93 results about "Transfer model" patented technology

A dynamic optimization system for energy consumption of a refrigeration house based on digital twinning is characterized by comprising a data acquisition module used for acquiring basic structure data of the refrigeration house, technical parameters of a refrigeration system, real-time operation data and historical operation data, preprocessing the data and then outputting a standardized multi-dimensional real-time data stream; the model construction module is used for constructing a 3D geometric model, a thermodynamic transfer model and a refrigeration system mathematical model according to the multi-dimensional real-time data flow, performing machine learning calibration on model parameters through historical operation data, and performing fusion to construct a refrigeration house digital twin model; the prediction analysis module is used for predicting future energy consumption demand and load change according to the refrigeration house digital twin model and the real-time operation data, and outputting an energy consumption prediction result and a load analysis report; a strategy generation module; an execution feedback module; and a learning optimization module. Overall energy consumption of the refrigeration house is reduced, energy utilization efficiency is remarkably improved, and goods storage safety is guaranteed.

The invention discloses a personalized information accurate pushing system and method based on artificial intelligence, and relates to the technical field of personalized recommendation, and the method comprises the steps: fusing user multi-platform behavior data and external space-time environment information, and generating a situation label with confidence through employing an improved space-time density clustering algorithm; constructing a causal directed acyclic graph by adopting a causal forest algorithm, quantitatively analyzing a heterogeneity causal effect, inverting a potential intention of the user, and outputting standardized intention inversion probability distribution; in combination with a historical intention and a behavior sequence, training a Transform intention state transition model constrained by causality of a causality directed acyclic graph, and performing multi-step probability deduction to generate an intention evolution path; information is retrieved from the dynamic knowledge graph according to the prediction path, a pushing copywriting matched with the situation is generated through the NLP technology, the optimal pushing opportunity is calculated in combination with the position track of the user, and accurate reaching of personalized information is achieved.

The invention discloses a load transfer method, system, equipment and medium considering new energy and load power fluctuation, and belongs to the technical field of relay protection, and the method comprises the steps: taking a switch motion moment as a first stage of load transfer, taking a set time after the switch motion as a second stage of load transfer, building a dual-stage load transfer model, and taking the set time as a second stage of load transfer; obtaining a load transfer scheme; double-stage constraint verification is executed, and operation constraint verification is carried out on the load transfer schemes of the first stage and the second stage; introducing a constraint out-of-limit penalty factor, and quantifying the degree of the load transfer scheme deviating from the constraint condition; constructing a load transfer scheme comprehensive evaluation function, and performing weighted integration; and optimizing the load transfer scheme comprehensive evaluation function by adopting an improved binary particle swarm algorithm, and determining an optimal load transfer scheme. According to the method, the limitation of a traditional static model is overcome, and the problem of operation constraint violation caused by photovoltaic output change is avoided.

The invention relates to the technical field of energy testing, in particular to a self-adaptive dynamic energy testing method based on combined heat and power generation, which comprises the following steps: constructing a thermoelectric coupling transfer model and a working condition identification model, and inputting power generation power and heat supply power into the thermoelectric coupling transfer model to correct model parameters, inputting the environmental parameters into a working condition recognition model, recognizing a current working condition type based on a preset working condition feature library, calling an initial test scheme from a test scheme library according to the current working condition type, and generating a dynamic test scheme through a machine learning algorithm in combination with the parameters corrected by the thermoelectric coupling transfer model; and controlling the test equipment to execute a test based on the dynamic test scheme, collecting test data in real time, calculating a system performance index according to the test data, comparing the system performance index with a preset threshold value, and generating feedback information for adjusting the test scheme. Therefore, the problems that in the prior art, a test scheme is fixed and cannot adapt to complex working conditions, and performance evaluation is incomplete are solved.

The invention provides a ground vibration spectrum acceleration prediction method, a transfer learning method, equipment and a medium, a global model is obtained through first training by using a basic knowledge data set, the first training is evaluated by using a regional knowledge data set, and then a transfer model is obtained through second training by using the regional knowledge data set. And the second training is evaluated by adopting the regional knowledge data set, and the establishment method of the migration model retains the data features of the regional database and learns the difference between the global data set and the regional data set, so that the prediction precision of the model is ensured, and the generalization ability of the model is enhanced.

