Integrated rotary detector for soil carbon sink monitoring, laying method and tracking and influence zoning method

Abstract

The invention discloses an integrated rotary detector for soil carbon sink monitoring, a laying method and a tracking and influence zoning method, and relates to the technical field of environmental protection and monitoring. A plurality of fan-shaped transmitting windows and a plurality of rectangular receiving windows are uniformly arranged around the annular direction of the shell to form a fan-shaped annular transmitting area and a rectangular annular receiving area; the rotating shaft drives the transmitting plane mirror and the receiving plane mirror to rotate by 360 degrees, and the transmitting plane mirror and the receiving plane mirror can adjust pitch angles. According to the invention, after being reflected by the rotating emission plane mirror, laser is precisely projected to soil through the fan-shaped annular window to form a fan-shaped annular illumination area; diffuse reflection light is collected by a synchronously rotating rectangular receiving window, and is captured by a fixed receiving array after being reflected by a receiving plane mirror; and the probe rotates 360 / theta times in a stepping manner by theta degrees, so that 360 / theta fan-shaped ring measurement areas are seamlessly spliced, and finally, a complete, continuous and dead-angle-free circular ring-shaped monitoring area taking the probe as the center is formed.

Description

Technical Field

[0001] This invention relates to the field of environmental protection and monitoring technology, specifically a spatiotemporal trajectory tracking and monitoring system and method for carbon sinks in power transmission and transformation projects. Background Technology

[0002] Achieving the "dual carbon" goal requires not only accurately understanding the static carbon sinks of ecosystems, but more importantly, a deep analysis of their dynamic evolution and driving mechanisms to enable scientific prediction, management, and regulation. Existing technologies mainly suffer from the following shortcomings: 1. Most existing soil spectrometers are single-point or simple linear scanning detectors. A single measurement can only acquire data from a single point or a small area, resulting in significant monitoring blind spots and making it impossible to achieve measurement without blind spots.

[0003] 2. Existing monitoring networks mostly adopt uniform or random point distribution, and their spatial structure fails to match the gradient influence field formed by point disturbance sources such as iron towers. This results in insufficient monitoring of the core disturbance area in the near field, while there is a waste of resources in the far field, making it impossible to accurately capture the spatial heterogeneity of carbon sinks.

[0004] 3. Existing technologies cannot effectively couple "spatial distribution" with "time series", and therefore cannot answer key scientific and management questions such as "how do high-value and low-value areas of carbon sinks migrate" and "how do the 'source / sink' attributes of carbon sink functions transform in the spatiotemporal dimension".

[0005] Therefore, there is an urgent need in this field for an innovative technological solution that can achieve long-term, continuous, high spatial resolution monitoring in an economically feasible manner, and ultimately quantify the spatiotemporal evolution trajectory of carbon sinks. Summary of the Invention

[0006] The technical problem to be solved by this invention is how to achieve long-term, continuous, high spatial resolution monitoring in an economically feasible manner, and ultimately quantify the spatiotemporal evolution trajectory of carbon sinks.

[0007] The present invention solves the above-mentioned technical problems through the following technical means: An integrated rotary detector for soil carbon sequestration monitoring includes a housing, inside which are electrically connected solar power supply module, control and communication module, and optical monitoring module; the housing has multiple fan-shaped transmission windows and multiple rectangular reception windows evenly arranged around it, forming a fan-shaped annular transmission area and a rectangular annular reception area; the fan-shaped annular transmission area is located above the rectangular annular reception area; The optical monitoring module includes, from top to bottom, a transmitting fiber array, a rotating optical module, a receiving fiber array, a beam splitting system, and a transmission fiber, all connected by communication. The transmitting fiber array and the receiving fiber array are respectively fixed to the top and bottom walls of the housing. The rotating optical module includes a transmitting plane mirror, a receiving plane mirror, and a rotating shaft; the center points of the transmitting plane mirror and the receiving plane mirror are respectively fixed at the upper and lower ends of the rotating shaft and are arranged symmetrically; the rotating shaft drives the transmitting plane mirror and the receiving plane mirror to rotate 360°, and the transmitting plane mirror and the receiving plane mirror can adjust the pitch angle.

[0008] Furthermore, the optical fibers at different positions in the transmitting fiber array have different emission angles, and the spatial distribution of their horizontally emitted light can be precisely matched to the fan-shaped annular emission area.

[0009] Furthermore, the angle of the fan-shaped emission window is θ; the fan-shaped emission windows are evenly distributed in groups of 360 / θ with an interval of θ.

[0010] The present invention also provides a method for deploying a soil carbon sink radial gradient monitoring network, including a radial baseline deployment and a gradient monitoring ring deployment; The radial reference system establishes a polar coordinate system with the center of the power transmission tower as the origin. Four radial monitoring baselines are set within this coordinate system, symmetrically distributed along the 0°, 90°, 180°, and 270° directions, forming a cross-shaped monitoring framework. A gradient monitoring ring system sets M monitoring node positions along each radial line, where M ≥ 3 and the first monitoring node is the origin. The monitoring node positions are distributed according to an adjustable parameter progression sequence, specifically including: Let R1 be the distance between the second monitoring node and the center of the tower, and Ri be the distance between the i-th monitoring node and the center of the tower. Then the node spacing satisfies the progressive relationship: R{i+1}-Ri=k×(Ri-R{i-1})+d; where k is the progressive coefficient, d is the baseline spacing, and i=2,3,M-1.

