Intelligent assessment system for habitat environment of crested ibis

Abstract

The present application relates to the technical field of crested ibis protection, and discloses an intelligent evaluation system for crested ibis habitat environment. The water quality dynamic evaluation module of the system acquires water quality sensor data, adjusts the proportion of acid-base balancing agent and mineral supplement; the vegetation coverage gradient analysis module compares the vegetation coverage density change rate with the crested ibis habitat demand based on the water quality regulation result and adjusts the repair rate; the soil moisture regulation module calculates the soil moisture content of multiple sampling points according to the vegetation adjustment result and adjusts the water management strategy; the meteorological change prediction and regulation module acquires meteorological data, calculates the future humidity change and adjusts the proportion of water and mineral supplement; the ecological factor linkage optimization module extracts the crested ibis activity frequency and food source data, calculates the diurnal ecological factor supply proportion and adjusts the management time sequence, and finally generates the intelligent evaluation result of the crested ibis habitat environment, so as to realize the accurate and systematic regulation of the crested ibis habitat environment.

Description

Technical Field

[0001] This invention relates to the field of crested ibis conservation technology, specifically to an intelligent assessment system for the crested ibis habitat environment. Background Technology

[0002] As an endangered and rare bird species, the crested ibis is highly sensitive to its habitat environment for survival and reproduction. Ecological factors such as water quality, vegetation cover, soil moisture, and meteorological conditions directly affect its habitat selection, food acquisition, and reproductive success. In recent years, with increased efforts in ecological protection, habitat restoration and management for the crested ibis have been gradually promoted. However, in practice, there are still many limitations in the assessment and regulation of the habitat environment.

[0003] Water quality management in crested ibis habitats largely relies on periodic sampling and testing. This method struggles to monitor water quality changes in real time, and the lack of scientific data to adjust the proportions of pH balancers and mineral supplements often leads to ineffective water quality control, failing to meet the crested ibis's water quality requirements. Regarding vegetation cover management, current technologies primarily focus on static data such as vegetation cover density, failing to consider the impact of water quality changes on vegetation growth or dynamically adjust vegetation restoration rates according to the crested ibis's habitat needs. This mismatch between vegetation cover and the crested ibis's actual habitat requirements impacts their activity space and food sources.

[0004] Traditional methods for regulating soil moisture typically monitor and manage soil moisture content at individual sampling points, neglecting the differences in soil moisture between different sampling points and the matching relationship between water supply rate and soil moisture content. This results in a lack of holistic and precise soil moisture regulation, making it difficult to maintain a stable soil moisture environment, which in turn affects vegetation growth and the survival of crested ibis prey. Regarding meteorological conditions, existing management measures are mostly reactive, taking action only after weather changes occur. They cannot predict future humidity changes based on weather station humidity data and evaporation rates, nor can they adjust the water and mineral replenishment ratios in a timely manner in conjunction with soil moisture and other ecological factors, making it difficult to proactively mitigate the impact of adverse weather conditions on crested ibis habitats.

[0005] In existing technologies, the management of various ecological factors is independent, lacking consideration of the interrelationships between ecological factors such as water quality, vegetation, soil, and meteorology. This makes it impossible to optimize the ecological environment of the habitat as a whole, resulting in an incomplete assessment and unsystematic regulation of the crested ibis habitat environment. Consequently, it is difficult to provide a suitable habitat for the crested ibis in a sustainable manner, which restricts the stable growth of the crested ibis population and the sustainable development of its habitat. Summary of the Invention

[0006] The purpose of this invention is to provide an intelligent assessment system for the habitat environment of the crested ibis, in order to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides an intelligent assessment system for the habitat environment of the crested ibis, the system comprising:

[0008] The habitat water quality dynamic assessment module acquires water quality sensor data, adjusts the ratio of acid-base balancers and mineral supplements, and generates habitat water quality regulation results.

[0009] The vegetation cover gradient analysis module, based on the habitat water quality regulation results, calculates the current vegetation cover density change rate and compares it with the crested ibis habitat requirements to adjust the vegetation restoration rate, thereby obtaining the habitat vegetation cover adjustment results.

[0010] The soil moisture regulation module, based on the habitat vegetation cover adjustment results, calculates the soil moisture content at multiple sampling points, adjusts the water management strategy according to the matching degree between the water supply rate and the soil moisture content, and generates habitat moisture retention results.

[0011] The meteorological change prediction and control module acquires humidity data and evaporation rate from meteorological stations in the crested ibis habitat, calculates future humidity changes, adjusts the water and mineral replenishment ratio based on the habitat moisture retention results, and generates meteorological prediction and control results.

[0012] The ecological factor linkage optimization module obtains the current activity frequency data and food source abundance data of crested ibises from the meteorological forecast and control results, calculates the diurnal ecological factor supply ratio, adjusts the ecological factor management time series according to the supply ratio, and generates intelligent assessment results of the crested ibis habitat environment.

[0013] Preferably, the habitat water quality control results include water pH adjustment values, mineral concentration adjustment ratios, and water neutralizer application rates; the habitat vegetation cover adjustment results include vegetation restoration rate adjustment values, vegetation distribution area allocation ratios, and vegetation growth balance; the habitat water retention results include soil moisture retention index, water permeability assessment values, and soil moisture characteristics classification; the meteorological forecast control results include evaporation rate change trends, water replenishment adjustment coefficients, and diurnal evaporation dynamic correction amounts; and the crested ibis habitat environment intelligent assessment results include diurnal ecological factor supply optimization ratios, ecological factor linkage adjustment coefficients, and crested ibis habitat cycle environment matching index.

[0014] Preferably, the habitat water quality dynamic assessment module includes:

[0015] The water quality change rate calculation submodule acquires water quality sensor data in the waters of the crested ibis habitat, records the water quality index values ​​at each time point, calculates the water quality change rate of the waters, and obtains the dynamic change value of water quality.

[0016] The water quality dynamic threshold setting submodule obtains the crested ibis growth stage data based on the water quality dynamic change value, determines whether the water quality change rate exceeds the stage change threshold based on the water quality demand range of the crested ibis growth stage, sets the water quality dynamic threshold, and obtains the water quality control target range.

[0017] The mineral ratio regulation submodule monitors the current water quality value based on the water quality regulation target range, calculates the deviation between the current water quality value and the water quality regulation target range, adjusts the application ratio of acid-base balancer and mineral supplement, and obtains the habitat water quality regulation result.

[0018] Preferably, the vegetation cover gradient analysis module includes:

[0019] The vegetation density data acquisition submodule acquires the data from the vegetation density sensor in each sampling area of ​​the crested ibis habitat, as well as the density value of the vegetation in the corresponding area. It also summarizes the data according to the sensor deployment area, calculates the density change range at adjacent time points, and correlates the density change range data with the water quality control results of the habitat recorded at the corresponding time point. By comparing the matching degree between the density change range and the mineral ratio, the vegetation density change data is obtained.

[0020] The vegetation density gradient calculation submodule calculates the vegetation density change rate of each layer according to the vegetation density change data, and obtains the vegetation density change information between each region.

[0021] The vegetation restoration and regulation submodule calculates the difference between the current vegetation restoration amount and the target vegetation requirement based on the vegetation density change information between each region. The difference is used as the vegetation restoration regulation amount. The operating rate of the restoration equipment is adjusted according to the vegetation restoration regulation amount to obtain the habitat vegetation cover adjustment result.

