CN115266708B - Embedded sand content measuring method based on cyclic neural network - Google Patents
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 32
- 239000004576 sand Substances 0.000 title claims abstract description 29
- 125000004122 cyclic group Chemical group 0.000 title claims abstract description 28
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- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 description 5
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- 210000002569 neuron Anatomy 0.000 description 1
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Abstract
The invention discloses an embedded sand content measuring method based on a cyclic neural network, which comprises the following steps: initial setting; collecting data; data processing; calculating data; displaying data; storing data; and (5) data transmission. The invention is applied to river basin water resources such as river channels and the like and water environment monitoring, has convenient operation, can carry out multichannel measurement, has high measurement accuracy, is convenient to store and view data, and can realize remote control and data transmission.
Description
Technical Field
The invention relates to the technical field of turbidity and sand content measurement, in particular to an embedded sand content measurement method based on a circulating neural network.
Background
The water resource inspection is a basic work necessary for national economy construction, and along with the rapid development of national economy, the contradiction between supply and demand of water resources is more prominent, so that the hydrologic work is required to support sustainable utilization of water resources by using higher-quality water resource information, wherein the turbidity and the sand content of the water body are important two indexes. Therefore, based on market demands and engineering projects, a set of measuring system capable of measuring turbidity and sand content of water in real time is urgently needed for accurately monitoring water quality.
Disclosure of Invention
In view of the above-mentioned defects or shortcomings in the prior art, it is desirable to provide an embedded sand content measuring method based on a cyclic neural network, which is applied to river basin water resources such as river channels and the like and water environment monitoring, and has the advantages of convenient operation, multi-channel measurement, high measurement accuracy, convenient data storage and viewing, and realization of remote control and transmission.
The invention provides an embedded sand content measuring method based on a cyclic neural network, which comprises the following steps:
S11: the method comprises the steps of (1) initializing a singlechip program, setting a sampling time interval Ts, setting communication serial port parameters, and configuring turbidity sensor coefficients a, b and c and circulating neural network parameters V, U and W;
s12: data acquisition, namely selecting a measurement channel, acquiring a group of temperature values and a group of turbidity analog quantities per TS, wherein the turbidity analog quantities are photoelectric signals acquired by a turbidity sensor, and acquiring longitude and latitude and measurement time of a measurement point;
S13: data processing, namely taking a current temperature value as an effective temperature value according to a sampling sequence, carrying out A/D conversion on N groups of turbidity analog quantities after taking, and obtaining a current signal value by averaging;
S14: calculating data, namely calculating turbidity value through curve fitting equation y m＝a(xm)2+b(xm) +c; wherein: m is a positive integer, x m is a current signal value obtained in the m time, and y m is a turbidity value corresponding to x m; correcting the turbidity value according to the effective temperature value and the temperature control array TEP [ n ] = { t 1,t2,···,tn }, and obtaining a corrected turbidity value Y m;
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti),ti<temperture≤ti+1;
Where n is a natural number greater than 1, t n is the nth control temperature value, and t n>tn-1, i= {1,2, temperture _scale is the temperature correction coefficient, temperture is the effective temperature value;
Calculating a sand content value M m by using a cyclic neural network system according to the corrected turbidity value Y m;
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s 0 = 0;
The formula (1) is a calculation formula of an output layer of the cyclic neural network, wherein the output layer is a full-connection layer, namely, each node of the output layer is connected with each node of the hidden layer, V is a weight matrix of the output layer, and g is an activation function of the output layer;
the formula (2) is a calculation formula of a hidden layer of the cyclic neural network, the hidden layer is a cyclic layer, U is a weight matrix of an input layer, W is a weight matrix of the cyclic layer, and f is a cyclic layer activation function;
s15: displaying the data, namely displaying the corrected turbidity value and the sand content value in a curve form in real time;
S16: data storage, namely storing a group of data according to the current signal value-corrected turbidity value-sand content value-longitude and latitude-measurement time in a data acquisition sequence;
S17: and data transmission, namely sending the stored data to a local computer through an RS485 communication module, sending the stored data to a management center server through an NB-IoT communication module, and sending the stored data to a mobile terminal through Bluetooth.