The invention discloses a micro-seismic signal identification method based on transfer learning and BiLSTM-DCNN, belongs to the technical field of mining engineering micro-seismic monitoring and signal processing, and solves the problems of data scarcity in the initial stage of mine monitoring and low identification precision under the condition of small samples. Firstly, Mel spectrum feature extraction is carried out on a micro-seismic signal; the method comprises the following steps: constructing a BiLSTM-DCNN model comprising a bidirectional long-short term memory network and a deep convolutional neural network, and carrying out pre-training by using large-scale source mine data; and the model is adapted to target mine small sample data through transfer learning, and model parameters are finely adjusted, so that high-precision classification of the signals is realized. According to the method, the recognition accuracy and the model generalization ability of the micro-seismic signals under the small sample condition are remarkably improved, the test accuracy reaches 0.9444 and is improved by 80.85% compared with an unmigrated model, and the method is suitable for an intelligent early warning and safety monitoring system for mine dynamic disasters.

A defect detection method and a model transfer method. The model transfer method comprises: acquiring a source-domain image and a corresponding annotation, and a baseline model obtained from a source domain; acquiring target-domain images, which are fully annotated, partially annotated, or unannotated; inputting the annotation of the source-domain image into a denoiser to obtain a denoised annotation; inputting the unannotated target-domain images into an initial baseline model to obtain second target-domain prediction results, performing data augmentation on the unannotated target-domain images to obtain second target-domain augmented images, and using the second target-domain prediction results as pseudo-annotations of the second target-domain augmented images; and using the source-domain image and the corresponding denoised annotation, the annotated target-domain images and the corresponding annotations, and / or the second target-domain augmented images and the corresponding pseudo-annotations to train the baseline model, so as to obtain a transfer model. The method can complete training by using a small number of annotated target-domain images, thereby solving the problem of model transfer performance being poor in the case of insufficient target-domain images.

This invention discloses a lightweight BIM data transfer method based on 3DE, comprising the following steps: S1, acquiring components; S2, acquiring structure tree nodes and updating the transfer; S3, acquiring components and generating hierarchical folders; S4, acquiring lightweight geometric files and material map files, and associating the lightweight geometric files and material map files; S5, acquiring lightweight attribute files, and associating the lightweight geometric files and lightweight attribute files; S6, compressing and outputting lightweight data files. This invention solves the problem of information loss during BIM lightweight transfer caused by traditional single-feature lightweight methods, and ensures the accuracy of the association between geometric information, material maps, and attribute information of the transferred model, as well as the consistency of coordinate information and structural organization of the transferred model, thereby improving the quality and efficiency of BIM lightweight transfer.

The application discloses a metallogenic fluid main channel inference model based on particle filtering. The model comprises the following steps: collecting relevant data of a target deposit, establishing an inference database, and defining a state sequence of spatial discrete elements; performing statistics on ore-bearing elements known in all exploration information, taking the known ore-bearing elements as an initial particle group of particle filtering, and generating a spatial channel path of each particle; establishing a metallogenic fluid ion state transfer model coupled with probability and velocity according to the spatial position of the particle and fluid dynamics; establishing a particle weight observation model based on a particle state transfer likelihood function, and dynamically updating the weight of the fluid particle; performing spatial sampling on the particle through a resampling algorithm, updating the weight particle, and outputting a maximum posterior probability path through particle number threshold judgment. The model combines multivariate data constraint and particle filtering algorithm to dynamically estimate the main channel path of the metallogenic fluid in a three-dimensional geological space, and fundamentally solves the problems of high cost and difficult sampling sample acquisition of the traditional method.

