A soil and water loss monitoring and early warning system and method based on the Internet of Things

Abstract

The present invention relates to the field of soil and water loss monitoring technology, specifically a soil and water loss monitoring and early warning system and method based on the Internet of Things. The system includes: a soil and water data acquisition and processing unit that uses NB-IoT communication technology based on Internet of Things sensors to collect meteorological data, soil environmental data, and regional basic data; a soil state monitoring unit that uses a soil state labeling mechanism to divide the soil state monitoring period into a freezing period, a transition period, and a rainy period; a soil and water loss monitoring unit that combines snowmelt factors and rainfall factors into a USLE model to obtain soil erosion models for different periods and calculates the soil erosion amount; and a soil and water loss early warning unit that analyzes the soil and water loss risk for each period and issues an early warning. Based on a soil state labeling mechanism for dividing freezing periods, transition periods, and rainy periods and a method for constructing a phased soil and water loss monitoring model, the present invention dynamically switches the dominant erosion factor based on the freeze-thaw characteristics of Northeast China's black soil to achieve soil and water loss monitoring.

Description

Technical Field

[0001] The present invention relates to the technical field of soil and water loss monitoring, and in particular to a soil and water loss monitoring and early warning system and method based on the Internet of Things. Background Art

[0002] At present, the traditional USLE model or its modified model is used to prevent and control soil erosion. The traditional USLE model regards factors such as rainfall, soil, slope, and vegetation as long-term averages or annual average data, and does not have the ability to divide and respond to short-term dynamic stages. Since the state of black soil is different under different seasonal climates, the soil loosens due to snowmelt in spring, heavy rain directly washes away the black soil in summer, and the soil is frozen in winter. In addition, the traditional USLE model cannot conduct targeted monitoring of soil erosion in the Northeast Black Soil Region, which has significant freeze-thaw seasonality, resulting in inaccurate soil erosion monitoring in each season and difficulty in precise prevention and control. Summary of the Invention

[0003] The purpose of the present invention is to provide a soil erosion monitoring and early warning system and method based on the Internet of Things, so as to solve the problem raised in the above background technology that the state of black soil is different in different seasonal climates, the soil is loosened by snowmelt in spring, the black soil is directly washed away by heavy rain in summer, and the soil is frozen in winter, and the traditional USLE model cannot carry out targeted monitoring of soil erosion in the Northeast black soil area with significant freeze-thaw seasonality, which will lead to inaccurate soil erosion monitoring in each season and difficult to accurately prevent and control it.

[0004] To achieve the above objectives, the invention provides a soil and water loss monitoring and early warning system based on the Internet of Things, comprising:

[0005] The soil and water data acquisition and processing unit uses NB-IoT communication technology based on IoT sensors to collect meteorological data, soil environmental data, and regional basic data in real time for monitoring soil and water loss areas;

[0006] Soil state monitoring unit, based on meteorological data and soil environmental data, uses a soil state labeling mechanism to divide the soil state monitoring period into freezing period, transition period, and rainy period;

[0007] The soil erosion monitoring unit is based on the land soil status data during the freezing period, transition period and rainy period. The snowmelt factor and rainfall factor are combined into the USLE model to obtain the soil erosion amount model for different periods. The soil erosion amount during the freezing period, transition period and rainy period is calculated to monitor the soil erosion in different periods respectively.

[0008] The soil and water loss early warning unit analyzes the soil and water loss risk in each period and issues early warnings based on the amount of soil erosion in different periods.

[0009] Preferably, the meteorological data includes: temperature, precipitation, snowfall, wind speed, solar radiation and relative humidity;

[0010] Basic regional data include: terrain slope, slope length, surface roughness, vegetation coverage and land use type;

[0011] Soil environment data is divided into air layer soil environment data, surface layer soil environment data and soil layer soil environment data;

[0012] Among them, the air layer soil environmental data and the surface layer soil environmental data include snow depth, snow water equivalent and surface temperature; the soil layer soil environmental data include soil temperature, number of freeze-thaw cycles, soil moisture content, soil electrical conductivity and soil porosity.

