19 results about "Demand modeling" patented technology

The invention discloses an electric vehicle charging station dynamic pricing method based on graph attention multi-agent reinforcement learning, and the method comprises the steps: S1.1, constructing a double-layer user equalization (UE) demand modeling frame, constructing a dynamic origin-destination (OD) distribution model based on point-of-interest (POI) distribution and road network topology at an upper layer, and achieving traffic flow guided charging demand distribution at a lower layer through a probability model, according to the electric vehicle charging station dynamic pricing method based on graph attention multi-agent reinforcement learning, the problems of demand modeling disjunction, insufficient space competition perception and low efficiency of multi-agent cooperation of a traditional method are effectively solved, the system income is improved, the congestion node proportion is reduced, the utilization rate variation coefficient optimization is reduced, and the method is suitable for large-scale popularization and application. And support can be provided for urban charging facility operation and power grid dispatching.

The invention relates to the technical field of computer software and information management, and discloses a product demand classification method, which comprises the following steps of: performing data acquisition of multi-source data according to product demands to obtain a multi-source data set; performing data preprocessing on the multi-source data set; performing multi-modal feature fusion, cross-process semantic alignment and demand modeling storage on the multi-source data set after data preprocessing to obtain a standardized demand model; performing full-process association on the standardized demand model through process mapping and weight calculation, and establishing a multi-dimensional association relationship between the demand and the business process; and solving cross-process demand contradictions through conflict management and an iteration mechanism, and optimizing the standardized demand model to complete closed-loop feedback of demand optimization. By utilizing the scheme of the invention, the problems of extensive demand classification, weak cross-process integration capability, poor dynamic adaptability and the like in the prior art can be solved, and efficient management and integration of demands can be realized.

The application provides a development demand modeling analysis method of a mechatronic hydraulic complex system, comprising the following steps: S1, performing stakeholder analysis to identify stakeholders and stakeholder demands, and generating a tracking matrix between the stakeholders and the stakeholder demands; S2, performing use case analysis to determine system function demands of a system to be developed based on the tracking matrix; S3, performing system environment analysis to determine system interface demands of the system to be developed; S4, performing index analysis to determine system performance demands of the system to be developed; and S5, determining system development demands according to the system function demands, the system external interface demands and the system performance demands. The technical scheme of the application can improve the system development demand obtaining speed and reduce the product research and development cycle.

The invention provides a traction converter control software design method fused with a large language model, and the method comprises the steps: analyzing a historical document through a large language model subjected to the fine adjustment of traction converter field data, generating a structured demand, and building MATLAB demand tracing; an initial model is built in Simulink according to requirements, and multi-target iterative optimization is completed through model recommendation parameters; normal, extreme and fault working condition scenes are generated through the large language model, MATLAB batch simulation is driven, and a semantic analysis result is given; codes are automatically generated through an Embedded Coder, readability and real-time optimization is implemented through a large language model, and a final code is output after PIL verification; codes are downloaded to a real control board card, a closed loop is formed in combination with a virtual traction system built by a semi-physical simulation platform, a test case is automatically generated by a large language model, software and hardware collaboration data are analyzed, and iteration is carried out until the performance reaches the standard. According to the method, full-process AI driving including requirements, modeling, simulation, codes and testing is achieved, the development period is remarkably shortened, and the software reliability is improved.

This invention relates to the field of artificial intelligence technology and discloses a method and system for optimizing feed formulation for caged laying ducks. The method includes collecting multidimensional dynamic data and environmental parameter data of caged laying ducks; preprocessing and extracting features from the collected multidimensional dynamic data and environmental parameter data to generate standardized dynamic feature data and environmental stress factor data of laying ducks; and constructing a process flow that includes data collection, feature extraction, demand modeling, formula optimization, precise execution, feedback evaluation, and iterative updates, and building a corresponding collaborative system to achieve closed-loop optimization from perception, decision-making, execution to feedback, so that the feed formulation can be dynamically adjusted according to the real-time status of the laying ducks and environmental changes.

