132 results about "Function model" patented technology

The invention provides a power equipment three-dimensional dynamic updating method and system for virtual reality training, and the method comprises the steps: collecting the working state data of power equipment in real time through a sensor; processing the working state data based on a preset function model, and calculating the physical state change of the power equipment; according to the physical state change, adjusting physical attributes of the three-dimensional model of the power equipment in real time so as to simulate physical effects of thermal expansion, stress concentration or structural deformation; loading the adjusted three-dimensional model into a virtual reality environment, and performing real-time display through a virtual reality rendering platform; and after the user executes the interactive operation through the virtual reality equipment, the working state of the power equipment is updated, and the influence of the operation on the three-dimensional model of the equipment is dynamically fed back, so that a virtual-real linkage closed-loop training process is formed. The virtual model can change in real time along with the collected data, and the technical verisimilitude and operability of virtual training are remarkably improved.

The invention discloses a linear guide rail optimization design method and device, a medium and a program product, and the method comprises the steps: (1) constructing a simulation model and a MaOP design model which can optimize the rigidity, modal and weight at the same time based on the linear guide rail structure and static load analysis; (2) generating an elite population based on a Latin hypercube and diversity criterion, obtaining target values of the elite population, establishing a database, and constructing a Gaussian process function model; (3) designing mixed mutation operation based on OCC to generate a filial generation guide rail set; (4) designing a DEP-driven DPM evolutionary strategy to generate a candidate guide rail set, and selecting an optimal candidate guide rail; and (5) obtaining each target value of the optimal candidate guide rail, updating the database and the Gaussian process function model, returning to the step (3) until all optimization targets meet requirements, and outputting an optimal parameter value. According to the method, the MaOP process aiming at the rigidity, the modal and the weight of the linear guide rail can be effectively balanced, and higher precision and better comprehensive performance are achieved.

The invention relates to a new energy micro-grid multi-objective optimization control method and device, equipment and a medium. The method comprises the following steps: firstly, acquiring real-time output data of renewable energy sources of the micro-grid, operation parameters of energy storage equipment and load demand records, and performing time sequence analysis to obtain an energy output variation rule and a load demand curve; constructing a multi-objective function model containing economy-stability double objectives and a coupling correction term based on the law and the curve, generating an initial balance scheme through linear programming, and iteratively determining resource collaborative allocation parameters through a particle swarm optimization algorithm; the distribution parameters and the energy storage available capacity are input into the agent model, a real-time optimization decision instruction is generated, and an economic evaluation value is calculated in combination with running log data; and if the evaluation value does not reach the power grid stability threshold value, recalculating a balance scheme through a multi-objective function model, and generating a standardized equipment instruction set. According to the method, collaborative optimization of economy and stability of the micro-grid is realized, and stable operation of the micro-grid in different scenes is guaranteed.

The invention relates to a method for establishing an SIL dynamic evaluation model based on Weibull distribution, and belongs to the technical field of safety protection systems. The method comprises the following steps: (1) establishing a dynamic cumulative failure probability function model based on Weibull distribution according to obtained failure data through a reliability test; (2) calculating Weibull distribution parameters by using a median rank method, a linear regression fitting method and a least square method; (3) drawing a function model image by using MATLAB software; and (4) in combination with a fault tree method, establishing an SIL dynamic evaluation model based on Weibull distribution. According to the SIL dynamic evaluation model for the final executive component of the safety instrument system based on Weibull distribution, the dynamic change trend of the final executive component PFDavg along with time obtained by the model basically accords with the reality, the accuracy of SIL evaluation is improved, and scientific method support is provided for design personnel of the safety instrument system.