[0013] Preferably, the soil state labeling mechanism is a multi-condition judgment and time series driven mechanism based on meteorological data and soil environmental data, which is used to divide the soil state of the monitoring area into freezing period, transition period and rainy period. The soil state labeling mechanism is specifically as follows:

[0014] During soil condition monitoring:

[0015] If the soil temperature is below zero, there is snow on the surface, and no surface liquid runoff occurs, the soil condition monitoring period is the freezing period;

[0016] If the surface temperature gradually rises and is greater than zero degrees, but the deep soil temperature is still below zero degrees, the soil moisture content increases rapidly, the snow water equivalent decreases, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the transition period;

[0017] If the soil temperature is always greater than zero degrees, the snow water equivalent is zero, the change in soil moisture content is lower than the set threshold, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the rainy period.

[0018] Preferably, the soil and water loss monitoring unit includes a freezing period monitoring module, a transition period monitoring module and a rainy period monitoring module;

[0019] Among them, the freezing period monitoring module is used to construct the snowmelt factor based on soil environment data and meteorological data during the freezing period. , and the snowmelt factor The soil erosion model during the freezing period was obtained by introducing the USLE model to calculate the soil erosion during the freezing period.

[0020] The transition period monitoring module is used to monitor the soil environment data, meteorological data, and basic rainfall factors during the transition period. Combined with the vertical moisture ratio, the enhanced rainfall factor is constructed , and the snowmelt factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model during the transition period was obtained, and the soil erosion during the transition period was calculated.

[0021] Preferably, in the freezing period monitoring module, a freezing period soil erosion amount model is constructed and the freezing period soil erosion amount is calculated. The specific method steps are as follows:

[0022] S3.1.1 Basic soil erodibility factor based on the USLE model Combined with the number of freeze-thaw cycles affected by the soil freeze-thaw cycle disturbance, the frozen soil erodibility factor is constructed. ;

[0023] S3.1.2. Constructing snowmelt factors based on snow depth, freeze-thaw cycles, terrain slope, and soil moisture ;

[0024] S3.1.3. Frozen soil erodibility factor The snowmelt factor is introduced into the USLE model to obtain the soil erosion model during the freezing period and calculate the soil erosion during the freezing period;

[0025] The soil erosion model during the freezing period is:

[0026] ;

[0027] in, is the amount of soil erosion during the freezing period; is the snowmelt factor; is the frozen soil erodibility factor; is the slope length; is the USLE standard slope function.

[0028] Preferably, in the transition period monitoring module, a transition period soil erosion amount model is constructed and the transition period soil erosion amount is calculated. The specific method steps are as follows:

[0029] S3.2.1. Calculation of soil saturation based on average soil moisture content and total soil porosity ;

[0030] S3.2.2 Basic rainfall factors based on the USLE model , soil saturation Combined with the vertical moisture content ratio, the enhanced rainfall factor is constructed :

[0031] ;

[0032] in, To enhance the rainfall factor; is the basic rainfall factor in the USLE model; To enhance the adjustment coefficient; is the soil saturation; is the average moisture content of the soil layer during the transition period; is the average moisture content of the surface layer during the transition period; is the vertical moisture content ratio;

[0033] S3.2.3. Snowmelt Factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model of the transition period was obtained, and the soil erosion amount of the transition period was calculated;

[0034] Soil erosion model during the transition period:

[0035] ;

[0036] in, is the snowmelt factor; is the snowmelt factor weight; To enhance the rainfall factor; To enhance the weight of rainfall factor; is the frozen soil erodibility factor; is the basic soil erodibility factor; is the vegetation coverage rate; is the amount of soil erosion during the transition period.

[0037] Preferably, the snowmelt factor weight and enhanced rainfall factor weights , as follows:

[0038] ;

[0039] ;

[0040] in, It is the sensitivity coefficient for adjusting saturation and weight changes.

[0041] Preferably, the rainy season monitoring module is used to calculate the amount of soil erosion during the rainy season using the traditional USLE model. .

[0042] Preferably, the soil erosion early warning unit analyzes the soil erosion risk in each period and issues an early warning based on the amount of soil erosion in different periods, as follows:

[0043] If the freezing period snowmelt factor , then a high-risk warning for the freezing period will be triggered;

[0044] If the soil erosion during the transition period , then a medium-level transitional warning will be triggered;

[0045] Soil erosion during rainy season , then trigger the heavy rain warning during the rainy season.

