VEP accounting-based ecological product value realization and accounting method

Abstract

The invention relates to the technical field of ecological environment monitoring and natural resource asset evaluation, and discloses an ecological product value realization and accounting method based on VEP accounting. The method comprises the following steps: setting an ecological self-maintenance reference threshold in a digital grid model; performing time domain waveform shaping on the low-frequency remote sensing data by using the high-frequency environment driving factor, and reconstructing a high-frequency background value; constructing a geographic topological impedance matrix, and performing spatial weighted correction on the instantaneous deviation of the ground monitoring data based on the minimum cumulative resistance distance; a hysteresis comparison logic and a dead zone control mechanism are introduced, and effective output states exceeding a reference threshold value are screened; integral is carried out on the net physical quantity of the effective time period, and when the accumulated value reaches the standard transaction package quota, a digital warrant is generated. According to the method, through space-time dual reconstruction and steady-state screening, the problems of multi-source data frequency mismatch and complex terrain interpolation deviation are solved, and accurate quantification and digital circulation of the ecological overflow value are realized.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring and natural resource asset assessment technology, specifically to a method for realizing and calculating the value of ecological products based on VEP (Various Ecological Product) accounting. Background Technology

[0002] With the advancement of ecological civilization construction and the proposal of "dual carbon" targets, the mechanism for realizing the value of ecological products by transforming ecological resources into economic value has become a research hotspot. Accurately quantifying the physical output of ecosystems (such as forest carbon sequestration and water conservation) and converting it into transferable and tradable digital rights is the core foundation for establishing ecological compensation mechanisms and green finance systems. This requires accounting methods to not only reflect the regional ecological status on a macro level, but also to provide accurate, traceable, and biophysically compliant quantitative evidence at the micro-scale.

[0003] Existing ecological value accounting techniques typically rely on satellite remote sensing inversion models to estimate net primary productivity or evapotranspiration within a region, and then correct for this by incorporating data from ground flux observation stations. In practice, technicians often calculate total ecological output based on long-term (e.g., monthly or annual) remote sensing data, and then extend limited ground station observation data to the entire region using Kriging interpolation or inverse distance weighting methods, thus providing data support for regional ecological asset assessment.

[0004] While existing technologies have played a role in macro-level assessments, they still have significant shortcomings in high-precision accounting. First, the spatiotemporal frequency mismatch of multi-source data limits accounting accuracy. Simple linear interpolation ignores the nonlinear fluctuations of ecological processes within long-period remote sensing intervals, causing systematic biases in time-period integration. Second, traditional interpolation algorithms based on Euclidean distance neglect the heterogeneity of geographic space and fail to consider the physical obstruction effects of complex terrain on material and energy transport, leading to erroneous corrections of monitoring weights across geographical barriers and causing spatial distortion of the data. Furthermore, existing methods lack effective mechanisms to separate the self-sustaining metabolic consumption of ecosystems from measurement noise, often misinterpreting basal metabolic rates or random sensor fluctuations as tradable spillover value, resulting in ecological asset certificates lacking true net physical quantity support.

[0005] Therefore, this invention proposes a method for realizing and calculating the value of ecological products based on VEP accounting to address the shortcomings of existing technologies. Summary of the Invention

[0006] To address the shortcomings of existing technologies, this invention provides a method for realizing and calculating the value of ecological products based on VEP (Vacuum-Effective Product) accounting. This method solves the problems of spatiotemporal frequency mismatch of multi-source data, spatial interpolation distortion under complex terrain, and inability to accurately separate ecological self-sustaining consumption and measurement noise in existing ecological accounting technologies.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a method for realizing and calculating the value of ecological products based on VEP accounting, comprising the following steps: S1. Establish a digital grid model of the area to be accounted for and set the ecological self-sustaining baseline threshold. S2, receive the low-frequency isometric remote sensing inversion feature values ​​of the area to be calculated, the measured ecological output data of the ground reference station, and the environmental driving factor data of the area to be calculated; S3, using environmental driving factor data to perform time-domain waveform shaping on low-frequency planar remote sensing inversion feature values ​​to generate reconstructed high-frequency background values; S4, based on the geographic topological impedance matrix and the minimum cumulative resistance distance, spatially interpolates the instantaneous deviation ratio between the measured ecological output data of the ground benchmark station and the reconstructed high-frequency background value, and uses the obtained spatial correction coefficient to correct the output instantaneous physical output rate of the reconstructed high-frequency background value. S5 utilizes hysteresis comparison logic with dead zone control, combined with the ecological self-sustaining benchmark threshold to determine the validity of instantaneous physical output rate, and determines the accounting state bit. S6, based on the determined accounting status bit, performs net physical quantity integration, and generates digital warrants when the value reaches the standard transaction package quota.

[0008] Preferably, in step S1, the ecological self-sustaining baseline threshold is determined using the historical mean correction method. Specifically, the set of biological dormancy periods in historical years with daily average temperatures below 5 degrees Celsius is selected, the arithmetic mean of the measured or inverted basic metabolic physical quantities within the set is calculated, and the arithmetic mean is multiplied by an adjustment coefficient with a value range of 0.8 to 1.2.

[0009] Preferably, in step S1, the pixel side length of the spatial grid unit in the digital grid model ranges from 30 meters to 250 meters; in step S2, the time span of the first update cycle of the low-frequency planar remote sensing inversion feature value is greater than the second update cycle of the ground reference station data, and the second update cycle is synchronized with the minimum time step ranging from 15 minutes to 60 minutes.

[0010] Preferably, in step S3, the time-domain waveform shaping involves calculating the waveform normalization coefficient of the measured environmental driving factor at the location of the ground reference station relative to its mean value in the current remote sensing update cycle, and mapping this coefficient to the entire domain; the reconstructed high-frequency background value is obtained by multiplying the low-frequency planar remote sensing inversion feature value with the mapped waveform normalization coefficient.

[0011] Preferably, in step S3, when the accounting object is forest carbon sink, the environmental driving factor data is selected as photosynthetically active radiation; when the accounting object is water conservation, the environmental driving factor data is selected as net radiation; the mapping adopts an inverse distance weighted interpolation algorithm, wherein the distance attenuation power exponent is set to 2.0.

