43 results about "Extended model" patented technology

The invention discloses a building carbon emission analysis method based on an STIRPAT algorithm and a building information model technology, and the method comprises the steps: collecting the whole life cycle information of a building through the BIM technology in a data collection and integration stage, covering the data of all links of design, construction and operation, widely collecting the external environment data of a project location, such as social economy, energy technology, etc. Multi-source data such as geographic space and weather are introduced and fused, and real-time monitoring is performed by means of an intelligent sensing technology. Secondly, an extended STIRPAT model is constructed, core independent variables such as population, economy and technology and control variables such as building scale are comprehensively considered, and the influence of factors is accurately quantified through dynamic parameter adjustment and scene simulation prediction; according to the method, carbon emission attributes are given to components and equipment based on a BIM model, full-life-cycle carbon emission is simulated and calculated, integrated optimization is carried out with solar photovoltaic, intelligent control, waste management and other technologies, finally, results are comprehensively analyzed, carbon emission distribution and trend are displayed by utilizing a BIM visualization function, and a building low-carbon decision is provided.

The invention relates to the technical field of data processing, in particular to a main data management method and system based on enterprise data governance, and the method comprises the steps: defining a basic model structure based on an enterprise general main data object, and storing the basic model structure in a metadata form; obtaining a business system change event and a manual expansion demand and converting the event and the demand into a structured description; zero-coding extension configuration of attributes, classification and association relationships is completed through a visual interface; executing interface adaptation, data mapping adaptation and rule conflict detection; and synchronizing the effective extension model to a service layer, a cleaning layer and a monitoring layer. The system comprises a demand awareness module, a dynamic modeling module, an extension configuration module, an adaptation engine module and a life cycle management module. According to the method, full-process management of the master data model from demand capture to business landing is realized, and the model expansion efficiency and the business adaptation capability are improved.

The present application relates to the technical field of data processing, and more particularly to a kind of master data management method and system based on enterprise data governance, including based on enterprise general master data object definition basic model structure and store in the form of metadata;Business system change event and artificial extension demand are obtained and converted into structured description;Attribute, classification and correlation are completed by visual interface Zero coding extension configuration;Interface adaptation, data mapping adaptation and rule conflict detection are executed;Synchronization of effective extension model to service layer, cleaning layer and monitoring layer.The system includes requirement perception, dynamic modeling, extension configuration, adaptation engine and life cycle management module.The present application realizes the whole process management of master data model from requirement capture to business landing, improves model extension efficiency and business adaptation capability.

The invention discloses a village-town crossing road maintenance construction safety risk dynamic evaluation method, and belongs to the field of artificial intelligence modeling and road engineering safety, and the method comprises the steps: building a risk index system comprising multi-level indexes; based on the risk indicator system, a dynamic Bayesian network model is constructed, and the model comprises a root node and a conditional dependency relationship between the nodes; determining the fuzzy probability of each root node in the dynamic Bayesian network model by adopting a fuzzy set theory in combination with an expert scoring method; the conditional probability of a non-root node in the dynamic Bayesian network model is corrected on the basis of a Leak Noise-or Gate expansion model; forward reasoning is carried out through a dynamic Bayesian network model, and a dynamic probability curve of the town-crossing highway maintenance construction safety risk is generated; backward reasoning is carried out through a dynamic Bayesian network model, and the posterior probability of the key risk factors is calculated. According to the method, the safety in the village-town-penetrating maintenance construction process can be guaranteed.

The invention relates to the technical field of power system dynamic safety analysis, and discloses a mechanism-data fusion new energy power system minimum inertia demand rapid evaluation method, which comprises the following steps: in an offline stage, establishing an expansion system frequency response model containing new energy; generating a plurality of sample scenes in a given disturbance power range and a new energy permeability range; a model minimum inertia demand and a simulation minimum inertia demand are obtained through expansion of a system frequency response model and stepping type time domain simulation calculation, a polynomial regression method and a polynomial regression equation are utilized, and the coefficient and order of the polynomial regression equation are determined by adopting an optimization algorithm; during online evaluation, real-time disturbance power and new energy permeability of a power grid are obtained; and obtaining a model minimum inertia demand and a corresponding minimum inertia demand deviation compensation value, and outputting a final system minimum inertia demand evaluation result. According to the method, the final evaluation result has the calculation efficiency of the extended SFR model and the evaluation precision of stepping simulation.

