230 results about "Resource supply" patented technology

The invention relates to the technical field of economic resource management, and discloses an economic resource management optimization method based on intelligent decision making. According to the method, multi-source heterogeneous data, including resource stock, demand fluctuation and the like, of an economic system are collected firstly; a dynamic resource pool is divided based on multi-dimensional feature analysis, and a nonlinear optimization model is constructed to predict resource supply and demand changes so as to generate an allocation scheme; and then optimizing a distribution path by using a multi-stage decision tree, updating model parameters through an adaptive learning mechanism according to market feedback and constraint condition changes, and correcting a deployment scheme in real time. In addition, key technical details such as an elastic quota adjustment formula and a fuzzy clustering algorithm membership function are given. According to the method, complex data can be effectively integrated, resources are scientifically scheduled, supply and demand are accurately predicted, a distribution path is optimized, environmental changes are adapted, the efficiency and benefits of economic resource management are remarkably improved, and scientific and reasonable resource management decision support is provided for economic subjects.

The invention discloses a high-efficiency computer software system resource scheduling method, system and device and a medium, and the method comprises the steps: deploying a hardware counter in an operating system, and monitoring the CPU utilization rate, memory occupation, disk I / O, network bandwidth, the use condition of data aggregation system resources and the resource demand of each process or thread in real time; based on monitoring data, analyzing a current resource supply and demand state by adopting an advanced algorithm, and predicting a future resource demand trend; according to an analysis result, a resource allocation strategy is dynamically adjusted, it is ensured that a high-priority task obtains needed resources in time, and meanwhile fairness of a low-priority task is considered; data feedback after scheduling execution is collected, a scheduling algorithm is continuously optimized, and the scheduling efficiency is improved; the system, the equipment and the medium are used for realizing the high-efficiency computer software system resource scheduling method. The problems that an existing computer system is low in resource utilization rate, poor in key task stability and high in operation and maintenance cost are solved.

The invention relates to the technical field of civil construction scheduling, and discloses a BIM-based civil construction intelligent scheduling method and system. The method comprises various resource parameters, construction procedures and site arrangement information. Performing construction progress dynamic simulation on the BIM model, generating a multi-dimensional progress plan in combination with a resource database, identifying a process logic relationship and potential resource conflict points, and forming a conflict early warning list with priorities; establishing an intelligent scheduling model according to the progress plan and the conflict early warning list, optimizing a scheduling algorithm by using a conflict early warning result, and dynamically adjusting resource allocation parameters; comparing the real-time construction data with the intelligent scheduling model, identifying resource supply and demand deviations and process delay risks, marking key conflict events and feeding the key conflict events back to the BIM model for visual analysis, and obtaining a conflict influence range; and performing construction process deduction on the BIM model based on a conflict identification result, identifying a potential optimization path and a deployment scheme, and determining an optimal scheduling strategy and implementation steps through feasibility verification.

The invention relates to the technical field of project management, in particular to a BIM-based building project resource scheduling optimization method, which comprises the following steps: based on the current progress state of a construction task, collecting the consumption of constructors, mechanical equipment and materials, comparing the task plan progress with the real-time progress, screening out construction links with the backward task progress, and establishing a construction project resource scheduling optimization model; and judging a resource type, and establishing a task progress offset. According to the method, the construction progress is accurately monitored, real-time evaluation of resource consumption is achieved, waste and scheduling lag are avoided, the construction path is optimized by means of the BIM model, the key path monitoring precision is improved, progress management and control effectiveness is ensured, key path progress deviation is analyzed, the resource shortage risk is recognized, stable supply of construction resources is guaranteed, and on the basis of the influence of resource shortage, the construction efficiency is improved. The resource distribution sequence is optimized, the resource utilization efficiency is improved, the supply chain delivery cycle is matched, the resource scheduling is optimized, the influence of resource supply and demand imbalance on the construction progress is reduced, and the continuous and stable construction progress is ensured.

The invention discloses a supply and demand data management system based on human resources, and relates to the technical field of human resource management, and the system comprises a human resource management center which is in communication connection with a data collection module, a knowledge graph construction module, a post supply and demand matching module, a dynamic adjustment optimization module and a user interaction module. Wherein the modules are connected through electric signals, and the data collection module is used for collecting and preprocessing human resource supply and demand data from different channels. By constructing the knowledge graph based on recruitment data and employee information, the matching relationship between post demands and job seeker skills can be accurately identified, the efficiency of human resource allocation is remarkably improved, and compared with a traditional manual matching mode, the system can quickly analyze a large amount of data, automatically generate post matching coefficients, and improve the efficiency of human resource allocation. And the matching degree is quantitatively evaluated, so that the key post is ensured to obtain the most appropriate talent configuration, and the recruitment cycle is shortened.

