56 results about "Demand modeling" patented technology

The invention relates to the technical field of intelligent traffic, in particular to an intelligent traffic signal edge computing resource dynamic allocation method and system.The method comprises the steps that firstly, real-time and historical traffic data of multiple intersections are obtained, a traffic prediction model is constructed, and future flow changes are pre-judged; thirdly, forming an edge computing resource demand matrix according to a prediction result and computing resource demands, and dynamically adjusting resource allocation to generate a resource slicing scheme; then, the flow gradient between intersections is analyzed, an edge node cooperative calculation model is established, and calculation task distribution and migration are optimized; and finally, in combination with system operation and traffic control effects, forming closed-loop feedback, continuously optimizing resource allocation and a cooperative strategy, and through traffic flow prediction driven resource demand modeling, realizing active pre-allocation of computing resources, converting resource allocation from passive response to active pre-adaptation, and improving the system resource utilization rate by 40%-60%.

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 discloses an aviation three-dimensional aided design integration method and system based on a large language model. The method comprises the steps of obtaining multi-modal design demand data submitted by a user; analyzing the multi-modal design demand data submitted by the user; and calling a large language model to generate a corresponding modeling command according to the analyzed multi-modal design demand data, calling a computer aided design system by utilizing the modeling command to carry out modeling design, and optimizing the large language model at the same time. The system comprises a user interaction module for receiving multi-modal design demand data input of a user; the data analysis module is used for analyzing the multi-modal design demand data and extracting a design demand; the modeling instruction generation module is used for calling a large language model to generate a modeling instruction according to a design requirement; the modeling execution module is used for calling a computer aided design system API to complete modeling by utilizing the modeling instruction; and the model optimization module is used for feeding back the model data modified by the user to the large language model for optimization.

The invention discloses an AI system for generating customized contents based on user preferences, which belongs to the technical field of artificial intelligence, and comprises a user preference modeling module for constructing and continuously updating a user feature vector library and realizing personalized demand modeling and classification; the interactive input and early warning module is used for receiving user input, implementing risk control and providing a visual interactive correction tool; an intelligent parameter conversion module; a video preprocessing and enhancing module; a cross-modal semantic generation module; the dynamic training and optimizing module is used for controlling a model training process and balancing repair quality and style migration; the multi-version generation and evaluation module is used for outputting a differentiated repair result and quantitatively evaluating the performance; the feedback learning and iteration module is used for collecting user preference data and driving the system to continuously optimize; and a distributed task scheduling module. According to the method, the real-time performance and accuracy of the user portrait are ensured through the layered modeling mode, accurate matching of personalized content generation is supported, and user experience and content recommendation are effectively improved.

The invention relates to a flexibility demand modeling method based on a probability box, and belongs to the field of flexibility demands of power systems. According to the method, a probability box theory P-Box is introduced to describe random uncertainty and cognitive uncertainty of wind power output, photovoltaic output and load demand prediction errors; introducing a conditional value-at-risk theory CVaR to a confidence interval boundary of the probability box, and intercepting an uncertainty factor boundary; and establishing a source-load double-side flexibility quantification model based on a prediction error of intercepting an uncertain factor boundary.

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 relates to the technical field of display system driving, and discloses a high-refresh-rate driving chip power consumption optimization control system which comprises an image content complexity modeling module for extracting region sparsity features, a visual attention prediction module for generating an attention heat map, and a display module for displaying the attention heat map. The combined graph modeling and optimization module fuses the information to construct a graph structure and predicts a refresh level, the refresh strategy generation module generates an optimal refresh strategy based on the level, the soft and hard translation execution module converts the strategy into a driving instruction and issues and executes the driving instruction, and the feedback scheduling and self-adaptive correction module dynamically adjusts the graph structure according to chip feedback. The method is used for optimizing next frame refresh control. According to the method, through region division and graph structure modeling, image region refreshing demand modeling is carried out in combination with an attention mechanism and loss feedback, differential refreshing is realized, invalid refreshing of a low-change region is effectively avoided, the content perception ability of a refreshing strategy is improved, and thus the overall power consumption is reduced.

The invention discloses a method for supporting complex system demand integrity modeling and evaluation, and relates to the technical field of complex system design, model-based system engineering and demand management. The method comprises the following steps: 1, constructing a Chinese general demand template; 2, combining the Chinese general demand template with a demand modeling standard Req I F; step 3, constraining the attribute content of the demand item based on the Chinese general demand template; 4, constructing a demand model; 5, constructing a demand integrity evaluation criterion; step 6, evaluating the integrity of the attribute content of the demand item; 7, modifying the content which does not meet the requirements to the template which meets the requirements, the method is based on the EARS template, a universal Chinese requirement template is expanded and put forward, the method adapts to the expression mode and habits of the Chinese requirements, structural constraint on the Chinese requirement content is supported through the template, and therefore complete expression of the Chinese requirements is achieved.

The invention discloses a model-based human factor analysis integrated modeling method and system. The method comprises the following steps: establishing an operation task list from a stakeholder perspective by using an operation concept analysis method; constructing a task scene case based on the operation task list and a SysML modeling language; human factor task analysis is carried out based on the task scene use case, the operation task list and the interaction relation, and human factor requirements related to the task are identified; a task, demand and system mapping table is formed based on the SysML modeling language, and a task and demand integrated model is obtained; and carrying out satisfaction verification on the task and demand integrated model by adopting professional human factor analysis software, and taking the task and demand integrated model passing the verification as a human factor analysis integrated model. According to the method, human factor task and demand modeling is carried out on the SysML modeling language, system engineering and human factor engineering are closely linked together, and the problems that in the complex system design process, human factor problems are not fully considered, and the human factor problem iteration period span is long are solved.

