35 results about "Demand modeling" patented technology

The invention discloses a software requirement modeling method and device based on a natural language, equipment and a medium, and relates to the field of software requirement modeling. According to the software demand modeling method and device based on the natural language, the equipment and the medium, through automatic demand analysis and model generation, the manual workload is greatly reduced, and meanwhile, errors caused by manual understanding deviation are reduced; close connection among the steps is ensured by sharing intermediate representation and a unified data model, output of the previous step provides structured information for input of the next step, and information loss or distortion is avoided; a demand provider is allowed to participate in a model confirmation process through an iterative demand acquisition and modeling framework of man-machine cooperation, and the quality of a demand model is continuously improved; by means of the model consistency analysis technology based on the data flow dependency relationship, the problem of inconsistency between the requirement specification and the software model can be automatically detected, and the software product quality is improved.

The invention provides a progress and resource management linkage application system based on BIM, relates to the technical field of building management, and is suitable for full life cycle management of design, construction and operation of a building. The system comprises a demand modeling module, an intelligent equipment optimization module, a collision detection and progress optimization module, a multi-professional data integration and real-time simulation module, a building full-life-cycle intelligent management module and the like. By optimizing arrangement and streamline design of equipment, the space utilization rate and the medical service efficiency are improved; in the construction stage, a collision detection function and a digital twinning technology of the BIM are utilized to realize multi-specialty collaborative design and progress optimization; in the operation stage, the operation state of the equipment is monitored in real time in combination with an Internet of Things sensor, a preventive maintenance plan is generated, and energy consumption management is optimized through energy consumption data analysis. The system improves the design precision and construction efficiency of the building and the intelligent level of equipment management and operation maintenance, and has a wide application prospect.

The invention discloses a multi-mode continuous transportation supply and demand matching degree evaluation method for a comprehensive transportation junction, and the method comprises the steps: carrying out the collection, cleaning, standardization and time-space alignment of multi-source passenger flow and transport capacity data of inter-city traffic and an intra-city continuous mode, and constructing a unified data basis; according to operation characteristics of different connection modes, a differential supply and demand measurement model is established, and accurate mapping of multi-mode passenger flow and transport capacity is realized through a total passenger flow-connection mode distribution matrix; and introducing a cloud model theory, and carrying out weighted fusion on matching degree indexes of each continuing mode to describe fuzziness and uncertainty in a supply-demand relationship, so as to realize quantitative evaluation of the multi-mode transport capacity coordination level of the comprehensive hub. Through differential supply and demand modeling and cloud model fusion, the supply and demand matching state of multi-mode continuous transportation of the comprehensive passenger transport hub can be accurately reflected, and a scientific decision basis is provided for operation scheduling and resource optimization of a hub management department.

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 energy digitization, in particular to a micro-grid energy storage capacity demand calculation method and system. The invention discloses a micro-grid energy storage capacity demand calculation method. The method comprises the following steps: S1, data acquisition and multi-scene demand modeling; s2, micro collaborative parameter mapping is configured; s3, dynamically optimizing the discharge depth; and S4, electric power and electric quantity balance calculation and capacity optimization. A system for calculating the energy storage capacity demand of the microgrid is used for implementing the method and comprises a computer readable storage medium in which computer program instructions are stored. The invention provides a user energy storage capacity demand calculation method based on a micro-distribution collaborative grid management model, and the method achieves the precise configuration of the energy storage capacity of a user through multi-scene demand modeling, micro-distribution collaborative parameter mapping and refined electric power and electric quantity balance calculation. And the energy storage utilization rate and the full life cycle economy are improved.

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 relates to a code generation method suitable for field requirement modeling, and belongs to the field of software modeling. Comprising the following steps: extracting four-level entities and attributes of superior and subordinate dependence based on domain semantic analysis to construct a demand template ontology model; demand template grammar and semantic constraints are obtained based on four-level entities and attributes of superior and subordinate dependencies, and a demand template expression mode is constructed based on the demand template grammar and semantic constraints; the method comprises the steps of constructing a demand template on the basis of a demand template ontology model and a demand template expression mode, generating a database script on the basis of the demand template, constructing a CRUD business logic template, a collaborative logic template and an atomic component template, and automatically generating front-end and rear-end codes on the basis of the templates. According to the method, the front-end codes and the rear-end codes corresponding to the demand templates are automatically generated by constructing different templates, front-end and rear-end code architectures are simplified, multi-user collaborative demand modeling is supported, the method is suitable for demand modeling in fields with huge scales and complex modeling tasks, and the demand modeling efficiency is improved.

