157 results about "Adaptive management" patented technology

A system is provided for managing Internet of Things (IoT) networks. The system includes a learning module configured to employ machine learning models with hyperparameters optimized through a hyperparameter optimization process; wherein the process includes evaluating a set of hyperparameters against a performance metric to select optimal hyperparameters that enhance the adaptability and efficiency of dynamic membership functions within an adaptive fuzzy logic engine (AFLE).

The invention relates to the field of power data management, and discloses a power big data adaptive management method and system fused with spatial-temporal feature mapping, and the method comprises the steps: obtaining a real-time operation data flow from a multi-source power terminal, and constructing an original power data set with a time sequence label and a device identifier; the method comprises the following steps: dividing an original power data set into parallel processing units based on a storage-while-computing architecture, performing dynamic index updating by adopting an event-driven index mapping rule, and constructing a multi-dimensional data index cache system with real-time responsiveness; identifying a key abnormal trajectory through a time-varying feature nesting mechanism, and performing hierarchical measurement and entropy disturbance analysis on a data fluctuation degree in the key abnormal trajectory by using a streaming feature aggregation network; identifying potential security risk nodes in combination with the structure matching degree between the historical abnormal event evolution graph and the key abnormal trajectory; and generating a multi-level response instruction chain based on the risk assessment result. The method has the advantage of improving the operation safety of the power grid.

The invention relates to the technical field of smart community data management, in particular to a smart community metadata interaction method and system based on an edge computing framework, and the method comprises the steps: carrying out the dynamic storage of multi-dimensional data through a hierarchical rolling cache; generating a uniform resource vector through sequential progressive mapping; community semantic tags are added for the uniform resource vectors through extensible tag slots, connection is established according to association rules, and a resource semantic graph oriented to community services is constructed; receiving a resident task request, constructing a multi-factor weighted scoring engine based on the resource semantic graph, and generating a task data packet with a priority label; a two-channel hybrid scheduling network is constructed, a first channel outputs resource availability and node association degree, and a second channel outputs time sequence characteristics and load prediction parameters; and fusing dual-channel output to obtain task-node correlation and generate an optimal matching strategy. According to the method, task collaboration and resource adaptive management are realized through semantic resource modeling and hybrid scheduling.

The invention relates to a self-adaptive control method for a rain and snow mode of rail transit, electronic equipment and a medium, and the method comprises the steps: comprehensively studying and judging a rain condition based on multi-sensor data and numerical weather forecast data, and calculating a weather grade in the rain and snow mode, the multi-sensor data including video detection data, laser radar data and rainfall data; whether a rain and snow mode is set or not is judged according to the weather level, if yes, the current track slip degree is evaluated according to the multi-sensor data, and the GEBR value is adaptively updated based on the slip degree; the CC calculates the running safety braking envelope of the train by using the updated GEBR value, and feeds back a slip detection result and a dynamic parameter adjustment result in real time; and the ATS dynamically adjusts the operation plan of the train according to the slip detection result reported by the CC, and optimizes the operation scheduling of the whole train. Compared with the prior art, the method has the advantages that self-adaptive management of the rain and snow mode is achieved based on the dynamically-adjusted GEBR, and the running safety and reliability of the train in the rain and snow weather are effectively improved.

The invention discloses an air conditioner energy consumption adaptive management system and method based on dynamic feature selection, and relates to the technical field of air conditioner energy consumption management, and the method comprises the steps: building a system energy consumption digital twin model as a theoretical optimal energy consumption baseline; operating parameters are collected in real time, and key parameter subsets are screened through working condition recognition and feature importance dynamic evaluation; inputting the key parameters into the model to obtain theoretical energy consumption, and comparing the theoretical energy consumption with a measured value to generate an energy consumption deviation rate; smooth processing is carried out on the deviation ratio sequence, recognition is carried out in combination with a dynamic threshold value and various anomaly detection algorithms, and grading early warning is triggered; the system comprises four core modules, namely a digital twinborn model construction module, a key parameter dynamic screening module, an energy consumption deviation calculation module and a grading early warning triggering module. The method can adapt to different working conditions, accurately capture energy consumption abnormities, and effectively improve the intelligent level and accuracy of energy efficiency management of the air conditioning system.