[0046] On the other hand, the present invention provides a soil and water loss monitoring and early warning method based on the Internet of Things, which is used in the soil and water loss monitoring and early warning system based on the Internet of Things described above, comprising the following steps:

[0047] S10.1. Using NB-IoT communication technology based on IoT sensors, collect meteorological data, soil environmental data, and regional basic data in real time in the areas where soil erosion is to be monitored;

[0048] S10.2. Based on meteorological data and soil environmental data, use a soil state labeling mechanism to divide the soil state monitoring period into freezing period, transition period, and rainy period;

[0049] S10.3. Based on land and soil condition data during the freezing period, transition period, and rainy period, combine the snowmelt factor and rainfall factor into the USLE model to derive soil erosion models for different periods. Calculate soil erosion during the freezing period, transition period, and rainy period, and monitor soil and water loss in different periods.

[0050] S10.4. Based on the amount of soil erosion in different periods, analyze the soil and water loss risk in each period and issue early warnings.

[0051] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0052] 1. In this invention, based on the soil state labeling mechanism for the freezing period, transition period and rainy period and the method for constructing a staged soil and water loss monitoring model, it can dynamically switch the snowmelt factor and rainfall factor to target the significant freeze-thaw characteristics of the black soil in Northeast China, realize chain modeling and accurate response of the entire process of soil and water loss, and improve the continuity, real-time and accuracy of monitoring;

[0053] 2. In the present invention, by integrating the NB-IoT communication architecture with a multi-layer soil environment perception sensor network, key parameters such as meteorology, soil and slope structure are collected in real time, and an erosion factor enhancement mechanism with soil saturation, freeze-thaw cycles and moisture gradient as the core is constructed to achieve dynamic modeling and accurate early warning of the complex erosion process. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1A functional block diagram of an embodiment of the present invention;

[0055] Figure numerals: 1. Soil and water data acquisition and processing unit; 2. Soil status monitoring unit; 3. Soil and water loss monitoring unit; 31. Freezing period monitoring module; 32. Transition period monitoring module; 33. Rainy period monitoring module; 4. Soil and water loss early warning unit. DETAILED DESCRIPTION

[0056] Example 1, as Figure 1 As shown, a soil and water loss monitoring and early warning system based on the Internet of Things is provided, including:

[0057] The water and soil data acquisition and processing unit 1 uses NB-IoT communication technology based on IoT sensors to collect meteorological data, soil environment data and regional basic data of the area required for soil and water loss monitoring in real time;

[0058] In this embodiment, the meteorological data includes: temperature, precipitation, snow amount, wind speed, solar radiation and relative humidity;

[0059] Basic regional data include: terrain slope, slope length, surface roughness, vegetation coverage and land use type;

[0060] Soil environment data is divided into air layer soil environment data, surface layer soil environment data and soil layer soil environment data;

[0061] Among them, the soil environmental data of the air layer and the surface layer include snow depth, snow water equivalent and surface temperature; the soil environmental data of the soil layer include soil temperature, number of freeze-thaw cycles, soil moisture content, soil electrical conductivity and soil porosity;

[0062] In this embodiment, the basic regional data collection method is as follows: using LiDAR mapping and slope sensors to collect terrain slope; using GPS + slope model to calculate terrain slope; using remote sensing image recognition to collect surface roughness; using multispectral remote sensing combined with AI segmentation to identify vegetation coverage;

[0063] Soil environmental data collection methods for the soil layer are as follows: soil temperature is collected using a deep-buried thermal probe, which can also measure freezing depth and freeze-thaw cycles; soil moisture content is collected using a dielectric constant soil moisture meter; soil conductivity is collected using a TDR sensor; soil porosity is collected using laboratory analysis for initial surveys and regular updates;

[0064] In this embodiment, after obtaining the initial meteorological data, soil environment data, and regional basic data, the obtained data is preprocessed as follows:

[0065] An equal-interval resampling mechanism is used, with every 30 minutes as the basic time frame. Missing values ​​in meteorological data are preprocessed using forward filling combined with time window averaging, and missing values ​​in snow depth and soil moisture are preprocessed using time series interpolation combined with trend extrapolation. An anomaly detection mechanism is used to identify and correct out-of-bounds values, sudden changes, and redundant data. Physical logic checks are used to ensure consistency between variables at different levels, such as verifying whether the frozen layer depth matches the snow water equivalent. The raw data are derived into key indicators for stage judgment and model control, such as frozen depth, freeze-thaw times, and moisture gradient. The processed data are constructed into factor vectors that can be used for USLE modeling.