The invention discloses a project progress and resource early warning method and system based on dynamic regulation and control, and the method comprises the steps: 1) collecting project progress and resource data in real time, and constructing a multi-source data fusion model; 2) based on the constructed multi-source data fusion model, judging whether the construction progress and the resource consumption are abnormal or not, and if so, executing the step 3); otherwise, continuing conventional construction progress tracking and resource management; 3) triggering a grading early warning mechanism, generating and executing an optimization regulation and control strategy, and adjusting the construction progress and resource configuration; and 4) evaluating the regulation and control effect, and returning to the step 2) after collecting feedback data. According to the method, the actual construction progress deviation and the resource supply and demand imbalance condition are comprehensively considered, the current overall operation condition of the construction project is analyzed, the construction management strategies are flexibly switched, the management parameters are flexibly adjusted, the construction progress can be rapidly and effectively guaranteed, resource utilization is optimized, and the construction efficiency is improved. And the intelligent level and the response efficiency of construction management are obviously improved.

The invention relates to the field of operation and maintenance management, in particular to an operation and maintenance management system and method based on artificial intelligence, and the system comprises an operation baseline modeling module, a resource consumption prediction module, an automatic resource allocation module, a data correlation analysis module, an abnormal data detection module and a fault path analysis module. According to the method, accurate data support is provided for resource management through real-time monitoring of operation data and baseline performance modeling, resource demands are analyzed and predicted in combination with a time sequence, shortage and excess in resource configuration are identified, automatic allocation of resources is achieved, optimal matching between resource supply and demands is ensured, and the optimal resource allocation efficiency is improved through construction of a dependency graph. The interaction between the data stream and the application is analyzed, abnormal data detection and fault path analysis are helped, fault problems are found in real time, reasons are identified, the downtime of the system is shortened, the continuity of services and the integrity of data are ensured, and the automation degree and the intelligent level of operation and maintenance management are improved.

Provided is a method, system, and computer program product for adaptatively training a large language model based on predicted changes in computing resources. A processor may identify data elements of computing resources for training a large language model. The identified data elements include a configured model topology. The processor may generate a forecast vector capturing a predicted change in the configured model topology over a time period. The processor may execute, using an optimization algorithm and the forecast vector, a series of optimization experiments using each training type of a plurality of training types to determine an optimal computing resource supply and demand over the time period. The processor may determine, based on the series of optimization experiments, an accuracy penalty value associated with each training type. The processor may train the large language model using a first training type that has a lowest accuracy penalty.

The invention belongs to the technical field of cloud computing and edge computing collaboration, and particularly discloses an intelligent scheduling and optimizing method for a cloud-edge collaboration network based on reinforcement learning. By constructing the state sensing matrix and generating the action decision vector, the problem that a traditional scheduling method is insufficient in correlation analysis of multi-dimensional operation state data in a complex network environment is solved, and the comprehensive sensing capability of the operation state of the network node is improved; a dynamic mapping mechanism among the running state, the resource limitation and the task allocation strategy is established, the task allocation and resource scheduling strategy is automatically and differentially adjusted according to the real-time state of the node, and the optimal matching between the task demand and the resource supply and the dynamic balance between the performance and the efficiency are realized; through performance index monitoring and closed-loop feedback optimization, the scheduling effect is mastered in real time, continuous iterative optimization is performed on the reinforcement learning model according to objective data, and resource waste and scheduling delay are reduced.

The embodiment of the invention discloses a scientific research project matching recommendation method and system based on artificial intelligence. The method comprises the steps of obtaining a historical project demand data set of a scientific research project demand side and a historical resource supply data set of a scientific research resource side; performing demand feature extraction processing on the historical project demand data set to generate a demand project feature set; performing resource feature extraction processing on the historical resource supply data set to generate a resource supply feature set; calling a trained deep matching learning model, performing dynamic matching degree evaluation processing on the demand item feature set and the resource supply feature set, and generating a matching degree evaluation result between each demand item description text and the resource supply description text; and generating a scientific research project matching recommendation strategy according to a matching degree evaluation result, and synchronizing the scientific research project matching recommendation strategy to the corresponding scientific research project demand side and the scientific research resource side to trigger scientific research collaborative optimization operation.

