CN115266708A - Embedded sand content measuring system based on recurrent neural network and control method - Google Patents
Embedded sand content measuring system based on recurrent neural network and control method Download PDFInfo
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- 238000013528 artificial neural network Methods 0.000 title claims abstract description 37
- 230000000306 recurrent effect Effects 0.000 title claims abstract description 33
- 239000004576 sand Substances 0.000 title claims abstract description 33
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- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 55
- 238000002955 isolation Methods 0.000 claims abstract description 26
- 230000003993 interaction Effects 0.000 claims abstract description 25
- 238000005259 measurement Methods 0.000 claims abstract description 22
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- 238000004364 calculation method Methods 0.000 claims description 10
- WHXSMMKQMYFTQS-UHFFFAOYSA-N Lithium Chemical compound [Li] WHXSMMKQMYFTQS-UHFFFAOYSA-N 0.000 claims description 9
- 125000004122 cyclic group Chemical group 0.000 claims description 9
- 229910052744 lithium Inorganic materials 0.000 claims description 9
- 239000011159 matrix material Substances 0.000 claims description 9
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- 238000005070 sampling Methods 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 4
- 230000005059 dormancy Effects 0.000 claims description 3
- 238000001914 filtration Methods 0.000 claims description 3
- 239000013049 sediment Substances 0.000 claims 1
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 238000001514 detection method Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/75—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated
- G01N21/77—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator
- G01N21/82—Systems in which material is subjected to a chemical reaction, the progress or the result of the reaction being investigated by observing the effect on a chemical indicator producing a precipitate or turbidity
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S19/00—Satellite radio beacon positioning systems; Determining position, velocity or attitude using signals transmitted by such systems
- G01S19/38—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system
- G01S19/39—Determining a navigation solution using signals transmitted by a satellite radio beacon positioning system the satellite radio beacon positioning system transmitting time-stamped messages, e.g. GPS [Global Positioning System], GLONASS [Global Orbiting Navigation Satellite System] or GALILEO
- G01S19/42—Determining position
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
- H04L67/125—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks involving control of end-device applications over a network
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/01—Arrangements or apparatus for facilitating the optical investigation
- G01N2021/0106—General arrangement of respective parts
- G01N2021/0112—Apparatus in one mechanical, optical or electronic block
Abstract
The invention discloses an embedded sand content measuring system based on a recurrent neural network and a control method thereof, wherein the measuring system comprises: a water surface parameter display unit; an underwater parameter acquisition unit; an isolation protection unit; a power supply unit; a temperature acquisition unit; a voltage acquisition unit; a human-computer interaction unit; a communication unit and a GPS module. The method is applied to monitoring of water resources and water environments in river channels and other watersheds, is convenient to operate, can perform multi-channel measurement, is high in 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 system based on a recurrent neural network and a control method.
Background
The water resource patrol is essential basic work for national economy construction, along with the rapid development of national economy, the contradiction between water resource supply and demand is more prominent, and hydrology work is required to support the sustainable utilization of water resources by high-quality water resource information, wherein the turbidity and the sand content of the water body are two important indexes. Therefore, based on market demands and engineering project requirements, a set of measuring system capable of measuring the turbidity and the sand content of the water body in real time is urgently needed for accurately monitoring the water quality.
Disclosure of Invention
In view of the above defects or shortcomings in the prior art, it is desirable to provide an embedded sand content measuring system and a control method based on a recurrent neural network, which are applied to river basin water resource and water environment monitoring, such as river channels, etc., and have the advantages of convenient operation, multi-channel measurement, high measuring accuracy, convenient data storage and viewing, and realization of remote control and transmission.