This invention provides a method, system, and medium for channel prediction in a massive MIMO TDD system using a base-extended extrapolation model. The method includes: modeling the uplink channel using a base-extended model, representing the uplink channel as a small number of unknown BEM coefficients, and reconstructing the uplink channel by estimating these unknown BEM coefficients; representing the estimated uplink channel as a linear combination of a discrete ellipsoidal BEM and its corresponding coefficients to reduce the complexity of downlink channel prediction; predicting the coefficients of a downlink time-varying DPS-BEM, and using the predicted DPS-BEM coefficients for downlink channel recovery. This invention alleviates the channel aging problem in massive MIMO TDD systems.

The invention provides a process construction method and BIM-based carbon emission accounting and tracking method and system, and the method comprises the steps: constructing an extended BIM model fusing geology, process and carbon emission attributes, and building a construction carbon emission model through employing a mixed life cycle method; and mechanical energy consumption is simulated and predicted through discrete events, and model components are associated, so that dynamic tracking and visualization of the carbon footprint during the construction period are realized. According to the invention, accurate accounting, dynamic tracking and visual display of carbon emission in the construction period can be realized, a scientific basis is provided for carbon emission management in the construction process, and optimization of construction organization, reduction of carbon emission and improvement of environmental benefits and sustainability of construction projects are facilitated.

The application provides a kind of guarantee resource configuration optimization method based on aircraft use maintenance activity, including the following steps: aircraft use maintenance activity classification, basic model construction, construction extension analogy derivation model, guarantee resource configuration project set acquisition, guarantee resource demand set acquisition, subset, function integration, guarantee resource configuration list.The application uses a basic model and three extension models to derive the preliminary configuration of guarantee resources for aircraft field use maintenance activities, and then uses set operation and function integration to finally form the standard configuration of guarantee resources, which solves the problem of aircraft grounding due to the shortage of certain guarantee resources during aircraft field use maintenance, affecting the completion of the task, and solves the problem of excessive guarantee resources, resulting in increased guarantee costs, affecting the efficiency and cost ratio target and aircraft seller market, and also reduces the specifications of guarantee resources through function integration, improving the aircraft transfer capacity.

The invention provides a human-in-the-loop Text2SQL iterative data labeling method fused with a large model. The method comprises the following steps: firstly, carrying out training and fine tuning on a generative model by utilizing a pre-constructed manual labeling training sample; performing same-semantic diversification expansion on the training sample by using the expansion model; inputting the plurality of extended training samples into a generative model to obtain a first SQL query statement; for each first SQL query statement, reward and punishment assignment is carried out on model parameters of the expansion model according to an expert analysis result and a reinforcement learning mechanism to determine a target training sample; performing manual annotation on the second SQL query statement, and performing incremental training on the generation model by utilizing a manual annotation result to obtain a second SQL query statement; and when the verification result of the second SQL query statement shows that the query expectation is not met, manually marking the target training sample circularly until the verification result meets the query expectation. The efficiency of data annotation is improved through man-machine cooperation and reinforcement learning.

