural network and the 2 nd integrator input of the second loop, and the 2 nd integrator output of the second loop is used as the J input of the RBF neural network.
The RBF neural network consists of six input nodes T, H, I, J, K and L, ten intermediate nodes and P, Q two output nodes, wherein the P, Q two output nodes are respectively used as input control amounts of the first electromagnetic directional valve 7 and the second electromagnetic directional valve 8, and the two-path water flow intelligent decoupling controller realizes decoupling control on two-path water supply flow and ensures that the two-path water supply flow can meet the user requirements.
In the invention, the design of the intelligent decoupling controller for the two paths of water flow is realized (the first loop GM water pressure prediction model and the second loop GM water pressure prediction model of the application are both GM (1, 1) water pressure prediction models):
(1) Design of GM (1, 1) hydraulic pressure prediction model
The GM (1, 1) water pressure prediction model comprises a first loop GM water pressure prediction model and a second loop GM water pressure prediction model, the GM (1, 1) gray prediction method has more advantages than the traditional statistical prediction method, whether the prediction variables are subjected to normal distribution or not does not need to be determined, large sample statistics is not needed, the prediction model does not need to be changed at any time according to the change of the water pressure input variable of the water supply pipe loop, a unified differential equation model is established through the accumulation generation technology of the water pressure of the water supply pipe output loop, the prediction result is obtained after the original value of the water pressure of the water supply pipe output loop is accumulated, and the differential equation model has higher prediction precision. The essence of establishing the water pressure prediction model of the water supply pipe GM (1, 1) is that the original data of the water pressure of the water supply output pipe are accumulated once, so that the generated sequence presents a certain rule, and a water pressure fitting curve of the water output pipe is obtained by establishing a differential equation model so as to predict the water pressure of the first water outlet pipe 3 and the second water outlet pipe 4 to achieve the purpose of predicting the water flow of the water supply pipe.
(2) Fuzzy least square support vector machine predictive controller design
The input and output variables of the first loop fuzzy least square support vector machine prediction controller and the second loop fuzzy least square support vector machine prediction controller are respectively a given water outlet pipe loop water pressure amount, a loop GM (1, 1) water pressure prediction model value and a water outlet pipe loop water pressure control amount, and the fuzzy proportional relation between fuzzy quantities E, EC and U and actual quantities E, EC and U in a decision process of the fuzzy support vector machine is adopted by adopting k e 、k ec 、k u Blurring processing is performed, and blurring region division is performed on the spaces of the blurring processing and the blurring region division. The least square support vector machine regression can be represented by a 3-layer network structure, wherein the node numbers of an input layer, a hidden layer and an output layer are respectively 2, 5 and 1, and the connection weights between the input layer and the hidden layer and between the hidden layer and the output are respectively 1 and alpha k (k＝1,2,…)。
I, input layer: the input variables e and ec are blurred and used as the input x of the control system.
II, hidden layer: and realizing the kernel operation of the two-dimensional input x and the least square support vector machine.
K(x,x i )＝exp(-|x-x i | 2 /2σ 2 ) (2)；
III, output layer: and (3) realizing regression operation of the least square support vector machine to obtain the actual input control quantity u of the RBF neural network inverse decoupling controller.
