142 results about "Model switching" patented technology

The invention discloses a method for intelligently regulating and controlling production parameters in a fruit concentrated juice production process, which comprises the following steps of: acquiring multi-dimensional process parameters such as temperature, pressure, flow, concentration, equipment state and the like in real time through a multi-channel sensor network, and forming a standardized data sequence after filtering, normalization and drift correction; extracting stage features by using technologies such as a sliding window and Fourier transform, and inputting the stage features into the lightweight classification model to realize production stage identification; in combination with an identification result, dynamically calling a corresponding multi-target optimization sub-model, and realizing nonlinear prediction and optimal solution selection of process parameter setting by adopting an LSTM and a multi-target genetic algorithm; on the basis of real-time feedback, the performance of the model is automatically evaluated, self-adaptive adjustment and optimization of the optimization algorithm are achieved through reinforcement learning and an incremental updating mechanism, multi-target collaborative optimization, self-adaptive adjustment and model switching in the production process can be achieved, and the consistency of production efficiency and product quality is improved.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention belongs to the technical field of intelligent unmanned systems and edge intelligent reasoning, and discloses an unmanned aerial vehicle emergency inspection system integrating low-altitude communication and edge calculation. The system is composed of a link state monitoring and scoring module, a reasoning calculation migration and model loading module, a task graph modeling and dynamic segmentation module, a multi-unmanned aerial vehicle task collaboration and trajectory continuation module, a flight state fusion and path adjustment module, a redundancy criterion driven fault-tolerant control module and a result consistency verification and data caching module. According to the method, the communication link scoring function fusing the packet loss rate, the bandwidth, the time delay and the signal-to-noise ratio is constructed, the comprehensive quality evaluation of different low-altitude communication links is realized, and the reasoning model switching and task migration strategy is driven by the scoring result, so that the communication adaptation capability of the system in a complex, dynamic or emergent scene is remarkably improved, and the system performance is improved. Compared with an existing communication mechanism depending on a fixed link or a preset priority.

The invention relates to the technical field of data processing, and discloses an automobile industrial database rapid management method and system based on virtual-real mapping. The method comprises the steps that a vehicle type mixed line recognizer recognizes the vehicle type switching state of a production line, and dynamic data of a vehicle type identifier, a process path and a resource demand are acquired; adjusting a partition strategy of the heterogeneous database according to the dynamic data, and generating a partition mapping table and an access weight matrix; binding the mapping table with the three-dimensional twin model through a time sequence association algorithm to form an automatically switched virtual-real mapping object pool; predicting a cross-vehicle-type data demand based on the object pool, and generating a preloading queue and a conflict avoidance strategy; and the vehicle type switching trigger processes the preloading queue to realize zero-delay data switching and consistency verification. The response speed and the data access efficiency of database management in the vehicle model line changing process are improved.

A method for a first apparatus, the method comprising: obtaining (713) at least one matching factor, wherein the at least one matching factor indicates a degree matching between inputs used during a machine learning training phase and inputs used during a machine learning inference phase; obtaining at least one threshold matching factor from a second apparatus; comparing (715) the at least one threshold matching factor and the at least one matching factor; performing at least a machine learning model switching based on the monitoring decision request from the second apparatus, the monitoring decision request based at least on: the obtained at least one matching factor; and at least one channel characteristic.

The invention relates to a fresh air conditioner control system and method based on double-model safety evolution. The fresh air conditioner control system comprises a credible data acquisition module, a mechanism main model controller and a shadow AI model training and verification module. A dynamic anchoring control module; a man-machine collaborative decision-making interface; and a fault isolation and model switching module. On the premise of guaranteeing control safety, the invention provides an industrial air conditioner control system architecture with evolution capability, adaptive capability and deployment flexibility, breaks through the limitation of the traditional control technology in nonlinear, multivariable and strong coupling scenes, and has relatively high engineering application value and popularization potential.