The invention discloses a resource metering method and device, electronic equipment and a storage medium, and relates to the technical field of computers.According to the resource metering method and device, due to the fact that a sliding window aggregation algorithm is adopted to merge use features of heterogeneous resources according to dynamic step length and duration, metering information of burst loads can be accurately captured, and metering efficiency is improved. Meanwhile, the charging rule is determined by combining the pre-training pricing model of real-time resource supply and demand and user characteristics, and resource use metering and adaptive charging which are finer than minute-level sampling are realized, so that the problems that the deviation between the resource use amount and the charging amount is obvious and the charging efficiency is low due to the fact that an existing charging method adopts a minute-level sampling mechanism can be solved. The technical problems of the prior art are solved, and the technical effects of improving the accuracy of heterogeneous resource charging and enhancing the credibility and fairness of the charging system are achieved.

The invention relates to the technical field of cloud computing, and discloses a multi-tenant cloud native database horizontal extension prediction method, which comprises the following steps of: A, collecting and preprocessing multi-channel data, collecting working load data of database instances, and preprocessing the collected original data; step B, resource load prediction: a Transform network is adopted to form a resource load prediction model, the Transform network predicts the resource load, and a prediction result is output; and step C, performing horizontal elastic expansion scheduling, optimizing a K8s HPA strategy based on a prediction result, and realizing automatic expansion and capacity reduction of a model reasoning service instance. According to the method, the number of database instances is adjusted based on the demand prediction result, it is guaranteed that resource supply is within a reasonable range, and hysteresis of elastic expansion and resource waste are avoided. The invention further discloses a multi-tenant cloud native database horizontal extension prediction system and device and a medium.

The invention discloses a production optimization method and system combined with logistics management, and relates to the technical field of production optimization. The invention discloses a production optimization system combined with logistics management. The production optimization system comprises a personalized order analysis module, a logistics state acquisition module, an internal logistics modeling module, an external logistics optimization module, a supply strategy management module, a joint scheduling optimization module, a scheduling execution module and a congestion monitoring and simulation feedback module. According to the invention, an external logistics resource supply strategy is structured and expressed as a transport time window matrix and a dynamic transport capacity allocation table, and the transport time window matrix and the dynamic transport capacity allocation table are used as boundary conditions to be introduced into a joint scheduling optimization model, so that a scheduling system can dynamically adjust a production and logistics collaborative plan according to an external resource state; and the adaptive capacity of the scheduling strategy to external supply changes is effectively enhanced.

The invention provides an operation and maintenance process optimization method and system applied to operation and maintenance technical services, and relates to the technical field of operation and maintenance technical services. Firstly, scene features, execution association relationships and resource demand attributes in an operation and maintenance task demand document are collected to form an operation and maintenance task information set; and meanwhile, collecting real-time operation state data and capability dimension information of the service nodes to construct dynamic capability portraits of the service nodes. And then, based on the scene features and the scene adaptation capability, executing bidirectional adaptation processing to generate a preliminary adaptation result, identifying and resolving resource supply conflicts in the preliminary adaptation result, and generating a final adaptation scheme. And finally, integrating an operation and maintenance task execution sequence and service node resource supply arrangement according to the final adaptation scheme to form an operation and maintenance process optimization scheme, and dynamically evolving and calibrating the scheme content according to real-time data in the execution process. The operation and maintenance task can be accurately allocated, the flexibility and adaptability of the operation and maintenance process are improved, and the operation and maintenance service quality and efficiency are improved.

The invention discloses a cross-cloud task arrangement optimization method based on multi-cloud resource scheduling, and the method comprises the steps: constructing a task topological weighted graph, and recognizing a parallelizable node set; constructing a task node preliminary scheduling feasibility table according to task topological weighted hierarchy information; constructing a granularity feature vector, and updating the task structure matrix; identifying granularity collaborative attenuation abnormal nodes, and performing cross-cloud task path repair and structure update; generating a cross-cloud scheduling time matrix based on each cloud platform resource supply capability index; predicting resource competition conflicts in combination with task execution index historical data, and generating a conflict prediction table; identifying granularity strategy deviation, and determining a repair trigger point; and generating a cross-cloud task granularity adaptive arrangement configuration file based on the repair trigger point information. According to the method, high reliability, high flexibility and high adaptability of task scheduling can be realized in a complex multi-cloud heterogeneous environment, and the stability, the resource utilization rate and the overall cooperation efficiency of cross-cloud task execution are remarkably improved.

