Small watershed surface matrix multi-dimensional intelligent monitoring method and system

Abstract

The invention relates to the technical field of geological monitoring, in particular to a small watershed surface matrix multi-dimensional intelligent monitoring method and system. Data acquisition is performed through a monitoring module, then acquired first data, second data and third data are sent to a cloud server, the cloud server corrects data errors through a calibration algorithm, and calculation and analysis of the soil erosion amount, the heavy metal content, the nutrient migration rule and the like are completed through a special model; the small watershed comprises a low mountain and hilly area, a low hilly area and a plain cultivated land area which correspond to first data, second data and third data respectively and focus on monitoring of physical and chemical properties such as geological disasters, soil erosion, earth surface matrix heavy metal and nutrients, and all the modules are parallel and can achieve early warning; the scheme has the following beneficial effects: the pertinence is strong: a differential monitoring scheme is constructed, and the problem of one-step monitoring in the traditional monitoring is solved; the monitoring dimensionality is complete: multi-dimensional indexes of meteorology, geology, hydrology, soil physicochemistry and the like are integrated, and the data coverage degree is improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of geological monitoring, in particular to a small watershed surface matrix multi-dimensional intelligent monitoring method and system. BACKGROUND

[0002] The surface matrix layer is the basic material layer supporting various natural resources, and its type, distribution and physicochemical properties directly affect the ecological environment characteristics and natural resource quality. The terrain in the red soil region in the south is complex, geological disasters easily occur in low mountain and hilly areas, soil erosion is serious in low hilly areas, and agricultural non-point source pollution and heavy metal enrichment problems are faced in plain cultivated areas, so it is urgent to monitor the surface matrix.

[0003] The existing monitoring methods have many shortcomings: first, the monitoring is not strong in pertinence, and the topography and ecological needs of different regions are not fully combined; second, the monitoring means is single, and is mostly dependent on single equipment or manual measurement, and the data is seriously fragmented; third, the data processing efficiency is low, and lacks intelligent analysis and prediction functions. SUMMARY

[0004] The main purpose of the present application is to provide a small watershed surface matrix multi-dimensional intelligent monitoring method and system, which aims to solve the problems of single monitoring means, poor pertinence and low data processing efficiency in the existing monitoring of the surface matrix.

[0005] The technical solution provided by the present application is: A small watershed surface matrix multi-dimensional intelligent monitoring method is applied to a small watershed surface matrix multi-dimensional intelligent monitoring system; the system comprises a monitoring module, a transmission module, a cloud server and an early warning module; the method comprises: The cloud server divides the monitoring area into low mountain and hilly areas, low hilly areas and plain cultivated areas; The monitoring module collects the first data corresponding to the low mountain and hilly area in real time, and sends it to the cloud server through the transmission module, wherein the first data includes meteorological parameters, geological disaster displacement data and aerial images; The monitoring module collects the second data corresponding to the low hilly area in real time, and sends it to the cloud server through the transmission module, wherein the second data includes topographic landscape image and vegetation coverage, land use type, soil and water conservation type and soil data; The monitoring module collects the third data corresponding to the plain cultivated area in real time, and sends it to the cloud server through the transmission module, wherein the third data includes surface layer monitoring data; The cloud server obtains the geological disaster displacement amount of the low mountain and hilly area based on the first data, and carries out geological disaster early warning for the low mountain and hilly area based on the disaster displacement amount; The cloud server obtains the soil erosion grade of the low hilly region based on the second data, and performs soil erosion early warning on the low hilly region based on the soil erosion grade, wherein the soil erosion grade is any one of light, moderate, strong, extremely strong, and severe; The cloud server obtains the physicochemical properties of the surface matrix heavy metal and nutrients of the plain cultivated region based on the third data, and performs ecological risk early warning on the plain cultivated region based on the physicochemical properties of the surface matrix heavy metal and nutrients; When early warning is needed, the cloud server controls the early warning module to issue an alarm.

[0006] Preferably, the system further comprises a management terminal in communication connection with the cloud server; the management terminal comprises a display screen; the cloud server divides the region to be monitored into low mountain hilly region, low hilly region, and plain cultivated region, comprising: The management terminal displays the topographic map of the region to be monitored through the display screen, and obtains the division data considering the soil type, vegetation coverage, and land use of the region to be monitored after artificial input, and sends the division data to the cloud server; The cloud server divides the region to be monitored into low mountain hilly region, low hilly region, and plain cultivated region based on the division data.

