阅读说明：本技术 一种基于热脉冲全同弱光纤光栅阵列的土壤含水率智能监测系统及原位标定方法 (Soil moisture content intelligent monitoring system based on heat pulse identical weak fiber grating array and in-situ calibration method ) 是由 刘喜凤 朱鸿鹄 王家琛 曹鼎峰 程刚 施斌 李�杰 刘威 于 2021-06-16 设计创作，主要内容包括：本发明涉及一种基于热脉冲全同弱光纤光栅阵列的土壤含水率智能监测系统及原位标定方法,该智能监测系统由全同弱光纤光栅阵列含水率传感器、弱光纤光栅解调分析仪、气象站和远程电源管理器组成。标定方法如下：将全同弱光纤光栅阵列含水率传感器埋入待测土体,选取标定弱光纤光栅在其周围布置常规传感器,并配置给水管；通过给水管给土体定点输水,对土体含水率进行监测；用常规传感器测得的土壤含水率值对弱光纤光栅传感器测得的最大升温值进行标定；拟合得含水率与最大升温值的标定曲线。本发明采用全同弱光纤光栅阵列传感器构建传感网络,并结合人工神经网络算法,能实现对土体含水率长距离、高密度的精细化智能监测,并通过原位标定方法提高传感器标定曲线精度。(The invention relates to a soil moisture content intelligent monitoring system based on a heat pulse identical weak fiber grating array and an in-situ calibration method. The calibration method comprises the following steps: embedding the identical weak fiber grating array water content sensor into a soil body to be detected, selecting a calibration weak fiber grating, arranging a conventional sensor around the calibration weak fiber grating, and configuring a water supply pipe; the water supply pipe is used for supplying water to the soil body at a fixed point, and the water content of the soil body is monitored; calibrating the maximum temperature rise value measured by the weak fiber grating sensor by using the soil moisture content value measured by the conventional sensor; and fitting to obtain a calibration curve of the water content and the maximum temperature rise value. The invention adopts the identical weak fiber grating array sensor to construct the sensing network, and combines the artificial neural network algorithm, thereby realizing the long-distance and high-density refined intelligent monitoring of the water content of the soil body, and improving the calibration curve precision of the sensor by the in-situ calibration method.)

1. An intelligent soil moisture content monitoring system based on a heat pulse identical weak fiber grating array is characterized by comprising four parts, namely a heat pulse identical weak fiber grating array moisture content sensor, a weak fiber grating demodulation analyzer, a weather station and a power manager with remote control capability, wherein the heat pulse identical weak fiber grating array moisture content sensor and the weather station transmit data to the weak fiber grating demodulation analyzer for processing, and the weak fiber grating demodulation analyzer is wirelessly connected with the power manager with the remote control capability through Bluetooth; the heat pulse congruent weak optical fiber grating array water content sensor is characterized by comprising a high-thermal conductivity tube, an optical fiber and a heating device, wherein the optical fiber and the heating device penetrate through the high-thermal conductivity tube, congruent weak optical fiber gratings are distributed on the optical fiber, heat conduction materials are filled in pores between the high-thermal conductivity tube and the heating device and the optical fiber, the heating device is connected with a power supply manager with remote control capability, the optical fiber is connected with a weak optical fiber grating demodulation analyzer through an optical fiber lead, the weak optical fiber grating demodulation analyzer is composed of a demodulation module, an analysis module and a control module, and an artificial neural network algorithm is arranged in the analysis module.

2. The intelligent soil moisture content monitoring system based on the heat pulse homodyne weak fiber grating array as claimed in claim 1, wherein the specific method for monitoring the moisture content by the heat pulse homodyne weak fiber grating array sensor is as follows: the method comprises the steps that a power manager with remote control capability is used for conducting pulse heating on a heating device in a heat pulse identical weak fiber grating array water content sensor, a demodulation module of a heat pulse identical weak fiber grating demodulation analyzer is used for collecting and recording central wavelength readings of all identical weak fiber gratings in the heating process, data are transmitted to an analysis module, the analysis module converts wavelength data into temperature information of the arrangement position of the heat pulse identical weak fiber gratings, and then the temperature information is converted into water content information according to a sensor calibration curve.

3. The system as claimed in claim 1, wherein the weather station transmits the measured weather data including temperature, pressure, rainfall condition to the analysis module of the demodulation analyzer of the weak fiber grating.