The invention provides a disease progress probability prediction method and device based on a Monte Carlo-Markov chain, and relates to the technical field of disease progress prediction. The method comprises the following steps: acquiring multi-modal historical follow-up visit data of a patient to be tested, and constructing a state space with transfer constraint; extracting and checking time series data according to the state space; performing Monte Carlo sampling on the time sequence data, establishing a non-homogeneous transfer model and forming an individualized non-homogeneous transfer kernel set; completing path interpolation and probability filling of missing fragments based on the transfer kernel set to obtain continuous complete state sequence data; a Markov chain Monte Carlo algorithm is used for parameter updating, an individualized transfer model subjected to posteriori updating is obtained, a calibration probability result is obtained, and dynamic prediction and uncertainty evaluation of individualized disease progression are achieved. According to the method, the problems of inaccurate state determination, single transfer modeling, incomplete observation and insufficient prediction calibration in the prior art are solved.

The invention discloses a multi-stage rotor assembly method based on four-in-one-axis cooperative regulation and control, and belongs to the technical field of aero-engine manufacturing. The four-in-one is a combination of a rotation axis, a geometric axis, an inertia main shaft and a mass center axis. The multi-stage rotor assembling method is realized through the following steps of S1, obtaining rotor size characteristics, geometric errors, mass center coordinates and the inertia main shaft; s2, establishing a geometric error representation model of the single-stage rotor; s3, establishing an error transfer model of multi-stage rotor assembly; s4, calculating end surface centroid and centroid coordinates of each stage of rotor after assembly; s5, fitting the rotation axis, the geometric axis and the mass center axis; s6, solving an inertia tensor matrix under the assembly coordinate system; s7, calculating an inertia main shaft inclination angle under the assembly coordinate system; and S8, four-axis-in-one assembling regulation and control are carried out. The problem that vibration exceeds the standard under the ultrahigh rotating speed due to low manufacturing precision of an aero-engine rotor can be solved.

The invention provides a fault rate calculation method for a transformer cooling system considering a leakage fault, which comprises the following steps of: firstly, analyzing a dynamic transfer process among a normal operation state, a fracture fault state and a leakage fault state of a cooler pipeline, and constructing a three-state Markov transfer model of the cooler pipeline; then, sensor information such as main transformer oil temperature, winding temperature, cooler outlet oil pressure, water pressure, oil temperature and water temperature is collected, and a knowledge and data driving-based fracture fault related transfer rate calculation method is provided; thirdly, based on the frequency and the transfer rate which are easy to count or calculate, an analytical expression of the transfer rate and the frequency is established by combining a Markov model and a frequency duration method, and the transfer rate related to the leakage fault in the model is calculated; and finally, integrating the transfer rate, constructing a fault rate calculation model of the transformer cooling system, and proposing to predict the future fault rate of the cooling system based on a time convolutional network and a Transform hybrid model. According to the method provided by the invention, fault rate monitoring, analysis and early warning of the transformer cooling system can be realized.

This invention discloses a diffusion model-driven method for generating multilingual human motion, comprising: constructing a sample library; performing multilingual text translation and feature encoding using the Ali Tongyi Qianwen model; constructing a customized CondUNet1D motion denoising network with residual linear multi-head cross-attention, Dropout layer, and Rearrange layer optimization; and training using a joint strategy of exponential moving average and classifier-free guidance. During the inference phase, starting with pure noise, iterative denoising is achieved using a variant of the second-order DPMSolver++ sampler combined with training-free acceleration techniques. Simultaneously, foot slippage is identified and corrected using a vGRFs model transfer model. After secondary optimization of the posture using the diffusion model, a slippage-free motion sequence is obtained. Finally, a video is generated by a remote server and transmitted back to the local terminal. This invention achieves multilingual-driven, efficient, and highly realistic human motion generation.

This invention discloses a high-speed train bearing fault diagnosis method based on transfer learning in the field of rail transit safety monitoring technology. The method preprocesses source and target domain data separately, extracts multi-dimensional features, and uses the t-SNE algorithm for dimensionality reduction and filtering to construct source and target domain feature sets. Based on the source domain feature set, a Stacking ensemble model is constructed and trained as the source domain baseline fault diagnosis model. A multi-scale subspace alignment transfer model is constructed, projecting the source and target domain feature sets onto a shared subspace and performing geometric alignment to obtain domain-invariant features. Based on the aligned target domain features and combined with a multi-scale decision fusion strategy, fault classification is performed on the target domain data, outputting the fault diagnosis result. This method constructs a high-precision baseline model using labeled source domain data and utilizes transfer learning to align cross-domain features, achieving high-accuracy fault diagnosis in the unlabeled target domain.

