An ecological security pattern intelligent simulation optimization system based on reinforcement learning

Abstract

The application relates to the technical field of ecological safety optimization, and discloses an ecological safety pattern intelligent simulation optimization system based on reinforcement learning. An experience pool forming module of the system divides historical optimization strategies into multiple experience layers to form a multilayer experience pool based on the gap between ecological indexes and strategy confidence of the historical optimization strategies. A strategy determining module determines intelligent optimization strategies at each stage based on a set ecological safety target state, and screens the multilayer experience pool to obtain experience optimization strategies in combination with strategy confidence and target matching degree. A strategy adjusting module adjusts the two strategies by using a dynamic mechanism to cope with real-time ecological changes, and determines a target optimization strategy. A simulation optimization module simulates and optimizes the ecological safety pattern based on the target optimization strategy. A state acquisition module acquires an actual state. An experience pool updating module updates the content of the experience layers based on the difference factors between the actual state and the target state, and perfects the multilayer experience pool data. The system can improve the dynamics and adaptability of ecological safety pattern optimization.

Description

Technical Field

[0001] This invention relates to the field of ecological security optimization technology, specifically to an intelligent simulation and optimization system for ecological security patterns based on reinforcement learning. Background Technology

[0002] The construction and optimization of ecological security patterns are important measures to address ecological and environmental changes and maintain regional ecological stability. Their core lies in adjusting the ecosystem's state through scientific strategies to gradually approach predetermined security targets. However, traditional methods for optimizing ecological security patterns often rely on static analysis models. These models are typically based on fixed ecological indicator thresholds and empirical strategy libraries, making it difficult to adapt to the complex dynamic characteristics of ecosystem changes.

[0003] In practical applications, traditional methods face multiple limitations. On the one hand, changes in ecosystem indicators are non-linear and interconnected. For example, changes in vegetation cover may simultaneously affect multiple indicators such as soil erosion rate and habitat quality. Traditional models typically treat each indicator as an independent variable, leading to optimization strategies failing to fully consider the interactions between indicators and thus affecting the accuracy of optimization results. On the other hand, updating traditional strategy libraries relies on manual review and summarization of historical data, resulting in long update cycles. When sudden changes occur in the ecological environment (such as abrupt changes in ecological indicators caused by extreme weather events), existing strategy libraries cannot provide timely and effective solutions, leaving the optimization process reactive.

[0004] Traditional methods lack a dynamic evaluation mechanism for strategy effectiveness during strategy selection. In most cases, strategy selection is based solely on its average performance in historical cases, ignoring the differences between the current ecological state and historical examples, as well as the varying adaptability of strategies under different conditions. This leads to some strategies that performed well in historical cases failing to achieve the desired results in the current ecological state, or even negatively impacting the ecosystem. Furthermore, due to the lack of real-time simulation and feedback of the optimization process, traditional methods struggle to adjust strategy direction based on changes in actual ecological indicators during optimization. They can only conduct a post-optimization review after the entire optimization cycle, significantly reducing optimization efficiency and increasing ecological security risks. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent simulation and optimization system for ecological security patterns based on reinforcement learning, so as to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, this invention provides an intelligent simulation and optimization system for ecological security patterns based on reinforcement learning, the system comprising: The data acquisition module is used to acquire several ecological indicator data points of the ecological security pattern; The experience pool formation module is used to divide historical optimization strategies into multiple experience layers based on the gap between historical optimization strategies and ecological indicators and the confidence of the strategies, so as to form a multi-layer experience pool. The strategy determination module is used to determine intelligent optimization strategies for each stage of the ecological security pattern based on the set ecological security target status, and to filter multi-layer experience pools based on strategy confidence and target matching degree to obtain experience optimization strategies. The strategy adjustment module is used to adjust intelligent optimization strategies and experience-based optimization strategies using a dynamic mechanism to respond to real-time ecological changes, thereby determining the target optimization strategy; The simulation optimization module is used to simulate and optimize the ecological security pattern based on the target optimization strategy. The status acquisition module is used to obtain the actual status of the ecological security pattern; The experience pool update module is used to update the content of the experience layer based on the differences between the actual state and the target state, so as to improve the multi-layer experience pool data.

[0007] Preferably, the data acquisition module includes: Obtain ecological indicator data points of ecological security patterns in multiple spatial locations; Based on the changes in ecological indicator data points over time, ecological indicator change parameters are generated. The ecological index change parameter is used to divide the historical optimization strategy in the experience pool formation module.

[0008] Preferably, the experience pool forming module includes: The weight values ​​of historical optimization strategies are calculated based on the ecological indicator change parameters and strategy confidence. Based on the weight values, the historical optimization strategies are divided into the expert experience layer, the key information experience layer, and the general information experience layer. The multi-layered experience pool is used by the strategy determination module to filter experience-based optimization strategies.

[0009] Preferably, the strategy determination module includes: Intelligent optimization strategies are generated based on the ecological security target state using reinforcement learning algorithms; Candidate experience strategies are extracted from a multi-layered experience pool based on policy confidence. Calculate the matching parameters between candidate empirical strategies and the target strategy; Determine the empirical optimization strategy based on the matching parameters; The intelligent optimization strategy and the experience-based optimization strategy are used by the strategy adjustment module for adjustments.

[0010] Preferably, the strategy adjustment module includes: Monitor real-time ecological change parameters; When real-time ecological change parameters exceed preset thresholds, the experience-based optimization strategy in the dynamic mechanism is activated. Otherwise, execute the intelligent optimization strategy; Output target optimization strategy based on dynamic mechanism; The target optimization strategy is used by the simulation optimization module for simulation optimization.

[0011] Preferably, the simulation optimization module includes: Generate ecological pattern simulation instructions based on the target optimization strategy; Execute ecological pattern simulation commands to optimize the spatial configuration of ecological security patterns; Output the optimized ecological pattern configuration parameters; The optimized ecological pattern configuration parameters are used by the status acquisition module to obtain the actual status.

[0012] Preferably, the status acquisition module includes: Collect actual ecological indicator data corresponding to the optimized ecological pattern configuration parameters; calculate the deviation value between the actual ecological indicator data and the preset benchmark; determine the actual state of the ecological security pattern based on the deviation value. The actual state is used by the experience pool update module to update the experience layer content.

