332 results about "Function model" patented technology

The invention relates to the technical field of storage equipment management, in particular to an intelligent control method and system for storage equipment, and aims to solve the problems of task allocation, path optimization and equipment collaboration in a complex storage scene. According to the method, a storage equipment function model comprising an upper-layer frame model and a lower-layer equipment model is constructed, task allocation and path planning are optimized by using a neural network model in combination with task requirements, and a storage equipment control instruction is generated. In the task execution process, task priorities and path planning are adjusted in real time to adapt to dynamic environment changes, and it is ensured that tasks are completed efficiently. According to the method, a task distribution diagram and a path planning scheme are further optimized through a dynamic task diagram and a dynamic energy field, the problems of equipment congestion, too high energy consumption and task delay are effectively solved, the collaboration efficiency and the resource utilization rate of storage equipment are improved, and the method is suitable for various complex storage scenes.

The invention relates to the technical field of data processing, and discloses a cross-border trade data intelligent processing method and system based on an intelligent port. The method comprises the steps of performing functional model hierarchical processing on cross-border trade business data, obtaining a functional module calling relation network, performing process template construction processing, obtaining a dynamic process assembly rule base, performing service burying point processing, obtaining a user behavior analysis data set, performing guide strategy construction processing, and obtaining a user behavior analysis result set; and obtaining a service guidance rule base, carrying out knowledge graph construction processing, obtaining a service knowledge inference rule set, and carrying out cross-border trade data processing to obtain a port service intelligent decision scheme. According to the method, intelligent processing of cross-border trade data is realized, a complete technical chain from data acquisition, behavior analysis to decision support is constructed, accurate business guidance and intelligent decision support are provided, and the problems of low business processing efficiency, inaccurate service guidance, weak decision support capability and the like in the prior art are solved.

The invention relates to the technical field of traffic planning, in particular to a transfer connection optimization method in multi-mode traffic integrated planning, which comprises the steps of data acquisition and demand analysis, multi-mode transfer network topology design, multi-mode timetable collaborative optimization, transfer service system development and facility transport capacity dynamic matching. According to the transfer connection optimization method in the multi-mode traffic integrated planning, a dynamic and static combined multi-source data acquisition framework is constructed based on a multi-dimensional data fusion system, the data precision can be improved, the network can be perceived in real time, association modeling of weather events and demand fluctuation is realized based on construction of a multi-agent simulation system, and the optimization of the transfer connection in the multi-mode traffic integrated planning is realized. A dynamic weight mechanism is introduced based on a hypergraph model, the robustness of the algorithm is improved, a layered optimization framework processes global optimization through upper-layer integer programming, dynamic adjustment is achieved through lower-layer deep reinforcement learning, a buffer time intelligent insertion strategy can improve the late absorption capacity, and a facility utility function model is established to improve the transfer efficiency.

The invention relates to the technical field of air conditioning energy consumption optimization, and discloses an HVAC multi-unit air conditioning system energy consumption optimization matching system and method, a polynomial function model is constructed based on historical data and real-time operation parameters, and a nonlinear relation between energy consumption and power distribution of each unit is accurately described; secondly, the adaptability of the model is enhanced by means of principal component analysis, segmented modeling and the like, global optimization of model parameters is realized in combination with a genetic algorithm, and the prediction precision and robustness of the model are improved; in the optimization control level, a multi-unit collaborative weight factor is introduced, a weighted particle swarm optimization algorithm is adopted, a power distribution strategy is dynamically adjusted, and collaborative optimization control among multiple units is achieved. Through a complete acquisition-modeling-optimization-execution closed-loop control structure, intelligent sensing of the running state of the multi-unit air conditioning system and optimal energy consumption scheduling are achieved.