[0011] Furthermore, the monitoring nodes are arranged in a staggered "cross-and-star" pattern in space: even-numbered layers of nodes are arranged on the cross-shaped frame; odd-numbered layers of nodes are uniformly rotated and offset by 45°, and arranged on the star-shaped frame at 45°, 135°, 225°, and 315°.

[0012] Furthermore, the initial values ​​of the reference spacing d and the progression coefficient k are no greater than twice the detector radius.

[0013] The present invention also provides a soil carbon sink radial gradient monitoring network, including a radiation baseline layout and a gradient monitoring ring layout; The radial reference system establishes a polar coordinate system with the center of the power transmission tower as the origin. Four radial monitoring baselines are set within this coordinate system, symmetrically distributed along the 0°, 90°, 180°, and 270° directions, forming a cross-shaped monitoring framework. A gradient monitoring ring system sets M monitoring node positions along each radial line, where M ≥ 3 and the first monitoring node is the origin. The monitoring node positions are distributed according to an adjustable parameter progression sequence, specifically including: Let R1 be the distance between the second monitoring node and the center of the tower, and Ri be the distance between the i-th monitoring node and the center of the tower. Then the node spacing satisfies the progressive relationship: R{i+1}-Ri=k×(Ri-R{i-1})+d; where k is the progressive coefficient, d is the baseline spacing, and i=2,3,M-1.

[0014] Furthermore, the monitoring nodes are arranged in a staggered "cross-and-star" pattern in space: even-numbered layers of nodes are arranged on the cross-shaped frame; odd-numbered layers of nodes are uniformly rotated and offset by 45°, and arranged on the star-shaped frame at 45°, 135°, 225°, and 315°.

[0015] This invention also provides a method for tracking the spatiotemporal trajectory and zoning the impact of carbon sinks in power transmission and transformation projects, comprising the following steps: S1. Establish a single-point soil organic carbon inversion model; S2. Calculate the soil organic carbon content at a single point using the detector data and the model established in S1. Each monitoring node collects in-situ spectral data and calculates organic carbon content at a fixed time period. S3. Construct a carbon sink spatial field. Using a physical information neural network algorithm, the soil organic carbon data obtained from discrete monitoring nodes is integrated with the physical laws of the soil carbon cycle. Through a combination of data-driven and physical constraints, the dynamic change trajectory of the continuous carbon sink spatiotemporal field is reconstructed with high precision. S4. Based on the dynamic change trajectory of the carbon sink spatiotemporal field constructed in S3, analyze the centroid motion trajectory, contour dynamic evolution and spatial gradient field; S5. Dynamic impact zoning and generation of trajectory maps: Based on the spatiotemporal trajectory analysis results of S4, the monitoring area is divided into direct disturbance zone, indirect impact zone and transition zone, and a carbon sink spatiotemporal trajectory map is generated.

[0016] Furthermore, step S1 includes the following sub-steps: S11. Within the monitoring area, systematically collect representative original soil samples; in the laboratory, use the potassium dichromate oxidation-external heating method or an elemental analyzer to determine the soil organic carbon baseline value of each sample and establish a database of true values. S12. Using a near-infrared spectrometer with the same core parameters as the optical monitoring detector, collect diffuse reflectance spectra of all standard soil samples in a controlled laboratory environment to form a standard spectral database. S13. First, the Savitzky-Golay algorithm is used for smoothing and denoising. Then, standard normal variable transformation or multivariate scattering correction is performed to eliminate baseline drift. Finally, first-order or second-order derivative processing is performed to enhance spectral characteristic peaks. On this basis, competitive adaptive reweighted sampling or continuous projection algorithm is applied to select the subset of characteristic wavelength variables most related to soil organic carbon content from the full spectrum data. S14. Using the preprocessed characteristic spectral data as independent variables and the corresponding organic carbon baseline value as dependent variables, a partial least squares regression or machine learning algorithm is used for training to generate a soil organic carbon quantitative inversion model. The k-fold cross-validation method is used to evaluate the model performance to ensure that its validation set determination coefficient is not less than 0.8 and the relative analysis error is greater than 2.0.

