Ecological environment monitoring method and system based on ecological function data analysis

Abstract

The invention relates to the technical field of ecological environment monitoring methods, in particular to an ecological environment monitoring method and system based on ecological function data analysis. According to the method, an upstream engineering data set and a downstream monitoring data set are endowed with initial credibility weights, weight values are dynamically adjusted based on the deviation degree of an actual ecological response index and an expected effect in an engineering influence range, and a checking task list is generated when the deviation of an accounting result exceeds a threshold value. And updating a weight adjustment rule set by executing a verification task, generating an optimized data packet containing a calibration data table, an abnormal list and a rule set configuration file, and realizing dynamic tracking and data comparison of ecological protection measure effects. The system comprises a memory and a processor, and realizes data processing and analysis through a distributed architecture. According to the method, the problems of data authenticity verification and causal association proof in ecological compensation are effectively solved, and a scientific decision basis is provided for drainage basin ecological management.

Description

Technical Field

[0001] This invention relates to the field of ecological environment monitoring methods, and more specifically, to an ecological environment monitoring method and system based on ecological function data analysis. Background Technology

[0002] With the deepening of ecological civilization construction, ecological environment monitoring technology provides a scientific basis for ecological management by quantitatively assessing the status of core ecosystem services such as water conservation and soil and water conservation. Particularly in the context of watershed ecological compensation, this technology is used to evaluate the effects of upstream protection measures (such as returning farmland to forest and soil and water conservation projects) on downstream ecological functions (such as improvements in water quality and quantity) to support compensation accounting. Existing technologies typically rely on manual data collection combined with remote sensing interpretation to obtain upstream data, and then use spatial interpolation or hydrological models to supplement data from sparse downstream stations.

[0003] Although existing technologies provide basic data support to some extent, a key challenge remains in practical applications: the lack of effective means to verify the authenticity of upstream measures data and the representativeness of downstream monitoring data makes it difficult to guarantee the accuracy of the ecological function improvement calculated based on these data. It is also impossible to reliably establish a causal relationship between upstream protection measures and the actual ecological effects downstream. This makes the final compensation standard lack a solid objective basis and easily leads to disputes. Summary of the Invention

[0004] This invention provides an ecological environment monitoring method and system based on ecological function data analysis. It assigns initial weights to upstream engineering data and downstream monitoring data, dynamically adjusts these weights based on the deviation between actual monitoring data and expected results, triggers manual verification when the deviation exceeds a threshold, and optimizes the weight rule set based on the verification results. This generates weight-optimized data including a calibration data table, an anomaly list, and an updated rule set. By increasing the decision weight of real data, eliminating interference from abnormal data, and optimizing future weight allocation rules based on historical verification experience, it ensures that the compensation calculation results effectively reflect the true ecological benefits, thereby solving the problems mentioned in the background art.

[0005] In watershed ecological compensation scenarios, the lack of effective means to verify the authenticity and completeness of upstream protection measures data and the representativeness of downstream monitoring data leads to significant uncertainty in the accounting of ecological function improvement effects, making it difficult to clearly prove the actual correlation between upstream measures and downstream improvements.

[0006] To achieve the above objectives, this ecological environment monitoring method includes the following steps:

[0007] S1. Collect upstream engineering datasets and downstream monitoring datasets, and perform standardized preprocessing to generate preprocessed data packets;

[0008] S2. Based on the preprocessed data packet, establish a dynamic data credibility evaluation and optimization mechanism, the steps of which are as follows:

[0009] S2.1 Assign initial confidence weights to the upstream engineering dataset and the downstream monitoring dataset respectively;

[0010] S2.2 Extract the actual change values ​​of downstream ecological response indicators within the influence range of upstream projects based on the initial credibility weight, and obtain the expected effect values ​​of the corresponding projects;

[0011] S2.3. Dynamically adjust the initial credibility weight based on the degree of deviation between the actual change value and the expected effect value;

[0012] S2.4 Substitute the adjusted weights into the ecological compensation accounting model to generate ecological compensation accounting results. When the deviation between the ecological compensation accounting results and historical compensation data exceeds the preset threshold, generate a structured verification task list.

[0013] S2.5 Execute the verification tasks in the structured verification task list, update the weight adjustment rule set according to the verification results, generate weight optimization data including the upstream project data table, the abnormal project list and the updated weight adjustment rule set configuration file, and dynamically monitor and compare the implementation effects of upstream and downstream ecological protection measures.