[0013] Preferably, the experience pool update module includes: The actual state is compared with the target state to generate a difference value; the difference value is analyzed to identify the difference factors; an update strategy is formulated based on the difference factors; the update strategy is applied to the corresponding experience layer in the multi-layer experience pool; the updated multi-layer experience pool data is used by the experience pool forming module to form a new multi-layer experience pool.

[0014] Preferably, the strategy determination module further includes: Based on the time series of ecological indicator data points, generate ecological indicator trend parameters; Intelligent optimization strategies are corrected using ecological indicator trend parameters; The revised intelligent optimization strategy is used by the strategy adjustment module for adjustment.

[0015] Preferably, the experience pool forming module further includes: Confidence level of strategy adjustment based on ecological indicator trend parameters; Based on the adjusted strategy confidence, historical optimization strategies are reclassified into multiple empirical layers; The newly divided multi-layered experience pool is used by the strategy determination module to filter experience to optimize strategies.

[0016] Compared with the prior art, the beneficial effects of the present invention are: From the perspective of experience pool construction, the system divides historical optimization strategies into multiple experience layers based on the gaps in ecological indicators and strategy confidence levels through an experience pool formation module, forming a multi-layered experience pool instead of using a traditional single strategy library. This hierarchical structure makes strategy storage and retrieval more targeted. Different experience layers correspond to different ranges of ecological indicator differences and strategy confidence levels. In the subsequent strategy selection process, it can quickly locate a set of strategies that match the current ecological state and target needs, avoiding the inefficiency of strategy selection caused by the disorganized strategy library in traditional methods. It also lays the foundation for accurate strategy selection, enabling strategy selection to no longer rely on a single historical average performance, but to select strategies with appropriate confidence levels based on the gap between the current ecological indicators and the target.

[0017] In the strategy determination and adjustment phase, the system's strategy determination module determines intelligent optimization strategies for each stage based on the set ecological security target state. It then filters multi-layered experience pools using strategy confidence and target matching to obtain empirical optimization strategies, thus combining intelligently generated strategies with empirical strategies. Compared to traditional methods that rely solely on empirical strategies or single-model strategy generation, this combined approach leverages the advantages of reinforcement learning in dynamic decision-making, dynamically generating adaptive intelligent strategies based on the target state. Furthermore, the introduction of empirical strategies reduces potential decision-making biases that may arise from insufficient data in the initial stages of purely intelligent strategies. Simultaneously, the strategy adjustment module utilizes a dynamic mechanism to adjust both strategies to respond to real-time ecological changes. This overcomes the limitations of traditional methods, such as rigid strategies and difficulty in responding to sudden changes. It allows optimization strategies to be flexibly adjusted in response to real-time changes in ecological indicators, ensuring the optimization process maintains its correct direction even when the ecological environment fluctuates.

[0018] The collaboration between the simulation optimization module and the state acquisition module enables real-time linkage between the optimization process and the actual ecological state. The simulation optimization module simulates and optimizes the ecological security pattern based on the target optimization strategy, while the state acquisition module acquires the actual state of the ecological security pattern in real time, forming a closed-loop feedback. This closed-loop mechanism transforms the optimization process from a traditional linear flow of "one-time decision-execution-result evaluation" to one that continuously fine-tunes the optimization strategy by referencing changes in the actual ecological state during the simulation optimization process. This enhances the dynamism and adaptability of the optimization process, ensuring that the optimization direction always aligns with the actual ecological condition and avoiding deviations in optimization results caused by a disconnect between simulation and reality.

[0019] The experience pool update module updates the experience layer content based on the differences between the actual state and the target state, further improving the multi-layered experience pool data and forming a continuous iteration mechanism for strategies. Traditional methods rely on manual operation for strategy library updates, resulting in long update cycles and high subjectivity. This system, through automated difference factor analysis and experience layer updates, allows the experience pool to automatically replenish new effective strategies and ecological state-related data with each optimization process, continuously improving the completeness and adaptability of the experience pool. As the system continues to operate, the strategies in the experience pool become increasingly aligned with the changing patterns of the actual ecological environment, thereby improving the accuracy of subsequent strategy selection and generation, forming a virtuous cycle of "optimization-feedback-update-re-optimization," and driving the continuous improvement of the system's optimization capabilities. Attached Figure Description

[0020] Figure 1 This is a time sequence diagram of the intelligent simulation and optimization system for ecological security patterns based on reinforcement learning described in this invention. Figure 2 A flowchart illustrating the working principle of the data acquisition module; Figure 3 A flowchart illustrating the working principle of the experience pool module; Figure 4 A flowchart illustrating the working principle of the strategy determination module. Detailed Implementation

[0021] 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.

[0022] Please see Figure 1 This invention provides an intelligent simulation and optimization system for ecological security patterns based on reinforcement learning, the system comprising: The system collects multi-source ecological indicator data points of the ecological security pattern through a data acquisition module, including multi-dimensional data such as vegetation cover index, biodiversity indicators, and soil and water conservation parameters. An experience pool formation module divides historical optimization strategies into multiple experience layers based on the gap between historical optimization strategies and ecological indicator change parameters, as well as strategy confidence, constructing a multi-layered experience pool structure. A strategy determination module generates intelligent optimization strategies for each stage based on a preset ecological security target state using reinforcement learning algorithms, and simultaneously selects experience optimization strategies from the multi-layered experience pool by combining strategy confidence and target matching. A strategy adjustment module monitors real-time ecological change parameters through a dynamic mechanism, adaptively adjusting the intelligent optimization strategy and experience optimization strategy, and outputting the target optimization strategy. A simulation optimization module executes the target optimization strategy, simulating spatial configuration optimization of the ecological security pattern. A state acquisition module collects the optimized actual ecological indicator data and calculates the deviation value from the target state. An experience pool update module analyzes the difference factors and updates the experience layer content in the multi-layered experience pool, forming a closed-loop optimization process. By integrating reinforcement learning and a multi-layered experience pool mechanism, the system achieves dynamic simulation and continuous optimization of the ecological security pattern.