[0012] Preferably, in step S4, the method for constructing the geographic topological impedance matrix is ​​as follows: the baseline resistance value for plain forest land is set to 1, and the baseline resistance value for construction land is set to 500 to 1000; for areas with a slope greater than 25 degrees, the baseline resistance value is multiplied by a slope correction coefficient, which is equal to 1 plus the product of a preset slope sensitivity factor and the sine value of the slope angle.

[0013] Preferably, in step S4, the instantaneous deviation ratio is the ratio of the measured ecological output data of the ground reference station to the reconstructed high-frequency background value; the spatial weighted interpolation adopts the inverse distance weighting method based on the minimum cumulative resistance distance, and the corrected instantaneous physical output rate is obtained by multiplying the spatial correction coefficient obtained by interpolation with the reconstructed high-frequency background value.

[0014] Preferably, in step S5, the specific rules of the hysteresis comparison logic are as follows: calculate the opening threshold and the closing threshold based on the ecological self-sustaining baseline threshold and the dead zone width coefficient; if the instantaneous physical output rate is higher than the opening threshold, the accounting state position is 1; if it is lower than the closing threshold, the accounting state position is 0; if it is between the opening threshold and the closing threshold, the accounting state position of the previous moment remains unchanged.

[0015] Preferably, in step S6, the net physical quantity integral is calculated as the portion of the instantaneous physical output rate that exceeds the ecological self-sustaining baseline threshold, and this portion is multiplied by the effective physical area of ​​the spatial grid cell, the duration of the minimum time step, and the unit conversion factor; when the accounting object is carbon sink, the unit conversion factor is 44 multiplied by 10 to the power of negative 6.

[0016] Preferably, in step S6, the standard trading package quota is a preset constant, set at 1000 kg of carbon dioxide equivalent for forest carbon sink accounting and 1000 cubic meters of fresh water for water conservation accounting; when digital warrants are generated, an integer multiple of the standard trading package quota is deducted from the accumulated value and the remainder is retained.

[0017] This invention provides a method for realizing and calculating the value of ecological products based on VEP (Value for Ecosystem Products) accounting. It has the following beneficial effects: 1. This invention utilizes high-frequency environmental driving factors to perform time-domain waveform shaping on low-frequency remote sensing data, reconstructing long-period remote sensing inversion values ​​into high-frequency background values ​​with minute-level characteristics. This method restores intraday fluctuation details while maintaining overall data constraints, avoids integration errors caused by traditional linear interpolation, and effectively achieves precise alignment between satellite data and ground-based measured data in the time dimension.

[0018] 2. This invention constructs a geographic topological impedance matrix and calculates the minimum cumulative resistance distance, replacing the traditional Euclidean distance interpolation. By quantifying the hindering effect of topographic relief and land cover on ecological transfer, this method prevents erroneous corrections of monitoring data across geographical barriers such as mountains, significantly improving the spatial accuracy of gridded accounting for heterogeneous habitats. 3. This invention sets an ecological self-sustaining baseline threshold and introduces hysteresis comparison logic, utilizing a dead zone mechanism to filter out sensor noise and random fluctuations near the baseline. This mechanism ensures that only net output that continuously and significantly exceeds its own metabolic needs is included in the accumulation register, avoiding false quotas due to numerical fluctuations and guaranteeing the authenticity and validity of the physical quantities corresponding to the digital warrants. Attached Figure Description

[0019] Figure 1 The flowchart shows the method for realizing and calculating the value of ecological products based on VEP accounting according to the present invention. Figure 2 This is a schematic diagram illustrating the specific process of constructing a discretized spatiotemporal accounting basis according to the present invention; Figure 3 This is a schematic diagram illustrating the specific process of performing spatiotemporal downscaling reconstruction based on covariates according to the present invention. Figure 4 This is a schematic diagram illustrating the specific process of dynamic anchoring under geographical topological impedance constraints according to the present invention. Figure 5 This is a schematic diagram illustrating the specific process of the hysteresis comparator determination based on dead-time control in this invention. Figure 6 This is a schematic diagram illustrating the specific process of the discrete integral of net service value and the settlement of fixed-rate packages according to the present invention. Detailed Implementation

[0020] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and 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.

[0021] See attached document Figure 1 , Figure 1This is a flowchart illustrating a method for realizing and calculating the value of ecological products based on VEP (Value for Energy) accounting, according to an embodiment of the present invention. The present invention provides a method for realizing and calculating the value of ecological products based on VEP accounting, comprising the following steps: S1. Establish a digital grid model of the area to be calculated in the computer storage medium, divide the area into multiple spatial grid units, and set the minimum time step for the discrete sampling time series. For each spatial grid unit, set a corresponding ecological self-sustaining baseline threshold based on land use type and historical climate data.

[0022] S2 receives data for the area to be calculated via a data interface. The data includes: low-frequency isometric remote sensing inversion feature values ​​with a first update cycle; measured ecological output data from ground reference stations with a second update cycle; and environmental driving factor data from ground reference stations with a second update cycle. The first update cycle has a longer time span than the second update cycle, and the second update cycle is synchronized with the minimum time step.

[0023] S3. Using environmental driving factor data, time-domain waveform shaping is performed on the low-frequency isometric remote sensing inversion feature values. The waveform coefficients of the environmental driving factors at the ground reference station location relative to their periodic mean are calculated, and these waveform coefficients are mapped to global spatial grid cells. Based on the product of the mapped waveform coefficients and the low-frequency isometric remote sensing inversion feature values, the reconstructed high-frequency background value of each spatial grid cell at each minimum time step is generated.

[0024] S4 constructs a geographic topological impedance matrix based on the digital elevation model and land cover data of the area to be calculated, and calculates the instantaneous deviation ratio between the measured ecological output data at the ground benchmark station and the reconstructed high-frequency background value. The minimum cumulative resistance distance from the non-benchmark station grid to the benchmark station is calculated based on the geographic topological impedance matrix. Spatially weighted interpolation is performed on the instantaneous deviation ratio based on the minimum cumulative resistance distance to obtain the spatial correction coefficient for each spatial grid cell. The reconstructed high-frequency background value is corrected using the spatial correction coefficient, and the corrected instantaneous physical output rate is output.

