162 results about "Function mapping" patented technology

The invention relates to an intelligent equipment access system and access method based on an AI large model, and relates to the field of artificial intelligence and Internet of Things. According to the technical scheme, a dynamically adaptive universal object model access framework is constructed, functional attributes, control instructions and data formats of intelligent devices of different brands and protocols are mapped into a standardized logic model, meanwhile, a multi-mode AI large model engine is combined, high-precision voice recognition, voiceprint feature detection and multi-level semantic intention analysis capabilities are integrated, and the intelligent devices of different brands and protocols can be obtained. And an intelligent interaction closed loop of end-cloud collaboration is formed. Semantic analysis adopts a mixed strategy, a natural language instruction is converted through function mapping, a local knowledge base is utilized to match user scene requirements, and external data is queried by means of cloud networking to optimize instruction logic. According to the scheme, the multi-device access and operation and maintenance cost is remarkably reduced, the semantic understanding accuracy and scene adaptability are improved, and the method is widely applied to the fields of smart home, smart medical treatment and industrial Internet of Things.

The invention belongs to the field of digital delivery, and particularly relates to a digital delivery topology mapping method and system for multi-source real-time data fusion, and the method comprises the steps: obtaining factory building distribution, equipment distribution and operation control logic and preset function block operation logic, and constructing a hierarchical clustering function mapping space through combining an association analysis and clustering algorithm; in response to a target function demand, obtaining a layered response mapping path in combination with a deep search algorithm; layered synchronous response and distributed node anomaly monitoring are realized based on the path, the three-dimensional simulation model and the display system equipment performance and the network state. Tracing abnormities based on a monitoring result in combination with a hidden Markov algorithm and a forward reasoning model, performing iterative verification after conflict resolution until the function is free of abnormities, and updating a mapping space; and adjusting the demand repeating steps to obtain a complete and updated mapping space, and realizing accurate function and picture collaboration under multi-source data fusion.

The invention discloses a multi-modal interaction control method for a Bluetooth knob screen, and relates to the technical field of multi-modal interaction control, and the method comprises the steps: synchronously collecting and forming a multi-modal feature vector set, and outputting a final operation instruction through a multi-modal fusion model; performing function mapping according to the final operation instruction, and identifying a control command; judging whether to resend the same instruction or adjust the instruction parameter based on the control command, and dynamically adjusting the tactile feedback parameter based on the function mapping of the current scene mode; performing low power consumption management based on the dynamically adjusted tactile feedback parameters, and triggering scene mode mapping update according to the wake-up instruction type; and optimizing the instruction output accuracy and the function mapping rule of the multi-modal fusion model based on the updated user interaction data. According to the method, deep fusion and intelligent cooperation of multiple interaction modes are realized, and accurate output of operation instructions, intelligent management of tactile feedback and adaptive updating of scene modes are achieved through dynamic function mapping, intelligent feedback adjustment and continuous learning optimization.

The invention discloses an elevator fault prediction method and system based on the big data technology, and the method comprises the steps: collecting and preprocessing elevator multi-source data, and generating a standardized data set; performing kernel function mapping and kernel principal component analysis dimensionality reduction to obtain a dimensionality-reduced feature sequence; the sequence is sent into a gating Transform in a segmented mode, and a time sequence modeling result is output; identifying a fault type based on a time sequence modeling result and predicting a future fault probability; the risk threshold is compared, the fault risk is judged, and early warning information is generated; and sending a judgment result and early warning information to an operation and maintenance end to assist in maintenance decision making. According to the method, efficient prediction and intelligent early warning of elevator faults are achieved by fusing kernel principal component analysis and gating Transform, and the operation and maintenance response efficiency and the equipment operation safety are improved.