[0023] Example 1: See Figure 2 The data acquisition module collects data through a multi-source sensor network and remote sensing monitoring platform deployed in specific ecological areas. These devices continuously monitor and record ecological indicator data at multiple spatial locations. Taking the monitoring of a watershed ecosystem as an example, the sensor network covers typical ecological units from the upstream water conservation area to the downstream estuary wetland. Each monitoring point is equipped with soil moisture sensors, vegetation spectrometers, and bioacoustic monitoring equipment to collect ecological indicator data such as soil moisture content, vegetation index, and bird activity frequency. These data points are encoded and stored according to a unified timestamp and geographic coordinates, forming a raw dataset with spatiotemporal characteristics.

[0024] The data acquisition process follows a systematic sampling strategy, employing a grid-based deployment of monitoring points in space. Fixed monitoring points are established within each ecological functional zone, supplemented by mobile monitoring units. A continuous monitoring mode is used temporally, with all sensors automatically recording data at preset time intervals. Taking vegetation index monitoring as an example, the spectrometer at the fixed points collects NDVI data hourly, while the mobile monitoring unit conducts a full-coverage survey weekly to obtain more detailed spatial variability data. This combined point-and-area, static-and-dynamic monitoring approach enables the acquisition of detailed data reflecting the dynamic changes in the ecological pattern.

[0025] After acquiring the raw data, the system performs data preprocessing, including data cleaning, format standardization, and missing value handling. Data cleaning primarily removes obvious outliers and noise interference, such as abnormal peaks caused by equipment malfunctions or monitoring interruptions due to weather conditions. Format standardization converts data from different sources and formats into a unified spatiotemporal data format, facilitating subsequent analysis. Missing value handling employs spatiotemporal interpolation methods, using data from adjacent points and time periods to fill in missing values ​​and ensure the integrity of the data sequence. Based on the preprocessed ecological indicator data points, the system generates ecological indicator variability parameters. This process is achieved by analyzing the variation characteristics of each data point over time. The system establishes a time series database of ecological indicators for each monitoring point, recording the observed values ​​of each indicator at different time points. Through dynamic analysis of these time series, parameters such as the magnitude, rate of change, and fluctuation characteristics of each indicator are calculated. Taking soil moisture as an example, the system analyzes daily monitoring data to calculate characteristic values ​​such as the daily rate of change, weekly fluctuation amplitude, and monthly trend of soil moisture content. These characteristic values ​​are weighted and integrated to form quantitative parameters that comprehensively reflect the dynamic changes of ecological indicators.

[0026] The generation of ecological indicator variability parameters takes into account the characteristics and importance differences of various ecological indicators. The system assigns different weight coefficients to various indicators; for example, water-related indicators are given higher weights in water conservation areas, while species-related indicators are given higher weights in biodiversity hotspots. This differentiated weighting allows the generated variability parameters to more accurately reflect the ecological change characteristics of specific regions. Simultaneously, the system also considers change characteristics at different time scales, including short-term fluctuations, seasonal variations, and long-term trends, obtaining a comprehensive variability assessment through multi-timescale analysis.

[0027] The generated variability parameters not only reflect the current state but also contain historical change information. The system saves historical variability parameter records and establishes time series of variability parameters. These historical records can be used to analyze the evolution trend of variability parameters and identify the periodic characteristics and abrupt change points of ecological changes. For example, by analyzing multi-year vegetation index variability parameters, interannual variation patterns of vegetation growth and characteristics of anomalous years can be discovered.

[0028] When the data acquisition module transmits ecological indicator data points and variability parameters to downstream modules, it uses a structured data format. Each data packet contains spatial coordinate information, a timestamp, ecological indicator type, monitoring values, and calculated variability parameter values. Data transmission employs encryption and verification mechanisms to ensure data integrity and security. The system establishes a data quality control system to detect and repair data loss or errors in real time during transmission. The module's implementation also considers the characteristics and differences of different ecological regions. Different monitoring schemes and parameter calculation methods are adopted for different types of ecosystems, such as forest ecosystems, wetland ecosystems, and grassland ecosystems. For example, in forest ecosystems, the focus is on monitoring the tree layer vegetation index and soil organic matter content; in wetland ecosystems, the focus is on water level changes and water quality indicators. This targeted monitoring strategy ensures that the acquired data accurately reflects the characteristics and changes of various ecosystems. The operation of the data acquisition module requires hardware infrastructure support. The system deploys high-performance data acquisition equipment, including multispectral remote sensing sensors, underground sensor networks, and UAV patrol platforms. These devices are connected via IoT technology to achieve real-time data transmission and centralized management. Simultaneously, it is equipped with large-capacity data storage devices and high-speed computing resources to support the processing and analysis of massive amounts of ecological data. The system also establishes an equipment maintenance and calibration mechanism to regularly maintain and verify the accuracy of monitoring equipment, ensuring the accuracy and continuity of data collection.

[0029] By optimizing the monitoring network layout and equipment configuration, gaps and time discontinuities in data collection are minimized. For unavoidable data gaps, specialized data reconstruction algorithms are developed, making reasonable extrapolations based on surrounding data and historical patterns. This pursuit of data integrity ensures the reliability of subsequent analysis results. The calculation methods for ecological indicator variability parameters are continuously optimized and updated. The system establishes an evaluation mechanism for parameter calculation methods, regularly comparing the effects of different methods to select the most suitable algorithm. Simultaneously, as monitoring data accumulates and ecological understanding deepens, the weight settings and algorithm parameters for parameter calculation are adjusted in a timely manner, ensuring that variability parameters more accurately reflect the actual changes in the ecosystem. The interface design between the data acquisition module and other modules emphasizes efficiency and stability. Standardized data exchange protocols and interface specifications are adopted to ensure smooth and accurate data transmission. A data buffering mechanism is established to cope with peak data traffic and prevent data loss or congestion. A data quality feedback mechanism is also designed, allowing downstream modules to evaluate and provide feedback on data quality, promoting continuous improvement in the data collection process. The module implementation also considers emergency monitoring needs. When a sudden ecological event occurs, the system can activate an emergency monitoring mode, increasing monitoring frequency, expanding the monitoring scope, and acquiring more detailed on-site data. These emergency monitoring data provide important support for responding to sudden ecological changes, and also enrich the connotation and application value of change parameters.