[0014] The above technical solution achieves accurate assessment of ecological data by constructing a dynamic weight adjustment and closed-loop verification mechanism. The core concept of this design is that static weight allocation alone cannot identify data quality differences, leading to low-confidence and high-confidence data participating in the calculation equally, ultimately affecting the accuracy of the compensation results. Without manual verification, the system relies solely on algorithms to adjust weights, making it difficult to detect hidden data fraud or anomalies, rendering weight optimization superficial. Without a feedback loop of rule sets, weights need to be readjusted for each calculation, reducing efficiency and hindering experience accumulation. Specifically, in step S2.3, a dynamic adjustment using an effect deviation model (rather than fixed threshold judgment) avoids misjudgments caused by natural environmental fluctuations. The three types of weight optimization data generated in step S2.5 (rather than a single output) meet current calculation needs and provide a basis for long-term data quality management. This design effectively overcomes the shortcomings of traditional methods, such as coarse data quality assessment and delayed human intervention, making ecological compensation calculation both real-time and reliable.

[0015] Based on this, the initial credibility weight is assigned according to the authority of the data source and the quality control status of the monitoring equipment.

[0016] In another technical solution, the weight adjustment is calculated by using an effect deviation evaluation model.

[0017] This technical solution designs the initial credibility weights based on the authority of the data source and the equipment quality control status. If weights are assigned according to a uniform standard, it will be impossible to distinguish the reliability differences between satellite remote sensing data and manually entered data, resulting in low-quality data receiving excessive weights. If the equipment quality control status is ignored, faulty sensor data may be treated the same as normal data. Similarly, using an effect deviation evaluation model (rather than nonlinear adjustment) for weight calculation can more sensitively capture the nonlinear relationship between the actual effect and the expectation. If a fixed step size adjustment is used instead, it may not react adequately when the actual effect deviates slightly from the expectation, while it may over-adjust when the deviation is severe.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0019] 1. This invention constructs an adaptive data quality optimization system. By quantifying data credibility and embedding it into the entire accounting process, it realizes dynamic collaborative optimization of data quality and ecological assessment, so that the compensation results can not only reflect the real ecological benefits, but also have verifiable scientific basis.

[0020] 2. This invention establishes an intelligent evolution mechanism of "data-model-decision". Through the continuous evolution of the weight adjustment rule set, the system can accumulate historical verification experience and optimize processing strategies. This not only solves the current data quality problem, but also forms a self-improvement capability for the future, providing sustainable technical support for ecological compensation. Attached Figure Description

[0021] Figure 1 This is a schematic diagram of the overall process structure of the ecological environment monitoring method based on ecological function data analysis of the present invention;

[0022] Figure 2 This is a schematic diagram of the specific process of step S1 in the ecological environment monitoring method based on ecological function data analysis of the present invention;

[0023] Figure 3 This is a schematic diagram of the specific process of step S2 in the ecological environment monitoring method based on ecological function data analysis of the present invention. Detailed Implementation

[0024] The technical solutions of 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.

[0025] Here are some explanations of technical terms:

[0026] The WGS84UTM projection coordinate system is a globally applicable geographic coordinate system that uses the WGS84 ellipsoid datum and divides the Earth's surface into 60 longitude zones for local projection using the UTM (Universal Transverse Mercator) projection method.

[0027] The dominant category refers to a data processing method that fills in null or missing values ​​in raster data with the most frequent land cover type (dominant category) among its surrounding neighboring cells.

[0028] Currently, in watershed ecological compensation scenarios, the lack of effective means to verify the authenticity and completeness of upstream protection measures data (such as the area of ​​farmland converted to forest and the effects of soil and water conservation projects) and the representativeness of downstream monitoring data (such as water quality and quantity) leads to significant uncertainty in the calculation of ecological function improvement effects, making it difficult to clearly prove the actual correlation between upstream measures and downstream improvements. This invention provides an ecological environment monitoring method and system based on ecological function data analysis. (See [link to relevant documentation]). Figure 1 As shown, it includes the following steps:

[0029] S1. Collect upstream engineering datasets and downstream monitoring datasets, and perform standardized preprocessing to generate preprocessed data packets;

