Desert soil evaporation field in-situ monitoring system and data processing method and application

Abstract

The application discloses a kind of desert soil evaporation field in-situ monitoring system and data processing method and application, system includes sensor acquisition module, data transmission module and output platform.Taking soil bucket contains undisturbed soil column, bottom support is weighed on the weighing disc in weighing cylinder, and weighing module is composed of weighing disc, sensor support, weighing sensor and bottom sealing disc.Taking soil bucket and the clearance of weighing cylinder are equipped with sand and water proof sealing ring.Weighing transmitter and 4G collector are packaged in joint sealing windproof sand box, and power supply uses solar panel with local latitude plus 5 ° and buried battery combination.Output platform is based on dynamic threshold method processing quality data: mean and standard deviation are calculated in sliding time window, and k times standard deviation is dynamic threshold value;When continuous multiple data points exceed the sum of mean and threshold value and continuously rise, it is determined that it is rainfall event and data is retained, to avoid false deletion real signal.The application effectively solves the problem of evaporation calculation distortion under desert extreme environment, realizes long-term automatic monitoring.

Description

Technical Field

[0001] This invention belongs to the field of desert hydrological monitoring technology, specifically relating to an automated in-situ monitoring system and method for soil evaporation suitable for extremely arid environments. Background Technology

[0002] Desert regions have abundant sunshine and strong evaporation, which has a particularly significant impact on the restoration of vegetation for desertification control. Soil micro-evaporators are an important tool for studying soil moisture evaporation.

[0003] The annual evaporation rate in desert regions can be dozens of times higher than the precipitation rate. Soil evaporation dynamics are a core parameter for vegetation restoration and water resource management in desert areas, and a key parameter for establishing hydrological models for desert regions. Therefore, soil evaporation is of great significance for studying the evaporation patterns in desert regions. Existing evaporation monitoring methods have significant limitations: traditional micro-evaporator methods rely on manual, timed on-site excavation and sampling, which is difficult to implement continuously and for a long period in the remote, uninhabited areas of the desert, where transportation is extremely inconvenient and the environment is harsh. Furthermore, frequent excavation operations severely damage the original soil structure and moisture distribution at the observation points, leading to a decrease in data representativeness. Although electronic weighing lysimeters can achieve automatic recording, the equipment purchase and installation costs are high. Their complex installation process often requires large machinery and mains power access, which is difficult to achieve in remote deserts. Moreover, they generally lack wind and sand protection design, and the sensors are easily covered or eroded by sand and dust. Strong sunlight causes significant temperature drift errors. These factors together result in a large number of outliers (anomalies) in the weighing data. Current data filtering methods generally employ static thresholding, which removes points with offsets exceeding a certain absolute value. However, in the context of desert showers and complex environmental disturbances, this approach easily misinterprets genuine, short-term, and dramatic increases in soil mass, such as rainfall, as outliers and removes them. This misrepresentation leads to unreasonably negative evaporation values ​​in subsequent calculations during rainfall periods, severely distorting the actual dynamics of soil evaporation. Furthermore, remote sensing inversion / meteorological modeling methods suffer from low spatial resolution and cannot capture small-scale soil heterogeneity.

[0004] Therefore, there is an urgent need for an in-situ evaporation monitoring system that can operate autonomously for a long time in extreme desert environments, resist interference, and support intelligent data recording and identification, to fill the technological gap in this field. Summary of the Invention

[0005] To address the problems of existing soil evaporation monitoring, such as heavy reliance on manual field recording and difficulties in implementation under extreme environmental conditions like deserts, this invention provides a desert soil evaporation in-situ monitoring system, data processing method, and application. This system enables long-term automatic monitoring in the field and solves the problems of calculating and identifying anomalies in desert soil evaporation data under wind and sand interference.

[0006] To solve the above-mentioned technical problems, the desert soil evaporation in-situ monitoring system of the present invention includes: The sensor acquisition module includes a soil sampling bucket for containing the undisturbed soil column, a weighing cylinder fitted outside the soil sampling bucket, and a weighing module located in the lower part of the weighing cylinder for measuring the total mass of the soil sampling bucket and the soil column. The weighing module consists of a weighing pan, a weighing sensor bracket, a weighing sensor, and a sealing plate. The soil sampling bucket is placed on the weighing pan by a bottom-mounted support. The weighing sensor is connected to the bottom of the weighing pan and fixed to the weighing sensor bracket. The bottom of the weighing sensor bracket is connected to the sealing plate, which also serves as the base of the weighing cylinder. The data transmission module is used to remotely transmit the mass data acquired by the weighing sensor to an output platform. The output platform is used to receive quality data transmitted remotely and calculate soil evaporation according to a set time interval; The output platform is configured to perform the following operations: Set a sliding time window; For a sequence of quality data points within each time window, calculate the dynamic threshold of the data in that window. Based on the dynamic threshold, outlier filtering is performed on the quality data points; During the screening process, rainfall event data points that meet the characteristics of continuous increase are identified and retained.