The invention provides an embedded sand content measuring system based on a recurrent neural network, which comprises:
the water surface parameter display unit comprises a microprocessor, and a hardware reset module, a real-time clock module, a parameter display module, a dormancy awakening module, a data downloading module, a data storage module and a serial port communication module which are respectively connected with the microprocessor;
the underwater parameter acquisition unit is connected with the water surface parameter display unit through the isolation protection unit and is used for acquiring related information of underwater turbidity;
the isolation protection unit comprises a power isolation circuit and a signal isolation circuit;
the power supply unit is respectively connected with the water surface parameter display unit and the underwater parameter acquisition unit;
the temperature acquisition unit is connected with the water surface parameter display unit through the isolation protection unit;
the voltage acquisition unit is connected with the underwater parameter acquisition unit and used for acquiring the working voltage of the underwater parameter acquisition unit, and the voltage acquisition unit is connected with the water surface parameter display unit through the isolation protection unit;
the human-computer interaction unit is connected with the water surface parameter display unit;
the communication unit is connected with the water surface parameter display unit;
and the GPS module is connected with the water surface parameter display unit.
Furthermore, the underwater parameter acquisition unit consists of a plurality of mutually independent turbidity sensors.
Further, the power supply unit includes:
the lithium battery is respectively connected with the water surface parameter display unit and the underwater parameter acquisition unit through the rectifying and filtering circuit, the voltage conversion circuit, the low-power consumption processing circuit, the overload overcurrent protection circuit and the reverse connection protection circuit in sequence;
and the charging module is connected with the lithium battery.
Further, the human-computer interaction unit includes:
the voice interaction module is connected with the microprocessor through the serial port communication module;
and the Bluetooth interaction module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and the mobile terminal.
Further, the communication unit includes:
the RS485 communication module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and the computer;
and the NB-IoT communication module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and the remote server.
In addition, the invention also provides a control method of the embedded sand content measuring system based on the recurrent neural network, which comprises the following steps:
s11: the method comprises the following steps of initially setting, initializing a single chip microcomputer 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, and acquiring a group of temperature values and a group of turbidity analog quantity every Ts, wherein the turbidity analog quantity is a photoelectric signal acquired by a turbidity sensor and acquires longitude and latitude of a measurement point and measurement time;
s13: data processing, namely taking the current temperature value as an effective temperature value according to a sampling sequence, taking N groups of turbidity analog quantities, performing A/D conversion, and calculating an average value to obtain a current signal value;
s14: data calculation by curve fitting equation ym＝a(xm)2+b(xm) + c, calculating to obtain a turbidity value; wherein: m is a positive integer, xmCurrent signal value, y, determined for the m-th timemIs xmA corresponding turbidity value; according to the effective temperature value and the temperature comparison array TEP [ n ]]＝{t1,t2,···,tnCorrecting the turbidity value to obtain a corrected turbidity value Ym；
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti)，ti<temperture≤ti+1；
Wherein n is a natural number greater than 1, tnIs the nth comparison temperature value, and tn>tn-1I = {1,2, ·, n-1}, temperature _ scale is a temperature correction coefficient, and temperature is an effective temperature value;
according to the corrected turbidity value YmUsing cyclesThe annular neural network system calculates the sand content value Mm；
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s is0＝0；
Formula (1) is a calculation formula of a recurrent neural network output layer, wherein the output layer is a fully-connected layer, namely each node of the output layer is connected with each node of a hidden layer, V is a weight matrix of the output layer, and g is an output layer activation function;
formula (2) is a calculation formula of a recurrent neural network hidden layer, wherein the hidden layer is a recurrent layer, U is a weight matrix of an input layer, W is a weight matrix of the recurrent layer, and f is a recurrent layer activation function;
s15: data display, namely displaying the corrected turbidity value and the sand content value in real time in a curve form;
s16: storing data, namely storing the data according to a group of data comprising current signal values, modified turbidity values, sand content values, longitude and latitude and measuring time according to a data acquisition sequence;
s17: and data transmission, namely, respectively 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 the mobile terminal through Bluetooth.
Further, 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.
Compared with the prior art, the invention has the beneficial effects that:
n groups of turbidity analog quantities are obtained through an underwater parameter acquisition unit, after A/D conversion is carried out on the N groups of turbidity analog quantities, the average value is obtained to obtain a current signal value, and an equation y is fitted according to a curvem＝a(xm)2+b(xm) And c, calculating to obtain a turbidity value, correcting by temperature compensation to obtain a corrected turbidity value, and calculating the sand content value by using a recurrent neural network according to the corrected turbidity value. Underwater parametric productionData transmission between the collection unit and the water surface parameter display unit is protected by the power isolation circuit and the signal isolation circuit, and the safety of the system is improved. The invention takes the singlechip as the core, realizes automatic measurement, has accurate detection and ensures that a user can accurately know the water quality condition.