The invention discloses a carbon emission intensity prediction simulation and land utilization management and control method and system, and belongs to the technical field of carbon emission calculation prediction.The method comprises the steps that firstly, multi-source space and statistical data are integrated to establish a basic database, and the carbon emission intensity of all kinds of space is calculated according to land space classification; then, constructing an extended STIRPAT model to screen a core driving factor, and constructing a feature project containing residual, lag and trend features based on the output of the extended STIRPAT model; training an XGBoost machine learning model by adopting a Bayesian optimization algorithm so as to capture a complex nonlinear relationship between the carbon emission intensity and the influence factors; and finally, inputting future scene feature data, generating a territorial space carbon emission intensity distribution map in combination with a PLUS model, and identifying a high-emission hot spot region. According to the method, the problem that the technical progress influence is difficult to quantify and the spatial explicit prediction is difficult to carry out in the prior art is solved, the prediction precision is remarkably improved, and a scientific basis and decision support are provided for regional territorial space planning and low-carbon management and control.

The application relates to the field of power distribution system simulation, in particular to a distributed power distribution system unified scale load modeling simulation method and system, which comprises the following steps: denoising and fitting a wideband signal to obtain a multiscale signal; obtaining a Hankel matrix time-varying extended model based on the multiscale signal and historical modal parameters and solving to obtain real-time modal parameters; constructing a coupling matrix based on the real-time modal parameters, fitting a fast action layer state equation and a slow action layer state equation by using the coupling matrix to obtain a unified coupling equation; transforming and decoupling the unified coupling equation to obtain a mixed differential-algebraic equation group; and solving the mixed differential-algebraic equation group by using a preset step length switching rule based on a dominant dynamic type to obtain full-time domain simulation results of the distributed power distribution system. The application can effectively solve the problems of time scale fragmentation and insufficient parameter identification accuracy in traditional load modeling.

An Artificial Intelligence (AI) based data processing system transforms a plurality of time series data sets for processing by one or more deep learning (DL) models for generating forecasts. The DL models are initially trained on training data generated from the historical data. During operation, a plurality of transformed time series data sets are generated from the plurality of time series data sets associated with different entities in an entity hierarchy via data flattening and data stacking. A primary model of the one or more DL models is trained on first-party data for generating the forecasts. An extended model of the one or more DL models is trained on third-party data from external data sources. Whenever new data is available in the first-party data or the third-party data, the primary model and the extended model are correspondingly updated.

The present invention discloses a method and system for automatically constructing a programming task context based on an Eclipse plug-in, which automatically adapts and selects a context pattern library by identifying programming task features; tracks code elements accessed by a developer within a sliding time window, forms a context model view of the code elements according to the declaration relationships between the code elements and uses the Eclipse SWT technology to display the view; extends the context model by using the AST technology, and then assigns abstract role types; extracts subgraphs of the extended model, performs pattern matching on the subgraphs by using the context pattern library, and records all successfully matched subgraphs; generates a list of recommended code elements by using the Eclipse SWT technology and displays the list. The developer can locate and analyze the recommended code elements by clicking on them. The present invention explicitly captures the programming task context model by a visual method, helps developers quickly understand the programming task context model, and effectively improves the efficiency of software development.

The embodiment of the application provides a kind of airborne software security modeling and automated testing method, comprising: according to the field of airborne software, the security property description required for security testing is extracted using security property extraction module, while the SysML model is designed, the XML file of SysML model and security property description are uploaded to model automatic expansion module;Model automatic expansion module is expanded according to the uploaded security property description XML file of SysML model, and the node type and path information of SysML model are supplemented, to generate the extended model;Test case automatic generation module generates the comprehensive test path and test data of the extended model according to the extended model output by model automatic expansion module. Since the scheme adds security testing through security property extraction module, model automatic expansion module and test case automatic generation module, the efficiency of test case generation is improved.

The invention discloses a self-evolution multi-agent event semantic understanding system and method based on a dynamic extensible model, and the system comprises a multi-agent cooperation framework construction module, a demand understanding and task planning module, a capability verification module, a model dynamic extension module, a task execution module, and a completion module which are connected in sequence. The multi-agent collaboration framework construction module comprises a central control type multi-agent, a task execution type multi-agent and a model expansion type multi-agent, the method focuses on a news event semantic understanding scene, a dynamic extensible system architecture is constructed through a multi-agent collaboration mechanism, a capability verification link is introduced to optimize a task execution process, and the task execution efficiency is improved. And on-demand iteration of the model capability is realized by using an active learning framework, and adaptive evolution of the agent capability is realized, so that the problems of model stiffness, poor adaptability and the like of a traditional event semantic understanding system in a complex news event scene are solved, and the dynamic adaptability and accuracy of news event semantic understanding are remarkably improved.