[0025] S5 introduces hysteresis comparison logic with dead zone control to effectively screen the instantaneous physical output rate. Based on the ecological self-sustaining baseline threshold and the preset dead zone width parameter, the opening and closing thresholds are determined. The instantaneous physical output rate at the current moment is compared with the opening and closing thresholds, and combined with the accounting status bit of the previous moment, the accounting status bit at the current moment is determined.

[0026] S6, based on the determined accounting status bit, performs net physical quantity integration on the period when it is in an effective state, calculates the portion of the instantaneous physical output rate that exceeds the ecological self-sustaining benchmark threshold, converts it into the corresponding value increment, and stores it in the accumulation register. The value of the accumulation register is monitored, and when the value reaches the preset standard transaction package quota, a digital warrant containing a timestamp and geographic tag is generated, and the corresponding amount is deducted from the accumulation register.

[0027] See attached document Figure 2 , Figure 2 This is a schematic diagram illustrating the specific process of constructing a discretized spatiotemporal computational basis in an embodiment of the present invention. In step S1, the process of constructing the discretized spatiotemporal computational basis mainly includes the discretization of the spatial dimension, the serialization setting of the time dimension, and the initialization setting of physical parameters.

[0028] Using Geographic Information System (GIS) technology, a multi-core processor accesses map data stored in memory to acquire administrative boundary vector data or natural watershed boundary vector data for the area to be calculated. The basic geographic data specifically includes: a digital elevation model in GeoTIFF format, sourced from SRTM or ASTERGDEM, with a vertical accuracy better than 20 meters; and a land use vector layer in Shapefile format, sourced from GlobeLand30 or the Third National Land Survey data. Rasterization processing is performed to divide the area to be calculated into a regular two-dimensional planar grid matrix. This two-dimensional planar grid matrix consists of several independent ecological computing units. Each ecological computing unit is instantiated as an object or structure in the computer's RAM, storing a unique spatial coordinate index and spatial attribute information.

[0029] Spatial attribute information is obtained by overlaying digital elevation models and land use vector layers. Specifically, it includes land use type codes, altitude values, slope values, and aspect values. Land use type codes follow the GlobeLand30 global land cover data classification standard, where codes 10-19 represent cultivated land, 20-29 represent forest land, 30-39 represent grassland land, and 60-69 represent water bodies. The spatial resolution of the ecological computing unit is set based on the physical scale of the object being analyzed and the accuracy of the remote sensing data source, with specific pixel side lengths ranging from 30 meters to 250 meters.

[0030] For incomplete grids located at the boundary of the region to be computed, the processor determines the area weight coefficient of the grid by calculating the ratio of the effective geometric area inside the region boundary line to the area of ​​the standard grid. During the initialization phase, the effective physical area (unit: square meters) of each ecological computing unit is stored as a static attribute in the data index. The effective physical area is equal to the area of ​​the standard grid multiplied by the area weight coefficient of the grid. All instantiated spatial grid units together constitute the digital grid model.

[0031] The time interval between adjacent time points is defined as the minimum computation time slice. The specific value of this minimum computation time slice is determined based on the characteristics of photosynthetic rate variation in ecological processes and the sampling frequency of ground monitoring equipment, with a value range of 15 to 60 minutes. All dynamic monitoring data from the computer equipment are aligned in the time domain using this minimum computation time slice as a reference, through linear interpolation or nearest neighbor resampling methods.

[0032] For each ecological computing unit, a corresponding ecological self-sustaining baseline threshold is set. This ecological self-sustaining baseline threshold physically characterizes the rate of material or energy consumption required for the ecosystem within that unit to maintain its survival and basal metabolism per unit time. Only when the actual ecological output rate exceeds this baseline is it determined that spillover value available for exchange or transfer has been generated.

[0033] Methods for determining the baseline threshold for ecological self-sustainability include the lookup table method and the historical mean correction method. In the lookup table method, the processor accesses a pre-set ecological metabolic parameter database, constructed as a key-value storage structure, such as Redis, or a relational database table, such as PostgreSQL, storing the baseline respiration consumption coefficient or baseline evapotranspiration coefficient corresponding to different land cover types. The corresponding coefficient is retrieved based on the land use type code of the ecological computing unit as the baseline threshold. The specific data structure and numerical settings are as follows: When the accounting object is forest ecosystem carbon sink, the key in the database is the land use type code, and the value is the sum of the fluxes of basic soil respiration and vegetation maintenance respiration (unit: micromoles per square meter per second). ).For example: When the Key is 21, corresponding to evergreen coniferous forest, the set Value is 3.5 to 4.5; When the Key is 22, corresponding to deciduous broad-leaved forest, the Value is set to 5.0 to 6.5; When Key is 30, corresponding to shrubland, the set Value is 2.0 to 3.0. This value range is determined based on the long-term average of ecosystem respiration measured by the eddy covariance flux tower during the nighttime period without photosynthesis.

[0034] When the accounting object is water conservation services, the Value in the database is the basic evapotranspiration rate (unit: millimeters per hour, mm / h). For example: When the Key is 21, corresponding to a woodland, the Value is set to 0.25 to 0.35; When the Key is 30, corresponding to grassland, the Value is set to 0.15 to 0.20.

[0035] In the historical mean correction method, the processor selects data from historical years during periods of biological dormancy or non-growing seasons for benchmark calculation. The specific calculation formula is as follows: ; In the formula, The ecological self-sustaining baseline threshold for the ecological computing unit to be calculated; This refers to the set of periods of biological dormancy in the region throughout history, specifically the set of dates when the average daily temperature is below 5 degrees Celsius. For this ecological computing unit in historical time period Measured or inverted basal metabolic physical quantities; For set Total number of time samples included; The adjustment factor is used to fine-tune historical benchmarks based on climate change trends, and its value ranges from 0.8 to 1.2.

[0036] See attached document Figure 2 In step S2, the process of acquiring dual-scale asynchronous monitoring data involves collecting data from different types of monitoring sources and preprocessing the collected data to establish a unified data index. The data collected in this step is divided into three categories: low-frequency areal remote sensing inversion feature values, measured ecological output data from ground reference stations, and environmental driving factor data from ground reference stations.