The invention provides a function mapping management method for a data acquisition system and analog simulation software, and relates to the technical field of data processing, the method comprises the following steps: acquiring an original data set from target equipment; performing noise filtering and abnormal value detection on the original data set; extracting a plurality of feature data corresponding to the analog simulation software model; generating an initial mapping parameter set according to a mapping rule between the feature data and an analog simulation software model input parameter; dynamically adjusting the initial mapping parameter set; converting the feature data into input format data required by a simulation software model; consistency verification is carried out; inputting analog simulation software; verifying the simulation result data, and if the verification is not passed, generating a feedback adjustment parameter according to the difference degree between the simulation result data and the feature data; if the verification is passed, taking the simulation result data as output data of operation evaluation of the target equipment; according to the invention, the autonomy and accuracy of function mapping are improved.

The invention discloses a multi-dimensional layered current limiting method based on spatio-temporal feature fusion. The method comprises the following steps: acquiring a hardware limit speed, and generating a global quota based on a reinforcement learning model; calculating a variable coefficient, and if the variable coefficient exceeds a preset critical value, introducing a conservative coefficient and generating a weight factor through nonlinear function mapping to suppress the micro network jitter; constructing a priority factor matrix based on the service priority and the service type, and obtaining a service priority factor; correcting the average bandwidth of the node by integrating the multi-dimensional dynamic weight factor and the service priority factor to obtain a suggested speed, inputting a historical performance index into the LSTM model to predict the load of the next period, and if the historical performance index exceeds a critical value, triggering connection migration before executing current limiting to realize active avoidance; otherwise, comparing and selecting the minimum value of the global quota, the suggested speed and the hardware limit speed as the final current-limiting speed. The problems of current limiting strategy lag and wide oscillation can be solved, and the bandwidth utilization rate and the system stability are improved.

The invention relates to the technical field of batteries, in particular to a battery pack equalization method for improving the performance and prolonging the service life. Comprising the following steps: predicting the real capacity of a battery cell by adopting a neural network algorithm, fitting SOC and OCV function mapping according to open-circuit voltage method data on the basis of outputting the real capacity of the battery cell, mapping the SOC of each battery cell, and dynamically adjusting and balancing in a full life cycle; compared with the prior art that direct prediction of the SOC value lacks detailed understanding of the internal state of the battery, the method for predicting the SOC value based on the real capacity firstly estimates the current capacity of the battery and then calculates the SOC according to the capacity, so that the accuracy of SOC value measurement is improved; moreover, accumulative errors are corrected more accurately, capacity estimation is adjusted dynamically, the accumulative errors are reduced, and aging of the battery can be better adapted by dynamically estimating the current capacity of the battery.

The invention discloses a multi-dimensional information-based adaptive gateway traffic scheduling method, which comprises the following steps of: acquiring server performance indexes, network delay, bandwidth and geographic position data in real time, and establishing a historical database; performance load weights are generated through weighted calculation of CPU, memory and disk indexes, and routing priorities are dynamically adjusted by adopting exponential function mapping; performing normalization processing on network bandwidth, delay and geographic distance, and calculating a service access weight to optimize a transmission path in combination with a step function; analyzing historical traffic data based on an LSTM model to predict a future trend, and generating a traffic prediction weight through a sigmoid function to realize pre-equalization; and combining the three types of weights, weighting to generate dynamic routing configuration, eliminating abnormal nodes in real time in combination with a health check mechanism, triggering high-load early warning and linking a predictive balancing strategy. According to the method, multi-dimensional index collaborative decision-making is realized, routing distribution is dynamically optimized, network delay is effectively reduced, and system throughput and stability in a high-concurrency scene are improved.

A method for integrating data from different data sources into a knowledge graph storage unit including:transferring data from different data sources into a receiving and extraction module of an extraction-transformation-loading, ETL, data pipeline framework;extracting the loaded data using an extraction data frame and transferring the extracted data to a transformation module;transforming the extracted data using transformation functionalities and transferring the transformed data to a mapping module;mapping the transformed data using mapping rule functionalities and transferring the mapped data to a post-processing module;post-processing the mapped data using a library and transferring the post-processed data to a loading module; andloading the processed data by the loading module to the knowledge graph storage unit;wherein the mapping module uses a JSON-based mapping notation to convert the transformed data into a serialized form, is provided.

