Ecological product value analysis method and system based on multi-source data fusion

Abstract

The invention discloses an ecological product value analysis method and system based on multi-source data fusion, and relates to the technical field of ecological product value analysis. Original ecological data are divided into two-dimensional space grid units according to a fixed scale through a space grid construction and index unit; according to the method, automatic mapping from an actual geographic coordinate system to an analysis grid unit is realized, so that multi-source data has a unified projection basis in space, the problems that multiple data source space references are inconsistent and results are difficult to transversely compare in original ecological evaluation are solved, and a strict space organization structure is provided for subsequent ecological supply modeling. In the process of mapping the ecological indexes to the grid units, the system designs a neighborhood interpolation-based mechanism for averaging and filling multiple observation points and invalid units, so that the problem of local deficiency caused by non-uniform distribution of the observation points or influence of shielding on remote sensing data in field sampling is effectively relieved, and the integrity and analysis reliability of a spatial expression matrix are improved.

Description

Technical Field

[0001] This invention relates to the field of ecological product value analysis technology, specifically to an ecological product value analysis method and system based on multi-source data fusion. Background Technology

[0002] This invention relates to the field of ecological environment monitoring and ecological value assessment, specifically to the quantitative evaluation of ecosystem service functions. More specifically, it is applicable to the structural identification and value expression of the supply capacity of ecological products in natural ecological units such as forests and grasslands. Traditional ecological assessment techniques often use single-factor calculation methods to estimate ecological services such as carbon sequestration, water conservation, and soil retention separately. This makes it difficult to form a unified and structured expression of ecological supply capacity, limiting the widespread application of cross-regional comparison of ecological resources, ecological compensation zoning, and multi-value transformation.

[0003] In existing technologies, the assessment of ecological product value often relies on the individual modeling or estimation of a specific ecological function. For example, carbon sequestration is used to characterize the carbon sink value of forest land, and soil moisture content is used to characterize water conservation capacity. However, various ecological services are often coupled with structural linkages, resource sharing, and spatial overlap, making it impossible for a single indicator to reflect the comprehensive structural performance of the ecosystem's supply capacity. Especially in forest or grassland areas, due to the temporal cumulative and spatial heterogeneous nature of ecological processes, ignoring the multi-factor structural combination leads to fragmented evaluation results, a lack of horizontal comparability, and difficulty in supporting the formulation of practical resource allocation and zoning management strategies.

[0004] The root cause of these problems lies in the lack of a collaborative modeling and structural quantity fusion mechanism for multi-source ecological factors in the existing system. Once ecosystem service functions are assessed in isolation, it is difficult to make horizontal comparisons between different regions, even if they have similar supply capacities, through a unified standard. This, in turn, affects key aspects such as ecological functional zoning, ecological compensation pricing, and ecological performance evaluation. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a method and system for analyzing the value of ecological products based on multi-source data fusion, thus solving the problems mentioned in the background section.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solution: an ecological product value analysis system based on multi-source data fusion, including a heterogeneous ecological factor data acquisition module, an indicator dimension unified mapping module, a spatial unit unified analysis module, a multi-dimensional indicator fusion response construction module, a unified ecological supply index construction module, and a regional comparability output and analysis module. The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; The unified spatial unit parsing module maps the ecological dataset SW to consistent spatial segmentation units, constructing a spatial representation matrix M; The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; The unified ecological supply index construction module is based on the ecological supply response function Ψeco, and constructs a unified ecological supply expression value Ψsup(i,j). The regional comparability output and analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

[0007] Preferably, the heterogeneous ecological factor data acquisition module includes a structural response parameter acquisition unit and a productivity and hydrological response acquisition unit; The structural response parameter acquisition unit collects forest vegetation environment data through sensors, including surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), and surface stability disturbance index Dstab(x,y). Among them, the surface rainfall interception rate Ri(x,y) was obtained by standard rain gauge and canopy rainfall collector; The specific method is as follows: First, obtain the total rainfall that actually falls to the ground and the amount of rainfall that actually reaches the ground after passing through forests or vegetation; then divide the amount of rainfall that actually reaches the ground after passing through forests or vegetation by the total rainfall that actually falls to the ground to obtain the rainfall ratio; finally, subtract the rainfall ratio from 1 to obtain the surface rainfall interception rate Ri(x, y). The relative humidity flux response coefficient Hs(x, y) was collected by an array of SHT series humidity sensors installed in the forest; The specific method is as follows: First, record the changes in relative humidity at two time points in the same spatial location, read the relative humidity values, then calculate the difference between the two humidity values, and then divide the difference by the time interval between the two time points to obtain the relative humidity flux response coefficient Hs(x,y). The surface stability disturbance index Dstab(x,y) was obtained using a LiDAR 3D terrain scanner and soil profile measurement tools. The specific method is as follows: First, obtain the slope value of the target area in the forest; then measure the thickness of the humus layer and the average distribution density of the vegetation root system; finally, use the slope value as the numerator and divide it by the sum of the thickness of the humus layer and the average distribution density of the vegetation root system to obtain the surface stability disturbance index Dstab(x, y). The hydrological response acquisition unit collects data on the water maintenance and carbon fixation capacity of the forest ecosystem through acquisition equipment, including soil capillary water response period Tmc(x,y) and net primary productivity NPP(x,y). Among them, the soil capillary water reaction period Tmc(x,y) is acquired by TDR / FDR soil moisture sensor; The specific method is as follows: First, in a rainfall event, when the rain stops, record the time at this moment, which is called the rainfall end time point; then, conduct high-frequency monitoring of soil moisture and observe the changing trend of moisture data, and record the steady state time point; finally, subtract the rainfall end time point from the steady state time point to obtain the soil capillary water reaction period Tmc(x, y). The net primary productivity (NPP) is obtained as follows: First, the efficiency of plants in converting sunlight into organic matter is collected to obtain the light energy utilization efficiency; then, the photosynthetic utilization factor is derived through NDVI; next, the photosynthetically active radiation is obtained through satellite remote sensing or ground meteorological observation data; finally, the light energy utilization efficiency, photosynthetic utilization factor and photosynthetically active radiation are multiplied together to obtain the net primary productivity (NPP) (x, y). The obtained surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), soil capillary water reaction period Tmc(x,y), net primary productivity NPP(x,y) and surface stability disturbance index Dstab(x,y) were fitted to obtain the multi-source dataset DW.