[0037] Computer equipment uses the satellite data receiving interface or remote sensing data service platform interface to request and parse metadata in JSON or XML format via HTTP / HTTPS protocol to acquire multispectral remote sensing image data covering the area to be calculated. The sources of this multispectral remote sensing image data include the Landsat series, Sentinel series, or medium resolution imaging spectrometers. For the acquired raw image data, the processor performs radiometric calibration, atmospheric correction, and geometric fine correction processing to generate a reflectance image with spatial coordinate information. The processor then georegisters the processed image raster with a digital grid model. If the image resolution is inconsistent with the grid model resolution, bilinear interpolation or the nearest neighbor method is used for resampling to ensure that each spatial grid cell corresponds to a specific remote sensing inversion value.

[0038] For the calculation of low-frequency isometric remote sensing inversion characteristic values, if the object of calculation is forest carbon sink, the CASA light energy utilization rate model is adopted. This model calculates net primary productivity by inputting normalized vegetation index, solar radiation and temperature data, and converts the units to flux units consistent with ground monitoring.

[0039] If the object of the calculation is water conservation, the Penman-Montes formula is used. This formula calculates the evapotranspiration by inputting net radiation, soil heat flux, air density, specific humidity and aerodynamic drag.

[0040] The low-frequency area remote sensing inversion feature values ​​have a first update cycle, which is determined by the satellite revisit cycle and cloud cover conditions, with a specific time span ranging from 5 to 16 days. For any given moment within a single update cycle, the processor treats the inversion feature values ​​for that time period as constant values ​​before subsequent reconstruction processing. The processed raster data is then stored in a spatial database, establishing a mapping relationship between it and the spatial coordinate index of the ecological computing unit.

[0041] Computer equipment connects to ground-based reference stations deployed within the area to be calculated via an Internet of Things (IoT) communication protocol in the data communication interface. Specific types of ground-based reference stations include eddy covariance flux observation towers or automatic weather stations. Measured ecological output data with a second update cycle is acquired from the ground-based reference stations. This measured ecological output data refers to ecological flux values ​​directly measured by instruments, specifically including net ecosystem exchange of carbon dioxide or surface runoff depth.

[0042] The measured ecological output data has a second update cycle, which is synchronized with the minimum computation time slice set in the aforementioned steps, specifically ranging from 15 to 60 minutes. Before the data is stored, the processor performs a quality control process, specifically including physical extreme value detection and continuity detection. For example, if a shortwave radiation value is detected to exceed the solar constant of 1361 W / m², the processor will perform a quality control process. 2 If the temperature exceeds the local historical extreme value, such as ±50℃, it is determined to be invalid data; at the same time, the rate of change of adjacent time steps is monitored. If the rate of change exceeds 3 standard deviations, abnormal data is removed to ensure the physical validity of the data entering the database.

[0043] Environmental driving factor data are acquired synchronously from ground-based reference stations. Environmental driving factor data refers to meteorological parameters that directly drive the biophysical processes of the ecosystem and exhibit significant diurnal periodic fluctuations. Specifically, these include photosynthetically active radiation and air temperature.

[0044] The selection of photosynthetically active radiation (PARF) as the primary driving factor is based on its direct determination of the instantaneous rate of plant photosynthesis, and its waveform characteristics accurately reflect sunrise, sunset, and cloud cover processes. Air temperature was selected as a secondary driving factor because it affects the rate of plant respiration. The sampling frequency of these environmental driving factor data was synchronized with the measured ecological output data, both being high-frequency continuous data. The computer equipment simultaneously recorded the geographic coordinates of each reference station; these coordinates are used in subsequent steps to construct the spatial distance matrix and calculate geographic topological impedance.

[0045] See attached document Figure 3 , Figure 3 This is a schematic diagram illustrating the specific process of performing spatiotemporal downscaling reconstruction based on covariates in an embodiment of the present invention. In step S3, the processor reads environmental driving factor data and low-frequency areal remote sensing inversion feature values ​​from memory, and performs temporal waveform shaping to solve the frequency mismatch problem of multi-source data. Specifically, this includes two sub-steps: waveform normalization coefficient calculation and full-domain high-frequency background value reconstruction.

[0046] For each ground reference station, a time-domain waveform template is constructed using the station's environmental driving factor data within a single remote sensing data update cycle. This process is based on ecological principles, namely, that within a short period, the instantaneous change trend of ecological outputs and the change trend of environmental driving factors show a high positive correlation.

[0047] The waveform normalization coefficient for each ground reference station under each minimum computation time slice is calculated using the following formula: ; In the formula, Indicates the first Each ground reference station at time The waveform normalization coefficient; Indicates the first Each ground reference station at time The measured values ​​of environmental driving factors are used. When the accounting object is forest carbon sink, the photosynthetically active radiation value is selected, and when the accounting object is wetland evapotranspiration, the net radiation value is selected. Indicates the current time The corresponding remote sensing data update cycle; This represents the total number of minimum computation time slices included within the remote sensing data update period; the denominator represents the arithmetic mean of environmental driving factors within that period.

[0048] The waveform normalization coefficient obtained through the above calculations is a dimensionless ratio. When the instantaneous value of the environmental driving factor is greater than its periodic mean, the coefficient is greater than 1; when it is less than the periodic mean, the coefficient is less than 1. This coefficient, in the form of a numerical ratio, fully preserves the high-frequency fluctuation characteristics of the environmental factor within the day.

[0049] The waveform normalization coefficients of the aforementioned discrete stations are extended to the global spatial grid and fused with low-frequency remote sensing data. An inverse distance weighted interpolation algorithm is then used to map the waveform normalization coefficients of the ground reference station set to each ecological computing unit, obtaining the waveform normalization coefficients of each grid in the global domain at the current time. When performing inverse distance weighted interpolation, the distance decay exponent is set to 2.0, and the search radius is set to the 3 to 5 nearest neighbor reference stations to ensure a smooth transition of local climate characteristics.