[0066] Soil state monitoring unit 2, based on meteorological data and soil environmental data, uses a soil state labeling mechanism to divide the soil state monitoring period into freezing period, transition period and rainy period;

[0067] In this embodiment, the soil state labeling mechanism is a multi-condition judgment and time series driven mechanism based on meteorological data and soil environmental data. It is used to classify the soil state of the monitoring area into freezing period, transition period, and rainy period. The soil state labeling mechanism is specifically as follows:

[0068] During soil condition monitoring:

[0069] If the soil temperature is below zero, there is snow on the surface, and no surface liquid runoff occurs, the soil condition monitoring period is the freezing period;

[0070] If the surface temperature gradually rises and is greater than zero degrees, but the deep soil temperature is still below zero degrees, the soil moisture content increases rapidly, the snow water equivalent decreases, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the transition period;

[0071] If the soil temperature is always greater than zero degrees, the snow water equivalent is zero, the change in soil moisture content is lower than the set threshold, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the rainy period;

[0072] In this embodiment, the switching between the three monitoring periods of freezing period, transition period and rainy period cannot jump across levels, and the switching between freezing period and rainy period requires a transition period; the soil status monitoring period can be one day, one week, one month, etc., and is set differently by experts according to different seasons.

[0073] Soil and water loss monitoring unit 3: Based on the land soil status data during the freezing period, transition period and rainy period, the soil and water loss monitoring unit 3 combines the snowmelt factor and the rainfall factor into the USLE model to obtain soil erosion models for different periods, calculates the soil erosion amount during the freezing period, transition period and rainy period, and monitors soil and water loss in different periods respectively;

[0074] In this embodiment, the soil and water loss monitoring unit 3 includes a freezing period monitoring module 31, a transition period monitoring module 32 and a rainy period monitoring module 33;

[0075] Among them, the freezing period monitoring module 31 is used to construct a snowmelt factor based on soil environmental data and meteorological data during the freezing period. , and the snowmelt factor The soil erosion model during the freezing period was obtained by introducing the USLE model to calculate the soil erosion during the freezing period.

[0076] The transition period monitoring module 32 is used to monitor the soil environment data, meteorological data, basic rainfall factors and the Combined with the vertical moisture ratio, the enhanced rainfall factor is constructed , and the snowmelt factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model during the transition period was obtained, and the soil erosion during the transition period was calculated.

[0077] In this embodiment, the construction of the soil erosion model during the freezing period is to identify the surface erosion risk caused by "snow load + snowmelt sudden release + freeze-thaw disturbance" under the frozen state during the freezing period, and to analyze the snowmelt factor. Dynamic estimation; when the snow has not completely receded but there is runoff on the surface, the combined effects of snowmelt erosion and rainfall erosion are integrated to calculate the snowmelt factor for soil and water loss. and rainfall factor The soil erosion model during the transition period was obtained through collaborative modeling; under the conditions of no snow accumulation, stable soil structure and rainfall-driven, the soil erosion during the rainy period was calculated using the traditional USLE standard model structure.

[0078] In the freezing period monitoring module 31 of this embodiment, a freezing period soil erosion amount model is constructed and the freezing period soil erosion amount is calculated. The specific method steps are as follows:

[0079] S3.1.1 Basic soil erodibility factor based on the USLE model Combined with the number of freeze-thaw cycles affected by the soil freeze-thaw cycle disturbance, the frozen soil erodibility factor is constructed. ;

[0080] In this embodiment, the frozen soil erodibility factor The construction formula is as follows:

[0081] ;

[0082] in, is the freeze-thaw adjustment coefficient, which indicates the attenuation strength of the corrosion resistance due to freeze-thaw cycles. In this embodiment, the value is 0.2; is the number of freeze-thaw cycles; The maximum freeze-thaw times threshold for the whole year is set, which is 30 times in this embodiment; is the basic soil erodibility factor;

[0083] S3.1.2. Constructing snowmelt factors based on snow depth, freeze-thaw cycles, terrain slope, and soil moisture ;

[0084] In this embodiment, the snowmelt factor The construction formula is as follows:

[0085] ;

[0086] in, is the snow depth; is the number of freeze-thaw cycles; is the terrain slope angle; is the soil moisture content; In order to adjust the coefficient of surface moisture response and control the amplification effect of moisture on meltwater runoff formation, the value is set to 0.4 in this embodiment;

[0087] S3.1.3. Frozen soil erodibility factor The snowmelt factor is introduced into the USLE model to obtain the soil erosion model during the freezing period and calculate the soil erosion during the freezing period;

[0088] The soil erosion model during the freezing period is:

[0089] ;

[0090] in, is the amount of soil erosion during the freezing period; is the snowmelt factor; is the frozen soil erodibility factor; is the slope length; is the USLE standard slope function.