4. The system as claimed in claim 3, wherein the weak fiber grating demodulation analyzer module inputs the meteorological information and the moisture content information collected by the meteorological station and the thermal pulse identical weak fiber grating array moisture content sensor into a built-in artificial neural network algorithm for modeling and learning, that is, the artificial neural network model for the disturbance of temperature, air pressure and rainfall on the soil moisture field is established by performing centralized processing and deep learning on the soil moisture content change data in different natural climate environments; the method comprises the steps of firstly determining a time interval to divide the periods of the whole monitoring process and the like, then dividing the periods of the periods into a plurality of time nodes, building a multilayer forward artificial neural network by an analysis module built-in neural network algorithm, inputting data into an input layer, wherein the weather values of rainfall, temperature and air pressure monitored by a weather station in any time interval and the initial soil water content value in the time interval monitored by a weak fiber grating sensor, determining the number of layers and the number of nodes of a hidden layer by monitoring precision and data acquisition quantity, outputting the output layer as the final soil water content value at the end of the time interval, and training and learning the neural network model by respectively determining the weather data of all time nodes except the last time node in any time interval and the initial soil water content value in the time interval Inputting an input layer of an artificial neural network model, and performing supervised learning by taking a water content value of a last time node, namely a soil termination water content value at the end of a time interval as an output layer number value, namely determining water content data of the last time node through meteorological data of the time interval except the last time node and an initial water content value of soil of the time interval, and training the created artificial neural network model for multiple times by using monitoring data of a plurality of divided time intervals, thereby improving the calculation and prediction precision of the model; the artificial neural network model only considers the influence of single meteorological factors such as rainfall on the disturbance of the soil moisture field by changing the data types of the input layer, or simultaneously considers the influence of a plurality of meteorological factors on the disturbance of the soil moisture field by combination.

5. The soil moisture content intelligent monitoring system based on the heat pulse identical weak optical fiber grating array as claimed in claim 1, wherein when the monitoring period is long enough, a large number of time intervals are obtained by dividing, the monitoring data is used for artificial neural network model training, and the model performance is continuously improved, on the basis, the weak optical fiber grating demodulation analyzer analysis module predicts the soil moisture migration condition according to the known meteorological information and the soil moisture content information based on the trained artificial neural network model; according to the specific method of model prediction, according to a time interval and divided time nodes, a weak fiber grating demodulation analyzer analysis module inputs meteorological data of all time nodes and the initial soil moisture content value of the time interval into a neural network input layer except the last time node in the time interval monitored by a meteorological station, and the trained artificial neural network model output layer predicts the water content data of the last time node of the time interval, namely the final soil moisture content value of the time interval; the analysis module transmits the predicted water content to the control module, the control module can preset a soil water content change threshold, and if the predicted value exceeds the threshold, the control module transmits an early warning signal through a cloud.

6. The system as claimed in claim 1, wherein the control module of the weak fiber grating demodulation analyzer can intelligently control the power manager, flexibly adjust the monitoring period according to the weather conditions, or manually control remotely.

7. The intelligent soil moisture content monitoring system based on the hot pulse homodyne weak fiber grating array as claimed in claim 1, wherein the heating device is a nickel-chromium heating wire.

8. The soil moisture content intelligent monitoring system based on the heat pulse homodyne weak fiber grating array as claimed in claim 1, wherein the heat pulse homodyne weak fiber grating array moisture content sensor is used alone or connected in series or in parallel to form a monitoring net.

9. An in-situ calibration method of an intelligent soil moisture content monitoring system based on a heat pulse homomorphic weak fiber grating array is characterized by comprising the following steps:

the method comprises the following steps: embedding a heat pulse identical weak fiber grating array water content sensor into a soil body to be detected according to monitoring design requirements, selecting a representative and special weak fiber grating sensor as a calibration weak fiber grating, arranging a conventional sensor in a certain range at the same peripheral horizontal position, and configuring a water supply pipe, wherein the sensor is ensured to be in good contact with the soil body in the embedding process;

step two: inputting water to the soil body at fixed points by a water supply pipe in a grading manner, and monitoring the water content of the soil body by using a heat pulse identical weak fiber grating array sensor and a conventional sensor in a water delivery interval;

step three: calibrating the in-situ soil volume water content value measured by the conventional sensor at the same time to the maximum temperature rise value measured by the calibration weak fiber grating sensor at the corresponding position;

step four: fitting the functional relation between the water content and the maximum temperature rise value by using different function models based on a least square method, selecting the model with the highest fitting precision to determine the soil water content calibration curve of each calibration weak fiber grating, and further obtaining the whole identical weak fiber grating array water content sensor calibration curve for subsequent soil water content monitoring.