The invention relates to the technical field of cognitive behavior therapy, in particular to a scheme intelligent pushing and task self-adaptive management method based on cognitive behavior therapy, which comprises the following steps: S1, acquiring physiological signals, behavior logs and environment interaction data of a user through a multi-modal data acquisition module; s2, constructing a dynamic psychological assessment model based on a cognitive behavior theory, and fusing multi-source data by adopting a Bayesian network to generate a user cognitive state map; s3, generating a personalized intervention scheme through a reinforcement learning algorithm according to the cognitive state map and a preset CBT intervention rule base; according to the method, through multi-modal data acquisition, dynamic psychological assessment, reinforcement learning scheme generation, task decomposition optimization, difficulty adaptive adjustment and digital twinborn simulation, full-process closed-loop management from data acquisition to intervention optimization is constructed, and efficient, safe and personalized cognitive behavior treatment scheme intelligent pushing and task adaptive management are realized.

The invention discloses an intelligent analysis-based security vulnerability intelligent research and judgment and co-processing system and method, and relates to the technical field of network security, and the system comprises a multi-source data collection module which obtains security data in real time by adopting a multi-level intelligent hierarchical collection architecture; the intelligent analysis module adopts a three-level progressive analysis architecture to analyze and collect data; the intelligent agent scheduling module designs a unified control framework, realizes standardized management and control of security equipment through hierarchical abstraction, receives analysis results, integrates a self-adaptive scheduling algorithm based on multi-objective optimization and an alarm vulnerability intelligent circulation strategy relying on an optimization mechanism of the self-adaptive scheduling algorithm, and executes a security mechanism by means of an adapter factory mode, a strategy arrangement engine and a four-level execution guarantee mechanism. Equipment self-adaptive management, strategy intelligent scheduling and vulnerability grading processing are supported, and a processing result is fed back; and the report generation and evidence collection module displays the disposal result and automatically generates an electronic evidence collection package meeting the standard. According to the invention, intelligent research and judgment of vulnerabilities and automatic generation of disposal suggestions can be realized.

The invention relates to the technical field of intelligent storage operation and maintenance management, and discloses a jasmine tea intelligent storage dynamic operation and maintenance management system, which comprises an aroma modeling and acquisition module, which is used for acquiring batch information and key volatile matter concentration and determining an aroma fidelity rate; the environment quota distribution module is used for determining an environment quota based on the microclimate parameters and distributing a storage location; the risk assessment and early warning module is used for calculating an aroma risk index and generating an early warning mark; the prediction operation and maintenance management module is used for carrying out equipment trend evaluation and generating an operation and maintenance work order; the scheduling decision module is used for generating a turnover priority queue according to the aroma residual margin and the risk index; the job resource scheduling module is used for executing joint scheduling to generate a job scheduling table; and the rebalance auditing module is used for updating the environment quota and the priority queue based on the execution receipt so as to realize dynamic closed-loop management. According to the invention, dynamic monitoring, intelligent decision making and closed-loop adaptive management of the storage environment, aroma quality and operation and maintenance scheduling of the jasmine tea are realized.

The invention provides a cloud-edge collaborative enterprise knowledge graph automatic construction method and system, electronic equipment and a storage medium, and relates to the technical field of computer and knowledge graph construction. Link data and inter-service communication monitoring data are called by collecting interface endpoints, and data are fused and associated by edge nodes; a dependency graph containing the service calling relation is generated; analyzing the time delay contribution degree and the error propagation probability, positioning a performance bottleneck link, measuring and calculating a service state in combination with a historical mode, adjusting flow distribution and isolating substandard instances; meanwhile, a multi-entity enterprise knowledge graph is constructed on the cloud based on dependency graph topology, performance attributes of service instances and interface endpoints and dependency relationship attributes among services are associated, and graph updating is automatically triggered when the attributes change. And finally, adaptive management strategies and configuration suggestions are generated through graph analysis, and automatic construction and application support of the enterprise knowledge graph can be realized.

The invention discloses a factory production process digital management method and system based on the Internet of Things, and belongs to the technical field of factory production management, and the method specifically comprises the steps: collecting related data of production equipment in a production line, carrying out the semantic fusion processing of the related data, generating a data stream with an incidence relation, and storing the data stream in a database; establishing a multi-relational graph structure based on the data flow, constructing a multi-dimensional digital model of the production process, receiving production intention information input by a manager, generating a production scheduling scheme according to the multi-dimensional digital model, selecting a scheduling scheme which is most matched with the production intention information, and sending the scheduling scheme to the manager; monitoring production data in the process of executing the scheduling scheme, determining influence factors according to a causal relationship when abnormality or deviation is detected, and updating the multi-dimensional digital model; according to the method, the production scheme can be dynamically adjusted under complex constraint conditions, so that a factory has self-adaptive management capability, and the intelligent level of production and the resource utilization efficiency are improved.