[0017] Furthermore, step S2 includes the following sub-steps: S21. The monitoring node starts according to a preset cycle or command, and controls the communication module to drive the optical detection detector to rotate; the near-infrared light emitted by the transmitting fiber array is reflected by the rotating transmitting plane mirror and projected onto the soil surface through the fan-shaped transmitting window; the generated diffuse reflection signal is captured by the synchronously rotating rectangular receiving window and transmitted to the spectral detector through the receiving fiber array. S22. The control and communication module performs analog-to-digital conversion on the acquired raw spectral signal and automatically calls the preprocessing process and the set of characteristic wavelengths determined in step S13 to perform standardization processing and feature extraction on the real-time spectrum. S23. Input the preprocessed and feature-extracted spectral data into the quantitative inversion model generated in step S14 and integrated into the control and communication module; the model performs real-time calculations and outputs the predicted value of soil organic carbon content in the fan-shaped annular emission area corresponding to the detector at the current rotation angle. S24. The calculated organic carbon content data is encapsulated with timestamps, geographic coordinates, node numbers, and sector ring spatial orientation identifiers. The system synchronously performs data quality self-checks. If the spectral signal-to-noise ratio or predicted value exceeds a reasonable range, a quality doubt mark is added. Finally, the data packet is transmitted to the central data processing server through the wireless communication module.

[0018] The advantages of this invention are: This invention utilizes a fixed transmitting fiber array to emit laser light calibrated at a preset angle. This laser light is reflected by a rotating transmitting plane mirror and precisely projected onto the soil through a fan-shaped annular window, forming a fan-shaped annular illumination area. The diffusely reflected light is collected by a synchronously rotating rectangular receiving window, reflected by a receiving plane mirror, and then captured by the fixed receiving array. The probe rotates 360 / θ times in θ° steps, seamlessly stitching together the 360 / θ fan-shaped annular measurement areas to ultimately form a complete, continuous, and blind-spot-free circular monitoring area centered on the probe. This design achieves low-cost, high signal-to-noise ratio panoramic dynamic monitoring without blind spots through "beam pre-adjustment + rotational scanning."

[0019] The multiple soil carbon sink monitoring systems employ a radial gradient monitoring network deployment scheme, characterized by including a radial baseline system and a gradient monitoring ring system. Addressing the dynamic and spatial heterogeneity of the impact of power transmission and transformation projects on surrounding soil carbon sinks, a three-tiered, progressive spatiotemporal trajectory tracking monitoring system ("point-line-surface") is constructed. A systematic method for identifying and delineating direct disturbance zones, indirect influence zones, and transition zones is provided. The carbon sink spatiotemporal trajectory map ultimately transforms complex monitoring data into a comprehensive decision-making map, providing a scientific basis for accurate assessment and zonal management of the ecological impacts of power transmission and transformation projects.

[0020] This invention controls the initial density by adjusting the baseline interval *d*, and controls the density decay rate with increasing distance by adjusting the progression coefficient *k*. By correlating the upper limit of the baseline interval *d* with the effective detection diameter of the detector, it ensures that within the core disturbance zone surrounding the tower, the maximum distance between any adjacent monitoring nodes does not exceed the detection range of a single node. Combined with a "cross-and-star" staggered deployment scheme, the monitoring spatial coverage within this core area is no less than 90%, fundamentally eliminating monitoring blind spots and achieving a leap from "sparse point measurement" to "near-continuous surface coverage." Furthermore, the values ​​of *k* and *d* can be fine-tuned within the specified range based on the terrain complexity, spatial heterogeneity, and sensor budget of the monitoring area. After the system is initially deployed and preliminary data is obtained, the values ​​of *k* and *d* can be optimized and adjusted in reverse based on the actual monitored carbon sink spatial variation characteristics, and the optimized parameters can be applied in subsequent deployments or system expansions. Moreover, a scientific deployment of "dense near the edge and sparse far away" can be achieved in any monitoring scenario. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the detector structure in Embodiment 1 of the present invention; Figure 2 This is an enlarged view of the transmitting fiber array in Embodiment 1 of the present invention; Figure 3 This is a schematic diagram of the detector arrangement structure in Embodiment 2 of the present invention. Detailed Implementation

[0022] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below in conjunction with the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0023] Example 1 This embodiment provides an integrated rotary detector for soil carbon sequestration monitoring. The detector includes a housing 1, which is preferably cylindrical in shape. Inside the housing 1, there is a solar power supply module (not shown in the figure), a control and communication module (not shown in the figure), and an optical monitoring module.

[0024] The solar module includes perovskite-silicon tandem solar cell units and packaging systems; the top cell of the tandem solar cell unit uses CsO. 17 FA0. 83 The module consists of a PbI2.5Br0.5 perovskite layer with a thickness of 400-500 nm and an optical bandgap of 1.68 eV. The bottom cell is an n-type TOPCon silicon cell with an 80 nm thick ITO interconnect layer. The encapsulation system includes an olefin-based encapsulating film and a polyvinyl fluoride / aluminum foil / polyester composite backplane, with water and oxygen permeability of less than 0.5 g / m²·day and 0.1 cm³ / m²·day, respectively. The module has a photoelectric conversion efficiency of ≥29.5%, and the efficiency decay is <5% after 1000 hours of dual 85 testing.

[0025] The control and communication module uses a liquid crystal polymer substrate with a dielectric constant of 2.86~2.94 and a loss factor ≤0.002 as the high-frequency circuit carrier; the power amplifier uses a gallium nitride high electron mobility transistor with a gate width of 0.15μm; a 32-bit multi-core processor and a CMOS RF chip are integrated through low-temperature co-fired ceramic system-in-package technology, and hydrogenated nitrile rubber is used for sealing and protection; the module maintains a bit error rate of less than 10 when operating in an ambient temperature range of -40℃ to +125℃. -12 .