Further, the communication serial port parameters include a voice interaction serial port parameter, a Bluetooth interaction serial port parameter, an RS485 communication serial port parameter and an NB-IoT communication serial port parameter.
Compared with the prior art, the invention has the beneficial effects that:
according to the application, N groups of turbidity analog quantities are obtained through an underwater parameter acquisition unit, the current signal value is obtained by averaging the N groups of turbidity analog quantities after A/D conversion, the turbidity value is calculated according to a curve fitting equation y m＝a(xm)2+b(xm) +c, then the corrected turbidity value is obtained through temperature compensation correction, and the sand content value is calculated by using a cyclic neural network according to the corrected turbidity value. The data transmission between the underwater parameter acquisition unit and the water surface parameter display unit is protected by the power isolation circuit and the signal isolation circuit, so that the safety of the system is improved. The application takes the singlechip as the core, realizes automatic measurement, has accurate detection, and ensures that a user accurately knows the water quality condition.
The voice interaction module has voice recognition and broadcasting functions, and the microprocessor can perform corresponding operation by itself after recognizing voice results; the invention has the Bluetooth communication function, and can be connected to a mobile phone by opening Bluetooth to cooperate with mobile phone app to receive and check data; the invention has the RS485 communication function, and a user can be connected to a computer in a wired mode and cooperate with local software to receive and check data; the invention has the NB-IoT wireless communication function, can be connected to a server of a management system after the corresponding IP and port are configured, and is convenient for remote data viewing and receiving.
It should be understood that the description in this summary is not intended to limit the critical or essential features of the embodiments of the invention, nor is it intended to limit the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
Other features, objects and advantages of the present invention will become more apparent upon reading of the detailed description of non-limiting embodiments, made with reference to the accompanying drawings in which:
FIG. 1 is a block diagram of an embedded sand content measurement system based on a recurrent neural network;
FIG. 2 is a flow chart of an embedded sand content measurement method based on a recurrent neural network;
Fig. 3 is a schematic diagram of a recurrent neural network.
Reference numerals in the drawings: 1. a water surface parameter display unit; 2. an underwater parameter acquisition unit; 3. an isolation protection unit; 4. a power supply unit; 5. a temperature acquisition unit; 6. a voltage acquisition unit; 7. a man-machine interaction unit; 8. a communication unit; 9. a GPS module;
11. A microprocessor; 12. a hardware reset module; 13. a real-time clock module; 14. a parameter display module; 15. a dormancy wakeup module; 16. a data downloading module; 17. a data storage module; 18. a serial port communication module;
21. a turbidity sensor;
31. a power isolation circuit; 32. a signal isolation circuit;
41. A lithium battery; 42. a rectifying and filtering circuit; 43. a voltage conversion circuit; 44. a low power consumption processing circuit; 45. an overload overcurrent protection circuit; 46. a reverse connection protection circuit; 47. a charging module;