[0007] Preferably, the monitoring module comprises a weather station, a tilt displacement monitor, and a aerial unmanned aerial vehicle; the first data comprises weather parameters, geological disaster displacement data, and aerial images; the monitoring module collects the first data corresponding to the low mountain hilly region in real time, and sends to the cloud server through the transmission module, comprising: The weather station collects the weather parameters of the low mountain hilly region in real time, and sends to the cloud server through the transmission module, wherein the weather parameters include air temperature, air humidity, rainfall, and evaporation; The tilt displacement monitor collects the geological disaster displacement data of the low mountain hilly region in real time, and sends to the cloud server through the transmission module, wherein the geological disaster displacement data includes X-axis direction tilt displacement, Y-axis direction tilt displacement, and Z-axis direction tilt displacement; The aerial unmanned aerial vehicle collects aerial images of the low mountain hilly region in real time, and sends to the cloud server through the transmission module.

[0008] Preferably, the monitoring module further comprises remote sensing monitoring equipment, a drill array, runoff plot monitoring equipment, and physicochemical property sensors; the runoff plot monitoring equipment is provided with a preset number of parallel plots, each runoff plot monitoring equipment is provided with a different planting mode, and each runoff plot monitoring equipment is installed with a rainfall sensor, a runoff flow meter, and a sand production collection instrument; the second data includes topographic landscape image data, vegetation coverage data, rainfall data, land use data, plot runoff, soil erosion thickness, and sediment data; the monitoring module collects the corresponding second data of the low hilly area in real time and sends it to the cloud server through the transmission module, including: The remote sensing monitoring equipment collects topographic landscape image data and vegetation coverage data, land use data, and soil and water conservation measure data of the low hilly area and sends them to the cloud server through the transmission module; The runoff plot monitoring equipment collects rainfall data, runoff, sediment, and soil data of the low hilly area and sends them to the cloud server through the transmission module; The drill array collects soil erosion thickness of the low hilly area and sends it to the cloud server through the transmission module.

[0009] Preferably, the monitoring module further comprises physicochemical property sensors; the second data further comprises soil data; the drill array collects soil erosion thickness of the low hilly area and sends it to the cloud server through the transmission module, and then further includes: The physicochemical property sensors collect soil data of three levels of slope vertical depth of the low hilly area, which are first, second, and third preset values, respectively, wherein the soil data includes soil temperature, soil humidity, soil PH value, and soil nutrient data.

[0010] Preferably, the third data includes surface layer monitoring data; the monitoring module further comprises a multi-point vertical automatic monitoring device; the monitoring module collects the corresponding third data of the plain cultivated area in real time and sends it to the cloud server through the transmission module, including: The multi-point vertical automatic monitoring device collects surface layer monitoring data of the plain cultivated area in real time and sends it to the cloud server through the transmission module, wherein the surface layer monitoring data includes soil nutrient data, vertical distribution of heavy metal elements, migration status, and physicochemical properties.

[0011] Preferably, the cloud server obtains the soil erosion grade of the low hilly area based on the second data and performs soil erosion early warning on the low hilly area based on the soil erosion grade, including: The cloud server obtains a digital elevation model of the low hilly area; The cloud server calculates the soil erosion amount of the low hilly region based on a digital elevation model of the low hilly region, a topographic landscape image, vegetation cover data, rainfall data, land use data, small area runoff, soil erosion thickness, sediment data, and soil data.

[0012] Preferably, the cloud server obtains a geological disaster displacement amount of the low hilly region based on the first data, and performs geological disaster early warning on the low hilly region based on the disaster displacement amount, and further comprises: The cloud server obtains a displacement amount of the low hilly region in a preset time length in the past based on geological disaster displacement data of the low hilly region. The cloud server obtains the physicochemical properties of the surface matrix heavy metals and nutrients of the plain cultivated region based on the third data, and performs ecological risk early warning on the plain cultivated region based on the physicochemical properties of the surface matrix heavy metals and nutrients, comprising: The cloud server obtains a heavy metal enrichment trend of the plain cultivated region based on the surface layer monitoring data, wherein the heavy metal enrichment trend is any one of increasing, decreasing or remaining unchanged.

[0013] The application provides a small watershed surface matrix multi-dimensional intelligent monitoring system and a small watershed surface matrix multi-dimensional intelligent monitoring method.