[0030] The data acquisition module systematically acquires multi-source ecological indicator data on ecological security patterns and generates quantitative parameters reflecting dynamic ecological changes. These data and parameters provide the necessary information foundation for subsequent experience pool construction and strategy optimization, supporting the normal operation and functional realization of the entire system. The module's implementation emphasizes data accuracy, completeness, and timeliness, continuously optimizing the monitoring network and calculation methods to improve data quality and support capabilities.

[0031] Example 2: See Figure 3 The experience pool formation module divides historical optimization strategies into multiple experience layers based on ecological indicator change parameters and strategy confidence levels, forming a structured multi-layered experience pool. Simultaneously, the module integrates trend analysis functionality, dynamically adjusting strategy confidence levels based on ecological indicator trend parameters to achieve a re-division of experience layers. The experience pool formation module receives ecological indicator change parameters and historical optimization strategy datasets from the data acquisition module. These historical optimization strategies originate from various ecological intervention schemes accumulated during the system's long-term operation, including vegetation restoration measures, water resource allocation schemes, and bio-corridor construction plans. Each historical strategy is accompanied by detailed execution records, including the implementation area, time period, resource input, and ecological indicator changes before and after execution. The module first standardizes the historical strategies, unifying the data format and units of measurement to facilitate subsequent quantitative analysis and comparison.

[0032] The calculation of strategy confidence is based on the evaluation of the implementation effects of historical strategies. The system establishes a multi-dimensional evaluation framework, including ecological benefit indicators, implementation cost indicators, and sustainability indicators. The confidence score of each strategy is derived by weighting the results of these indicators, reflecting the reliability and effectiveness of the strategy. For example, a wetland restoration strategy that excels in improving water level stability and increasing species diversity will have a correspondingly high confidence score. Conversely, a vegetation planting strategy, while showing significant short-term effects, has excessively high maintenance costs, and its confidence score will be appropriately lowered. The ecological indicator variability parameter plays a crucial role in the weighting calculation. The module analyzes the change characteristics of the ecological indicators targeted by each historical strategy and combines the variability parameter with the strategy confidence. For ecological indicators with more dramatic changes, the weighting calculation of related strategies will assign a higher weight to the variability parameter to reflect the value of strategies that address dynamic changes. This weighted calculation method ensures that strategy evaluation considers not only absolute effects but also the dynamic characteristics of the ecosystem. Based on the calculated weight values, the module divides historical optimization strategies into three empirical layers. The expert experience layer contains strategies with the highest weights. These strategies have typically been validated through repeated practice and demonstrate significant and stable effects under specific ecological conditions. The key information experience layer contains strategies with medium weights. These strategies have some effectiveness but may have limitations under certain conditions. The general information experience layer contains strategies with lower weights. These strategies are less effective or have limited applicability. Each experience layer employs different storage and management strategies, with the expert experience layer having the highest data access priority and a lower update frequency.

[0033] The multi-layered experience pool adopts a hierarchical storage architecture, with each experience layer establishing an independent database and indexing system. The strategy data in the expert experience layer is equipped with detailed metadata descriptions, including applicable environmental conditions, expected effect range, and implementation considerations. The strategy data in the key information experience layer includes effect evaluation reports and descriptions of limiting conditions. The strategies in the general information experience layer mainly store basic execution records and effect data. This hierarchical storage method ensures both rapid retrieval and application of important strategies and the complete preservation of all historical data. The integrated trend analysis submodule continuously monitors the time series of ecological indicator data points, generating ecological indicator trend parameters. These trend parameters are derived from the analysis of long-term monitoring data, reflecting the evolution direction and development speed of various ecological indicators. For example, by analyzing vegetation cover data over several consecutive years, trend parameters can indicate whether regional vegetation is in a trend of improvement, stability, or degradation. Trend parameters of water quality monitoring data reflect the long-term changing characteristics of water environmental quality.

[0034] Based on ecological indicator trend parameters, the module dynamically adjusts strategy confidence levels. When trend parameters show systematic changes in certain ecological indicators, the confidence levels of relevant strategies are adjusted accordingly. For example, if climate trend parameters show a significant trend towards regional aridification, the confidence levels of ecological restoration strategies that rely on sufficient water resources will be appropriately lowered. Conversely, the confidence levels of drought-resistant vegetation restoration strategies may be raised. This dynamic adjustment mechanism allows strategy evaluation to adapt to long-term changes in the ecological environment. Adjustments to strategy confidence levels trigger a reclassification process of the experience layers. The module periodically recalculates the weight values ​​of all historical strategies and redistributes strategies to different experience layers based on the new weight value ranges. Some strategies may be promoted from the general information layer to the key information layer due to changes in the ecological environment or new implementation data, while others may be downgraded due to changes in environmental conditions. This reclassification ensures that the multi-layered experience pool always reflects current best practices.

[0035] The experience pool formation module establishes a rigorous quality control mechanism. The weight calculation and hierarchy division of each strategy undergo multiple verifications, including data integrity checks, calculation logic verification, and expert review. For strategies with hierarchical changes, the module records the reason for the change and the time, establishing a complete audit trail. Simultaneously, a special handling mechanism is set up to perform additional evaluations on strategies near boundary weight values, avoiding frequent hierarchical changes.

[0036] The implementation of the module takes into account the characteristics of different ecological types, establishing differentiated weight calculation standards and hierarchical classification thresholds for ecosystems such as forests, wetlands, and grasslands. For example, in forest ecosystems, biodiversity indicators and carbon sequestration functions have higher weights; in wetland ecosystems, hydrological regulation and water purification functions have more important weights. This differentiated approach makes the empirical pool classification more in line with the actual needs of various ecosystems.