[0022] Preferably, the soil moisture regulation module includes:

[0023] The soil moisture data acquisition submodule obtains the habitat vegetation cover adjustment results, calls the moisture sensor data deployed at each sampling point in the crested ibis habitat, and classifies them according to the sampling point depth. By calculating the moisture changes at adjacent time points, it calls the habitat moisture supply rate data at the corresponding time points, analyzes the trend of change between moisture changes and moisture supply rate, and obtains the soil moisture change analysis results.

[0024] Based on the soil moisture change analysis results, the moisture retention calculation submodule calculates the soil moisture retention intensity at depth points and obtains moisture retention distribution information.

[0025] The water stability assessment submodule, based on the water retention distribution information, analyzes the matching degree between the water supply rate and the water retention distribution value according to the habitat water supply rate, analyzes the water retention trend in each soil layer, judges the water retention stability in the habitat, and obtains the habitat water retention results.

[0026] Preferably, the meteorological change prediction and control module includes:

[0027] The humidity change calculation submodule acquires humidity data from meteorological stations, regional microclimate data, and evaporation rate sensor data, calculates the humidity change value for a specified future time period, and generates future humidity change analysis results.

[0028] The evaporation rate fluctuation judgment submodule, based on the analysis results of future humidity changes, calls the evaporation rate sensor data, calculates the evaporation rate change value, determines the direction of evaporation rate fluctuation, and obtains evaporation rate change trend information.

[0029] The water and mineral adjustment submodule calculates the water and mineral replenishment ratio based on the evaporation rate change trend information and habitat moisture retention results, and adjusts the water and mineral replenishment ratio accordingly to generate meteorological forecast adjustment results.

[0030] Preferably, the ecological factor linkage optimization module includes:

[0031] The ecological factor supply ratio calculation submodule, based on the meteorological forecast and control results, obtains the current activity frequency data and food source richness data of the crested ibis, and calculates the diurnal water supply ratio and diurnal mineral supply ratio respectively to obtain the ecological factor supply ratio information;

[0032] The ecological factor supply time series adjustment submodule adjusts the ecological factor supply time series based on the ecological factor supply ratio information, taking into account the diurnal cycle characteristics and the periods when the crested ibis needs water and minerals, and obtains the ecological factor supply time arrangement results;

[0033] The environmental assessment results generation submodule analyzes the ecological factor supply schedule based on the ecological factor supply schedule and the ecological resources of the habitat area, and generates intelligent environmental assessment results for the crested ibis habitat.

[0034] Preferably, the system further includes:

[0035] The habitat data preliminary processing module extracts initial environmental indicator keywords to create search terms, obtains searchable data from the environmental database, classifies it into search datasets, and divides them into preliminary and discardable search datasets based on the number of subsets, discarding the latter.

[0036] The environmental data comparison module receives the preliminary search dataset, sorts it in descending order by the number of subsets, selects the top few as the preferred dataset, calculates its proportion in the preliminary search dataset and compares it with the screening proportion threshold; and based on the proportion result, uses the preliminary search dataset or comparison data as the first search result, analyzes and estimates the impact index and issues a verification instruction.

[0037] When the evaluation index optimization module receives the verification instruction, it performs secondary classification of the preferred dataset according to the database classification, analyzes the evaluation value, and outputs optimization instructions based on historical retrieval trends. The feedback instructions are secondary instructions, which are used to filter and output secondary classification retrieval data.

[0038] The evaluation effect verification module obtains the proportion of the preferred dataset, the estimated impact index, and the number of subsets before and after instruction optimization, and generates the environmental assessment efficiency evaluation coefficient. When the coefficient is lower than the evaluation effect threshold, the evaluation scheme correction strategy is retrieved.

[0039] Preferably, the system further includes:

[0040] The historical environmental data retrieval module retrieves historical environmental records and divides them into zones based on the monitored environmental type and the ecological mode of the crested ibis habitat to obtain environmental monitoring points;

[0041] The ecological network simulation module performs graph neural network simulation based on the ecological factor distribution topology of the crested ibis habitat and constructs a baseline value configuration unit. The input node of the baseline value configuration unit is the ecological factor distribution node, the input is the ecological factor change duration, the output node is the environmental measurement point, and the output is the environmental indicator baseline value.

[0042] The evaluation model configuration module integrates the output mean of multiple benchmark configuration units based on the variance of the change duration of multiple ecological factors, constructs a benchmark configuration model, processes the change duration of the multiple ecological factors, and obtains the benchmark values ​​of environmental monitoring points.

[0043] When the monitoring environment of the first measuring point is inconsistent with the benchmark value of the environmental measuring point, the environmental data verification module performs delayed prompt rule data verification.

[0044] Preferably, the environmental data verification module includes:

[0045] The delayed notification rule unit is configured with thresholds for the duration of inconsistent states and the percentage of inconsistent state data.

[0046] When the monitored data meets the threshold for the duration of the non-consistent state or the threshold for the proportion of the non-consistent state data, the anomaly judgment unit outputs an anomaly execution instruction.

[0047] The continuous monitoring unit continues to collect environmental monitoring data when the monitored data does not meet the threshold for the duration of the non-consistent state and the threshold for the proportion of non-consistent state data.

[0048] Compared with the prior art, the beneficial effects of the present invention are:

[0049] This intelligent assessment system for crested ibis habitat environment, through the establishment of multiple functional modules, enables precise monitoring, dynamic regulation, and coordinated optimization of various ecological factors in the crested ibis habitat, creating a suitable habitat for the bird. Among these modules, the habitat water quality dynamic assessment module can acquire water quality sensor data in real time and adjust the proportions of pH balancers and mineral supplements based on actual water quality conditions. This makes water quality control more targeted and effectively maintains the water quality at a stable level that meets the survival needs of the crested ibis, preventing adverse effects on their drinking water and prey survival due to water quality fluctuations.

[0050] The vegetation cover gradient analysis module, based on water quality control results and combined with the crested ibis's habitat requirements, analyzes the rate of change in current vegetation cover density and adjusts the vegetation restoration rate to ensure that the vegetation cover is compatible with the crested ibis's activity needs. This provides ample space for the crested ibis to move around while also ensuring a suitable habitat for its prey, thus providing a stable food source. The soil moisture regulation module, based on the vegetation cover adjustment results, comprehensively calculates the soil moisture content at multiple sampling points. It optimizes water management strategies based on the matching degree between water supply rate and soil moisture content, enabling holistic and precise control of soil moisture, maintaining a stable soil moisture environment, providing favorable conditions for vegetation growth, and creating a suitable soil environment for the crested ibis's prey.

[0051] The meteorological change prediction and control module acquires humidity data and evaporation rates from weather stations, calculates future humidity changes in advance, and adjusts the water and mineral replenishment ratio based on soil moisture retention results. This proactively addresses potential adverse weather conditions, preventing damage to the habitat ecosystem caused by sudden humidity changes or excessive evaporation, thus ensuring the stability of the crested ibis habitat. The ecological factor linkage optimization module extracts current activity frequency and food source abundance data from meteorological prediction and control results, calculates the diurnal ecological factor supply ratio, and adjusts the management time series. This achieves synergistic optimization of multiple ecological factors, including water quality, vegetation, soil, and meteorology, breaking the limitations of traditional independent management of each factor and comprehensively improving the suitability of the habitat environment.