The invention discloses an LLM-based system-level demand modeling and analysis method and platform, and belongs to the field of demand engineering and system modeling. The invention provides an LLM (Logical Language Model)-driven automatic demand behavior model construction and conflict detection method aiming at the problem that accurate modeling and consistency verification of natural language system-level software demands are difficult. According to the method, control flow logic in a demand text is recursively analyzed by simulating an expert stratified analysis process, and a behavior model is automatically generated. The modeling process comprises the steps of recognizing atomic activities, extracting a hierarchical relation and constructing a behavior model. On the basis, a system-level behavior tree is constructed based on preposition and postposition conditions of behavior nodes, a plurality of demand models are comprehensively analyzed, and behavior consistency analysis and conflict detection on the system level are achieved. According to the method, automatic expansion from the demand-level behavior model to the system-level behavior model can be realized, and an effective technical scheme is provided for automatic modeling and analysis of system-level demands.

The invention relates to the technical field of charging piles, and discloses a charging pile operation state reproduction method, system, equipment and medium, and the method comprises the steps: constructing a three-in-one adjustable working condition simulation system of a power grid side-load side-fault side, and sequentially executing the steps of demand modeling, module design, calibration debugging, performance verification and the like. A DSP + FPGA dual-core controller and a closed-loop control algorithm are adopted, high-precision sensing and error compensation are combined, and accurate and rapid reproduction of voltage fluctuation, harmonic waves, three-phase imbalance, dynamic loads and various fault scenes is achieved. According to the method, the test coverage rate and reproduction fidelity are remarkably improved, and the method is suitable for research, development, detection and operation and maintenance verification of various alternating-current and direct-current charging piles.

The invention discloses a cloud-side collaborative substation intelligent decision analysis operator library construction method, belongs to the technical field of power system automation and intelligent decision, and aims to solve the problems that a traditional substation decision analysis system is dispersed in operators, poor in reusability, insufficient in cloud-side resource adaptation, high in decision delay and difficult to maintain. Firstly, operator demand classification and specification definition are completed through substation business scene disassembly; secondly, an atomic operator and a composite operator are designed according to the function modules, and standardized interface packaging is adopted; unified management and index construction of operator metadata are realized through a cloud edge collaborative registration center; then dynamically configuring a cloud edge scheduling strategy based on resource awareness and service priority; and finally, iterative optimization of the operator library is realized through incremental updating and version management and control. Based on a cloud edge collaborative architecture, a unified, reusable and dynamically adaptive intelligent decision analysis operator library is constructed through a five-step core process of'demand modeling-operator design-packaging registration-collaborative scheduling-iterative maintenance '.

The invention discloses a data modeling method and device for demand modeling in software development, equipment and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring enterprise-level business data; dividing and establishing an enterprise subject domain model based on the business domain; for each subject domain model, forming an enterprise concept data model; mapping the business concepts into business entities, defining entity relationships among the business entities, and determining attributes and attribute domains for the business entities; dividing the plurality of attribute domains into an attribute domain group, and determining an attribute domain instance for the attribute domain belonging to the code class; forming an enterprise logic data model based on the business entity, the entity relationship, the attribute and the attribute domain; generating an application physical data model based on the enterprise logic data model; wherein the enterprise concept data model is visually presented to business personnel in the form of a classification hierarchy graph; and presenting the enterprise logic data model to technicians in the form of an entity relation graph. The data quality of an enterprise is improved.

The application provides a modeling and model conversion method for automatic driving demand analysis, comprising the following steps: step 1, defining a UML extension meta-model; step 2, obtaining an intermediate model; and step 3, generating a target extension model. The application combines the abstracted concepts with the UML analysis model by analyzing and extracting the automatic driving related terms and concepts and the behavior characteristics of the automatic driving vehicle, realizes the extension of the UML class diagram and the activity diagram in the automatic driving field, formulates the model conversion rules according to the automatic driving concepts of the automatic driving system limited use case modeling language, and realizes the model conversion method based on the model conversion engine written in the Java language, so that the defects that the general model conversion method cannot process the RUCM4ADS model are made up, the efficiency and accuracy of the model conversion in the automatic driving field are promoted, the subsequent analysis and testing of the system demand are facilitated, and the accuracy of the demand modeling in the automatic driving field is improved.