(3) Design of RBF neural network inverse decoupling controller
The RBF neural network inverse decoupling controller consists of an integral first loop, an integral second loop and an RBF neural network, wherein the integral first loop and the integral second loop are respectively formed by serially connecting a 1 st integrator and a 2 nd integrator, the output of a water flow composite controller of the first loop is the input of the 1 st integrator of the integral first loop and the I input of the RBF neural network, the output of the 1 st integrator of the integral first loop is the H input of the RBF neural network and the 2 nd integrator input of the integral first loop, and the output of the 2 nd integrator of the integral first loop is used as the T input of the RBF neural network; the output of the water flow composite controller of the second loop is the input of the 1 st integrator of the second loop and the L input of the RBF neural network, the 1 st integrator output of the second loop is the K input of the RBF neural network and the 2 nd integrator input of the second loop, and the 2 nd integrator output of the second loop is used as the J input of the RBF neural network; the RBF neural network consists of six input nodes T, H, I, J, K and L, ten intermediate nodes and P, Q two output nodes, wherein the P, Q two output nodes are respectively used as input control amounts of the first electromagnetic directional valve 7 and the second electromagnetic directional valve 8, and the two-path water flow intelligent decoupling controller realizes decoupling control on two-path water supply flow and ensures that the two-path water supply flow can meet the user requirements; the function (basis function) in the hidden layer node of the RBF neural network will locally respond to the input signal, and the radial basis function is most commonly a Gaussian function as shown in the formula (4):
the RBF neural network inverse decoupling controller realizes decoupling control on the opening degree of the electromagnetic directional valve of the water flow of the two water outlet pipes which are mutually influenced and mutually coupled, and improves the control accuracy of the water flow of each output loop.
In order to enable a user to know various parameters of water quality on mobile portable equipment in real time, a drinking water source monitoring system based on a ZigBee network is introduced, wherein the system is characterized in that a singlechip 19 collects data of a monitoring water quality sensor 12, a wireless communication module 24 transmits Zigbee signals to transmit the data of the monitoring water quality sensor 12 to a data aggregation processing node 21 and a web server 22, and the data is transmitted to an equipment processor through the data aggregation processing node 21 and the web server 22 to be transmitted to the mobile portable equipment.
As shown in fig. 6-8, the ZigBee network-based drinking water source monitoring system includes a terminal detection node 20, a data aggregation processing node 21, a web server 22, and a data monitoring processor 23, where the terminal detection node 20 is composed of several monitoring devices, and the monitoring devices include a monitoring water quality sensor 12, a singlechip 19, and a wireless communication module 24; the data aggregation processing node 21 and the web server 22 are realized by a program which builds a Linux system by raspberry pie and runs a Django framework 26 and a MySQL database 25; the data monitoring processor 23 is composed of a mobile portable device, and a user can monitor, control and analyze data in real time through a browser of a computer or a mobile phone.
The web server 22 is a web system website developed based on Django and MySQL, manages a plurality of ZigBee-based monitoring devices at the same time, and monitors the device status of each site at the same time.
The singlechip 19 adopts a singlechip processor with the model of STM32F103c8t6, and the wireless communication module 24 adopts a ZigBee communication module with the chip model of CC2530F 256; the monitoring water quality sensor 12 comprises a DS18B20 temperature sensor, a TDS water quality conductivity sensor, a pH value sensor and a turbidity sensor; the data information of the water quality parameters collected by each monitoring device is communicated with the web server 22 through the ZigBee composition network.
The web server 22 is a PC, connected to the ZigBee data aggregation processing node 21, and receives data, and provides web services to implement data query and analysis. The web server 22 runs a c++ program to receive and store data of temperature, TDS value, turbidity and pH value sent back by the ZigBee-based monitoring device into a database.
The information data of each index of the water quality processed by the web server 22 is obtained by the access server of 3G, 4G, wiFi and the like and is displayed on the mobile device in real time; meanwhile, the web server 22 collects data of each monitoring device in the terminal detection node 20, analyzes the data packet, sequentially classifies each data into a MySQL database 25, compares the stored data with a set threshold value when the data is stored in the MySQL database 25, and if the stored data is too large or too small, the web server 22 timely informs a manager and alarms; the data monitoring processor 23 and the MySQL database 25 are implemented by means of UDP communication, and UDP can exchange data on different platforms.