The invention relates to the technical field of electric vehicle charging infrastructure intelligent management, and discloses a charging pile load balancing method and system based on a neural network, and the method comprises the steps: analyzing the state, environment and user behavior data of a charging pile through a multi-mode auto-encoder network; generating a unified scene representation; recognizing a typical scene by using a variational auto-encoder and a density clustering algorithm; decomposing the charging load balancing knowledge into cross-scene shared knowledge and scene specific knowledge; automatically generating a neural network architecture adaptive to the current scene based on the scene similarity; using a meta reinforcement learning algorithm to train a meta strategy network to generate a load balancing decision; scene smooth transition is realized through gradual model switching; according to the method, multi-scene adaptation, knowledge sharing and smooth switching are realized, the problems of system redundancy, knowledge isolation, scene switching performance fluctuation and the like in a traditional method are effectively solved, and the operation efficiency of the charging infrastructure is improved.

The invention relates to the technical field of cross of storage controllers and artificial intelligence acceleration, and discloses a large model reasoning system and method based on combination of a flash memory controller and an NPU (Network Processing Unit), and the large model reasoning system comprises the flash memory controller, the NPU and a flash memory array, the flash memory controller integrates a host interface module, a flash memory interface module, an independent AI acceleration interface module and an AI management engine, and the AI management engine autonomously completes NPU initialization, model weight direct loading, KV Cache hierarchical management, RAG knowledge base retrieval and model switching; the flash memory array is divided into a firmware partition, an AI special partition and a user storage partition, and different data storage requirements are met. According to the method, large model reasoning with low delay and low CPU dependence can be realized, and the model loading delay is reduced from 5-30 seconds to lt; after 500 milliseconds, the CPU occupancy rate of the host is reduced from 15-25% to lt; 2%, and concurrent operation of 4-8 models is supported. According to the invention, integration of storage and calculation is realized, and edge end, data center and mobile equipment scenes are adapted.

The invention relates to the technical field of FSRU regasification control systems, and particularly discloses an FSRU regasification heat source intelligent switching control system based on multi-source coupling optimization, and the system comprises a multi-source grading collection and edge preprocessing module which is used for collecting heat source-load-environment-heat storage-abnormity five-dimensional basic data and dynamic operation constraint data, and sending the data to a data processing module; carrying out hierarchical transmission and normalization processing on the data; and the dual-mode adaptive multi-source-heat storage coupling model module is used for pre-constructing a lightweight coupling model and a high-precision coupling model, and dynamically switching between the lightweight coupling model and the high-precision coupling model according to a working condition trigger signal. By introducing a transient asynchronous compensation mechanism, the system effectively solves the problem of control window period caused by time consumption of high-precision model calculation, ensures that the system can still maintain smooth and continuous instruction output at the moment of extreme load fluctuation, and eliminates the risk of pressure oscillation caused by model switching.

The application discloses a low-altitude intelligent networking dynamic collaborative reasoning method based on multi-agent reinforcement learning, relates to the technical field of low-altitude intelligent networks and edge artificial intelligence, and comprises the following steps: preloading a light model and a complex model on each unmanned aerial vehicle (UAV), deploying a complete complex model by means of a ground station, and constructing an air-ground integrated intelligent reasoning system. By introducing an enhanced multi-agent deep reinforcement learning algorithm, each UAV can dynamically select a model type, determine a model segmentation point, and reasonably allocate bandwidth and ground computing resources based on the state of the UAV, network conditions and task characteristics during task execution, so that multi-DNN flow heterogeneous resource-aware collaborative reasoning is realized. The application can effectively improve reasoning accuracy and reduce average delay under different device performance, bandwidth conditions and task density, has good system scalability and adaptability, and solves the problems of limited single-machine processing capacity, restricted communication resources and inefficient model switching.

The embodiment of the invention provides a management operation execution method and device, a storage medium and an electronic device, and the method comprises the steps: determining a priority set for at least one model in a first node, and determining the occupation number of model processing units (MPU) of each model in the at least one model and the processing time of each model, the MPU occupation number and the processing time have a first association relationship; and executing a management operation on the at least one model through the priority and / or the first association relationship, wherein the management operation comprises at least one of the following: deactivating the model, adjusting the number of occupied MPUs, activating the model, selecting the model, switching the model, updating the model and adjusting the processing time. By adopting the technical scheme, the technical problem of how to manage a plurality of artificial intelligence models or computing units at the same time under the condition of limited computing, storage and other resources at the terminal side in the related technology is solved.