The invention provides a quantitative evaluation method for the bolt connection precision retentivity of a multi-axis numerical control machine tool based on digital twinning, and the method integrates ANSYS multi-physics field simulation, Unity 3D virtual-real interaction and machine learning technologies by constructing a real-time interaction closed loop of a physical entity and a virtual model. The method comprises the following specific steps: establishing an error transfer model to quantify the influence of static offset, vibration and creep; carrying out pre-tightening force simulation and creep-vibration coupling analysis by utilizing ANSYS; by expanding Kalman filtering calibration parameters, virtual and physical entity errors are ensured; parameter adjustment and visual interaction are realized by means of Unity 3D; and finally, calculating a precision retention index, and evaluating a precision state according to a third-level judgment standard. According to the method, the limitation of single factor analysis is broken through, multi-physical field dynamic coupling capture is realized, and traditional qualitative evaluation is upgraded into a data-driven quantitative system.

The invention provides a consistency verification method and system for a state transition model and a storage medium, and the method comprises the steps: obtaining the state transition model for a protocol and an implementation code, and obtaining the description of a configuration item and a state of the state transition model; according to the description of the configuration items, determining the corresponding configuration items between the state transition models, and obtaining the position arrangement of the configuration items based on a position mapping function; screening out an effective configuration item digital value sequence set based on the configuration item option conflict condition, the self-defined digital meaning and the position arrangement of the configuration item; instantiating the state transition model by using the effective configuration item digital value sequence set to obtain a state transition sequence set; and according to the description of the state, determining and comparing corresponding state parameters in the corresponding state transition sequence, and taking a comparison result of the state transition sequence as a consistency verification result of the state transition model. According to the invention, verification resources can be reasonably utilized while the reliability and accuracy of verification results are ensured.

The application discloses a kind of photoelectric detection model transfer sharing method and internet of things monitoring evaluation system of cloud service, the transfer sharing method specifically is: by calling temperature compensation model and spectrum transfer model, the spectrum information of agricultural product sample is corrected, calling detection model is calculated to the corrected spectrum information, obtains the detection result of agricultural product sample;Internet of things monitoring evaluation system utilizes spectrum transfer sharing method to carry out spectrum information correction, and then calls detection model to calculate, and detection result is returned to detection terminal in real time, realizes remote monitoring and evaluation of agricultural product quality.The application can realize that detection model is shared in different detection terminal, and has wide application prospect in agricultural product quality sampling evaluation.

An electronic component manufacturing system is configured to identify a machine learning model trained to generate analysis or prediction data for a first substrate processing domain associated with a type of substrate processing system. The system is further configured to obtain first trajectory data relating to the first domain to train the machine learning model. The system is further configured to a migration model for a second substrate processing domain associated with the type of substrate processing system. The migration model is generated based on the first trajectory data related to the first substrate processing domain and second trajectory data related to the second substrate processing domain. At least one of the machine learning model associated with the second substrate processing domain, or current trajectory data, using the migration model, is modified to enable the machine learning model to generate analysis or prediction data associated with the second substrate processing domain.

The invention discloses a correction method and device, a storage medium and a program product, relates to the technical field of material processing, and is used for improving the accuracy and stability of operation parameter control in the execution process of to-be-executed operation. The method comprises the steps of obtaining a target parameter curve of a to-be-executed operation, wherein the target parameter curve comprises target parameter values corresponding to operation parameters of the to-be-executed operation at at least two time points; in response to the target parameter curve, executing the to-be-executed operation to obtain a first response parameter curve; determining a first transfer model, wherein the first transfer model is associated with the target parameter curve and the first response parameter curve; based on the first transfer model, correcting the target parameter curve to obtain a corrected target parameter curve; and in response to the corrected target parameter curve, executing the to-be-executed operation to obtain a second response parameter curve.