[0052] Through the collaborative work of its various modules, the entire system forms a complete habitat environment assessment and regulation system. It can respond to changes in various ecological factors in real time, dynamically adjust management strategies, and ensure that the habitat environment is always in a state that meets the survival and reproduction needs of the crested ibis. This helps to promote the stable growth of the crested ibis population and also provides a feasible technical solution for the intelligent management of endangered bird habitats, promoting ecological protection work in a more scientific and efficient direction. Attached Figure Description

[0053] Figure 1 This is a schematic diagram illustrating the working principle of the intelligent assessment system for the habitat environment of the crested ibis described in this invention.

[0054] Figure 2 A diagram illustrating the working principle of habitat water quality regulation results;

[0055] Figure 3 This is a schematic diagram illustrating the working principle of the vegetation cover gradient analysis module.

[0056] Figure 4 This is a schematic diagram illustrating the working principle of the meteorological change prediction and control module. Detailed Implementation

[0057] 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.

[0058] Please see Figure 1 This invention provides an intelligent assessment system for the habitat environment of the crested ibis. The system includes: a habitat water quality dynamic assessment module, a vegetation cover gradient analysis module, a soil moisture regulation module, a meteorological change prediction and control module, and an ecological factor linkage optimization module. Specific implementation methods are as follows:

[0059] The habitat water quality dynamic assessment module acquires water quality sensor data, adjusts the proportions of acid-base balancers and mineral supplements, and generates habitat water quality regulation results. The vegetation cover gradient analysis module, based on the habitat water quality regulation results, calculates the current vegetation cover density change rate and compares it with the crested ibis's habitat needs, adjusting the vegetation restoration rate to obtain habitat vegetation cover adjustment results. The soil moisture regulation module, based on the habitat vegetation cover adjustment results, calculates soil moisture content at multiple sampling points and adjusts water management strategies according to the matching degree between water supply rate and soil moisture content, generating habitat moisture conservation results. The meteorological change prediction and control module acquires humidity data and evaporation rates from meteorological stations in the crested ibis habitat, calculates future humidity changes, and adjusts the water and mineral supplementation ratios based on the habitat moisture conservation results, generating meteorological forecast control results. The ecological factor linkage optimization module acquires current crested ibis activity frequency data and food source richness data from the meteorological forecast control results, calculates the diurnal ecological factor supply ratio, adjusts the ecological factor management time series based on the supply ratio, and generates intelligent environmental assessment results for the crested ibis habitat. Each module is connected through a data interface to enable real-time collection, analysis, and feedback control of environmental indicators.

[0060] Example 1: See Figure 2 The results of habitat water quality control include pH adjustment values, mineral concentration adjustment ratios, and the dosage of neutralizing agents. The process for generating the pH adjustment value is as follows: The system monitors the water's pH in real time using pH sensors deployed in the water, collecting continuous time-series pH values. These monitoring data are compared with a preset pH range suitable for crested ibises, calculating the deviation between the current measured value and the median of the ideal range. Based on the magnitude and direction of the deviation, the system calculates the amount of acidic or alkaline neutralizing agent to be added. This calculation process comprehensively considers factors such as water volume, current water temperature, and water flow velocity, ultimately yielding an accurate pH adjustment value. This value directly guides the setting of the operating parameters for the automatic dosing system.

[0061] The determination of the mineral concentration adjustment ratio relies on data acquisition from multi-parameter water quality monitors, which continuously detect the content of key minerals such as calcium, magnesium, and potassium in the water. The system compares and analyzes the detected mineral concentrations with the optimal mineral ratios required by crested ibises at different growth stages. By establishing a quantitative model for mineral deficiency or excess, the system calculates the proportions of various minerals that need to be supplemented or reduced. This proportion is a multi-dimensional adjustment parameter that considers not only the interactions between various minerals but also the changing trends of the original background mineral values ​​in the water. The final mineral concentration adjustment ratio is output in the form of a numerical matrix, where each element represents the adjustment coefficient for a particular mineral.

[0062] The calculation of the dosage of water neutralizer is a dynamic optimization process. Based on real-time water quality monitoring data, combined with pH adjustment values ​​and mineral concentration adjustment ratios, the system comprehensively calculates the type and quantity of neutralizer to be added. This process considers the chemical properties, dissolution rates, duration of action, and chemical reaction relationships of various neutralizers. The system establishes a correspondence model between the neutralizer dosage and changes in water quality parameters, finding the optimal dosing scheme through iterative calculations. The final dosage data includes the precise grams of each neutralizer, the dosing time interval, and the coordinates of the dosing location; this data is directly transmitted to the automatic dosing device for execution.

[0063] The results of habitat vegetation cover adjustment include vegetation restoration rate adjustment values, vegetation distribution area ratios, and vegetation growth evenness. The vegetation restoration rate adjustment values ​​are generated based on multi-source remote sensing data and monitoring information from ground-based vegetation sensors. The system acquires the macroscopic distribution of habitat vegetation through high-resolution satellite imagery and UAV aerial photography data, while simultaneously collecting microscopic vegetation growth data using ground-deployed vegetation density sensors. After data fusion processing, the system calculates the current rate of vegetation cover change and compares this rate with the ideal vegetation cover required for the crested ibis habitat. Based on the comparison results, the system dynamically adjusts the pace of vegetation restoration work; this adjustment is reflected in the setting of mechanical equipment operating parameters and the intensity of manual maintenance work.

[0064] The calculation of vegetation distribution area allocation ratio involves spatial analysis technology using Geographic Information System (GIS). The system divides the habitat into several ecological units, each with its own vegetation configuration requirements. By analyzing environmental factors such as soil characteristics, hydrological conditions, and light intensity in each unit, and combining this with data on the crested ibis's activity preferences in different regions, the system calculates the optimal vegetation type distribution scheme. This allocation ratio is a multi-dimensional spatial allocation parameter that specifies the area proportion of different vegetation types in each ecological unit, as well as the spatial configuration relationships between different vegetation types. This parameter guides the specific implementation of vegetation restoration projects, including the layout planning of seedling planting and the density control of seed sowing.

[0065] Assessing vegetation growth evenness is a comprehensive process involving the calculation of indicators. The system continuously monitors the growth status of different regions and vegetation types, collecting growth data across multiple dimensions, including plant height, canopy size, and leaf area index. After standardization, the system uses statistical methods to calculate the coefficient of variation and dispersion of vegetation growth in each region, thereby assessing the overall balance of vegetation growth across the habitat. The growth evenness index reflects the overall effectiveness of vegetation restoration efforts and guides adjustments to subsequent maintenance measures, such as differentiated regional allocation of fertilizer application and zoned regulation of irrigation water.

[0066] Habitat moisture retention results include the soil moisture retention index, water permeability assessment, and soil moisture characteristics classification. The soil moisture retention index is calculated based on monitoring data from a distributed soil moisture sensor network. These sensors are deployed in soil profiles at different depths to continuously record changes in soil moisture content. The system calculates the water retention capacity and loss rate in the soil by analyzing the characteristics of the water content change curve over time. The retention index is a comprehensive parameter that reflects the combined effects of soil water-holding capacity, water evaporation loss rate, and plant water uptake; this index is used to assess the soil moisture retention status of the habitat.

[0067] The determination of water permeability assessment values ​​is achieved through a combination of infiltration tests and model calculations. Standardized soil infiltration tests are conducted in typical areas to measure the rate and total amount of water entering the soil. Simultaneously, a soil water movement model is established using soil texture analysis data and organic matter content data. By comparing and correcting the measured data with the model calculation results, the system obtains an accurate water permeability assessment value. This assessment value reflects the ease with which the soil allows water to pass through, and is used to guide the design of irrigation systems and the management of rainfall runoff.