[0014] The above technical solution can achieve the following beneficial effects: The small watershed surface matrix multi-dimensional intelligent monitoring method can solve the problems of single monitoring means, weak pertinence and low data processing efficiency of the existing monitoring means for the surface matrix. First, data acquisition is performed through the monitoring module, and then the collected first data, second data and third data are sent to the cloud server. The cloud server corrects data errors through a calibration algorithm, and completes calculation and analysis of soil erosion amount, heavy metal content and nutrient migration law by using a special model. The geological disaster early warning level of the to-be-monitored region is determined based on the first data, the second data and the third data. When the geological disaster early warning level is early warning or danger, the cloud server controls the warning module to issue an alarm. Compared with the existing scheme, the present scheme has the following beneficial effects: strong pertinence: based on the ecological characteristics of different terrain partitions in the southern red soil region, a differentiated monitoring scheme is constructed, solving the problem of traditional monitoring "one size fits all"; full monitoring dimension: multiple dimensions such as meteorology, geology, hydrology and soil physicochemical properties are integrated to realize all-around monitoring of the surface matrix, greatly improving the data coverage; high degree of intelligence: through intelligent analysis and machine learning model of the cloud server, data processing, trend prediction and intelligent early warning are realized, greatly reducing the cost of manual intervention; fast response speed: double-mode communication technology and fast early warning mechanism ensure real-time transmission of monitoring data and timely delivery of early warning information, which saves time for emergency disposal. BRIEF DESCRIPTION OF DRAWINGS

[0015] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed to be used in the embodiments or prior art description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained according to the structures shown in the drawings without creative labor for those skilled in the art.

[0016] Figure 1 A flow chart of the first embodiment of the small watershed surface substrate multi-dimensional intelligent monitoring method proposed by the present application. DETAILED DESCRIPTION

[0017] It should be understood that the specific embodiments described herein are only used to explain the present application and not to limit the present application.

[0018] The present application proposes a small watershed surface substrate multi-dimensional intelligent monitoring method and system.

[0019] As shown in the drawings, Figure 1 In the first embodiment of the small watershed surface substrate multi-dimensional intelligent monitoring method proposed by the present application, the small watershed surface substrate multi-dimensional intelligent monitoring method is applied to a small watershed surface substrate multi-dimensional intelligent monitoring system; the system includes a monitoring module, a transmission module, a cloud server, and an early warning module; the embodiment includes the following steps: Step S110: The cloud server divides the to-be-monitored area into low mountain and hilly areas, low hilly and gully areas, and plain cultivated areas.

[0020] Step S120: The monitoring module collects the first data corresponding to the low mountain and hilly area in real time, and sends the first data to the cloud server through the transmission module, wherein the first data includes meteorological parameters, geological disaster displacement data, and aerial images.

[0021] Specifically, the transmission module adopts a dual-mode communication mode of "4G / 5G communication protocol + LoRa communication protocol" to realize real-time transmission and breakpoint continuation of monitoring data. In the signal good area, high-speed transmission is realized through the 4G / 5G communication protocol, and in the signal weak area such as mountainous area, it is automatically switched to the LoRa communication model, and the transmission distance can reach 3km. The transmission module is built-in data buffer chip, which can store 72 hours monitoring data, realize breakpoint continuation, and guarantee data integrity.

[0022] Step S130: The monitoring module collects the second data corresponding to the low hilly and gully area in real time, and sends the second data to the cloud server through the transmission module, wherein the second data includes topographic landscape image and vegetation coverage data.

[0023] Step S140: The monitoring module collects the third data corresponding to the plain cultivated area in real time and sends it to the cloud server through the transmission module. The third data includes surface layer monitoring data.

[0024] Step S150: The cloud server obtains the geological disaster displacement in the low mountain and hilly area based on the first data, and conducts geological disaster early warning based on the disaster displacement.

[0025] Specifically, when the displacement of a disaster exceeds a set threshold, it indicates a high risk of geological disasters such as landslides, and a geological disaster early warning is required.

[0026] Specifically, the cloud server processing module has built-in data calibration algorithms, soil erosion estimation models, heavy metal migration prediction models, and risk level assessment models; the early warning response module triggers graded early warnings based on monitoring data thresholds and outputs early warning information through three methods: SMS, APP push, and on-site audible and visual alarms.

[0027] Step S160: The cloud server obtains the soil erosion level of the low hilly area based on the second data, and issues a soil erosion warning for the low hilly area based on the soil erosion level, wherein the soil erosion level is any one of light, moderate, strong, very strong, and severe.

[0028] Specifically, soil erosion warnings are issued when the soil erosion level in low hilly areas is moderate or above.

[0029] Step S170: The cloud server obtains the physicochemical properties of heavy metals and nutrients in the surface matrix of the plain cultivated area based on third data, and conducts ecological risk warning for the plain cultivated area based on the physicochemical properties of heavy metals and nutrients in the surface matrix.