[0026] like Figure 1The optical monitoring module comprises, from top to bottom, a transmitting fiber array 2, a rotating optical module 3, a receiving fiber array 4, a beam splitting system, a transmission fiber, and a detector temperature control system (not shown in the figure). The rotating optical module 3 is driven by a motor to rotate around its central axis, while the transmitting fiber array 2 and the receiving fiber array 4 are fixed to the top and bottom walls of the housing. Specifically, the transmitting fiber array 2 consists of multiple transmitting fibers arranged in a rectangular or square pattern, with physical dimensions (length × width) preferably 15mm × 15mm to 100mm × 50mm. The core diameter of the fibers is 50μm to 400μm, and the center-to-center distance between the fibers is 0.5mm to 3mm. The transmitting fiber light source uses a halogen tungsten lamp supercontinuum light source, with an output spectral range covering 780-2500nm, an output power of not less than 50mW / nm at 2100nm, and a power stability better than ±0.3dB / 24h. Figure 2 As shown, the optical fibers at different positions in the array have different emission angles, which are determined by an elevation angle φ and an azimuth angle ω. This ensures that the spatial distribution of the horizontally emitted light rays after reflection precisely matches the preset fan-shaped annular emission area (detailed description follows). The elevation angle φ ranges from 15° to 75° and is used to control the radial distance of the beam illumination. The azimuth angle ω ranges from -60° to +60° and is used to control the tangential angle of the beam illumination, achieving fan-shaped area coverage with a single measurement angle θ of 30° to 120°.

[0027] The rotating optical module 3 consists of a transmitting plane mirror 31, a fan-shaped transmitting window 32, a rectangular receiving window 33, a receiving plane mirror 34, a rotating shaft 35, and a motor.

[0028] The center points of the transmitting plane mirror 31 and the receiving plane mirror 34 are fixed at the upper and lower ends of the rotating shaft 35, respectively, and are symmetrically arranged at an angle of 45° to 60° with the horizontal plane. The middle part of the rotating shaft 35 is driven to rotate by a worm gear, one end of which is fixed to the inner wall of the outer casing 1, thereby driving the plane mirrors 34 at its upper and lower ends to rotate 360°. In addition, micro motors are fixed at the upper and lower ends of the rotating shaft 35, and the two plane mirrors are fixed to the output ends of the micro motors. The micro motors can drive the plane mirrors to adjust their pitch angle. The detector is 35 to 40 cm long and its bottom is 10 cm above the ground (height of the support). The transmitting plane mirror 31 is 30 to 35 cm above the ground.

[0029] The angle of the transmitting fiber, the angle of the transmitting plane mirror 31, and the height together control the detection range of the detector, and the detection radius of the detector is 10~110cm.

[0030] The fan-shaped emission window 32 has an opening shape of a fan-shaped ring with an inner diameter to outer diameter ratio ranging from 0.15 to 0.7. The fan-shaped ring's opening angle θ matches the azimuth angle range of the beam, preferably 30° to 120°. 360 / θ fan-shaped emission windows 32 are evenly distributed around the cylindrical outer shell 1 at intervals of θ, forming a fan-shaped ring-shaped emission area. Similarly, the number of rectangular receiving windows 33 is the same as the emission windows, located directly below the fan-shaped emission windows 32. Their width W is preferably 30mm to 150mm, and their height H is preferably 20mm to 100mm. Their dimensions should ensure that they completely cover the projection of the upper fan-shaped emission window 32 onto the detector outer shell 1, forming a rectangular ring-shaped receiving area. The emission and receiving windows, as optical windows, are made of double-sided anti-reflection sapphire material with a thickness of 1.5mm. The average transmittance in the 800-2500nm wavelength band is greater than 98%, and the surface hardness reaches Mohs 9.

[0031] The physical arrangement of the receiving fiber array 4 can be the same as or similar to that of the transmitting array, and is preferably rectangular.

[0032] The receiving fiber optic detector employs an extended indium gallium arsenide focal plane array detector with an effective spectral response range of 800–2500 nm, a detectivity D* greater than 2 × 10¹¹ Jones at 2100 nm, and a pixel size of 25 μm × 25 μm. The beam splitting system is an imaging spectrometer using a convex holographic grating, achieving a spectral resolution better than 6 nm and a stray light suppression ratio greater than 10. -4 It has wavelength repeatability better than 0.1nm and is equipped with a built-in standard whiteboard for spectral self-calibration.

[0033] The transmission optical fiber adopts a hybrid bundle of low-hydroxyl silica fiber and fluoride fiber. The silica fiber transmits the 780-1800nm ​​band with a loss of less than 1dB / km at 1300nm; the fluoride fiber transmits the 1800-2500nm band with a loss of less than 0.2dB / m at 2200nm. The overall numerical aperture is 0.22NA.