[0008] Preferably, the index dimension unification mapping module includes a multi-scale tolerance-driven cleaning unit and a dimensionless mapping unit; The multi-scale tolerance-driven cleaning unit cleans the multi-source dataset DW to obtain the cleaned dataset CW. Among them, the cleaning process includes trend-drift hybrid anomaly identification and ecological continuity response jump detection; The processing method for trend-drift hybrid outlier identification is as follows: for each parameter in the multi-source dataset DW, a local sliding window sequence is constructed, such as a 5×5 grid neighborhood, and the local gradient variation rate is calculated; The local gradient mutation rate is obtained as follows: First, a spatial location point is determined, which is the location of a certain index value, such as a certain cell or sampling point in a certain region; then, a 5×5 grid neighborhood window is set around the spatial location point; next, for each point in the neighborhood window, the specific data value is calculated, and the absolute difference is calculated with the data value of the center point itself; that is, the difference between the value of each point in the neighborhood and the center point is calculated one by one, whether it is greater or less, only the magnitude of the difference is considered; finally, all these differences are added together and then divided by the total number of valid points in the neighborhood to obtain the local gradient mutation rate. When the local gradient variability rate is greater than the preset variability rate threshold, the spatial location point is marked as a spatial anomaly drift point. The variability threshold is obtained by adding 3 × local median difference to the local distribution mean. The processing method for detecting ecological continuity response jumps is as follows: perform slope response curvature analysis on the time series of spatial location points to determine whether there is a single-point jump behavior; First, the data in the multi-source dataset DW is continuously observed in spatial location. The direction of observation can be along a spatial path, such as a row of grid points in one direction, or along time, such as one day or multiple days at a single point; obtain continuous data values; for this set of continuous data values, first analyze the rate of change and obtain the first derivative; Next, we will further analyze whether its rate of change is also changing—that is, whether the slope of this indicator is becoming steeper or gentler, and obtain the second derivative, i.e., curvature. When the curvature shows a large outlier within a fixed period, it indicates the presence of non-ecological disturbances, which are then eliminated and replaced by interpolation.

[0009] Preferably, the dimensionless mapping unit performs dimensionless processing on the cleaned dataset CW to obtain the ecological dataset SW; Dimensionless processing eliminates the dimensions of data by introducing a median-dominant bidirectional mapping mechanism, specifically including constructing the range of the distribution kernel function and performing median symmetric extended normalization; The method for constructing the range of the distributed kernel function is as follows: S1. Obtain the quartile information of the data in the cleaned dataset CW; for a given data point, first sort the values ​​of the data across all observation points or regions; then, determine two key points: the first quartile (Q1): indicating that 25% of the data are smaller than it; the third quartile (Q3): indicating that 75% of the data are smaller than it. Subtract the first quartile from the third quartile to obtain the interquartile range; this is used to represent the core distribution width of this parameter value. S2. Construct a reasonable range of values; based on the third quartile and the first quartile, expand to both sides by 1.5 times the interquartile range. Extending downwards, we obtain the lower boundary: the first quartile minus 1.5 times the interquartile range; Extending upwards, we obtain the upper boundary: the third quartile plus 1.5 times the interquartile range; The range between the upper and lower boundaries is the reasonable distribution range; The method for performing median symmetric extended normalization is as follows: normalize the data within a reasonable distribution range using the relative median deviation ratio method to eliminate the dimensions of the data.

[0010] Preferably, the unified spatial unit parsing module includes a spatial raster construction and indexing unit and an index filling and matrix expression unit; The spatial grid construction and indexing unit divides the forest study area into grids at a fixed scale (e.g., 100m × 100m) to construct a two-dimensional spatial grid G(i,j). Each two-dimensional spatial grid G(i,j) corresponds to the actual geographic coordinate range [(xi, xi+Δx), (yj, yj+Δy)]. Where Δx and Δy represent the actual physical length of the raster in the x and y directions, respectively; (i, j) represents the spatial index number; (Supports integration with raster coding and geographic information systems) Map the coordinates (x, y) of each data point in the ecological dataset SW to the corresponding two-dimensional spatial grid G(i, j): The matching logic is as follows: ; In the formula, (x0, y0) represents the starting coordinates of the analysis region, and ⌊⋅⌋ represents the floor function; The index filling and matrix expression unit maps the data in the ecological dataset SW to a two-dimensional spatial grid G(i,j) based on the spatial index table, generating a spatial expression matrix M; The spatial representation matrix M is established by determining whether the coordinate position belongs to the two-dimensional spatial grid G(i,j) based on the correspondence of the geographic coordinate positions (x, y) in the ecological dataset SW. Given that the geographic coordinates (x, y) belong to the two-dimensional spatial grid G(i, j), the data corresponding to the geographic coordinates (x, y) are assigned to the corresponding matrix positions to establish the spatial representation matrix M. When there are multiple coordinate points in the two-dimensional spatial grid G(i,j), the average value of the data at all points is calculated and then filled into the corresponding matrix positions; when there are no valid observation points in the two-dimensional spatial grid G(i,j), completion is performed based on the data of the spatial neighborhood to generate a complete spatial representation matrix M.

[0011] Preferably, the multidimensional index fusion response construction module includes an ecological factor coupling structure generation unit and a fusion response function construction unit; The ecological factor coupling structure generation unit restructures the spatial expression matrix M to construct multiple ecological factors, including the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst. The hydrological regulation factor Qhd is obtained using the following formula: ; In the formula, Qhd(i,j) represents the hydrological moderating factor at spatial grid (i,j), MRi(i,j) represents the surface rainfall interception rate at spatial grid (i,j), MHs(i,j) represents the relative humidity flux response coefficient at spatial grid (i,j), MTmc(i,j) represents the soil capillary water response period at spatial grid (i,j), and ex represents the minimum constant; (to prevent the denominator from being zero) The biological impact factor Qpr is obtained using the following formula: ; In the formula, Qpr(i,j) represents the biological impact factor at spatial grid (i,j), and MNPP(i,j) represents the net primary productivity at spatial grid (i,j). The structural stability adjustment factor Qst is obtained using the following formula: ; In the formula, Qst(i,j) represents the structural stability adjustment factor at spatial grid (i,j), ln represents the logarithmic function, and MDstab(i,j) represents the surface stability disturbance index at spatial grid (i,j). The fusion response function construction unit combines the acquired hydrological regulation factor Qhd, biological impact factor Qpr, and structural stability regulation factor Qst to obtain the ecological supply response function Ψeco; The ecological supply response function Ψeco is obtained by multiplying the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst.

[0012] Preferably, the unified ecological supply index construction module includes a structural adjustment factor calculation unit and a unified supply index output unit; The structural adjustment factor calculation unit obtains the terrain elevation at the two-dimensional spatial grid G(i,j), labels it as Ev(i,j), and introduces the spatial neighborhood Ω(i,j) to calculate the micro-topographic variation rate Δtopo. The micro-topographic deformation rate Δtopo is obtained using the following formula: In the formula, Δtopo(i,j) represents the micro-topographical variation rate at the spatial grid (i,j), and Ev(u,v) represents the elevation of the neighboring grid. The obtained micro-topographic deformation rate Δtopo is mapped to the adjustment coefficient Φ(i,j) through an exponential adjustment structure. The adjustment coefficient Φ(i,j) is obtained using the following formula: Φ(i,j)=1+λ×tanh(Δtopo(i,j)); In the formula, tanh represents the hyperbolic tangent function, and λ represents the adjustment factor (with a value range of 0.5 to 1.5).