10. The intelligent soil moisture content monitoring system in-situ calibration method based on the heat pulse homomorphic weak fiber grating array as claimed in claim 9, wherein the conventional sensor in the first step is connected with a data acquisition instrument through a reading line, the reading line and a water supply pipe penetrate through soil through a hard PVC pipe, and the data acquisition instrument is connected with the weak fiber grating demodulation analyzer through a data line.

Technical Field

The invention relates to the technical field of soil moisture content monitoring, in particular to a soil moisture content intelligent monitoring system based on a heat pulse identical weak fiber grating array and an in-situ calibration method.

Background

The water content of the soil directly influences the water circulation and growth process of plants and influences the strength and stability of soil bodies. The accurate determination of the soil moisture content has important significance to theoretical research and engineering practice. In the soil moisture testing technology at the present stage, the optical fiber sensing technology can monitor the moisture content of the soil body in situ in real time, has the advantages of high sensitivity, corrosion resistance, interference resistance and the like, and is rapidly developed and widely applied in recent years.

The fiber Bragg grating has the characteristic of reflecting light waves with specific wavelengths, and the central wavelength of the reflection spectrum of the grating can be shifted due to strain and temperature, so that the strain and temperature variation of a corresponding sensor can be obtained by analyzing the reflection spectrum of the fiber Bragg grating. If the fiber grating sensor embedded in the soil body is subjected to pulse heating, the diffusion speed of heat in the soil body with different water content can be different due to different heat conductivity coefficients of water, soil particles and air, so that the aim of testing the soil moisture can be fulfilled according to the temperature change characteristics of the fiber grating sensor, such as the maximum temperature rise value. It should be noted that: firstly, an empirical relationship between the maximum temperature rise value of the fiber grating sensor and the soil moisture content needs to be established through calibration, and the soil moisture content of the soil body to be measured can be obtained according to a measured value of the maximum temperature rise value obtained on site.

The traditional fiber grating sensor adopts the wavelength division multiplexing technology to construct a sensing network, is limited by the working wavelength range of a light source of a demodulator, has limited single-channel multiplexing sensors, and cannot realize a large-capacity sensing network. The newly developed fiber grating array distributed sensing technology adopts a mixed multiplexing technology of wavelength division and time division, combines the advantages of distributed measurement of the time division technology, realizes accurate positioning of gratings at different positions through the arrival time of reflected light of different gratings, can greatly expand the number of grating sensors which can be connected in series in the same channel, and further meets the monitoring requirements of long distance, large capacity and high density. The reflectivity of a common fiber grating sensor is high, if the fiber grating sensor is used for forming a grating array, most incident light can be reflected by the grating at the front section of the array, so that the attenuation of the transmitted light intensity at the rear section of the array is obvious, and the signal detection of the rear section sensor is influenced. Therefore, weak fiber gratings with low reflectivity and narrow reflection bandwidth are required to form a grating array, so that the transmission loss of optical signals is reduced, and the multiplexing capability of the fiber gratings is enhanced.

According to the foregoing, the weak fiber grating array sensor for monitoring the water content of the soil has the outstanding advantages of long distance, large capacity, high density and the like. However, at present, the empirical relationship between the maximum temperature rise value and the water content of the sensor is usually obtained through indoor calibration, that is, soil samples with different water contents are configured to be subjected to calibration tests in a laboratory, and when a monitoring site relates to a complex soil type and structure, the indoor calibration method is large in workload and complex in operation. In addition, the sampling and configuration process can cause disturbance to the soil body, factors such as the porosity of the soil body, the structure of the soil body and boundary conditions can be correspondingly changed, so that the thermodynamic property of the soil body is influenced, meanwhile, the contact condition between a sensor and the soil body in indoor calibration and in-situ monitoring and the embedded disturbance caused by sensor arrangement are different, and the factors can cause that the indoor calibration result after on-site sampling cannot accurately accord with the soil layer of an actual field, so that the monitoring precision of the water content of the soil body is influenced.

In addition, the geological and geotechnical engineering monitoring field has the characteristics of long time effect, wide range and complex environment, and the related data has large volume, more variables and large analysis difficulty. With the continuous development of machine learning research in recent years, monitoring data can be effectively analyzed by using a powerful data processing function of an artificial intelligence algorithm.