The invention relates to the technical field of compressors, and discloses a compressor running state recognition method and device, a medium and equipment. The method comprises the steps that the running state of the compressor is defined as double state spaces, and the double state spaces are the load state space and the health state space of the compressor; identifying the load state of the compressor and marking the health state of the compressor; establishing a running state space transfer model; performing parameter estimation based on an EM algorithm, and obtaining a parameter calculation result of the operation state space transfer model; compressor health state evaluation of the operation state space transfer model is obtained, and health indexes of the compressor are redefined to be in a continuous expression form; and the real-time load state, the health grading state and the health degree index of the compressor are obtained, and operation management personnel are prompted to conduct corresponding compressor health management operation. According to the method, the abnormality handling efficiency can be improved, and abnormal false alarms caused by compressor load switching and parameter fluctuation can be reduced.

A generator set control method and apparatus, and a device. Comprising: constructing a state transition model for sub-problems of a single unit, and adding as a state in the model a penalty price corresponding to a Lagrange multiplier for each time period Using a reinforcement learning algorithm to train a startup / shutdown strategy and a power increase / decrease strategy for each unit; using a surrogate sub-gradient method to relax constraints coupled to different units in a UC problem, using the surrogate sub-gradient method to perform iteration and Lagrange multiplier updating, solving sub-problems in the iteration process using a trained reinforcement learning agent to perform sequential decision-making, and iterating repeatedly until convergence, so as to obtain an optimal solution to a dual problem; and performing a feasibility operation on a resulting unit commitment state, and controlling generator set nodes.

The application discloses a kind of equipment life prediction methods based on particle filtering's LSTM model, it is related to system reliability field.The method proposes the RUL prediction method of fusion PF and LSTM, state transition model in PF filtering process is established using LSTM, the resampling method in PF is improved so that resampling can retain sampling gradient information for network model update.PF-LSTM model realized can give full play to LSTM on time series modeling and the state estimation capability of PF to nonlinear, non-stationary and non-Gaussian system.Original data is directly sent into network after standardization processing, and end-to-end learning ensures that the network only focuses on features related to the prediction target during the learning process, improving network sequence efficiency and accuracy.

The application discloses a multi-stage interference transfer modeling-based electromagnetic compatibility design method and system for power intelligent equipment, and belongs to the technical field of electromagnetic interference. The method comprises the following steps: synchronously collecting external port excitation and internal loop node interference data under multiple working conditions, and establishing a transfer matrix from the port to the loop; synchronously collecting loop node and chip key pin response data, fitting a transfer matrix from the loop to the pin, and cascading to form a full-link interference transfer model; positioning an electromagnetic sensitive point by calculating the sensitivity S of each level node in a preset time window i ; iteratively updating associated electromagnetic compatibility design parameters, repeating the modeling and evaluation process until all nodes Si meet the threshold requirement; and finally outputting multi-stage transfer model parameters, sensitive point positioning results and final design parameter configuration. Through full-link quantitative modeling and closed-loop iterative optimization, the application solves the blindness of traditional empirical design, and significantly improves the electromagnetic compatibility design efficiency and the on-site reliability of the equipment.

The invention discloses a load transfer switch control method and related equipment, and the method comprises the steps: obtaining the operation parameters of a power distribution network line according to a preset data structure based on a power distribution network line topological graph of a target power distribution network; determining a target function for the power distribution network line according to a rule of lowest transmission loss in a transfer process, and constructing an initial transfer model based on the target function and a preset constraint condition; through a second-order cone optimization auxiliary variable, converting the initial wheeling model to obtain a target wheeling model; and substituting the operation parameters into the target transfer model to obtain a to-be-solved model, and performing second-order cone optimization solution on the to-be-solved model to obtain the control method for the load transfer switch.