[0013] Preferably, the unified supply index output unit combines the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j) to obtain the ecological supply expression value Ψsup(i,j). The ecological supply expression value Ψsup(i,j) is obtained by multiplying the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j).

[0014] Preferably, the regional comparability output and analysis module includes a regional supply statistics extraction unit and a regional supply statistics extraction unit; The regional supply statistics extraction unit aggregates the ecological supply expression value Ψsup(i,j) according to the boundaries of ecological functional zones, extracting basic statistical indicators for each region, including the regional supply mean and coefficient of variation (CV), providing a quantifiable data foundation for horizontal comparisons. The regional supply mean is obtained by averaging the calculated ecological supply expression values ​​Ψsup(i,j) for any region; The coefficient of variation (CV) is obtained by dividing the standard within any region by the regional supply mean. The horizontal supply difference feedback unit performs horizontal parallel analysis of statistical indicators from multiple regions, constructs a supply comparison function and hierarchical feedback logic between regions, and obtains the supply level classification function L. The supply grade classification function L is obtained by matching in the following way. When the ecological supply expression value Ψsup(i,j) is greater than the preset high supply judgment threshold TH and the coefficient of variation CV is less than the preset upper judgment threshold CH, then the supply level classification function L represents a class of high supply; complete ecological service functions and high structural stability; maintain the existing ecological utilization structure, strictly control the development intensity, and ensure the in-situ operation of the ecosystem; benchmark demonstration area construction: can be selected as a high-value ecological product area, an ecological compensation output area, and a key area for green finance investment; Monitoring indicator optimization: High-frequency monitoring can be appropriately reduced, and only trend-tracking monitoring points can be retained to improve the efficiency of monitoring resource allocation; Regional ecological product spillover identification: Marking possible ecological service spillover paths to provide a support foundation for downstream low- and medium-supply areas.

[0015] When the preset low supply threshold TL is less than or equal to the ecological supply expression value Ψsup(i,j) and less than or equal to the preset high supply threshold TH, the supply level classification function L represents the second category, medium supply; the ecological supply function is sound, but there are fluctuations; for local low supply points, small-scale ecological restoration and degraded land management are implemented; local capacity enhancement measures are implanted, such as restoring vegetation diversity, optimizing forest stand structure, micro-topography modification, and embedding hydrological retention facilities. A monitoring and grading mechanism is established: a grid-level trend assessment model is used to identify potential points for supply growth; Pilot deployment for ecological function enhancement: Select representative sub-regions to carry out enhancement-type engineering interventions, such as artificial wetland construction and understory structure optimization; When the ecological supply expression value Ψsup(i,j) is less than the preset low supply judgment threshold TL, and the coefficient of variation CV is greater than the preset lower judgment threshold CL, the supply level classification function L represents three categories: low supply; ecosystem degradation and no service function; as a key area for ecological governance, priority is given to implementing ecological restoration projects; zoning control and management mechanism: limiting the expansion range of human interference sources and setting "ecological capacity constraint line"; High-frequency dynamic monitoring and control: Establish multi-source monitoring nodes to dynamically track the ecological restoration process and human disturbance pressure; Priority areas for receiving ecological compensation: Areas that can be designated as recipients of ecological compensation, with supporting mechanisms for horizontal compensation and fiscal ecological transfer.

[0016] The high supply threshold TH is obtained by calculating the 75th percentile Q3 of the mean of the ecological supply expression values ​​Ψsup(i,j) of all regions; setting: TH=Q3+0.5×IQR; IQR=Q3-Q1, which is the interquartile range; the high supply threshold TH is set to [0.70, 0.90]. The low supply threshold TL is obtained by taking the 25th percentile (Q1) of the average supply of all regions; setting: TL = Q1 - 0.5 × IQR; the low supply threshold TL is set to [0.25, 0.40]. The threshold value CH is set between 0.2 and 0.35. The threshold value CL is set between 0.45 and 0.60. The method for analyzing the value of ecological products based on multi-source data fusion includes the following steps: Step 1: The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). Step 2: The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; Step 3: The spatial unit unified parsing module maps the ecological dataset SW to consistent spatial segmentation units and constructs the spatial representation matrix M; Step 4: The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; Step 5: The unified ecological supply index construction module constructs a unified ecological supply expression value Ψsup(i,j) based on the ecological supply response function Ψeco. Step Six: The Regional Comparability Output and Analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

[0017] This invention provides a method and system for analyzing the value of ecological products based on multi-source data fusion, which has the following beneficial effects: (1) During system operation, a multi-dimensional index fusion response construction module is introduced to logically reorganize and coordinate the response of multi-dimensional structural factors such as hydrological regulation, biomass accumulation, and ecological stability, forming a comprehensive response function with the physical logic of ecological processes, thereby enhancing the model's ability to express the actual state of the ecosystem. Through the unified ecological supply index construction module, the system can reduce the multi-dimensional response function to a single spatial expression value, which has the attributes of being sortable, hierarchical, and partitionable, providing a unified data foundation for practical applications such as horizontal ecological capacity comparison, ecological compensation calculation, and performance evaluation.

[0018] (2) By constructing and indexing spatial grids, the original ecological data is divided into two-dimensional spatial grid units at a fixed scale, realizing the automatic mapping from the actual geographic coordinate system to the analysis grid units. This enables multi-source data to have a unified projection basis in space, overcoming the problems of inconsistent spatial benchmarks and difficulty in horizontal comparison of results among multiple data sources in the original ecological assessment, and providing a strict spatial organization structure for subsequent ecological supply modeling. In the process of mapping ecological indicators to grid units, the system designed a mechanism for filling multiple observation points by averaging and for interpolating invalid units based on neighborhood, which effectively alleviated the problem of uneven distribution of observation points or local missing data due to occlusion in field sampling, and improved the integrity and reliability of the spatial representation matrix.

[0019] (3) By calculating the micro-topographic variation rate and dynamically introducing the adjustment coefficient, the original ecological response results in areas with drastic topographic changes can be specifically corrected, which significantly enhances the adaptability of the index model to the complexity of the actual ecological environment. By establishing the product linkage between the response function and the topographic adjustment coefficient, the final ecological supply index result reflects both ecological output capacity and structural adaptability, forming a response-adjustment fusion structure, which effectively solves the limitation of traditional methods that ignore the influencing factors of ecological structure.