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 discloses a modeling method for inverting surface high-precision three-dimensional deformation through InSAR and GNSS data fusion, and belongs to the technical field of surface deformation monitoring. The method includes the steps of resolving InSAR line-of-sight deformation, unifying InSAR and GNSS data reference systems, establishing a stress model based on an elastic theory, calculating a three-dimensional deformation component for the first time based on a least square theory, obtaining an optimal weight matrix by applying a variance component estimation method, calculating final three-dimensional deformation and the like. According to the method, three-dimensional deformation and strain parameters are estimated by fusing InSAR measurement data and GNSS measurement data based on the elastic theory, the stress relation between adjacent points is considered, and unnecessary spatial interpolation of GNSS measurement values is avoided. The variance component estimation method solves the problem of unreasonable weight ratio of different types of observation values, and according to the adjusted optimal weight matrix, a function model is solved based on the least square method, and high-precision and high-spatial-resolution earth surface three-dimensional deformation information within the research range is obtained.

The invention discloses a dial plate processing optimization system based on big data, and relates to the technical field of dial plate processing, data is collected, anti-fact sample data is generated, a causal atlas structure including processing process nodes and an edge weight function model is constructed, processing path performance is evaluated based on an abstract objective function, and the processing process is optimized. According to the method, the causal atlas is constructed based on multi-source data such as tool wear degree data and spindle vibration amplitude data, and path deduction and parameter optimization of a target state are realized in combination with an anti-fact sample; self-adaptive control over the machining state is achieved through a structured deviation matrix, a causal relationship and a weight function model are dynamically corrected through a feedback learning mechanism, and data-driven optimization and defect prevention in the machining process are achieved.

The invention discloses a multi-source heterogeneous learning path planning method based on personalized constraints, which solves the problems of single path, data staticization and the like in the prior art, and comprises the following steps: acquiring multi-source behavior data of a learner, carrying out feature modeling, and constructing a high-dimensional behavior portrait; according to the high-dimensional behavior portrait, utilizing a neural collaborative filtering algorithm to predict the interest and mastering probability of a learner to any knowledge point, and generating a preliminary learning path; carrying out dominant and implicit evaluation on the knowledge mastering state of the learner by adopting a cognitive diagnosis model, and carrying out dynamic correction on the preliminary learning path to obtain a corrected learning path; performing dimension reduction on the multi-source behavior data by adopting a principal component analysis algorithm to extract a core factor, and forming an evaluation basis for path optimization; and constructing a multi-objective path function model, and obtaining an optimal dynamic learning path by adopting a multi-objective optimization path algorithm in combination with the corrected learning path and the core factor. The method has high practical value and popularization value in the technical field of learning path planning.

The invention relates to the field of materials and chemical calculation, in particular to a machine learning potential function model construction method of a solid-solid interface system, which comprises the following steps: constructing the solid-solid interface system; the method comprises the following steps: calculating a solid-solid interface system based on a first principle, recording output data of each configuration in the solid-solid interface system in each step of a simulation process, and forming a high-precision data set; performing fine tuning on a pre-trained MACE model by adopting a high-precision data set, and performing optimization by adopting a physical constraint loss function to obtain a preliminary machine learning potential function model; performing hyper-parameter tuning on the preliminary machine learning potential function model to obtain a fine tuning machine learning potential function model; according to the method, the machine learning potential function model based on the high-order equivariant graph neural network architecture is adopted, and the characteristics of the solid-solid interface system are effectively learned.

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 new energy equipment system maintenance decision-making method and system based on a fault coupling model, and relates to the technical field of equipment predictive maintenance, and the method comprises the steps: collecting system structure information and system fault data of a complex new energy equipment system, and carrying out the maintenance decision-making of the complex new energy equipment system according to the system structure information and the system fault data; respectively constructing a function model view, a system level fault model view and each flow fault model view; constructing a fault coupling model corresponding to the complex new energy equipment system; probability estimation is carried out on the system fault data according to a kernel density estimation method, the fault probability of each fault node in the complex new energy equipment system is obtained, and key fault nodes in all the fault nodes and key node information corresponding to the key fault nodes are obtained; and mapping the key node information into a risk level corresponding to the key node information, and obtaining an optimal maintenance decision based on a decision tree analysis method and a plurality of maintenance decisions. According to the method, multi-dimensional analysis of a complex system can be realized, and fault modes of different components and subsystems can be identified more clearly, so that the fault detection and diagnosis efficiency is improved.