The voice interaction module has the functions of voice recognition and broadcasting, and the microprocessor can automatically perform corresponding operation after recognizing the voice result; the mobile phone has a Bluetooth communication function, can be connected to a mobile phone by opening Bluetooth, and is matched with mobile phone app to receive and check data; the invention has RS485 communication function, the user can be connected to the computer by wire, and cooperates with local software to receive and check data; the invention has NB-IoT wireless communication function, after configuring corresponding IP and port, can be connected to the server of the management system, and is convenient for remote data viewing and receiving.
It should be understood that the statements herein reciting aspects are not intended to limit the critical or essential features of any embodiment of the invention, nor are they intended to limit the scope of the invention. Other features of the present invention will become apparent from the following description.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
FIG. 1 is a block diagram of a cyclic neural network-based embedded sand content measurement system;
FIG. 2 is a flow chart of a control method of an embedded sand content measuring system based on a recurrent neural network;
FIG. 3 is a schematic diagram of a recurrent neural network.
Reference numbers in the figures: 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 human-computer 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 sleep wake-up module; 16. a data download 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 rectification filter circuit; 43. a voltage conversion circuit; 44. a low power consumption processing circuit; 45. an overload and overcurrent protection circuit; 46. 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 present invention will be described in further detail with reference to the accompanying drawings and examples. It is to be understood that the specific embodiments described herein are merely illustrative of the relevant invention and not restrictive of the invention. It should be noted that, for convenience of description, only the portions related to the present invention are shown in the drawings.
It should be noted that the embodiments and features of the embodiments may be combined with each other without conflict. The present invention will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.
Referring to fig. 1, an embodiment of the present invention provides an embedded sand content measuring 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 awakening module 15, a data downloading 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 related information of underwater turbidity;
an isolation protection unit 3 including a power 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 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 human-computer 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;
and the GPS module 9 is connected with the water surface parameter display unit 1.
In this embodiment, a recurrent neural network is configured in the microprocessor 11 of the water surface parameter display unit 1, the underwater turbidity analog quantity is acquired 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 to perform data calculation. According to the curve fitting equation y = ax2And calculating a turbidity value through + bx + c, obtaining a corrected turbidity value through temperature compensation correction, and calculating a sand content value by using a recurrent neural network according to the corrected turbidity value.
The corrected turbidity value and the sand content value are displayed in real time in the form of a curve by the parameter display module 14, and the parameter display module 14 is preferably a touch screen. Simultaneously storing data and transmitting the data, and storing the current signal value-corrected turbidity value-sand content value-longitude and latitude-measuring time as a group of data; the transmission of data is done by the communication unit 8.
The invention takes the singlechip as the core and realizes automatic measurement. The operation is convenient and fast, and the detection is accurate. The data storage is checked conveniently, remote control and transmission can be realized, and a user can be ensured to know the water quality condition accurately.
In a preferred embodiment, as shown in fig. 1, the underwater parameter collecting unit 2 is composed of a plurality of independent turbidity sensors 21, and the collection of the turbidity information of the water body is realized by the plurality of independent turbidity sensors 21.
In a preferred embodiment, as shown in fig. 1, the power supply unit 4 comprises:
the lithium battery 41 is respectively connected with the water surface parameter display unit 1 and the underwater parameter acquisition unit 2 through a rectifying and filtering circuit 42, a voltage conversion circuit 43, a low power consumption processing circuit 44, an overload overcurrent protection circuit 45 and a reverse connection protection circuit 46 in sequence;
and a charging module 47 connected with the lithium battery 41.
In this embodiment, the system uses a lithium battery 41 for power supply, and the power supply safety of the system is ensured by various protection circuits, and the lithium battery 41 is charged by a 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;
and 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, voice control of the measurement system is achieved by the voice interaction module 71. The operation control and information viewing through the mobile terminal are realized 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;
and the NB-IoT communication module 82 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 remote server.