The application relates to the technical field of low-orbit satellite communication, and discloses a satellite communication-oriented traffic prediction method, device, equipment and medium, which are applied to a satellite-borne system, wherein the method comprises the following steps: receiving a self-description model package sent by a ground terminal in a standby area, and keeping an old model currently running in an active area to execute an inference task; obtaining original traffic data in a preset sliding window, determining a data correction factor of an adaptive ground model based on the difference between the original traffic data and statistical information; loading the data correction factor to the ground model to obtain a corrected new model; and in response to a model switching instruction, updating a global active model pointer from a memory address pointing to the old model to a memory address pointing to the new model by atomic operation. The technical scheme provided by the application can realize seamless hot updating of a model from the ground to the satellite without interrupting real-time inference services of the satellite-borne system.

The invention discloses a style dynamic adaptation switching method for switching cue words along with a large model, and relates to the field of style dynamic adaptation switching of cue words, which comprises the following operation steps: S1, core hypothesis and benchmark establishment; s2, an input initialization stage; s3, performing a first reasoning and evaluation process; and S4, iteratively optimizing the process. According to the style dynamic adaptation switching method for switching the cue word along with the large model, style deviation of different models can be automatically recognized, the cue word of a system is corrected in real time through feedback circulation, an adaptation rule does not need to be written manually, intelligent optimization of the cue word is achieved, automatic adaptation can be achieved for model updating or supplier adding scenes, and the user experience is improved. According to the method, the cue word does not need to be manually readjusted, the human input is reduced, the maintenance cost of the cross-model cue word is remarkably reduced, the manual adaptation workload is reduced through automatic iterative optimization, the newly-added model access period is greatly shortened, and the time of several days of a traditional scheme is shortened to the hour level.

The invention discloses an asymmetric fault equivalence method and system for short-circuit current calculation of a flexible direct network construction system, and the method comprises the steps: building a fault equivalence model of network-following and network-construction power electronic equipment through obtaining the information of a power grid, building a system-level composite sequence network based on the fault type, and carrying out the fault equivalence calculation of the short-circuit current of the flexible direct network construction system. And constructing a system-level asymmetric fault equivalent model of the new energy base sending end power grid under the flexible direct construction network, and constructing a fault composite sequence network of the new energy base sending end power grid under the flexible direct construction network. According to the method, the problems of poor model switching index applicability and insufficient asymmetric fault modeling in the prior art are solved, and the reliability of fault analysis of the flexible direct structure network system is improved.

This disclosure provides an information processing method, apparatus, and electronic device. The method includes: receiving a model switching request for a target function of an application; displaying a model list, the model list including one or more available models for a target user group; receiving a target available model selected from the model list; and associating the target function with the target available model so that the target function is implemented by the target available model. This provides users with a channel to switch models for applications, which can improve the compatibility of models used by applications and also improve the problem of poor flexibility in implementing AI functions in applications.

The invention relates to the field of model deployment, and discloses an edge AI model control method and device, edge equipment and a storage medium, and the method comprises the steps: obtaining network state parameters and hardware performance parameters of the edge equipment in real time, judging whether the network state parameters and the hardware performance parameters are matched with a currently deployed AI model or not, and triggering a model switching condition when the network state parameters and the hardware performance parameters are not matched with the currently deployed AI model. And selecting a target model matched with the current environment parameters from a model library comprising the reference model and a plurality of lightweight AI models with different compression ratios, and executing seamless switching. The problem that an existing edge deployment scheme cannot adapt to diversified device performance and dynamic network environments due to the fact that a single fixed model is adopted is solved, dynamic balance between the model performance and resource occupation is achieved, and the self-adaptive capacity and user experience of the edge device in a cross-border scene are remarkably improved.

The invention discloses an interaction control system and an interaction control method, and belongs to the technical field of interaction control, the interaction control system comprises an intention understanding module, an intention processing module and an execution module; the intention understanding module comprises at least one large model and is used for sending the current intention output by any large model to the intention processing module; the intention processing module is used for determining current attention scores of at least part of text fragments in previous text information of the current intention, selecting a target text fragment from the at least part of text fragments according to a sequence of the current attention scores from high to low, generating a target task according to the current intention and the target text fragment, converting the target task into a control instruction and sending the control instruction to an execution module; and the execution module is used for executing the control instruction. Therefore, intention understanding and task execution can be decoupled, and the flexibility of model switching is achieved.