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 discloses a formalization-based demand modeling and SysML graph automatic test verification method, and belongs to the technical field of computer software development. The method comprises the following steps: demand text format conversion; generating a test case table; carrying out formalized modeling on the demand text; test verification is executed, a test case table is read and assigned to a system model variable, the system runs according to internal modeling logic and generates output, and the output is obtained and written into the test case table at a corresponding position; and designing a test report generation tool. According to the method, the generation efficiency and accuracy of the test data are improved, a complete closed-loop process from demand capture to verification analysis is formed, the standardization and consistency of development are improved, automatic execution of the test cases is realized, verification is completed by automatically comparing model output with an expected result, and the test efficiency is improved. And the method can quickly adapt to demand change and function extension of a complex system.

The invention relates to the technical field of data processing and information security, in particular to a thermal management model sharing method based on a block chain and homomorphic encryption. Creating a point account, completing user identity information registration and block account application, and applying for a corresponding block chain account for the user after basic identity information registration is completed; issuing the shared heat management model, namely completing the issuing of the user shared heat management model in the block chain and the storage function of the model in the IPFS; downloading the shared model, retrieving the thermal management model, and after the credit is paid to the model provider, downloading the model by the user; a user issues a thermal management model through a block chain system, other users retrieve the issued thermal management model in the system through a block chain intelligent contract, and the user needs to pay a certain amount of points to a sharing party, so that joint simulation of digital models in different subject fields and repeated use of the models can be realized; and the problem of multi-functional demand modeling of a complex system can be solved.

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 present invention belongs to the technical field of electric vehicle charging demand modeling, and discloses an electric vehicle charging demand modeling method, system, medium, device and terminal, which analyzes the influence of electric vehicle user differences and electric vehicle performance differences, and learns user driving strategies and user travel strategies based on a generative adversarial model; based on XGboost, a machine learning electric vehicle performance model; based on the real-time road conditions of Baidu Maps, it is proved that the algorithm can extract user driving characteristics and charging characteristics. By combining the real-time traffic flow rate of Baidu Maps and the real user driving trajectory data, the obtained regional electric vehicle cluster charging demand model has a broader application prospect, and the prediction result has good generalization ability. The results prove that compared with the current mainstream prediction methods, the present invention has better prediction accuracy and robustness, and at the same time can combine real-time traffic data, which can provide a powerful model support for the planning of urban area charging stations and charging guidance.

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 present invention relates to the field of intelligent transportation technology, in particular to an intelligent transportation signal edge computing resource dynamic allocation method and system thereof. First, real-time and historical traffic data of multiple intersections are obtained to construct a traffic prediction model to predict future traffic flow changes; then, according to the prediction results, resource requirements are calculated to form an edge computing resource requirement matrix, and the resource allocation is dynamically adjusted to generate a resource slicing scheme; subsequently, the traffic flow gradient between intersections is analyzed to establish an edge node collaborative computing model to optimize the calculation task allocation and migration; finally, combined with the system operation and traffic control effects, a closed-loop feedback is formed to continuously optimize the resource allocation and collaborative strategy. Through the resource demand modeling driven by traffic flow prediction, the active pre-allocation of computing resources is realized, the resource allocation is changed from passive response to active pre-adaptation, and the system resource utilization rate is increased by 40% to 60%.

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 invention provides a modeling method, a medium and equipment for a formal demand model with hierarchical characteristics, which comprises the following steps of: according to a natural language demand to be processed, dividing according to a system hierarchy, dividing demand entries in a demand document into sets according to a logical relationship, and dividing each set into a module; according to the divided demands, mode conversion type demands are separated from each module to be summarized, and for the remaining demands, the remaining demands are classified into conditional demands and event type demands according to the modules; constructing a domain concept library according to the divided original requirements; modeling the condition / event type demand according to the demand standardization template and generating an equivalent formalized description process; and generating an HVRM model according to the traceability relationship among the modeling elements. According to the method, the model-driven technology is adopted, the problem can be effectively recognized from the software requirement level, the problem can be accurately positioned, and therefore the development quality and safety of an airborne software system are greatly improved.

The application discloses a kind of based on demand dependency graph and logic conflict quantification feedback with the terminal demand dynamic modeling and verification method, system and medium, including obtaining original terminal equipment demand, standardization is handled, and standardization demand text is obtained;Standardized demand text is processed based on the knowledge enhancement of pre-set exclusive knowledge base, and fusion feature vector is generated;Fusion feature vector is carried out semantic and symbolic logic joint inference, constructs demand dependency directed graph and calculates the strength of logic conflict, including each graph node single branch conflict intensity quantization value and comprehensive conflict intensity quantization value;According to comprehensive conflict intensity quantization value, the demand modeling granularity of corresponding graph node is dynamically adjusted, and tree-shaped demand model is generated;The tree-shaped demand model is carried out multidimensional compliance verification, and verification report is generated.The application can greatly reduce the field demand fault escape rate, and adapt to large quantities of state grid terminal demand automation modeling scene.

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.