The web system website comprises a user login unit, an analysis query unit, a fortification early warning unit, an equipment management unit and a system management unit; the analysis query unit comprises a detailed query subunit, an option query subunit and an icon query subunit; the fortification early warning unit comprises a monitoring list subunit and a monitoring diagram subunit; the equipment management unit comprises an equipment state subunit and an equipment design subunit; the system management unit includes a user management subunit, a user setting subunit, a hardware status subunit, and a system log subunit, as shown in fig. 8.
The main recorded data of MySQL database 25 are as follows:
1) A user table for recording the user name, the encryption value of the password MD5, the user state, the creation time, the last login time, whether the user is an administrator user or not, and the like;
2) The equipment list records equipment addresses, equipment states, equipment online time, whether temperature monitoring is started or not, a temperature monitoring upper limit value and a temperature monitoring lower limit value; whether to start the TDS monitoring, the upper limit value of the TDS monitoring and the lower limit value of the TDS monitoring; whether to start turbidity monitoring, a turbidity upper limit value and a turbidity lower limit value; whether to start pH monitoring, pH value upper limit value and pH value lower limit value;
3) Each monitoring device corresponds to a device information table, and records device addresses, capturing time, reporting time, current temperature data, TDS data, turbidity data, PH value data and the like.
Working principle: the networked water source monitoring and grading water supply device is connected to the water meter outlet of a household user, a water source passes through the water inlet pipe 5 of the device, the monitoring water quality sensor 12 in the water source monitoring device starts to monitor various parameters of a water factor, and according to parameter values set by the user, the various parameters of the water factor reach user set values, the first electromagnetic directional valve 7 is opened, and the water source directly flows out through the first water outlet pipe 3 which is matched with the valve port of the electromagnetic directional valve; if the values of all parameters of the water factors do not reach the set values of users, the second electromagnetic directional valve 8 is opened, a water source flows out through the second water outlet pipe 4 which is matched with the second electromagnetic directional valve 8, and passes through the hollow fiber ultrafiltration membrane UF filter element 901, the granular fine-washing coconut shell activated carbon filter element 902 and the PP cotton filter element 903 to reach the reservoir 13, so that the water quality sensor 12 in the reservoir 13 can accurately measure all parameters of the water factors, and the stepping motor 10 drives the first baffle 14 and the second baffle 15 which are connected in a cross way to stir the water in the reservoir 13 to be more uniform; meanwhile, in order to meet the requirements of different users, the first electromagnetic directional valve 7 and the second electromagnetic directional valve 8 controlled by the single chip microcomputer 19 can act simultaneously, the first water outlet pipe 3 and the second water outlet pipe 4 are both discharged, two paths of intelligent water flow decoupling controllers are introduced into the single chip microcomputer 19 to control the opening of the first electromagnetic directional valve 7 and the opening of the second electromagnetic directional valve 8, the water flow of the two water pipes of the first water outlet pipe 3 and the second water outlet pipe 4 is regulated, the control accuracy of the water flow of each water outlet pipe is improved, the response speed and the control accuracy are improved, and the stability of the system is improved. And simultaneously, various parameter values of the water quality monitored by the water quality monitoring sensor 12 are transmitted back to the mobile equipment end of the user or the water supply department in real time through the Zigbee network, so that the user and the water supply department can macroscopically control the water quality.