This invention discloses a collaborative scheduling method, device, and medium for decoupling parameters and computation, applied to an AIGC inference system containing at least one computing node. The method includes: receiving a user inference request, the inference request indicating a target model; in response to determining that computing resources need to be allocated to the target model, performing a resource allocation operation on the target computing node based on a preset collaborative tidal scheduling strategy, the resource allocation operation including an atomically executed ebb and flow operation; wherein: the ebb operation includes: releasing computing resources on the target computing node that have been allocated to one or more low-priority tasks; the flow operation includes: allocating the released computing resources to the target model to process the user inference request. This invention enables high resource utilization, low-latency model switching, and intelligent resource scheduling for AIGC inference services.

The application relates to a multi-model optimal load prediction method and device based on reinforcement learning. The method can automatically select an optimal model or weight combination according to real-time characteristics and historical performance of the model based on a Markov decision mode. The method uses a reinforcement learning decision mechanism to realize dynamic selection and adaptive fusion of the prediction model, thereby overcoming the shortcomings of the prior art in terms of working condition adaptability, prediction accuracy and stability. Meanwhile, through a reward function and an error feedback mechanism, the method can realize continuous optimization of prediction performance. By using the reward function to consider risk-sensitive rewards and switching penalty terms, the method can avoid oscillation caused by frequent model switching, improve model prediction stability, and ensure the accuracy of the results.

A quality monitoring apparatus includes a processor configured to execute monitoring processing of monitoring a quality of a manufacturing process of a biopharmaceutical by using a plurality of state prediction models that predict a state of a liquid produced in the manufacturing process, which is related to the quality of the manufacturing process, using spectroscopic spectrum acquired inline from the liquid as input data, and model switching processing of switching the state prediction models during the monitoring processing based on step management information for managing at least one step included in the manufacturing process, in which the plurality of state prediction models have been trained before the monitoring processing is started, and the step management information includes at least one of step identification information, usage device information, or step quality information.

Computer-implemented machines, systems and methods for providing insights about uncertainty of a machine learning model. A method includes determining an uncertainty value associated with a first machine learning model output of a first machine learning model. The method further includes generating a confidence interval for the first machine learning model output associated with an input. The method further includes switching, responsive to the uncertainty value satisfying a threshold, from the first machine learning model to a second machine learning model, the second machine learning model generating a second machine learning model output. The method further includes generating the second machine learning model. The method further includes providing, responsive to the switching, the machine learning output, the uncertainty value, the confidence interval, and the second machine learning output to a user interface.

The application discloses a multi-mechanism cold supply load prediction method and system, which establishes a machine learning model based on load physical composition and containing a sub-model cooperation prediction system, and a historical data matching model that can construct a hybrid model system with self-defined parameters and adaptive matching algorithms, and automatically selects and runs an adaptive adjustment model for actual prediction. The model self-improvement mechanism is also deployed for the machine learning model and the historical data matching model, respectively, to ensure that the model has self-improvement characteristics during operation, which will ensure that the two models are continuously optimized and always use the better model, significantly improving the scene coverage rate and the ability of the model after data accumulation.

A method performed between a user equipment (UE) or gNodeB (gNB) and a location management function (LMF). The UE / gNB obtains at least one matching factor, which indicates a degree matching between inputs used during a machine learning (ML) training phase and inputs used during a ML inference phase. The UE / gNB performs ML model switching based on a monitoring decision request from the LMF, the monitoring decision request is based on the obtained matching factor and at least one channel characteristic. The monitoring decision request may be further based on a positioning accuracy parameter. The inputs may be TRP measurements for a location management service or positioning determination. The LMF may request the UE / gNB to initiate obtaining the matching factor, and the request may include a threshold matching factor corresponding to the at least one channel characteristic and / or the positioning accuracy parameter.