The invention discloses an engineering construction digital intelligent supervision management and control system, and relates to the technical field of engineering construction supervision. The system comprises an information acquisition module which is used for acquiring equipment state, image and environment data; the data fusion layer module is used for carrying out cleaning, alignment and Kalman filtering fusion on the multi-source data to generate a multi-modal construction data flow; a progress calculation and prediction unit, a deviation judgment unit and a digital modeling unit are arranged in the analysis monitoring module, and the analysis monitoring module is used for updating a progress state based on an XGBoost model, analyzing a node dependency relationship by using a graph neural network, calculating a deviation propagation risk coefficient and generating a hierarchical deviation signal in combination with three-level judgment conditions; and the management and control layer module executes hierarchical response according to the level of the deviation signal, and feeds back execution effect data to the front-end module to realize model optimization and closed-loop iteration. According to the invention, intelligent perception, risk prediction and adaptive management and control of the construction process are realized, and the real-time performance, accuracy and autonomy of project supervision are improved.

The invention relates to the technical field of intelligent agriculture, provides an unmanned farm AI autonomous operation and life cycle management method and system, and aims to solve the problems that traditional agricultural production depends on artificial experience, management is extensive and resource utilization efficiency is low. An integrated sensor network is deployed, multi-source environment and image data in the crop growth process are periodically collected, the data are preprocessed and standardized, and the processed data are input into an AI model fused with deep learning for intelligent analysis so as to identify the crop growth stage and predict the demand of the crop growth stage. The system further has a closed-loop feedback mechanism, crop response data are recorded, an online learning technology is utilized to continuously optimize a decision model, autonomy and adaptive management and knowledge iteration of the whole life cycle are realized, and the system has a wide application prospect. And the intelligent level and the resource utilization efficiency of agricultural production are remarkably improved.

The invention discloses a chronic disease risk prediction and dynamic management system based on multi-modal data fusion, and relates to the technical field of chronic disease management, the chronic disease risk prediction and dynamic management system comprises a data acquisition layer, a data fusion processing layer, a risk prediction management layer and an application service layer, the data fusion processing layer performs preprocessing, feature extraction and fusion analysis, the risk prediction management layer constructs a risk prediction model according to result analysis, and the application service layer provides a risk prediction result, a personalized management scheme and an interactive interface service for a user. Through the arrangement of the data acquisition layer, the data fusion processing layer and the risk prediction management layer, 'symptom-physiology-image 'full-dimensional data is covered, more comprehensive health state evaluation is supported, dynamic intelligent prediction can be carried out, diet, exercise and medication suggestions are automatically adjusted according to patient execution feedback and the latest prediction result, and the health state evaluation efficiency is improved. And a self-adaptive management cycle is formed.

The invention discloses a dynamic adaptive management system for road traffic safety planning driven by artificial intelligence. According to the system, multi-source data are acquired through a data acquisition and preprocessing layer and are cleaned and standardized; the knowledge engineering layer constructs a policy knowledge graph and a rule engine; the large model service layer realizes natural language generation, semantic understanding and multi-modal reasoning; the intelligent generation layer uses a GAN model to generate multi-modal planning content and dynamically adapts the multi-modal planning content; the implementation process management layer monitors planning implementation, middle-stage evaluation and last-stage summarization in real time; and the interaction and output layer provides visual editing and multi-format output. The system integrates the technologies of deep learning, generative adversarial network and the like, realizes intelligent planning generation and dynamic supervision, has the capabilities of multi-modal data processing, dynamic self-adaption and full-process management, can effectively improve the scientificity, high efficiency and adaptability of road traffic safety planning, and provides powerful support for road traffic safety management.

The invention discloses an intelligent self-adaptive solar power supply system and an energy management method, and relates to the technical field of solar intelligent power supply. A multispectral dynamic capture module improves the spectrum utilization rate and the photoelectric conversion efficiency; the global environment sensing module accurately predicts shadow and weather; the multi-mode MPPT cooperative control module reduces the power loss under the shadow; the heterogeneous energy storage cooperative scheduling module optimizes energy storage distribution; the intelligent load interaction module realizes accurate load management; the edge intelligent decision center realizes high-efficiency energy decision and safety guarantee, and all the modules work cooperatively, so that intelligent adaptive management of the solar power supply system is realized. Through multi-module innovation and cooperation, the performance of a solar power supply system is remarkably improved, the power generation efficiency is improved, environmental changes are accurately coped, power loss is reduced, energy storage and load management are optimized, efficient energy decision is achieved, safe operation of the system is guaranteed, the operation and maintenance cost is reduced, and intelligent development of solar power supply is promoted.