[0050] The low-frequency planar remote sensing inversion feature values ​​are modulated using the normalization coefficients of the interpolated waveform, and the reconstructed high-frequency background value of each ecological computing unit at the current time is calculated. The specific calculation formula is as follows: ; In the formula, Indicates the first Each ecological computing unit at time Reconstructing high-frequency background values; Indicates the first Each ecological computing unit in the remote sensing data update cycle The low-frequency planar remote sensing inversion characteristic value within the data is a periodic constant in the original data; Indicates the first Each ecological computing unit at time The waveform normalization coefficient.

[0051] After this processing step, the low-frequency remote sensing data, which was originally distributed in a stepped manner on the time axis, is transformed into a continuous curve with minute-level variation characteristics. This processing mathematically ensures that the total integral of the reconstructed high-frequency data within the remote sensing period is approximately equal to the original low-frequency remote sensing values. Thus, while maintaining the total constraint of remote sensing inversion, the diurnal variation details of ecological processes are restored, providing a frequency-based benchmark for subsequent accurate anchoring with high-frequency ground-based measured data.

[0052] See attached document Figure 4 , Figure 4 This is a schematic diagram illustrating the specific process of dynamic anchoring under geographic topological impedance constraints in an embodiment of the present invention. In step S4, a geographic topological impedance model is introduced to quantify the impact of spatial heterogeneity on ecological interpolation, thereby calculating spatial correction coefficients and generating the final physical output rate. This process includes three sub-steps: instantaneous bias calculation, impedance matrix construction, and corrected physical quantity output.

[0053] At the spatial location of each ground reference station, the ratio between the measured ecological output data and the reconstructed high-frequency background value generated in step S3 is calculated. Since the two are now aligned in frequency over time, this ratio directly reflects the local systematic bias of the remote sensing inversion model.

[0054] The specific calculation formula is as follows: ; In the formula, Indicates the first Each ground reference station at time The instantaneous deviation ratio; Indicates the first Each ground reference station at time Actual measured ecological output data; Indicates the first Each ground reference station at time The reconstructed high-frequency background value.

[0055] To prevent calculation errors caused by a denominator of zero or abnormally amplified values, the algorithm incorporates numerical protection logic. This logic prevents errors when the reconstructed high-frequency background value is less than a preset small positive number, specifically 10. -6 At that time, the processor forcibly determines that the instantaneous deviation ratio at that position is 1.0, that is, during the period of micro-production, deviation correction is ignored and the original background value is maintained.

[0056] To accurately generalize the instantaneous deviation ratio of discrete stations to the entire domain, a spatial interpolation logic based on the minimum cumulative resistance model is constructed.

[0057] First, the processor constructs a global geographic topological impedance matrix in RAM. This matrix is ​​a two-dimensional data grid with the same spatial resolution as the ecological computing unit, represented as a double-precision floating-point two-dimensional array in the computer's RAM. The matrix is ​​generated by the processor reading land cover type raster data and slope raster data, performing numerical reclassification and algebraic operations on each pixel location. The value of each pixel in the matrix represents the relative difficulty of matter or energy passing through that unit. The resistance value is determined by the land cover type baseline resistance and the terrain slope correction factor. A baseline resistance value of 1 is set for plains and forests as a normalization reference. For non-ecological land that hinders ecological flow, such as construction land, its ecological connectivity is extremely low, and its baseline resistance value is set to 500 to 1000. For steep areas with a slope greater than 25 degrees, the baseline resistance value is multiplied by a slope correction factor. The specific calculation logic for this correction factor is as follows: ; In the formula, This is the slope correction factor. The slope angle, The preset slope sensitivity factor, The values ​​range from 5.0 to 10.0 to simulate the significant hindering effect of terrain undulations on material transport.

[0058] Next, the processor uses shortest path search algorithms from graph theory, such as Dijkstra's algorithm or the CostDistance function, to calculate the minimum cumulative drag distance from the ecological computing unit to each ground reference station. This algorithm iterates through a two-dimensional drag array in RAM, calculating the sum of the products of the drag values ​​of all grid cells traversed along the minimum cost path from the center point of the computing unit to the center point of the reference station, and the geometric distance between the grid center points.

[0059] Based on this minimum cumulative resistance distance, the spatial correction coefficient of each ecological computing unit at the current moment is calculated using the inverse distance weighting method, as shown in the following formula: ; In the formula, Indicates the first Each ecological computing unit at time Spatial correction factor; This represents the set of valid ground reference stations participating in the interpolation; Indicates the first The first ecological computing unit to the first Minimum cumulative drag distance for each ground reference station; The distance decay index is set to 2.0 to ensure rapid convergence of spatial weights.

[0060] Multiplying the reconstructed high-frequency background value by the spatial correction coefficient yields the final instantaneous physical output rate after spatiotemporal dual correction.

[0061] The specific calculation formula is as follows: ; In the formula, Indicates the first Each ecological computing unit at time The corrected instantaneous physical output rate is the ecological physical quantity output rate that the processor ultimately approves.

[0062] By introducing geographic topological impedance, this step allows the correction weights to propagate further over flat terrains, while suppressing the propagation of weights between complex terrains or heterogeneous habitats, thus conforming to the spatial autocorrelation decay law in ecology.

[0063] See attached document Figure 5 , Figure 5 This is a schematic diagram illustrating the specific process of performing hysteresis comparator determination based on dead-time control in an embodiment of the present invention. In step S5, hysteresis comparator logic is introduced to screen the instantaneous physical output rate for validity, in order to eliminate frequent jumps in the calculation state caused by measurement noise or minor environmental fluctuations. This process specifically includes three sub-steps: dead-time threshold definition, hysteresis comparator logic operation, and validity status bit output.

[0064] Read the ecological self-sustaining baseline threshold initialized in step S1, and combine it with the preset dead zone width coefficient to calculate the opening threshold and closing threshold used for state reversal determination. The opening threshold refers to the high-level critical point at which the ecosystem begins to generate effective spillover value, and the closing threshold refers to the low-level critical point at which the ecosystem stops generating effective spillover value.

[0065] The dead zone width factor is a dimensionless empirical parameter, set based on the measurement noise level of the ground monitoring equipment and the random fluctuation amplitude of the environmental background. This factor is typically set to 1.5 to 2.0 times the reciprocal of the signal-to-noise ratio of the monitoring equipment to ensure that the threshold bandwidth covers more than 95% of the random white noise. In this embodiment, the dead zone width factor is set to a range of 0.05 to 0.15.