In addition, referring to fig. 2 and fig. 3, an embodiment of the present invention further provides a control method of an embedded sand content measurement system based on a recurrent neural network, including the following steps:
s11: the method comprises the steps of initial setting, single chip microcomputer program initialization, setting sampling time interval Ts, setting communication serial port parameters, 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 quantity every Ts, wherein the turbidity analog quantity is a photoelectric signal acquired by a turbidity sensor, and acquiring longitude and latitude of a measurement point and measurement time;
s13: data processing, namely taking the current temperature value as an effective temperature value according to a sampling sequence, taking N groups of turbidity analog quantities, performing A/D conversion, and calculating an average value to obtain a current signal value;
s14: data calculation by curve fitting equation ym＝a(xm)2+b(xm) + c, calculating to obtain a turbidity value; wherein: m is a positive integer, xmThe current signal value, y, of the m-th measurementmIs xmA corresponding turbidity value; according to the effective temperature value and the temperature comparison array TEP [ n ]]＝{t1,t2,···,tnCorrecting the turbidity value to obtain a corrected turbidity value Ym；
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti)，ti<temperture≤ti+1；
Wherein n is a natural number greater than 1, tnIs the nth comparison temperature value, and tn>tn-1I = {1,2, ·, n-1}, temperature _ scale is a temperature correction coefficient, and temperature is an effective temperature value;
curve fitting equation ym＝a(xm)2+b(xm) + c is the functional relationship between the turbidity value and the current signal value obtained by the calibration test in the standard laboratory environment;
according to the corrected turbidity value YmCalculating the sand content value M by using a recurrent neural network systemm；
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s is0＝0；
Formula (1) is a calculation formula of a recurrent neural network output layer, wherein the output layer is a full-connection layer, namely each node of the output layer is connected with each node of a hidden layer, V is a weight matrix of the output layer, and g is an output layer activation function;
formula (2) is a calculation formula of a cyclic neural network hidden layer, 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 h functions, i.e. tan h (x) = (e)x-e-x)/(ex+e-x) Wherein e is the natural logarithm;
s15: data display, namely displaying the corrected turbidity value and the sand content value in real time in a curve form;
s16: storing data, namely storing the data according to a group of data comprising current signal values, modified turbidity values, sand content values, longitude and latitude and measuring time according to a data acquisition sequence;
s17: data transmission, namely respectively 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, array TEP [9 ] is temperature-referenced]For example, = {0 ℃,5 ℃,10 ℃,15 ℃, 20 ℃,25 ℃,30 ℃,35 ℃,40 ℃ } when the actual temperature value obtained by measurement is 18 ℃, the turbidity value Y is correctedm＝ym+temperture_scale*(18-15)/(20-15)，15<18≤20。
Different sand samples have different parameters of the recurrent neural network, and the samples are shown in table 1. The parameters of the recurrent neural network of black mud are shown in table 2.
Table 1: parameter table (sample table) of recurrent neural network
| Serial number | Parameter(s) | Format |
| 1 | U | [U1,U2,……，U8]’ |
| 2 | V | [V1,V2,……，V8] |
| 3 | W | [W1,W2,……，W8]’ |
Table 2: recurrent neural network parameter table (Black mud, initial parameter)
In the description of the present specification, the description of the terms "one embodiment," "some embodiments," etc., means 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, the schematic representations of the terms used above 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 description is only a preferred embodiment of the present application and is not intended to limit the present application, and various modifications and changes may be made to the present application by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (7)
1. An embedded sand content measuring system based on a recurrent neural network is characterized by comprising:
the water surface parameter display unit comprises a microprocessor, and a hardware reset module, a real-time clock module, a parameter display module, a dormancy awakening module, a data downloading module, a data storage module and a serial port communication module which are respectively connected with the microprocessor;
the underwater parameter acquisition unit is connected with the water surface parameter display unit through the isolation protection unit and is used for acquiring related information of underwater turbidity;
the isolation protection unit comprises a power isolation circuit and a signal isolation circuit;
the power supply unit is respectively connected with the water surface parameter display unit and the underwater parameter acquisition unit;
the temperature acquisition unit is connected with the water surface parameter display unit through the isolation protection unit;
the voltage acquisition unit is connected with the underwater parameter acquisition unit and used for acquiring the working voltage of the underwater parameter acquisition unit, and the voltage acquisition unit is connected with the water surface parameter display unit through the isolation protection unit;
the human-computer interaction unit is connected with the water surface parameter display unit;
the communication unit is connected with the water surface parameter display unit;
and the GPS module is connected with the water surface parameter display unit.