The invention discloses a tensor-kernel neural network for efficient prediction modeling and a modeling method thereof, and the tensor-kernel neural network comprises a kernel function mapping module which is used for carrying out the nonlinear kernel mapping of input data to obtain high-dimensional data; the tensor decomposition module is used for performing low-rank reconstruction on high-dimensional data and extracting low-dimensional features; and the deep neural network is used for training and predicting the extracted data low-dimensional features and outputting a prediction result. The loss function can effectively balance the reconstruction error and the prediction error in the model training process, and the generalization ability of the model is improved. In combination with Gaussian radial basis kernel function nonlinear kernel mapping, nonlinear input data is mapped to a high-dimensional feature space, and low-rank reconstruction is performed on high-dimensional data through a CP decomposition method; and finally, training and predicting the extracted low-dimensional features through a deep neural network. According to the method, the calculation efficiency and the prediction precision can be remarkably improved in high-dimensional data modeling; and the calculation complexity is reduced, and meanwhile, accurate extraction of data features and efficient training of the model are ensured.

To generate a control program of a programmable logic controller in an automation system based on an AI pipeline that contains at least one AI model with optional preprocessing of input data and / or postprocessing of output data, the following are carried out: providing a latency model for predicting a computing time for an execution of an AI pipeline based on hardware configurations and software configurations and a compatibility model for mapping AI pipeline functions to software configurations; detecting a hardware configuration and a software configuration of the programmable logic controller; creating a set of AI pipeline candidates based on the compatibility model;Selecting an AI pipeline from the set of AI pipeline candidates by evaluating the performance of the AI ​​pipeline candidates after training the AI ​​pipeline candidates, taking into account a prediction of a computing time based on the latency model; and creating a source code of the control program with the selected AI pipeline for execution on the programmable logic controller in the automation system.

The invention relates to the technical field of intelligent label generation, in particular to a user label generation method and system based on artificial intelligence, and the method comprises the steps: based on user behavior data, through difference preserving function mapping and behavior label generation, outputting a user behavior label set; and performing label fusion based on the user behavior label set and preset user historical interaction data to generate a comprehensive behavior label set. According to the method and the device, emergent behaviors of the user in different time periods, places and scenes are reserved, and information loss caused by averaging is avoided. The difference preserving mapping does not need to depend on complex weight training, is logically transparent, and can perform online rapid calculation; and meanwhile, traceable input with fine granularity is provided for subsequent steps, so that the real-time interest and intention of the user can be accurately reflected by tag generation, and the response speed and personalized precision of downstream recommendation, risk control and other systems are remarkably improved.

Provided are a computer system and method for generating and providing intelligent recommendations using artificial intelligence (“AI”). The system includes a memory for storing user feedback data, user resource data, and user goal data, and a processor in communication with the memory. The processor is configured to execute a first AI model for user interface (“UI”) effectiveness optimization, a second AI model for transaction optimization, a model mapping module configured to implement a functional mapping between the first AI model and the second AI model through which the first AI model and second AI model communicate and mutually update each other, and a user interface generator module for generating a user interface for outputting the intelligent recommendations and receiving the user feedback data.

Some examples of the present disclosure relate to executing configuration files generated from specification files. In one particular example, a system can receive a specification file defining functions for an application programming interface (API) associated with a software entity. The system can generate a graph identifying keywords and relationships associated with the functions. The system can generate, based on the graph, a mapping dictionary that maps the functions to tasks usable in an executable file associated with the API. The system can generate the executable file including one or more of the tasks based on the mapping dictionary and execute the executable file to perform operations associated with the API and the software entity at a node.