The invention discloses a photoelectric system digital prototype function model generation method and device based on artificial intelligence, and belongs to the technical field of artificial intelligence and photoelectric system digital prototypes, and the method comprises the steps: inputting training data of an artificial intelligence model, carrying out semantic analysis, marking and key parameter extraction operation, and forming a knowledge graph; establishing a logic association and constraint relationship among the parameters; training an artificial intelligence model by using the analyzed training data, optimizing a preset general rule base of the artificial intelligence model, and constructing a photoelectric system modeling rule base and a photoelectric system artificial intelligence model; calling the trained artificial intelligence model of the photoelectric system, extracting a model demand, calling the modeling rule base of the photoelectric system, and generating a function model of the photoelectric system; and visually displaying the photoelectric system function model, modifying and updating the photoelectric system function model, and dynamically updating the optimization strategy. Different function model modeling languages can be selected according to actual system requirements, and an artificial intelligence algorithm is adjusted.

The invention relates to the technical field of computers, in particular to a load awareness and task migration scheduling method of a heterogeneous multi-core power terminal and related equipment. A real-time load state vector is constructed by collecting core load state data, tasks are classified, dynamic priorities are calculated, and task priority data are generated. And calculating a global load imbalance degree according to the real-time load state vector, and triggering a task migration scheduling process when the global load imbalance degree exceeds a set threshold value. In the process, a utility function model is constructed based on task priority, performance gain and migration cost, utility calculation is carried out on a task and target core combination, an optimal migration scheme is screened, migration operation is executed, and efficient distribution of tasks and dynamic optimization of system energy consumption are achieved. According to the method, real-time load balancing and optimal performance scheduling of the heterogeneous multi-core power terminal are realized, so that the task processing efficiency and the operation stability are remarkably improved.

The present application provides a reinforcement learning method of learning machine, the learning machine is equipped with: the first agent, the manufacturing condition of manufacturing device is adjusted based on the observation data obtained by observing the state of manufacturing device; and the second agent, the function model or function approximator that has the relationship of observation data and manufacturing condition expressed in a different way from the first agent, the manufacturing condition searched by the first agent in reinforcement learning is adjusted using observation data and the function model or function approximator of the second agent, the reward data corresponding to the state of the product manufactured by the manufacturing device under the adjusted manufacturing condition is calculated, and the first agent and the second agent are reinforced learning based on observation data and calculated reward data.

The embodiment of the invention provides a multiphase pipe flow multi-parameter optimization method and system based on a genetic algorithm, and belongs to the field of multiphase fluid mechanics and oil and gas pipeline engineering. The method comprises the following steps: constructing a corresponding multi-objective function model based on a decomposition problem of a target multi-phase pipe flow problem; wherein the decomposition problem comprises any one or more of a liquid holdup accuracy problem, a friction resistance coefficient accuracy problem and an on-way length accuracy problem; calling a genetic algorithm based on a multi-objective function model, and carrying out individual coding and group initialization operation; executing the genetic algorithm, and executing optimization solution on the multi-objective function model; and repeating iteration until the optimized multiphase pipe flow calculation model meets the target precision, and outputting an individual with the highest fitness as a final optimization result. According to the scheme, the problems that a traditional method cannot give consideration to multi-objective optimization and the global search capability is insufficient are solved, and reliable technical support is provided for simulation and control of oil and gas field multiphase flow.

The application discloses an aging-aware precision reconfigurable logic synthesis method and system, and belongs to the technical field of integrated circuit EDA. The application aims to solve the problems of limited optimization space, introduced error from the beginning and lack of aging modeling in the existing aging-aware synthesis. The application constructs an aging standard cell library, iteratively applies constant replacement to the original circuit to generate multiple approximate candidate circuits, and then constructs a precision reconfigurable circuit composed of reconfigurable modules, which dynamically switches between accurate mode and approximate mode. Through aging-aware timing modeling, a power function model of gate delay with running time is established, and the aging continuity during mode switching is processed. The accurate mode life and the approximate mode remaining life are predicted, and the circuit with the highest score is automatically selected according to the score function. The application can improve the circuit life by about 9.5 times with an additional area overhead of about 3.72% under the premise of meeting the error constraint, which significantly enhances the long-term reliability of digital circuits.

