221 results about "Function model" patented technology

The invention discloses a beneficiation process intelligent optimization control method and system based on a knowledge graph, and relates to the technical field of beneficiation process control, and the method comprises the steps: collecting static attribute data and dynamic operation data, constructing a causal knowledge graph, and obtaining a causal relationship between nodes in the causal knowledge graph and an optimization target constraint; based on a causal relationship between nodes in the causal knowledge graph and optimization target constraints, screening data from a historical database to form a high-value historical data set; according to the high-value historical data set, a reward function model is constructed, a strategy function is trained, and a reinforcement learning control strategy is obtained; running the reinforcement learning control strategy, generating a control instruction, and issuing the control instruction to the target equipment to obtain dynamic optimization control; and performing real-time monitoring on the dynamic optimization control, and performing anti-fact reasoning based on a causal knowledge graph when abnormal deviation of the running state is detected to obtain an anti-fact reasoning result.

The invention provides a chip verification method and device, equipment, a medium and a program product, and the method comprises the steps: receiving a first operation instruction of a to-be-verified module and a second operation instruction of a reference model in parallel, the reference model being a function model used for simulating the expected behavior of the to-be-verified module; searching a first instruction record matched with the first operation instruction from a request queue of the reference model, and searching a second instruction record matched with the second operation instruction from a request queue of the to-be-verified module; and determining a verification result of the to-be-verified module according to a comparison result of the first operation instruction and the first instruction record and / or a comparison result of the second operation instruction and the second instruction record. According to the invention, a structured and reusable universal chip verification method is realized, so that a tedious customized scoreboard design is replaced, and the efficiency and accuracy of verification work are greatly improved.

The invention discloses a method and system for solving a partial differential equation based on a KAN and MLP parallel structure, and belongs to the technical field of deep learning. The method comprises the following steps: constructing a parallel neural network comprising a KAN branch and an MLP branch, and introducing a fusion factor to carry out weighted fusion on the output of the KAN branch and the output of the MLP branch; based on a physical information neural network principle, constructing a loss function comprising an equation residual term, an initial condition term and a boundary condition term; training the parallel neural network by using an adaptive optimization algorithm, and minimizing the loss function; and inputting variables in the partial differential equation into the trained parallel neural network, and outputting a solution of the partial differential equation. By effectively combining the nonlinear expression ability of MLP and the function modeling advantage of KAN, the expression and solution ability of a multi-scale, non-stationary and complex physical field is improved; the modeling precision and convergence stability of a complex physical system, especially a partial differential equation driving problem, can be effectively improved.

The invention discloses an adaptive multi-angle elbow airtightness detection method, which comprises the following steps: arranging a flexible pressure sensor array and a micro inertial measurement unit on the inner wall of an elbow, collecting multi-point sealing pressure and attitude signals in real time, fusing geometric parameters and a spatial mapping relation, and completing denoising, normalization and multi-curvature region mapping of pressure data; a distributed pressure-attitude database is combined, a function model associated with sealing pressure and pipeline geometric parameters is established, inflation, exhaust and deformation of each node are dynamically adjusted through an intelligent closed-loop regulation and control algorithm, collaborative optimization of multi-node pressure and a fitting state is achieved, and the sealing performance of the pipeline is improved. According to the method, the accuracy and the self-adaptive adjusting capacity of sealing pressure distribution under a complex bent pipe structure are effectively improved, the tiny leakage real-time positioning and self-learning optimization capacity is achieved, and the method is suitable for sealing quality guarantee of various types of curved surface pipelines.

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 the field of chip verification, and discloses a construction method of a verification environment, a verification method and a system.The construction method of the verification environment comprises the steps that the connection relation between a to-be-verified component and bus function models is established, and communication connection is established between the matched bus function models and proxy models; initializing each agent model in the software end environment, and completing the initialization process of each agent model after receiving a reset release signal transmitted by the to-be-verified component in the hardware end environment; calling a preset configuration function in the hardware end environment to construct a configuration transaction in the software end environment through the preset configuration function; and driving the configuration transaction through the bus function model in the hardware end environment, and finishing a transaction configuration process of the to-be-verified component in the hardware end environment based on a driving result. According to the technical scheme provided by the invention, the verification process of the to-be-verified component can be accelerated, and the verification efficiency of the to-be-verified component is improved.