[0068] Soil moisture content classification is based on detailed soil profile survey data. The system obtains physical property parameters of soil layers at different depths through soil sampling and analysis, including soil bulk density, porosity, and field capacity. Using these parameters, the system employs cluster analysis to classify soil layers by their moisture content, dividing the soil profile into different hydrological functional layers. Each soil layer has its own water retention and movement characteristics. This classification provides a foundation for precise water management and helps in developing differentiated irrigation and drainage measures.

[0069] The meteorological forecast and control results include the evaporation rate trend, water replenishment adjustment coefficient, and diurnal evaporation dynamic correction. The analysis of the evaporation rate trend is based on meteorological station monitoring data and numerical weather prediction products. The system collects observational data on meteorological elements such as temperature, humidity, wind speed, and sunshine duration, and uses evaporation calculation models such as the Penman formula to calculate the actual evaporation rate. Simultaneously, combined with weather forecast information, it predicts the evaporation rate trend over a future period. This trend is output in time series form, showing the expected direction and magnitude of the evaporation rate change.

[0070] The calculation of the water replenishment adjustment coefficient is a multi-factor comprehensive decision-making process. The system comprehensively considers information such as real-time soil moisture content, vegetation water demand characteristics, evaporation rate prediction, and rainfall probability forecast. By establishing a water balance equation, it calculates the amount of water replenishment required to maintain ideal soil moisture conditions. This replenishment amount is converted into an adjustment coefficient, which reflects the degree and direction of adjustment needed for the current water management strategy and is used to guide the water quantity control of the irrigation system.

[0071] The determination of the diurnal evaporation dynamic correction focuses on the diurnal variation characteristics of the evaporation process. A systematic analysis of the diurnal variation pattern of the evaporation rate reveals significant differences in the evaporation mechanisms during the day and night. By separating the evaporation contributions from daytime and nighttime, the system calculates the proportional relationship between diurnal and evaporation. Based on this proportional relationship, the total evaporation forecast is spatiotemporally allocated to obtain more accurate diurnal and nighttime evaporation forecasts. This correction is used to optimize irrigation scheduling, achieving an effective match between water replenishment and evaporation loss.

[0072] The intelligent environmental assessment results for crested ibis habitats include the diurnal ecological factor supply optimization ratio, the ecological factor linkage adjustment coefficient, and the crested ibis habitat cycle environmental matching index. The calculation of the diurnal ecological factor supply optimization ratio is based on in-depth research into the behavioral ecology of crested ibises. The system collects data on the diurnal activity patterns of crested ibises through technologies such as infrared camera monitoring and GPS tracking, analyzing their behavioral characteristics such as drinking and foraging at different times. Combining this behavioral data with the diurnal variation patterns of environmental factors, the system calculates the optimal time ratio for the supply of ecological factors such as water and minerals. This optimization ratio ensures that the supply of ecological factors and the actual needs of crested ibises are optimally matched in time.

[0073] The generation of ecological factor linkage adjustment coefficients focuses on the interaction relationships between various environmental factors. Through long-term monitoring data accumulation, the system establishes correlation models among factors such as water, minerals, vegetation, and soil. When a factor changes, the system can predict its impact on other factors and calculate the corresponding adjustment coefficient. This coefficient is a multi-dimensional adjustment parameter that specifies the magnitude and direction of adjustment required for other related factors when a factor changes, maintaining the coordination among all elements of the entire ecosystem.

[0074] The assessment of the crested ibis habitat lifecycle environmental matching index employs a multi-indicator comprehensive evaluation method. Environmental demand data for different life stages of the crested ibis, including breeding, molting, and migration, were systematically collected to establish a complete environmental suitability evaluation index system. By monitoring the degree of conformity between the current environmental status and the requirements of each indicator, a weighted scoring method was used to calculate the environmental matching index. This index reflects the degree to which habitat environmental quality supports the crested ibis throughout its complete life cycle and is used to guide long-term planning and improvement of habitat management.

[0075] Example 2: See Figure 3The habitat water quality dynamic assessment module includes a water quality change rate calculation submodule, a water quality dynamic threshold setting submodule, and a mineral ratio control submodule. The water quality change rate calculation submodule collects data through a network of multi-parameter water quality monitoring buoys deployed in the crested ibis habitat waters. These buoys are equipped with pH sensors, conductivity probes, dissolved oxygen sensors, and temperature sensors, collecting water quality parameters at a frequency of once per minute. The system records water quality index values ​​at each time point, including key parameters such as pH, mineral content, and turbidity. Using time series analysis methods, the changes in water quality indicators between adjacent time points are calculated, and then divided by the time interval to obtain the change rate. These calculations consider not only instantaneous changes but also hourly averages and daily trends, thus obtaining a comprehensive water quality dynamic change value that reflects the real-time fluctuation characteristics of the aquatic environment.

[0076] The dynamic water quality threshold setting submodule accesses the crested ibis biological database to obtain physiological requirement data for different growth stages. Chicks require a near-neutral aquatic environment, while adult birds require a specific range of mineral content during their breeding season. Based on this requirement data, the system establishes a phased water quality requirement range model and compares the real-time calculated rate of water quality change with these phased thresholds. When the detected rate of water quality change approaches or exceeds the threshold range, the system automatically adjusts the dynamic threshold setting. This adjustment process considers external factors such as seasonal changes and rainfall effects to ensure that the threshold setting both meets the crested ibis's growth needs and adapts to environmental changes. The final water quality control target range is a dynamically changing range that is automatically optimized and adjusted based on real-time monitoring data.

[0077] The mineral ratio control submodule acquires the current water quality value of the water area through a real-time monitoring network based on the target water quality control range. The system calculates the deviation between the current water quality value and the target range, including not only the absolute deviation value but also the duration and trend of the deviation. For pH control, the system calculates the type and quantity of acidic or alkaline neutralizing agents to be added based on the direction and magnitude of the deviation. The control of mineral supplements is more complex, requiring analysis of the proportional relationships between various minerals to ensure that the content of various minerals reaches a balanced state after supplementation. During the control process, the system refers to historical control effect data to optimize the application ratio of supplements. The final habitat water quality control results include a detailed chemical dosing plan and implementation schedule.

[0078] The vegetation cover gradient analysis module comprises a vegetation density data acquisition submodule, a vegetation density gradient calculation submodule, and a vegetation restoration and regulation submodule. The vegetation density data acquisition submodule utilizes a multi-platform observation system, including satellite remote sensing, UAV aerial photography, and a ground-based sensor network. High-resolution satellite imagery provides extensive vegetation distribution information, UAVs equipped with multispectral cameras perform detailed monitoring, and ground-based vegetation density sensors collect detailed data for local areas in real time. This data is fused and processed to calculate the vegetation density value for each sampling area. The system categorizes and summarizes the data according to the areas where the sensors are deployed. By comparing monitoring data from adjacent time points, the system calculates the magnitude of vegetation density changes and correlates these changes with the corresponding water quality control results at those time points.

[0079] The vegetation density gradient calculation submodule employs a spatial stratification analysis method, dividing the habitat into different layers based on ecological function, such as core areas, buffer zones, and edge zones. Within each layer, the system calculates the spatial distribution characteristics and temporal variation trends of vegetation density. By establishing a three-dimensional gradient model, the system analyzes the variation patterns of vegetation density in both the horizontal and vertical directions. This calculation process considers the influence of environmental factors such as topographic relief, soil type, and water distribution, ultimately obtaining vegetation density variation information for each region. This information reflects the spatiotemporal dynamic characteristics of vegetation distribution.