The invention relates to a resource quota dynamic management and control method for multi-tenant sub-accounts in a cloud communication platform, and belongs to the technical field of cloud computing and software. The method comprises the steps of obtaining sub-account data and a tenant grading system and a standardization protocol preset by a platform, performing verification and format regulation, and generating a standardization data set; secondly, based on a grading system, binding a permission boundary, extracting resource and service features for verification, constructing an associated weight distribution mechanism, and forming a grading quota evaluation benchmark; various thresholds and rules are preset, a reference quota is generated in combination with real-time monitoring data and a service load prediction result, and a differentiated adjustment scheme is formed by constructing an elastic adjustment mechanism and dynamically adapting to a resource quota. And finally, quota allocation is executed, indexes such as use saturation and response time delay are monitored through a two-dimensional verification system, a full-link management and control log is generated, and a resource dynamic management and control closed loop is formed. Refined and adaptive management and efficient utilization of resource quotas are realized.

The invention discloses a forest seedling raising period management optimization method based on an adaptive algorithm. The method comprises the following steps: S1, collecting multi-source environment data and seedling growth state data in a forest seedling raising process; s2, the original index data set is processed, a standardized index matrix is obtained, and a stage index evaluation input set is constructed; s3, constructing a multi-target periodic scheduling model based on the stage index evaluation input set; s4, introducing an entropy weight adaptive grey wolf optimization algorithm to solve the multi-target scheduling function set, and outputting a periodic scheduling optimal solution; s5, generating a seedling culture management execution strategy based on the period scheduling optimal solution; s6, forming a feedback monitoring data set by the environment feedback data collected in real time and the seedling feedback data, and generating an updated seedling management execution strategy; and S7, synchronizing the updated seedling culture management execution strategy to the seedling culture management platform. According to the invention, intelligent scheduling and adaptive management optimization of the whole forest seedling raising process are realized.

The invention discloses a multi-protocol self-adaptive management docking station core board control method and system, and belongs to the technical field of docking station core board monitoring control, and the method comprises the steps: analyzing the stability risk analysis necessity of a docking station core board, analyzing the stability risk score of the docking station core board, and determining the stability risk of the docking station core board; and performing control optimization adjustment on the docking station core board based on the stability risk score of the docking station core board, performing protocol layer clock recovery circuit sampling phase drift analysis, analyzing a protocol layer clock recovery circuit sampling phase drift characterization factor, and executing control adjustment of sampling phase drift. According to the method, the stability and reliability of system operation can be improved, the link retraining frequency is reduced, sampling phase adjustment is optimized, sampling point dislocation, inter-frame phase lock out-of-step and systematic communication faults are prevented under the condition of multi-protocol concurrent working, and long-term stable operation and multi-protocol compatibility of the docking station core board are guaranteed.

The invention discloses a labor capability allocation method and device based on a dynamic weight, a medium and a product, and the method comprises the steps: obtaining the complexity and personnel skill data of each task in response to a project labor capability allocation request; based on the dynamic coupling model, calculating the real-time matching degree of the personnel and the tasks in combination with the dynamic weight factors corresponding to the tasks; then, according to a matching result and multi-dimensional constraints such as project cost and time, a multi-objective optimization algorithm is adopted to generate an optimal labor force and energy distribution scheme and expected progress data; after the scheme is executed, the actual progress of the project is monitored in real time and compared with expected data; if the deviation exceeds a set threshold value, a feedback mechanism is triggered, the personnel skill data and the dynamic weight factor are updated, the matching and optimizing process is restarted, and closed-loop self-adaptive management of the whole period of the project is achieved until the project is completed, and the technical scheme of the embodiment of the invention remarkably improves the accuracy of resource allocation and the self-adaptive capacity of project management.