Disclosure of Invention

The invention aims to provide an intelligent soil moisture content monitoring system based on a heat pulse identical weak fiber grating array and an in-situ calibration method. The invention adopts the identical weak optical fiber grating array water content sensor to construct the sensing network to realize the long-distance, large-capacity and high-density in-situ monitoring of the water content of the soil body. Meanwhile, an artificial neural network model of disturbance of environmental factors such as temperature, air pressure and rainfall on the soil moisture field is established by using an artificial neural network algorithm, and the soil moisture migration condition is predicted according to the known meteorological information and the moisture content information on the basis of accumulating certain data. In addition, the intelligent monitoring system saves the sampling, transportation and sample preparation processes required by sampling calibration through in-situ calibration, so that the calibration process is simpler and more efficient, meanwhile, the influence of the sampling calibration on the disturbance of the soil body is reduced, the water content calibration curve precision of the sensor is improved, and the monitoring precision of the sensor on the water content of the soil body is further improved.

Through the introduction, the invention adopts the following technical scheme: an intelligent soil moisture content monitoring system based on a heat pulse identical weak optical fiber grating array is composed of four parts, namely a heat pulse identical weak optical fiber grating array moisture content sensor, a weak optical fiber grating demodulation analyzer, a weather station and a power supply manager with remote control capability, wherein the heat pulse identical weak optical fiber grating array moisture content sensor and the weather station transmit data to the weak optical fiber grating demodulation analyzer for processing, and the weak optical fiber grating demodulation analyzer is wirelessly connected with the power supply manager with the remote control capability through Bluetooth; the heat pulse congruent weak optical fiber grating array water content sensor is characterized by comprising a high-thermal conductivity tube, an optical fiber and a heating device, wherein the optical fiber and the heating device penetrate through the high-thermal conductivity tube, congruent weak optical fiber gratings are distributed on the optical fiber, heat conduction materials are filled in pores between the high-thermal conductivity tube and the heating device and the optical fiber, the heating device is connected with a power supply manager with remote control capability, the optical fiber is connected with a weak optical fiber grating demodulation analyzer through an optical fiber lead, the weak optical fiber grating demodulation analyzer is composed of a demodulation module, an analysis module and a control module, and an artificial neural network algorithm is arranged in the analysis module.

The specific method for monitoring the water content of the heat pulse homomorphic weak fiber grating array sensor comprises the following steps: the method comprises the steps that a power manager with remote control capability is used for conducting pulse heating on a heating device in a heat pulse identical weak fiber grating array water content sensor, a demodulation module of a heat pulse identical weak fiber grating demodulation analyzer is used for collecting and recording central wavelength readings of all identical weak fiber gratings in the heating process, data are transmitted to an analysis module, the analysis module converts wavelength data into temperature information of the arrangement position of the heat pulse identical weak fiber gratings, and then the temperature information is converted into water content information according to a sensor calibration curve.

The meteorological station transmits the measured meteorological data including temperature, air pressure and rainfall conditions to an analysis module of the weak fiber grating demodulation analyzer.

The weak fiber grating demodulation analyzer analysis module inputs meteorological information and water content information acquired by a meteorological station and a heat pulse identical weak fiber grating array water content sensor into a built-in artificial neural network algorithm for modeling and learning, namely, the artificial neural network model for disturbing the soil water content field by factors such as temperature, air pressure and rainfall is established for centralized processing and deep learning of soil water content change data in different natural climate environments. And respectively creating artificial neural network models by using the weak fiber bragg grating sensors with different embedding depths and positions. The specific implementation method of the model establishment comprises the steps of firstly determining a time interval, dividing the periods such as the whole monitoring process and the like, and then dividing the periods such as the periods or unequal periods of the time intervals into a plurality of time nodes. The analysis module is internally provided with a neural network algorithm which can create a multilayer forward artificial neural network, input layer input data are weather values such as rainfall, temperature and air pressure and the like monitored by a weather station in any time interval and initial soil moisture content values in the time interval monitored by a weak fiber grating sensor, the number of layers and the number of nodes of a hidden layer are determined by monitoring precision requirements and data acquisition quantity, and an output layer is a soil termination moisture content value at the end of the time interval. The specific method for training and learning the neural network model comprises the steps of inputting meteorological data of all time nodes except the last time node in any time interval and the initial soil moisture content value of the time interval into an input layer of the artificial neural network model, and performing supervised learning by taking the moisture content value of the last time node, namely the final soil moisture content value at the end of the time interval as an output layer value. Namely, the water content data of the last time node is determined through the meteorological data of the time interval except the last time node and the initial soil water content value of the time interval. And training the created artificial neural network model for multiple times by using the divided monitoring data of the multiple time intervals, so that the model calculation and prediction precision is improved. The artificial neural network model can only consider the influence of a single meteorological factor (such as rainfall) on the disturbance of the soil moisture field by changing the data type of the input layer, and can also simultaneously consider the influence of a plurality of meteorological factors on the disturbance of the soil moisture field by combination.