[0007] Furthermore, the output platform performs the operation of "calculating the dynamic threshold of the window data", specifically as follows: Calculate the mean and standard deviation of all data points within this time window; The dynamic threshold Δ is calculated using the formula Δ=k×σ, where σ is the standard deviation and k is a preset constant.

[0008] Furthermore, the output platform performs the operation of "identifying and retaining rainfall event data points that meet the characteristics of continuous increase". Specifically, if there are multiple consecutive data points whose values ​​are all greater than the sum of the average value in the window and the dynamic threshold, and the values ​​show a continuous upward trend, then it is determined that a rainfall event has occurred in the period, and the multiple consecutive data points are retained without being marked as an anomaly.

[0009] Furthermore, the sensor acquisition module also includes a sand-proof and waterproof sealing ring. After the soil sampling bucket is placed into the weighing cylinder, it is higher than the height of the weighing cylinder. The sand-proof and waterproof sealing ring is fitted onto the soil sampling bucket to block the gap between the soil sampling bucket and the weighing cylinder and prevent foreign objects from entering.

[0010] Furthermore, the data transmission module includes a weighing transmitter, a 4G data acquisition unit, and a 4G signal transmission antenna; the weighing sensor is connected to the weighing transmitter via a sensor signal line; the weighing transmitter is connected to the 4G data acquisition unit via a data line; the weighing transmitter and the 4G data acquisition unit are housed in a windproof sandbox, which is a sealed box with sealing material filling the seams to prevent sand and dust intrusion and reduce the impact of temperature and humidity fluctuations on the electronic equipment.

[0011] Furthermore, the monitoring system also includes a power supply module, which comprises a solar panel and a battery. The tilt angle θ of the solar panel satisfies: θ = φ + 5°, where φ is the local latitude. This angle is used to promote sand grain sliding. The battery is installed below ground level to reduce temperature fluctuations in its working environment and extend its service life in the high-temperature desert environment. The solar panel and battery supply power to the sensor acquisition module and data transmission module via power lines.

[0012] A data processing method for the aforementioned desert soil evaporation in-situ monitoring system includes the following steps: Acquire continuous mass data generated by the load cell; Set a sliding time window; For each time window, the sequence of quality data points {x1, x2, ..., x} n}: a) Calculate the average value of the data within this window. and sample standard deviation σ; b) Calculate the dynamic threshold Δ according to the formula Δ = k × σ, where k is a preset constant with a value range of 2 to 3; c) Determine the data points If | - If |>Δ, then mark the point as a potential outlier. d) Identify rainfall events: If multiple consecutive data points satisfy... > If the value of +Δ shows a continuous upward trend, it is determined that a rainfall event occurred during that period, and the relevant data points are retained without being marked as outliers. Soil evaporation is calculated based on the screened quality data and within a set time interval.

[0013] Furthermore, following step d), a secondary verification step is also included: When a rainfall event is determined to have occurred, further data such as rain gauge pulse signals and / or soil moisture sensor data within the same time period are obtained. If the rain gauge pulse signal data and / or soil moisture sensor data also indicate rainfall or a sudden increase in humidity, then the rainfall event is confirmed. If no valid evidence is found, it is determined to be a suspected abnormal event, and an alarm is issued or a more conservative static threshold is used to perform a second screening of the data for that period.

[0014] A computer-readable storage medium having a computer program stored thereon, characterized in that the computer program, when executed by a processor, implements the above-described method.

[0015] A computing device includes: one or more processors; and a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform the method described above.

[0016] The beneficial effects of this invention are as follows: Soil samples are taken in situ during installation, and the monitoring system acquires the weighing data of the soil column through sensor modules. This data is then transmitted to an output platform for soil evaporation calculation. Simultaneously, abnormal values ​​are differentiated, effectively identifying and retaining signals of sudden increases in soil mass caused by real hydrological events such as rainfall, preventing them from being mistakenly excluded in evaporation calculations and thus preventing distortion of soil evaporation calculations. This invention has a simple structure, is easy to install, enables long-term field monitoring, and is highly adaptable, especially suitable for soil moisture evaporation monitoring in extreme and special environments such as deserts, possessing significant potential for widespread application. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the overall system structure of the present invention.