71. A voice interaction module; 72. a Bluetooth interaction module;
81. An RS485 communication module; 82. NB-IoT communication module.
Detailed Description
The invention is described in further detail below with reference to the drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the invention and are not limiting of the invention. It should be noted that, for convenience of description, only the portions related to the invention are shown in the drawings.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
Referring to fig. 1, an embodiment of the present invention provides an embedded sand content measurement system of a recurrent neural network, including:
the water surface parameter display unit 1 comprises a microprocessor 11, and a hardware reset module 12, a real-time clock module 13, a parameter display module 14, a dormancy wakeup module 15, a data download module 16, a data storage module 17 and a serial port communication module 18 which are respectively connected with the microprocessor 11;
The underwater parameter acquisition unit 2 is connected with the water surface parameter display unit 1 through the isolation protection unit 3 and is used for acquiring the related information of underwater turbidity;
an isolation protection unit 3 including a power supply isolation circuit 31 and a signal isolation circuit 32;
the power supply unit 4 is respectively connected with the water surface parameter display unit 1 and the underwater parameter acquisition unit 2;
the temperature acquisition unit 5 is connected with the water surface parameter display unit 1 through the isolation protection unit 3;
the voltage acquisition unit 6 is connected with the underwater parameter acquisition unit 2 and is used for acquiring the working voltage of the underwater parameter acquisition unit 2, and the voltage acquisition unit 6 is connected with the water surface parameter display unit 1 through the isolation protection unit 3;
The man-machine interaction unit 7 is connected with the water surface parameter display unit 1;
the communication unit 8 is connected with the water surface parameter display unit 1;
the GPS module 9 is connected with the water surface parameter display unit 1.
In this embodiment, a circulating neural network is configured in the microprocessor 11 of the water surface parameter display unit 1, the underwater turbidity analog quantity is obtained by the underwater parameter acquisition unit 2, the turbidity analog quantity is converted into a current signal value, and the current signal value is transmitted to the water surface parameter display unit 1 through the power isolation circuit 31 and the signal isolation circuit 32 for data calculation. And calculating a turbidity value according to a curve fitting equation y=ax 2 +bx+c, obtaining a corrected turbidity value through temperature compensation correction, and calculating a sand content value according to the corrected turbidity value by using a cyclic neural network.
The corrected turbidity value and the sand content value are displayed in real time by the parameter display module 14 in the form of a curve, and the parameter display module 14 is preferably a touch screen. Simultaneously carrying out data storage and data transmission, and storing the current signal value, the corrected turbidity value, the sand content value, the longitude and latitude and the measurement time as a group of data; the transmission of data is completed by the communication unit 8.
The invention takes the singlechip as the core, thereby realizing automatic measurement. The operation is convenient, and the detection is accurate. The data storage is convenient to check, remote control and transmission can be realized, and accurate water quality understanding of a user is ensured.
In a preferred embodiment, as shown in fig. 1, the underwater parameter acquisition unit 2 is composed of a plurality of mutually independent turbidity sensors 21, and the acquisition of the turbidity information of the water body is realized through the plurality of mutually independent turbidity sensors 21.
In a preferred embodiment, as shown in fig. 1, the power supply unit 4 includes:
The lithium battery 41 is respectively connected with the water surface parameter display unit 1 and the underwater parameter acquisition unit 2 through a rectification filter circuit 42, a voltage conversion circuit 43, a low-power consumption processing circuit 44, an overload overcurrent protection circuit 45 and an inverse connection protection circuit 46 in sequence;
the charging module 47 is connected to the lithium battery 41.
In this embodiment, the system uses the lithium battery 41 to supply power, the power supply safety of the system is ensured by various protection circuits, and the lithium battery 41 is charged by the charging module 47.
In a preferred embodiment, as shown in fig. 1, the human-computer interaction unit 7 comprises:
the voice interaction module 71 is connected with the microprocessor 11 through the serial port communication module 18;
The bluetooth interaction module 72 is connected with the microprocessor 11 through the serial port communication module 18, and is used for communication connection between the water surface parameter display unit 1 and the mobile terminal.
In the present embodiment, the voice control of the measurement system is realized by the voice interaction module 71. Operation control and information viewing by the mobile terminal is achieved through the bluetooth interaction module 72. The convenience of system operation is improved.
In a preferred embodiment, as shown in fig. 1, the communication unit 8 comprises:
the RS485 communication module 81 is connected with the microprocessor 11 through the serial port communication module 18 and is used for communication connection between the water surface parameter display unit 1 and a computer;
the NB-IoT communication module 82 is connected to the microprocessor 11 through the serial communication module 18 for communication connection between the surface parameter display unit 1 and a remote server.