[0030] Specifically, when heavy metal enrichment or soil nutrient loss occurs in plain farmland, ecological risk warnings are issued.

[0031] Step S180: When a geological disaster early warning is required, the cloud server controls the early warning module to issue an alarm.

[0032] The multi-dimensional intelligent monitoring method for surface matrix in small watersheds proposed in this invention solves the problems of existing monitoring methods for surface matrix that are singular, lack specificity, and have low data processing efficiency. First, data is collected through a monitoring module. Then, the collected first, second, and third data are sent to a cloud server. The cloud server corrects data errors using a calibration algorithm and uses a dedicated model to calculate and analyze soil erosion, heavy metal content, and nutrient migration patterns. Based on the first, second, and third data, the geological disaster early warning level of the monitored area is determined. When the geological disaster early warning level is warning or dangerous, the cloud server controls the early warning module to issue an alarm. Compared with existing solutions, this approach offers the following advantages: Highly targeted: Based on the ecological characteristics of different topographical zones in the southern red soil region, a differentiated monitoring scheme is constructed, solving the problem of the traditional "one-size-fits-all" approach. Comprehensive monitoring dimensions: Integrating meteorological, geological, hydrological, and soil physicochemical indicators, it achieves comprehensive monitoring of the surface matrix, significantly improving data coverage. High level of intelligence: Through intelligent analysis and machine learning models using cloud servers, it achieves data processing, trend prediction, and intelligent early warning, greatly reducing the cost of manual intervention. Fast response speed: Dual-mode communication technology and a rapid early warning mechanism ensure real-time transmission of monitoring data and timely delivery of early warning information, buying time for emergency response.

[0033] Furthermore, this solution addresses the differences in topography and ecological characteristics among low mountain and hilly areas, low hilly regions, and plain cultivated areas in the southern red soil region. It integrates functional modules such as meteorological parameter monitoring, geological disaster early warning, soil erosion tracking, physicochemical property analysis, and vertical migration monitoring. Employing a three-dimensional monitoring architecture of "satellite remote sensing + UAV patrol + ground-based automated equipment + stratified sampling analysis," and combining it with intelligent analysis and early warning algorithms from a data cloud platform, it achieves real-time perception, precise quantification, and intelligent prediction of dynamic changes in the land surface matrix. This invention solves the problems of insufficient targeting, data fragmentation, and delayed early warning in traditional monitoring methods. It can provide precise support for ecological security, the development of specialty industries, and food security protection, significantly improving the comprehensiveness, timeliness, and intelligence level of land surface matrix monitoring in small watersheds.

[0034] In a second embodiment of the multi-dimensional intelligent monitoring method for surface matrix in a small watershed proposed in this invention, based on the first embodiment, the system further includes a management terminal communicatively connected to the cloud server; the management terminal includes a display screen; step S110 includes the following steps: Step S210: The management terminal displays a topographic map of the area to be monitored on the display screen, and obtains the division data after considering the soil type, vegetation cover and land use of the area to be monitored by manual input, and sends the division data to the cloud server.

[0035] Step S220: The cloud server divides the area to be monitored into low mountain and hilly areas, low hilly areas, and plain farmland areas based on the division data.

[0036] In the third embodiment of the multi-dimensional intelligent monitoring method for surface matrix in a small watershed proposed in this invention, based on the first embodiment, the monitoring module includes a weather station, a tilt displacement monitoring instrument, and an aerial drone; the first data includes meteorological parameters, geological disaster displacement data, and aerial images; step S120 includes the following steps: Step S310: The meteorological station collects meteorological parameters of the low mountain and hilly area in real time and sends them to the cloud server through the transmission module. The meteorological parameters include air temperature, air humidity, rainfall and evaporation.

[0037] Specifically, the weather station includes a platinum resistance temperature sensor, a humidity-sensitive capacitive humidity sensor, a tipping bucket rain gauge, and an ultrasonic evaporation sensor. According to the monitoring requirements of each area, the sensors are installed, fixed, and calibrated to ensure that the platinum resistance temperature sensor error is ≤ ±0.2℃, the tipping bucket rain gauge resolution is 0.1mm, and the pH sensor measurement range is 3-11 with an accuracy of ±0.01.

[0038] Step S320: The tilt displacement monitoring instrument collects geological disaster displacement data in the low mountain and hilly area in real time and sends it to the cloud server through the transmission module. The geological disaster displacement data includes tilt displacement in the X-axis direction, tilt displacement in the Y-axis direction, and tilt displacement in the Z-axis direction.