[0020] (4) By extracting key response factors representing ecological functions in a structured manner and combining them to form an ecological supply response function, the supply status of the ecosystem under the combined action of multiple factors is reflected. This mechanism can capture features such as nonlinear fluctuations and structural coupling changes, making up for the structural defects in traditional assessment models that do not consider the intrinsic correlation between factors.

[0021] Based on the supply response function, a structural adjustment mechanism is further introduced to dynamically generate a unified ecological supply index, achieving dimensionless integration and spatially normalized expression of heterogeneous ecological factors. This index can be directly used for cross-sectional assessments at different regions and scales, possessing high versatility and compatibility, effectively overcoming the problems of scattered indicators and difficulty in comparing results in traditional methods. Attached Figure Description

[0022] Figure 1 This is a flowchart illustrating the ecological product value analysis system based on multi-source data fusion of the present invention. Figure 2 This is a schematic diagram illustrating the steps of the ecological product value analysis method based on multi-source data fusion of the present invention; Figure 3 This is a schematic diagram of the spatial representation matrix acquisition process of the present invention; Figure 4 This is a schematic diagram of the ecological supply expression value judgment process of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0024] Example 1 This invention provides an ecological product value analysis system based on multi-source data fusion. Please refer to [link / reference]. Figures 1 to 4 It includes a heterogeneous ecological factor data acquisition module, an indicator dimension unified mapping module, a spatial unit unified analysis module, a multi-dimensional indicator fusion response construction module, a unified ecological supply index construction module, and a regional comparability output and analysis module. The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; The unified spatial unit parsing module maps the ecological dataset SW to consistent spatial segmentation units, constructing a spatial representation matrix M; The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; The unified ecological supply index construction module is based on the ecological supply response function Ψeco, and constructs a unified ecological supply expression value Ψsup(i,j). The regional comparability output and analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

[0025] In this embodiment, by constructing a heterogeneous ecological factor data acquisition module, the joint acquisition of multiple structurally significant ecological parameters (such as water conservation capacity, net primary productivity, and understory microenvironment regulation capacity) in the forest ecosystem is realized. This breaks through the limitation of traditional assessment methods that rely on a single indicator to express ecosystem service functions, and allows the structural information of ecological supply to be systematically preserved.

[0026] By introducing a unified mapping module for indicator dimensions, trend-driven cleaning and dimensionless mapping are performed on data from different sources and with different physical dimensions. This constructs a highly comparable and structurally continuous ecological factor expression system, laying the foundation for the unified construction of the ecological supply index and improving the stability of spatial analysis and the consistency of system output.

[0027] This invention introduces a multi-dimensional index fusion response construction module, which logically reorganizes and synergistically processes multi-dimensional structural factors such as hydrological regulation, biomass accumulation, and ecological stability to form a comprehensive response function with the physical logic of ecological processes, thereby enhancing the model's ability to express the actual state of ecosystems. Through a unified ecological supply index construction module, the system can reduce the multi-dimensional response function to a single spatial expression value, possessing the attributes of being sortable, hierarchical, and partitionable, providing a unified data foundation for practical applications such as horizontal ecological capacity comparison, ecological compensation calculation, and performance evaluation.

[0028] Example 2 Please refer to Figure 1 Specifically: the heterogeneous ecological factor data acquisition module includes a structural response parameter acquisition unit and a productivity and hydrological response acquisition unit; The structural response parameter acquisition unit collects forest vegetation environment data through sensors, including surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), and surface stability disturbance index Dstab(x,y). Among them, the surface rainfall interception rate Ri(x,y) was obtained by standard rain gauge and canopy rainfall collector; The relative humidity flux response coefficient Hs(x, y) was collected by an array of SHT series humidity sensors installed in the forest; The surface stability disturbance index Dstab(x,y) was obtained using a LiDAR 3D terrain scanner and soil profile measurement tools. The hydrological response acquisition unit collects data on the water maintenance and carbon fixation capacity of the forest ecosystem through acquisition equipment, including soil capillary water response period Tmc(x,y) and net primary productivity NPP(x,y). Among them, the soil capillary water reaction period Tmc(x,y) is acquired by TDR / FDR soil moisture sensor; The net primary productivity (NPP) is obtained as follows: First, the efficiency of plants in converting sunlight into organic matter is collected to obtain the light energy utilization efficiency; then, the photosynthetic utilization factor is derived through NDVI; next, the photosynthetically active radiation is obtained through satellite remote sensing or ground meteorological observation data; finally, the light energy utilization efficiency, photosynthetic utilization factor and photosynthetically active radiation are multiplied together to obtain the net primary productivity (NPP) (x, y). The obtained surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), soil capillary water reaction period Tmc(x,y), net primary productivity NPP(x,y) and surface stability disturbance index Dstab(x,y) were fitted to obtain the multi-source dataset DW.

[0029] The indicator dimension unification mapping module includes a multi-scale tolerance-driven cleaning unit and a dimensionless mapping unit; The multi-scale tolerance-driven cleaning unit cleans the multi-source dataset DW to obtain the cleaned dataset CW. Among them, the cleaning process includes trend-drift hybrid anomaly identification and ecological continuity response jump detection; The approach to identifying trend-drift hybrid outliers is as follows: for each parameter in the multi-source dataset DW, construct a local sliding window sequence and calculate the local gradient mutation rate; The method for obtaining the local gradient mutation rate is as follows: First, determine a spatial location point; then, set a 5×5 grid neighborhood window around the spatial location point; next, calculate the absolute difference between the specific data value of each point in the neighborhood window and the data value of the center point itself; finally, add up all these differences and divide by the total number of effective points in the neighborhood to obtain the local gradient mutation rate. When the local gradient variability rate is greater than the preset variability rate threshold, the spatial location point is marked as a spatial anomaly drift point. The processing method for detecting ecological continuity response jumps is as follows: perform slope response curvature analysis on the time series of spatial location points to determine whether there is a single-point jump behavior; First, the data in the multi-source dataset DW are continuously observed in spatial location; continuous data values ​​are obtained; for this set of continuous data values, the rate of change is analyzed to obtain the first derivative; then, the second derivative, i.e., curvature, is obtained. When the curvature shows a large outlier within a fixed period, it indicates the presence of non-ecological disturbances, which are then eliminated and replaced by interpolation.

[0030] The dimensionless mapping unit performs dimensionless processing on the cleaned dataset CW to obtain the ecological dataset SW. Dimensionless processing eliminates the dimensions of data by introducing a median-dominant bidirectional mapping mechanism, specifically including constructing the range of the distribution kernel function and performing median symmetric extended normalization; The method for constructing the range of the distributed kernel function is as follows: S1. Obtain the quartile information of the data in the cleaned dataset CW; First quartile (Q1): indicates that 25% of the data are smaller than it; Third quartile (Q3): indicates that 75% of the data are smaller than it. Subtract the first quartile from the third quartile to obtain the interquartile range. S2. Construct a reasonable range of values; based on the third quartile and the first quartile, expand to both sides by 1.5 times the interquartile range. Extending downwards, we obtain the lower boundary: the first quartile minus 1.5 times the interquartile range; Extending upwards, we obtain the upper boundary: the third quartile plus 1.5 times the interquartile range; The range between the upper and lower boundaries is the reasonable distribution range; The method for performing median symmetric extended normalization is as follows: normalize the data within a reasonable distribution range using the relative median deviation ratio method to eliminate the dimensions of the data.

