CN110988295A - Floating type sewage detection system and method based on NB _ IoT - Google Patents
Floating type sewage detection system and method based on NB _ IoT Download PDFInfo
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- CN110988295A CN110988295A CN201911408826.0A CN201911408826A CN110988295A CN 110988295 A CN110988295 A CN 110988295A CN 201911408826 A CN201911408826 A CN 201911408826A CN 110988295 A CN110988295 A CN 110988295A
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Abstract
The invention discloses a floating sewage detection system based on NB _ IoT, which comprises a main control module, a data acquisition module, an NB _ IoT communication module, a power supply module, a display module, a data processing module and a positioning module, wherein the data acquisition module, the NB _ IoT communication module, the power supply module and the display module are respectively connected with the main control module; the data acquisition module is used for acquiring water quality information, performing primary treatment and transmitting the water quality information to the main control module, and the main control module transmits the primarily treated water quality information to the data processing module through the communication module for deep treatment and display to obtain a relevant water quality information result for broadcasting; the positioning module is used for acquiring the current position of the detection data and feeding back the current position in time; the NB-IOT communication module is adopted for real-time communication, so that the power consumption is low, and the long-term data transmission function can be guaranteed; and the collected water quality information is subjected to prediction model training to obtain a water quality prediction model, so that the water quality information is predicted, and the change of the water quality information can be predicted in advance to carry out early warning.
Description
Technical Field
The invention relates to the research field of water quality detection, in particular to a floating sewage detection system and method based on NB _ IoT.
Background
China is short of water resources, and water systems of a sea river, a Liaojiang river, a Huai river, a yellow river, a Songhua river, a Yangtze river and a Zhujiang river 7 Dajiang river are polluted to different degrees. The water pollution is particularly serious in Guangdong areas in Yangtze river basin. The environmental protection department adopts a plurality of measures to invest a great deal of manpower and financial resources to monitor the water quality so as to hope to adopt measures in advance to reduce pollution. However, the traditional water quality monitoring mode is still manual-based timing acquisition, and the time for taking measures to prevent water pollution is often missed due to insufficient real time.
The most common prior art has two forms, namely a buoy monitoring system and a shore upright monitoring system; the prior art has the following defects: (1) the buoy is deployed in the center of a water area, so that power cannot be supplied in a wired mode, monitoring data need to be transmitted remotely, and the buoy is floating in an indefinite position and is easy to float away from an original definite position; (2) the GPRS module has large power consumption and often has insufficient power supply; (3) the volume is large, the appearance is attractive, and some lawless persons can easily bypass the system to implement illegal path of polluted water source; (4) there is a lack of prediction of water quality changes.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a floating sewage detection system based on NB _ IoT, which can quickly monitor water quality change in real time, generate a predicted water quality model, realize synchronous check of a mobile phone and a computer through calculation of a neural network model, ensure low power consumption, concealment, intelligence and portability, and greatly save the financial and manpower of a monitoring department.
Another object of the present invention is to provide a NB _ IoT-based floating sewage detection method.
The purpose of the invention is realized by the following technical scheme:
a floating sewage detection system based on NB _ IoT is characterized by comprising a main control module, a data acquisition module, an NB _ IoT communication module, a power supply module, a display module, a data processing module and a positioning module, wherein the data acquisition module, the NB _ IoT communication module, the power supply module and the display module are respectively connected with the main control module; the data acquisition module is used for acquiring water quality information, performing primary treatment and transmitting the water quality information to the main control module, and the main control module transmits the primarily treated water quality information to the data processing module through the communication module for deep treatment and display to obtain a relevant water quality information result for broadcasting; the positioning module is used for acquiring the current position of the detection data and feeding back the current position in time; the power module is connected with the main control module and the display module.
Further, the data acquisition module comprises a digital quantity sensor and an analog quantity sensor.
Further, the analog quantity sensor includes: a temperature sensor, a pH value sensor, a turbidity sensor and a dissolved oxygen sensor; the digital quantity sensor comprises a TDS sensor.
Further, the analog sensor is connected to the main control module through a signal acquisition processing circuit, the signal acquisition processing circuit includes: the signal transmitting circuit and the ADC analog-to-digital conversion circuit; the digital sensor is connected to the main control module through a TDS data transmission circuit.