[0039] Specifically, relevant equipment for the zone monitoring module is installed in each monitoring area to complete sensor calibration, communication debugging, and data acquisition parameter settings.

[0040] Step S330: The aerial drone collects aerial images of the low hilly area in real time and sends them to the cloud server through the transmission module.

[0041] Specifically, meteorological parameters and geological disaster displacement data are collected in real time in low mountain and hilly areas, and topographic and geomorphological landscape images are collected every six months or one year.

[0042] Specifically, the weather station in this embodiment adopts a windproof and anti-shake design for its Stevenson screen, and the sensor installation height is precisely controlled at 150cm; the tilt displacement monitor has a measurement accuracy of 0.01°, can capture small displacements of the mountain in real time, and has an alarm response time of ≤3 seconds; combined with high-resolution satellite remote sensing and low-altitude drone aerial photography, it can achieve full coverage monitoring of key areas such as open-pit mines and subsidence areas.

[0043] In the fourth embodiment of the multi-dimensional intelligent monitoring method for surface matrix in small watersheds proposed in this invention, based on the third embodiment, the monitoring module further includes remote sensing monitoring equipment, a measuring array, runoff plot monitoring equipment, and physicochemical property sensors; the runoff plot monitoring equipment is set with a preset number (e.g., 3) of parallel plots, and each runoff plot monitoring equipment is set with different planting patterns according to the habits of the area to be monitored, and each runoff plot monitoring equipment is equipped with a rainfall sensor, a runoff flow meter, and a sediment yield acquisition instrument; the second data includes topographic and geomorphological landscape images, vegetation cover data, rainfall data, land use data, plot runoff, soil erosion thickness, and sediment load data; step S130 includes the following steps: Step S410: The remote sensing monitoring device collects topographic and landscape images and vegetation cover data of the low hilly area, and sends them to the cloud server through the transmission module.

[0044] Step S420: The runoff monitoring equipment collects rainfall data and land use data in the low hilly area and sends them to the cloud server through the transmission module.

[0045] Step S430: The measuring array collects data on runoff, soil erosion thickness, and sediment load in the low hilly area, and sends the data to the cloud server via the transmission module.

[0046] Specifically, the measuring rod array is arranged with a spacing of 2.5m×2.5m, and the top of the steel rod is coated with wear-resistant reflective paint for easy and rapid observation; the runoff monitoring equipment is equipped with an automatic sampling valve, which can automatically adjust the sampling frequency according to the rainfall intensity; the sensors of the physicochemical property monitoring subunit adopt a waterproof and corrosion-resistant design, and the vertical layered arrangement can accurately capture the migration patterns of water and nutrients.

[0047] In the fifth embodiment of the multi-dimensional intelligent monitoring method for surface matrix in a small watershed proposed in this invention, based on the fourth embodiment, the monitoring module further includes a physicochemical property sensor; the second data further includes soil data; step S430, followed by the following steps: Step S510: The physicochemical property sensor collects soil data in real time at three levels of vertical depth on the slope of the low hill area, namely the first preset value (20cm), the second preset value (40cm), and the third preset value (60cm). The soil data includes soil temperature, soil moisture, soil pH value, and soil nutrient data.

[0048] Specifically, the physicochemical property sensors include temperature and humidity sensors, pH sensors, nitrogen, phosphorus and potassium sensors, and organic matter sensors arranged vertically in layers. The depth interval of the sensors is 20-30cm, which can monitor the dynamic changes of the physicochemical properties of the surface matrix layer at any time.

[0049] In the sixth embodiment of the multi-dimensional intelligent monitoring method for surface matrix in a small watershed proposed in this invention, based on the fifth embodiment, the third data includes surface stratification monitoring data; the monitoring module further includes a multi-point vertical automated monitoring device; step S140 includes the following steps: Step S610: The multi-point vertical automated monitoring equipment collects surface layer monitoring data of the plain farmland in real time and sends it to the cloud server through the transmission module. The surface layer monitoring data includes soil nutrient data, vertical distribution and migration status of heavy metal elements (such as cadmium, mercury, arsenic, etc.), and physicochemical properties.

[0050] Specifically, the multi-point vertical automated monitoring equipment employs a layered sensor array, deployed at depths of 20cm, 50cm, 80cm, 110cm, and 140cm. Each layer is equipped with temperature and humidity sensors, salinity sensors, nitrogen, phosphorus, and potassium nutrient sensors, pH sensors, and rapid heavy metal detection sensors. The equipment uploads layered monitoring data every 1-2 hours. With a detection limit of 0.001mg / kg, it can monitor the vertical distribution, migration, and physicochemical changes of heavy metal elements (such as cadmium, mercury, and arsenic) in real time.