[0034] The detector temperature control system uses a two-stage semiconductor cooler combined with a PID control algorithm to maintain the internal temperature of the optical cavity at a constant 25±0.5℃ and the detector chip temperature at a stable -20±0.1℃. This ensures that the wavelength repeatability of the system's spectral data is better than 0.1nm and the intensity fluctuation is less than ±0.5% in extreme ambient temperatures ranging from -30℃ to +60℃.

[0035] In this embodiment, a fixed transmitting fiber array 2 emits a laser beam calibrated at a preset angle. This beam is reflected by a rotating transmitting plane mirror 31 and precisely projected onto the soil through a fan-shaped annular window, forming a fan-shaped annular illumination area. The diffusely reflected light is collected by a synchronously rotating rectangular receiving window 33 and reflected by a receiving plane mirror 34 before being captured by the fixed receiving array. The detector rotates 360 / θ times in θ° steps, seamlessly stitching together the 360 / θ fan-shaped annular measurement areas to ultimately form a complete, continuous, and blind-spot-free circular monitoring area centered on the detector. This design achieves low-cost, high signal-to-noise ratio panoramic dynamic monitoring without blind spots through "beam pre-adjustment + rotation scanning."

[0036] Example 2 Based on the integrated detector of Example 1, this example provides a soil carbon sink monitoring scheme using a radial gradient monitoring network, including a radiation reference system and a gradient monitoring ring system.

[0037] The radial reference system establishes a polar coordinate system with the center of the power transmission tower as the origin. Within this coordinate system, four radial monitoring baselines (N=4) are symmetrically distributed along the 0°, 90°, 180°, and 270° directions, forming a cross-shaped monitoring framework. A gradient monitoring ring system sets M monitoring node positions along each radial line, where M≥3 and the first monitoring node is the origin. The monitoring node positions are distributed according to an adjustable parameter progression sequence, specifically including: Let R1 be the distance from the second monitoring node to the center of the tower, and Ri be the distance from the i-th monitoring node to the center of the tower. Then the node spacing satisfies the progressive relationship: R{i+1}-Ri=k×(Ri-R{i-1})+d. Where k is the progressive coefficient, d is the baseline spacing, and i=2,3,…,M-1.

[0038] like Figure 3 As shown, the monitoring nodes are arranged in a staggered "cross-and-star" pattern in space: even-numbered layers of nodes (such as the 2nd, 4th, 6th... nodes) are strictly arranged in the aforementioned cross-shaped framework (0°, 90°, 180°, 270° directions). Figure 3 The solid line represents a cross-shaped framework. Odd-numbered layer nodes (such as the 3rd, 5th, 7th... nodes) are uniformly rotated and offset by 45°, and placed on the star-shaped framework (in the 45°, 135°, 225°, 315° directions). Figure 3 The dotted line in the middle represents the cross-shaped framework.

[0039] The values ​​of the progressive coefficient k and the reference spacing d are determined by the effective detection radius range (10cm~110cm) of the optical monitoring detector. In order to achieve seamless cross-coverage of monitoring nodes in the area with the most severe disturbance around the tower, and at the same time avoid resource waste, the initial value of the reference spacing d should not be greater than twice the detector radius, that is, d≤2.2m, and the preferred range is 1.5m~2.2m; the preferred initial value of the progressive coefficient k is 1.4~1.8, so as to achieve a rapid and smooth decay of monitoring density with increasing distance.

[0040] This embodiment controls the initial density by adjusting the baseline interval d and the density decay rate with increasing distance by adjusting the progression coefficient k. By correlating the upper limit of the baseline interval d with the effective detection diameter of the detector, it ensures that within the core disturbance zone of 0-5 meters around the tower, the maximum distance between any adjacent monitoring nodes does not exceed the detection range of a single node. Combined with the "cross-and-star" staggered deployment scheme, the monitoring spatial coverage within this core area is no less than 90%, fundamentally eliminating monitoring blind spots and achieving a leap from "sparse point measurement" to "near-continuous surface coverage." Furthermore, the values ​​of k and d can be fine-tuned within their range based on the terrain complexity, spatial heterogeneity, and detector budget of the monitoring area. After the system's initial deployment and the acquisition of preliminary data, the values ​​of k and d can be optimized and adjusted in reverse based on the actual monitored carbon sink spatial variation characteristics, and the optimized parameters can be applied in subsequent deployments or system expansions. Moreover, a scientific deployment of "dense near the edge and sparse far away" can be achieved in any monitoring scenario.

[0041] Example 3 Based on the soil carbon sequestration monitoring scheme of Example 2, which employs a radial gradient monitoring network deployment, this example proposes a method for tracking the spatiotemporal trajectory and zoning the impact of carbon sequestration in power transmission and transformation projects based on multi-source detector data. Specifically, it includes the following steps: S1. Establish a single-point soil organic carbon inversion model.

[0042] S2. The soil organic carbon content at a single point is calculated using the detector data and the model established in S1. In-situ spectral data is collected and organic carbon content is calculated at each monitoring node at a fixed time period. Preferably, this fixed time period is set to 7 days.