[0037] The construction of the multi-layered experience pool also emphasizes the analysis of the correlation between strategies. The module identifies frequently used strategy combinations and records their synergistic effects. For example, certain vegetation restoration strategies, when implemented in conjunction with soil and water conservation measures, show significantly improved results; such combinations are marked in the experience pool. The trend analysis submodule uses various analytical methods to generate trend parameters, including time series analysis, change detection algorithms, and predictive models. These methods reveal the evolution patterns of ecological indicators from different perspectives, providing comprehensive trend judgments. Trend parameters are updated regularly to reflect the latest changes in the ecological environment. For indicators with obvious trends, the module increases the monitoring frequency to ensure the accuracy of trend judgments. The module establishes a connection mechanism with external knowledge bases, incorporating the latest ecological protection research results and successful cases. This external knowledge, after standardization, is integrated into the multi-layered experience pool, enriching strategy resources. Simultaneously, the module supports manual intervention, allowing ecological experts to adjust the strategy levels or add annotation information, combining professional knowledge and system analysis. The output of the experience pool formation module uses a standardized data interface to provide structured experience layer data to downstream modules. Each experience layer's strategy data contains complete attribute descriptions and metadata information, supporting flexible querying and retrieval. The module also provides data usage statistics, recording the invocation and implementation effects of each strategy, providing a reference for subsequent confidence level adjustments.

[0038] The experience pool formation module effectively organizes and manages historical optimization strategies, establishing a structured knowledge base system. A confidence adjustment mechanism based on the dynamic characteristics and trend analysis of ecological indicators ensures the experience pool remains timely and adaptable. The multi-level classification architecture highlights the importance of key strategies while preserving the value of all historical data.

[0039] Example 3: See Figure 4The strategy determination module generates intelligent optimization strategies based on the ecological security target state, while simultaneously selecting empirical optimization strategies from a multi-layered experience pool and revising the strategy content through trend analysis. The module first receives the ecological security target state set by the system. These targets typically include specific ecological indicator thresholds, spatial pattern requirements, and time nodes. For example, the target state may specify quantitative indicators such as vegetation coverage, biodiversity index, or soil and water conservation capacity that a certain area needs to achieve within a specific time period. These target states constitute the basic constraints and optimization directions for strategy generation. The module uses reinforcement learning algorithms to generate intelligent optimization strategies, primarily employing the policy gradient method. The algorithm operates by constructing a state space, action space, and reward function. The state space encompasses the current values ​​and distribution characteristics of various ecological indicators, while the action space includes various feasible ecological interventions, such as vegetation planting, hydrological regulation, and habitat restoration. The reward function design comprehensively considers ecological benefits, economic costs, and feasibility, guiding the algorithm towards the overall optimal strategy. During strategy generation, the algorithm continuously adjusts action parameters through multiple iterations and simulated executions, gradually bringing the generated strategy closer to the target state requirements.

[0040] While generating intelligent optimization strategies, the module extracts candidate experience strategies from a multi-layered experience pool. The extraction process sorts and filters strategies based on their confidence levels, prioritizing those from the expert experience layer and then considering those from the key information experience layer. Each candidate experience strategy comes with complete historical execution records and performance evaluation data, including a description of the implementation environment, execution process records, and performance monitoring results. This detailed information provides a sufficient basis for subsequent matching degree calculations.

[0041] The module calculates matching parameters between candidate empirical strategies and the target state, employing a multi-dimensional feature comparison method. The calculation of matching parameters considers not only the degree of alignment between the strategy's expected effect and the target state, but also the compatibility of the strategy's applicable conditions with the current environment. For example, for a soil and water conservation strategy, it is necessary to assess whether its required soil conditions, topographic features, and climatic factors are consistent with the current regional environment. The formula for calculating the matching parameters is:

[0042] in: This represents the matching parameter, with a value range from 0 to 1; The number of feature dimensions represented in the comparison; It is the first The weight coefficients of each feature dimension reflect the importance of that feature in the matching evaluation; It is a similarity calculation function that measures the degree of matching between policy features and target features; Indicates the candidate experience strategy in the 1st Values ​​can be taken in each feature dimension; Indicates the target state at the th The required values ​​for each feature dimension. This calculation formula comprehensively considers the matching situation of multiple feature dimensions and reflects the differences in importance of different features through weight coefficients.

[0043] Based on the calculation results of the matching parameters, the module determines the final empirical optimization strategy. A matching parameter threshold is set; only candidate strategies with matching parameters higher than this threshold are adopted. For multiple strategies with similar matching parameters, their complementarity and synergistic effects are further analyzed to select the optimal strategy combination. After determining the empirical optimization strategy, the module records the matching parameter value and the reason for adoption for each strategy, establishing a complete decision trajectory.

[0044] The module also integrates trend prediction functionality, generating trend parameters for ecological indicators based on time series data points. The generation of these trend parameters employs time series analysis methods, including an autoregressive ensemble moving average model. These trend parameters reveal the evolutionary patterns and development directions of ecological indicators; for example, some indicators show a continuous improvement trend, while others may show signs of degradation. Trend parameters not only reflect historical patterns but also contain predictive information for future trends. Using these ecological indicator trend parameters, the module modifies intelligent optimization strategies. The modification process primarily adjusts the action parameters and timing of the strategies to better align them with the actual evolutionary trends of the ecosystem. For example, if trend parameters indicate a significant aridification trend in a certain area, the module will adjust the species selection in the vegetation restoration strategy accordingly, increasing the proportion of drought-resistant species, while also adjusting the intensity and timing of irrigation measures. If trend parameters show a positive trend in water quality improvement, the module may appropriately reduce the intensity of wastewater treatment measures and optimize resource allocation.

[0045] The strategy revision process emphasizes maintaining the integrity and consistency of the strategy. Revision operations are based on strict rules and constraints to avoid disrupting the strategy's internal logic and action sequence. For each revision step, the reason for the revision, the content of the revision, and the expected impact are recorded to ensure the traceability and explainability of the revision process. The revised intelligent optimization strategy needs to be re-simulated and verified to confirm its continued effectiveness in achieving the target state requirements. The module establishes a strategy effectiveness evaluation mechanism to conduct simulation tests on the generated intelligent optimization strategies and the selected empirical optimization strategies. By constructing an ecosystem simulation model, various possible scenarios after strategy implementation are simulated to evaluate the expected effects and potential risks of the strategy. Simulation tests consider different environmental conditions and implementation deviations to test the robustness and adaptability of the strategy. Based on the simulation test results, the strategy is further adjusted and optimized.