When the monitoring period is long enough, a large number of time intervals can be obtained by dividing, the monitoring data are used for artificial neural network model training, and the model performance is continuously improved. On the basis, the weak fiber grating demodulation analyzer analysis module can predict the soil body water migration condition according to the known meteorological information and the soil water content information based on the trained artificial neural network model. According to the specific method of model prediction, according to a time interval and divided time nodes, a weak fiber grating demodulation analyzer analysis module inputs meteorological data of all time nodes except the last time node in the time interval and the initial soil moisture content value of the time interval, which are monitored by a meteorological station, into a neural network input layer, and a trained artificial neural network model output layer can predict the water content data of the last time node in the time interval, namely the final soil moisture content value of the time interval. The analysis module transmits the predicted water content to the control module, the control module can preset a soil water content change threshold, and if the predicted value exceeds the threshold, the control module transmits an early warning signal through a cloud.

The weak fiber grating demodulation analyzer control module can intelligently control the power manager, flexibly adjust the monitoring period according to the meteorological condition, or manually control remotely.

The heating device is a nickel-chromium heating wire.

The heat pulse identical weak fiber grating array water content sensor is used alone or connected in series or in parallel to form a monitoring network.

An in-situ calibration method of an intelligent soil moisture content monitoring system based on a heat pulse congruent weak fiber grating array comprises the following steps:

the method comprises the following steps: embedding a heat pulse identical weak fiber grating array water content sensor into a soil body to be detected according to monitoring design requirements, selecting a representative and special weak fiber grating sensor as a calibration weak fiber grating, arranging a conventional sensor in a certain range at the same peripheral horizontal position, and configuring a water supply pipe, wherein the sensor is ensured to be in good contact with the soil body in the embedding process;

step two: inputting water to the soil body at fixed points by a water supply pipe in a grading manner, and monitoring the water content of the soil body by using a heat pulse identical weak fiber grating array sensor and a conventional sensor in a water delivery interval;

step three: calibrating the in-situ soil volume water content value measured by the conventional sensor at the same time to the maximum temperature rise value measured by the calibration weak fiber grating sensor at the corresponding position;

step four: fitting the functional relation between the water content and the maximum temperature rise value by using different function models based on a least square method, selecting the model with the highest fitting precision to determine the soil water content calibration curve of each calibration weak fiber grating, and further obtaining the whole identical weak fiber grating array water content sensor calibration curve.

The conventional sensor in the first step is connected with a data acquisition instrument through a reading line, the reading line and a water supply pipe penetrate through the soil body through a hard PVC pipe, and the data acquisition instrument is connected with a weak fiber bragg grating demodulation analyzer through a data line.

Has the advantages that:

1. the invention introduces an intelligent soil moisture content monitoring system based on a heat pulse identical weak optical fiber grating array. Meanwhile, the intelligent monitoring system can establish an artificial neural network model for disturbing the soil moisture field by environmental factors such as temperature, air pressure and rainfall, can predict the soil moisture migration condition based on the established artificial neural network model according to the known meteorological information and the water content information on the basis of accumulating certain data, and can timely early warn the geological environment problem closely related to the soil moisture change.

2. Compared with the traditional indoor calibration method, the method disclosed by the invention can be used for carrying out in-situ calibration on the identical weak fiber grating array moisture content sensor in a monitoring field, does not need the processes of sampling, transporting and sample preparation, has the characteristics of simplicity and high efficiency, and is particularly suitable for in-situ monitoring of the moisture content of the soil body with a large range of complex soil types. Meanwhile, the invention can avoid the disturbance of the sampling and configuration process to the soil body, eliminate the influence caused by factors such as the contact condition difference between the sensor and the soil body, the embedding disturbance difference caused by the sensor arrangement and the like, and improve the precision of the calibration curve of the water content of the weak optical fiber grating array sensor.

3. According to the invention, the change range of the water content can be accurately controlled through the fixed-point water delivery calibration of the water supply pipe, compared with the calibration in the natural rainfall process, the problem that rainfall depends on climatic conditions and hardly influences the moisture field of deep soil can be solved, and the controllability is stronger; compared with the calibration in the process of utilizing the surface artificial precipitation, the method can avoid the problems of deformation and stability possibly caused by large-range disturbance of the soil body, and has stronger operability.

4. According to the invention, the reading line of the conventional sensor and the water supply pipe penetrate through the soil body through the same hard PVC pipe, so that the pipeline utilization rate and the layout cost performance are improved. Meanwhile, the hard PVC pipe can protect and fix the reading line and the water supply pipe besides providing a connecting channel.