Furthermore, the main control module adopts K STC15F2K60S2 as a main control chip and is divided into a master machine and a slave machine.
Further, the host and the slave are connected in the form of a jumper cap and communicate through a UART.
Furthermore, the power supply module adopts a DC-DC power supply module to be set as a multi-channel power supply circuit.
Furthermore, the data processing module comprises a database module and a neural network training module, water quality information data are collected through the database module, the water quality information data are trained through the neural network training module, a water quality prediction model is obtained, and then water quality change is predicted.
The other purpose of the invention is realized by the following technical scheme:
a floating sewage detection method based on NB _ IoT is characterized by comprising the following steps:
s1, acquiring water quality information data through a data acquisition module;
s2, converting the water quality information data into digital quantity signals and transmitting the digital quantity signals to the main control module;
s3, the main control module forwards the digital quantity signal to the cloud server through the communication module, the digital quantity signal is cached in a temporary database of the cloud server, and the local server database downloads the digital quantity signal from the temporary database at regular time and stores the digital quantity signal in the local server database;
s4, training the sample through a data processing module to obtain a neural network model;
s5, predicting the water quality change rule through the neural network model, outputting a predicted value in real time, predicting, broadcasting and early warning the water quality information through the predicted value, and transmitting the water quality information to a user side; the method specifically comprises the following steps: extracting a section of historical water quality data from a local server database as sample data of neural network training, firstly, using ADF to perform sample data stationarity detection, using a difference mode to correct the sample data to stabilize the data, then performing normalization processing on the stabilized data, inputting the processed data as time series effective data into a neural network, and training by using a deep LSTM neural network model to enable the output error to meet the requirement of an error loss function; and outputting the model parameters of the trained model in a JSON mode, and calling the model parameters in upper computer monitoring software so as to predict the water quality change in real time and display the result.
Further, the method also comprises the following steps: through orientation module location detecting system position, when skew primary standard position, through remote control host system, remove detecting system and get back to primary standard position, install aquatic driving motor promptly, the thick liquid of rotatable direction is connected to the motor, thereby the target azimuth data through the calculation with GPS fix data difference, thereby convey driving motor with distance and direction and realize removing the function of getting back to the original position.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the NB-IOT communication module is adopted for real-time communication, so that the power consumption is low, and the long-term data transmission function can be guaranteed; and the collected water quality information is subjected to prediction model training to obtain a water quality prediction model, so that the water quality information is predicted, and the change of the water quality information can be predicted in advance to carry out early warning.
2. The invention adopts the positioning module to detect the position, has strong maneuverability and is convenient to be transferred to other water areas in time for monitoring and checking.
3. The invention can transmit the predicted water quality information to the user side in time, and the user can monitor the water quality change in real time through the user side.
The invention can adopt a portable structure with small volume and low cost, can realize distributed arrangement and distributed detection.
Drawings
FIG. 1 is a block diagram of a floating sewage detection system based on NB _ IoT according to the present invention;
FIG. 2 is a block diagram of the connection of the temperature sensor to the main control module according to the embodiment of the present invention;
FIG. 3 is a block diagram of a neural network prediction process according to an embodiment of the present invention;
FIG. 4 is a block diagram of the software flow in the embodiment of the present invention;
fig. 5 is a flowchart of a NB _ IoT-based floating sewage detection method according to the present invention.
Detailed Description
The present invention will be described in further detail with reference to examples and drawings, but the present invention is not limited thereto.
Example (b):
a floating sewage detection system based on NB _ IoT is provided, which is provided with a hardware module and a software functional module for real-time monitoring of water quality. Research and development are carried out based on NB-IOT communication technology, parameters such as temperature, turbidity degree, PH, TDS and DO of a water body are collected by adopting an MCU (microprogrammed control unit) taking STC15F2K60S2 as a main control unit, and data are transmitted in real time through an NB-IOT module, so that flexible and efficient monitoring is realized. Deployment is carried out through the cloud server, a database system and a mobile phone end monitoring program are developed, and a complete Internet of things system architecture is constructed.