[0031] In this embodiment, through the collaborative design of the structural response parameter acquisition unit and the productivity and hydrological response acquisition unit, the system integrates ecological parameters covering different dimensions such as meteorological and hydrological data, biological production, and microstructural stability under the same ecological acquisition mechanism for the first time. This completes the observation dimensions missing in traditional assessments of understory microenvironment, soil dynamics, and carbon-water processes, enabling multi-source factors to have a homomorphic observation basis in time and space, which is helpful for the overall analysis of multi-scale ecological processes.

[0032] By synergistically fitting surface rainfall interception rate, relative humidity flux response coefficient, soil capillary water response cycle, net primary productivity, and surface disturbance index to form a unified dataset (DW), a structure-driven multi-source ecological information representation method was constructed. This overcomes the problems of scattered indicator sources and inconsistent parameter structures in the original ecological assessment, providing a unified input interface for subsequent indicator fusion and response modeling. During multi-source data processing, the system introduces two mechanisms: "trend-drift mixed anomaly identification" and "ecological continuity response jump detection." This enables comprehensive identification and cleaning of spatial abrupt changes, temporal disturbances, and ecologically unstable behaviors, fundamentally eliminating data pollution and analytical misleading problems caused by equipment errors, sudden environmental changes, or blind spots in measurement point coverage in traditional acquisition systems, thus improving the stability of the ecological representation of the input data.

[0033] Unlike previous strategies that modeled each ecological indicator independently, this system uses a unified data collection and standard mapping process to ensure consistency in response rhythm, fluctuation range, and stability characteristics among various ecosystem service factors. This provides a collaborative basis for subsequent model construction, including response function fitting, supply expression generation, and comparability analysis.

[0034] Example 3 Please refer to Figure 1 and Figure 3 Specifically: the unified parsing module for spatial units includes spatial raster construction and indexing units and index filling and matrix expression units; Spatial grid construction and indexing unit divides the forest study area into grids at a fixed scale to construct a two-dimensional spatial grid G(i,j); Each two-dimensional spatial grid G(i,j) corresponds to the actual geographic coordinate range [(xi, xi+Δx), (yj, yj+Δy)]. Where Δx and Δy represent the actual physical length of the raster in the x and y directions, respectively; (i, j) represents the spatial index number; and it supports integration with raster coding and geographic information systems. Map the coordinates (x, y) of each data point in the ecological dataset SW to the corresponding two-dimensional spatial grid G(i, j): The matching logic is as follows: ; In the formula, (x0, y0) represents the starting coordinates of the analysis region. This represents the floor function; The index filling and matrix expression unit maps the data in the ecological dataset SW to a two-dimensional spatial grid G(i,j) based on the spatial index table, generating a spatial expression matrix M; The spatial representation matrix M is established by determining whether the coordinate position belongs to the two-dimensional spatial grid G(i,j) based on the correspondence of the geographic coordinate positions (x, y) in the ecological dataset SW. Given that the geographic coordinates (x, y) belong to the two-dimensional spatial grid G(i, j), the data corresponding to the geographic coordinates (x, y) are assigned to the corresponding matrix positions to establish the spatial representation matrix M. When there are multiple coordinate points in the two-dimensional spatial grid G(i,j), the average value of the data at all points is calculated and then filled into the corresponding matrix positions; when there are no valid observation points in the two-dimensional spatial grid G(i,j), completion is performed based on the data of the spatial neighborhood to generate a complete spatial representation matrix M.

[0035] The multidimensional index fusion response construction module includes an ecological factor coupling structure generation unit and a fusion response function construction unit; The ecological factor coupling structure generation unit restructures the spatial expression matrix M to construct multiple ecological factors, including the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst. The hydrological regulation factor Qhd is obtained using the following formula: ; In the formula, Qhd(i,j) represents the hydrological regulation factor at spatial grid (i,j), MRi(i,j) represents the surface rainfall interception rate at spatial grid (i,j), MHs(i,j) represents the relative humidity flux response coefficient at spatial grid (i,j), MTmc(i,j) represents the soil capillary water response period at spatial grid (i,j), and ex represents the minimum constant. The biological impact factor Qpr is obtained using the following formula: ; In the formula, Qpr(i,j) represents the biological impact factor at spatial grid (i,j), and MNPP(i,j) represents the net primary productivity at spatial grid (i,j). The structural stability adjustment factor Qst is obtained using the following formula: ; In the formula, Qst(i,j) represents the structural stability adjustment factor at spatial grid (i,j), ln represents the logarithmic function, and MDstab(i,j) represents the surface stability disturbance index at spatial grid (i,j). The fusion response function construction unit combines the acquired hydrological regulation factor Qhd, biological impact factor Qpr, and structural stability regulation factor Qst to obtain the ecological supply response function Ψeco; The ecological supply response function Ψeco is obtained by multiplying the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst.

[0036] In this embodiment, the original ecological data is divided into two-dimensional spatial grid cells at a fixed scale through spatial grid construction and indexing units. This achieves automatic mapping from the actual geographic coordinate system to the analysis grid cells, enabling multi-source data to have a unified spatial projection basis. This overcomes the problems of inconsistent spatial benchmarks and difficulty in horizontal comparison of results from multiple data sources in the original ecological assessment, providing a rigorous spatial organization structure for subsequent ecological supply modeling. In the process of mapping ecological indicators to grid cells, the system designs a mechanism for averaging and filling multiple observation points and interpolating invalid cells based on neighborhood. This effectively alleviates the problem of uneven distribution of observation points or local missing data due to occlusion in remote sensing data during field sampling, improving the integrity of the spatial representation matrix and the reliability of the analysis.

[0037] By recombining different ecological factor indicators in the spatial expression matrix M according to their ecosystem service function attributes, three dominant ecological function factors—hydrological regulation, biomass production, and structural stability—are clearly constructed. This transforms the indicators from isolated variables into a structural response system centered around the ecosystem's output capacity, better aligning with the logic of ecological product value expression. Since the three types of factors—hydrological regulation, biological response, and structural regulation—correspond to key processes in the ecosystem—energy regulation, water and carbon cycling, and stability maintenance, respectively, the fusion response function Ψeco is based on these factors, possessing a clear ecological mechanism orientation. This avoids the analytical pitfalls of simply superimposing multiple indicators or fuzzy combinations of correlations, making supply assessment more closely aligned with actual ecological functions.