The invention discloses a formal verification method based on a hierarchical verification framework, which comprises the following steps of: firstly, dividing a target program into a C code and an assembly code, modeling and verifying the assembly code, then modeling and verifying the C code, and in the modeling process of the C code, carrying out verification on the C code; and defining a specific state and an abstract state of each data structure, establishing mapping through a refinement relationship, defining a function model based on the abstract state, constructing a function API specification, and finally defining system properties for a target program according to the function model and verifying the system properties. According to the method, a hierarchical verification framework and a formalization tool are combined, formalization verification is carried out on the target program from specific implementation to abstract division into three layers, comprehensive verification from bottom-layer codes to high-layer system properties is realized, the risk of security vulnerabilities is reduced, and the reliability of the target program is improved.

The present disclosure relates to systems, non-transitory computer-readable media, and methods that utilize a generative stochastic model and an action-value function model to build a biochemical structure. Indeed, in one or more implementations, the disclosed systems generate a flow measure for a constructive object option in building a biochemical structure and further generate an action-value for the constructive object option. For instance, the disclosed systems combine the flow measure and the action-value to select the constructive object option from a plurality of constructive object options. Moreover, in some instances, the disclosed systems generate the biochemical structure using the selected constructive object option.

The invention discloses a new energy system frequency domain modeling and equivalent circuit order reduction analysis method, and relates to the technical field of new energy power system modeling and stability analysis. The method is used for solving the problems that in an existing frequency domain modeling means, the automation degree of data acquisition is low, fitting precision and stability are poor, equivalent circuit universality is poor, and order reduction analysis is not systematic. The method comprises the following steps: acquiring system frequency domain response data based on disturbance injection; constructing a system rational function model by adopting an improved vector fitting algorithm; and then model order reduction is realized through state space modeling and an SVD-based balanced truncation method, and response consistency and stability analysis is carried out in a frequency domain and a time domain. The method can be widely applied to modeling analysis of new energy equipment such as wind power equipment, voltage source converters and high-voltage direct-current power transmission equipment, and has the advantages of being high in automation degree, high in modeling precision, good in visualization, clear in structure, suitable for simulation and control design and the like.

The invention provides a multi-source heterogeneous data integrated management method based on a digital main line, a medium and a system, and belongs to the technical field of industrial digital main lines. A digital main line platform is used for collecting data of various formats and conducting standardization processing, a deep ontology fusion model is adopted for achieving cross-domain semantic understanding, and the data of various formats is obtained. Semantic understanding efficiency is improved through adaptive pooling feature dimensionality reduction and separable attention calculation, a storage space is optimized by adopting similarity aggregation de-duplication or a distributed independent storage strategy according to a data overlap ratio, and a digital principal line data consanguinity tracing relation is established to realize full-life-cycle data link construction. Based on the semantic mapping deviation value, a fine fine tuning or coarse tuning mode is adopted to optimize semantic understanding parameters, longitudinal integrated mapping from a function model to a performance model and a physical model and a transverse integrated framework of multi-domain comprehensive simulation are constructed, and the technical problem that cross-domain model semantic understanding accuracy is insufficient is solved.

The invention discloses a new energy uncertainty interval fine modeling method based on ELM-Copula. Comprising the following steps: 1) data reading and preprocessing: obtaining a power prediction value and an actual output value by reading historical operation data of a new energy electric field, performing kernel density estimation on the per-unit power prediction value and the actual output value, and calculating an error absolute value according to the per-unit power prediction value and the actual output value; 2) constructing a dynamic Copula function model; 3) measuring goodness of fit of the model; 4) calculating a confidence interval of a prediction error, analyzing uncertainty of power generation power prediction, and giving a confidence interval of a prediction value; according to the method, a multi-type dynamic Copula model is introduced to construct a dynamic dependency structure between a prediction error and power, the ELM is applied to a post-processing stage of a dynamic Copula prediction interval, a correction coefficient is generated by learning historical deviation characteristics, and an original interval is shrunk, so that the prediction precision and practicability are improved on the premise that the coverage rate is not reduced.

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 configurable building system and method for an energy management system. The system comprises an infrastructure layer, a configuration management layer, an atomic function library layer, an atomic function combination layer and an application scene layer. The infrastructure layer provides a containerization facility and a distributed data storage facility of the atomic function model; the configuration management layer is used for visualized interaction, gateway and protocol configuration and atomic function model instantiation; the atomic function library layer is used for creating and storing an atomic function model; the atomic function combination layer is used for polymerizing atomic function model examples to obtain atomic function combinations; and the application scene layer is used for calling the atomic function combination and finally forming an energy management system suitable for a corresponding scene. The invention provides a highly flexible, configurable and extensible energy management system development solution.