[0066] The specific calculation formulas for the enable and disable thresholds are as follows: ; ; In the formula, This indicates the threshold for enabling the ecological computing unit; This represents the shutdown threshold of the ecological computing unit; This represents the ecological self-sustaining baseline threshold for the ecological computing unit; This represents the dead zone width coefficient.

[0067] Through the above calculations, a numerical buffer is established between the on threshold and the off threshold. Within this interval, the system's decision logic is no longer a simple single threshold comparison, but introduces a historical state dependency mechanism, meaning that the current decision result depends on both the current input value and the decision state at the previous moment.

[0068] At each minimum computation time slice, the corrected instantaneous physical output rate is input into a hysteresis comparator. This hysteresis comparator is a piece of logical judgment code running in computer memory, maintaining a Boolean or binary status bit to mark the current computational state. A status bit of 1 indicates that the current state is in a valid value-producing state, while a status bit of 0 indicates that the current state is invalid or self-sustaining.

[0069] The hysteresis comparator's specific decision-making logic follows these rules: When the instantaneous physical output rate at the current moment is higher than the activation threshold, regardless of the state at the previous moment, the current accounting state bit is forcibly set to 1. At this time, it is determined that the ecosystem has exceeded its self-sustaining requirements and entered net output mode. When the instantaneous physical output rate at the current moment is lower than the shutdown threshold, regardless of the state at the previous moment, the current accounting state bit is forcibly set to 0. At this time, it is determined that the ecosystem's output is insufficient to maintain its own metabolism or that it is in a dormant state. When the instantaneous physical output rate at the current moment is between the activation and shutdown thresholds, the processor keeps the accounting state bit unchanged from the previous moment. That is, if the previous moment's record was a valid state, the current moment continues to maintain a valid determination; if the previous moment's record was an invalid state, the current moment continues to maintain an invalid determination.

[0070] The mathematical expression for the status bit is as follows: ; In the formula, Indicates the ecological computing unit at the current moment The accounting status bit; This indicates that the ecological computing unit was at the previous moment. The accounting status bit; Indicates the ecological computing unit at the current moment The corrected instantaneous physical output rate.

[0071] After processing by the hysteresis comparator, the processor generates a binary state sequence corresponding to the time series. This mechanism physically simulates the hysteresis characteristics of a Schmitt trigger. During critical periods of photosynthesis, such as sunrise or sunset, or when cloudy weather causes slight fluctuations in the photosynthetic rate near the baseline, it effectively filters out spurious state-switching signals caused by numerical jitter, ensuring that only output that consistently and significantly exceeds the baseline is accumulated. For the first moment after a system cold start or data interruption, the processor resets the initial state bits to 0 by default.

[0072] See attached document Figure 6 , Figure 6 This is a schematic diagram illustrating the specific process of executing the discrete integration of net service value and the settlement of fixed-rate packages in an embodiment of the present invention. In step S6, the instantaneous physical output rate after multiple corrections and state filtering is converted into the total accumulated ecological service value, and a digital settlement data packet is generated according to the standard transaction quota. This process includes three sub-steps: discrete integration of net physical quantity, cumulative register update, and triggering of fixed-rate package settlement.

[0073] Read the corrected instantaneous physical output rate output in step S4 and the accounting status bit output in step S5. The processor subtracts the ecosystem self-sustaining baseline threshold from the corrected instantaneous physical output rate and only performs integration calculations on the portion exceeding the baseline.

[0074] At the end of each minimum computation time slice, the net ecological physical quantities generated within that time slice are calculated. During the specific calculation process, the processor executes numerical truncation logic to prevent negative values ​​and converts the physical rate into a total physical quantity, as shown in the following formula: ; In the formula, This represents the net ecological physical quantity generated by the ecological computing unit within the current time slice; This represents the corrected instantaneous physical output rate; This represents the baseline threshold for ecological self-sustainability. This represents the effective physical area of ​​the ecological computing unit, in square meters, and is used to convert the flux rate per unit area back to the total amount within the spatial unit. This represents the accounting status bit, which can be either 0 or 1. This item is used as a multiplication mask in the formula. When the status bit is 0, the calculation result is forced to zero, thereby realizing the logic of accumulating only the output of the valid status period. This indicates the duration of the minimum computation time slice, converted to seconds or hours to match the rate unit. This represents a unit conversion factor used to convert micro-level monitoring fluxes into macro-level transaction units of mass or volume. The value of this factor varies depending on the object being calculated. When the accounting object is carbon sink, the input flux unit is usually 1. In order to convert to g, The value is 44× (i.e., carbon dioxide molar mass 44g / ) Multiply by micromolar conversion factor ) When the accounting object is water conservation, the input rate unit is usually [unit missing]. (millimeters per hour), in order to convert to cubic meters ( ), The value is 0.001 (which converts millimeters to meters).

[0075] An accumulation register is maintained in memory for each ecological computing unit. This accumulation register is defined as a double-precision floating-point variable and a data type for representing real numbers with decimals, typically occupying 8 bytes (64 bits) to ensure that the precision of minute values ​​is not lost during long-cycle accumulation. The processor adds the calculated net ecological physical quantity to the current value of this accumulation register.

[0076] After each update of the accumulator register, the system compares the current value in the accumulator register with the preset standard transaction package quota. The standard transaction package quota is a preset constant threshold, and the specific setting varies depending on the accounting object: for forest carbon sequestration accounting, the quota is set to 1000 kg, which is equivalent to 1 ton of carbon dioxide; for water conservation accounting, the quota is set to 1000 cubic meters of freshwater.

[0077] When the value in the accumulation register is greater than or equal to the standard transaction package quota, the settlement logic is triggered: the processor generates a unique standardized settlement data packet. This data packet's data structure includes a universally unique identifier (UUID), a generation timestamp, the corresponding ecological computing unit spatial coordinate hash index, and the physical quantity value for this settlement, i.e., the specific value of the standard transaction package quota. The processor subtracts an integer multiple of the standard transaction package quota from the current value in the accumulation register, and retains the fractional part (less than one quota) in the register through a modulo operation. This fractional part continues to participate in the next round of time integration, ensuring the conservation of the total ecological output across cycles. The generated standardized settlement data packet is sent to a data storage module, such as a distributed ledger node or a secure relational database, for persistent storage, serving as a digital certificate for subsequent value transfers.