[0018] Figure 2 This is a schematic diagram of the partial system structure of the present invention.

[0019] In the diagram: 1. Soil sampling bucket; 2. Weighing cylinder; 3. Sand and water resistant sealing ring; 4. Sensor lead port; 5. Weighing pan; 6. Soil sampling handle; 7. Weighing sensor; 8. Sealing plate; 9. Weighing sensor bracket; 10. Solar photovoltaic panel; 11. Support pole; 12. 4G signal transmission antenna; 13. Windproof sand box; 14. 4G data acquisition unit; 15. Weighing transmitter; 16. Sensor signal line; 17. Power cord; 18. Buried battery; 19. Data cable. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0021] In the description of this invention, it should be noted that the terms "upper", "middle", "lower", "inner", "outer", "both sides", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the purpose of simplifying the description of this invention and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limiting this invention.

[0022] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installed," "equipped with," "connected," etc., should be interpreted broadly. For example, "connection" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium; it can be a connection within two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0023] like Figure 1 , 2 As shown, the desert soil evaporation in-situ monitoring system of the present invention includes: The sensor acquisition module includes a soil sampling bucket 1 for containing the undisturbed soil column, a weighing cylinder 2 fitted outside the soil sampling bucket 1, and a weighing module located in the lower part of the weighing cylinder 2 for measuring the total mass of the soil sampling bucket 1 and the soil column. The weighing module consists of a weighing pan 5, a weighing sensor bracket 9, a weighing sensor 7, and a bottom sealing plate 8. The soil sampling bucket 1 is placed on the weighing pan 5 by a bottom-mounted support. The weighing sensor 7 is connected to the bottom of the weighing pan 5. The weighing sensor 7 is fixed to the weighing sensor bracket 9. The bottom of the weighing sensor bracket 9 is connected to the bottom sealing plate 8, which also serves as the base of the weighing cylinder 2. The data transmission module is used to remotely transmit the mass data acquired by the weighing sensor 7 to an output platform. The output platform is used to receive quality data transmitted remotely and calculate soil evaporation according to a set time interval; The output platform is configured to perform the following operations: Set a sliding time window; For a sequence of quality data points within each time window, calculate the dynamic threshold of the data in that window. Based on the dynamic threshold, outlier filtering is performed on the quality data points; During the screening process, rainfall event data points that meet the characteristics of continuous increase are identified and retained.

[0024] Furthermore, the output platform performs the operation of "calculating the dynamic threshold of the window data", specifically as follows: Calculate the mean and standard deviation of all data points within this time window; The dynamic threshold Δ is calculated using the formula Δ = k × σ, where σ is the standard deviation and k is a preset constant.

[0025] Furthermore, the output platform performs the operation of "identifying and retaining rainfall event data points that meet the characteristics of continuous increase". Specifically, if there are multiple consecutive data points whose values ​​are all greater than the sum of the average value in the window and the dynamic threshold, and the values ​​show a continuous upward trend, then it is determined that a rainfall event has occurred in the period, and the multiple consecutive data points are retained without being marked as an anomaly.

[0026] Furthermore, the sensor acquisition module also includes a sand-proof and waterproof sealing ring 3. After the soil sampling bucket 1 is placed into the weighing cylinder 2, it is higher than the height of the weighing cylinder 2. The sand-proof and waterproof sealing ring 3 is fitted on the soil sampling bucket 1 to block the gap between the soil sampling bucket 1 and the weighing cylinder 2 and prevent foreign objects from entering.

[0027] Furthermore, the data transmission module includes a weighing transmitter 15, a 4G data acquisition unit 14, and a 4G signal transmission antenna 12; the weighing sensor 7 is connected to the weighing transmitter 15 via a sensor signal line 16; the weighing transmitter 15 is connected to the 4G data acquisition unit 14 via a data line 19; the weighing transmitter 15 and the 4G data acquisition unit 14 are housed in a windproof sandbox 13, which is a sealed box with sealing material filling the seams to prevent sand and dust intrusion and reduce the impact of temperature and humidity fluctuations on electronic equipment.