2. The embedded sand content measuring system based on the recurrent neural network as claimed in claim 1, wherein the underwater parameter collecting unit is composed of several independent turbidity sensors.
3. The recurrent neural network-based embedded sediment concentration measurement system of claim 1, wherein the power supply unit comprises:
the lithium battery is respectively connected with the water surface parameter display unit and the underwater parameter acquisition unit through the rectifying and filtering circuit, the voltage conversion circuit, the low-power consumption processing circuit, the overload overcurrent protection circuit and the reverse connection protection circuit in sequence;
and the charging module is connected with the lithium battery.
4. The embedded sand content measuring system based on the recurrent neural network as claimed in claim 1, wherein the human-computer interaction unit comprises:
the voice interaction module is connected with the microprocessor through the serial port communication module;
and the Bluetooth interaction module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and the mobile terminal.
5. The recurrent neural network-based embedded sand content measuring system according to claim 1, wherein said communication unit comprises:
the RS485 communication module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and a computer;
and the NB-IoT communication module is connected with the microprocessor through the serial port communication module and is used for communication connection between the water surface parameter display unit and the remote server.
6. A control method of an embedded sand content measuring system based on a recurrent neural network is characterized by comprising the following steps:
s11: the method comprises the steps of initial setting, single chip microcomputer program initialization, setting sampling time interval Ts, setting communication serial port parameters, 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, and acquiring a group of temperature values and a group of turbidity analog quantity every Ts, wherein the turbidity analog quantity is a photoelectric signal acquired by a turbidity sensor and acquires longitude and latitude of a measurement point and measurement time;
s13: data processing, namely taking the current temperature value as an effective temperature value according to a sampling sequence, taking N groups of turbidity analog quantities, performing A/D conversion, and calculating an average value to obtain a current signal value;
s14: data calculation by curve fitting equation ym＝a(xm)2+b(xm) + c, calculating to obtain a turbidity value; wherein: m is a positive integer, xmCurrent signal value, y, determined for the m-th timemIs xmA corresponding turbidity value; according to the effective temperature value and the temperature comparison array TEP [ n ]]＝{t1,t2,···,tnCorrecting the turbidity value to obtain a corrected turbidity value Ym；
Ym＝ym+temperture_scale*(temperture-ti)/(ti+1-ti)，ti<temperture≤ti+1；
Wherein n is a natural number greater than 1, tnIs the nth comparison temperature value, and tn>tn-1I = {1,2, ·, n-1}, temperature _ scale is a temperature correction coefficient, and temperature is an effective temperature value;
according to the corrected turbidity value YmCalculating the sand content value M by using a recurrent neural network systemm；
Mm＝g(Vsm) (1)
sm＝f(UYm+Wsm-1) (2)
Wherein s is0＝0；
Formula (1) is a calculation formula of a recurrent neural network output layer, wherein the output layer is a fully-connected layer, namely each node of the output layer is connected with each node of a hidden layer, V is a weight matrix of the output layer, and g is an output layer activation function;
formula (2) is a calculation formula of a cyclic neural network hidden layer, wherein 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: data display, namely displaying the corrected turbidity value and the sand content value in real time in a curve form;
s16: storing data, namely storing the data according to a group of data comprising current signal values, modified turbidity values, sand content values, longitude and latitude and measuring time according to a data acquisition sequence;
s17: and data transmission, namely, respectively 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 the mobile terminal through Bluetooth.
7. The method as claimed in claim 6, wherein the communication serial port parameters include voice interaction serial port parameters, bluetooth interaction serial port parameters, RS485 communication serial port parameters, and NB-IoT communication serial port parameters.
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