Claims (8)
1. A monitoring system of networked water source monitoring and hierarchical water supply device, its characterized in that: the networked water source monitoring and grading water supply device comprises an integral frame (1), a water inlet pipe (5) is connected into the integral frame (1), the water inlet pipe (5) is communicated with a water source monitoring device (6), a first electromagnetic reversing valve (7) is arranged on the end face of the water source monitoring device (6), a water outlet of the first electromagnetic reversing valve (7) is communicated with a first water outlet pipe (3), a second electromagnetic reversing valve (8) is arranged at the bottom of the water source monitoring device (6), a reservoir (13) is arranged below the water outlet of the second electromagnetic reversing valve (8), and the water outlet of the reservoir (13) is communicated with a second water outlet pipe (4); a first loop water pressure sensor (18) is arranged at the water inlet end of the first water outlet pipe (3), and a second loop water pressure sensor (17) is arranged at the water inlet end of the second water outlet pipe (4); the monitoring system is a drinking water source monitoring system based on a ZigBee network and comprises a terminal detection node (20), a data convergence processing node (21), a web server (22) and a data monitoring processor (23), wherein the terminal detection node (20) consists of monitoring equipment, and the monitoring equipment comprises a monitoring water quality sensor (12), a singlechip (19) and a wireless communication module (24); the data aggregation processing node (21) and the web server (22) are realized by a program which is formed by constructing a Linux system by a raspberry group and running a Django framework (26) and a MySQL database (25); the monitoring devices collect data information of water quality parameters and communicate with a web server (22) through a ZigBee composition network; the data monitoring processor (23) consists of mobile portable equipment, and a user monitors, controls and processes and analyzes data in real time through a browser of a computer or a mobile phone;
two paths of intelligent water flow decoupling controllers are embedded in the singlechip (19), and the singlechip (19) controls the water flow of the first water outlet pipe (3) and the second water outlet pipe (4) by adjusting the opening degrees of the first electromagnetic directional valve (7) and the second electromagnetic directional valve (8); the two-path water flow intelligent decoupling controller comprises a first loop fuzzy least square support vector machine prediction controller, a first loop PID real-time controller, a first loop GM water pressure prediction model, a second loop fuzzy least square support vector machine prediction controller, a second loop PID real-time controller, a second loop GM water pressure prediction model and an RBF neural network inverse decoupling controller; the first loop fuzzy least square support vector machine prediction controller and the first loop PID real-time controller are connected in parallel to be used as a water flow composite controller of the first loop, the second loop fuzzy least square support vector machine prediction controller and the second loop PID real-time controller are connected in parallel to be used as a water flow composite controller of the second loop, and the outputs of the water flow composite controller of the first loop and the water flow composite controller of the second loop are respectively used as inputs of an RBF neural network inverse decoupling controller; the output of the first loop water pressure sensor (18) is used as the input of a first loop GM water pressure prediction model, and the output of the first loop GM water pressure prediction model is used as the feedback quantity of a first loop fuzzy least square support vector machine prediction controller to form the prediction control of the first loop water supply flow; the output of the second loop water pressure sensor (17) is used as the input of a second loop GM water pressure prediction model, and the output of the second loop GM water pressure prediction model is used as the feedback quantity of a second loop fuzzy least square support vector machine prediction controller to form the prediction control of the second loop water supply flow.
2. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: the RBF neural network inverse decoupling controller consists of an integral first loop, an integral second loop and an RBF neural network, wherein the RBF neural network consists of T, H, I, J, K and L six input nodes, ten intermediate nodes and P, Q two output nodes, and the P, Q two output nodes are respectively used as input control amounts of a first electromagnetic directional valve (7) and a second electromagnetic directional valve (8).
3. The networked water source monitoring and staged water supply monitoring system of claim 2, wherein: the output of the water flow composite controller of the first loop is the input of the 1 st integrator of the first loop and the I input of the RBF neural network, the output of the 1 st integrator of the first loop is the H input of the RBF neural network and the 2 nd integrator input of the first loop, and the output of the 2 nd integrator of the first loop is used as the T input of the RBF neural network; the output of the water flow composite controller of the second loop is the input of the 1 st integrator of the second loop and the L input of the RBF neural network, the 1 st integrator output of the second loop is the K input of the RBF neural network and the 2 nd integrator input of the second loop, and the 2 nd integrator output of the second loop is used as the J input of the RBF neural network.
4. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: the web server (22) is a web system website developed based on Django and MySQL, manages a plurality of monitoring devices based on ZigBee at the same time, and monitors the device status of each site at the same time.
5. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: the singlechip processor is characterized in that the model of the singlechip (19) is an STM32F103c8t6 singlechip processor, and the chip of the wireless communication module (24) is a ZigBee communication module of CC2530F 256; the monitoring water quality sensor (12) comprises a DS18B20 temperature sensor, a TDS water quality conductivity sensor, a pH value sensor and a turbidity sensor.
6. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: the web server (22) is a PC, is connected with the ZigBee data aggregation processing node (21) and receives data, and provides web service to realize data query and analysis; the web server (22) operates a C++ program to receive data of temperature, TDS value, turbidity and pH value sent back by the ZigBee-based monitoring equipment and store the data into a database; the information data access server of each index of the water quality processed by the web server (22) is obtained and displayed on the mobile device in real time; meanwhile, the web server (22) collects data of each monitoring device in the terminal detection node (20), analyzes the data packet, sequentially classifies each data into a MySQL database (25), compares the stored data with a set threshold value when the data is stored into the MySQL database (25), and if the stored data is too large or too small, the web server (22) can timely inform a manager and give an alarm; the data monitoring processor (23) and the MySQL database (25) are realized through UDP communication, and the UDP can exchange data on different platforms.
7. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: a first baffle (14) and a second baffle (15) are arranged in the reservoir (13) in a mutually perpendicular manner, the first baffle (14) and the second baffle (15) are fixed in a staggered manner through grooves, a rotating shaft (16) is arranged at the staggered fixing position, and the rotating shaft (16) penetrates through the joint of the first baffle (14) and the second baffle (15); a stepping motor (10) is arranged below the reservoir (13), and the stepping motor (10) drives the first baffle (14) and the second baffle (15) through a rotating shaft (16).
8. The networked water source monitoring and staged water supply monitoring system of claim 1, wherein: a filter screen (9) is arranged above the inner wall of the reservoir (13); the filter screen (9) comprises a filter box (904), wherein a hollow fiber ultrafiltration membrane UF filter element (901) is arranged in the filter box (904) from bottom to top, and a coconut shell activated carbon filter element (902) and a PP cotton filter element (903) are subjected to particle fine washing.
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Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100839515B1 (en) * | 2007-09-28 | 2008-06-19 | (주)서흥이엔지 | A small scale water supply system |
| CN101615888A (en) * | 2009-07-14 | 2009-12-30 | 中国船舶重工集团公司第七一五研究所 | A kind of signal demodulation method of portable multifunctional optical fiber hydrophone |
| CN101694583A (en) * | 2009-10-14 | 2010-04-14 | 东北大学 | Ore grinding process operation control method based on multivariable decoupling (IMC) technology |
| CN101968649A (en) * | 2010-10-18 | 2011-02-09 | 淮阴工学院 | Network type control system for live pig culturing environment and intelligent environment factor control method |
| US8699558B1 (en) * | 2011-02-25 | 2014-04-15 | Pmc-Sierra Us, Inc. | Decoupling and pipelining of multiplexer loop in parallel processing decision-feedback circuits |
| CN203890176U (en) * | 2014-04-09 | 2014-10-22 | 安徽华盛科技控股股份有限公司 | Ultrapure water integrated machine for CIT laboratory |