The application discloses a kind of SysML model to code generation method and device, belong to model-driven development technical field, including: first, by embedding code generation configuration parameter and the semantic structure of target programming language through extending SysML model;Then, the model element instance repository containing model element instance and its associated relationship is built and validity check is carried out;Subsequently, the SysML model element instance that passes through check is converted into the abstract syntax tree of target programming language using conversion engine;Finally, the correct syntax tree of verification is filled to pre-defined code template, and executable code is generated.By the application, the structure and behavior semantic mapping of system model to code is realized, the code automatic generation of dynamic behavior such as state machine, activity diagram is supported, and the code generation efficiency and system development quality are significantly improved.

The invention provides a method for identifying and classifying danger sources of medium and large unmanned ships based on a deviation path, which comprises the following steps of: firstly, comprehensively describing a task process from four dimensions of events, processes, information and states through an MVMOS multi-view modeling method so as to realize panoramic description of personnel, a ship body, an environment and an interaction relationship thereof, secondly, realizing deviation analysis and task process adaptation, and finally, identifying and classifying the danger sources of the medium and large unmanned ships. By expanding time deviation and state deviation guide words, the HAZOP method better fits the characteristics of high task time sequence and discrete state of the unmanned ship; injecting the deviation attribute into a SysML behavior graph, constructing an expansion model based on a deviation path, visually simulating and deducing the propagation and influence of the deviation in a task process, and achieving the dynamic positioning of a hazard source and the analysis of a causal chain; and finally, a four-layer hazard source hierarchical structure is established, typical hazard sources are classified and merged, risk sources, influence paths and associated elements are clarified, systematicness and operability of hazard source management are improved, and clear and reliable input is provided for subsequent risk assessment and safety measure formulation.

The invention relates to the technical field of unmanned aerial vehicle autonomous control, and discloses an unmanned aerial vehicle control method and device, an unmanned aerial vehicle and a storage medium, and the method comprises the steps: obtaining a kinetic equation of the unmanned aerial vehicle under a rigid coordinate system, and determining a relational expression of motion parameters, such as position, linear velocity, angular velocity and attitude, with respect to control quantity; constructing an extended state vector, and obtaining an extended state space model through linear conversion; based on a motion parameter measurement value under a world system, the extended model and an observation equation containing observation noise, state estimation is carried out on motion parameters of the unmanned aerial vehicle, and a state estimation value composed of a state item and a disturbance item containing disturbance force / torque is obtained; the target control quantity is solved according to the state item and the disturbance item in the estimated value, the unmanned aerial vehicle is controlled according to the target control quantity, disturbance force / torque is modeled as a dynamic state variable, online real-time estimation is conducted, the disturbance force / torque is fed back to the control quantity, dynamic compensation is conducted, and the strong interference resistance and the control precision of the unmanned aerial vehicle are remarkably improved.

The present invention relates to an audio classification method, a model training method, a system, a device and a medium. The training method comprises: obtaining an extended audio set and a corresponding audio tag set; inputting the extended audio set into a trained original model, extracting the generated reference original category labels and original features; inputting the extended audio set into an extended model, extracting the generated original category labels and / or newly added category labels and extended features generated by the same network layer as the original model; based on the difference between the original category labels and the reference original category labels, the difference between the extended features and the original features, and the difference between the original category labels, the newly added category labels and the corresponding audio tags in the audio tag set, updating the parameters of the extended model, and using the trained extended model as an audio classification model. The present invention improves the problem of knowledge forgetting that may occur when the extended model learns new categories, while enhancing its learning ability for newly added categories.