[0030] S2. Based on the preprocessed data packet, establish a dynamic data credibility evaluation and optimization mechanism, the steps of which are as follows:

[0031] S2.1 Assign initial confidence weights to the upstream engineering dataset and the downstream monitoring dataset respectively;

[0032] S2.2 Extract the actual change values ​​of downstream ecological response indicators within the influence range of upstream projects based on the initial credibility weight, and obtain the expected effect values ​​of the corresponding projects;

[0033] S2.3. Dynamically adjust the initial credibility weight based on the degree of deviation between the actual change value and the expected effect value;

[0034] S2.4 Substitute the adjusted weights into the ecological compensation accounting model to generate ecological compensation accounting results. When the deviation between the ecological compensation accounting results and historical compensation data exceeds the preset threshold, generate a structured verification task list.

[0035] S2.5 Execute the verification tasks in the structured verification task list, update the weight adjustment rule set according to the verification results, generate weight optimization data including the upstream project data table, the abnormal project list and the updated weight adjustment rule set configuration file, and dynamically monitor and compare the implementation effects of upstream and downstream ecological protection measures.

[0036] By constructing a dynamic credibility weight adjustment mechanism based on spatiotemporal consistency verification and introducing a closed-loop feedback process that uses compensation accounting results to drive manual verification, the quality of key data for watershed ecological compensation has been continuously optimized and verified, providing core assurance for generating fair, credible, and data-supported ecological compensation decisions.

[0037] See Figure 2 As shown, step S1 of this invention involves multi-source heterogeneous data acquisition and preprocessing, which is a fundamental data guarantee step for achieving accurate analysis of ecological functions. Specifically, the primary task of this step is to extract key upstream protection measure data from the watershed ecological compensation business system and acquire downstream ecological status monitoring data through the Internet of Things sensing network, thereby constructing a multi-dimensional data acquisition system covering the entire watershed.

[0038] Data collection began with upstream engineering datasets. By connecting to the database interface of the watershed ecological compensation management platform, boundary vector data for the Grain for Green project was automatically obtained. This boundary vector data includes core attribute fields such as the spatial coordinates of each plot, the declared implementation area, and the specific implementation year. Simultaneously, a point attribute table for soil and water conservation projects was extracted. This table details the spatial coordinates of various projects, project type classification codes, and design sediment reduction efficiency parameters verified by experts. These parameters will serve as important benchmarks for subsequent evaluation of the actual effectiveness of the projects.

[0039] To establish the capability for correlation analysis between upstream measures and downstream ecological responses, this step simultaneously integrates real-time ecological effect data transmitted from IoT monitoring devices deployed within the watershed. The downstream monitoring dataset used for this integration is as follows: a water quality sensor network collects turbidity, total nitrogen, and total phosphorus concentrations at each monitoring section at a minute-by-minute frequency, forming a highly timely water quality time-series dataset. Hydrological monitoring stations periodically upload daily average flow observations and corresponding water level elevations for each control section. These data, after quality control marking, are stored in the hydrological operational database for use by this system.

[0040] Considering the spatiotemporal correlation of ecological function analysis, this step also requires the integration of supporting data. Two-meter resolution remote sensing images of the watershed area are acquired through a satellite remote sensing data service platform. These images, after radiometric and geometric correction, can be used for dynamic interpretation and analysis of vegetation cover. Soil data utilizes a kilometer-level raster dataset published by the state, which includes physical parameters such as the sand-to-clay content ratio for each grid cell. Rainfall data provided by the meteorological department provides a spatial distribution matrix of precipitation across the entire watershed at the hourly granularity, used to eliminate the interference of climate fluctuations on the ecological effect assessment.

[0041] All collected multi-source heterogeneous data undergoes a rigorous standardized preprocessing procedure. In the spatial data unified projection conversion stage, all vector layers and raster data are uniformly converted to the WGS84UTM projection coordinate system. Control point matching ensures that the spatial location deviation of data from different sources is less than one pixel. To address potential missing values ​​in the monitoring data, anomalous breakpoint data from water quality sensors are appropriately filled using linear interpolation algorithms, while missing grids in the soil type raster are filled using the dominant category of neighboring regions to maintain data continuity, thus completing the missing value filling for the monitoring data. To eliminate dimensional differences among different monitoring indicators, numerical features such as engineering sediment reduction efficiency parameters and water quality monitoring concentration values ​​are normalized to a standardized range of zero to one. In the final fusion processing stage, spatial data of soil and water conservation engineering points are overlaid with remote sensing images to generate a buffer raster layer containing the engineering influence radius. Simultaneously, the vector plot data of land converted from farmland to forest is converted into a land use classification map with the same resolution as the soil raster, ensuring spatial scale consistency for subsequent analyses.