[0028] Furthermore, it also includes a power supply module, which comprises a solar panel 10 and a battery 18; the tilt angle θ of the solar panel 10 satisfies: θ = φ + 5°, where φ is the local latitude, and the angle is used to promote sand grain sliding; the battery 18 is installed below the ground to reduce temperature fluctuations in its working environment and extend the battery's service life in the high-temperature environment of the desert; the solar panel 10 and the battery 18 supply power to the sensor acquisition module and the data transmission module through the power cable 17.

[0029] A data processing method for an in-situ monitoring system of desert soil evaporation includes the following steps: Acquire continuous mass data generated by the weighing sensor 7; Set a sliding time window; For each time window, the sequence of quality data points {x1, x2, ..., x} n}: a) Calculate the mean x and sample standard deviation σ of the data within this window; b) Calculate the dynamic threshold Δ according to the formula Δ = k × σ, where k is a preset constant with a value range of 2 to 3; c) Determine the data points If | - If |>Δ, then mark the point as a potential outlier. d) Identify rainfall events: If multiple consecutive data points satisfy... > If the value of +Δ shows a continuous upward trend, it is determined that a rainfall event occurred during that period, and the relevant data points are retained without being marked as outliers. Soil evaporation is calculated based on the screened quality data and within a set time interval.

[0030] Furthermore, following step d), a secondary verification step is also included: When a rainfall event is determined to have occurred, further data such as rain gauge pulse signals and / or soil moisture sensor data within the same time period are obtained. If the rain gauge pulse signal data and / or soil moisture sensor data also indicate rainfall or a sudden increase in humidity, then the rainfall event is confirmed. If no valid evidence is found, it is determined to be a suspected abnormal event, and an alarm is issued or a more conservative static threshold is used to perform a second screening of the data for that period.

[0031] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method.

[0032] A computing device includes: one or more processors; and a storage device for storing one or more programs that, when executed by the one or more processors, cause the one or more processors to perform the method described above.

[0033] The data transmission module includes a weighing transmitter 15, a 4G signal transmission antenna 12, and a 4G data acquisition unit 14; the weighing sensor 7 is connected to the weighing transmitter 15 via a sensor signal line 16, and the weighing transmitter 15 is connected to the 4G data acquisition unit 14 via a data line 19; wherein, the weighing transmitter 15 is used to convert the analog signal of the weighing sensor 7 into a digital signal, and the 4G data acquisition unit 14 is used to remotely transmit the digital signal to the output platform via the 4G signal transmission antenna 12.

[0034] The solar panel 10 is installed on top of the pole 11 with an inclination angle θ satisfying: θ = φ + 5°, where φ is the local latitude. Its output is connected to the charging input of the battery 18 via a power cable 17. The battery 18 is located underground and supplies power to the sensor acquisition module and data transmission module via the power cable 17. The underground installation of the battery 18 reduces temperature fluctuations in its operating environment and extends its lifespan in the high-temperature desert environment.

[0035] The usage method of the above-mentioned desert soil evaporation in-situ monitoring system includes: ① Select a flat measurement site; ② At the test site, install the soil sampling handle 6 above the soil sampling bucket 1, knock the soil sampling bucket into the ground to collect the wind-blown sand and soil to be measured, and dig it out from the original monitoring location after the soil is collected. Seal the bottom of the soil sampling bucket 1 with plastic PVC film for later use. ③ Before installation, combine the weighing cylinder 2 with the weighing module consisting of the weighing pan 5, the weighing sensor bracket 9, the weighing sensor 7 and the bottom plate 8, so that the sensor wire is led out from the lead-in port 4; ④ During installation, the monitoring pit will be expanded and deepened to allow the assembled weighing cylinder 2 to be placed in place; ⑤ Place the soil sampling bucket 1 into the weighing cylinder 2, adjust it to be centered, and install the sand-proof and waterproof sealing ring 3; ⑥ Select a location near the original monitoring site to install the pole 11, and then install the 4G data acquisition device 14 and the weighing transmitter 15 in the windproof sand box 13 and fix them on the pole; ⑦ Connect sensor wire 16 to weighing transmitter 15; ⑧ Install solar panels 10 on the pole and connect power lines 17 to power on the pole. Bury a storage battery 18 in the ground.

[0036] ⑨ Obtain data from the platform and calculate soil evaporation according to the formula based on the set calculation interval.

[0037] Example 1 The project was implemented in the northern part of the Kubuqi Desert Photovoltaic Industrial Park in Hangjin Banner, Inner Mongolia, located in the heart of the Kubuqi Desert, with a monitoring period of 12 months.