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 provides a method for additive forming of a high-performance Ti2AlNb three-period minimal curved surface lattice structure in a nitrogen atmosphere, and belongs to the technical field of additive manufacturing. Comprising the following steps: step 1) modeling an implicit function of a TPMS configuration: designing a CAD model of the TPMS configuration and exporting the CAD model as an STL file; 2, pretreatment of the Ti2AlNb lattice structure formed through L-PBF, wherein an STL file is subjected to slicing treatment, and technological parameters are set; 3, L-PBF forming of the Ti2AlNb lattice structure is conducted, specifically, in the nitrogen-argon mixed atmosphere, the Ti2AlNb lattice structure is formed through the L-PBF technology; and (4) the TPMS configuration Ti2AlNb lattice structure is subjected to surface treatment. According to the method, the Ti2AlNb lattice structure with the TPMS configuration is designed through implicit function modeling, the Ti2AlNb lattice structure with the TPMS configuration is formed through L-PBF in the nitrogen-argon reaction atmosphere, the problems that in traditional particle strengthening, reinforcements are distributed unevenly, and the solid solution strengthening effect is limited are solved, and good matching of the strength, plasticity and toughness of the lattice structure is achieved.

The invention provides a shale oil fracturing effect evaluation method and device, a terminal and a storage medium, and belongs to the technical field of petroleum engineering. According to the method provided by the embodiment of the invention, the fuzzy comprehensive evaluation method is utilized to perform fuzzy processing on the basic parameters such as the target reservoir parameters and the target engineering parameters, so that the values of the basic parameters are more representative and are not certain values, and therefore, an evaluation result close to the reality can be obtained, and the accuracy of a calculation result is improved; and optimizing the shale oil fracturing effect evaluation method. Moreover, by fitting the function model of the main control factors and the yield and utilizing the function model to evaluate the fracturing effect, the model construction process is simplified, and the evaluation efficiency is higher.

The invention discloses a method for evaluating the function operation state of a dispatching automation master station system, and solves the problems that the efficiency is low and the operation stability and reliability of a power grid are influenced after the function detection of the dispatching automation master station system in the prior art occurs after a fault occurs, and the method comprises the following steps: constructing a function model of the dispatching automation master station system; determining an influence factor set of the function failure of the master station system; determining the weight value of the influence factor set by using an analytic hierarchy process and checking the consistency of the influence factor set; constructing a fuzzy comprehensive evaluation matrix by using a single-factor fuzzy evaluation method; the fuzzy evaluation matrix and the influence factor set weight value are combined to evaluate the operation state of the main station system function; and constructing a main station system function failure fault tree model, solving a minimum cut set and a minimum path set, and tracing the reason of the main station system function failure. The function failure probability of the dispatching automation master station system can be evaluated, the function failure reason can be traced, and safe and stable operation of a power grid is ensured.

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.

Systems and method for estimating treatment effects for a target trial in accordance with embodiments of the invention are illustrated. One embodiment includes a method. The method defines a skedastic function model, wherein defining the skedastic function model depends, at least in part, on target trial data. The method designs trial parameters for the target trial based in part on the skedastic function model. The method applies the trial parameters to a loss function to derive at least one minimizing coefficient, wherein a minimizing coefficient corresponds to a regression coefficient for an expected outcome to the target trial based on the trial parameters. The method computes standard errors for the at least one minimizing coefficient. The method quantifies, using the standard errors, values for uncertainty associated with the target trial. The method updates the trial parameters according to the uncertainty.