[0046] The strategy determination module outputs intelligent optimization strategies, experience-based optimization strategies, and related evaluation data. Each strategy comes with a detailed description, including strategy content, applicable conditions, expected results, and implementation requirements. A comparative analysis report is also provided, illustrating the characteristics and applicable scenarios of different strategies, offering a reference for subsequent strategy selection. Output data uses a standardized format to ensure seamless integration with downstream modules. The module's implementation considers computational efficiency and real-time requirements. A distributed computing architecture is employed to handle large-scale strategy generation and evaluation tasks, accelerating processing speed through parallel computing. A caching mechanism is established to store intermediate computation results, avoiding redundant calculations. For applications with high real-time requirements, dedicated high-speed computing nodes are configured to ensure timely strategy generation.

[0047] The strategy determination module also establishes a knowledge learning mechanism to record the process and results of each strategy generation and selection. These records are used to analyze patterns and rules in strategy decision-making, and optimize the strategy generation algorithm and matching parameter calculation methods. Through continuous learning and improvement, the quality and efficiency of strategy generation are enhanced. Simultaneously, a feedback collection mechanism is established to receive feedback from downstream modules on the effectiveness of strategy implementation, which is used to adjust strategy evaluation standards and methods. The module's quality control includes algorithm verification, data validation, and result review. Algorithm verification ensures the correctness and rationality of the strategy generation logic, data validation ensures the accuracy and completeness of input data, and result review involves ecosystem experts to conduct professional evaluations of important strategies. This multi-layered quality control mechanism ensures the scientific validity and feasibility of the output strategies.

[0048] The strategy determination module effectively generates intelligent optimization strategies, rationally selects experience-based optimization strategies, and revises strategies through trend analysis. The module's implementation emphasizes the relevance, practicality, and adaptability of the strategies, ensuring that the output strategies effectively guide the optimization of the ecological security landscape. The multi-source strategy generation and selection mechanism provides abundant options and sufficient evidence for ecological protection decision-making, supporting the continuous improvement and optimization of the ecological security landscape.

[0049] Example 4: The strategy adjustment module deploys a real-time data acquisition interface to continuously monitor three types of ecological change parameters: climate data, including temperature, precipitation, and frequency of extreme weather events; sudden pollution event indicators, covering abnormal fluctuations in water quality and sudden changes in soil heavy metal concentration; and ecosystem disturbance indices, involving the scale of pest and disease outbreaks and the degree of invasive alien species. These parameters are updated every 5 minutes through a distributed sensor network, forming a dynamic monitoring data stream. The module's preset threshold system is based on statistical analysis of ecological events over the past ten years. For example, a drought warning is triggered if precipitation is 30% lower than the historical average for three consecutive days, and a pollution alarm is triggered if dissolved oxygen concentration in water drops by 40%.

[0050] The dynamic decision-making mechanism employs a dual-track system of rule engines and fuzzy logic. The rule engine has over 300 built-in conditional judgment rules, such as "activate the fire prevention emergency plan when the forest fire risk index > 85 and there is no rain for three consecutive days." The fuzzy logic system handles uncertain scenarios, quantifying the gradual changes in ecological state through membership functions. Taking a wetland ecosystem as an example, when water level fluctuations are on the fuzzy boundary between "normal" and "drought," the system calculates the fitness scores of each strategy and selects the optimal response.

[0051] The strategy switching mechanism establishes a priority response model. When real-time monitoring shows a sudden industrial pollution event in a watershed, and the chemical oxygen demand (COD) monitoring value exceeds the threshold by 125%, the system immediately interrupts the execution of the intelligent optimization strategy and invokes the heavy metal pollution emergency response plan from the expert experience layer. This plan includes emergency pollution interception measures, a deployment location map of activated carbon adsorption devices, and a protection plan for affected biological populations. If the monitoring parameters are all within the threshold range, the intelligent optimization strategy generated based on reinforcement learning continues to be executed, such as proceeding with the riparian vegetation restoration project as originally planned. The target optimization strategy output adopts a structured instruction format, including three parts: spatial operation instructions, time-series instructions, and a resource allocation matrix. The spatial operation instructions specify the geographical scope of ecological restoration measures, such as designating 32°15'-32°45' N latitude as a priority restoration area; the time-series instructions specify the implementation sequence of measures, such as requiring wetland vegetation replanting to be completed within 60 days after the completion of the hydrological regulation project; and the resource allocation matrix quantifies the proportion of manpower, equipment, and funds invested.

[0052] After receiving the target optimization strategy, the simulation optimization module executes the optimization operation through the ecological pattern simulation platform. The platform integrates a geographic information system, a hydrological model, and an ecological process simulator to construct a three-dimensional virtual ecological space. When executing ecological pattern simulation commands, the system first parses spatial configuration parameters, such as setting the width of biological corridors to a core area of ​​200 meters + a buffer zone of 100 meters, and setting the minimum area of ​​habitat patches to 5 hectares. Then, it calls the landscape pattern optimization algorithm to automatically generate three spatial configuration schemes for decision-making reference.

[0053] The spatial optimization process employs an iterative correction mechanism. After the initial scheme is implemented, the system detects insufficient connectivity between a certain ecological node and its surrounding habitats, automatically adjusting the corridor orientation and adding stepping stone patches. After five iterations of optimization, a spatial configuration scheme that meets the requirements of the minimum cumulative resistance model is finally formed. The optimized ecological pattern configuration parameters include: the proportion of core ecological source area increased to 28.7%, the landscape connectivity index optimized to 0.92, and the average ecological resistance surface reduced to 45.3.

[0054] The module establishes a configuration parameter verification mechanism. Before outputting the optimization results, a virtual environment stress test is performed: simulating the stability of the ecological pattern under a once-in-a-decade flood event and assessing the water conservation capacity under extreme drought conditions. Only solutions that pass the test are confirmed as the final output, and a corresponding implementation guide is generated, detailing the management requirements for each spatial unit (see Table 1).

[0055] Table 1: Correspondence between monitoring parameters of ecological change and strategy response.