[0043] S3. Constructing a carbon sink spatial field: Employing a physical information neural network algorithm, soil organic carbon data acquired from discrete monitoring nodes is integrated with the physical laws of the soil carbon cycle. Through a combination of data-driven and physical constraints, a continuous spatiotemporal field of carbon sinks is reconstructed with high precision. Specifically, the spatial coordinates and timestamps of monitoring points are used as inputs, and the measured organic carbon content of the corresponding points is used as the training objective. Simultaneously, the partial differential equations describing the spatiotemporal dynamics of carbon sinks are embedded as physical constraint terms in the loss function of the neural network. By jointly optimizing the data fit and consistency with physical laws, the trained network model can directly deduce and generate a carbon sink spatiotemporal dataset with a resolution of 1 meter × 1 meter × 7 days within the entire monitoring area. This dataset constitutes a physically reliable three-dimensional spatiotemporal cube, completely and reasonably recording the spatiotemporal dynamic changes of carbon sinks within the monitoring area.

[0044] S4. Based on S3, the spatiotemporal trajectory of carbon sink spatiotemporal field is quantitatively analyzed, including the analysis of the centroid motion trajectory, the dynamic evolution of contour lines, and the spatial gradient field.

[0045] S5. Dynamic Impact Zoning and Trajectory Map Generation: Based on the spatiotemporal trajectory analysis results of S4, this step divides the monitoring area into direct disturbance zone, indirect impact zone, and transition zone, and generates a carbon sink spatiotemporal trajectory map. Furthermore, the carbon sink spatiotemporal trajectory map is a comprehensive map generated based on a carbon sink spatiotemporal cube, capable of dynamically visualizing the spatiotemporal evolution of soil carbon sinks. This map, in a form similar to a dynamic cloud map in weather forecasts, intuitively presents the spatial distribution and temporal dynamics of carbon sinks within the monitoring area, and simultaneously integrates the following core analytical elements: 1) Clearly identifying and dynamically tracking the spatial boundaries and evolution of the direct disturbance zone, indirect impact zone, and transition zone; 2) Quantifying and superimposing the centroid movement trajectory to reveal the movement path, direction, and speed of the core of carbon sink spatial distribution; 3) Depicting the dynamic evolution of characteristic contour lines to reflect the expansion, contraction, and morphological changes of the carbon sink "source / sink" pattern; 4) Displaying the intensity and direction of the spatial gradient field to identify areas of drastic carbon sink changes and ecological boundaries.

[0046] Furthermore, S1 includes the following sub-steps: S11. Within the monitoring area, systematically collect representative original soil samples; in the laboratory, use the potassium dichromate oxidation-external heating method or an elemental analyzer to determine the soil organic carbon baseline value of each sample and establish a database of true values.

[0047] S12. Using a near-infrared spectrometer with the same core parameters as the optical monitoring detector, diffuse reflectance spectra of all standard soil samples are collected in a controlled laboratory environment, covering the characteristic band of 780nm~2500nm, forming a standard spectral database.

[0048] S13. First, the Savitzky-Golay algorithm is used for smoothing and denoising. Then, standard normal variable transformation or multivariate scattering correction is performed to eliminate baseline drift. Finally, first-order or second-order derivative processing is performed to enhance spectral characteristic peaks. On this basis, competitive adaptive reweighted sampling or continuous projection algorithm is applied to select the subset of characteristic wavelength variables most related to soil organic carbon content from the full spectrum data.

[0049] S14. Using the preprocessed characteristic spectral data as independent variables and the corresponding organic carbon baseline value as dependent variables, a partial least squares regression or machine learning algorithm is used for training to generate a soil organic carbon quantitative inversion model. The k-fold cross-validation method is used to evaluate the model performance to ensure that its validation set determination coefficient is not less than 0.8 and the relative analysis error is greater than 2.0.

[0050] Furthermore, S2 includes the following sub-steps: S21. The monitoring node starts according to a preset cycle or command, and controls the communication module to drive the optical detection detector to rotate. The near-infrared light emitted by the transmitting fiber array 2 is reflected by the rotating transmitting plane mirror 31 and projected onto the soil surface through the fan-shaped transmitting window 32. The generated diffuse reflection signal is captured by the synchronously rotating rectangular receiving window 33 and transmitted to the spectral detector through the receiving fiber array 4.

[0051] S22. The control and communication module performs analog-to-digital conversion on the acquired raw spectral signal and automatically calls the preprocessing procedures (including smoothing, scattering correction, and derivative processing) that have been solidified in step S13 and the set of characteristic wavelengths determined in step S13 to perform standardization processing and feature extraction on the real-time spectrum.

[0052] S23. Input the preprocessed and feature-extracted spectral data into the quantitative inversion model generated in step S14 and integrated into the control and communication module; the model performs real-time calculations and outputs the predicted value of soil organic carbon content in the fan-shaped annular emission area corresponding to the detector at the current rotation angle.

[0053] S24. The calculated organic carbon content data is encapsulated with timestamps, geographic coordinates, node numbers, and sector ring spatial orientation identifiers. The system synchronously performs data quality self-checks. If the spectral signal-to-noise ratio or predicted value exceeds a reasonable range, a quality doubt mark is added. Finally, the data packet is transmitted to the central data processing server through the wireless communication module.