The invention relates to a flexible scheduling method and device for soft switch resources of a cloud native architecture, equipment and a medium. The method comprises the following steps: performing feature extraction and marking on computing nodes, storage resources and network resources by using a resource classification model to generate a resource tag library representing initial classification of each resource, extracting resource feature data from a resource tag library based on each resource initial classification, generating current supply and demand data in combination with resource use state data collected in real time, calculating a resource allocation priority sequence by using a multi-objective optimization algorithm fusing service response time and cost balance optimization parameters, and generating a preliminary sorting result; and detecting a resource supply and demand imbalance state based on the preliminary sorting result and the resource supply and demand change data flow monitored in real time, if the imbalance state is detected, executing resource allocation adjustment calculation based on the current supply and demand data, and generating a preliminary resource allocation scheme, thereby realizing flexible scheduling and optimal allocation of soft switch resources under the cloud native architecture.

The invention relates to the technical field of cloud edge collaboration, in particular to a production scheduling intelligent management and control system based on cloud edge collaboration, and the system comprises a task feature aggregation module which is used for extracting a task type code based on an input task request, querying a task type and calculation amount mapping table in a pre-stored knowledge base, matching the task type and extracting a calculation amount parameter. According to the method, the mapping relation between the task type and the pre-stored calculation amount is dynamically matched, the task data packet size, the real-time requirement threshold value and the bandwidth reference value are linked to calculate the transmission time consumption parameter, quantitative mapping of task requirements and resource supply is achieved, and parameter deviation caused by traditional artificial experience estimation is eliminated. The CPU occupancy rate value of the edge node process queue is collected in real time, and the multi-dimensional node state vector is generated in combination with the number of cloud virtual machine instances and memory surplus data, so that the limitation of traditional single resource index monitoring is broken through, and the space-time fine granularity of load evaluation is improved.

The invention discloses a GIS resource management method, system and device and a medium, and belongs to the technical field of resource management, and the method comprises the steps: obtaining a city planning map, constructing a city three-dimensional model, and collecting multi-source city feature information; constructing a multi-source data fusion matrix of a GIS graph and generating three layers of attribute tags; a spatial gravity model is constructed, and reachability analysis is carried out on urban resources; constructing a dynamic prediction model, and triggering a model iteration mechanism; and generating a resource health degree index based on a model iteration mechanism result, and triggering a resource allocation control signal. The method has the beneficial effects that through multi-source data fusion matrix construction, precise space-time association of geographic space, Internet of Things time sequence and visual feature data is realized, and the integrity and accuracy of data integration are improved; quantitative evaluation and prospective pre-judgment of resource supply and demand are realized based on reachability analysis of a space gravity model and a dynamic prediction model of a space-time diagram convolutional network.

The invention relates to the technical field of cloud computing, in particular to a container cloud elastic scaling method based on a GRU-attention mechanism, and the method comprises the following steps: S1, constructing a GRU-attention mixed model, and generating a load prediction result through the GRU-attention mixed model; s2, based on the load prediction result generated in the step a), through an improved HPA mechanism, calculating the number of Pods required in a period of time in the future, and executing a corresponding capacity expansion and contraction strategy; and S3, continuously analyzing resource supply indexes through a resource supply evaluation module, and optimizing capacity expansion and shrinkage decision parameters according to quantitative results of an insufficient supply rate and an excessive supply rate so as to improve resource utilization efficiency. According to the invention, by optimizing resource allocation and reducing resource waste, the operation cost of the container cloud environment is effectively reduced, and especially in a large-scale and high-dynamic load scene, the method has remarkable economic benefits.

The invention relates to a park green land service level optimization evaluation method and device, a terminal and a medium, and the method comprises the steps: coding environment monitoring data and tourist positioning data into a unified space-time grid through a multi-source data fusion technology, and constructing a dynamic supply and demand prediction model; based on collaborative analysis of vegetation irrigation capability and tourist gathering intensity, quantifying a time-space conflict degree between a resource strategy and peak activities; a dynamic weight distribution mechanism is adopted to optimize collaborative parameters of an irrigation period and a diversion path, and an adjustment scheme considering both resource efficiency and tourist experience is generated; and finally, realizing closed-loop optimization of the park service level through multi-dimensional index fusion evaluation. According to the method, the problems of resource supply and tourist demand separation, response lag, experience decision deviation and the like in a traditional management mode are effectively solved, the irrigation conflict rate is remarkably reduced, the tourist satisfaction degree is improved, and a decision basis is provided for intelligent park management.