Disclosed herein are embodiments of systems, methods, and products comprises a server for efficiently processing electronic requests. The server receives a plurality of predictive computer models and generates a specification file for each model by parsing the source code of each model. When the server receives an electronic request, the API layer of the server validates the request by verifying the inputs of the request satisfying validation codes in the specification file of the corresponding model. If the electronic request is invalid, the server imputes valid values for the request and sends the imputed values to the model execution layer. Within the model execution layer, the server utilizes an integrated development environment of a third-party server to call the function of the corresponding model. The model execution layer transmits the function's output results back to the API layer, which transmits the output results to the user device.

The invention discloses a ship information system reliability simulation and evaluation method. The method comprises the following steps: 1) ship information system fault identification based on a function model; the fault is comprehensively identified from three dimensions of a function domain, a physical domain and a system level; 2) building a ship information system reliability simulation model based on fault logic; and 3) realizing quantitative evaluation of reliability for the constructed reliability simulation model. The invention provides a fault logic-based ship information system reliability simulation model construction method, which is characterized in that a reliability simulation algorithm is designed according to the actual operation process of a ship information system, and statistical analysis is carried out after analogue simulation to obtain a ship information system reliability evaluation result.

The application relates to a used commodity feature mining method and system, wherein the method comprises the following steps: reading a commodity quality inspection report to obtain text information; extracting one or more pending commodity features from the text information; adding the pending commodity features to a first information feature set of a function model to form a second information feature set; taking the first information feature set and the second information feature set as inputs of the function model to obtain a first prediction evaluation value and a second prediction evaluation value; and comparing the first prediction evaluation value and the second prediction evaluation value; and in response to the second prediction evaluation value being greater than the first prediction evaluation value, confirming that the pending commodity features are available. The application extracts available and effective commodity features from a commodity quality inspection report, thereby refining the description of the commodity, enabling downstream storage such as search, recommendation and other services to better distinguish commodities and provide more accurate recall commodities.

The application discloses an interface-based passenger cabin simulation analysis auxiliary method and tool based on StarCCM+, and the method comprises the following steps: in response to user operation on a user operation interface, the following tasks are performed: reading the user operation interface, determining the input settings of the user for parameters of the user operation interface; calling a model setting module macro file, generating a model setting module macro file with parameters based on simulation analysis functions, model files and calculation parameters; calling StarCCM+ software to run the model setting module macro file with parameters, automatically generating a post-simulation calculation model; calling a model post-processing module macro file, generating a model post-processing module macro file with parameters based on a post-processing auxiliary file; running the model post-processing module macro file with parameters, automatically generating a post-processing output file based on the post-simulation calculation model; and running a report generation program, automatically generating a simulation analysis report based on the post-processing output file. The application can improve the simulation analysis efficiency.

The present invention relates to a method, system, and terminal for matching and scheduling technological achievements. By inputting a set total input quantity, it retrieves the technological needs to be traded and information on technological achievements traded in previous years. Using a trained demand function model, it obtains one or more matching technological achievements. It calculates the text similarity and technological productivity similarity between the technological needs to be traded and their corresponding technological achievements, and calculates the corresponding comprehensive matching similarity. A technological achievement scheduling sequence is constructed, and the matching and scheduling results based on the scheduling sequence are displayed on the user's graphical interface. This invention is operable while ensuring high output and high system operating efficiency. It optimizes the technological achievement transaction queuing and scheduling algorithm, saving queuing and matching time. It can automatically match using a similarity algorithm to prevent human matching errors. Furthermore, during information system development, queuing can be solved using a computable scheduler, making queuing matching management convenient and controllable.