The invention relates to the technical field of large model service management, and discloses a system for carrying out self-adaptive current limiting on a token number and a QPS called by a large model. A dynamic monitoring module of the system is responsible for collecting a request flow data set comprising a token consumption sequence, a request frequency sequence and a response delay sequence; a traffic feature extraction module performs multi-dimensional feature analysis on the data set to generate a traffic feature matrix containing token consumption rate, request frequency fluctuation coefficient and delay sensitivity index; the adaptive current-limiting decision module dynamically matches a load balancing strategy based on the matrix, and generates a current-limiting control parameter set containing a token quota threshold and a QPS upper limit threshold; the real-time regulation and control module dynamically adjusts the request queue according to the request queue to generate a new request scheduling sequence; and the feedback optimization module monitors the execution effect and iteratively updates the load balancing strategy. According to the invention, through real-time perception and dynamic adjustment, refined and adaptive management of large model service resources is realized.

The invention relates to an electromechanical system energy efficiency scheduling method based on edge-cloud collaboration, and relates to the technical field of computer processing, and the method comprises the following steps: S01, collecting motor operation parameters, environment data and energy consumption data in real time through edge devices deployed at all nodes of an electromechanical system to obtain original data at the same time bit, processing the original data by using an edge computing platform to obtain first processed data; s02, deploying a lightweight energy efficiency analysis model on an edge computing platform, calculating a current energy efficiency value of the motor in real time based on the first processed data, and if the energy efficiency is detected to be lower than a preset threshold value, triggering a local optimization strategy and recording optimization effect data; and S03, storing the optimization effect data uploaded by the edge device into a cloud database. According to the method, efficient, accurate and adaptive management of the energy efficiency of the electromechanical system is realized by constructing an intelligent scheduling architecture with edge real-time response and cloud global optimization collaboration.

The application provides a kind of intelligent delivery and replenishment management method and system based on multi-dimensional inventory early warning, belong to goods management technical field, this method includes: obtaining the global demand data and full-link supply data of inventory object, and determining demand fluctuation intensity sequence and supply interruption frequency sequence;The demand fluctuation intensity sequence and supply interruption frequency sequence are cross recursive analysis, determine the mismatch risk evolution path between the goods demand and goods supply;According to the mismatch risk evolution path, the adaptive adjustment coefficient when safety stock is determined, and the safety stock threshold curve that evolves smoothly with time is obtained;When the inventory level of inventory object is lower than safety stock threshold curve, generate out-of-stock risk early warning, when inventory level is higher than safety stock threshold curve, generate backlog risk early warning.The technical scheme provided by the application can realize the adaptive management of delivery and replenishment under the nonlinear coupling effect of demand fluctuation and supply interruption.

The present application relates to the technical field of intelligent pension robot control, and discloses a task adaptive management system of a pension robot, which comprises a multi-modal task access module, an old person portrait and multi-source sensing module, an adaptive task decision engine, a local pension robot execution terminal, a task closed-loop feedback optimization module and a cloud-based pension management and control platform; the multi-modal task access module is connected with the adaptive task decision engine; the adaptive task decision engine is connected with the local pension robot execution terminal; the local pension robot execution terminal and the adaptive task decision engine are both connected with the task closed-loop feedback optimization module; and the cloud-based pension management and control platform and the adaptive task decision engine are in bidirectional communication. The management system has the advantages of realizing task dynamic addition, postponement, replacement, queuing, splitting and collaborative shunting on demand, and solves the problems of existing pension robots, such as task fixation, poor emergency response, insufficient personalization, low fault tolerance for environmental changes and chaotic multi-machine cooperation.

The invention discloses a patch card state adaptive management method, which comprises the following steps of: actively scanning and constructing a detailed card slot information mapping table for persistent storage by pre-configuring an initialization flag bit when equipment is powered on for the first time; in the subsequent starting process, the cache information is directly read to skip a time-consuming physical discovery process, so that the starting efficiency is greatly improved. Meanwhile, a closed-loop self-repairing mechanism is built in the method, and by continuously monitoring the network registration state and verifying the data integrity, physical rescanning can be automatically triggered and the cache can be updated when cache failure or data errors are detected, so that the system can always return to the most reliable state acquisition mode.

