22 results about "Extended model" patented technology

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 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 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.

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.

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 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.

This invention discloses a time-varying channel estimation method, device, and medium combining UKF and stacked ELM. The method first characterizes the time-varying channel using a basis extension model. Then, based on historical channel basis coefficient estimates, it introduces the UKF method to predict an initial estimate of the basis coefficients at the current time. This initial estimate is then used to train and test a stacked ELM network to obtain a more accurate basis coefficient estimate. Finally, based on the transformation relationship between the basis coefficients and the frequency domain channel, the final frequency domain channel estimate is obtained. To address the parameter estimation problem under nonlinear state-space models, this invention, based on historical channel basis coefficient estimates, first introduces unscented Kalman filtering (UKF) to predict an initial estimate of the basis coefficients at the current time. This initial estimate is then used to train a stacked Extreme Learning Machine (ELM) network to extract deep channel features and further improve the accuracy of channel estimation. This invention offers higher estimation accuracy than existing methods and is suitable for acquiring time-varying channel information in high-speed mobile scenarios.

The invention discloses an industrial robot kinetic parameter identification and optimal friction model selection method, and relates to the technical field of robots. The method comprises the steps that a robot dynamic model containing unknown parameters is decoupled into a friction-free model and a joint friction model, a non-linear friction model candidate set is established, and iteration is conducted on the nth candidate model: in the tth round, an expansion model is obtained through the friction-free model and the linear friction model, a first parameter is obtained based on the expansion model and a torque actual measurement value, and a second parameter is obtained based on the first parameter; eliminating linear friction parameters to obtain inertial parameters; obtaining a first model through the inertial parameters and the candidate model, obtaining a likelihood function according to model output and a prediction error of a torque measured value, and solving friction parameters; and fusing the two parameters to obtain a (t + 1) th round model, if an error of adjacent rounds is smaller than a threshold value, determining the model as an nth candidate model, otherwise, continuing iteration. Candidate model comprehensive indexes are obtained according to the robustness and precision indexes, the model with the lowest comprehensive index serves as the target model, and the robot dynamics modeling refinement degree and accuracy can be improved.

The application discloses a kind of based on the frequency safety fast judgment method of extended SFR model, comprising the following steps: for real-time detection of power grid, construct each frequency response expression, establish the relevant information of collection judgment power grid, including the capacity of generator, load size, the size of new energy output etc.;Determine whether the relevant parameter is updated, and update the parameter of extended SFR model, so as to obtain the corresponding calculation parameter of new state, or use the parameter of last calculation period;The frequency safety when N-1 fault occurs is quickly calculated from the collected data, and finally the frequency is quickly judged.The frequency safety fast judgment method based on the extended SFR model provided by the application can predict the frequency safety, thereby enhancing the stability of power system, and has important academic significance and engineering practical value.

This invention discloses a keyword-free semantic retrieval and intelligent comparison method and system for massive data, belonging to the field of information technology. The method includes: acquiring query text and generating a global intent feature tensor based on the query text; transmitting the global intent feature tensor to an external target database based on an extended model context protocol; filtering candidate datasets that match the global intent feature tensor from the target database using a distance metric algorithm; and performing multi-dimensional analysis based on the global intent feature tensor and the candidate datasets, outputting a corresponding comparative analysis report. This enables semantic retrieval to bypass keyword bottlenecks, thereby obtaining more accurate, relevant, and insightful results when dealing with complex and ambiguous professional queries.

This invention relates to the field of power line maintenance technology, and more particularly to a method and system for constructing a power line defect database. The method first acquires multiple image sets, each containing multiple images representing the same power line condition. Then, multiple feature sets and features generated by an extended model are fed into a classification model. The parameters of the extended model are adjusted based on the output of the classification model until the classification result is below a threshold. Next, multiple extended features are generated using the extended model. Finally, these extended features are fed into the classification model, and based on the classification result, they are added to the multiple feature sets. This invention generates a relatively large defect feature database using a relatively small number of defect image samples, requiring fewer defect image samples but generating more defect samples, thus reducing the workload of constructing the defect database.

This application discloses a method for extending a model, electronic equipment, and a storage medium, which belong to the art of computers. The method includes the steps of: setting an index field in a TR181 extended model file for target traffic based on the type of target traffic; setting at least one description field in the TR181 extended model file for target traffic based on the description parameters of the target traffic, wherein one of the description fields is for expressing the characteristics of the description parameters; and setting the length of each of the description fields based on the globalization adaptability and global standards of the target traffic, thereby realizing the extension of the TR181 extended model file.

This application provides a predistortion extended model and a method and apparatus for implementing predistortion, used to improve the linearity of a power amplifier (PA) and reduce the power consumption of a base station system under conditions of limited sampling rate. The method includes: determining the input and output predicted signals corresponding to any time interval between two adjacent sampling times of the power amplifier using linear interpolation; extending the GMP model by using the input and output predicted signals as the input and output signals of the generalized memory polynomial model (GMP) to obtain an extended GMP model; querying the first lookup table (LUT) value corresponding to the first input signal using the original GMP model, and querying the second LUT value corresponding to the first input signal using the extended model; and generating the predistortion signal based on the first and second LUT values.

The invention provides a fault diagnosis method and equipment for an air conditioning system. The method comprises the steps that modeling is conducted on each component in the air conditioning system based on the architecture of the air conditioning system, specifically, multiple models are established for each component, the multiple models of the same component adopt the same input independent variable and the same output dependent variable, and at least one equation and at least one performance coefficient corresponding to the models are provided; the multiple models comprise a normal model and a normal expansion model, and the normal model and the normal expansion model are established within and outside a normal operation range according to the value range of the input independent variable respectively; fault diagnosis is carried out on each component in the air conditioning system until diagnosis of all the components is completed, and the method comprises the steps that for any component, whether actual measurement values of all input independent variables of the component are within a normal operation range or not is judged; and selecting one of a normal model and a normal expansion model to carry out fault diagnosis on the component according to a judgment result.

The invention discloses an RPA multi-process integration verification method based on a colored Petri net, and the method comprises the steps: defining a colored Petri net RCPN model used for representing an RPA process for a multi-component cooperation scene in the RPA process; according to the RCPN model, constructing a corresponding RCPN subnet for each RPA process to be verified; based on the constructed RCPN subnets, integrating a plurality of subnets into an extended RCPN model; defining a determination condition set, and based on each RCPN subnet and the extended RCPN model, constructing a state reachable set of the extended model and a corresponding transition sequence set; and carrying out compatibility verification on the expansion model by utilizing the judgment condition set, and outputting a verification result. According to the method, the compatibility of RPA multi-process integration can be verified under a formalized framework, and the problems that an existing method is insufficient in support of control flow and data flow, lacks unified semantics and is difficult to ensure the integration correctness are solved.