8 results about "Resource based" patented technology

This invention discloses an optimization method for a financial management system for construction workers, relating to the field of financial management technology. After identifying financial anomalies, a fund utilization stability index is constructed based on expenditure change curves. Simultaneously, a resource optimization index is constructed based on resource utilization. The fund utilization stability index and the resource optimization index are jointly input into an intelligent optimization model trained on historical data. This model automatically calculates a financial coordination index. If the financial coordination index is lower than a preset standard, it automatically determines that financial management and resource allocation have not reached an optimal state. Based on the real-time calculated financial coordination index and its fluctuations over different time periods, the model autonomously selects and executes a series of optimization strategies, including intelligent budget adjustment and expenditure control, dynamic optimization of working hours and resources, and real-time generation of financial health reports and intelligent reminders. This optimization method, through an intelligent optimization model, achieves timely identification of financial anomalies and resource optimization, effectively improving financial management efficiency.

This invention relates to the field of computer system resource management technology, and in particular to a method for dynamic allocation and adjustment of computer system resources based on big data analysis. This method involves collecting and standardizing hardware resource status data, business load characteristic data, and historical allocation data. A two-dimensional evaluation model is established to determine resource allocation priorities. An optimal resource allocation scheme is obtained by constructing an objective function that maximizes resource utilization and minimizes business performance deviations. This reduces resource allocation problems caused by human error. Furthermore, a dynamic adjustment mechanism prioritizes high-urgency business needs in mild conflict scenarios. The allocation effect is monitored in real time through a three-dimensional scoring system of resource utilization, business performance, and system stability, with feedback processing of abnormal indicators to obtain a list of abnormal indicators. This effectively avoids the risk of the optimal allocation scheme failing, ensuring that the implemented scheme matches theoretical expectations and preventing resource waste.

The application relates to the technical field of artificial intelligence, can be applied to business system platforms such as medical health and financial technology, and discloses a resource pool management method based on clustering analysis, which comprises the following steps: acquiring multidimensional data, pre-processing the multidimensional data, and outputting numerical characteristics; acquiring industry characteristic data, updating parameters of a clustering model based on a contour coefficient, an elbow rule and the industry characteristic data, inputting customer resources into the updated clustering model, classifying the customer resources based on the numerical characteristics by the clustering model, and outputting clustering grouping results; scoring the customer resources according to the clustering grouping results and customer dynamic indexes, generating customer dimension indexes, scoring business personnel according to business personnel dynamic indexes, generating business dimension indexes, calculating priority evaluation results through the customer dimension indexes and the business dimension indexes; and matching optimal selected business personnel for to-be-matched customers according to the priority evaluation results.

This application provides a medium- to long-term power system planning method that considers the flexible production mode of nuclear power. The method includes constructing a system operation model with multiple power source categories based on resource data and power parameters, and constructing a capacity planning model with the objective of minimizing the annualized comprehensive cost of the system, combined with first-cost data. Constraints include nuclear power flexibility operation constraints, power supply and demand balance constraints, system reserve constraints, installed capacity constraints, cross-sectional constraints, carbon emission constraints, and energy storage operation constraints. Based on the nuclear power flexibility operation constraints and output data, the system operation model is used to perform production simulation to obtain load forecast data and second-cost data. Based on the load forecast data and second-cost data, the capacity planning model is solved to obtain the power system planning scheme. This method simultaneously considers economic efficiency, low carbon emissions, and flexible regulation capabilities, achieving multi-objective collaborative planning and providing scientific support for the optimization of the power source structure of new power systems.

This invention provides a resource intelligent matching method and system based on a metaverse. It constructs a virtual-real data association graph by collecting multi-dimensional data; performs semantic analysis on tourist demand data to generate text feature vectors and obtain target tasks; analyzes resource status based on scene state and cultural resource data to construct a fragmented resource graph; configures fragmented resource combination rules according to the obtained target tasks, and selects fragmented resources that meet the target tasks from the fragmented resource graph to combine them into a resource block set; calculates the similarity between the text feature vectors and the resource block set, and dynamically selects resources that meet preset constraints from the resource block set to obtain resource matching results; and generates corresponding scheduling instructions based on the resource matching results. By accurately analyzing tourist needs, constructing a fragmented resource graph, and dynamically matching cultural and tourism resources according to constraints, it is possible to achieve intelligent matching of cultural and tourism resources based on tourist needs, thereby improving tourist satisfaction.

This invention discloses a pricing method for computing power services based on resource stress prediction and electricity price prediction, belonging to the field of cloud computing and data center operation and management technology. The method includes the following steps: Step S1. Establishing a multi-timescale computing power resource stress prediction model; Step S2. Constructing an opportunity cost mapping and comprehensive cost calculation model based on resource stress; Step S3. Constructing a differentiated pricing model and outputting the computing power service price. This method integrates long-term basic load, medium-term planned load, and short-term elastic load into a tiered occupancy ratio model, and corrects them respectively through planned execution correction coefficients, seasonal factors, special event factors, industry characteristic factors, and acceptable delay correction coefficients. Compared with existing technologies, it can more accurately reflect the actual occupancy status and dynamic changes of computing power resources. This invention can also improve the rationality and stability of the pricing mechanism, and is more conducive to the market-oriented trading and packaged delivery of computing power services.

The application provides a kind of computing power resource configuration method and system based on network intention analysis, comprising: obtaining user natural language input and obtaining standardized intention text by text preprocessing;By large language model, semantic understanding is carried out, business type is identified and explicit intention primitive is extracted;Based on business knowledge graph, explicit intention primitive is inferred and enhanced, and complete intention requirement set is obtained by completing implicit demand;After demand standardization processing and resource semantic mapping, resource description framework is generated;The resource description framework is matched with the computing power identifier of the computing power node, and the candidate computing power node set is screened in combination with real-time network state;Based on resource description framework, candidate node, network state and business constraint, adaptive prompt word is constructed, and candidate configuration scheme is generated by large language model, and the final configuration scheme is output after verification and optimization.The application can improve the integrity and accuracy of intention analysis, realize intelligent generation and adaptive optimization from natural language business intention to computing power configuration scheme.

The application discloses a data service configuration method and system based on resource field modeling and configuration self-construction, and belongs to the technical field of power secondary equipment management configuration. The method comprises the following steps: according to the current secondary equipment type and the current business demand, corresponding meta-models are matched from the meta-model instantiation templates according to the policy constraint instructions, and instantiated resource points and instantiated functions are generated; the association relationship between the instantiated resource points and the instantiated functions is obtained; the target standard model file is parsed, the association relationship between the target standard model file and the instantiated resource points and the instantiated functions is respectively established, and the association relationship is allocated with a short address mapping relationship, and a target standard model configuration is generated; a standardized interactive description file is generated and deployed to the target power secondary equipment, and a data service configuration result is obtained. The application can solve the problems of non-standard plug-in modeling, complicated target standard model adaptation and low instantiation configuration efficiency.