The invention relates to a method for generating an extended FEM model (570) with a 3D geometry of a formed technical component (10), in particular a sheet metal component for a vehicle body, for an FEM simulation for calculating, testing and / or validating design parameters of the technical component (10), wherein the extended FEM model (570) comprises a forming profile (380) of at least one forming parameter (370) occurring during a forming of the technical component (10), comprising the following steps: - Input of at least one FEM model (270); - Performing a modeling of a spatial forming profile (380) of at least one forming parameter (370) by a trained AI model (350), - Generating an extended FEM model (570) with the 3D geometry of the component (10) and the integrated spatial forming profile (380); - Performing an FEM simulation of the component (10) for the calculation, testing and / or validation of design parameters of the component (10), in particular a crash simulation, using the extended FEM model (570).

1. Name of the design product: Bathroom cabinet set (extended model - Explore the Boundary series). 2. Use of the design product: This design is a bathroom cabinet set for washing and storing items. 3. Design key points of the design product: lie in the shape and pattern of the product. 4. Picture or photo that best shows the design key points: combined state reference drawing. 5. Other situations that need to be explained Kit description: Kit 1 is a bathroom cabinet; Kit 2 is a storage cabinet.

The application discloses a kind of large-scale recommendation system training method based on distributed heterogeneous computing in the field of communication technology, comprising the following steps: Cache initialization;Cache replacement algorithm construction;Index mapping algorithm construction;In GPU, the calculation of Sparsefeature is carried out using the Embedding or Embedding_bag function provided by PyTorch, and the calculation of the subsequent Dense part of the model, the technical solution can make full use of GPU computing resources, and the calculation speed is increased;Replace in and out by row optimization is replaced in and out by block, and the throughput is improved;Finally, the scale of the model is further expanded by making full use of computing resources, and the model is distributedly expanded, and the method has creativity and market promotion value.

This invention provides a method and apparatus, electronic device, computer storage medium, and computer program product for model training, model routing, and model service. The model training method includes: acquiring a training dataset, which at least includes query data, first response scoring labels of a portion of the models in an expanded model pool to the query data, and first model description data; acquiring a routing model including a pre-trained encoding module and an adaptive module; inputting the query data and the first model description data into the pre-trained encoding module and the adaptive module, while maintaining the pre-training parameters of the pre-trained encoding module, and training the routing model using at least the first response scoring labels as a supervised condition. This invention reduces the training cost of the routing model while ensuring the routing capability of the expanded model pool.

The invention relates to the technical field of business process management, in particular to a business arrangement method and system based on a hierarchical specification model and electronic equipment, and the method comprises the steps: building a core specification model based on predefined basic specification elements; deriving from the basic specification elements through the inheritance relationship to obtain target extension elements of the target domain; establishing a domain extension model based on the target extension element; constructing a target domain business process model according to the core specification model and the domain extension model; and when the target domain business process model is submitted to be executed, the execution engine creates a corresponding runtime instance for the process node of the target domain business process model, and determines a model execution sequence, allocates resources and transmits data according to the process dynamic attribute and the node relationship of the runtime instance. The universality and the specificity of semantics are structurally decoupled, so that the semantic stability of core specification elements is ensured, and the business process arrangement modeling requirements of multiple scenes and multiple fields can be flexibly met.

This invention discloses a method, system, device, and medium for modeling the electro-magnetic-thermal coupling characteristics of a three-phase integrated high-temperature superconducting cable. The method includes: establishing an electrical model based on the equivalent circuit of parallel multilayer tapes; considering interlayer and interphase electromagnetic coupling in the current distribution model to obtain the current in each tape layer; establishing a critical characteristic and equivalent resistance calculation model using the gold extended model considering magnetic field anisotropy and combining it with power-law relations, and calculating AC losses; establishing a two-dimensional heat transfer model considering interlayer thermal resistance with AC losses as a heat source term, and correcting the circumferential non-uniform convection heat transfer conditions and interphase heat conduction channels through angular weighting functions and shape factors; iterating based on time parameters, feeding the temperature field and equivalent resistance back to the current distribution model to achieve closed-loop updates, and outputting electro-magnetic-thermal coupling simulation results, thereby providing a calculation model that more closely matches the actual operating conditions for the steady-state and fault condition simulation of a three-phase integrated high-temperature superconducting cable.