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 discloses a demand modeling and management method and system supporting multi-level demand tracing and baseline management. The method comprises the following steps: constructing a demand information architecture comprising a tree hierarchical structure, a demand type definition and an association relationship definition; generating and storing demand entries in a unified format by integrating a demand import interface based on an attention mechanism deep learning model; dynamically updating a state mark of the associated entry based on the association relationship, wherein a user can instruct to create a demand baseline; serializing the demand set and calculating a hash value, and generating a non-tampering baseline snapshot; a demand consistency report is finally generated by constructing a demand traceability graph, utilizing a graph traversal algorithm and adopting an innovative mathematical model combining time distribution and associated weight to quantitatively calculate a change influence index. According to the invention, automation, intelligentization and quantitative analysis of the whole life cycle of demand management are realized, and the consistency maintenance, change control and tracing analysis efficiency of demands are significantly improved.

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 application discloses a kind of public transport passenger travel behavior space-time semantic similarity measurement method, the implementation steps of this method include: (1) based on the card data of passenger extraction departure place, departure time, travel mode, activity type and destination 5 kinds of travel attributes, further construct public transport passenger individual travel sequence;(2) the travel attribute in passenger travel sequence is expressed as discrete variable;(3) the travel behavior attribute and travel sequence are analogized as word and sentence respectively, and the travel attribute is converted into word vector by applying Word2vec model, so as to realize the space-time semantic embedding representation of travel sequence;(4) the space-time semantic similarity of passenger multi-day travel behavior is measured by using improved word shift distance.The application solves the defect that traditional travel behavior similarity measurement model cannot consider its space-time semantic correlation, can provide support for passenger market segmentation, individual travel demand modeling and public transport policy making etc..

The invention provides a method for customizing specific business scene construction based on a model view, and belongs to the technical field of system modeling, and the method specifically comprises the steps: carrying out unified system model construction through system analysis and architecture decomposition; performing business scene demand modeling according to the business intention and the core target; based on the unified system model, defining a model view cutting rule; generating a model view set according to business requirements on the basis of a model view cutting rule; constructing and organizing the business scene based on the constructed business scene, the unified system model and the model view set; performing scene execution based on the constructed business scene; and feeding back an execution result, and optimizing the constructed service scene according to the execution result. Through the processing scheme provided by the invention, the suitability, practicability and operability of the system model in the specific business scene are improved, the modeling threshold is reduced, and the business scene construction efficiency is improved.

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 application discloses a target-driven and data-mining-based abnormal modeling behavior detection method, relates to target-oriented requirement modeling analysis and data mining technology, and comprises the following steps: firstly, a target-oriented requirement modeling analysis technology is used to model normal modeling behaviors of a modeler; then, data mining technology is used to mine and identify real modeling operation data, to obtain normal modeling behavior patterns from the data, and to verify the normal modeling behaviors defined in the model of the previous step; finally, the screened normal modeling behavior patterns are used to detect abnormal modeling behaviors in the modeling operation data. The application is suitable for collecting modeling process data of the modeler on a modeling platform, modeling the modeling behaviors of the modeler to obtain a normal modeling behavior target model, then combining real modeling process data to extract normal modeling behavior patterns to form a normal modeling behavior pattern set, and finally detecting an abnormal modeling behavior process.

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 application discloses a device for supporting complex system requirement integrity modeling and evaluation, and relates to the technical field of system engineering, and comprises requirement modeling, requirement integrity evaluation, design process modeling, requirement and design process ontology integration, requirement traceability expression and evaluation; the requirement modeling module comprises requirement entry modeling and requirement content constraint definition; the requirement modeling module mainly functions to realize requirement model creation based on requirement integrity expression rules and constraints, and guarantee requirement content integrity in the created requirement model; in the requirement modeling module, the requirement content constraint definition submodule provides constraints to the requirement entry modeling submodule; the application uses entry modeling technology to decompose high-level requirements into specific and operable entries in the requirement modeling module, which promotes in-depth understanding of the requirements and ensures that each requirement is accurately defined and recorded.