[0091] In the transition period monitoring module 32 of this embodiment, a transition period soil erosion amount model is constructed and the transition period soil erosion amount is calculated. The specific method steps are as follows:

[0092] S3.2.1. Calculation of soil saturation based on average soil moisture content and total soil porosity ;

[0093] In this example, soil saturation is calculated as follows:

[0094] ;

[0095] in, is the soil saturation; Soil moisture content average value during the monitoring period; is the total soil porosity, which depends on the soil type;

[0096] S3.2.2 Basic rainfall factors based on the USLE model , soil saturation Combined with the vertical moisture content ratio, the enhanced rainfall factor is constructed :

[0097] ;

[0098] in, To enhance the rainfall factor; is the basic rainfall factor in the USLE model; To enhance the adjustment coefficient; is the soil saturation; is the average moisture content of the soil layer during the transition period; is the average moisture content of the surface layer during the transition period; is the vertical moisture content ratio;

[0099] S3.2.3. Snowmelt Factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model of the transition period was obtained, and the soil erosion amount of the transition period was calculated;

[0100] Soil erosion model during the transition period:

[0101] ;

[0102] in, is the snowmelt factor; is the snowmelt factor weight; To enhance the rainfall factor; To enhance the weight of rainfall factor; is the frozen soil erodibility factor; is the basic soil erodibility factor; is the vegetation coverage rate; is the amount of soil erosion during the transition period.

[0103] In this embodiment, during the transition period, the snowmelt process has not yet ended, but external rainfall has begun. It is necessary to construct an enhanced rainfall factor that reflects the current rainfall scouring capacity. ;

[0104] When snowmelt and rainfall jointly drive the erosion process, it is necessary to introduce the snowmelt factor weight and enhanced rainfall factor weights , adjust the sensitivity coefficient of saturation and weight change The value is 0.8; when the soil is more saturated, the rainfall is more likely to form runoff, so it should be increased weight to reduce the impact of snowmelt;

[0105] Vertical moisture ratio It is the difference in moisture content between the surface and deep soil, reflecting whether the soil is in a water-blocked state. If the surface is saturated and the lower layer is dry, it is easier to form surface runoff, and the enhanced rainfall factor can be obtained. .

[0106] In this embodiment, the snowmelt factor weight and enhanced rainfall factor weights , as follows:

[0107] ;

[0108] ;

[0109] in, It is the sensitivity coefficient for adjusting saturation and weight changes.

[0110] In this embodiment, the rainy season monitoring module 33 is used to calculate the soil erosion amount during the rainy season using the traditional USLE model. ;

[0111] In this example, under the conditions of no snow accumulation and rainfall-dominated surface runoff, a standard USLE model is constructed and applied to calculate erosion. The snowmelt factor is completely excluded, and the rainfall factor is dynamically enhanced to enable the model to capture the soil erosion risk under complex climate events such as heavy rain, short-term heavy rainfall, and surface drainage blockage. The basic form of the traditional USLE model is:

[0112] ;

[0113] in, is the amount of soil erosion during the rainy season;

[0114] In order to enhance the response capability of the USLE model to high-frequency heavy rainfall events during the rainy season, the basic rainfall factor Using dynamic calculation instead of static value, the construction method is as follows:

[0115] ;

[0116] in, For the Total rainfall kinetic energy of a rainfall event; The maximum rainfall intensity within 30 minutes during the monitoring period; is the number of rainfalls during the rainy season;

[0117] No. Total rainfall kinetic energy of a rainfall event The following empirical formula is used for calculation:

[0118] ;

[0119] in, is the average rainfall intensity; Total rainfall.