The invention relates to the technical field of data management, and discloses a cloud computing-based technical service integrated management system, which comprises a resource management module, a service monitoring module, a security control module, a data processing module and a user interface module, when dynamic allocation of cloud computing resources is carried out, resource load indexes are monitored in real time through an elastic telescopic unit in the resource management module, and resource demand estimation is carried out in combination with the prediction model, so that the system can actively adjust resource supply according to the fluctuation trend of technical service demands, the hysteresis of a resource allocation strategy is avoided, and the resource allocation efficiency is improved. Dynamic matching between cloud computing resource allocation and a real load is ensured, the resource utilization efficiency is improved, multi-dimensional monitoring data is collected and associated through a fault detection unit and a log analysis unit in the service monitoring module, so that the system can quickly position a core problem from an isolated alarm event, and the system reliability is improved. And the troubleshooting complexity and the time cost are reduced.

The invention provides an emergency resource data sharing method based on deep learning and a storage medium. The method comprises the following steps: acquiring original resource data sets distributed in different emergency response nodes; performing feature unification processing on the original resource data set to generate an emergency resource feature set with semantic consistency; inputting the emergency resource feature set into a pre-trained sharing relation modeling model for correlation analysis processing, and generating a sharing correlation feature set reflecting a resource supply and demand matching relation; generating an emergency resource data sharing rule set based on the sharing association feature set; and executing sharing operation of the multi-source emergency resource data according to the emergency resource data sharing rule set. According to the invention, complementary information of multi-source data can be fully utilized in an emergency resource data sharing process, and a sharing strategy can be flexibly adjusted according to actual scene requirements, so that the pertinence of emergency resource allocation and the effectiveness of information transmission are improved, and the accuracy of resource supply and demand matching is ensured.

The invention discloses a cloud mobile phone resource allocation method and related equipment, and relates to the technical field of cloud computing, and the method comprises the steps: obtaining multi-dimensional operation feature data of a user on a cloud mobile phone interface; based on the multi-dimensional operation feature data, determining a resource demand prediction result of the user in the target time period through a behavior prediction model; and dynamically configuring heterogeneous computing resources of the cloud mobile phone based on a resource demand prediction result. According to the method, the multi-dimensional operation feature data of the user on the cloud mobile phone interface is acquired, behavior details such as touch pressure and sliding tracks are captured, and fine-grained input is provided for resource prediction; time rules and space distribution of operation characteristics are analyzed based on a behavior prediction model, operation intention prejudgment is achieved, and the accuracy of resource demand prediction is improved; finally, heterogeneous computing resources are dynamically configured according to a prediction result, process-level GPU video memory partition and CPU core binding is completed before a user actually triggers high-load operation, and resource supply and behavior requirements are aligned in real time.

The application discloses a data management platform for smart park construction and belongs to the technical field of smart park management, and specifically comprises the following steps: collecting resource demand data and resource supply data of each enterprise in the park, performing demand feature extraction operation on the collected resource demand data to generate a structured demand description, performing supply feature extraction operation on the resource supply data to generate a structured supply description, calculating the matching degree between demand and supply based on the structured demand description and the structured supply description and generating an initial matching scheme, performing constraint condition checking operation on the initial matching scheme according to the park operation strategy to identify matching pairs that violate the constraint conditions, performing scheme adjustment operation on the matching pairs that violate the constraint conditions to generate an optimized matching scheme, and finally scheduling the park resources to complete resource data sharing between enterprises; and the application realizes intelligent management and efficient sharing of resource data in the park, and improves the overall resource utilization efficiency of the park.

The invention relates to the technical field of manufacturing execution systems, in particular to an intelligent scheduling method integrating demand prediction and supply matching, which comprises the following steps of: acquiring to-be-scheduled order information and a workshop real-time resource state in a manufacturing execution system, constructing an order demand feature vector, the method comprises the following steps: acquiring an equipment precision retentivity value, a current load rate value, an energy consumption level value and an operator skill proficiency value, and constructing a resource supply capability vector; according to the method, when an urgent order insertion instruction is received, a clear decision basis is provided, a rigid scheduling locking block which must be kept unchanged and an adjustable liquid adjustable scheduling block which can be adjusted can be distinguished, only the latter is subjected to accurate splitting and backward moving operation, and the target-clear local adjustment mode is adopted, so that the operation efficiency is greatly improved. The huge calculation overhead and the violent fluctuation of the production plan caused by the traditional global rescheduling are avoided, and the agility of the manufacturing system to deal with the market change is enhanced.