The invention relates to a potential function model construction method, system and device and a storage medium, and belongs to the technical field of digital data processing. The construction method of the potential function model comprises the following steps: determining a neighbor atom set for each atom in an atom system according to a truncation radius; constructing a neighbor environment descriptor of each atom based on the neighbor atom set of each atom; inputting the neighbor environment descriptor of each atom into an independently configured neural network corresponding to the element type of the neighbor environment descriptor, and performing forward propagation calculation from an input layer to an output layer of the neural network to obtain energy of each atom; performing back propagation calculation and descriptor back calculation on the energy of each atom from the output layer to the input layer through each independently configured neural network to obtain partial derivative of the energy of each atom to the position of the adjacent atom; and obtaining the stress of each atom based on the partial derivative of the energy of each atom to the position of the adjacent atom. The potential function model has both precision and calculation efficiency.

The invention discloses a computer interlocking system capacity evaluation method and system. The method comprises the following steps: acquiring station yard data of an interlocking station; signal equipment models are established for various types of signal equipment, and interlocking system function models are established for various functions of the station interlocking system; establishing an association relationship between the signal equipment model and the interlocking system function model to obtain an interlocking system resource calculation system; the interlocking system resource calculation system calculates the total amount of system resources occupied by operation of the signal equipment of the target station; the total quantity of resources consumed by interlocking system function execution of the target station; and comparing the sum of the total amount of system resources occupied by the operation of the signal equipment and the total amount of resources consumed by the function execution of the interlocking system with the design capacity of the interlocking system to evaluate the scale applicability of the station yard of the target station. The method can accurately evaluate whether the capacity of the computer interlocking system adapts to the scale of a large station yard of a project or not.

The application belongs to the technical field of electric machines, and provides an electric machine efficiency optimization method based on magnetic flux density balance, comprising: establishing a first function model according to an electric machine main size relationship mathematical model; wherein the first function model represents the relationship between magnetic flux density and core length and stator winding; calculating stator winding parameters according to a magnetic flux density balance condition and the first function model; establishing a second function model according to the stator winding parameters; wherein the second function model represents the relationship between stator loss and stator winding parameters and slot fill rate; establishing a third function model according to the first function model and the second function model; wherein the third function model represents the relationship between electric machine efficiency and core length and winding parameters; adopting simulation calculation fitting to obtain an electric machine efficiency trend line according to the third function model, and determining the optimal efficiency of the electric machine and the core length and winding parameters corresponding to the optimal efficiency according to the electric machine efficiency trend.

The application discloses a kind of multi-stage system task reliability modeling evaluation methods considering complex dynamic characteristics, belong to system reliability modeling and evaluation technical field, including: according to the structure of system component unit, function principle and task flow, establish system function model and stage model;Based on the finite state machine with constraint, the state-transition model of each structure component unit is established respectively;According to the task activity of each stage and the success and failure criterion of task, the task flow model of system in the corresponding stage is constructed;Integrate the above steps, obtain the reliability model of multi-stage task, carry out task reliability simulation, calculate the occurrence probability of the structure component unit state or structure component unit state combination concerned.This application guarantees the efficient and stable operation of complex dynamic multi-stage system.

Communication methods and an apparatus, capable of ensuring normal use of functions or models and improving the actual use effect of functions or models. In a method, a first node may acquire first information corresponding to function / model granularity, and send the first information to a second node, the first information comprising target performance indicator information of a first function / first model and / or stability information corresponding to the target performance indicator information, the target performance indicator information being used for representing target performance of the first function / first model, and the stability information being used for representing the stability of the first function / first model. In this way, the second node may determine the performance and / or stability performance of the first function / first model, thereby ensuring normal use of functions or models and improving the actual use effect of functions or models.

The invention provides a sequential circuit characterization learning method based on a graph neural network and a state space model, and belongs to the field of electronic design automation, and the method comprises the steps: obtaining a sequential circuit diagram, and constructing a graph neural network reasoning module through designing a quadrupole embedding space; performing dynamic function modeling on the time sequence circuit diagram by using a diagram neural network reasoning module to obtain a node embedding vector; the method comprises the following steps: constructing a Mama memory module on the basis of a token method and a Mama framework; and performing dual structure modeling on node embedding by using a Mamba memory module to obtain an embedded vector, and completing sequential circuit characterization learning. The method solves the problems that an existing sequential circuit characterization learning method destroys the original structure of the circuit, cannot capture the core of the sequential circuit, is low in processing efficiency, is low in training speed, is limited in performance improvement, and is not obvious in precision advantage.