[0080] The vegetation restoration regulation submodule is based on vegetation density change information combined with an ecological demand model for crested ibis habitat. The system first determines the target vegetation density requirements for different areas, based on factors such as crested ibis foraging behavior, nest site selection preferences, and concealment needs. By comparing the current vegetation density with the target density, the system calculates the amount of vegetation to be restored and the restoration urgency. Based on these calculations, the system formulates differentiated restoration strategies: rapid replanting is used in areas with severely insufficient density; natural regeneration is supplemented by artificial promotion in areas with mild insufficiency; and maintenance management is implemented in areas with suitable density. The operating rate of the restoration equipment is dynamically adjusted according to the area of ​​the restoration zone, the current vegetation conditions, and the target requirements to ensure efficient progress of the restoration work.

[0081] Data exchange and collaborative operation between modules are achieved through a central data processing platform. The control results generated by the water quality dynamic assessment module are transmitted in real time to the vegetation cover analysis module as background parameters for vegetation density change analysis. Simultaneously, vegetation cover change data is also fed back to the water quality assessment module to optimize water quality control strategies. This two-way data flow ensures the coordinated operation of the two modules, achieving unified management of the aquatic and vegetation environments. All data acquisition, transmission, and processing processes are based on a unified spatiotemporal benchmark, guaranteeing the consistency and comparability of data from different sources.

[0082] For water quality data, a multi-sensor cross-validation method was employed to ensure the accuracy of the monitoring data; for vegetation data, the reliability of remote sensing monitoring results was verified through ground-based field surveys. All computational models underwent historical data validation and expert evaluation to ensure their scientific validity and practicality. The system also established an anomaly data processing mechanism, capable of identifying and eliminating abnormal data caused by sensor malfunctions, weather interference, and other factors, ensuring the reliability of the analysis results. The entire implementation process embodies the characteristics of multi-technology integration, multi-data integration, and multi-module collaboration. Through the comprehensive application of advanced sensing technology, spatial information technology, data analysis technology, and automatic control technology, refined management and intelligent regulation of the water quality and vegetation environment of the crested ibis habitat have been achieved.

[0083] Example 3: See Figure 4 The soil moisture regulation module includes a soil moisture data acquisition submodule, a moisture retention calculation submodule, and a moisture stability assessment submodule. The soil moisture data acquisition submodule acquires data through a stratified soil monitoring network deployed in the crested ibis habitat. This network consists of TDR-type soil moisture sensors buried at depths of 10cm, 30cm, and 60cm, collecting volumetric water content data every 15 minutes. When the system accesses this stratified data, it first establishes a three-dimensional coordinate system based on depth, binding the spatial location of each sampling point with depth information. By comparing the moisture data at the same location at adjacent time points, the gradient of change is calculated, and the corresponding water supply rate record is correlated. The analysis process employs a time-series cross-correlation algorithm to identify the hysteretic response relationship between water supply events and soil moisture fluctuations, establishing a moisture transport characteristic matrix. For example, if monitoring shows that after 20mm / h irrigation, the moisture content of the 10cm layer increases by 8% within 1 hour, while the 30cm layer only increases by 3% after 3 hours, the system determines that water infiltration is obstructed.

[0084] Based on the above analysis results, the water retention calculation submodule constructs a water retention intensity model for each depth point. This model comprehensively considers soil texture parameters, organic matter content, and root density. A depth attenuation factor is introduced into the calculation process, with deeper soil layers contributing more to water retention than the surface layer. The final generated water retention distribution information is expressed in raster layer form, with each raster cell containing a water-holding capacity score for different depths. The scoring criteria are as follows:

[0085]

[0086] in: The water-holding capacity index represents the water-holding capacity at time t. The volumetric water content at depth d (in %). The duration of water retention (unit: hours). The depth attenuation coefficient (taken as 0.05-0.2 depending on soil type) Soil depth (unit: meters) This represents the total number of monitoring layers. This formula quantifies the water retention characteristics within a profile, such as sandy soil layers. When the value is 0.15, the water retention contribution at a depth of 60cm is 2.7 times that of the surface layer.

[0087] The water stability assessment submodule performs a matching analysis between water retention distribution information and real-time water supply rates. The system establishes a three-dimensional water balance model, dividing the habitat into 50m×50m grids spatially, hourly units temporally, and 10cm intervals vertically. By comparing the difference between water supply input and soil water holding capacity output within each grid cell, the system analyzes the redistribution process of water in the soil layer. When the water holding capacity of the 30-60cm layer in a grid is consistently below the theoretical value by 20%, the system determines that water leakage exists in that area; when the surface water holding capacity exceeds the theoretical value by 30% for 6 consecutive hours, the system determines the risk of surface water accumulation. The final habitat water retention results include a water holding stability grading map, marking the geographical distribution of high leakage areas, drought-prone areas, and water-saturated areas.

[0088] The meteorological change prediction and control module includes a humidity change calculation submodule, an evaporation rate fluctuation judgment submodule, and a moisture and mineral adjustment submodule. The humidity change calculation submodule integrates multi-source meteorological data: temperature and humidity data uploaded minute by minute from automatic weather stations, atmospheric water vapor content retrieved from microwave radiometers, and cloud top temperature information provided by meteorological satellites. The system uses a time-series prediction model, taking the current humidity H as a baseline and combining it with temperature, pressure, and humidity parameters from the numerical weather prediction for the next 48 hours, to calculate the hourly humidity change ΔH. The calculation process incorporates a habitat micro-topography correction factor; when the monitoring area is located in a valley, the nighttime inversion effect increases the humidity prediction by 15%; when located at the top of a slope, the enhanced wind effect decreases the prediction by 8%. The generated future humidity change analysis results include a high-resolution humidity field animation, which can display the spatial shift of hourly humidity isopleths.

[0089] The evaporation rate fluctuation judgment submodule calls upon data from the evaporation monitoring system, which consists of a 20cm diameter evaporating dish, a weighing automatic recorder, and an eddy covariance flux tower. This is achieved by analyzing the hourly weight changes of the evaporating dish. By combining data from water surface temperature sensors, the actual evaporation rate is calculated. Simultaneously, the theoretical evaporation rate was calculated based on the Penman formula. ,Establish The ratio sequence is used. When the ratio is below 0.7 for 3 consecutive hours, evaporation is considered to be in a suppressed phase; when the ratio exceeds 1.3 for 2 consecutive hours, evaporation is considered to be enhanced. This submodule pays special attention to diurnal differences: daytime evaporation fluctuations are mainly affected by solar radiation, with fluctuations reaching 0.8 mm / h; nighttime evaporation is dominated by wind speed, with fluctuations controlled within 0.2 mm / h. The output evaporation rate trend information includes an evaporation curve for the next 24 hours, marking periods of strong evaporation (usually 10:00-15:00) and periods of weak evaporation.