[0042] Following the above processing steps, this step ultimately outputs a structured preprocessed data package. This package contains a spatially aligned upstream engineering dataset, a time-complete downstream monitoring dataset, and a quality-controlled set of auxiliary parameters. These data are transmitted to subsequent analysis steps via a unified interface. The upstream engineering dataset and the downstream monitoring dataset undergo dynamic reliability weighting analysis in step S2, while remote sensing imagery, soil data, and rainfall data are directly provided for ecological function model calculations in step S3, forming a clearly defined data flow architecture.

[0043] like Figure 3 As shown, step S2 of this invention establishes a dynamic evaluation and optimization mechanism for data credibility. This involves quantitatively analyzing the correlation between the implementation effects of upstream engineering projects and the downstream ecological response, and continuously revising the data weight values. The system first performs an initial credibility weight allocation on the upstream engineering dataset and the downstream monitoring dataset from step S1. For upstream engineering data, the system establishes a multi-level weight evaluation system, comprehensively considering the authority of the data source, the reliability of the collection method, and the completeness of the parameter information. For data on land converted from farmland to forest, differentiated weights are assigned based on different collection methods such as remote sensing interpretation, UAV aerial surveying, and manual reporting; soil and water conservation engineering data are weighted according to the characteristics of the engineering type and the completeness of the design documents. During this process, the system automatically generates an upstream engineering data table containing complete metadata, achieving efficient data management and rapid access through a distributed storage architecture.

[0044] For downstream monitoring data, the system constructs a dynamic weighting system based on monitoring network level and data quality status. Water quality and flow monitoring data are managed with tiered weights according to the certification level and operation and maintenance standards of the monitoring stations. Simultaneously, the system accesses the quality control status information of the monitoring equipment in real time, continuously evaluating the continuity and accuracy of data collection and dynamically adjusting data weights accordingly. All weight determination rules and adjustment records are fully saved to the system log, providing a traceable basis for subsequent analysis. This refined weighting management mechanism ensures that high-quality monitoring data gains greater decision-making influence in ecological function assessments.

[0045] During this process, the system established a complete weight management database, containing core data such as basic project information, weight assignment criteria, and dynamic adjustment records. This data interfaces with subsequent processing modules through standard interfaces, providing fundamental support for dynamic credibility assessment. Simultaneously, the system maintains a complete weight determination log, including assignment rule versions, operation timestamps, and executor information, ensuring the auditability of the entire weight management process.

[0046] The dynamic weight adjustment process is achieved through spatiotemporal correlation analysis. For each upstream engineering unit, the changes in ecological indicators at downstream monitoring sections within its influence range are extracted and compared with baseline data before project implementation. The system uses an effect deviation assessment model to calculate the weight adjustment amount, which is calculated using the following formula:

[0047]

[0048] In the formula, ΔW represents the weight adjustment amount (the final value is directly added to the current weight);

[0049] α represents the adjustment coefficient (ranging from 0.1 to 0.5);

[0050] O represents the actual observed rate of change of ecological indicators;

[0051] E represents the expected effect value of the engineering design.

[0052] After the weight adjustment calculation is completed, the system updates the weight value field in the upstream project data table in real time and records the adjustment timestamp and calculation parameters.

[0053] When the turbidity reduction rate downstream of a certain area of ​​land conversion from farmland to forest is significantly lower than the expected vegetation restoration rate, its weight value will be adjusted downward according to the model output; conversely, if the actual sediment reduction effect of the soil and water conservation project exceeds the design target, the weight value will be increased accordingly. All adjustment operations retain a complete decision log, including adjustment time, basis data, and calculation parameters. The compensation accounting and reverse verification process constitutes a technical closed loop. The current weight value is substituted into the compensation calculation model to generate ecological compensation accounting results. Potential anomalies are identified through deviation analysis with historical compensation data or protection cost benchmarks for the region. During the deviation analysis phase, the system automatically calls an anomaly detection algorithm. When a compensation deviation is detected to exceed a preset threshold (e.g., ±15%), a structured verification task list is generated. The list includes fields such as engineering coordinates, deviation values, and associated monitoring data IDs, and is stored in the verification task database.