Specifically, as shown in fig. 1: the hardware module comprises a main control module, a data acquisition module, an NB _ IoT communication module, a power supply module and a display module which are respectively connected with the main control module, and a data processing module and a positioning module which are connected through the communication module;
wherein, the data acquisition module includes digital quantity sensor and analog quantity sensor, analog quantity sensor includes: a temperature sensor, a pH value sensor, a turbidity sensor and a dissolved oxygen sensor; digital sensor includes the TDS sensor for gather quality of water information and carry out primary treatment, transmit host system, wherein, temperature sensor is LM35DZ temperature sensor, promptly analog sensor passes through signal acquisition processing circuit and is connected to host system, signal acquisition processing circuit includes: the signal transmitting circuit and the ADC analog-to-digital conversion circuit; the digital sensor is connected to the main control module through a TDS data transmission circuit. The ADC analog-to-digital conversion circuit adopts a high-performance analog-to-digital converter ADS1256, a glass electrode PH value sensor (DOLE6211) adopted by the invention is dissolved in the turbidity of the oxygen sensor, the PH electrode outputs a mV signal, namely an analog signal, through BNC, if the PH electrode is directly connected with the single chip microcomputer, the single chip microcomputer cannot identify the mV signal returned by the PH sensor, at the moment, an external ADC module is required to perform analog-to-digital conversion, and the specific PH value is returned to the single chip microcomputer. In addition, after the dissolved oxygen sensor (DOLE6211) performs analog-to-digital conversion, the obtained minimum division value is 0.01mg/L, and the obvious change of oxygen cannot be intuitively understood, at this time, signals need to be amplified, and the specific change condition of DO can be intuitively understood on the upper computer by means of the measured DO standard values under various environments, including 64 times of amplification and standard values without amplification. In summary, the four sensors of PH, DO, TDS, and temperature are externally connected to the ADS1256 module and then connected to the single chip, i.e., the main control module, so as to achieve accurate data acquisition, wherein the temperature sensor is connected to the main control module as shown in fig. 2.
The main control module adopts K STC15F2K60S2 as a main control chip and is divided into a host and a slave. The host computer and the slave computer are connected in a jumper cap mode and communicate through UART. The main control module transmits the primarily processed water quality information to the data processing module through the communication module for advanced processing and display, and related water quality information results are obtained for broadcasting;
the power module adopts a DC-DC power module to be a multi-channel power circuit, and the power module is connected with the main control module and the display module to supply power.
The data processing module comprises a database module and a neural network training module, water quality information data are collected through the database module, the water quality information data are trained through the neural network training module to obtain a water quality prediction model, then water quality change and early warning are predicted, and the prediction process is shown in figure 3.
The positioning module is used for acquiring the current position of the detection data and feeding back the current position in time.
The invention has strong concealment, is provided with a plurality of sensors, monitors water quality by using two analysis methods of physics and chemistry, is suitable for rapidly monitoring and knowing the water body condition, and supports the use of a mobile phone APP or PC end to check data. Has certain concealment, is suitable for short-term dark monitoring and is not easy to be found. The system has high function integration degree and mobility, and supports multi-site and wide-area monitoring. And (3) analyzing and processing the acquired data in real time by using upper computer software, so that the time period of the next occurrence of the pollution source can be predicted and judged. The device is particularly suitable for secondary monitoring of factories with emission pollution, greatly saves manpower, material resources and financial resources, and achieves the purpose of remote monitoring. The data acquisition precision of temperature, pH value, conductivity, dissolved oxygen and turbidity is less than or equal to 0.1; the corresponding time for collecting the sensing data to the main control end is less than or equal to 5 s; the service life of the general battery in works is more than or equal to 1 year.
The software module is designed as follows:
the system comprises a sensing layer, a network layer and an application layer; wherein, the sensing layer is a sewage detection system and is used for acquiring water quality information; the network layer comprises a base station and a detection server, the sewage detection system is communicated with the base station through the NB _ IOT module and transmits the water quality information to the server layer, and the detection server processes the water quality information; the application layer comprises a remote monitoring terminal which can be a PC terminal and a mobile terminal, and a user can remotely detect through the remote monitoring terminal and perform feedback and control.