[0090] The water and mineral adjustment submodule constructs a multi-objective optimization model based on evaporation trend information and water retention results. This model uses a soil moisture stability grading map as a constraint, setting a water replenishment priority coefficient of 1.2 in drought-prone areas and 0.8 in high-permeability areas. Evaporation prediction data is converted into water loss, which is then added to the current soil water deficit to calculate the total water demand. Mineral supplementation The system will then dynamically adjust based on historical control data from the water quality module: when persistently low mineral concentrations are detected in the preceding period, Increase compensation amount When a mineral excess is detected, Reduce by 30%. The final water and mineral replenishment ratio is output in the form of a decision matrix, where rows represent different regions, columns represent time windows, and matrix elements are ( , (A binary pair.) For example, the supplementary plan for the core area at noon might be (5L / m², 0.3g / L), which is transmitted to the smart irrigation system via a wireless network for execution.

[0091] Data fusion between modules is achieved through a spatiotemporal matching engine. Soil moisture and meteorological data are uniformly converted to the WGS84 coordinate system, and timestamps are synchronized to UTC standard time. When processing vegetation cover adjustment results, the system automatically extracts water-related parameters (such as root depth and leaf transpiration coefficient) as correction factors for water retention calculations. The generated meteorological forecast control results contain a set of geocoded control instructions. Each instruction includes parameters such as the target area's GPS range, execution time window, water replenishment amount, and mineral concentration, forming a complete closed-loop control chain.

[0092] Example 4: The ecological factor linkage optimization module includes an ecological factor supply ratio calculation submodule, an ecological factor supply time series adjustment submodule, and an environmental assessment result generation submodule. The ecological factor supply ratio calculation submodule receives meteorological forecast control results and extracts humidity change trends and mineral replenishment ratio data from them. Simultaneously, it connects to the crested ibis behavior monitoring system to obtain activity frequency heatmaps recorded by infrared cameras and aerial photography data of food sources from drones. The system divides the daytime period into 6:00-18:00 and the nighttime period into 18:00-6:00 the next day, respectively, and counts the frequency of crested ibises appearing around the water area (times / hour) and food source biomass (g / m²) in both periods. By establishing a day-night demand weight model, the water supply ratio factor α = daytime activity frequency / nighttime activity frequency, and the mineral ratio factor β = daytime food richness / nighttime food richness are calculated. When α > 1.5, daytime water demand is determined to be dominant, and a control scheme with a daytime water supply ratio of 70% is generated; when β < 0.8, the nighttime mineral replenishment intensity is increased to 60% of the total.

[0093] The ecological factor supply time series adjustment submodule optimizes the time window based on the aforementioned proportional factors and microclimate monitoring data. The system calls upon historical meteorological databases to analyze the peak evaporation periods (typically 13:00-15:00) and low humidity periods (typically 03:00-05:00) of the same period over the past five years. To avoid high-temperature evaporation losses, water replenishment in the core area is adjusted to the period of weaker evaporation (07:00-09:00); considering the tendency of minerals to settle, their replenishment is scheduled for the period of faster flow (11:00-13:00). The time series optimization results form a scheduling instruction table, which includes the coordinates of the execution area, start time, duration, ecological factor type, and distribution amount (see Table 1).

[0094] Table 1: Schedule of Ecological Factor Supply.

[0095] Area code Execution period Ecological factor types Distribution volume Duration A-03 07:00-08:30 Moisture 15L / m² 90 minutes B-12 11:20-12:00 minerals 0.45g / L 40 minutes C-07 16:00-16:45 Moisture + Minerals 8L / m² + 0.2g / L 45 minutes

[0096] The environmental assessment results generation submodule integrates the time schedule and habitat resource distribution map. The system calls upon the water distribution layer, vegetation type layer, and soil permeability coefficient layer from the geographic information system to assess the ecological carrying capacity of each scheduling unit. When a unit simultaneously contains highly permeable sandy soil (permeability coefficient > 20 cm / h) and shallow root vegetation, the water replenishment amount for that unit is reduced by 20%; when the unit contains deep water areas (water depth > 1.5 m), the mineral replenishment concentration is increased by 0.1 g / L. The final generated intelligent environmental assessment results for the crested ibis habitat include a three-dimensional spatiotemporal climate control map, annotating the optimized supply parameters for different areas at different time periods.

[0097] The system's extended modules include a habitat data preliminary processing module, an environmental data comparison module, an assessment indicator optimization module, and an assessment effect verification module. The habitat data preliminary processing module extracts initial search terms from a cloud-based environmental database, including 12 core indicators such as "soil moisture content," "vegetation cover," and "water pH." Monitoring data from the past three months was collected via distributed web crawling and categorized by data type into three subsets: real-time sensor data (68%), manual survey data (22%), and remote sensing inversion data (10%). After discarding the remote sensing inversion data subset, the first two categories were retained as the initial search dataset.

[0098] The environmental data comparison module prioritizes the initial search datasets: sensor data is sorted by update frequency (updates per minute > updates per hour > updates per day), and manual survey data is sorted by spatial density (10 points per hectare > 5 points per hectare). The top 80% of the data is selected to form the preferred dataset, and its proportion in the initial search dataset is calculated (e.g., 82%). This proportion is compared with a preset screening threshold of 0.65. If the threshold is exceeded, the preferred dataset is directly output as the initial search result. The system automatically analyzes the estimated impact index of this dataset on the habitat suitability of the crested ibis. For example, if the correlation between soil moisture content changes and nest site selection reaches 0.73, a data verification command is sent to the management terminal.

[0099] After receiving the verification instruction, the evaluation index optimization module performs secondary classification on the selected dataset. Data is reorganized according to ecological factor types: hydrological (water content, permeability coefficient), vegetation (coverage, biomass), and meteorological (humidity, evaporation). Dynamic evaluation values ​​are calculated for each data type. For hydrological data, a weighted average method is used (0.6 for deep soil, 0.4 for shallow soil), and for vegetation data, spatial interpolation is used to generate a continuous distribution surface. Based on historical search trends (60% of queries in the past month involved water indicators), an optimization instruction is output requiring an increase in the hydrological data collection frequency to 15 minutes / time. This instruction is fed back to the sensor network to form a secondary instruction, triggering the monitoring equipment to adjust its sampling strategy.

[0100] The evaluation and verification module tracks key parameters throughout the process: the percentage of the preferred dataset (82%), the estimated impact index (0.73), the number of subsets before optimization (1200 sets), and the number of subsets after optimization (980 sets). The environmental assessment efficiency coefficient η is calculated as: η = (number of subsets after optimization × estimated impact index) / (number of subsets before optimization × data acquisition energy consumption coefficient). When η < 0.48 (evaluation effect threshold), the preset correction strategy library is automatically retrieved, and the "increase mobile monitoring device density" option is selected, dispatching a drone equipped with a portable sensor to supplement data sampling in blind spots.

[0101] The modules collaborate via a chain of instructions. When the ecological factor linkage optimization module generates assessment results, it automatically triggers the habitat data preliminary processing module to initiate a new round of data collection. The optimized dataset output by the environmental data comparison module is synchronously shared with the assessment index optimization module, forming a closed-loop optimization mechanism. All data exchanges are encapsulated in JSON-LD format to ensure the accuracy of semantic transmission. The time series adjustment results are sent to the field execution equipment via the LoRaWAN network, and the actual execution rate reported by the equipment (e.g., 92% completion rate of water replenishment) is sent back to the assessment effect verification module to update the efficiency assessment coefficient calculation parameters.