The invention provides a grasshopper-based planing boat parametric modeling method and system, belongs to the technical field of computer aided design, and aims to solve the problems that in the planing boat modeling process, due to the fact that parameters need to be repeatedly determined, the modeling efficiency is low, the modeling design flexibility is insufficient, the interactivity and visual experience are poor, and the modeling cost is high. The method comprises the following steps: constructing a main line segment and carrying out draught control; controlling a hull chord line through curvilinear function mapping; constructing a hull bilge edge line; constructing a boat body keel line; constructing a middle parallel section of the hull; mirroring to generate a complete hull; the system comprises a user interaction interface, a parameter processing module and a model generation module.

The invention discloses a visual synchronous updating method and system for real-time data streams of the Internet of Things, and the method comprises the steps: obtaining basic features, and calculating the comprehensive importance score of each data stream through employing an analytic hierarchy process; establishing a dynamic weight distribution table, determining a priority and a display resource distribution proportion by utilizing piecewise function mapping, and setting a differential updating frequency; dynamically adjusting the visual size of each data stream based on the priority and the updating frequency to form a hierarchical interface layout; establishing a synchronous updating scheduling queue, managing updating tasks with different priorities, and triggering interface refreshing according to a preset frequency; executing load balancing control, and dynamically adjusting the actual updating frequency of each priority data stream; and continuously tracking the importance change of the data flow, recalculating the comprehensive importance score when abnormal fluctuation is detected, and updating the weight distribution table and the priority. According to the method, accurate distribution of display resources, self-optimization of interface layout and efficient synchronization of updating are realized.

The invention relates to the technical field of man-machine interaction, in particular to an intelligent ring gesture recognition method and system based on a sensor, and the method comprises the steps of acquiring hand state information, monitoring a motion track, monitoring posture change, judging gesture triggering conditions, extracting and matching pressure features, dynamically optimizing, mapping functions and the like. According to the method, the real-time performance and stability of gesture recognition are improved through multi-sensor collaborative acquisition and data fusion analysis; a dynamic optimization mechanism is utilized to enhance adaptability in a complex environment, user habits are dynamically updated through a gesture function association table, and an identification strategy is optimized. According to the method, the accuracy and adaptability of gesture recognition can be remarkably improved, and the requirements of diversified application scenes are met.

The invention discloses a visual arrangement method and device for an automobile scene function, equipment and a storage medium, and relates to the technical field of new energy automobile control, and the method comprises the steps: responding to a mixed configuration operation of a user on function entries and event entries, and generating a function arrangement list; responding to execution time configuration of each function in the function arrangement list by a user; mapping each function in the function arrangement list into a corresponding system code and an operation code based on a pre-stored function comparison table; combining the system code, the operation code, the execution time configuration and the event trigger identifier into scene description data; the scene description data is encoded, the arrangement data packet is generated, and the shareable arrangement data packet is generated by responding to user mixed configuration and mapping, so that user definition, event triggering and social propagation of scene functions are realized, individual requirements can be met without modifying vehicle end software, and the development cycle is shortened.

The present invention discloses a personalized graph federated learning method, system and storage medium based on signature clustering. The method includes the steps: a client receives a model sent by a server for training and aggregates according to the parameters of the model; generates a signature of the client through function mapping; adds a personalized factor to the local model and uploads the model layer parameters and signature except the personalized factor to the server; the server clusters according to the signatures of each client and performs federated averaging within the clustering clusters; the client receives the federated averaged model parameters sent by the server and performs local training in combination with the personalized factor. The present invention solves the problems of poor clustering learning effect and data heterogeneity between and within clients in the related art.