The mobile terminal can check the data transmitted by the sewage monitoring system in real time through the sensor data function of the WeChat small program, and the data is updated every 10 seconds, so that the accuracy of real-time monitoring is achieved. Meanwhile, an early warning value function is set on the interface, a user can set warning values of sensors to be monitored, and when monitoring data reach the early warning values, software can automatically send out abnormal warnings to the user. The sensor location function of the wechat applet can view the specific location of the device in real time. The sensing data line graph of the WeChat small program provides a real-time data curve observation function, and can count the water quality condition monitored by the system within a period of time (2 hours). The user can know the environmental condition of the water area through the intuitive curve chart. The user can utilize APP software to look over sewage monitoring system real time transmission's data, and data update once every second reaches real-time supervision's accuracy. Meanwhile, the APP sets an early warning value, and when the monitoring data reaches the early warning value, the software can automatically give an alarm to a user. The software links the functions of the curve table and can count the data monitoring conditions of daily, weekly and monthly. The user can know the environmental condition of the water area through the intuitive curve chart. Meanwhile, the time period of the next occurrence of the pollution source can be analyzed and predicted.
And the PC end, namely the Python end of the computer, realizes the functions of collecting the numerical values of all sensors in one day and displaying the numerical values by a normalized line graph. An alert is provided that certain sensor data is abnormal for a certain period of time during the day. The method specifically comprises the following steps: using a program developed by C #, carrying out data analysis by Python, and calling the Python analysis result in the upper computer software by writing a C # command; the second method is that a Python program generates a neural network model, parameters are transmitted in a Json field (Key, Value) mode, and the C # also obtains the neural network model by taking the parameters in the Json field (Key, Value) mode, so that the upper computer software written from the C # can be displayed
Introduction of software flow: when the system is powered on, the SPI, UART and AD modules are initialized. For the stability of the system and the accuracy of data, a sensor initialization function is added; considering that a delay function exists in the sensor when monitoring data, in order to avoid the situation that the normal monitoring of the next sensor is skipped and the monitoring data is sent, a timer 0 (serial port 1) is set in the design to count for 1 second, when the time reaches 1 second, the single chip microcomputer is applied for interruption to acquire and send the sensor data, then the timer is reset, the counting is carried out again, and a new round of data monitoring and sending is started; after each sensor is collected and processed, another serial port of the host can transmit data to the NB _ IoT module in time, and the NB _ IoT module reports the data to the cloud in time, so that the cloud can conveniently send the data to the software of the upper computer, as shown in FIG. 4.
Because the external analog-to-digital conversion module exists, the SPI initialization needs to be added in front of the analog-to-digital conversion module, so that the module can normally operate. When the sensor is opened for the first time and data detection is carried out, errors are often generated due to the fact that the sensor is not used for a long time, the normal phenomenon exists, for the stability of a system and the accuracy of data, a sensor initialization function is added after an analog-to-digital conversion module is initialized, each sensor is enabled to carry out 6000 times of detection, and the situation that errors occur in the data is greatly reduced. In addition, considering that a delay function exists when the sensor detects data, in order to avoid the situation that normal detection of the next sensor is skipped and the detection data is sent, a timer 0 (a serial port 1) is set to count for 1 second, when the situation reaches 1 second, the single chip microcomputer is applied for interrupting the acquisition and sending of the sensor data (the first second is a PH sensor, the second is a DO sensor, the third is a turbidity sensor, the fourth is a TDS sensor, and the fifth is a temperature sensor), then the timer is cleared, the counting is carried out again, a new round of data detection and sending is started, and in addition, the situation that the acquisition of each sensor data is finished, the data is sent immediately, and then the interruption is waited. And the data is transmitted to the server by matching with a data transmission module (OnenetM5310-a) to realize the real-time detection and monitoring of the remote end.
A NB _ IoT-based floating sewage detection method, as shown in fig. 5, includes the following steps:
s1, acquiring water quality information data through a data acquisition module;
s2, converting the water quality information data into digital quantity signals and transmitting the digital quantity signals to the main control module;
s3, the main control module forwards the digital quantity signal to a cloud server through a communication module and stores the digital quantity signal in a database;
s4, training the sample through a data processing module to obtain a neural network model;
and S5, predicting the water quality change rule through the neural network model, outputting a predicted value in real time, and predicting and broadcasting the water quality information through the predicted value.
Further, the method also comprises the following steps: the position of the detection system is located through the positioning module, and when the detection system deviates from the original fixed position, the detection system is moved back to the original fixed position through the remote control main control module.