In addition, referring to fig. 2 and 3, the embodiment of the invention further provides a control method of the embedded sand content measurement system based on the recurrent neural network, which comprises the following steps:
S11: the method comprises the steps of (1) initializing a singlechip program, setting a sampling time interval Ts, setting communication serial port parameters, and configuring turbidity sensor coefficients a, b and c and circulating neural network parameters V, U and W; the communication serial port parameters comprise voice interaction serial port parameters, bluetooth interaction serial port parameters, RS485 communication serial port parameters and NB-IoT communication serial port parameters;
S12: data acquisition, namely selecting a measurement channel, acquiring a group of temperature values and a group of turbidity analog quantities per TS, wherein the turbidity analog quantities are photoelectric signals acquired by a turbidity sensor, and acquiring longitude and latitude and measurement time of a measurement point;
S13: data processing, namely taking a current temperature value as an effective temperature value according to a sampling sequence, carrying out A/D conversion on N groups of turbidity analog quantities after taking, and obtaining a current signal value by averaging;
s14: calculating data, namely calculating turbidity value through curve fitting equation y m＝a(xm)2+b(xm) +c; wherein: m is a positive integer, x m is the value of the current signal measured at the m-th time, and y m is the turbidity value corresponding to x m; correcting the turbidity value according to the effective temperature value and the temperature control array TEP [ n ] = { t 1,t2,···,tn }, and obtaining a corrected turbidity value Y m;
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti),ti<temperture≤ti+1;
Where n is a natural number greater than 1, t n is the nth control temperature value, and t n>tn-1, i= {1,2, temperture _scale is the temperature correction coefficient, temperture is the effective temperature value;
Curve fitting equation y m＝a(xm)2+b(xm) +c is the functional relationship between turbidity value and current signal value obtained by calibration test under laboratory standard environment;
Calculating a sand content value M m by using a cyclic neural network system according to the corrected turbidity value Y m;
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s 0 = 0;
The formula (1) is a calculation formula of an output layer of the cyclic neural network, the output layer is a full-connection layer, namely, each node of the output layer is connected with each node of the hidden layer, V is a weight matrix of the output layer, and g is an activation function of the output layer;
The formula (2) is a calculation formula of a hidden layer of the cyclic neural network, the hidden layer is a cyclic layer and is provided with 8 neurons, each node of an output layer is connected with the 8 nodes, U is a weight matrix of an input layer, W is a weight matrix of the cyclic layer, and f is a cyclic layer activation function;
f and g are tan functions, i.e., tan (x) = (e x-e-x)/(ex+e-x), where e is the natural logarithm;
s15: displaying the data, namely displaying the corrected turbidity value and the sand content value in a curve form in real time;
S16: data storage, namely storing a group of data according to the current signal value-corrected turbidity value-sand content value-longitude and latitude-measurement time in a data acquisition sequence;
S17: data transmission, namely sending the stored data to a local computer through an RS485 communication module, sending the stored data to a management center server through an NB-IoT communication module, and sending the stored data to a mobile terminal through Bluetooth;
In some embodiments, taking the temperature control array TEP [9] = {0 ℃,5 ℃,10 ℃,15 ℃,20 ℃,25 ℃,30 ℃,35 ℃,40 ℃ as an example, when the actual temperature value obtained by measurement is 18 ℃, the corrected turbidity value Y m＝ym + temperture _scale is (18-15)/(20-15), 15< 18.ltoreq.20.
Different sand samples have different parameters of the recurrent neural network, and the sample table is shown in table 1. The parameters of the black mud circulating neural network are shown in table 2.