The present invention relates to a control method and system for maintaining safe driving of high-speed trains under DoS attacks. The method comprises: setting pseudo-partial derivative step size, pseudo-partial derivative weight, control law step size, control law weight, adjustment factor, and control input linearization length; setting the expected trajectory of the leader car, the initial value of the train speed output by the following car, and the initial value of the train traction / braking force input by the following car; updating the predicted value of the pseudo-partial derivative; resetting the predicted value of the pseudo-partial derivative; updating the control input; updating the output based on the mathematical expectation of successful data transmission under DoS attacks; calculating the train speed; judging whether the train's travel time has reached the termination time, and if so, stopping the control; otherwise, repeating the above steps. The present invention proposes an extended model-free control, which introduces a control law update method with an input criterion function of an error adjustment factor, which can effectively shorten the convergence time of high-speed train control to achieve stability and improve the tracking effect of the system.

Systems, devices, and methods for local illumination compensation (LIC) with an extended model are described herein. An example device for video decoding may determine an LIC model of a block; obtaining a set of LIC parameters based on the determined LIC model of the block; and decoding the block based on the set of LIC parameters. An example device for video encoding may select an LIC model for a block from a plurality of LIC models; obtaining a set of LIC parameters based on an LIC model selected for the block; and encoding the block based on the set of LIC parameters.

The application discloses a kind of based on deep learning's collaborative voice gesture generation method and system, method includes: obtaining initial gesture sequence, initial gesture sequence is diffused forward, and obtains disturbance sequence;Obtain speech feature, initial attitude feature and time embedding vector, speech feature, initial attitude feature and time embedding vector are fused across modalities, and obtain multi-modal feature;Using extended long short-term memory network under the guidance of multi-modal feature to disturbance sequence iterative denoising, obtain new gesture sequence.The application combines using extended long short-term memory network and extended model, can effectively capture the correlation between different multi-modal features and gestures, help infer natural and expressive gestures from conditional information, so that the generated collaborative gesture has higher quality and diversity.

This invention belongs to the field of smart home, specifically an offline voice command word update method, comprising the following steps: S1: hierarchical model construction; S2: dynamic model compression and distribution; S3: hot patch writing in idle frames; S4: hierarchical collaborative recognition. This invention dynamically loads new command word models by allocating a dedicated extended model area, utilizes system idle frames for data writing, achieving "zero downtime" updates. It avoids redundant calculations through a dual-model parallelism and feature sharing mechanism, and only activates the extended model for secondary matching when the confidence of the base model is insufficient. Furthermore, it employs "8-bit vector quantization + cepstral residual coding technology" to compress the extended command words, reducing the size of a single command to an extremely small volume.

The invention relates to the technical field of computer vision, in particular to a point cloud gesture recognition method based on an extended Point LSTM (Long Short Term Memory) model, which takes the Point LSTM model as a reference, combines state information of past adjacent points with current features, updates a current state through a weight sharing LSTM layer, introduces an xlstm model and strengthens information propagation. Meanwhile, a stacked network is designed based on two training methods, and a hierarchical feature fusion method is designed for the characteristics of the network and is used for learning different network depth information in a point cloud sequence recognition network. Experimental verification is carried out through several widely used data sets, information transmission between frames of the point cloud sequence can be more effectively carried out so as to improve the information acquisition amount of the time dimension of the point cloud sequence, meanwhile, the understanding ability of the model to the point cloud sequence is improved, and the recognition ability of actions such as gestures is enhanced.