| CN204369739U (en) * | 2015-01-08 | 2015-06-03 | 新疆杰农种子有限责任公司 | A kind of melon and fruit wash seeds Waste Water Treatment |
| CN105955167A (en) * | 2016-06-16 | 2016-09-21 | 武克易 | Intelligent water quality monitoring system |
| CN106227042A (en) * | 2016-08-31 | 2016-12-14 | 马占久 | Dissolved oxygen control method based on fuzzy neural network |
| CN106325252A (en) * | 2016-09-28 | 2017-01-11 | 华北电力大学 | Multi-level large-span large data oriented power equipment state monitoring and evaluating system |
| CN107178398A (en) * | 2017-06-23 | 2017-09-19 | 西安西热节能技术有限公司 | A kind of thermoelectricity decoupled system for improving steam power plant's energy utilization quality |
| CN107700597A (en) * | 2017-10-31 | 2018-02-16 | 安徽舜禹水务股份有限公司 | A kind of water tank water turbidity intelligent protection device |
| CN107740465A (en) * | 2017-10-24 | 2018-02-27 | 四川省东宇信息技术有限责任公司 | It is a kind of that there is regulation hydraulic pressure and the running water valve of water filtration function |
| CN108589833A (en) * | 2018-04-02 | 2018-09-28 | 淮阴工学院 | A kind of monitoring of water source and intelligentized control method water supply by stage device |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| AU2014280840A1 (en) * | 2013-06-12 | 2016-01-07 | Applied Hybrid Energy Pty Ltd | Electrical power control method and system |
-
2018
- 2018-04-02 CN CN201810284475.6A patent/CN108425405B/en active Active
Patent Citations (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR100839515B1 (en) * | 2007-09-28 | 2008-06-19 | (주)서흥이엔지 | A small scale water supply system |
| CN101615888A (en) * | 2009-07-14 | 2009-12-30 | 中国船舶重工集团公司第七一五研究所 | A kind of signal demodulation method of portable multifunctional optical fiber hydrophone |
| CN101694583A (en) * | 2009-10-14 | 2010-04-14 | 东北大学 | Ore grinding process operation control method based on multivariable decoupling (IMC) technology |
| CN101968649A (en) * | 2010-10-18 | 2011-02-09 | 淮阴工学院 | Network type control system for live pig culturing environment and intelligent environment factor control method |
| US8699558B1 (en) * | 2011-02-25 | 2014-04-15 | Pmc-Sierra Us, Inc. | Decoupling and pipelining of multiplexer loop in parallel processing decision-feedback circuits |
| CN203890176U (en) * | 2014-04-09 | 2014-10-22 | 安徽华盛科技控股股份有限公司 | Ultrapure water integrated machine for CIT laboratory |
| CN204369739U (en) * | 2015-01-08 | 2015-06-03 | 新疆杰农种子有限责任公司 | A kind of melon and fruit wash seeds Waste Water Treatment |
| CN105955167A (en) * | 2016-06-16 | 2016-09-21 | 武克易 | Intelligent water quality monitoring system |
| CN106227042A (en) * | 2016-08-31 | 2016-12-14 | 马占久 | Dissolved oxygen control method based on fuzzy neural network |
| CN106325252A (en) * | 2016-09-28 | 2017-01-11 | 华北电力大学 | Multi-level large-span large data oriented power equipment state monitoring and evaluating system |
| CN107178398A (en) * | 2017-06-23 | 2017-09-19 | 西安西热节能技术有限公司 | A kind of thermoelectricity decoupled system for improving steam power plant's energy utilization quality |
| CN107740465A (en) * | 2017-10-24 | 2018-02-27 | 四川省东宇信息技术有限责任公司 | It is a kind of that there is regulation hydraulic pressure and the running water valve of water filtration function |
| CN107700597A (en) * | 2017-10-31 | 2018-02-16 | 安徽舜禹水务股份有限公司 | A kind of water tank water turbidity intelligent protection device |
| CN108589833A (en) * | 2018-04-02 | 2018-09-28 | 淮阴工学院 | A kind of monitoring of water source and intelligentized control method water supply by stage device |
Non-Patent Citations (3)
| Title |
|---|
| 基于ANSYS的电振给料机的设计与数学建模;唐俊;严辉容;杨辉;;西华大学学报(自然科学版)(02);全文 * |
| 基于现场总线技术的水产养殖过程智能监控系统;马从国;赵德安;秦云;陈前亮;刘喆;;农业机械学报(08);全文 * |
| 基于运行模式识别的球磨机自适应解耦模糊控制与仿真;王恒;贾民平;陈左亮;谢超;;热能动力工程(04);全文 * |
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