[0038] Example 4 Please refer to Figure 1 Specifically: the unified ecological supply index construction module includes a structural adjustment factor calculation unit and a unified supply index output unit; The structural adjustment factor calculation unit obtains the terrain elevation at the two-dimensional spatial grid G(i,j), labels it as Ev(i,j), and introduces the spatial neighborhood Ω(i,j) to calculate the micro-topographic variation rate Δtopo. The micro-topographic deformation rate Δtopo is obtained using the following formula: In the formula, Δtopo(i,j) represents the micro-topographical variation rate at the spatial grid (i,j), and Ev(u,v) represents the elevation of the neighboring grid. The obtained micro-topographic deformation rate Δtopo is mapped to the adjustment coefficient Φ(i,j) through an exponential adjustment structure. The adjustment coefficient Φ(i,j) is obtained using the following formula: Φ(i,j)=1+λ×tanh(Δtopo(i,j)); In the formula, tanh represents the hyperbolic tangent function, and λ represents the adjustment factor (with a value range of 0.5 to 1.5).

[0039] The unified supply index output unit combines the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j) to obtain the ecological supply expression value Ψsup(i,j). The ecological supply expression value Ψsup(i,j) is obtained by multiplying the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j).

[0040] In this embodiment, by calculating the micro-topographic variability rate and dynamically introducing an adjustment coefficient, targeted correction of the original ecological response results is achieved in areas with drastic topographic changes, significantly enhancing the index model's adaptability to the complexity of the actual ecological environment. By establishing a product-linkage relationship between the response function and the topographic adjustment coefficient, the final ecological supply index result reflects both ecological output capacity and structural adaptability, forming a response-adjustment fusion structure, effectively overcoming the limitation of traditional methods that ignore the influencing factors of ecological structure.

[0041] The ecological supply expression value output by this module has a unified spatial coordinate system and structural adjustment embedding characteristics. It can be used not only for ecological value assessment of a single region, but also as a direct input result for cross-regional comparison, time series tracking, GIS display system, etc., meeting the quantitative, objective, and structural integrated expression needs of cross-regional comparison of ecological product value and policy formulation.

[0042] By constructing topographic elevation differences as adjustable coefficients and introducing them into the ecological response expression process, the physical interpretability of the results is improved. At the same time, the λ factor, as an open parameter of the adjustment coefficient, also provides a controllable variable for subsequent policy simulation, management strategy optimization, or scenario analysis, thus transforming the ecological supply index from a static score value into a structured decision-making tool with adjustment capabilities.

[0043] Example 5 Please refer to Figure 1 and Figure 4 Specifically: the regional comparability output and analysis module includes a regional supply statistics extraction unit and a regional supply statistics extraction unit; The regional supply statistics extraction unit aggregates the ecological supply expression value Ψsup(i,j) according to the boundaries of ecological functional zones, extracting basic statistical indicators for each region, including the regional supply mean and coefficient of variation (CV), providing a quantifiable data foundation for horizontal comparisons. The regional supply mean is obtained by averaging the calculated ecological supply expression values ​​Ψsup(i,j) for any region; The coefficient of variation (CV) is obtained by dividing the standard within any region by the regional supply mean. The horizontal supply difference feedback unit performs horizontal parallel analysis of statistical indicators from multiple regions, constructs a supply comparison function and hierarchical feedback logic between regions, and obtains the supply level classification function L. The supply grade classification function L is obtained by matching in the following way. When the ecological supply expression value Ψsup(i,j) is greater than the preset high supply judgment threshold TH and the coefficient of variation CV is less than the preset upper judgment threshold CH, then the supply level classification function L represents a class of high supply; complete ecological service functions and high structural stability; maintaining the existing ecological utilization structure, strictly controlling the development intensity, and ensuring the in-situ operation of the ecosystem. When the preset low supply judgment threshold TL is less than or equal to the ecological supply expression value Ψsup(i,j) and less than or equal to the preset high supply judgment threshold TH, the supply level classification function L represents the second category, medium supply; the ecological supply function is sound, but there are fluctuations; for local low supply points, small-scale ecological restoration and degradation management are carried out. When the ecological supply expression value Ψsup(i,j) is less than the preset low supply judgment threshold TL, and the coefficient of variation CV is greater than the preset lower judgment threshold CL, the supply level classification function L represents three categories: low supply; ecosystem degradation and no service function; as a key area for ecological governance, priority should be given to implementing ecological restoration projects.

[0044] In this embodiment, by constructing a regional supply statistical extraction mechanism, the ecological supply expression value is aggregated within the boundary of the ecological functional zone, and core statistical indicators such as the regional supply mean and coefficient of variation are output, so that the indicators have cross-regional structural consistency and solve the technical difficulty of "incomparability" of ecological value assessment results.

[0045] This module introduces the coefficient of variation (CV) as a criterion parameter for the volatility of ecological response within a region, enabling the identification of imbalances in the supply index within the region. By setting high / low thresholds, it clearly identifies areas with poor ecosystem stability and large fluctuations, providing a basis for classifying ecosystems as "strong protected areas," "key governance areas," and "fluctuation control areas," thus promoting ecological governance from extensive to precise prioritization and targeted management.

[0046] Example 6 For a method of ecological product value analysis based on multi-source data fusion, please refer to [reference needed]. Figure 2 Specifically, it includes the following steps: Step 1: The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). Step 2: The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; Step 3: The spatial unit unified parsing module maps the ecological dataset SW to consistent spatial segmentation units and constructs the spatial representation matrix M; Step 4: The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; Step 5: The unified ecological supply index construction module constructs a unified ecological supply expression value Ψsup(i,j) based on the ecological supply response function Ψeco. Step Six: The Regional Comparability Output and Analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

[0047] In this embodiment, the method no longer relies on a single ecological indicator as a representative of ecological value. Instead, it integrates ecological factors from different sources to form a multi-dimensional and multi-indicator composite ecological supply expression method, promoting the transformation of ecosystem service functions from "single-item quantification" to "structural coupling", reflecting the integrity and interactivity of ecosystem functions.

[0048] By using a unified spatial unit analysis mechanism, ecological data from heterogeneous sources and with varying granularities are incorporated into a standard spatial grid system, reconstructing the spatial expression matrix. This effectively supports subsequent indicator alignment, factor fusion, and assessment operations at spatial scales, eliminating the horizontal mismatch problem caused by inconsistent sampling points and data scales, and improving the spatial integrity and organization of ecological value analysis.

[0049] This method extracts key response factors representing ecological functions in a structured manner and combines them to form an ecological supply response function, reflecting the supply status of the ecosystem under the combined effects of multiple factors. This mechanism can capture characteristics such as nonlinear fluctuations and structural coupling changes, making up for the structural deficiency of traditional assessment models that do not consider the intrinsic correlations between factors.