The invention relates to the technical field of agricultural internet of things and edge computing control, in particular to a multi-modal sensing fusion control system based on edge computing, which comprises a multi-source sensing module used for collecting environment scalar data in a controlled environment and visual image data of a target object and obtaining a multi-modal input data set; the potential field construction module is used for receiving a preset virtual potential energy function model and defining a mapping relation between a state space of the controlled environment and a potential energy value; the heterogeneous mapping module is used for mapping the visual image data into morphological parameters of a virtual potential energy function model; the gradient decision module is used for generating a gradient vector and converting the gradient vector into a control instruction; the execution feedback module is used for driving the state coordinate point to move towards the potential energy lowest point of the virtual potential energy function model; according to the invention, the problem of mutual confrontation of heating, dehumidification and other execution mechanisms in traditional control is effectively solved, and full-dimension and decoupling precise management of greenhouse microclimate is realized.

The application provides a multi-source heterogeneous data integration management method, medium and system based on a digital main line, belongs to the technical field of industrial digital main lines, and collects various formats of data and performs standardized processing by using a digital main line platform, realizes cross-domain semantic understanding by using a deep ontology fusion model, improves the semantic understanding efficiency by using adaptive pooling feature dimension reduction and separable attention calculation, optimizes the storage space by using a similarity aggregation deduplication or distributed independent storage strategy according to the data coincidence degree, establishes a digital main line data blood relationship tracing relationship to realize all-life-cycle data link construction, optimizes the semantic understanding parameters by using a fine-tuning or coarse-tuning mode based on a semantic mapping deviation value, constructs a vertical integration mapping from a function model to a performance model and a physical model and a horizontal integration framework of multi-domain comprehensive simulation, and solves the technical problem of insufficient cross-domain model semantic understanding accuracy.

The embodiment of the invention relates to the technical field of document processing, and discloses a function specification document design method and device, electronic equipment and a storage medium. The method comprises the following steps: after a function model is debugged, decomposing a model structure and a logical relationship of the function model to determine a chapter structure of a function specification document; designing a cover template, and obtaining data source information of the functional model; compiling files corresponding to the cover template and the functional structure based on the data source information and the chapter structure; and compiling and generating a function specification document based on all files. According to the method and the device, the problems of low efficiency and high error rate of a mode of manually compiling the function specification document are at least solved, the compiling efficiency of the function specification document is favorably improved, and the design error risk is reduced.

The invention relates to the technical field of production and manufacturing, and discloses a vibroseis earth parameter identification method, device and equipment, and the method comprises the steps: obtaining a function model built based on the system parameters of a vibroseis system; on the basis of the function model, parameter vectors needing to be identified are determined through an index window function and by means of least square; solving process parameters of parameter vectors on the basis of the collected acceleration of the heavy hammer and the acceleration of the flat plate, and determining the parameter vector at the current moment; and based on the parameter vector at the current moment, identifying the earth damping coefficient and the earth elastic coefficient. According to the scheme of the invention, on the basis of the function model, the exponential window function is introduced, and the forgetting factor adapting to the time variation is added for the least square, so that the process parameters for solving through the acceleration of the heavy hammer and the acceleration of the flat plate can accurately reflect the system condition at the current moment, thereby reflecting the time variation of the data, and improving the accuracy of the calculation. And a guarantee is provided for the accuracy of subsequent data identification.