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 invention belongs to the technical field of multi-level RCM, and discloses a multi-level RCM analysis and reliability structure construction method and a related device. The multi-level RCM analysis and reliability structure construction method comprises the following steps: establishing a unit system function model after constructing a unit system database, performing multi-level classification on equipment of a unit system in combination with a fuzzy clustering algorithm, and then determining levels and weights of various types of equipment in the unit system function model, analyzing a failure mode of each type of equipment after multi-level classification through a harmfulness analysis method in combination with a fault tree analysis technology, establishing a correlation model between the equipment failure mode and a unit system function, determining an influence weight of the equipment failure mode on the reliability of the unit system, and establishing a unit system reliability model; inputting the unit system database into the unit system reliability model to obtain a unit system maintenance strategy library; the analysis result accuracy and the system reliability can be greatly improved.

The invention discloses a method for constructing a two-dimensional layered material machine learning potential function, and belongs to the field of material science and computational simulation, and the method comprises the steps: S1, carrying out the modeling of a two-dimensional layered material through employing a first principle, completing the calculation of the first principle, and taking a result as a data source; s2, extracting structure and force information from the data source, and vectorizing the data to form a data set for training a neural network model; s3, training the processed data set based on a deep learning model DeepMD, and generating a potential function model of machine learning; s4, model detection: calculating an error between a model prediction value and a first principle calculation result; s5, carrying out molecular dynamics simulation; by reasonably designing the modeling process and optimizing the data processing and training steps, the generated potential function model is ensured to have high precision and high efficiency, and the method can be widely applied to the field of material science and molecular dynamics simulation.

The invention discloses an automobile part optimization method and device and electronic equipment, and the method comprises the steps: receiving an adjustment instruction for a target part of an automobile part, and determining a first potential defect corresponding to the adjustment instruction from potential defects contained in functional model information; wherein the functional model information comprises potential defects of a plurality of parts associated with the automobile part, and at least one potential optimization strategy corresponding to each potential defect; determining at least one defect category associated with the first potential defect; determining at least one second potential defect included in each defect category, and determining at least one potential optimization strategy corresponding to each second potential defect; and according to the at least one potential optimization strategy corresponding to each second potential defect, determining a target optimization strategy of the target part so as to optimize the automobile part. Through the technical scheme provided by the invention, the optimization result of the automobile part can be improved, the optimization efficiency is improved, and the optimization cost is reduced.

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 relates to the technical field of networks, in particular to a twin model construction method and device and computer equipment. The method comprises the following steps: in the case that a digital twin demand for a target service is acquired, constructing a network element slice model according to network element capabilities required for realizing the target service; constructing a network element function model according to network element functions required for possessing the network element capabilities; constructing a network element instruction model according to execution instructions required for possessing the network element functions; assembling the network element slice model, the network element function model and the network element instruction model to obtain a digital twin model; wherein the digital twin model is used for realizing the target service.

The present application relates to a kind of diesel engine electronic control system hardware PCB power supply function model calibration and identification method, this method is in the process of traditional mature PCB performance simulation commercial software to PCB is carried out performance evaluation, the concept of diesel engine electronic control system hardware PCB power supply function model is established, the model parameter class is reasonably divided, then on this basis, the calibration method that can effectively improve model precision is proposed, and the identification method that can verify model precision is correspondingly given.The proposed method breaks through the present situation that simulation precision is difficult to control in the traditional PCB performance evaluation process, and forms a scheme that can guarantee model precision by means of the two main links of calibration and identification.Its contribution lies in establishing the bridge between traditional commercial software simulation and equipment digital prototype construction, and it is an important part of diesel engine electronic control system digital prototype construction.

Provided in the embodiments of the present application are a beam failure recovery (BFR) method and apparatus. The method comprises: a terminal device receiving BFR configuration information from a network device; and the terminal device performing BFR on the basis of the BFR configuration information, wherein a candidate beam for BFR is determined on the basis of an AI / ML functionality / model.

The embodiment of the present application discloses a kind of method for calculating fitting parameter and positioning method, and is used to calculate fitting parameter by improving simulated annealing algorithm, to improve the accuracy of calculated fitting parameter.The method comprises the following steps: the step of initializing parameter, including: initializing multiple molecules (or variables), each molecule is at least composed of fitting parameter and energy value;The step of simulated annealing, including: using simulated annealing, according to test data and objective function model, the multiple molecules are iterated to calculate the final fitting parameter;Wherein, the test data is collected from the first environment, the objective function model is the algorithm model used by the second environment, and the final fitting parameter is used to fit the first data obtained from the first environment into the second data in the second environment, so that the algorithm model can realize predetermined function based on the second data.

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.