[0050] Based on the supply response function, a structural adjustment mechanism is further introduced to dynamically generate a unified ecological supply index, achieving dimensionless integration and spatially normalized expression of heterogeneous ecological factors. This index can be directly used for cross-sectional assessments at different regions and scales, possessing high versatility and compatibility, effectively overcoming the problems of scattered indicators and difficulty in comparing results in traditional methods.

[0051] The overall process is modular and clear, with a closed structure and open expansion capabilities. It can not only conduct value assessments of forest systems, but also be extended to various ecosystem types such as wetlands, grasslands, and farmlands by changing data collection sources and factor definitions. It has strong transplantability and upgrading potential, and is suitable for building a regional ecological product value monitoring network.

[0052] 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 technical solutions and their equivalents.

Claims

1. An ecological product value analysis system based on multi-source data fusion, characterized in that: It includes a heterogeneous ecological factor data acquisition module, an indicator dimension unified mapping module, a spatial unit unified analysis module, a multi-dimensional indicator fusion response construction module, a unified ecological supply index construction module, and a regional comparability output and analysis module. The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; The unified spatial unit parsing module maps the ecological dataset SW to consistent spatial segmentation units, constructing a spatial representation matrix M; The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; The unified ecological supply index construction module is based on the ecological supply response function Ψeco, and constructs a unified ecological supply expression value Ψsup(i,j). The regional comparability output and analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

2. The ecological product value analysis system based on multi-source data fusion according to claim 1, characterized in that: The heterogeneous ecological factor data acquisition module includes a structural response parameter acquisition unit and a productivity and hydrological response acquisition unit; The structural response parameter acquisition unit collects forest vegetation environment data through sensors, including surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), and surface stability disturbance index Dstab(x,y). Among them, the surface rainfall interception rate Ri(x,y) was obtained by standard rain gauge and canopy rainfall collector; The relative humidity flux response coefficient Hs(x, y) was collected by an array of SHT series humidity sensors installed in the forest; The surface stability disturbance index Dstab(x,y) was obtained using a LiDAR 3D terrain scanner and soil profile measurement tools. The hydrological response acquisition unit collects data on the water maintenance and carbon fixation capacity of the forest ecosystem through acquisition equipment, including soil capillary water response period Tmc(x,y) and net primary productivity NPP(x,y). Among them, the soil capillary water reaction period Tmc(x,y) is acquired by TDR / FDR soil moisture sensor; The net primary productivity (NPP) is obtained as follows: First, the efficiency of plants in converting sunlight into organic matter is collected to obtain the light energy utilization efficiency; then, the photosynthetic utilization factor is derived through NDVI; next, the photosynthetically active radiation is obtained through satellite remote sensing or ground meteorological observation data; finally, the light energy utilization efficiency, photosynthetic utilization factor and photosynthetically active radiation are multiplied together to obtain the net primary productivity (NPP) (x, y). The obtained surface rainfall interception rate Ri(x,y), relative humidity flux response coefficient Hs(x,y), soil capillary water reaction period Tmc(x,y), net primary productivity NPP(x,y) and surface stability disturbance index Dstab(x,y) were fitted to obtain the multi-source dataset DW.

3. The ecological product value analysis system based on multi-source data fusion according to claim 2, characterized in that: The indicator dimension unification mapping module includes a multi-scale tolerance-driven cleaning unit and a dimensionless mapping unit; The multi-scale tolerance-driven cleaning unit cleans the multi-source dataset DW to obtain the cleaned dataset CW. Among them, the cleaning process includes trend-drift hybrid anomaly identification and ecological continuity response jump detection; The approach to identifying trend-drift hybrid outliers is as follows: for each parameter in the multi-source dataset DW, a local sliding window sequence is constructed, and the local gradient mutation rate is calculated. The method for obtaining the local gradient mutation rate is as follows: First, determine a spatial location point; then, set a 5×5 grid neighborhood window around the spatial location point; next, calculate the absolute difference between the specific data value of each point in the neighborhood window and the data value of the center point itself; finally, add up all these differences and divide by the total number of effective points in the neighborhood to obtain the local gradient mutation rate. When the local gradient variability rate is greater than the preset variability rate threshold, the spatial location point is marked as a spatial anomaly drift point. The processing method for detecting ecological continuity response jumps is as follows: perform slope response curvature analysis on the time series of spatial location points to determine whether there is a single-point jump behavior; First, the data in the multi-source dataset DW are continuously observed in spatial location; continuous data values ​​are obtained; for this set of continuous data values, the rate of change is analyzed to obtain the first derivative; then, the second derivative, i.e., curvature, is obtained. When the curvature shows a large outlier within a fixed period, it indicates the presence of non-ecological disturbances, which are then eliminated and replaced by interpolation.

4. The ecological product value analysis system based on multi-source data fusion according to claim 3, characterized in that: The dimensionless mapping unit performs dimensionless processing on the cleaned dataset CW to obtain the ecological dataset SW. Dimensionless processing eliminates the dimensions of data by introducing a median-dominant bidirectional mapping mechanism, specifically including constructing the range of the distribution kernel function and performing median symmetric extended normalization; The method for constructing the range of the distributed kernel function is as follows: S1. Obtain the quartile information of the data in the cleaned dataset CW; First quartile (Q1): indicates that 25% of the data are smaller than it; Third quartile (Q3): indicates that 75% of the data are smaller than it. Subtract the first quartile from the third quartile to obtain the interquartile range. S2. Construct a reasonable value range; Based on the third and first quartiles, extend to both sides by 1.5 times the interquartile range; Extending downwards, we obtain the lower boundary: the first quartile minus 1.5 times the interquartile range; Extending upwards, we obtain the upper boundary: the third quartile plus 1.5 times the interquartile range; The range between the upper and lower boundaries is the reasonable distribution range; The method for performing median symmetric extended normalization is as follows: normalize the data within a reasonable distribution range using the relative median deviation ratio method to eliminate the dimensions of the data.