[0056] ; When monitoring data shows gaps or contradictions, a multi-source data verification process is initiated: comparing monitoring values ​​from neighboring stations, retrieving data from satellite remote sensing, and verifying the sensor's operational status. Simultaneously, a strategy execution traceability system is established to record the time point, triggering parameters, strategy source, and executor for each strategy switch, forming a complete decision-making and auditing chain.

[0057] The spatial configuration output of the simulation optimization module adopts the geospatial data interoperability standard, and the ecological pattern configuration parameters are exported in standard Shapefile format, including patch vector boundaries, corridor topology relationships, and resistance surface raster data. Simultaneously, a 3D visualization model is generated, supporting multi-angle viewing of ecological node distribution, biological migration path simulation, and landscape pattern perspective analysis. All output data are accompanied by metadata description files, detailing the data source, processing methods, and accuracy indicators.

[0058] The module implementation process emphasizes a balance between timeliness and accuracy. For sudden ecological events, a rapid response channel is established, completing the process from data collection to strategy output within 15 minutes. For long-term ecological process optimization, a refined simulation mode is adopted, with a single simulation operation lasting up to 72 hours and spatial resolution improved to the 1-meter level. This differentiated approach adapts to various ecological optimization needs. The strategy adjustment module enables real-time response to ecological changes and dynamic strategy adaptation, while the simulation optimization module completes the spatial reconstruction of the ecological pattern and parameter output. The close collaboration between the two modules forms a complete closed loop from decision-making to execution, providing a highly operational technical solution for optimizing the ecological security pattern. The entire process emphasizes data-driven, precise decision-making and the scientific nature of spatial configuration, establishing a traceable and verifiable optimization implementation system.

[0059] Example 5: After the target optimization strategy is implemented, the status acquisition module initiates the monitoring program, collecting actual ecological indicator data through a multi-platform collaborative observation network. Ground monitoring stations deploy high-precision sensor arrays to continuously record vegetation physiological parameters, soil physicochemical properties, and microclimate data. Taking a wetland ecological restoration project as an example, after the implementation of the water system connectivity project, the module collects pore water pressure data every 30 minutes through a groundwater level sensor network, acquires vegetation cover images daily through a multispectral camera, and records bird activity frequency in real time through passive acoustic monitoring equipment. A satellite remote sensing system provides supplementary macro-scale data, updating regional vegetation index distribution maps, surface temperature heat maps, and water turbidity inversion data weekly. A drone survey platform performs detailed scanning of key areas, generating centimeter-level precision 3D point cloud models of habitats. This integrated space-ground monitoring system ensures the acquisition of comprehensive and multi-dimensional information on the actual ecological status.

[0060] The collected raw data undergoes a standardized preprocessing procedure. The data cleaning stage filters out equipment noise and environmental interference, such as removing the anomalous impact of rainfall events on spectral data. Spatiotemporal registration unifies data from different sources to the same geographic coordinate system and time reference. Data fusion algorithms integrate multi-source heterogeneous data; for example, they correlate groundwater level monitoring values ​​with surface vegetation response characteristics to form a comprehensive ecological status dataset. The preprocessed data is stored in a distributed database, establishing a complete data traceability chain that records the collection time, location coordinates, and processing history of each data point.

[0061] Based on the preprocessed dataset, the module calculates the deviation values ​​between actual ecological indicators and preset benchmarks. The preset benchmarks are derived from quantitative standards set for ecological security target states, such as requiring a vegetation coverage rate of 65% and water transparency greater than 1.2 meters in the restoration area. Deviation calculation employs a multi-dimensional assessment method: spatial dimension analyzes the compliance status of each ecological unit, temporal dimension examines the trajectory of indicator changes, and functional dimension assesses the integrity of ecological processes. The specific calculation process includes: quantifying the difference between the measured values ​​and benchmark values ​​at each monitoring point, calculating the comprehensive deviation using the Euclidean distance formula; analyzing the relative deviation levels of different ecological indicators through a standardized difference index; establishing a time-series deviation curve to identify persistent deviation phenomena. The calculation results form a deviation value matrix, recording in detail the degree of deviation of each spatial unit at each time node.

[0062] Deviation analysis supports the determination of actual state, and the module establishes a three-level state classification system: stable state corresponds to a comprehensive deviation value below 5% and no continuous expansion trend; transitional state corresponds to a deviation value in the range of 5%-15% or fluctuating changes; and degraded state corresponds to a deviation value exceeding 15% or showing a continuous deterioration trend. State determination considers spatial heterogeneity, allowing different state zones within the same ecological area. For example, in a watershed restoration project, the upstream water conservation area is classified as stable due to good vegetation recovery, the midstream buffer zone is classified as transitional due to insufficient soil and water loss control, and the downstream area affected by pollution diffusion is classified as degraded. The state classification results are accompanied by detailed determination criteria, including key indicator deviation data and spatial distribution characteristics.

[0063] After receiving the actual state data, the experience pool update module initiates the difference analysis process. The module compares the quantitative indicators of the actual state with the target state, generating a difference value dataset. The difference value calculation not only focuses on absolute numerical differences but also analyzes spatial pattern differences, temporal evolution differences, and functional achievement differences. Taking biodiversity enhancement targets as an example, it not only calculates the numerical difference in species richness but also analyzes differences in species composition structure, key species absences, and ecological network connectivity differences. The difference value dataset contains numerical difference indicators and descriptive difference features, forming a complete difference map.

[0064] The identification of discrepancies was achieved using root cause analysis. A differential factor analysis matrix was constructed, linking observed discrepancies with potential influencing factors. The analysis process categorized three main types of factors: strategy implementation deviations, including inadequate implementation, resource allocation errors, and timeline delays; environmental abrupt changes, encompassing extreme weather events, sudden pollution incidents, and ecosystem cascading effects; and data acquisition errors, involving sensor drift, monitoring blind spots, and model calculation biases. Each identified differential factor was supported by a chain of evidence. For example, poor vegetation recovery in a region was attributed to an incorrect planting season (strategy implementation deviation), while also considering the impact of an abnormal frost (environmental abrupt change).