[0038] like Figure 1 As shown, the desert soil evaporation field in-situ monitoring system includes: a sensor acquisition module, a data transmission module, a power supply module, and an output platform.

[0039] The sand-proof and waterproof sealing ring 3 has an inner diameter of 164mm and an outer diameter of 220mm. The soil sampling bucket 1 has a height of 500mm, an outer diameter of 164mm, and an inner diameter of 160mm. The weighing cylinder 2 has a height of 500mm, an outer diameter of 200mm, and an inner diameter of 194mm. The weighing pan 5 has a diameter of 180mm and a thickness of 20mm. The bottom sealing plate has a diameter of 200mm and a thickness of 30mm.

[0040] The soil sampling bucket 1 is used in conjunction with the soil sampling handle 6. The soil sampling bucket 1 is placed vertically at the selected monitoring point, and the soil sampling handle 6 is placed on it. Then, the long end of the soil sampling handle 6 is tapped until the surrounding soil covers the soil sampling bucket 1, thus obtaining the original soil of the monitoring area.

[0041] Extend the in-situ soil sampling point, combine the weighing cylinder 2 with the weighing module consisting of the weighing pan 5, the weighing sensor bracket 9, the weighing sensor 7 and the sealing plate 8, so that the sensor wire is led out from the lead wire port 4 and buried in the in-situ, and then put the soil sampling bucket 1 in.

[0042] After the soil sampling bucket 1 is placed in the weighing cylinder 2, its height should be higher than that of the weighing cylinder 2. The sand-proof and waterproof sealing ring 3 should be embedded into the groove on the upper edge of the weighing cylinder 2 to ensure that the soil sampling bucket 1 is vertically centered.

[0043] The pole 11 has a diameter of 100mm and a length of 2m. It is installed 5m away from the original soil extraction site.

[0044] The 4G data acquisition unit 14 and the weighing transmitter 15 are fixed inside the windproof sandbox 13, with the seams of the box sealed with silicone. The analog signal output by the weighing sensor 7 is transmitted to the weighing transmitter 15 inside the windproof sandbox 13 via the sensor signal line 16. After being converted into a digital signal, it is input to the 4G data acquisition unit 14 via the data line 19, and finally sent to the cloud platform by the 4G signal transmission antenna 12. The windproof sandbox 13 is then fixed to the support pole 11, with the box and the support pole 11 connected by stainless steel clamps, and the seams sealed with silicone.

[0045] After connecting the 4G signal transmission antenna 12 to the 4G data acquisition unit 14, it is fixed above the windproof sand box 13 by magnetic attachment at the base of the antenna, and the antenna feed line is inserted into the box through a waterproof connector.

[0046] In this embodiment, the weighing sensor 7 is a high-precision resistance strain gauge weighing sensor with a range of 50kg and an accuracy of C3 level, such as the L6E series of ZEMIC or similar products; the weighing transmitter 15 is an RS485 output digital transmitter that supports the Modbus RTU protocol, such as the CMCU series of Shenzhen Xiaoyu Electronics; the 4G data acquisition unit 14 is a remote data acquisition terminal (RTU) that supports 4G full network connectivity, has multiple RS485 interfaces and Modbus RTU master station functions, such as the 4G smart cloud management platform of Hebei Pinggao Electronics Technology Co., Ltd.; the solar panel 10 is a monocrystalline silicon photovoltaic module with a power of 50W and an operating voltage of 18V, such as the corresponding small module of Henglong Optoelectronics New Energy Formula; the battery 18 is a 12V deep cycle gel battery with a capacity of 100Ah, such as the gel battery series of NPP. The windproof sand box 13 is customized according to the equipment size. It is made of 326 stainless steel and has a protection level of IP65 or above. The seams of the box are filled with silicone rubber sealing strips. The solar panel 10 is fixed to the top of the pole 11 with a clamp, facing due south. The tilt angle is set according to the formula of local latitude + 5°. For example, the latitude of Hangjin Banner is 40° and the tilt angle is 45°. This angle can promote the sliding of sand particles and reduce dust accumulation.

[0047] Meanwhile, a 40cm×30cm×50cm pit was dug at the bottom of the pole 11, 60cm away from the pole body, and the buried battery 18 was installed and buried. Actual tests showed that when the surface temperature in summer can reach 50℃, the temperature at a burial depth of 50cm is stable at 10-20℃, which effectively slows down the battery capacity decay.

[0048] After powering on, log in to the transmission platform to obtain the mass of the soil sampling bucket and the soil column inside the bucket. Set the calculation time interval according to the requirements.