[0078] Specific application examples: Implementation Scenario Background: The core protected area of ​​a national park located in China's subtropical monsoon climate zone was selected as the area to be accounted for, with a total area of ​​50 square kilometers. The accounting target is the net carbon dioxide capture of the forest ecosystem in this area.

[0079] Detailed implementation steps: S1 Model Construction and Baseline Setting: Gridding: Using the ArcGIS engine to load the SRTM 30-meter resolution DEM data of the area, the Yunling Experimental Zone was discretized into approximately 55,500 spatial grid units of 30 meters × 30 meters.

[0080] Time step: Sets the minimum computation time slice. It lasts for 30 minutes.

[0081] Baseline threshold determination: For grid numbered Cell_ID_0825, with a land type of evergreen coniferous forest, nighttime eddy flux observation data from January of historical years (daily average temperature <5℃) were selected for this area. The mean baseline respiration consumption was calculated as follows: Set adjustment coefficient. That is, the ecological self-sustaining baseline threshold of the grid. Fixed as .

[0082] S2 multi-source data reception: Remote sensing data: Sentinel-2 satellite imagery was acquired, and the average CASA model inversion NPP value for Cell_ID_0825 during the period from May 1st to May 10th (first update cycle) was calculated. (Periodic constant).

[0083] Ground data: Measured photosynthetically active radiation and emissions transmitted back every 30 minutes from three eddy flux towers (base stations A, B, and C) within the receiving area. Flux data.

[0084] S3 Time Domain Waveform Shaping: Scenario: At 12:00 noon on May 5th, the measured PAR at base station A surged, and the waveform normalization coefficient... The calculated value is 2.3 (far higher than the periodic average).

[0085] Reconstruction: After inverse distance interpolation, the waveform coefficient at Cell_ID_0825 is 2.1.

[0086] Result: The reconstructed high-frequency background value of the mesh at this moment. = 8.5 × 2.1 = This step "shapes" the originally flat remote sensing data into a peak curve that conforms to the characteristics of midday sunlight.

[0087] S4 Impedance Correction and Dynamic Anchoring: Geographic impedance: There is a ridge with a slope of 35 degrees between Cell_ID_0825 and base station A.

[0088] Calculate the slope correction factor: .

[0089] The cumulative resistance distance along this path increases significantly, causing the interpolation weight of the grid by reference station A to decrease.

[0090] Deviation correction: Base station B, located on the same hillside, was found to have thick cloud cover on the day of the measurement. The measured value was only 80% of the reconstructed value, with an instantaneous deviation ratio of 0.8.

[0091] Output: After synthesizing impedance weighting, the space correction factor for Cell_ID_0825 is calculated to be 0.85.

[0092] Final output: Adjusted instantaneous physical output rate = 17.85 × 0.85 = .

[0093] S5 Hysteresis Comparison and State Determination: Dead Zone Setting: Set the dead zone width coefficient . Enable threshold Shutdown threshold .

[0094] Judgment: At 18:00 in the evening, the light intensity decreased, and the output rate dropped to 4.0. Although 4.0 is lower than the baseline value of 4.2, it is still higher than the shutdown threshold of 3.78. Since the state was valid (1) at the previous moment (17:30), the hysteresis comparator determines that the current state should remain 1. This avoids frequent system start-ups and shutdowns caused by minor fluctuations in light intensity.

[0095] S6 Points and Digital Certificate Generation: Net physical quantity calculation: (15.17−4.2)×900m2×1800s×(44×10−6)≈782gCO2 (Points for a single 30-minute session).

[0096] Cumulative Settlement: This value is stored in the cumulative register. After approximately two months of accumulation, the register value reaches 1000.5 kg.

[0097] Generate warrant: The system automatically generates a 1-ton carbon sink digital warrant with the tag Yunling-0825-202405, and the register balance of 0.5kg is retained for the next round.

[0098] Cumulative Settlement: This value is stored in the cumulative register. After approximately two months of accumulation, the register value reaches 1000.5 kg.

[0099] Generating warrants: The system automatically generates a 1-ton carbon sink digital warrant with the tag Yunling-0825-202405, and the register balance of 0.5kg is retained for the next round.

[0100] Experimental verification and effect comparison: 1. Experimental setup Source of truth values: The measured data of the independent No. 4 flux tower (ValidationTower) in the experimental area, which was not involved in the modeling validation, were selected as the truth values.

[0101] Comparison method: Method A, traditional static remote sensing method: Only the CASA model is used to calculate the daily NPP, which is distributed on a 24-hour average basis, without time-domain shaping or impedance correction.

[0102] Method B, simple interpolation: It uses time-domain shaping in S3, but only uses Euclidean distance for spatial interpolation in S4, without a geographic topological impedance matrix.

[0103] Method C, the present invention: comprises complete steps S1-S6.

[0104] 2. Evaluation Indicators RMSE, Root Mean Square Error: Measures how well the calculated flux curve fits the actual flux curve.

[0105] R 2 Coefficient of determination: Measures correlation.

[0106] Pseudo-state switching rate: The frequency (times / day) of erroneous transitions of the system state bit between 0 and 1 at the critical point of photosynthesis during the dawn and dusk periods.

[0107] 3. Experimental Results Data Evaluation indicators Method A (Traditional Static Method) Method B (Simple Interpolation) Method C (This invention) Performance improvements (CvsA / B) RMSE of total daily flux 5.82 2.14 0.87 Accuracy improved by 85% / 59% Daily total accounting deviation (%) +18.5% -12.3% +1.2% The absolute value of the deviation decreased significantly. Pseudo-state switching rate during dawn and dusk (times / day) N / A (Static) 4.5 0.2 The false positive rate was reduced by 95%. Complex terrain area R2 0.45 0.68 0.92 Extremely adaptable to terrain 4. Results Analysis Charts and Tables Temporal consistency analysis: Method A, with its rectangular or stepped distribution, fails to reflect the peak photosynthesis at midday, resulting in a severe underestimation of daytime extremes and an overestimation of nighttime respiration.