Drawings

FIG. 1 is a schematic diagram of the internal structure of the isotactic weak optical fiber grating array water content sensor. Wherein, 1, high thermal conductivity tube; 2. an optical fiber; 3. a nickel-chromium electric heating wire; 4. weak fiber grating; 5. a thermally conductive material.

FIG. 2 is a schematic diagram of data transmission of the intelligent soil moisture content monitoring system based on the identical weak fiber grating array.

Fig. 3 is an artificial neural network model built in the analysis module of the weak fiber grating demodulation analyzer according to the present invention.

Fig. 4 is a schematic device diagram of an in-situ calibration method for an intelligent soil moisture monitoring system based on an identical weak fiber grating array in embodiment 1 of the present invention. Wherein, 6, a silt layer; 7. a fine sand layer; 8. a medium sand layer; 9. the identical weak optical fiber grating array water content sensor; 10. a weather station; 11. a demodulation analyzer for weak fiber grating; 12. a power manager; 13. a data line; 14. an optical fiber lead; 15. the conductor is disclosed; 16. calibrating a weak fiber bragg grating sensor; 17. a rigid PVC pipe; 18. a dielectric constant sensor; 19. a reading line; 20. a data acquisition instrument; 21. a water supply pipe.

FIG. 5 is a calibration curve of the water content of the weak fiber grating in embodiment 1 of the present invention.

Detailed Description

The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. The invention is further explained below with reference to the figures and examples.

An intelligent soil moisture content monitoring system based on a heat pulse congruent weak fiber grating array is composed of a congruent weak fiber grating array moisture content sensor, a weak fiber grating demodulation analyzer, a meteorological station and a power supply manager with remote control capability, and connecting lines among the parts. The heat pulse congruent weak optical fiber grating array water content sensor is composed of a three-hole high-thermal conductivity tube, optical fibers penetrating through the three-hole high-thermal conductivity tube and the tube, and a nickel-chromium heating wire, wherein congruent weak optical fiber gratings are distributed on the optical fibers, and the holes in the high-thermal conductivity tube, the nickel-chromium heating wire and the optical fibers are filled with heat conduction materials. The nickel-chromium heating wire in the sensor is connected with a power supply manager through a lead, and the optical fiber is connected with a weak optical fiber grating demodulation analyzer through an optical fiber lead. The identical weak fiber grating array water content sensor can be used independently, and can also be connected in series or in parallel to form a monitoring network. The weak fiber grating demodulation analyzer consists of a demodulation module, an analysis module and a control module, wherein an artificial neural network algorithm is arranged in the analysis module.

Further, the specific method for monitoring the moisture content of the isotactic weak optical fiber grating array sensor comprises the following steps: pulse heating is carried out on a nickel-chromium heating wire in the congruent weak fiber grating array water content sensor through a power supply manager, central wavelength readings of the congruent weak fiber gratings in the heating process are collected and recorded by a demodulation module of a weak fiber grating demodulation analyzer, data are transmitted to an analysis module, the analysis module converts wavelength data into temperature information of the congruent weak fiber grating layout, and then the temperature information is converted into water content information according to a sensor calibration curve. Furthermore, the weather station is connected with the weak fiber grating demodulation analyzer through a data line, and transmits the measured weather data including temperature, air pressure and rainfall conditions to the analysis module of the weak fiber grating demodulation analyzer.

Further, the weak fiber grating demodulation analyzer analysis module inputs the obtained meteorological information and the water content information into a built-in artificial neural network algorithm for deep learning, namely, the soil water content change data in different natural climate environments are processed in a centralized mode and the deep learning mode is carried out, and an artificial neural network model for disturbing the soil water field by factors such as temperature, air pressure, rainfall intensity and rainfall duration is established. On the basis of accumulating certain data, the weak fiber grating demodulation analyzer analysis module can predict the water migration condition of the soil body according to weather forecast information based on the established artificial neural network model, the prediction information is transmitted to the control module, the control module can preset a soil water content change threshold value, and if the prediction value exceeds the threshold value, the control module can transmit an early warning signal through a cloud.

Furthermore, the control module of the weak fiber grating demodulation analyzer can intelligently control the power supply manager, flexibly adjust the monitoring period according to the meteorological conditions and also can be manually controlled remotely.

The in-situ calibration method of the soil moisture content intelligent monitoring system based on the heat pulse congruent weak fiber grating array comprises the following steps:

the method comprises the following steps: according to the monitoring design requirement, the identical weak fiber grating array water content sensor is embedded into the soil body to be detected, the representative and special weak fiber grating sensor is selected as a calibration weak fiber grating, the conventional sensor is arranged in the range of 50cm at the same horizontal position around the sensor, a water supply pipe is configured, and the sensor is ensured to be in good contact with the soil body in the embedding process.