[0049] Soil evaporation is calculated using the formula based on the established calculation range:

[0050] Where: E is the evaporation rate (mm). m is the difference in weight (g) between the two ends of the time interval. R is the density of water (g / cm³), and R is the radius of the soil sampling bucket (mm). Furthermore, considering the large diurnal temperature range in the desert, the change in soil moisture density can introduce non-negligible calculation errors. Therefore, this invention also includes a water temperature expansion coefficient compensation method. This method uses an added temperature sensor (located near or built into the weighing module) to obtain the soil column temperature or near-surface temperature T in real time, and dynamically corrects the water density ρ in the evaporation calculation according to the following formula:

[0051] in: The reference temperature for water density is 4℃, which corresponds to the maximum density of pure water. The density of water at 4℃ is 1.000 g / cm³. =2.14×10 4 / ℃ is the coefficient of volume expansion of water; This refers to the measured temperature of the soil column or the near-surface air temperature.

[0052] The compensated dynamic density Substituting into the evaporation calculation formula, we get:

[0053] It can effectively eliminate the mass-volume conversion error caused by environmental temperature fluctuations, and further improve the accuracy and physical authenticity of evaporation monitoring under extreme temperature difference in desert environments.

[0054] Ideally, weighing data should be continuous and smooth. However, the raw data contains abnormally large signals (outliers), so it is necessary to filter the raw data. The data filtering method is simple: the deviation of each data point from its average value within a certain period of time should be less than a certain value. In actual data processing, a threshold is set to filter the data and remove data that does not meet the conditions.

[0055] Constant thresholds can lead to excessive data rejection, such as data jumps during rainfall being treated as outliers and rejected. However, dynamically set thresholds for grouping do not have this problem.

[0056] In the described dynamic thresholding algorithm, σ (standard deviation) is the core calculation parameter used to measure the volatility of data within a time window. Its calculation process follows standard statistical methods, and the specific implementation is as follows: In the example, rainfall events were monitored in the Kubuqi Desert and lasted for several hours. The time window size was defined as 1 hour (e.g., 1 data point per minute, with 60 points in the window).

[0057] Calculate the standard deviation σ of the data within the time window, assuming there are n quality data points within the time window: x1, x2, ..., xn. n (Unit: g) formula:

[0058] : The average value of the window data

[0059] n-1: Sample standard deviation correction (sensor data are treated as a sample rather than the population).

[0060] Generation of dynamic threshold Δ:

[0061] If a certain data point satisfy If so, it is marked as an exception.

[0062] If multiple points are consecutive If the temperature continues to rise, it is determined to be rainfall (data retained).

[0063] If the system is equipped with a rain gauge or soil moisture sensor, the platform will compare rainfall pulse signals or data on sudden increases in soil moisture to perform dual verification of rainfall events determined by dynamic thresholds, further reducing the false positive rate. This method fundamentally avoids the problem of negative value distortion caused by the loss of real rainfall signals in evaporation calculation.

[0064] When the dynamic threshold determines that a rainfall event has occurred, the system triggers the following verification: ① Check and compare the pulse signals of the rain gauge at the weather station; ② Manually check the sudden increase in data from the surface soil moisture sensor; ③ If double verification fails, revert to static threshold filtering.

[0065] This embodiment simultaneously equips the test site with sensors of the same specifications and a sealed reference container of roughly the same size and load to quantify the impact of the external environment on the measurement results. Further analysis and calibration of the monitoring results are then performed.

[0066] A sand collection tray with the same diameter as the soil sampling bucket was set up 50cm away from the sensor acquisition module at the test site, and its ground height was set to collect wind and sand sedimentation. The mass of the collected sand particles was weighed each time the monitoring equipment was maintained to further correct the error caused by wind and sand sedimentation and ensure the accuracy and scientific nature of soil evaporation calculation.

[0067] To verify the actual monitoring effect of this invention, a 12-month continuous monitoring experiment was conducted at a test site in the hinterland of the Kubuqi Desert in Hangjin Banner, Inner Mongolia. Data was collected every minute, with a sliding time window of 1 hour (containing 60 data points), and a dynamic threshold coefficient k of 2.5. The analysis results for typical time periods are described below.

[0068] The selected time period was the entire day of October 17, 2024, and the calculation results are shown in Table 1 below.

[0069] Table 1 Soil Evaporation Calculation Table as of October 17, 2024

[0070] Outlier filtering: No isolated outliers exceeding the mean were found in any hour, and the data was smooth overall, so no outliers were removed.