[0054] Next, inspectors upload the on-site verification results via mobile devices, and the system uses this information to perform a final calibration of the weight values. During the calibration process, the rule engine automatically analyzes the differences between the manual verification results and the system predictions, extracts new weight adjustment experience rules (such as "adjusting the efficiency verification cycle of terraced field projects from 1 year to 2 years"), updates them to the weight adjustment rule set XML configuration file, and feeds the updated rule set back to the initial credibility weight assignment process. For example, for falsely reported projects that have been verified multiple times, the initial weight preset value of similar data will be permanently reduced. The weight of data from projects that have been verified as genuine and valid is enhanced; while the weight of falsely reported project data that has been disproven is significantly reduced or even excluded, ensuring that the upstream data entering the accounting stage has a verifiable basis for authenticity.

[0055] After a complete processing flow, this step outputs three types of weight optimization data: an upstream project data table (containing final weight values ​​and historical adjustment records) stored in the weight management module; an abnormal project list (containing spatial location and deviation analysis results) written to the verification task database; and a versioned weight adjustment rule set configuration file. The optimized weight data is transmitted to the ecological function calculation process in step S3 via a standardized data interface. The automatically generated verification list is pushed to the workflow processing program of the regulatory system via a preset communication protocol. The updated weight adjustment rules are fed back to the initial weight allocation stage of this step, forming a closed-loop verification mechanism for continuous optimization. This data credibility management method, based on dynamic verification of actual effects, effectively solves the core problem of ensuring data authenticity in ecological compensation by establishing a complete technical chain of "data collection - weight adjustment - effect verification - rule optimization."

[0056] Step S3 of this invention involves calculating the improvement in ecological function. Specifically, this step achieves precise quantification of ecosystem service functions by hierarchically processing the heterogeneous data from steps S1 and S2. For the auxiliary dataset provided in step S1, the system performs differentiated preprocessing: remote sensing image data undergoes vegetation index inversion processing to extract the NDVI time-series variation curves for each plot of land converted from farmland to forest; soil data is spatially interpolated to generate a watershed-wide uniform permeability distribution raster; and rainfall data is statistically analyzed by hydrological response units. These preprocessed environmental factor data provide basic parameter support for the ecological model.

[0057] For the weighted optimization data passed in step S2, the system performs data weight fusion calculation: the declared area of ​​the land parcels converted from farmland to forest is multiplied by the corresponding weight value to generate the effective implementation area reflecting the data's credibility; the design parameters of soil and water conservation projects are discounted according to their weight values. For example, if a project's designed sediment reduction efficiency is 30% and its weight is 0.8, then 24% is used as the actual calculation parameter. This fusion ensures that the contribution of low-credibility data is reasonably suppressed.

[0058] Based on the above processing results, the system initiates multi-model coupled calculations: the water conservation module employs a soil-vegetation-atmosphere transport model, integrating rainfall data, canopy resistance retrieved from NDVI, and soil permeability parameters, calculating water balance grid-by-grid, and outputting an incremental water conservation layer in millimeters per year. The soil and water conservation module runs a modified general soil loss equation, inputting weighted engineering parameters, rainfall erosivity factors, and soil erodibility factors, generating a sediment reduction distribution matrix in tons per hectare per year. All model calculations retain intermediate variable logs, supporting traceability and verification of the calculation process.

[0059] Step S4 of this invention is the compensation decision output and visualization. Specifically, this step transforms the calculation results into a hierarchical decision-making basis through multi-dimensional data fusion. The system first establishes a spatial aggregation analysis pipeline: taking the hydrological response unit as the basic evaluation area, it summarizes the water conservation increment and sediment reduction data of all projects within the unit, combines them with the project weight values ​​from step S2, calculates the unit's comprehensive benefit index, and quantitatively evaluates the ecological benefit level of each unit. The calculation formula is as follows:

[0060]

[0061] In the formula, I j This represents the comprehensive benefit index of the j-th unit;

[0062] exp() represents the natural exponential function;

[0063] Q j This represents the normalized value of the water conservation increment in the j-th unit;

[0064] Sj This represents the normalized value of the sediment reduction in the j-th unit;

[0065] w1 represents the weight value of the water conservation index;

[0066] w2 represents the weight value of the sediment reduction index;

[0067] ln() represents the natural logarithm function.