The invention relates to the technical field of intelligent channel information, in particular to a coastal channel operation state real-time monitoring and early warning method based on digital twinning. Building a digital twin architecture and fusing multi-source operation data to realize real-time synchronous mapping from a physical channel to a virtual channel; introducing a multi-dimensional mechanism model and a historical mechanism function thereof to drive an AI model to perform function mapping, and constructing a back-silting dynamic prediction model to obtain a navigation risk index of a future leg; a space-time occupation matrix and space-time safety envelope model is constructed based on the navigational speed and course data of nearby ships and the ship, the future ship collision risk is predicted, studied and judged, and a navigation safety index is generated; performing coupling evaluation on the navigation risk and the navigation risk by using a marginal dependence distribution model to form a comprehensive risk and trigger dynamic early warning; and issuing early warning information and auxiliary decisions through the shipborne terminal and the VTS center. According to the invention, a complete ship-shore-cloud cooperative digital twin system is constructed, and global digital and intelligent management of coastal channels is realized.

The invention provides a method and device for generating feedback information, electronic equipment and a storage medium. The method comprises the following steps: acquiring query information input by a user, wherein the query information is multi-modal information; according to the query information and historical information, a prediction result is obtained, and the prediction result comprises topics related to the query information; obtaining a program function corresponding to the prediction result according to the prediction result and a function mapping table; executing the program function to convert the prediction result into input data of a target model; and generating feedback information corresponding to the query information based on the input data and the target model. By predicting the query information and executing the program function corresponding to the prediction result, the more accurate input data of the target model is obtained, and the feedback information generated based on the input data better meets the requirements of a user.

The invention discloses a self-adaptive multi-mode mouse based on scene recognition and a control method thereof, and relates to the technical field of man-machine interaction, the mouse comprises a behavior acquisition module, a local mode recognition module, a cloud analysis module, a mode optimization module and a model self-updating module; the control method specifically comprises the steps that DPI sensitivity and key function mapping are dynamically adjusted based on an identification result, progressive parameter switching and RGB state indication are supported, and model autonomous evolution is achieved through OTA incremental updating. The method has the functions of cross-device federated learning, privacy encryption transmission, abnormal fault tolerance and energy-saving management, basic intelligence can be maintained by depending on local cache in an offline state, high-precision, low-delay and self-adaptive man-machine interaction experience can be realized, the operation efficiency is remarkably improved, and the misoperation rate is reduced.

To utilize unused or underutilized input and output pins without significantly impacting power consumption and die area, an adaptive logic module (ALM) of a programmable logic device can be implemented with an additional 2LUT to improve low function packing density and broad function mapping coverage. The 2LUT can also serve as a route-through to provide direct access to ALM registers with or without input inversion. The extended ALM can also utilize route-through configurations of the additional 2LUT to enhance the connectivity of the ALM inputs and outputs.

The application provides a magnetic shaft keyboard RT value dynamic optimization method, system, medium and product, and relates to the technical field of automatic control. Including: for the keyboard global mapping scene, first collect the dynamic operation data such as pressing depth, pressing speed and pressing eccentricity characteristics of the keys within the user preset time, then call the preset key feature-finger function mapping table, and match to obtain the corresponding finger function characteristics. Then, combined with the characteristics and the physical topology structure of the keyboard keys, at least an independent control area containing a strong control area and a weak control area of finger function is divided, and the fatigue state is judged according to the change trend of the dynamic operation data of each area. Finally, according to the dynamic operation data and the fatigue state, an exclusive RT value adjustment strategy is generated for different independent control areas, the strategy is issued to the corresponding area to complete the RT value adjustment, and the RT value is dynamically adapted according to the finger function and the use state.

The invention discloses a multi-sensor track fusion control method and system based on a UWB technology. The UWB technology is adopted, high-precision remote controller positioning can be achieved, the precision reaches the centimeter level, and the accuracy of gesture recognition is ensured; the gesture operation history of the user is automatically analyzed through centimeter-level positioning and a deep learning algorithm, rapid response, real-time dynamic gesture recognition and rapid television function and application starting can be achieved, operation delay is reduced, and the timeliness and accuracy of gesture control are greatly improved. Furthermore, the method also supports user-defined gestures and function mapping, so that the individuation of the operation is improved.