This invention discloses an adaptive zoning management method for water environment pollutants based on differences in ecosystem service functions, belonging to the field of environmental risk assessment and pollutant management technology. The method includes the following steps: S1, ecosystem service function zone division; S2, establishment of a candidate pollutant inventory and concentration measurement; S3, construction of a differentiated multi-criteria evaluation system; S4, comprehensive subjective and objective weighting, determining the weights of each evaluation indicator for each ecosystem service function zone using a comprehensive subjective and objective weighting method; S5, pollutant priority ranking and inventory generation; S6, implementation of differentiated management measures. This invention, by constructing a zoning assessment framework and integrating multi-criteria decision analysis and a mixed subjective and objective weighting model, achieves accurate identification and scientific ranking of the environmental and health risks of pollutants in different functional zones. By generating a differentiated priority management inventory for each zone, it achieves the technical effect of transforming from unified supervision to precise adaptive management.

A system is provided for detecting and remediating computing system breaches using computing network traffic monitoring. In particular, the system may identify one or more technology elements within a network as well as relationships between computing systems associated with said elements to determine a network topology. Based on the network topology, the system may use historical network traffic data associated with the technology elements in the network to generate predicted entry points and lateral pathways of a security breach that may take place within particular computing systems. Then, based on the technology elements affected as well as entry points and path traversals of the breach, the system may generate and / or implement one or more remediation steps to address existing and / or future breaches. In this way, the system may provide an intelligent method of augmenting the security of a computing network.

The invention relates to the technical field of image transmission, in particular to an energy consumption self-adaptive management method and system of a wireless image transmission module. Comprising the steps of obtaining operation data of at least one node, and analyzing a network load to adjust interaction frequency configuration; load balancing parameters are determined according to interaction frequency configuration, transmission task priorities are dynamically adjusted, node energy consumption data are collected, and resource waste points are checked to update an energy consumption distribution scheme; predicting the traffic of a transmission path according to an energy consumption distribution scheme, optimizing path allocation and reallocating resources when the traffic is greater than a set value, and updating node state shared data; and finally, the network operation state is calibrated according to the node state shared data, and energy consumption optimization configuration is obtained, so that energy consumption is reduced, and network efficiency is improved. According to the invention, the problem that the energy consumption management of the wireless image transmission network is difficult to finely regulate and control along with the load change is solved, and the self-adaptive energy conservation and stable transmission of the distributed image transmission module are realized.

The invention belongs to the technical field of artificial intelligence, and particularly relates to an intelligent processing and storage optimization system for micro-wave audio data, which comprises an audio data acquisition module, a real-time preprocessing module, an intelligent value evaluation module, a dynamic processing strategy generation module, a differential data processing module, a multi-stage storage optimization module and a system feedback and self-learning module. And dynamic and adaptive processing and storage optimization of the full life cycle of the audio data are realized through a data value driven integrated mechanism. The processing storage efficiency and the retrieval performance are effectively improved, and resource self-adaptive management and system intelligence are achieved.

The invention relates to a self-adaptive management method and system for a database plan cache, and the method comprises the steps: firstly judging whether the plan cache is hit or not after a database receives an SQL statement, and triggering a hard analysis generation plan and caching the plan when the plan cache is not hit; if the cache is hit, further judging whether a corresponding multi-execution plan exists or not; and when only a single execution plan exists, analyzing the relationship between the number distribution of execution tuples and the number of execution times by adopting an execution efficiency algorithm to dynamically judge whether the data is inclined. And when a plurality of execution plans exist, entering a multi-plan cache management stage, selecting the execution plans according to a matching result of the current binding variable predicate selection rate and the cache plan historical selection rate, and generating and caching a new plan when the matching fails. According to the method, the data skew is accurately identified by executing the efficiency algorithm, intelligent matching and multiplexing of plans are realized by means of a multi-plan cache management mechanism, and the overall performance of the database in a data skew scene is improved.

This invention discloses an ecological management zoning method and system based on static and dynamic supply and demand matching, belonging to the field of ecological management technology. The invention constructs a node feature matrix by calculating a comprehensive static supply and demand matching index and a comprehensive dynamic change trend index, then generates a dynamic spatiotemporal adjacency matrix to construct a spatiotemporal physical connectivity graph. This graph is then input into a spatiotemporal graph neural network for processing, outputting predicted static supply and demand matching indices and predicted dynamic change trend indices. Based on the zero-value boundaries of these indices, the study area is divided into initial four-level basic management zones. A Markov decision process is then constructed, and a Pareto optimal solution set is obtained through reinforcement learning algorithms to fine-tune the spatial boundaries of the initial four-level basic management zones. The resulting refined ecological management zoning map and management priority sequence are output, making the zoning results more targeted for management. This solves the problem that existing ecological management zoning results lag behind actual changes and are difficult to effectively support adaptive management.