The above embodiments are preferred embodiments of the present invention, but the present invention is not limited to the above embodiments, and any other changes, modifications, substitutions, combinations, and simplifications which do not depart from the spirit and principle of the present invention should be construed as equivalents thereof, and all such changes, modifications, substitutions, combinations, and simplifications are intended to be included in the scope of the present invention.
Claims (10)
1. A floating sewage detection system based on NB _ IoT is characterized by comprising a main control module, a data acquisition module, an NB _ IoT communication module, a power supply module, a display module, a data processing module and a positioning module, wherein the data acquisition module, the NB _ IoT communication module, the power supply module and the display module are respectively connected with the main control module; the data acquisition module is used for acquiring water quality information, performing primary treatment and transmitting the water quality information to the main control module, and the main control module transmits the primarily treated water quality information to the data processing module through the communication module for deep treatment and display to obtain a relevant water quality information result for broadcasting; the positioning module is used for acquiring the current position of the detection data and feeding back the current position in time; the power module is connected with the main control module and the display module.
2. The NB _ IoT based floating sewage detection system according to claim 1 wherein the data acquisition module comprises a digital quantity sensor and an analog quantity sensor.
3. The NB _ IoT based floating sewage detection system according to claim 2 wherein said analog quantity sensor comprises: a temperature sensor, a pH value sensor, a turbidity sensor and a dissolved oxygen sensor; the digital quantity sensor comprises a TDS sensor.
4. The NB _ IoT-based floating sewage detection system according to claim 3 wherein the analog sensor is connected to the master control module through a signal acquisition and processing circuit comprising: the signal transmitting circuit and the ADC analog-to-digital conversion circuit; the digital sensor is connected to the main control module through a TDS data transmission circuit.
5. The NB _ IoT-based floating sewage detection system according to claim 1 wherein the master control module uses K STC15F2K60S2 as master control chips, and is divided into a master and a slave.
6. The NB _ IoT based floating sewage detection system according to claim 5 wherein the master and slave are connected by means of jumper caps and communicate via UART.
7. The NB _ IoT-based floating sewage detection system according to claim 1 where the power module is configured as a multi-channel power circuit using a DC-DC power module.
8. The floating type sewage detection system based on NB _ IoT as claimed in claim 1, wherein the data processing module comprises a database module and a neural network training module, and the water quality information data is collected by the database module and trained by the neural network training module to obtain a water quality prediction model, thereby predicting water quality changes.
9. A floating sewage detection method based on NB _ IoT is characterized by comprising the following steps:
s1, acquiring water quality information data through a data acquisition module;
s2, converting the water quality information data into digital quantity signals and transmitting the digital quantity signals to the main control module;
s3, the main control module forwards the digital quantity signal to the cloud server through the communication module, the digital quantity signal is cached in a temporary database of the cloud server, and the local server database downloads the digital quantity signal from the temporary database at regular time and stores the digital quantity signal in the local server database;
s4, training the sample through a data processing module to obtain a neural network model;
s5, predicting the water quality change rule through the neural network model, outputting a predicted value in real time, predicting, broadcasting and early warning the water quality information through the predicted value, and transmitting the water quality information to a user side; the method specifically comprises the following steps: extracting a section of historical water quality data from a local server database as sample data of neural network training, firstly, using ADF to perform sample data stationarity detection, using a difference mode to correct the sample data to stabilize the data, then performing normalization processing on the stabilized data, inputting the processed data as time series effective data into a neural network, and training by using a deep LSTM neural network model to enable the output error to meet the requirement of an error loss function; and outputting the model parameters of the trained model in a JSON mode, and calling the model parameters in upper computer monitoring software so as to predict the water quality change in real time and display the result.
10. The NB _ IoT-based floating sewage detection method according to claim 9, wherein the output error satisfies a requirement of an error loss function, specifically: and when the root mean square of the output value of the training sample and the actual output value is less than 0.1, the requirement of the error loss function is met.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112255388A (en) * | 2020-11-14 | 2021-01-22 | 杭州联芳科技有限公司 | Networked water quality real-time monitoring intelligent early warning device |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN112255388A (en) * | 2020-11-14 | 2021-01-22 | 杭州联芳科技有限公司 | Networked water quality real-time monitoring intelligent early warning device |
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