The invention provides a steel production energy consumption data processing method and device, a medium and electronic equipment. The method comprises the steps that production information and energy metering information of all working procedures in the steel production process are obtained, the production information comprises product identifiers and corresponding production time periods, and the energy metering information comprises energy medium types and corresponding consumption; performing time-space synchronization processing on the production information and the energy metering information, and establishing an association mapping relation between the production information and the energy metering information; calculating single-process energy consumption of each piece of secondary product in each process based on the association mapping relation and the material flow characteristics of each process; and according to the single-process energy consumption and the process circulation logic of steel production, constructing an energy consumption circulation model, and calculating the accumulated energy consumption of each product through the energy consumption circulation model. According to the invention, the accuracy of steel production energy consumption data processing can be improved.

This invention provides a controllable intelligent regeneration method for architectural contexts based on the Flux large-scale model ecosystem. The method involves inputting a design model drawing into the ComfyUI interface and reading its dimensions; determining the modeling accuracy of the design model drawing and matching it with the corresponding basic shape control algorithm; inputting an architectural context reference image and generating architectural context prompts; setting a style transfer model sequence and connecting it to a Flux large-scale model-based image generation module to obtain an architectural context rendering image; determining whether the architectural context rendering image conforms to the design intent; if it does, it is directly output; otherwise, precise modification and regeneration of the rendering image are completed through text control; finally, the architectural context rendering image is enlarged and restored to generate a high-resolution architectural context rendering image, which is then output. This invention solves problems such as insufficient intelligence in the image generation process, the need for manually written prompts, reliance on manual selection for modification results, and deficiencies in the quality and content matching of architectural context rendering images.

The invention discloses a visual interaction method, device and equipment based on a state probability transition model, and relates to the technical field of man-machine interaction, and the method comprises the steps: collecting multi-source data, extracting a state feature vector, and fusing the multi-source data; constructing a state transition model based on a state transition graph G = (V, E, P), and dynamically calculating a state transition probability matrix; wherein V is a state set, and the nodes represent different states; e is an edge set, and edges represent possible transfer paths between states; p is an edge weight set, represents a transition probability and can be dynamically updated; a state visualization graph is generated based on the state transition graph G, and dynamic visualization display is rendered through a graphic engine; receiving an interaction operation, triggering the state transition model to update parameters in response to the interaction operation, and recalculating; and responding to different target states and / or strategy parameters of the interaction operation, generating and comparing a plurality of strategy paths, and visually displaying a strategy deduction process. According to the invention, the precision, interaction capability and interpretability of the system are improved.

The invention provides a personalized workload intelligent evaluation method based on Bayesian multi-source fusion, and relates to the field of workload evaluation. According to the method, firstly, multi-source asynchronous data are collected and processed; secondly, reasonably initializing a learnable parameter set and a workload state under the condition of lack of individual historical data through a personalized meta-prior method of hysteresis distribution modeling incorporating biochemical response, and avoiding prior deviation; thirdly, acquiring an atlas aggregation item used for representing the causal lag influence to construct a state transition model, introducing a quality adaptive mechanism to construct an observation model, mapping low-quality observation into larger effective noise to automatically reduce the weight, and reducing the interference of asynchronization and noise on fusion; and finally, posterior value and uncertainty estimation is completed in the same framework, and the current working load state is intelligently evaluated, so that the method is superior to the prior art in the aspects of accuracy, interpretation and operability, and has a cross-scene migration capability.

The application discloses a blower bearing fault diagnosis method based on a multi-source feature fusion transfer model. The method synchronously collects multi-source operation data by deploying a sensor network on a target blower and extracts fusion features, and simultaneously trains a basic diagnosis model by using complete fault data of a laboratory benchmark blower. When deployed, the similarity of the feature distribution of the target blower and the benchmark blower is quantitatively evaluated to determine the feasibility of transfer, and the model is safely fine-tuned and lightened based on the screened source domain knowledge. Finally, the optimized model is deployed to the edge side of the target blower to realize real-time diagnosis. The method effectively reduces the dependence on the historical fault data of a new blower, realizes rapid, safe deployment and precise self-adaptation of the diagnosis model, improves the timeliness of fault early warning, and guarantees the safe operation of the blower.