The invention relates to the technical field of human resource and project management crossing, and particularly provides a multi-project personnel intelligent arrangement and dynamic balance method and device, and the method comprises the following steps: S1, carrying out the quantification of employee capability portraits, and generating an operable capability vector; s2, project demand modeling is carried out, and a demand standard of a matching capability vector is generated; s3, calculating an employee-project matching degree, and quantifying a suitability level; s4, constructing a'employee-project-neutral position 'bipartite graph, and solving global optimal distribution by using a minimum cost flow algorithm; s5, performing '1 + N' focusing and teaching copying on the high-ability employees, and establishing an L1-L3 gradient task pool and cross-project scheduling on the low-ability employees; and S6, performing dynamic iterative optimization, real-time monitoring and periodic redisk. Compared with the prior art, the method can achieve a remarkable effect through quantitative matching, global optimization and dynamic balance.

The invention relates to a substation TSN flow optimization scheduling method and system based on a near-end strategy optimization algorithm and a medium. The method comprises the following steps: environment deployment: deploying a TSN protocol stack and an AI scheduling engine in an embedded real-time operating system of a substation, and establishing a flow data acquisition and analysis environment; traffic modeling: performing feature extraction and demand modeling on multiple types of service traffic (such as telemetering, remote control and protection signals) of the transformer substation through a near-end strategy optimization algorithm, and generating a traffic priority and time delay constraint model; dynamic scheduling: based on AI model output, combining a QBV / QCI scheduling mechanism of a TSN to realize dynamic time slot allocation and priority adjustment of traffic; and hardware adaptation: designing a TSN intelligent network card based on the FPGA, integrating an AI lightweight reasoning module, and realizing real-time execution of a scheduling strategy. Through the adaptive decision-making capability of the near-end strategy optimization algorithm, the scheduling adaptability of the TSN to the complex dynamic flow of the transformer substation is improved, the deterministic transmission requirements of different services are met, and high reliability and low time delay of communication of a power system of the transformer substation are guaranteed.

The application discloses a parameter driving launch vehicle modeling method, device, equipment and medium based on MBSE, which comprises the following steps: demand modeling: capturing system demand, converting launch vehicle system stakeholder demand into complete specification full rocket system demand; parameter modeling: modeling physical parameters of the launch vehicle model, parameterizing the launch vehicle model, and forming a launch vehicle parameter index set; function modeling: through simulation of a function model, according to selected architecture; interface modeling; through a confirmation process of demand modeling of the launch vehicle, based on refinement and decomposition of a use case scenario of the launch vehicle, forming an interface of a launch vehicle system context model; physical modeling: describing the system architecture from the perspective of the launch vehicle physics, defining the relationship and parameters of the launch vehicle physical equipment and the interface thereof by organizing the launch vehicle physical elements, obtaining the physical architecture of the launch vehicle, and meeting the expected design attributes and technical indexes.

The invention belongs to the field of document tool integration, and relates to an integration method and device of a demand modeling tool and a demand management system and a medium. Comprising a data interface standard, a data format requirement, a data transmission protocol, a data conversion rule and a data adaptation requirement meeting a demand scene; analyzing the modeling tool, analyzing an engineering file in the tool, performing data model mapping with a demand management system, and establishing a relationship between the model engineering and the demand management system; and importing data of a demand management system through an interface by using a demand modeling tool, and performing entries of data management. According to the method, a unified integration specification is provided, data of modeling tools are effectively mapped to the demand management system, received demand information is tracked, monitored and managed in the demand management system, the difficulty of transmission and sharing of the demand information among different tools is broken through, management of the demand information is effectively improved, and the demand management efficiency is improved. The efficiency and accuracy of demand management are greatly improved, and efficient integration and cooperative work are realized.