Step two: the water is input to the soil body at fixed points by the water supply pipe in a grading way, and the water content of the soil body is monitored by the identical weak fiber grating array sensor and the conventional sensor in the water delivery interval.

Step three: and calibrating the in-situ soil volume water content value measured by the conventional sensor at the same time to the maximum temperature rise value measured by the calibration weak fiber grating sensor at the corresponding position.

Step four: fitting the functional relation between the water content and the maximum temperature rise value by using different function models based on a least square method, selecting the model with the highest fitting precision to determine the soil water content calibration curve of each calibration weak fiber grating, and further obtaining the whole identical weak fiber grating array water content sensor calibration curve for subsequent soil water content monitoring.

Furthermore, the conventional sensor in the first step is connected with a data acquisition instrument through a reading line, the reading line and the water supply pipe penetrate through the soil body through a hard PVC pipe, and the hard PVC pipe provides protection and fixing effects. The number and the positions of the calibration weak fiber gratings selected in the step one are determined by the structure and the property of the soil body to be detected, the requirement of monitoring precision, the complexity of a field and other factors. The representativeness refers to weak fiber bragg grating sensors which are buried in soil bodies with basically the same structure and properties, identical weak fiber bragg gratings are randomly selected to be calibrated, and the obtained calibration curve can represent the properties of all the identical weak fiber bragg gratings. The particularity is that the weak fiber bragg grating sensors are arranged at key monitoring positions, encryption is needed for accurate monitoring at the positions, and the arranged sensors need to be calibrated.

Furthermore, in the second step, the soil moisture field is disturbed through fixed-point water delivery, water is delivered for multiple times, so that the sensor can measure soil with different moisture contents, enough moisture content information is obtained for calibration of the identical weak fiber grating array moisture content sensor, and the size and the time interval of each water delivery are determined by the property of the soil to be measured and the monitoring design requirement. The specific method for monitoring the identical weak fiber grating array sensor in the second step is as follows: in the water delivery interval process, the identical weak fiber grating array water content sensor is subjected to pulse heating through the power manager, temperature information of the fiber grating arrangement position is obtained through demodulation and analysis of the weak fiber grating demodulation analyzer, and the maximum temperature rise value of each calibrated weak fiber grating is determined.

Further, the maximum temperature rise value in the third step is defined as follows: the weak fiber grating sensor is heated in a pulse mode, the temperature of the optical fiber rises first and then tends to be stable, and the temperature rise value is closely related to the water content of the soil body and can be converted into the water content information of the soil body. In order to reduce the influence of the thermal characteristics and the contact thermal resistance of the optical fiber at the early heating stage on the temperature data and errors caused by reasons of uneven soil body, unstable test system and the like, the arithmetic mean value of the temperature rise values after the temperature field is stable can be selected to replace the temperature rise data at a certain time point for analysis, and the arithmetic mean value is called as the maximum temperature rise value. The higher the soil moisture content is, the higher the heat conductivity coefficient is and the smaller the maximum temperature rise value is, namely the soil moisture content and the maximum temperature rise value are in a negative correlation relationship. The maximum temperature rise value can be obtained by processing the temperature information through the analysis module.

Further, the function model of the fourth step includes various function forms such as a linear function, a quadratic function polynomial, a power function and the like, the properties of the soil body to be measured are different, and the fitting accuracy of each function model is also different.

Further, the ambient temperature of the fifth step is provided by a weather station.

Example 1:

in the embodiment, the water content of the soil at each time fixed-point water delivery interval is monitored by the identical weak fiber grating array water content sensor and the conventional sensor, and the identical weak fiber grating array water content sensor is calibrated. The soil layer of the field to be measured is sequentially a silt layer, a fine sand layer and a medium sand layer from top to bottom. The specific implementation steps are as follows:

(1) the sensor is embedded. According to the monitoring design, three identical weak fiber grating array moisture content sensors are embedded into a soil body to be detected, the identical weak fiber grating array moisture content sensors are connected in series to form a monitoring network, and the sensors are ensured to be in good contact with the soil body in the embedding process.

(2) And determining and calibrating the weak fiber bragg grating. And selecting a weak fiber grating from each of the three soil layers to represent all the identical weak fiber gratings of the soil layers for calibration, and arranging a dielectric constant sensor at the same horizontal position around the selected three calibrated weak fiber gratings within the range of 50 cm.