[0068] Based on the unit comprehensive benefit index, the system constructs a complete decision support system that includes spatial visualization, numerical analysis and process traceability. Specifically, in the spatial dimension, the system transforms the comprehensive benefit index of each unit into an intuitive watershed compensation heat map, clearly displays the priority level of different areas through color gradients, and seamlessly overlays it with basic geographic information to provide managers with spatial decision-making basis.

[0069] In terms of numerical dimensions, the system automatically generates structured compensation analysis reports, organically combining the comprehensive benefit index with key indicators such as weight values ​​and funding allocation recommendations. This supports in-depth analysis from multiple perspectives, including administrative divisions and project types. The reports clearly indicate the comprehensive benefit level of each unit and its corresponding compensation plan recommendations, ensuring that the decision-making basis is transparent and verifiable.

[0070] At the process level, the system has developed a dynamic monitoring dashboard to continuously track the changing trends of the comprehensive benefit index. When abnormal fluctuations in the index of a specific unit are detected, the system can quickly display the full-process data of all projects within that unit, including original monitoring records, weight adjustment history, and model calculation results, providing a complete chain of evidence for tracing the source of the problem.

[0071] In the final output stage, the system presents the decision-making results in multiple dimensions through standardized interfaces: spatial data services provide interactive heatmap browsing, business interfaces transmit structured compensation schemes, and document services generate decision packages containing the complete analysis process. This multi-dimensional output approach not only meets the differentiated needs of different users but also signifies the realization of a complete technical chain from data collection to decision support. The comprehensive benefit index, as the core hub, runs through the entire process of ecological compensation decision-making, ensuring the scientific validity and reliability of the final solution.

[0072] This invention assigns initial weights to upstream engineering data and downstream monitoring data, dynamically adjusts the weight values ​​based on spatiotemporal consistency analysis, and triggers manual verification when the deviation between the engineering implementation effect and the expected downstream monitoring data exceeds a threshold. The rule engine analyzes the difference between the manual verification results and the system prediction, and generates weight-optimized data based on this difference. This effectively solves the problem of disputes over compensation standards caused by insufficient data authenticity in watershed ecological compensation, realizes real-time monitoring of upstream and downstream ecological data and scientific quantitative evaluation of the benefits of protection measures, and provides full-process and verifiable technical support for ecological compensation decision-making.

[0073] The second objective of this embodiment is to provide an ecological environment monitoring system based on ecological function data analysis, comprising a memory and a processor. The computer program stored in the memory is executed by the processor to implement the aforementioned method steps. The system operates collaboratively through a multi-source data acquisition module, a dynamic weight management module, an ecological function calculation module, and a decision support output module.

[0074] The multi-source data acquisition module integrates the watershed management platform interface and IoT sensing units to acquire upstream engineering spatial data and downstream ecological monitoring data in real time. It then combines this data with a satellite remote sensing analysis system to process land cover and environmental parameters, outputting a standardized pre-processed dataset. This module ensures unified access and spatial registration of multi-source heterogeneous data.

[0075] The dynamic weight management module constructs a data credibility assessment system, including a weight initialization unit and a spatiotemporal analysis engine. The system dynamically adjusts data weight values ​​based on the correlation between project implementation effects and downstream responses. When the monitored data deviates significantly from the expected target, a manual verification process is automatically initiated, forming a closed-loop feedback mechanism for weight optimization.

[0076] The ecological function calculation module injects weight information into the ecological model parameters through the data fusion unit, driving parallel computation of the water conservation and soil and water conservation function models. The module incorporates a cross-validation mechanism to ensure consistency between the model output and field monitoring data, providing credibly validated quantitative indicators for decision analysis.

[0077] The decision support output module transforms the ecological function calculation results into multi-dimensional decision-making products: spatial visualization units generate watershed-level ecological compensation heat maps, the numerical analysis engine outputs structured compensation scheme reports, and the end-to-end traceability system integrates the entire lifecycle of evidence from raw data collection to final decision-making. All results are pushed to users at different levels through standardized interfaces.