[0051] Data collection phase: A combination of "real-time monitoring + regular monitoring + emergency monitoring" is adopted. Key indicators such as meteorology and geological disasters are collected in real time, while topography, landforms and soil erosion intensity are collected regularly. Emergency intensified monitoring is activated in case of sudden events such as rainstorms and landslides.

[0052] In the seventh embodiment of the multi-dimensional intelligent monitoring method for surface matrix in small watersheds proposed in this invention, based on the sixth embodiment, step S160 includes the following steps: Step S710: The cloud server acquires the digital elevation model of the low hilly area.

[0053] Step S720: The cloud server calculates the soil erosion in the low hilly area based on the digital elevation model, topographic and geomorphological images, vegetation cover data, rainfall data, land use data, plot runoff, soil erosion thickness, sediment load data, and soil data. , In the formula, The value represents soil erosion in low hilly areas; LS is the LS factor, which is a combination of slope length (L) and slope gradient (S), dimensionless and usually greater than 0; P is the soil and water conservation measures factor, determined by digital elevation model and land use data; C is the land cover factor, determined by topographic and geomorphological landscape images and vegetation cover data; K is the soil erodibility factor, determined by soil data, plot runoff, soil erosion thickness, and sediment load data; R is the rainfall erosion factor, determined by rainfall data.

[0054] Specifically, the soil erosion level in low hilly areas is determined based on the amount of soil erosion. The greater the amount of soil erosion, the more severe the corresponding soil erosion level.

[0055] In the eighth embodiment of the multi-dimensional intelligent monitoring method for surface matrix in small watersheds proposed in this invention, based on the seventh embodiment, step S150 includes the following steps: Step S810: The cloud server obtains the displacement of the low mountain and hilly area within a preset time period (e.g., 1 year) based on the geological disaster displacement data of the low mountain and hilly area.

[0056] Specifically, when the displacement of low hilly areas exceeds a corresponding threshold within a preset time period, a geological disaster warning will be issued for the low hilly areas.

[0057] Step S170 includes the following steps: Step S820: The cloud server obtains the heavy metal enrichment trend in the plain farmland based on surface stratification monitoring data, wherein the heavy metal enrichment trend is any one of increasing, decreasing or remaining unchanged.

[0058] Specifically, when the trend of heavy metal enrichment is increasing, geological disaster warnings are issued for plain farmland areas.

[0059] In the ninth embodiment of the multi-dimensional intelligent monitoring method for surface matrix in small watersheds proposed in this invention, based on the eighth embodiment, the following steps are further included: Step S910: When the first preset condition is met, the cloud server determines that the geological disaster warning level of the area to be monitored is dangerous. The first preset condition is: the soil erosion in the low hilly area is greater than the first threshold (e.g., 5t / (km².a)), and the displacement in the low hilly area within the past preset time period is greater than the second threshold (e.g., 8mm), and the heavy metal enrichment trend in the plain cultivated area is increasing, and the nutrient loss in the plain cultivated area is greater than the first loss threshold (e.g., 3mg / kg).

[0060] Step S920: When the second preset condition is met, the cloud server determines that the geological disaster early warning level of the area to be monitored is safe. The second preset condition is: the soil erosion in the low hilly area is less than the third threshold (e.g., 1t / (km²·a)), and the displacement in the low hilly area within the past preset time period (e.g., 2mm) is less than the fourth threshold. The heavy metal enrichment trend in the plain cultivated area is decreasing, and the nutrient loss in the plain cultivated area is less than the second loss threshold (e.g., 1t / (km²·a)). The third threshold is less than the first threshold, the fourth threshold is less than the second threshold, and the second loss threshold is less than the first loss threshold.

[0061] Step S930: When the third preset condition is met, the cloud server determines the geological disaster early warning level of the area to be monitored to be an early warning. The third preset condition is: the amount of soil erosion in the low hilly area is greater than the first threshold (e.g., 5t / (km².a)), or the amount of displacement in the low hilly area within the past preset time period is greater than the second threshold (e.g., 8mm), or the heavy metal enrichment trend in the plain cultivated area is increasing, or the nutrient loss in the plain cultivated area is greater than the first loss threshold.

[0062] Step S940: When the first preset condition, the second preset condition, and the third preset condition are not met, the cloud server determines the geological disaster early warning level of the area to be monitored to be of concern.