5. The ecological product value analysis system based on multi-source data fusion according to claim 4, characterized in that: The unified parsing module for spatial units includes spatial raster construction and indexing units and index filling and matrix expression units; Spatial grid construction and indexing unit divides the forest study area into grids at a fixed scale to construct a two-dimensional spatial grid G(i,j); Each two-dimensional spatial grid G(i,j) corresponds to the actual geographic coordinate range [(xi, xi+Δx), (yj, yj+Δy)]. Where Δx and Δy represent the actual physical lengths of the grid in the x and y directions, respectively; (i, j) represents the spatial index number; Map the coordinates (x, y) of each data point in the ecological dataset SW to the corresponding two-dimensional spatial grid G(i, j): The matching logic is as follows: ； In the formula, (x0, y0) represents the starting coordinates of the analysis region. This represents the floor function; The index filling and matrix expression unit maps the data in the ecological dataset SW to a two-dimensional spatial grid G(i,j) based on the spatial index table, generating a spatial expression matrix M; The spatial representation matrix M is established by determining whether the coordinate position belongs to the two-dimensional spatial grid G(i,j) based on the correspondence of the geographic coordinate positions (x, y) in the ecological dataset SW. Given that the geographic coordinates (x, y) belong to the two-dimensional spatial grid G(i, j), the data corresponding to the geographic coordinates (x, y) are assigned to the corresponding matrix positions to establish the spatial representation matrix M. When there are multiple coordinate points in the two-dimensional spatial grid G(i,j), the average value of the data at all points is calculated and then filled into the corresponding matrix positions; when there are no valid observation points in the two-dimensional spatial grid G(i,j), completion is performed based on the data of the spatial neighborhood to generate a complete spatial representation matrix M.

6. The ecological product value analysis system based on multi-source data fusion according to claim 5, characterized in that: The multidimensional index fusion response construction module includes an ecological factor coupling structure generation unit and a fusion response function construction unit; The ecological factor coupling structure generation unit restructures the spatial expression matrix M to construct multiple ecological factors, including the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst. The hydrological regulation factor Qhd is obtained using the following formula: ； In the formula, Qhd(i,j) represents the hydrological regulation factor at spatial grid (i,j), MRi(i,j) represents the surface rainfall interception rate at spatial grid (i,j), MHs(i,j) represents the relative humidity flux response coefficient at spatial grid (i,j), MTmc(i,j) represents the soil capillary water response period at spatial grid (i,j), and ex represents the minimum constant. The biological impact factor Qpr is obtained using the following formula: ； In the formula, Qpr(i,j) represents the biological impact factor at spatial grid (i,j), and MNPP(i,j) represents the net primary productivity at spatial grid (i,j). The structural stability adjustment factor Qst is obtained using the following formula: ； In the formula, Qst(i,j) represents the structural stability adjustment factor at spatial grid (i,j), ln represents the logarithmic function, and MDstab(i,j) represents the surface stability disturbance index at spatial grid (i,j). The fusion response function construction unit combines the acquired hydrological regulation factor Qhd, biological impact factor Qpr, and structural stability regulation factor Qst to obtain the ecological supply response function Ψeco; The ecological supply response function Ψeco is obtained by multiplying the hydrological regulation factor Qhd, the biological impact factor Qpr, and the structural stability regulation factor Qst.

7. The ecological product value analysis system based on multi-source data fusion according to claim 6, characterized in that: The unified ecological supply index construction module includes a structural adjustment factor calculation unit and a unified supply index output unit; The structural adjustment factor calculation unit obtains the terrain elevation at the two-dimensional spatial grid G(i,j), labels it as Ev(i,j), and introduces the spatial neighborhood Ω(i,j) to calculate the micro-topographic variation rate Δtopo. The micro-topographic deformation rate Δtopo is obtained using the following formula: In the formula, Δtopo(i,j) represents the micro-topographical variation rate at the spatial grid (i,j), and Ev(u,v) represents the elevation of the neighboring grid. The obtained micro-topographic deformation rate Δtopo is mapped to the adjustment coefficient Φ(i,j) through an exponential adjustment structure. The adjustment coefficient Φ(i,j) is obtained using the following formula: Φ(i,j)=1+λ×tanh(Δtopo(i,j)); In the formula, tanh represents the hyperbolic tangent function, and λ represents the adjustment factor.

8. The ecological product value analysis system based on multi-source data fusion according to claim 7, characterized in that: The unified supply index output unit combines the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j) to obtain the ecological supply expression value Ψsup(i,j). The ecological supply expression value Ψsup(i,j) is obtained by multiplying the ecological supply response function Ψeco with the adjustment coefficient Φ(i,j).

9. The ecological product value analysis system based on multi-source data fusion according to claim 8, characterized in that: The regional comparability output and analysis module includes a regional supply statistics extraction unit and a regional supply statistics extraction unit; The regional supply statistics extraction unit aggregates the ecological supply expression value Ψsup(i,j) according to the boundaries of ecological functional zones, extracting basic statistical indicators for each region, including the regional supply mean and coefficient of variation (CV), providing a quantifiable data foundation for horizontal comparisons. The regional supply mean is obtained by averaging the calculated ecological supply expression value Ψsup(i,j) for any region; The coefficient of variation (CV) is obtained by dividing the standard within any region by the regional supply mean. The horizontal supply difference feedback unit performs horizontal parallel analysis of statistical indicators from multiple regions, constructs a supply comparison function and hierarchical feedback logic between regions, and obtains the supply level classification function L. The supply grade classification function L is obtained by matching in the following way. When the ecological supply expression value Ψsup(i,j) is greater than the preset high supply judgment threshold TH and the coefficient of variation CV is less than the preset upper judgment threshold CH, the supply level classification function L represents a class with high supply, complete ecological service functions, and high structural stability. Maintain the existing ecological utilization structure, strictly control the intensity of development, and ensure the in-situ operation of the ecosystem; When the preset low supply judgment threshold TL is less than or equal to the ecological supply expression value Ψsup(i,j) and less than or equal to the preset high supply judgment threshold TH, the supply level classification function L represents the second category, medium supply; the ecological supply function is sound, but there are fluctuations; for local low supply points, small-scale ecological restoration and degradation management are carried out. When the ecological supply expression value Ψsup(i,j) is less than the preset low supply judgment threshold TL, and the coefficient of variation CV is greater than the preset lower judgment threshold CL, the supply level classification function L represents three categories: low supply; ecosystem degradation and no service function; as a key area for ecological governance, priority should be given to implementing ecological restoration projects.

10. A method for analyzing the value of ecological products based on multi-source data fusion, applied to the ecological product value analysis system based on multi-source data fusion as described in any one of claims 1 to 9, characterized in that: Includes the following steps: Step 1: The heterogeneous ecological factor data acquisition module collects forest ecological data through acquisition sensors and acquisition devices, and fits it into a multi-source dataset (DW). Step 2: The indicator dimension unification mapping module cleans and standardizes the multi-source dataset DW to obtain the ecological dataset SW; Step 3: The spatial unit unified parsing module maps the ecological dataset SW to consistent spatial segmentation units and constructs the spatial representation matrix M; Step 4: The multi-dimensional index fusion response construction module fuses the ecological indicators of the spatial expression matrix M to construct the ecological supply response function Ψeco; Step 5: The unified ecological supply index construction module constructs a unified ecological supply expression value Ψsup(i,j) based on the ecological supply response function Ψeco. Step Six: The Regional Comparability Output and Analysis module performs regional statistical analysis on all ecological supply expression values ​​Ψsup(i,j) to obtain output results that support horizontal comparison and provides feedback.

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