[0078] The system's overall architecture employs a distributed design to support high-concurrency processing. The dynamic weight management module achieves sub-second response times, and the decision output module supports interactive visualization. In classic watershed validation, this system significantly improves the efficiency and credibility of ecological compensation accounting, providing a scientific decision-making tool for sustainable watershed development.

[0079] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely preferred examples and are not intended to limit the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. An ecological environment monitoring method based on ecological function data analysis, characterized in that, Includes the following steps: S1. Collect upstream engineering datasets and downstream monitoring datasets, and perform standardized preprocessing to generate preprocessed data packets; S2. Based on the preprocessed data packet, establish a dynamic data credibility evaluation and optimization mechanism, the steps of which are as follows: S2.1 Assign initial confidence weights to the upstream engineering dataset and the downstream monitoring dataset respectively; S2.2 Extract the actual change values ​​of downstream ecological response indicators within the influence range of upstream projects based on the initial credibility weight, and obtain the expected effect values ​​of the corresponding projects; S2.

3. Dynamically adjust the initial credibility weight based on the degree of deviation between the actual change value and the expected effect value; S2.4 Substitute the adjusted weights into the ecological compensation accounting model to generate ecological compensation accounting results. When the deviation between the ecological compensation accounting results and historical compensation data exceeds the preset threshold, generate a structured verification task list. S2.5 Execute the verification tasks in the structured verification task list, update the weight adjustment rule set according to the verification results, generate weight optimization data including the upstream project data table, the abnormal project list and the updated weight adjustment rule set configuration file, and dynamically monitor and compare the implementation effects of upstream and downstream ecological protection measures.

2. The ecological environment monitoring method based on ecological function data analysis according to claim 1, characterized in that: The upstream engineering dataset includes boundary vector data of the Grain for Green project and location attribute tables of the soil and water conservation project, while the downstream monitoring dataset includes water quality indicators and flow data.

3. The ecological environment monitoring method based on ecological function data analysis according to claim 1, characterized in that: The standardized preprocessing includes spatial data unified projection transformation, missing value imputation, and numerical feature normalization processing of the upstream engineering dataset and the downstream monitoring dataset.

4. The ecological environment monitoring method based on ecological function data analysis according to claim 1, characterized in that: The initial credibility weight is assigned based on the authority of the data source and the quality control status of the monitoring equipment.

5. The ecological environment monitoring method based on ecological function data analysis according to claim 1, characterized in that: The weight adjustment is calculated using an effect deviation evaluation model, and the weight adjustment is obtained through the following formula: In the formula, ΔW represents the weight adjustment amount (the final value is directly added to the current weight); α represents the adjustment coefficient (ranging from 0.1 to 0.5); O represents the actual observed rate of change of ecological indicators; E represents the expected effect value of the engineering design.

6. The ecological environment monitoring method based on ecological function data analysis according to claim 2, characterized in that: Based on the weighted optimization data, the declared area of ​​the land parcels for returning farmland to forest is multiplied by the corresponding weight value to generate the effective implementation area.

7. The ecological environment monitoring method based on ecological function data analysis according to claim 2, characterized in that: The design parameters of soil and water conservation projects are discounted according to their weight values.

8. The ecological environment monitoring method based on ecological function data analysis according to claim 1, characterized in that: The ecological benefit level of each unit is quantitatively assessed by calculating the unit comprehensive benefit index. The formula for calculating the unit comprehensive benefit index is as follows: In the formula, I j This represents the comprehensive benefit index of the j-th unit; exp() represents the natural exponential function; Q j This represents the normalized value of the water conservation increment in the j-th unit; S j This represents the normalized value of the sediment reduction in the j-th unit; w1 represents the weight value of the water conservation index; w2 represents the weight value of the sediment reduction index; ln() represents the natural logarithm function.

9. The ecological environment monitoring method based on ecological function data analysis according to claim 8, characterized in that: Based on the unit comprehensive benefit index, a complete decision support system is constructed, which includes spatial visualization, numerical analysis, and process traceability.

10. An ecological environment monitoring system based on ecological function data analysis, characterized in that: Includes a memory and a processor for using the ecological environment monitoring method based on ecological function data analysis as described in any one of claims 1-9.

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