[0108] The curve generated by this invention and the true curve, measured independently, highly overlap in waveform, proving the effectiveness of S3 time-domain waveform shaping.

[0109] The role of the impedance matrix in spatial heterogeneity analysis: In the verification of Tower 4, located on the leeward slope, Method B directly introduced the high values ​​of the benchmark station on the windward slope, resulting in an overestimation of the value, with an error of -12.3%.

[0110] This invention identifies the blocking effect of ridges through the S4 geographic topological impedance matrix, automatically reduces the weight of windward slope sites, and the calculation results deviate from the true value by only 1.2%.

[0111] Noise resistance stability analysis, the role of hysteresis comparison: During sunrise, from 6:00 to 7:00, light intensity fluctuates around the baseline due to cloud cover. Method B, lacking hysteresis control, causes the state bit to repeatedly jump between production and consumption 4-5 times, resulting in invalid and false billing.

[0112] This invention utilizes S5 dead-zone control to successfully filter out these minute fluctuations, only filtering out those when the output is stable above a certain level. The 1 is set only afterward, ensuring the rigor of the assets generated by the digital warrant.

[0113] Conclusion: Experiments show that the VEP-based accounting method proposed in this invention effectively solves the problems of low spatiotemporal resolution and poor terrain adaptability of traditional remote sensing models by introducing temporal waveform shaping and geographic topological impedance constraints. Simultaneously, the dead-zone hysteresis control mechanism significantly improves the robustness of the accounting system under critical conditions, making it more suitable for the stringent requirements of data accuracy and stability in financial-grade carbon asset trading. Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for realizing and calculating the value of ecological products based on VEP accounting, characterized in that, Includes the following steps: S1. Establish a digital grid model of the area to be accounted for and set the ecological self-sustaining baseline threshold. S2, receive the low-frequency isometric remote sensing inversion feature values ​​of the area to be calculated, the measured ecological output data of the ground reference station, and the environmental driving factor data of the area to be calculated; S3, using environmental driving factor data to perform time-domain waveform shaping on low-frequency planar remote sensing inversion feature values ​​to generate reconstructed high-frequency background values; S4, based on the geographic topological impedance matrix and the minimum cumulative resistance distance, spatially interpolates the instantaneous deviation ratio between the measured ecological output data of the ground benchmark station and the reconstructed high-frequency background value, and uses the obtained spatial correction coefficient to correct the output instantaneous physical output rate of the reconstructed high-frequency background value. S5 utilizes hysteresis comparison logic with dead zone control, combined with the ecological self-sustaining benchmark threshold to determine the validity of instantaneous physical output rate, and determines the accounting state bit. S6, based on the determined accounting status bit, performs net physical quantity integration, and generates digital warrants when the value reaches the standard transaction package quota.

2. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S1, the ecological self-sustaining baseline threshold is determined using the historical mean correction method. Specifically, a set of biological dormancy periods in historical years with daily average temperatures below 5 degrees Celsius is selected, the arithmetic mean of the measured or inverted basic metabolic physical quantities within the set is calculated, and the arithmetic mean is multiplied by an adjustment coefficient with a value range of 0.8 to 1.

2.

3. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S1, the pixel side length of the spatial grid unit in the digital grid model ranges from 30 meters to 250 meters; in step S2, the time span of the first update cycle of the low-frequency planar remote sensing inversion feature value is greater than the second update cycle of the ground reference station data, and the second update cycle is synchronized with the minimum time step ranging from 15 minutes to 60 minutes.

4. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S3, the time-domain waveform shaping involves calculating the waveform normalization coefficient of the measured environmental driving factor at the location of the ground reference station relative to its mean value in the current remote sensing update cycle, and mapping this coefficient to the entire domain; the reconstructed high-frequency background value is obtained by multiplying the low-frequency planar remote sensing inversion feature value with the mapped waveform normalization coefficient.

5. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 4, characterized in that, In step S3, when the accounting object is forest carbon sink, the environmental driving factor data is selected as photosynthetically active radiation; when the accounting object is water conservation, the environmental driving factor data is selected as net radiation; the mapping adopts an inverse distance weighted interpolation algorithm, wherein the distance attenuation power exponent is set to 2.

0.

6. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S4, the method for constructing the geographic topological impedance matrix is ​​as follows: the baseline resistance value for plain forest land is set to 1, and the baseline resistance value for construction land is 500 to 1000; for areas with a slope greater than 25 degrees, the baseline resistance value is multiplied by a slope correction coefficient, which is equal to 1 plus the product of a preset slope sensitivity factor and the sine value of the slope angle.

7. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S4, the instantaneous deviation ratio is the ratio of the measured ecological output data of the ground reference station to the reconstructed high-frequency background value; the spatial weighted interpolation adopts the inverse distance weighting method based on the minimum cumulative resistance distance, and the corrected instantaneous physical output rate is obtained by multiplying the spatial correction coefficient obtained by interpolation with the reconstructed high-frequency background value.

8. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S5, the specific rules of the hysteresis comparison logic are as follows: calculate the opening threshold and the closing threshold based on the ecological self-sustaining baseline threshold and the dead zone width coefficient; if the instantaneous physical output rate is higher than the opening threshold, the accounting state position is 1; if it is lower than the closing threshold, the accounting state position is 0; if it is between the opening threshold and the closing threshold, the accounting state position of the previous moment remains unchanged.

9. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S6, the net physical quantity integral is calculated as the portion of the instantaneous physical output rate that exceeds the ecological self-sustaining baseline threshold, and this portion is multiplied by the effective physical area of ​​the spatial grid cell, the duration of the minimum time step, and the unit conversion factor; when the accounting object is carbon sink, the unit conversion factor is 44 multiplied by 10 to the power of negative 6.

10. The method for realizing and calculating the value of ecological products based on VEP accounting according to claim 1, characterized in that, In step S6, the standard trading package quota is a preset constant, set at 1000 kg of carbon dioxide equivalent for forest carbon sink accounting and 1000 cubic meters of fresh water for water conservation accounting; when digital warrants are generated, an integer multiple of the standard trading package quota is deducted from the accumulated value and the remainder is retained.

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