[0120] Soil and water loss early warning unit 4, which analyzes the soil and water loss risk in each period and issues early warnings based on the amount of soil erosion in different periods;

[0121] In this embodiment, the soil erosion warning unit 4 analyzes the soil erosion risk in each period and issues a warning based on the soil erosion amount in different periods, as follows:

[0122] If the freezing period snowmelt factor , then a high-risk warning for the freezing period will be triggered;

[0123] If the soil erosion during the transition period , then a medium-level transitional warning will be triggered;

[0124] Soil erosion during rainy season , then trigger the heavy rain warning during the rainy season.

[0125] In a second embodiment, the present invention proposes a soil and water loss monitoring and early warning method based on the Internet of Things, which is used in the soil and water loss monitoring and early warning system based on the Internet of Things in the first embodiment, comprising the following steps:

[0126] S10.1. Using NB-IoT communication technology based on IoT sensors, collect meteorological data, soil environmental data, and regional basic data in real time in the areas where soil erosion is to be monitored;

[0127] S10.2. Based on meteorological data and soil environmental data, use a soil state labeling mechanism to divide the soil state monitoring period into freezing period, transition period, and rainy period;

[0128] S10.3. Based on land and soil condition data during the freezing period, transition period, and rainy period, combine the snowmelt factor and rainfall factor into the USLE model to derive soil erosion models for different periods. Calculate soil erosion during the freezing period, transition period, and rainy period, and monitor soil and water loss in different periods.

[0129] S10.4. Based on the amount of soil erosion in different periods, analyze the soil and water loss risk in each period and issue early warnings.

[0130] The embodiments of the present invention are described in detail above with reference to the accompanying drawings, but the present invention is not limited thereto. Various changes can be made within the scope of knowledge possessed by those skilled in the art without departing from the spirit of the present invention.

Claims

1. A soil and water loss monitoring and early warning system based on the Internet of Things, characterized by: include The water and soil data acquisition and processing unit (1) uses NB-IoT communication technology based on IoT sensors to collect meteorological data, soil environment data and regional basic data of the area required for monitoring water and soil erosion in real time; The soil state monitoring unit (2) divides the soil state monitoring period into a freezing period, a transition period, and a rainy period using a soil state labeling mechanism based on meteorological data and soil environmental data; Soil and water loss monitoring unit (3) is based on the land soil status data of the freezing period, transition period and rainy period, and combines the snowmelt factor and rainfall factor into the USLE model to obtain the soil erosion amount model in different periods, calculate the soil erosion amount in the freezing period, transition period and rainy period, and monitor the soil and water loss in different periods respectively; The soil and water loss monitoring unit (3) comprises a freezing period monitoring module (31), a transition period monitoring module (32) and a rainy period monitoring module (33); Among them, the freezing period monitoring module (31) is used to construct the snowmelt factor based on soil environmental data and meteorological data during the freezing period. , and the snowmelt factor The soil erosion model during the freezing period was obtained by introducing the USLE model to calculate the soil erosion during the freezing period. The transition period monitoring module (32) is used to monitor the soil environment data, meteorological data, basic rainfall factors and Combined with the vertical moisture ratio, the enhanced rainfall factor is constructed , and the snowmelt factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model of the transition period was obtained, and the soil erosion amount of the transition period was calculated; Soil and water loss early warning unit (4) is based on the amount of soil erosion in different periods, analyzes the soil and water loss risk in each period and issues early warnings.

2. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 1 is characterized in that: The meteorological data include: temperature, precipitation, snow amount, wind speed, solar radiation and relative humidity; Basic regional data include: terrain slope, slope length, surface roughness, vegetation coverage and land use type; Soil environment data is divided into air layer soil environment data, surface layer soil environment data and soil layer soil environment data; Among them, the air layer soil environmental data and the surface layer soil environmental data include snow depth, snow water equivalent and surface temperature; the soil layer soil environmental data include soil temperature, number of freeze-thaw cycles, soil moisture content, soil electrical conductivity and soil porosity.

3. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 1 is characterized in that: The soil state labeling mechanism is a multi-condition judgment and time series driven mechanism based on meteorological data and soil environmental data. It is used to divide the soil state of the monitoring area into freezing period, transition period and rainy period. The soil state labeling mechanism is as follows: During soil condition monitoring: If the soil temperature is below zero, there is snow on the surface, and no surface liquid runoff occurs, the soil condition monitoring period is the freezing period; If the surface temperature gradually rises and is greater than zero degrees, but the deep soil temperature is still below zero degrees, the soil moisture content increases rapidly, the snow water equivalent decreases, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the transition period; If the soil temperature is always greater than zero degrees, the snow water equivalent is zero, the change in soil moisture content is lower than the set threshold, the precipitation is greater than zero, and there are signs of liquid runoff in the surface layer, then the soil condition monitoring period is the rainy period.

4. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 3 is characterized in that: In the freezing period monitoring module (31), a freezing period soil erosion amount model is constructed and the freezing period soil erosion amount is calculated. The specific method steps are as follows: S3.1.1 Basic soil erodibility factor based on the USLE model Combined with the number of freeze-thaw cycles affected by the soil freeze-thaw cycle disturbance, the frozen soil erodibility factor is constructed. ; S3.1.

2. Constructing snowmelt factors based on snow depth, freeze-thaw cycles, terrain slope, and soil moisture ; S3.1.

3. Frozen soil erodibility factor The snowmelt factor is introduced into the USLE model to obtain the soil erosion model during the freezing period and calculate the soil erosion during the freezing period; The soil erosion model during the freezing period is: ； in, is the amount of soil erosion during the freezing period; is the snowmelt factor; is the frozen soil erodibility factor; is the slope length; is the USLE standard slope function.

5. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 4 is characterized in that: In the transition period monitoring module (32), a transition period soil erosion amount model is constructed and the transition period soil erosion amount is calculated. The specific method steps are as follows: S3.2.

1. Calculation of soil saturation based on average soil moisture content and total soil porosity ; S3.2.2 Basic rainfall factors based on the USLE model , soil saturation Combined with the vertical moisture content ratio, the enhanced rainfall factor is constructed : ； in, To enhance the rainfall factor; is the basic rainfall factor in the USLE model; To enhance the adjustment coefficient; is the soil saturation; is the average moisture content of the soil layer during the transition period; is the average moisture content of the surface layer during the transition period; is the vertical moisture content ratio; S3.2.

3. Snowmelt Factor Enhanced rainfall factor Combined with the introduction into the USLE model, the soil erosion model of the transition period was obtained, and the soil erosion amount of the transition period was calculated; Soil erosion model during the transition period: ； in, is the snowmelt factor; is the snowmelt factor weight; To enhance the rainfall factor; To enhance the weight of rainfall factor; is the frozen soil erodibility factor; is the basic soil erodibility factor; is the vegetation coverage rate; is the amount of soil erosion during the transition period.

6. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 5 is characterized in that: The snowmelt factor weight and enhanced rainfall factor weights , as follows: ； ； in, It is the sensitivity coefficient for adjusting saturation and weight changes.

7. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 6 is characterized in that: The rainy season monitoring module (33) is used to calculate the amount of soil erosion during the rainy season using the traditional USLE model. .

8. The soil and water loss monitoring and early warning system based on the Internet of Things according to claim 7 is characterized in that: The soil erosion early warning unit (4) analyzes the soil erosion risk in each period and issues early warnings based on the amount of soil erosion in different periods, as follows: If the freezing period snowmelt factor , then a high-risk warning for the freezing period will be triggered; If the soil erosion during the transition period , then a medium-level transitional warning will be triggered; Soil erosion during rainy season , then trigger the heavy rain warning during the rainy season.

9. A soil and water loss monitoring and early warning method based on the Internet of Things, used in a soil and water loss monitoring and early warning system based on the Internet of Things as claimed in any one of claims 1 to 8, characterized in that: The steps include: S10.

1. Using NB-IoT communication technology based on IoT sensors, collect meteorological data, soil environmental data, and regional basic data in real time in the areas where soil erosion is to be monitored; S10.

2. Based on meteorological data and soil environmental data, use a soil state labeling mechanism to divide the soil state monitoring period into freezing period, transition period, and rainy period; S10.

3. Based on land and soil condition data during the freezing period, transition period, and rainy period, combine the snowmelt factor and rainfall factor into the USLE model to derive soil erosion models for different periods. Calculate soil erosion during the freezing period, transition period, and rainy period, and monitor soil and water loss in different periods. S10.

4. Based on the amount of soil erosion in different periods, analyze the soil and water loss risk in each period and issue early warnings.

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