[0071] Soil evaporation calculation process, taking hour number 1 as an example: Table 2 Measured data per minute for hour number 1

[0072] average value;

[0073] Sample variance:

[0074] Standard deviation:

[0075] Δ calculation:

[0076] Calculate x1 + Δ:

[0077] Interval calculation:

[0078] The actual data range is 18195-18215, all of which fall within the range, with no outliers. The maximum value is 18215 < 18228.89, indicating no rainfall.

[0079] If the hourly near-surface air temperature monitored during the experiment was 13℃, then:

[0080]

[0081]

[0082] Base area:

[0083] The calculated result of soil evaporation is as follows:

[0084] The process of identifying a significant rainfall signal, taking hour number 17 as an example: Table 3 Measured data per minute for hour number 17

[0085] average value;

[0086] Sample variance:

[0087] Standard deviation:

[0088] Δ calculation:

[0089] Calculate x1 + Δ:

[0090] The maximum value of this hourly data, 18213, is greater than 18,205.3. Furthermore, starting from the 51st minute, several consecutive data points, such as 18206, 18208, 18209, 18210, 18211, 18212, and 18213, show values ​​greater than this. These points increase strictly in ascending order, therefore they are determined to be rainfall.

[0091] There exist multiple consecutive data points that satisfy > The data shows a continuous upward trend, and therefore is determined to be a rainfall event. All data points are retained, and the evaporation in this hour is not included in the cumulative evaporation.

[0092] The process of identifying weak rainfall signals, taking hour number 5 as an example: Table 4 Hour No. 5 Measured data per minute

[0093] average value:

[0094] Sample variance:

[0095] Standard deviation:

[0096] Δ calculation:

[0097] Calculation of X1+Δ:

[0098] The maximum value of this hourly data is 18166 > 18165.9675, and starting from the 40-minute mark, for example, multiple consecutive points are greater than this value. Therefore, it was determined to be rainfall.

[0099] The rainfall event identification results show that, at the 5th hour, multiple points are significantly higher than [the previous data point]. The dynamic threshold condition was correctly identified as rainfall, thus avoiding negative evaporation values. In the 17th hour, the mass suddenly increased by 68g, continued to rise, and met the condition of being significantly higher than [a certain value] at multiple consecutive points. The dynamic threshold condition was correctly identified as heavy rainfall. In the 18th hour, the threshold continued to rise, indicating continued rainfall. Smaller increases in other hours did not meet the requirement of being significantly higher than the threshold at multiple consecutive points. The dynamic threshold condition was reasonably classified as noise and was not misjudged as rainfall.

[0100] Based on the above embodiments, the soil evaporation monitoring system proposed in this application, designed for extreme desert environments, achieves the following significant technical effects by comprehensively considering the difficulties of manual monitoring in desert areas and the extreme climatic conditions: This invention successfully overcomes interference sources such as sand erosion and strong sunlight temperature drift in desert environments through windproof sandbox encapsulation of electronic modules, underground battery protection to avoid high-temperature aging, and a double-layer sealed structure design of soil sampling bucket and weighing cylinder, enabling continuous and reliable operation of the system in uninhabited areas. Simultaneously, through an innovative dynamic threshold algorithm, it effectively distinguishes between abnormal noise and actual rainfall events, significantly improving the accuracy of evaporation calculation and avoiding result distortion caused by erroneous deletion of rainfall signals. This addresses the industry pain points of traditional electronic lysimeters, which rely on mains power, are costly, and require frequent manual intervention. It provides a widely applicable automated monitoring tool for desert vegetation restoration, photovoltaic desertification control, and the protection of major engineering projects, filling the technological gap in in-situ monitoring of soil evaporation in desert areas.

[0101] The above description is merely a preferred embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A desert soil evaporation in-situ monitoring system, characterized in that, include: The sensor acquisition module includes a soil sampling bucket (1) for containing the original soil column, a weighing cylinder (2) fitted outside the soil sampling bucket (1), and a weighing module located in the lower part of the weighing cylinder (2) for measuring the total mass of the soil sampling bucket (1) and the soil column. The weighing module consists of a weighing pan (5), a weighing sensor bracket (9), a weighing sensor (7), and a sealing plate (8). The soil sampling bucket (1) is placed on the weighing pan (5) by a bottom support method. The weighing sensor (7) is connected to the bottom of the weighing pan (5). The weighing sensor (7) is fixed on the weighing sensor bracket (9). The bottom of the weighing sensor bracket (9) is connected to the sealing plate (8). The sealing plate (8) also serves as the base of the weighing cylinder (2). The data transmission module is used to remotely transmit the mass data acquired by the weighing sensor (7) to an output platform; The output platform is used to receive quality data transmitted remotely and calculate soil evaporation according to a set time interval; The output platform is configured to perform the following operations: Set a sliding time window; For a sequence of quality data points within each time window, calculate the dynamic threshold of the data in that window. Based on the dynamic threshold, outlier filtering is performed on the quality data points; During the screening process, rainfall event data points that meet the characteristics of continuous increase are identified and retained.