The application discloses a valve cavity air pressure control method and device and related media, the method comprising collecting a multi-dimensional time series data set, and performing data preprocessing on the multi-dimensional time series data set to obtain a feature engineering data set; the feature engineering data set is divided according to working conditions and pressure segments to obtain a two-dimensional model matrix; a model mapping table is established based on the two-dimensional model matrix to obtain a model switching configuration object; the current cavity air pressure is obtained according to the model switching configuration object, and data comparison is performed to obtain a pressure segment identification result; the pressure segment identification result is used to determine a pressure increase / decrease identifier and / or a pressure adjustment identifier, so that a sub-model of the two-dimensional model matrix is selected for reasoning to generate a control instruction. The pressure increase / decrease identifier and / or the pressure adjustment identifier are determined according to the pressure segment identification result obtained by calculation, so that the sub-model of the two-dimensional model matrix is selected for reasoning to generate the control instruction. In this way, the valve cavity air pressure can be stably and accurately controlled in the full pressure range.

The invention relates to a multi-model optimal load prediction method and device based on reinforcement learning. According to the method, an optimal model or a weight combination can be automatically selected based on a Markov decision mode according to real-time features and historical expressions of models; according to the method, a reinforcement learning decision mechanism is utilized to realize dynamic selection and adaptive fusion of a prediction model, so that the defects in the aspects of working condition self-adaption, prediction precision and stability in the prior art are overcome. Meanwhile, through a reward function and an error feedback mechanism, continuous optimization of prediction performance can be realized; the risk-sensitive reward and the switching penalty term are considered by using the reward function, so that oscillation caused by frequent model switching can be avoided, the model prediction stability is improved, and the result accuracy is ensured.

A system and method for automatically detecting the type of product being processed in an industrial machine and switching to the corresponding product model without manual operator input. The system includes a detection module configured to determine the product type based on real-time data such as visual classification, lot number association, or processing schedule. Upon identifying the product type, a model selection engine automatically activates the appropriate product model from among multiple models stored on the machine. This automation eliminates the risk of human error associated with manual switching, improves operational reliability, and ensures accurate processing for environments where multiple product types are handled on the same equipment.

A quality monitoring apparatus includes a processor configured to execute monitoring processing of monitoring a quality of a manufacturing process of a biopharmaceutical by using a plurality of state prediction models that predict a state of a liquid produced in the manufacturing process, which is related to the quality of the manufacturing process, using spectroscopic spectrum acquired inline from the liquid as input data, and model switching processing of switching the state prediction models during the monitoring processing based on step management information for managing at least one step included in the manufacturing process, in which the plurality of state prediction models have been trained before the monitoring processing is started, and the step management information includes at least one of step identification information, usage device information, or step quality information.

The invention discloses a model switching processing method, device and equipment. Comprising the following steps: determining a plurality of canvases which are sequentially arranged in a scrollable direction of a model display interface; a displayable model set is determined, the model set comprises a plurality of ordered models, and the number of the models is larger than that of the canvas; correspondingly loading a plurality of sequentially continuous models in the model set on the plurality of canvases; receiving an interface scrolling operation executed for the model display interface, and determining an actual scrolling direction of the interface scrolling operation; according to the interface scrolling operation, reordering the plurality of canvases, and adjusting at least one canvas at the front end of the actual scrolling direction to the rear end of the actual scrolling direction; and loading models except the plurality of models from the model set on the canvas at the rear end adjusted to the actual rolling direction so as to replace the previously loaded models.

The invention discloses a pattern float valve guide edge micro-curvature stamping die wear inhibition method, and relates to the technical field of stamping die manufacturing, and the method comprises the following steps: constructing a controllable wear profile based on a crystal orientation gradient, and controlling the tip of an edge to be lt through directional solidification; 111gt, 111gt; a crystal face, the base of which is lt; 001gt, 001gt; a crystal face forms a differential wear rate; according to the method, controllable abrasion of the edges and active compensation of forming deviation are achieved through crystal orientation gradient design, a traditional coating is replaced with matrix orientation abrasion, and the problems of poor coating uniformity and peeling off are solved; contact stress is dissipated by means of the nano twin crystal layer, a low-stress abrasion cycle is formed, and the material utilization rate is increased; programmable regulation and control of a wear profile are realized through an inverse parabola model, and the precision bottleneck of a traditional model is broken through; the surface energy gradient is used for driving the lubricating liquid to directionally migrate, and a high-speed stamping lubricating blind area is eliminated; the integrated piezoelectric-SMA composite support compensates abrasion deformation in real time, and the material guiding deviation is eliminated in combination with an intelligent model switching strategy.