[0054] Furthermore, S4 includes the following sub-steps: S41. Centroid Motion Trajectory: By calculating the weighted centroid of the carbon sink spatial distribution, the trajectory parameters (direction, velocity, distance, acceleration) of the centroid are analyzed and the motion mode (diffusion, contraction, stability, fluctuation) is identified.

[0055] S42. Dynamic evolution of contour lines: extract feature contour lines (mean, standard deviation, threshold), quantify contour line morphology (shape index, fractal dimension, density, elongation), and monitor the movement of contour line boundaries (movement distance, velocity field, hotspot identification, direction preference).

[0056] S43. Spatial gradient field: Calculate the spatial gradient (magnitude and direction), analyze the gradient direction distribution and gradient intensity evolution, and perform spatiotemporal coupling analysis.

[0057] This embodiment addresses the dynamic and spatial heterogeneous impact of power transmission and transformation projects on surrounding soil carbon sequestration. It constructs a three-tiered, progressive spatiotemporal trajectory tracking and monitoring system based on "points, lines, and surfaces," and provides a systematic method for identifying and dividing direct disturbance zones, indirect impact zones, and transition zones. The carbon sequestration spatiotemporal trajectory map ultimately transforms complex monitoring data into a comprehensive decision-making map, providing a scientific basis for accurate assessment and zonal management of the ecological impact of power transmission and transformation projects.

[0058] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. An integrated rotary detector for soil carbon sequestration monitoring, characterized in that, The device includes an outer casing, inside which are electrically connected solar power supply modules, control and communication modules, and optical monitoring modules. The outer casing has multiple fan-shaped transmission windows and multiple rectangular reception windows evenly arranged around it in a circumferential direction, forming a fan-shaped annular transmission area and a rectangular annular reception area. The fan-shaped annular transmission area is located above the rectangular annular reception area. The optical monitoring module includes, from top to bottom, a transmitting fiber array, a rotating optical module, a receiving fiber array, a beam splitting system, and a transmission fiber, all connected by communication. The transmitting fiber array and the receiving fiber array are respectively fixed to the top and bottom walls of the housing. The rotating optical module includes a transmitting plane mirror, a receiving plane mirror, and a rotating shaft; the center points of the transmitting plane mirror and the receiving plane mirror are respectively fixed at the upper and lower ends of the rotating shaft and are arranged symmetrically; the rotating shaft drives the transmitting plane mirror and the receiving plane mirror to rotate 360°, and the transmitting plane mirror and the receiving plane mirror can adjust the pitch angle.

2. The integrated rotary detector for soil carbon sequestration monitoring according to claim 1, characterized in that, The optical fibers at different positions in the transmitting fiber array have different emission angles, and the spatial distribution of their horizontally emitted light can be precisely matched to the fan-shaped annular emission area.

3. The integrated rotary detector for soil carbon sequestration monitoring according to claim 1 or 2, characterized in that, The angle of the sector-shaped emission window is θ; the sector-shaped emission windows are evenly distributed in a pattern of 360 / θ with an interval of θ.

4. A method for deploying a soil carbon sink radial gradient monitoring network using the integrated rotating detector described in any one of claims 1 to 3, characterized in that, This includes the deployment of radiation baselines and the deployment of gradient monitoring rings; The radial reference system establishes a polar coordinate system with the center of the power transmission tower as the origin. Four radial monitoring baselines are set within this coordinate system, symmetrically distributed along the 0°, 90°, 180°, and 270° directions, forming a cross-shaped monitoring framework. A gradient monitoring ring system sets M monitoring node positions along each radial line, where M ≥ 3 and the first monitoring node is the origin. The monitoring node positions are distributed according to an adjustable parameter progression sequence, specifically including: Let R1 be the distance between the second monitoring node and the center of the tower, and Ri be the distance between the i-th monitoring node and the center of the tower. Then the node spacing satisfies the progressive relationship: R{i+1}-Ri=k×(Ri-R{i-1})+d; where k is the progressive coefficient, d is the baseline spacing, and i=2,3,…,M-1.

5. The method for deploying a soil carbon sink radial gradient monitoring network according to claim 4, characterized in that, The monitoring nodes are arranged in a "cross-and-star" staggered pattern in space: even-numbered layers of nodes are arranged on the cross-shaped frame; odd-numbered layers of nodes are uniformly rotated and offset by 45°, and arranged on the star-shaped frame at 45°, 135°, 225°, and 315°.

6. The method for deploying a soil carbon sink radial gradient monitoring network according to claim 4 or 5, characterized in that, The initial values ​​of the reference spacing d and the progression coefficient k are not greater than twice the detector radius.