[0065] Based on the analysis of discrepancies, the module formulates targeted update strategies. These strategies comprise three levels: content updates adjust the strategy description, applicable conditions, and effect data in the experience layer; structural updates modify the strategy weight coefficients, confidence scores, and hierarchical affiliation; and mechanism updates optimize the experience pool's division rules, retrieval logic, and verification process. For example, taking a soil and water conservation strategy as an example, because the actual effect was lower than expected, the updated strategy lowered its confidence score, added restrictions on areas with excessively steep slopes to the metadata, and moved the strategy from the expert experience layer to the key information experience layer. For discrepancies caused by sudden environmental changes, a new emergency strategy data package, including extreme weather response plans, is added.

[0066] The application of update strategies follows a priority rule: expert experience layers and key information experience layers are updated first, while general information experience layers are updated in batches. Update operations include: modifying historical strategy metadata fields, adjusting the strategy's position in the multi-level structure, adding newly acquired strategy cases, and deleting policy records that have been verified as invalid. Each update operation records the operation type, modified content, supporting factors, and operator, establishing a complete version control history. A conflict detection mechanism is implemented during the update process; when multiple update commands conflict, an expert review process is initiated for arbitration.

[0067] The updated multi-layered experience pool undergoes consistency verification, which includes checking the integrity of the data structure, the rationality of logical relationships, and the timeliness of the data. The updated strategy is then sampled and simulated for verification, testing its applicability in typical scenarios. The verified experience pool data is regenerated into index files, and the caching system is updated to ensure that downstream modules can promptly access the latest knowledge base. The system automatically generates an update report, summarizing the update content, scope of impact, and expected effects, serving as a historical archive of the knowledge base's evolution.

[0068] The status acquisition module enables the scientific evaluation of ecological optimization effects, while the experience pool update module facilitates the dynamic evolution of the knowledge system. These two modules form a closed loop from effect monitoring to knowledge optimization, allowing the multi-layered experience pool to continuously accumulate practical wisdom and enhance its support for optimizing the ecological security landscape. The entire process emphasizes the depth of difference analysis and the precision of update operations, establishing a self-improving knowledge management system.

[0069] 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.

[0070] 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. A smart simulation and optimization system for ecological security patterns based on reinforcement learning, characterized in that, include: The data acquisition module is used to acquire several ecological indicator data points of the ecological security pattern; The experience pool formation module is used to divide historical optimization strategies into multiple experience layers based on the gap between historical optimization strategies and ecological indicators and the confidence of the strategies, so as to form a multi-layer experience pool. The strategy determination module is used to determine the intelligent optimization strategies for each stage of the ecological security pattern based on the set ecological security target status, and to filter multi-layer experience pools based on strategy confidence and target matching degree to obtain experience optimization strategies. The strategy adjustment module is used to adjust intelligent optimization strategies and experience-based optimization strategies using a dynamic mechanism to respond to real-time ecological changes, thereby determining the target optimization strategy; The simulation optimization module is used to simulate and optimize the ecological security pattern based on the target optimization strategy. The status acquisition module is used to obtain the actual status of the ecological security pattern; The experience pool update module is used to update the content of the experience layer based on the differences between the actual state and the target state, so as to improve the multi-layer experience pool data.

2. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 1, characterized in that, The data acquisition module includes: Obtain ecological indicator data points of ecological security patterns in multiple spatial locations; Based on the changes in ecological indicator data points over time, ecological indicator change parameters are generated. The ecological indicator change parameter is used to divide the historical optimization strategy in the experience pool formation module.

3. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 2, characterized in that, The experience pool formation module includes: The weight values ​​of historical optimization strategies are calculated based on the ecological indicator change parameters and strategy confidence. Based on the weight values, the historical optimization strategies are divided into the expert experience layer, the key information experience layer, and the general information experience layer. The multi-layered experience pool is used by the strategy determination module to filter experience-based optimization strategies.

4. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 3, characterized in that, The strategy determination module includes: Intelligent optimization strategies are generated using reinforcement learning algorithms based on the ecological security target state. Candidate experience strategies are extracted from a multi-layered experience pool based on policy confidence. Calculate the matching parameters between candidate empirical strategies and the target strategy; Determine the empirical optimization strategy based on the matching parameters; The intelligent optimization strategy and the experience-based optimization strategy are used by the strategy adjustment module for adjustments.

5. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 4, characterized in that, The strategy adjustment module includes: Monitor real-time ecological change parameters; When real-time ecological change parameters exceed preset thresholds, the experience-based optimization strategy in the dynamic mechanism is activated. Otherwise, execute the intelligent optimization strategy; Output target optimization strategy based on dynamic mechanism; The target optimization strategy is used by the simulation optimization module for simulation optimization.

6. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 5, characterized in that, The simulation optimization module includes: Generate ecological pattern simulation instructions based on the target optimization strategy; Execute ecological pattern simulation commands to optimize the spatial configuration of ecological security patterns; Output the optimized ecological pattern configuration parameters; The optimized ecological pattern configuration parameters are used by the status acquisition module to obtain the actual status.

7. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 6, characterized in that, The status acquisition module includes: Collect actual ecological indicator data corresponding to the optimized ecological pattern configuration parameters; calculate the deviation value between the actual ecological indicator data and the preset benchmark; determine the actual state of the ecological security pattern based on the deviation value; The actual state is used by the experience pool update module to update the experience layer content.

8. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 7, characterized in that, The experience pool update module includes: The actual state is compared with the target state to generate a difference value; the difference value is analyzed to identify the difference factors; an update strategy is formulated based on the difference factors; the update strategy is applied to the corresponding experience layer in the multi-layer experience pool; the updated multi-layer experience pool data is used by the experience pool forming module to form a new multi-layer experience pool.

9. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 8, characterized in that, The strategy determination module further includes: Based on the time series of ecological indicator data points, generate ecological indicator trend parameters; Intelligent optimization strategies are corrected using ecological indicator trend parameters; The revised intelligent optimization strategy is used by the strategy adjustment module for adjustment.

10. The intelligent simulation and optimization system for ecological security patterns based on reinforcement learning according to claim 9, characterized in that, The experience pool formation module also includes: Confidence level of strategy adjustment based on ecological indicator trend parameters; Based on the adjusted strategy confidence, historical optimization strategies are reclassified into multiple empirical layers; The newly divided multi-layered experience pool is used by the strategy determination module to filter experience to optimize strategies.

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