This invention discloses a method for optimizing the operating costs of low-altitude infrastructure based on a quantitative engineering model, comprising the following steps: obtaining a structured dataset; constructing a set of operating elements for low-altitude infrastructure; establishing a quantitative engineering model; performing demand modeling and load analysis to obtain the demand distribution of the service area set on a time series set; establishing an operating cost optimization model; solving the operating cost optimization model using a symplectic geometrical structure-preserving optimization algorithm to obtain resource allocation results and operation scheduling results; performing simulation evaluation to obtain operating cost results and resource utilization rate indicators, and updating the parameters of the quantitative engineering model; outputting the operating cost optimization results to improve resource utilization and demand satisfaction rate, and enhance the stability and intelligence level of the low-altitude infrastructure system operation.

The invention relates to the technical field of data analysis, and discloses a data prediction analysis method and system based on big data, and the analysis method comprises the steps: S1, data base construction, S2, on-demand modeling and preparation, S3, batch flow fusion and service packaging, and S4, intelligent embedding and continuous learning. According to the method, the original data directly enters the lake, and the structure is redefined during analysis, so that the processing efficiency of semi-structured and non-structured data is greatly improved, the data access and analysis path is shortened, and the trial and error cost is reduced; through the batch flow integrated architecture, the real-time and offline data processing logic unification is realized, the data aperture difference is fundamentally eliminated, the uniqueness of the decision basis is ensured, and meanwhile, the real-time decision and response in the event are supported; through automatic monitoring and feedback closed loop, the model can continuously sense data changes and actively complete self-iteration, it is ensured that the prediction accuracy is maintained at a high level for a long time, and large-scale intelligent operation is achieved with lower cost.

The application discloses a kind of system-level demand modeling and analysis method and platform based on LLM, belong to demand engineering and system modeling field.Aiming at the problem that natural language system-level software demand is difficult to accurately model and consistent verification, an automatic demand behavior model construction and conflict detection method driven by LLM is proposed.The method simulates the hierarchical analysis process of experts, recursively analyzes the control flow logic in the demand text, and automatically generates the behavior model.The modeling process includes identifying atomic activities, extracting hierarchical relationships and constructing behavior models.On this basis, based on the precondition and postcondition of the behavior node, a system-level behavior tree is constructed, and multiple demand models are analyzed comprehensively to realize behavior consistency analysis and conflict detection at the system level.The application can realize the automatic expansion from the demand-level behavior model to the system-level behavior model, and provides an effective technical solution for system-level demand automatic modeling and analysis.

This invention discloses a non-periodic dynamic detection and maintenance method for multi-state systems based on Gaussian demand and customized PPO (Progressive Point of Action), comprising the following steps: A) System and demand modeling: including a multi-state system model, time-varying demand modeling, and definition of detection and maintenance actions; B) Continuous-time MDP modeling: constructing the dynamic detection and maintenance decision problem as a continuous-time Markov decision process, including state space, action space, state transition probabilities, reward function, and objective function; C) Customized PPO algorithm framework: designing a deep reinforcement learning framework based on PPO, adapting to the hybrid action space, and efficiently solving the MDP model. The demand modeling of this invention is accurate: by using a Gaussian process to model time-varying demand, it can simultaneously capture the expected trend, random fluctuations, and time correlation of demand. Compared with traditional constant, linear, or simplified Markov demand modeling, it is more in line with actual industrial scenarios and effectively reduces the risk of supply-demand mismatch.

The invention provides a software security demand modeling method based on an improved clustering algorithm, which comprises the following steps: acquiring attack information data, classifying the attack information data, and generating a classification cluster number and a characteristic value; performing clustering analysis on the attack information data through a spectral clustering algorithm according to the number of the classification clusters and the characteristic value, and generating a software security demand model; acquiring unknown attack information data, and performing distance calculation on the unknown attack information data through a KNN algorithm according to a software security demand model to obtain candidate attack information; and analyzing and identifying the candidate attack information to obtain an attack target, and the invention provides an effective and efficient information security management method.