7. A soil carbon sink radial gradient monitoring network, characterized in that, This includes the deployment of radiation baselines and the deployment of gradient monitoring rings; The radial reference system establishes a polar coordinate system with the center of the power transmission tower as the origin. Four radial monitoring baselines are set within this coordinate system, symmetrically distributed along the 0°, 90°, 180°, and 270° directions, forming a cross-shaped monitoring framework. A gradient monitoring ring system sets M monitoring node positions along each radial line, where M ≥ 3 and the first monitoring node is the origin. The monitoring node positions are distributed according to an adjustable parameter progression sequence, specifically including: Let R1 be the distance between the second monitoring node and the center of the tower, and Ri be the distance between the i-th monitoring node and the center of the tower. Then the node spacing satisfies the progressive relationship: R{i+1}-Ri=k×(Ri-R{i-1})+d; where k is the progressive coefficient, d is the baseline spacing, and i=2,3,…,M-1.

8. The soil carbon sink radial gradient monitoring network according to claim 7, characterized in that, The monitoring nodes are arranged in a "cross-and-star" staggered pattern in space: even-numbered layers of nodes are arranged on the cross-shaped frame; odd-numbered layers of nodes are uniformly rotated and offset by 45°, and arranged on the star-shaped frame at 45°, 135°, 225°, and 315°.

9. A method for tracking the spatiotemporal trajectory and zoning the impact of carbon sequestration in power transmission and transformation projects based on the soil carbon sequestration radial gradient monitoring network as described in claim 7 or 8, characterized in that, Includes the following steps: S1. Establish a single-point soil organic carbon inversion model; S2. Calculate the soil organic carbon content at a single point using the detector data and the model established in S1. Each monitoring node collects in-situ spectral data and calculates organic carbon content at a fixed time period. S3. Construct a carbon sink spatial field. Using a physical information neural network algorithm, the soil organic carbon data obtained from discrete monitoring nodes is integrated with the physical laws of the soil carbon cycle. Through a combination of data-driven and physical constraints, the dynamic change trajectory of the continuous carbon sink spatiotemporal field is reconstructed with high precision. S4. Based on the dynamic change trajectory of the carbon sink spatiotemporal field constructed in S3, analyze the centroid motion trajectory, contour dynamic evolution and spatial gradient field; S5. Dynamic impact zoning and generation of trajectory maps: Based on the spatiotemporal trajectory analysis results of S4, the monitoring area is divided into direct disturbance zone, indirect impact zone and transition zone, and a carbon sink spatiotemporal trajectory map is generated.

10. The method for spatiotemporal trajectory tracking and impact zoning of carbon sinks in power transmission and transformation projects according to claim 9, characterized in that, Step S1 includes the following sub-steps: S11. Within the monitoring area, systematically collect representative original soil samples; in the laboratory, use the potassium dichromate oxidation-external heating method or an elemental analyzer to determine the soil organic carbon baseline value of each sample and establish a database of true values. S12. Using a near-infrared spectrometer with the same core parameters as the optical monitoring detector, collect diffuse reflectance spectra of all standard soil samples in a controlled laboratory environment to form a standard spectral database. S13. First, the Savitzky-Golay algorithm is used for smoothing and denoising. Then, standard normal variable transformation or multivariate scattering correction is performed to eliminate baseline drift. Finally, first-order or second-order derivative processing is performed to enhance spectral characteristic peaks. On this basis, competitive adaptive reweighted sampling or continuous projection algorithm is applied to select the subset of characteristic wavelength variables most related to soil organic carbon content from the full spectrum data. S14. Using the preprocessed characteristic spectral data as independent variables and the corresponding organic carbon baseline value as dependent variables, a partial least squares regression or machine learning algorithm is used for training to generate a soil organic carbon quantitative inversion model. The k-fold cross-validation method is used to evaluate the model performance to ensure that its validation set determination coefficient is not less than 0.8 and the relative analysis error is greater than 2.

0.

11. The method for spatiotemporal trajectory tracking and impact zoning of carbon sinks in power transmission and transformation projects according to claim 10, characterized in that, Step S2 includes the following sub-steps: S21. The monitoring node starts according to a preset cycle or command, and controls the communication module to drive the optical detection detector to rotate; the near-infrared light emitted by the transmitting fiber array is reflected by the rotating transmitting plane mirror and projected onto the soil surface through the fan-shaped transmitting window; the generated diffuse reflection signal is captured by the synchronously rotating rectangular receiving window and transmitted to the spectral detector through the receiving fiber array. S22. The control and communication module performs analog-to-digital conversion on the acquired raw spectral signal and automatically calls the preprocessing process and the set of characteristic wavelengths determined in step S13 to perform standardization processing and feature extraction on the real-time spectrum. S23. Input the preprocessed and feature-extracted spectral data into the quantitative inversion model generated in step S14 and integrated into the control and communication module; the model performs real-time calculations and outputs the predicted value of soil organic carbon content in the fan-shaped annular emission area corresponding to the detector at the current rotation angle. S24. The calculated organic carbon content data is encapsulated with timestamps, geographic coordinates, node numbers, and sector ring spatial orientation identifiers. The system synchronously performs data quality self-checks. If the spectral signal-to-noise ratio or predicted value exceeds a reasonable range, a quality doubt mark is added. Finally, the data packet is transmitted to the central data processing server through the wireless communication module.

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