(3) And (4) in-situ monitoring. The water is input to the soil body at fixed points by the water supply hose in a grading way, and the water content of the soil body is monitored by the identical weak fiber grating array sensor and the dielectric constant sensor in the water transmission interval. The specific method comprises the following steps: the nickel-chromium electric heating wire of the identical weak fiber grating array water content sensor is subjected to pulse heating through a power supply manager, the heating current is controlled to be 0.60A, and the heating time is 120 s. The central wavelength reading of the identical weak fiber grating in the whole process from heating to returning to the initial value is collected and recorded by using a weak fiber grating demodulation analyzer, the wavelength data is converted into temperature information at the position of the grating by using an analysis module in the weak fiber grating demodulation analyzer, and the corresponding maximum temperature rise value is obtained. And simultaneously, reading the soil moisture content value measured by the dielectric constant sensor by using a data acquisition instrument.

(4) And (6) drawing. And (3) correspondingly making the soil moisture content value measured by the dielectric constant sensor at the same time and the maximum temperature rise value measured by the calibration weak fiber grating sensor at the corresponding position into a maximum temperature rise value delta T-moisture content theta scatter diagram.

(5) And fitting a water content calibration curve. And fitting the functional relationship between the water content and the maximum temperature rise value by respectively using a linear function, a power function and a quadratic function polynomial model based on a least square method, wherein the power function has the highest fitting precision and is determined as a calibration curve of the sensor, as shown in FIG. 4. It can be seen from the figure that the maximum temperature rise value delta T of the weak fiber bragg grating sensors embedded in the silt layer, the fine sand layer and the medium sand layer of the monitoring field is monitored at the ambient temperature in the monitoring process_maxThe water cut theta calibration curves are respectively Delta T_max＝1.4779θ^-0.509、ΔT_max＝3.1095θ^-0.394、ΔT_max＝5.2487θ^-0.316。

Example 2:

the embodiment is that the field is monitored for a long time on the basis of obtaining the calibration curve of the identical weak fiber bragg grating array moisture content sensor of the field to be detected in the embodiment 1. The specific implementation steps are as follows:

and setting the switching time of a remote power supply manager, electrifying and heating the identical weak optical fiber grating array water content sensor for 1 time every 4 hours, wherein the heating current is 0.60A, and the heating time is 120 s. The central wavelength reading of the weak fiber grating in the whole process of heating the optical fiber to the initial value is collected and recorded by using a weak fiber grating demodulation analyzer, the wavelength data is converted into the temperature information of the fiber grating arrangement position by using a demodulation module in the weak fiber grating demodulation analyzer, and the data is transmitted to an analysis module. The analysis module utilizes the calibration curve to convert temperature information into moisture content information, and the obtained moisture content monitoring data can be transmitted through the cloud end, so that the real-time monitoring of the moisture content change condition of the monitoring site is realized. Meanwhile, an analysis module of the weak fiber grating demodulation analyzer automatically reads and records meteorological data measured by a meteorological station, including temperature, air pressure and rainfall conditions. The analysis module inputs the obtained meteorological data and the water content data into a built-in artificial neural network algorithm for deep learning, performs centralized processing and deep learning on soil water content change data in different natural climate environments, and establishes an artificial neural network model of disturbance of factors such as temperature, air pressure and rainfall on a soil water field. The specific implementation method of the model establishment comprises the steps of firstly determining each 12h as a time interval, dividing the whole monitoring process, and then dividing each time interval into 4 time nodes in equal periods (the period is 4 h). A4-layer forward artificial neural network is created through a neural network algorithm built in an analysis module, the number of layers is hidden, 2 layers are hidden, and the number of nodes in each layer is 10. The specific method for training and learning the neural network model comprises the steps of inputting temperature values, air pressure values and rainfall values of all time nodes except the last time node in any time interval and the initial soil moisture content value of the time interval into an input layer of the artificial neural network model, and performing supervised learning by taking the moisture content value of the last time node, namely the final soil moisture content value at the end of the time interval as an output layer value. And training the created artificial neural network model for multiple times by using the divided monitoring data of the multiple time intervals, so that the model calculation and prediction precision is improved. And inputting newly measured data into the obtained artificial neural network model every time in the continuous monitoring process, and further training the model. After model training is carried out by continuously acquiring data of 180 days, the established artificial neural network model is used for predicting the soil termination water content value at the end of a certain time interval according to the known meteorological information and the water content information, the prediction information is transmitted to the control module, and if the prediction value exceeds the soil water content change threshold value preset by the control module, the control module can transmit an early warning signal through a cloud end.