The invention relates to a multi-parameter fitting industrial equipment temperature threshold prediction method, belongs to the technical field of data analysis and prediction, and aims to solve the problems of weak nonlinear fitting capability, high error and lack of threshold standard accurate time positioning of temperature prediction in the prior art. The method comprises the following steps: firstly, respectively performing polynomial fitting on three parameter sequences by utilizing historical data, and establishing a function model about time; future time points are simulated through iteration, and whether time points meeting conditions exist or not is judged by combining a quadratic equation root judgment method and extreme value analysis, so that the temperature reaches a set target. If yes, the total number of days from the current starting time to the target temperature appearing time is calculated, and a result is output; and otherwise, giving out prompt information that the standard is not reached within the preset time period. According to the method, through multi-parameter dynamic fitting, the prediction error is smaller than or equal to 5%, complex temperature scenes are covered according to conditions, 100-year + prediction is supported, iteration verification is stable, and the method is remarkably superior to a traditional single / multi-parameter linear model.

The present disclosure relates to a method for generating a language model performed by at least one processor, the method including obtaining a base model pre-trained with a large-scale corpus, a functional model with a specified function added to the base model, and a target model additionally trained on the base model with learning data of a specified domain, calculating a first difference value between a first parameter of the functional model and a second parameter of the base model corresponding to the first parameter, calculating a change ratio of a third parameter of the target model corresponding to the second parameter with respect to the second parameter, and generating a new model from the target model based on the first difference value and the change ratio.

The invention relates to the cross technical field of artificial intelligence and reliability system engineering, and provides an MBSE-LLM-based functional danger intelligent analysis method and device. According to the method, an MBSE model is utilized to construct a function model of an analysis object, retrieval enhancement knowledge is injected to obtain an enhanced vector database, then basic description and associated documents of a target function are determined, the basic description and the associated documents are fused to obtain enhanced description, a risk factor list is generated based on the enhanced description, and the risk factor list is analyzed. According to the method, the defect that traditional FHA depends on manual completion of field experts is overcome, a model-based system engineering method and a large language model technology are comprehensively utilized, and the formed intelligent FHA method can significantly optimize the overall process of function risk analysis and improve the risk analysis efficiency. The accuracy of an FHA result and the efficiency of analysis work are effectively improved, and efficient iteration of traditional FHA can be guided.

The invention discloses a heterogeneous federal learning system. The system comprises an edge device layer, a regional aggregation layer and a global coordination layer, wherein the edge equipment layer is used for carrying out local model training and is also used for determining an equipment cost function model in real time according to resource occupation of local terminal equipment; the regional aggregation layer is used for carrying out local aggregation on the local training result of the edge device layer; and the global coordination layer is used for globally aggregating the local aggregation result of the regional aggregation layer, synchronizing the global aggregation result to the edge equipment layer, and dynamically adjusting a learning model training strategy according to the equipment cost function model. According to the technical scheme provided by the invention, the hierarchical aggregation structure is adopted, and dynamic cost perception and strategy adaptive adjustment are carried out, so that efficient cooperation and robust operation of federated learning in a heterogeneous environment is realized, the convergence speed and robustness of the model are improved, and meanwhile, the system cost is remarkably reduced.

This invention provides a method and system for penetrating spaceborne computers, comprising: identifying and parsing the physical layer, link layer, transport layer, and application layer protocols involved in the target spaceborne computer, completing communication link connectivity and protocol field mapping; designing and generating multiple types of attack payloads based on protocol and functional models, injecting various attack data packets including internal network penetration, lateral movement, flooding attacks, and command replay; real-time collection of multiple operational indicators, status parameters, and abnormal events of the spaceborne computer, performing data processing and feature extraction; setting multi-dimensional anomaly criteria and scoring models, identifying and quantifying abnormal indicators, and outputting risk levels; and performing graded risk assessment based on attack input, abnormal responses, and scoring results, outputting security vulnerabilities and rectification suggestions. This invention is applicable to ground testing, on-orbit evaluation, and related security protection fields of spaceborne computers, improving the security assurance capabilities of satellite systems.