[0102] Example 5: Description of the implementation of the historical environmental data retrieval module, ecological network simulation module, evaluation model configuration module, and environmental data verification module. The historical environmental data retrieval module distinguishes between three environmental types—wetland, woodland, and farmland ecotone—based on the actual monitoring needs of the crested ibis habitat. The system connects to the provincial ecological database, retrieving historical environmental records for the same period over the past five years, including parameters such as monthly average water level fluctuations, vegetation greenness index, and soil organic matter content. The retrieval process incorporates the ecological modal characteristics of the habitat: the wetland modality focuses on hydrological parameters, the woodland modality focuses on tree canopy closure, and the farmland ecotone analyzes the frequency of human activity disturbance. The system is spatially partitioned into 1-square-kilometer grids, with the center point of each grid designated as an environmental monitoring point. Ultimately, 56 environmental monitoring points are delineated in typical habitats, each bound to latitude, longitude, and altitude coordinates.

[0103] The ecological network simulation module constructs a simulation architecture based on graph neural networks. The system abstracts the habitat as a topological graph containing nodes and edges. Nodes represent the distribution points of ecological factors (e.g., water area nodes, vegetation patch nodes, soil sampling point nodes), and edges represent the interaction relationships between factors (e.g., water-vegetation transpiration correlation, soil-mineral adsorption correlation). The input layer sets 72 ecological factor distribution nodes, with each node inputting the duration data of ecological factor changes over the past 24, 48, and 72 hours. The output layer corresponds to 56 environmental monitoring points, outputting the baseline values ​​of environmental indicators calculated through simulation. The baseline value configuration unit adopts a three-layer graph convolutional network structure. The first layer aggregates factor change data from adjacent nodes, the second layer calculates the cross-media interaction strength, and the third layer generates the baseline values ​​for the monitoring points. For example, the water area node receives mineral migration duration data from the upstream soil node, and outputs the pH baseline value of the downstream water area after convolution operation.

[0104] The evaluation model configuration module handles the fluctuation characteristics of ecological factor changes over time. The system collects a 30-day duration sequence of changes at each location and calculates the variance over time. When the variance of moisture change duration at a location exceeds 15 hours, the hydrological state at that location is considered unstable, and the number of baseline value configuration units needs to be increased. The model integration uses a dynamic weighting method: units with variance less than 5 hours have a weight of 0.8, those with variance between 5 and 10 hours have a weight of 0.5, and those with variance greater than 10 hours have a weight of 0.3. The final environmental monitoring point baseline value is determined by the weighted average of the outputs from multiple units. For example, the baseline value for a soil monitoring point is calculated as 12.6% after three units output 12.3%, 11.8%, and 13.1% respectively, based on their weights. The model automatically updates the baseline value every 6 hours to adapt to dynamic environmental changes.

[0105] The environmental data verification module compares the monitoring data with the baseline value in real time. When the real-time soil moisture content of the first monitoring point is 10.2%, while the baseline value output by the model is 13.5%, the system determines that the monitoring point is in an inconsistent state. The delay prompt rule unit presets two thresholds: a duration threshold of 0.5 hours for inconsistent state and a data volume percentage threshold of 20% for inconsistent state. The system starts a continuous monitoring mechanism, collecting data from the monitoring point every 5 minutes, while simultaneously expanding monitoring to 3 surrounding related points. If the monitoring point continuously deviates from the baseline value for 6 consecutive monitoring cycles (30 minutes), the duration threshold is triggered; if more than one surrounding point (accounting for 33%) deviates simultaneously, the data volume percentage threshold is triggered.

[0106] When the threshold is triggered, the anomaly detection unit initiates root cause analysis. The system retrieves relevant events for the monitoring point in the past 24 hours: the water level sensor recorded a 0.5-meter drop in water level 3 hours ago, and vegetation monitoring showed a 15% increase in transpiration from surrounding reeds. Based on this data, the unit determines that the drop in water level caused an abnormal soil moisture content and outputs an anomaly execution instruction set including the "Initiate Emergency Water Replenishment" command. If the threshold is not reached, the continuous monitoring unit continues to collect data and dynamically update the deviation curve. When the deviation shows a downward trend for three consecutive cycles, it is determined that the environment is self-recovering, and the monitoring status is maintained without triggering any commands.

[0107] The environmental monitoring point coordinates output by the historical environmental data retrieval module are synchronized to the evaluation model configuration module as the spatial benchmark for benchmark value calculation. When the benchmark values ​​generated by the ecological network simulation module are transmitted to the environmental data verification module, confidence level labels are automatically attached: benchmark values ​​with more than 20 input nodes are marked as high confidence (confidence value > 0.9), and those with fewer than 10 nodes are marked as requiring verification (confidence value < 0.6). When the verification module receives a low-confidence benchmark value, it automatically requests the simulation module to increase the number of input nodes and recalculate. All abnormal events in the monitoring data are recorded to form a knowledge base, and the threshold parameters of the delay prompt rules are automatically updated weekly, such as adjusting the duration threshold of the dry season from 0.5 hours to 0.3 hours.

[0108] Before the rainy season, the historical environmental data retrieval module automatically increases the density of hydrological monitoring points, adding one monitoring point every 500 meters in the floodplain area. The ecological network simulation module adjusts the weight of input nodes according to seasonal characteristics: increasing the weight of rainfall nodes to 0.7 during the rainy season and increasing the weight of groundwater nodes to 0.6 during the dry season. The evaluation model configuration module activates an emergency mode during extreme weather warnings, increasing the benchmark update frequency from 6 hours to 1 hour. During red rainstorm warnings, the environmental data verification module temporarily increases the threshold for the proportion of inconsistent data from 20% to 35% to avoid frequent false alarms. The entire system achieves accurate generation of habitat environmental benchmarks and scientific handling of abnormal states through the closed-loop operation of these four modules.

[0109] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0110] 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 alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An intelligent assessment system for the habitat environment of the crested ibis, characterized in that, The system includes: The habitat water quality dynamic assessment module acquires water quality sensor data, adjusts the ratio of acid-base balancers and mineral supplements, and generates habitat water quality regulation results. The vegetation cover gradient analysis module calculates the rate of change of the current vegetation cover density based on the habitat water quality regulation results, and adjusts the vegetation restoration rate in combination with the crested ibis's habitat needs to obtain the habitat vegetation cover adjustment results. The soil moisture regulation module, based on the habitat vegetation cover adjustment results, calculates the soil moisture content at multiple sampling points, adjusts the water management strategy according to the matching degree between the water supply rate and the soil moisture content, and generates habitat moisture retention results. The meteorological change prediction and control module acquires humidity data and evaporation rate from meteorological stations in the crested ibis habitat, calculates future humidity changes, adjusts the water and mineral replenishment ratio based on the habitat moisture retention results, and generates meteorological prediction and control results. The ecological factor linkage optimization module, based on the meteorological forecast and control results, combined with the activity frequency heat map and food source drone aerial photography data, obtains the current activity frequency data and food source richness data of crested ibises, calculates the diurnal ecological factor supply ratio, adjusts the ecological factor management time series according to the supply ratio, and generates intelligent assessment results of the crested ibis habitat environment.

2. The intelligent assessment system for the habitat environment of the crested ibis according to claim 1, characterized in that, The habitat water quality control results include water pH adjustment values, mineral concentration adjustment ratios, and water neutralizer application rates. The habitat vegetation cover adjustment results include vegetation restoration rate adjustment values, vegetation distribution area allocation ratios, and vegetation growth balance. The habitat water retention results include soil water retention index, water infiltration capacity assessment values, and soil moisture characteristics classification. The meteorological forecast control results include evaporation rate change trends, water replenishment adjustment coefficients, and diurnal evaporation dynamic correction amounts. The crested ibis habitat environment intelligent assessment results include diurnal ecological factor supply optimization ratios, ecological factor linkage adjustment coefficients, and crested ibis habitat cycle environment matching index.