The invention discloses a development tool plug-in synchronization method based on artificial intelligence, and the method comprises the following steps: S1, collecting the multi-modal feature data of a plug-in, and generating an original feature data set; s2, processing the data by adopting a causal graph method, constructing a plug-in and context relation graph, and generating a context enhanced semantic vector; s3, constructing a cross-environment function mapping model matching semantic set to generate candidate plug-ins; s4, performing combination mapping on the candidate plug-in set, determining a one-to-one or multi-plug-in scheme and outputting a coverage rate index; s5, loading the candidate set in the virtual sandbox environment, executing a compilation test debugging process and collecting operation performance indexes; and S6, generating an anti-fact scene based on the operation indexes, and comparing actual and anti-fact results to screen a final synchronous plug-in scheme. According to the method, cross-platform function consistency synchronization and stability guarantee of the development tool plug-in are realized through context feature modeling, cross-environment function mapping and causal verification driven by the causal graph.

The invention discloses a substation interface configuration operation and maintenance method and system based on functional identity recognition, and belongs to the technical field of substation operation and maintenance. According to the invention, the anti-misplug guide keys are configured for the in-cabinet interface and the out-cabinet interface through the in-cabinet interface configuration data and the out-cabinet interface configuration data, so that misplug and misplug between the interfaces on an object quantity connection layer are avoided; by generating a function identity identification code and constructing a function mapping database, digital management of function logic configuration between interfaces is realized, and the complexity and error rate of manual parameter configuration are reduced; simple and efficient substation interface configuration is realized; then the to-be-overhauled interface is identified through the substation operation and maintenance requirement, the function mapping database and the function identity identification code, the to-be-overhauled interface needing to be isolated can be accurately identified from the interface function logic level, the risk of isolation leakage or misoperation is avoided, efficient and accurate substation interface operation and maintenance are achieved, and the operation efficiency is improved. And the efficiency and the accuracy of substation interface configuration operation and maintenance are improved.

The invention provides an adaptive configuration and switching method and device for a PHEV driving mode, electronic equipment and a storage medium, in the method, a preset scene mode list and a function mapping table of physical keys are dynamically generated according to vehicle driving form information, deep decoupling and adaptive matching of control logic and hardware configuration are achieved, and the driving mode switching efficiency is improved. The problem of interaction redundancy or function ineffectiveness caused by configuration difference in a traditional scheme is effectively solved, and the software maintenance cost of a multi-configuration vehicle type is remarkably reduced; meanwhile, in combination with a long-press direct mechanism of context awareness, a user can enter a specific high-performance mode by one key without experiencing tedious cyclic switching or sight line transfer under an emergency working condition, so that the operation convenience and blind operation safety in the driving process are greatly improved, intelligent interaction experience of'hardware self-adaption + operation high efficiency 'is constructed, and the user experience is improved. Therefore, the driving mode control performance of the PHEV model is comprehensively optimized.

The invention belongs to the technical field of remote controller control, and discloses a cloud data driving type universal remote controller control system based on APP and Bluetooth, and the system comprises an equipment recognition and protocol mapping module which carries out the equipment recognition and function mapping through a cloud management system; the personalized interface generation module is used for performing interface adaptation according to user operation habits; the instruction coding and transmission module is used for generating an infrared coding instruction and transmitting the infrared coding instruction through Bluetooth; and the equipment control execution module executes corresponding operation. Plug-and-play equipment adaptation is realized through cloud data driving, and the problem of complex configuration of a traditional universal remote controller is avoided; based on personalized interface generation of user operation habits, the layout is dynamically adjusted according to the function use frequency, and the limitation of function layout solidification is solved; bluetooth direct connection transmission is adopted, infrared light physical limitation is bypassed, the problems of directional constraint and shielding are solved, and stable and reliable equipment control is achieved.