Table 1: parameter table (sample table) of cyclic neural network
| Sequence number | Parameters (parameters) | Format of the form |
| 1 | U | [U1,U2,……,U8]’ |
| 2 | V | [V1,V2,……,V8] |
| 3 | W | [W1,W2,……,W8]’ |
Table 2: parameter table of cyclic neural network (Black mud, initial parameters)
In the description of the present specification, the terms "one embodiment," "some embodiments," and the like, mean that a particular feature, structure, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The above is only a preferred embodiment of the present application, and is not intended to limit the present application, but various modifications and variations can be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the protection scope of the present application.
Claims (2)
1. The embedded sand content measuring method based on the cyclic neural network is characterized by comprising the following steps of:
S11: the method comprises the steps of (1) initializing a singlechip program, setting a sampling time interval Ts, setting communication serial port parameters, and configuring turbidity sensor coefficients a, b and c and circulating neural network parameters V, U and W;
s12: data acquisition, namely selecting a measurement channel, acquiring a group of temperature values and a group of turbidity analog quantities per TS, wherein the turbidity analog quantities are photoelectric signals acquired by a turbidity sensor, and acquiring longitude and latitude and measurement time of a measurement point;
S13: data processing, namely taking a current temperature value as an effective temperature value according to a sampling sequence, carrying out A/D conversion on N groups of turbidity analog quantities after taking, and obtaining a current signal value by averaging;
S14: calculating data, namely calculating turbidity value through curve fitting equation y m＝a(xm)2+b(xm) +c; wherein: m is a positive integer, x m is a current signal value obtained in the m time, and y m is a turbidity value corresponding to x m; correcting the turbidity value according to the effective temperature value and the temperature control array TEP [ n ] = { t 1,t2,···,tn }, and obtaining a corrected turbidity value Y m;
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti),ti<temperture≤ti+1;
Where n is a natural number greater than 1, t n is the nth control temperature value, and t n>tn-1, i= {1,2, temperture _scale is the temperature correction coefficient, temperture is the effective temperature value;
Calculating a sand content value M m by using a cyclic neural network system according to the corrected turbidity value Y m;
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s 0 = 0;
The formula (1) is a calculation formula of an output layer of the cyclic neural network, wherein the output layer is a full-connection layer, namely, each node of the output layer is connected with each node of the hidden layer, V is a weight matrix of the output layer, and g is an activation function of the output layer;
the formula (2) is a calculation formula of a hidden layer of the cyclic neural network, the hidden layer is a cyclic layer, U is a weight matrix of an input layer, W is a weight matrix of the cyclic layer, and f is a cyclic layer activation function;
s15: displaying the data, namely displaying the corrected turbidity value and the sand content value in a curve form in real time;
S16: data storage, namely storing a group of data according to the current signal value-corrected turbidity value-sand content value-longitude and latitude-measurement time in a data acquisition sequence;
S17: and data transmission, namely sending the stored data to a local computer through an RS485 communication module, sending the stored data to a management center server through an NB-IoT communication module, and sending the stored data to a mobile terminal through Bluetooth.
2. The method for measuring the sand content based on the recurrent neural network as claimed in claim 1, wherein the communication serial port parameters comprise a voice interaction serial port parameter, a bluetooth interaction serial port parameter, an RS485 communication serial port parameter and an NB-IoT communication serial port parameter.