The invention discloses an incentive mechanism and power distribution method based on federated learning, and the method comprises the steps: building an FL system model, a time delay model and an edge device and server utility function model according to a system scene, putting forward a two-stage Stackelberg game optimization problem on this basis, and solving a resource optimization configuration scheme through a two-stage convex optimization iterative algorithm (TSPA). According to the method, the earnings of the server and the edge equipment are modeled by utilizing the two-stage Stackelberg game model, so that the power distribution is optimized, the model precision is improved, the model safety is guaranteed, and the training time delay is shortened. In the model, the server is used as a leader of the game and formulates a reward strategy, the edge device is used as a follower, and the uploading power and the interference power of the edge device are optimized based on the reward strategy of the server. And finally, the purpose of maximizing the benefits of both the server and the edge equipment is achieved by analyzing the equilibrium point of the game, iteratively optimizing model uploading and cooperative interference power distribution.

The invention provides a heterogeneous computing power distribution method and device, electronic equipment, a storage medium and a program product, and relates to the technical field of cloud computing resource energy efficiency evaluation. In the embodiment of the invention, the energy consumption condition of the server is quantified by using a power consumption function, the resource utilization efficiency is measured by using a load balance degree function, an energy + efficiency dual-objective function model is established to evaluate the energy efficiency of heterogeneous computing power, the two objective functions are optimized at the same time by using a dynamic multi-objective optimization algorithm, and meanwhile, the service index is taken as a constraint condition. According to the method, calculation tasks are distributed to the server with the highest energy efficiency, the dual purposes of high performance and low energy consumption are achieved, the purposes of improving the energy efficiency and reducing the cost are achieved, service level indexes are taken into consideration, and the response speed is improved while stable operation of a service system is guaranteed.

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 lightweight time sequence information fusion perception control method and system, and relates to the technical field of Internet of Things weak terminal data acquisition, and the method comprises the steps: obtaining key perception data, and tracking the local task load and communication load of a node in real time; based on a feasible space window of various non-linear fitting functions, carrying out adaptive segmentation on input sensing data and channel states; on the basis of curve features of the time series data, preprocessing the time series data, then performing feature extraction on a curve, and selecting important features; analyzing and predicting the compressed curve characteristic data, classifying the time sequence data into curves with different characteristics, selecting a corresponding empirical function model for fitting according to a classification result, and finally dynamically changing the running state of the node by a self-adaptive adjustment algorithm; and performing dynamic control based on the future trend of the data. According to the method, the future trend of the task and the communication load is coupled with the running state of the task and the communication load, and the change rate of node tracking data is combined with the historical condition of the node tracking data, so that the self-adaptive adjustment of the running state of the node is realized.

The invention provides a solid-solid interface structure global optimization method and device, equipment and a storage medium. Comprising the following steps: generating one or more initial solid-solid interface configurations; performing molecular dynamics simulation on each initial solid-solid interface configuration by adopting a minimum jump method, recording the structure and interface energy of the solid-solid interface configuration in the molecular dynamics simulation process, and forming a data set; constructing a machine learning potential function model according to the data set to describe the relationship between the solid-solid interface configuration and the interface energy; periodically performing molecular dynamics simulation on each initial solid-solid interface configuration by adopting a minimum jump method, calculating the interface energy of the solid-solid interface configuration obtained by each simulation by adopting a machine learning potential function model to obtain a plurality of solid-solid interface configurations with a local energy minimum value, and when the number of iterations is met, calculating the interface energy of the solid-solid interface configuration with a local energy minimum value. And outputting the obtained solid-solid interface configuration with the minimum interface energy.

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 relates to a GPU system verification method and device, electronic equipment, a storage medium and a program product. The method comprises the following steps: obtaining a target random test excitation sequence; respectively inputting the target random test excitation sequence into the GPU function model and the register transfer level model; wherein the GPU function model is a software model used for simulating functions of the GPU; the register transfer level model is a model written based on a hardware description language, and the register transfer level model is used for describing hardware implementation and behaviors of the GPU; obtaining a function simulation result output by the GPU function model in response to the target random test excitation sequence and a simulation result output by the register transfer level model in response to the target random test excitation sequence; and comparing the function simulation result with the simulation result to obtain a first random verification result of the GPU system. According to the method and the device, the GPU system verification flexibility can be remarkably improved.