[0063] Specifically, the cloud server in this embodiment also includes the following functions: Intelligent early warning: The cloud platform determines the risk level by comparing the analysis results with preset thresholds and outputs corresponding early warning information through the early warning response module; Dynamic adjustment: Based on monitoring data feedback and regional ecological changes, the system dynamically adjusts the layout of monitoring points, sensor deployment density, and early warning thresholds. This system is based on the topographic zoning characteristics of small watersheds in the southern red soil region and constructs a full-process architecture of "zonal monitoring - data transmission - cloud platform processing - early warning response".

[0064] Built-in multi-dimensional data processing models: The regional soil erosion estimation model is optimized based on the general soil erosion equation (soil erosion algorithm), which combines remote sensing data and ground measurement data to improve the estimation accuracy by 15%; The heavy metal migration prediction model adopts machine learning algorithms and can predict the migration path and enrichment trend of heavy metals in the surface matrix based on parameters such as soil physicochemical properties and irrigation methods; The risk level assessment model compares the monitoring data with preset thresholds and classifies them into four levels: safe, attention, warning, and dangerous.

[0065] This plan includes the following stages: Data processing stage: The cloud platform first removes and calibrates outliers from the raw data, and then performs in-depth analysis through professional models to generate quantitative results such as soil erosion, nutrient content, and heavy metal concentration. The data update frequency is synchronized with the collection frequency.

[0066] Intelligent early warning stage: Based on the analysis results of the cloud platform, the corresponding level of early warning response is automatically triggered, and the early warning information can be synchronized to management departments at all levels in real time to achieve collaborative handling.

[0067] Dynamic adjustment phase: Monitoring data are summarized and analyzed every quarter. Based on regional ecological changes and industrial development needs, the layout of monitoring points and early warning thresholds are optimized to ensure the adaptability and effectiveness of the monitoring system.

[0068] The present invention also proposes a multi-dimensional intelligent monitoring system for surface matrix in small watersheds, which applies a multi-dimensional intelligent monitoring method for surface matrix in small watersheds; the system includes a monitoring module, a transmission module, a cloud server, and an early warning module.

[0069] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0070] The embodiments of the present invention have been described above with reference to the accompanying drawings. However, the present invention is not limited to the specific embodiments described above. The specific embodiments described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms under the guidance of the present invention without departing from the spirit and scope of the claims. All of these forms are within the protection scope of the present invention.

Claims

1. A multi-dimensional intelligent monitoring method for surface matrix in a small watershed, characterized in that, A multi-dimensional intelligent monitoring system for surface matrix in small watersheds is applied; the system includes a monitoring module, a transmission module, a cloud server, and an early warning module; the method includes: The cloud server divides the area to be monitored into low mountain and hilly areas, low hilly areas, and plain farmland areas; The monitoring module collects first data corresponding to the low mountain and hilly area in real time and sends it to the cloud server through the transmission module. The first data includes meteorological parameters, geological disaster displacement data, and aerial images. The monitoring module collects the second data corresponding to the low hilly area in real time and sends it to the cloud server through the transmission module. The second data includes topographic and geomorphological landscape images and vegetation cover, land use type, rainfall, soil and water conservation measures, and soil data. The monitoring module collects third data corresponding to the plain cultivated area in real time and sends it to the cloud server through the transmission module. The third data includes surface layer monitoring data. The cloud server obtains the geological disaster displacement in the low mountain and hilly area based on the first data, and conducts geological disaster early warning for the low mountain and hilly area based on the disaster displacement. The cloud server obtains the soil erosion level of the low hilly area based on the second data, and provides soil erosion warning for the low hilly area based on the soil erosion level. The soil erosion level is any one of light, moderate, strong, very strong, and severe. The cloud server obtains the physical and chemical properties of heavy metals and nutrients in the surface matrix of the plain cultivated area based on third-party data, and conducts ecological risk warning for the plain cultivated area based on the physical and chemical properties of heavy metals and nutrients in the surface matrix. When an early warning is required, the cloud server controls the early warning module to issue an alarm.

2. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 1, characterized in that, The system also includes a management terminal communicatively connected to the cloud server; the management terminal includes a display screen; the cloud server divides the area to be monitored into low mountain and hilly areas, low hilly areas, and plain cultivated areas, including: The management terminal displays a topographic map of the area to be monitored on a screen, and obtains manually input division data after considering soil type, vegetation cover and land use of the area to be monitored, and sends the division data to the cloud server. The cloud server divides the area to be monitored into low mountain and hilly areas, low hilly areas, and plain farmland areas based on the division data.

3. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 1, characterized in that, The monitoring module includes a weather station, a tilt displacement monitor, and an aerial drone; the first data includes meteorological parameters, geological disaster displacement data, and aerial images; the monitoring module collects the first data corresponding to the low mountain and hilly area in real time and sends it to the cloud server through a transmission module, including: The meteorological station collects meteorological parameters in the low mountain and hilly area in real time and sends them to the cloud server through the transmission module. The meteorological parameters include air temperature, air humidity, rainfall and evaporation. The tilt displacement monitoring instrument collects geological disaster displacement data in the low mountain and hilly area in real time and sends it to the cloud server through the transmission module. The geological disaster displacement data includes tilt displacement in the X-axis direction, tilt displacement in the Y-axis direction, and tilt displacement in the Z-axis direction. The aerial photography drone collects aerial images of the low mountain and hilly areas in real time and sends them to the cloud server via a transmission module.

4. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 3, characterized in that, The monitoring module also includes remote sensing monitoring equipment, a measuring array, runoff plot monitoring equipment, and physicochemical property sensors; the runoff plot monitoring equipment is set with a preset number of parallel plots, each runoff plot monitoring equipment is set with a different planting mode, and each runoff plot monitoring equipment is equipped with a rainfall sensor, a runoff flow meter, and a sediment yield acquisition instrument; the second data includes topographic and geomorphological landscape images, vegetation cover data, rainfall data, land use data, plot runoff, soil erosion thickness, and sediment volume data; The monitoring module collects second data corresponding to the low hilly area in real time and sends it to the cloud server through the transmission module, including: The remote sensing monitoring equipment collects topographic and geomorphological landscape images, vegetation cover data, land use type data, and soil and water conservation measures data of low hilly areas and sends them to the cloud server through the transmission module. The runoff monitoring equipment collects rainfall data, runoff volume, sediment volume, and soil data in low hilly areas, and sends them to the cloud server via a transmission module. The probe array collects the soil erosion thickness in the low hilly area and sends it to the cloud server via a transmission module.

5. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 4, characterized in that, The monitoring module also includes physicochemical property sensors; the second data also includes soil data; the probe array collects soil erosion thickness data in low hilly areas and sends it to the cloud server via a transmission module, and then includes: The physicochemical property sensor collects soil data in real time at three levels: a first preset value, a second preset value, and a third preset value, representing the vertical depth of the slope in the low hilly area. The soil data includes soil temperature, soil moisture, soil pH value, and soil nutrient data.

6. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 5, characterized in that, The third data includes surface stratification monitoring data; the monitoring module also includes multi-point vertical automated monitoring equipment; the monitoring module collects the third data corresponding to the plain cultivated area in real time and sends it to the cloud server through the transmission module, including: The multi-point vertical automated monitoring equipment collects surface stratification monitoring data of the plain farmland in real time and sends it to the cloud server through the transmission module. The surface stratification monitoring data includes soil nutrient data, vertical distribution and migration of heavy metal elements, and physicochemical properties.

7. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 6, characterized in that, The cloud server obtains the soil erosion level of the low hilly area based on the second data, and provides soil erosion early warning for the low hilly area based on the soil erosion level, including: The cloud server acquires digital elevation models of low hilly areas; The cloud server calculates soil erosion in low hilly areas based on digital elevation models, topographic and geomorphological images, vegetation cover data, rainfall data, land use data, community runoff, soil erosion thickness, sediment volume data, and soil data.

8. The method for multi-dimensional intelligent monitoring of surface matrix in a small watershed according to claim 7, characterized in that, The cloud server obtains the geological hazard displacement in the low hilly area based on the first data, and provides geological hazard early warning for the low hilly area based on the hazard displacement. It also includes: The cloud server obtains the displacement of the low mountain and hilly area over a preset period of time based on geological disaster displacement data of the low mountain and hilly area. The cloud server obtains the physicochemical properties of heavy metals and nutrients in the surface matrix of the plain cultivated area based on third-party data, and conducts ecological risk early warning for the plain cultivated area based on these properties, including: The cloud server obtains the heavy metal enrichment trend in the plain farmland based on surface stratification monitoring data, wherein the heavy metal enrichment trend is any one of increasing, decreasing or remaining unchanged.

9. A multi-dimensional intelligent monitoring system for surface matrix in a small watershed, characterized in that, The method for multi-dimensional intelligent monitoring of surface matrix in small watersheds as described in any one of claims 1-8 is applied; the system includes a monitoring module, a transmission module, a cloud server, and an early warning module.

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