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Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11230905A (en) * | 1998-02-12 | 1999-08-27 | Hitachi Ltd | Turbidimeter |
| CN102004077A (en) * | 2010-10-08 | 2011-04-06 | 中国农业大学 | Turbidity transducer |
| CN102914519A (en) * | 2012-10-19 | 2013-02-06 | 中国科学院合肥物质科学研究院 | Optical fiber type laser liquid turbidity measuring device and measuring method |
| JP2013148376A (en) * | 2012-01-17 | 2013-08-01 | Dkk Toa Corp | Measuring device |
| EP2664264A1 (en) * | 2012-05-15 | 2013-11-20 | Electrolux Home Products Corporation N.V. | Method and device for measuring turbidity |
| CN104792737A (en) * | 2015-04-17 | 2015-07-22 | 上海众毅工业控制技术有限公司 | High-precision high-accuracy turbidity measurement device and method |
| CA3034738A1 (en) * | 2016-08-25 | 2018-03-01 | RS Hydro Limited | Water quality sensing |
| CN107907506A (en) * | 2017-11-08 | 2018-04-13 | 华东师范大学 | A kind of measuring device and method of wide-range and dynamic optimum resolution measurement silt content |
| CN108663347A (en) * | 2018-07-09 | 2018-10-16 | 山东省科学院海洋仪器仪表研究所 | Optical dissolved oxygen sensor multi-parameter interference compensation corrects system and method |
| CN110736723A (en) * | 2019-10-18 | 2020-01-31 | 常州罗盘星检测科技有限公司 | method and system for online simultaneous detection of low turbidity and high turbidity |
| CN111289557A (en) * | 2020-02-27 | 2020-06-16 | 鞍钢矿业爆破有限公司 | Arduino-based method and system for measuring crystallization point of ammonium nitrate solution |
| CN212988918U (en) * | 2020-09-11 | 2021-04-16 | 天津水运工程勘察设计院 | Sand content vertical layering real-time remote measurement remote reporting device |
| CN113588601A (en) * | 2021-06-15 | 2021-11-02 | 北京圣海林生态环境科技股份有限公司 | Silt amount automatic monitoring instrument and online monitoring system |
-
2022
- 2022-07-25 CN CN202210877906.6A patent/CN115266708B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH11230905A (en) * | 1998-02-12 | 1999-08-27 | Hitachi Ltd | Turbidimeter |
| CN102004077A (en) * | 2010-10-08 | 2011-04-06 | 中国农业大学 | Turbidity transducer |
| JP2013148376A (en) * | 2012-01-17 | 2013-08-01 | Dkk Toa Corp | Measuring device |
| EP2664264A1 (en) * | 2012-05-15 | 2013-11-20 | Electrolux Home Products Corporation N.V. | Method and device for measuring turbidity |
| CN102914519A (en) * | 2012-10-19 | 2013-02-06 | 中国科学院合肥物质科学研究院 | Optical fiber type laser liquid turbidity measuring device and measuring method |
| CN104792737A (en) * | 2015-04-17 | 2015-07-22 | 上海众毅工业控制技术有限公司 | High-precision high-accuracy turbidity measurement device and method |
| CA3034738A1 (en) * | 2016-08-25 | 2018-03-01 | RS Hydro Limited | Water quality sensing |
| CN107907506A (en) * | 2017-11-08 | 2018-04-13 | 华东师范大学 | A kind of measuring device and method of wide-range and dynamic optimum resolution measurement silt content |
| CN108663347A (en) * | 2018-07-09 | 2018-10-16 | 山东省科学院海洋仪器仪表研究所 | Optical dissolved oxygen sensor multi-parameter interference compensation corrects system and method |
| CN110736723A (en) * | 2019-10-18 | 2020-01-31 | 常州罗盘星检测科技有限公司 | method and system for online simultaneous detection of low turbidity and high turbidity |
| CN111289557A (en) * | 2020-02-27 | 2020-06-16 | 鞍钢矿业爆破有限公司 | Arduino-based method and system for measuring crystallization point of ammonium nitrate solution |
| CN212988918U (en) * | 2020-09-11 | 2021-04-16 | 天津水运工程勘察设计院 | Sand content vertical layering real-time remote measurement remote reporting device |
| CN113588601A (en) * | 2021-06-15 | 2021-11-02 | 北京圣海林生态环境科技股份有限公司 | Silt amount automatic monitoring instrument and online monitoring system |
Non-Patent Citations (1)
| Title |
|---|
| 基于OBS 3+传感器的实验室含沙量测量系统开发和应用;栾润润;张瑞波;;水道港口;20170228(第01期);第94-98页 * |
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