3. The intelligent assessment system for the habitat environment of the crested ibis according to claim 2, characterized in that, The habitat water quality dynamic assessment module includes: The water quality change rate calculation submodule acquires water quality sensor data in the waters of the crested ibis habitat, records the water quality index values ​​at each time point, calculates the water quality change rate of the waters, and obtains the dynamic change value of water quality. The water quality dynamic threshold setting submodule obtains the crested ibis growth stage data based on the water quality dynamic change value, determines whether the water quality change rate exceeds the stage change threshold based on the water quality demand range of the crested ibis growth stage, sets the water quality dynamic threshold, and obtains the water quality control target range. The mineral ratio regulation submodule monitors the current water quality value based on the water quality regulation target range, calculates the deviation between the current water quality value and the water quality regulation target range, adjusts the application ratio of acid-base balancer and mineral supplement, and obtains the habitat water quality regulation result.

4. The intelligent assessment system for the habitat environment of the crested ibis according to claim 3, characterized in that, The vegetation cover gradient analysis module includes: The vegetation density data acquisition submodule acquires the data from the vegetation density sensor in each sampling area of ​​the crested ibis habitat, as well as the density value of the vegetation in the corresponding area. It also summarizes the data according to the sensor deployment area, calculates the density change range at adjacent time points, and correlates the density change range data with the water quality control results of the habitat recorded at the corresponding time point. By comparing the matching degree between the density change range and the mineral ratio, the vegetation density change data is obtained. The vegetation density gradient calculation submodule calculates the rate of vegetation density change for each layer based on the vegetation density change data, and obtains the vegetation density change information between each region. The vegetation restoration and adjustment submodule calculates the difference between the current vegetation restoration amount and the target vegetation demand amount based on the vegetation density change information between each region, uses the difference as the vegetation restoration adjustment amount, adjusts the operation rate of the restoration equipment according to the vegetation restoration adjustment amount, and obtains the habitat vegetation cover adjustment result.

5. The intelligent assessment system for the habitat environment of the crested ibis according to claim 4, characterized in that, The soil moisture regulation module includes: The soil moisture data acquisition submodule obtains the habitat vegetation cover adjustment results, calls the moisture sensor data deployed at each sampling point in the crested ibis habitat, and classifies them according to the sampling point depth. By calculating the moisture changes at adjacent time points, it calls the habitat moisture supply rate data at the corresponding time points, analyzes the trend of change between moisture changes and moisture supply rate, and obtains the soil moisture change analysis results. Based on the soil moisture change analysis results, the moisture retention calculation submodule calculates the soil moisture retention intensity at depth points and obtains moisture retention distribution information. The water stability assessment submodule, based on the water retention distribution information, analyzes the matching degree between the water supply rate and the water retention distribution value according to the habitat water supply rate, analyzes the water retention trend in each soil layer, judges the water retention stability in the habitat, and obtains the habitat water retention results.

6. The intelligent assessment system for the habitat environment of the crested ibis according to claim 5, characterized in that, The meteorological change prediction and control module includes: The humidity change calculation submodule acquires humidity data from meteorological stations, regional microclimate data, and evaporation rate sensor data, calculates the humidity change value for a specified future time period, and generates future humidity change analysis results. The evaporation rate fluctuation judgment submodule, based on the analysis results of future humidity changes, calls the evaporation rate sensor data, calculates the evaporation rate change value, determines the direction of evaporation rate fluctuation, and obtains evaporation rate change trend information. The water and mineral adjustment submodule calculates the water and mineral replenishment ratio based on the evaporation rate change trend information and habitat moisture retention results, and adjusts the water and mineral replenishment ratio accordingly to generate meteorological forecast adjustment results.

7. The intelligent assessment system for the habitat environment of the crested ibis according to claim 6, characterized in that, The ecological factor linkage optimization module includes: The ecological factor supply ratio calculation submodule, based on the meteorological forecast and control results, combined with the activity frequency heat map and food source drone aerial photography data, obtains the current activity frequency data and food source richness data of the crested ibis, and calculates the diurnal water supply ratio and diurnal mineral supply ratio respectively to obtain the ecological factor supply ratio information. The ecological factor supply time series adjustment submodule adjusts the ecological factor supply time series based on the ecological factor supply ratio information, taking into account the diurnal cycle characteristics and the periods when the crested ibis needs water and minerals, and obtains the ecological factor supply time arrangement results; The environmental assessment results generation submodule analyzes the ecological factor supply schedule based on the ecological factor supply schedule and the ecological resources of the habitat area, and generates intelligent environmental assessment results for the crested ibis habitat.

8. The intelligent assessment system for the habitat environment of the crested ibis according to claim 7, characterized in that, The system also includes: The habitat data preliminary processing module extracts initial environmental indicator keywords to create search terms, obtains searchable data from the environmental database, classifies it into search datasets, and divides them into preliminary and discardable search datasets based on the number of subsets, discarding the latter. The environmental data comparison module receives the preliminary search dataset, sorts it in descending order by the number of subsets, selects the top few as the preferred dataset, calculates its proportion in the preliminary search dataset and compares it with the screening proportion threshold; and based on the proportion result, uses the preliminary search dataset or comparison data as the first search result, analyzes and estimates the impact index and issues a verification instruction. When the evaluation index optimization module receives the verification instruction, it performs secondary classification of the preferred dataset according to the database classification, analyzes the evaluation value, and outputs optimization instructions based on historical retrieval trends. The feedback instructions are secondary instructions, which are used to filter and output secondary classification retrieval data. The evaluation effect verification module obtains the proportion of the preferred dataset, the estimated impact index, and the number of subsets before and after instruction optimization, and generates the environmental assessment efficiency evaluation coefficient. When the coefficient is lower than the evaluation effect threshold, the evaluation scheme correction strategy is retrieved.

9. The intelligent assessment system for the habitat environment of the crested ibis according to claim 8, characterized in that, The system also includes: The historical environmental data retrieval module retrieves historical environmental records and divides them into zones based on the monitored environmental type and the ecological mode of the crested ibis habitat to obtain environmental monitoring points; The ecological network simulation module performs graph neural network simulation based on the ecological factor distribution topology of the crested ibis habitat and constructs a baseline value configuration unit. The input node of the baseline value configuration unit is the ecological factor distribution node, the input is the ecological factor change duration, the output node is the environmental measurement point, and the output is the environmental indicator baseline value. The evaluation model configuration module integrates the output mean of multiple benchmark configuration units based on the variance of the change duration of multiple ecological factors, constructs a benchmark configuration model, processes the change duration of the multiple ecological factors, and obtains the benchmark values ​​of environmental monitoring points. When the monitoring environment of the first measuring point is inconsistent with the benchmark value of the environmental measuring point, the environmental data verification module performs delayed prompt rule data verification.

10. The intelligent assessment system for the habitat environment of the crested ibis according to claim 9, characterized in that, The environmental data verification module includes: The delayed notification rule unit is configured with thresholds for the duration of inconsistent states and the percentage of inconsistent state data. When the monitored data meets the threshold for the duration of the non-consistent state or the threshold for the proportion of the non-consistent state data, the anomaly judgment unit outputs an anomaly execution instruction. The continuous monitoring unit continues to collect environmental monitoring data when the monitored data does not meet the threshold for the duration of the non-consistent state and the threshold for the proportion of non-consistent state data.

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