The invention discloses a higher vocational college quality evaluation system construction method based on production and teaching integration, and particularly relates to the technical field of production and teaching integration teaching assistance. Based on enterprise support resources and student actual receiving information, preset and actual knowledge network topological graphs are constructed respectively, and standardized knowledge points are extracted through natural language processing; generating a migration difficulty weight in combination with expert scores and industry standards, and forming a directed weighted graph; a support resource quality coefficient is generated by comparing a topological structure, semantic content and course stage integrating degree of the topological graph, calculating a skill knowledge matching degree, an aging synchronization rate and an operation integrating degree and performing conjoint analysis, and if the coefficient is lower than a threshold value, early warning is triggered to prompt optimization of resource supply; and by taking maximization of a quality coefficient as a target, redividing a course stage boundary based on a dynamic programming algorithm, correcting an operation integrating degree formula in combination with a migration path efficiency coefficient, preferentially adjusting a high migration difficulty stage, and realizing real-time alignment of a teaching rhythm and an industrial demand.

The invention discloses a cloud resource planning method based on time sequence prediction. The method comprises the following steps of collecting cloud resource data and performing storage processing to form time sequence data; preprocessing the time sequence data according to a target to obtain processed data; constructing a training sample set based on the processing data; constructing a time sequence prediction neural network model ScaleMixer, and training the time sequence prediction neural network model ScaleMixer based on the training sample set to obtain a trend prediction model; performing analysis based on a prediction result output by the trend prediction model in combination with actual cloud resource data to obtain an analysis result; and generating a cloud resource planning report based on the analysis result and the historical cloud resource data, and planning the cloud resources. Multi-dimensional accurate prediction of future cloud resource demands is realized, and a self-adaptive cloud resource allocation strategy is generated in combination with demand scene features, so that resource supply and demand fluctuation are effectively balanced.

The invention is suitable for the technical field of resource management, and provides a resource management system based on an artificial intelligence platform, which comprises a resource demand end, a resource scheduling center and a resource supply end, the resource demand end comprises a resource demand identification module used for identifying the resource type of the demand; the resource scheduling center comprises a resource demand receiving module, a resource supply feedback module, a resource state analysis module and a resource scheduling module; the resource demand receiving module is used for receiving demand information sent by the resource demand end; the resource supply feedback module is used for receiving supply information fed back by the demand supply end; the resource state analysis module is used for analyzing the state change of resource demand and supply; the resource scheduling module is used for performing resource scheduling according to the analyzed resource state change; the resource supply end comprises a resource checking module which is used for counting available resource information. According to the invention, dynamic resource scheduling can be carried out based on state change, and the resource utilization rate is effectively improved.

The invention discloses a network high-speed traffic acquisition method, system and device based on a clouded environment, and relates to the technical field of network communication. According to the invention, a clouded environment is created; a two-stage clouded environment capacity expansion and contraction strategy is constructed by using an optimized FCN network high-speed flow prediction model and a real-time load index, and pre-allocation and accurate adjustment of resources are realized; the optimal solution of the model learning rate is dynamically searched by introducing an optimization algorithm, the session continuity is guaranteed by adopting a consistent Hash algorithm, and the load balance degree and the processing throughput are improved in combination with a three-level dynamic filtering rule and a lightweight DPI (Deep Packet Inspection) technology; due to an annular buffer management strategy and a Kafka message queue synchronization mechanism, the key data loss rate and the data synchronization delay are reduced, and the requirements of real-time analysis and long-term storage in a high-speed network scene are met; according to the method, the problem of insufficient resource elasticity of a clouded environment is effectively solved, the resource supply response speed is improved, and the cost and the prediction error rate are reduced.

The invention relates to the technical field of policy resource intelligent matching, in particular to a policy resource intelligent matching method and system, and the method comprises the steps: data collection, matching strategy recognition, visual hierarchical feedback and strategy adjustment and optimization. The data acquisition is used for acquiring declaration subject attribute data and policy term text data, and the matching strategy identification is used for generating optimal target matching strategy data of the declaration subject attribute data and the policy term text data; according to the invention, technologies such as a BERT embedding model, a graph neural network algorithm module, a particle swarm search algorithm module and a natural language processing interface are introduced, so that efficient and accurate matching between a declaration subject and policy resources is realized; the conflict between policy dynamics and system staticity, the conflict between semantic complexity and man-machine interaction simplicity and the conflict between resource supply accuracy and service efficiency are effectively solved, and the scientificity and practicability of policy resource intelligent matching are remarkably improved.