The invention discloses a multi-target reconfigurable regional electronic and electric appliance framework self-generating system for a high-safety intelligent automobile. The system comprises a demand modeling module for establishing a six-dimensional target optimization model based on a vehicle function demand and a multi-dimensional constraint condition; the staged optimization module is in communication connection with the demand modeling module and is used for generating a candidate region architecture scheme according to the six-dimensional target optimization model; the system strengthening module is in communication connection with the staged optimization module and is used for designing a physical layer structure with a rapid fault recovery capability according to the candidate architecture scheme; and the scheme generation module is in communication connection with the system enhancement module and is used for integrating the optimization result and the system enhancement design and outputting a final regional electronic and electric appliance architecture design scheme comprising an electronic control unit integration strategy, dynamic reconfigurable communication configuration and a redundancy mapping relation. According to the method, the problems of multi-target splitting optimization, safety response lag, weak reconstruction capability, high life cycle cost and the like can be solved.

The invention discloses a charging demand-oriented city charging facility constant volume planning visualization system, which comprises a demand prediction module, which combines the market sales volume, vehicle performance parameters and regional driving characteristics of all electric vehicle types, and constructs an electric quantity demand estimation model in a vehicle type, version, region and season division mode; obtaining a predicted charging demand; a capacity optimization configuration module introduces a multi-objective optimization framework to construct a charging facility constant volume planning model on the basis of the predicted charging demand, and realizes optimization configuration under the condition of satisfying multiple constraints; and the display module constructs an interactive visual interface, and displays an output result of the charging facility constant volume planning model and auxiliary decision analysis. According to the invention, through key technologies such as multi-source data fusion, demand modeling and dynamic simulation, accurate planning and optimal configuration of the capacity of the charging facility are realized. Meanwhile, a three-dimensional visualization function is integrated to visually display a planning result, and the understanding and decision-making efficiency of the scheme 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 relates to a registration service layout optimization method based on multi-source data and GIS spatial analysis, which comprises the following steps: step S1, multi-source space-time data acquisition and preprocessing; step S2, space-time modeling based on passenger registration demand of multi-source space-time data; step S3, construction of a GIS network data set of a traffic station; step S4: establishment of a multi-objective location-allocation optimization model, comprising: step S4-1, setting of an objective function; step S4-2, setting of a constraint condition; step S4-3, solving of the multi-objective optimization model by using a genetic algorithm to obtain a set of Pareto optimal solutions; step S5: multi-scheme evaluation and three-dimensional visualization output. The demand modeling method of multi-source data fusion innovatively combines train schedules, passenger stay behaviors, individual attributes and building space data, constructs a more realistic "demand pulse" model and a demand intensity surface, and breaks through the limitation of relying only on passenger flow density.

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 relates to a domain semantic driven demand modeling method, and belongs to the field of software modeling. Comprising the following steps: extracting four-level entities and attributes of superior and subordinate dependence based on domain semantic analysis to construct a demand template ontology model; demand template grammar and semantic constraints are obtained based on the four-level entities and attributes of the superior and subordinate dependencies, and a demand template expression mode is constructed based on the demand template grammar and semantic constraints; and constructing a demand template based on the demand template ontology model and the demand template expression mode, and carrying out demand modeling based on the demand template. According to the method, the demand template ontology model is constructed by extracting four levels of entities and attributes which are dependent on the upper and lower levels, customization of the demand template is simplified, and a simple and easy-to-use demand template expression mode is constructed for the demand template ontology model based on demand template grammar and semantic constraints, so that the efficiency of demand template customization and demand modeling is improved.

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.

The application discloses a SysML requirement modeling method and device based on parameter sensitivity analysis and a medium, belongs to the technical field of complex equipment system design, and identifies key characteristic parameters affecting launch stability through multidisciplinary modeling and parameter sensitivity analysis, and realizes formalized description and design verification of requirement elements based on a SysML system modeling language. The application solves the problems of fuzzy parameter correlation and difficult requirement tracing in traditional modeling, and significantly improves the accuracy and reliability of digital mockup design of a rocket aircraft system.

The invention discloses a multi-modal data-driven tourism demand causal inference method and system oriented to active policy making. The method comprises the following steps: multi-modal tourism data acquisition and characterization preprocessing; constructing a tourism demand causal map based on a PC algorithm and domain knowledge; policy causal effect estimation based on a double difference model; carrying out travel demand modeling and active policy simulation based on fusion of causal effects; the system comprises a data acquisition and preprocessing module, a causal atlas construction module, a causal effect estimation module, a demand modeling and policy simulation module, a data storage module and a visualization and interaction module. According to the method, the problems of insufficient causal relationship mining, policy evaluation lagging, insufficient multi-modal data utilization, difficulty in consideration of model interpretation and prediction performance and the like in an existing tourism demand analysis and policy making method are solved, the policy effect can be actively evaluated, scientific decision making is supported, and tourism demands are driven.