2. The monitoring system according to claim 1, characterized in that, The output platform performs the operation of "calculating the dynamic threshold of the window data", specifically: Calculate the mean and standard deviation of all data points within this time window; The dynamic threshold Δ is calculated using the formula Δ = k × σ, where σ is the standard deviation and k is a preset constant.

3. The monitoring system according to claim 1, characterized in that, The output platform performs the operation of "identifying and retaining rainfall event data points that meet the characteristics of continuous increase". Specifically, if there are multiple consecutive data points whose values ​​are all greater than the sum of the average value in the window and the dynamic threshold, and the values ​​show a continuous upward trend, then it is determined that a rainfall event has occurred in the period, and the multiple consecutive data points are retained without being marked as an anomaly.

4. The monitoring system according to claim 1, characterized in that, The sensor acquisition module also includes a sand-proof and waterproof sealing ring (3). After the soil bucket (1) is placed into the weighing cylinder (2), it is higher than the height of the weighing cylinder (2). The sand-proof and waterproof sealing ring (3) is fitted on the soil bucket (1) to block the gap between the soil bucket (1) and the weighing cylinder (2) and prevent foreign objects from entering.

5. The monitoring system according to claim 1, characterized in that, The data transmission module includes a weighing transmitter (15), a 4G data acquisition unit (14), and a 4G signal transmission antenna (12); the weighing sensor (7) is connected to the weighing transmitter (15) via a sensor signal line (16); the weighing transmitter (15) is connected to the 4G data acquisition unit (14) via a data line (19); the weighing transmitter (15) and the 4G data acquisition unit (14) are housed in a windproof sand box (13), which is a sealed box with sealing material filling the seams to prevent sand and dust intrusion and reduce the impact of temperature and humidity fluctuations on electronic equipment.

6. The monitoring system according to claim 5, characterized in that, The monitoring system also includes a power supply module, which includes a solar panel (10) and a battery (18); the tilt angle θ of the solar panel (10) satisfies: θ=φ+5°, where φ is the local latitude, and the angle is used to promote the sliding of sand particles; the battery (18) is set below the ground to reduce the temperature fluctuation of its working environment and extend the service life of the battery in the high temperature environment of the desert; the solar panel (10) and the battery (18) supply power to the sensor acquisition module and the data transmission module through the power line (17).

7. A data processing method for the desert soil evaporation field in-situ monitoring system according to any one of claims 1-6, characterized in that, Includes the following steps: Acquire continuous mass data generated by the weighing sensor (7); Set a sliding time window; For each time window, the sequence of quality data points {x1, x2, ..., x} n }: a) Calculate the average value of the data within this window. and sample standard deviation σ; b) Calculate the dynamic threshold Δ according to the formula Δ=k×σ, where k is a preset constant with a value range of 2 to 3; c) Determine the data points If | - If |>Δ, then mark the point as a potential outlier. d) Identify rainfall events: If multiple consecutive data points satisfy... > If the value of +Δ shows a continuous upward trend, it is determined that a rainfall event occurred during that period, and the relevant data points are retained without being marked as outliers. Soil evaporation is calculated based on the screened quality data and within a set time interval.

8. The data processing method according to claim 7, characterized in that, Following step d), a secondary verification step is also included: When a rainfall event is determined to have occurred, further data such as rain gauge pulse signals and / or soil moisture sensor data within the same time period are obtained. If the rain gauge pulse signal data and / or soil moisture sensor data also indicate rainfall or a sudden increase in humidity, then the rainfall event is confirmed. If no valid evidence is found, it is determined to be a suspected abnormal event, and an alarm is issued or a more conservative static threshold is used to perform a second screening of the data for that period.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the method as described in claim 7 or 8.

10. A computing device, characterized in that, include: One or more processors; A storage device for storing one or more programs that, when executed by one or more processors, cause the one or more processors to perform the method as described in claim 7 or 8.

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