110 results about "Incremental computing" patented technology

The invention discloses a digital employee collaborative screening method and system, and belongs to the technical field of artificial intelligence, and the method comprises the steps: receiving multi-user concurrent requests, carrying out semantic isolation, generating an adjustment factor in combination with user portrait features, generating an initial semantic vector through two-channel semantic coding, and carrying out semantic conflict detection, thereby effectively avoiding semantic cross and confusion, and improving the user experience. The intention analysis of multi-user concurrent requests is realized; acquiring request features in real time, calculating computing power demand levels of different requests, sequencing the requests with the same computing power demand level, and performing resource allocation; a user adjustment instruction is obtained through a front end, a demand difference is identified, an incremental calculation scheme is generated, and state synchronization control is carried out; according to the method, digital employees are divided into analytic, computational and feedback intelligent agents, collaborative screening is carried out, feedback verification is carried out, it is ensured that a screening result better meets user requirements, and real-time capability iteration of a digital employee cluster is achieved.

The invention discloses a table-level data classification and grading method and system based on big data, and relates to the technical field of table-level data classification and grading. According to the scheme, second-level sensitive table grading response is achieved through streaming incremental calculation and dynamic semantic coupling; a finite-state machine and a Hash fingerprint technology accurately capture table structure and content mutation, and traditional batch processing delay is avoided; the multi-modal fusion model synchronously analyzes topological features and semantic vectors, and the field-level sensitivity misjudgment problem is solved; the timeliness weight mechanism guarantees real-time evaluation of a dynamic association table relationship, and is particularly suitable for a high-frequency change scene in a big promotion period; and the asynchronous architecture design isolates the calculation load, so that the processing priority of the core price list is effectively guaranteed.

The invention discloses a disaster multi-source data real-time fusion early warning system based on deep learning, which belongs to the technical field of disaster early warning and comprises a multi-source data space-time alignment module, a cross-modal semantic fusion module, a disaster evolution dynamic prediction module and a minute-level early warning generation module. According to the method, accurate alignment of multi-source data is realized through an adaptive space-time resampling algorithm, cross-modal deep semantic fusion is realized through a cross attention mechanism, a disaster evolution trend is predicted through a space-time diagram attention network, minute-level early warning response is realized through an incremental calculation strategy, and disaster early warning is realized. According to the method, precise space-time alignment of the multi-source heterogeneous data is realized through adaptive space-time resampling and timestamp synchronous correction, and the space-time alignment precision is improved.

The invention discloses a data deduplication method and system based on incremental calculation and feature clustering and electronic equipment, and the method comprises the steps: receiving multi-source heterogeneous text, image, audio and video data streams in real time, adding a timestamp to each data unit, and generating an input data set with a time attribute; performing multi-modal feature extraction on the input data set, and dynamically adjusting feature weights of the extracted multi-modal features through a sliding window model and a time decay factor to obtain an increment feature vector set with the weights; executing two-stage clustering based on the feature vector set to generate a plurality of fine-grained data clusters; and comparing the intra-cluster data of each fine-grained data cluster in pairs by adopting a composite similarity model to determine intra-cluster repeated data, and performing data deduplication on the input data set according to the intra-cluster repeated data to obtain a deduplicated input data set. According to the method, the processing efficiency of the real-time data is improved, and the storage and calculation cost is remarkably reduced.

The invention provides a cache elimination method and device, a storage medium and computer equipment, and relates to the technical field of computers.The method comprises the steps that a weight label is generated for each cache data item in a cache space, and the weight label comprises a service priority field and an access frequency field; for each cache data item, when the cache data item is accessed, calculating the current frequency score of the cache data item according to the historical frequency score and the current access increment of the cache data item, and updating the access frequency field of the cache data item; and when the cache space is full, determining a target elimination data set according to the weight tag of each cache data item, and eliminating the cache data items in the target elimination data set from the cache space. In this way, the problem of cache breakdown can be effectively relieved, and therefore the technical defect that in the prior art, due to the fact that hot data are eliminated, the system performance is reduced is overcome.

Disclosed in the present invention is a method for constructing a decoupling controller of a permanent-magnet-assisted bearingless synchronous reluctance electric motor. A decoupling controller formed by an IDBO-FNN dynamic prediction module and an IDBO-FNN system is connected to a composite controlled object, wherein the IDBO-FNN dynamic prediction module is formed by three parallel IDBO-FNN prediction modules, three parallel control increment calculation modules, three parallel prediction value calculation modules and a composite signal calculation module; and the IDBO-FNN system is formed by means of connecting a second-order difference processor and first-order difference processor of a fourth IDBO parameter optimization module before a fourth fuzzy neural network model, each IDBO parameter optimization module using an improved dung beetle optimizer to optimize the membership function width, membership function center value and weight of a fuzzy neural network. The structure of a fuzzy neural network is optimized, such that the degree of dependence of the fuzzy neural network on expert experience is reduced, the convergence speed of the weight and membership function of the fuzzy neural network is improved, high robustness is achieved, and various good static and dynamic performances such as rotor radial position control and electric motor speed control can be obtained.

The invention provides an automatic index calculation method based on an information model and real-time data driving, which relates to the technical field of data driving and comprises the steps of receiving multi-source heterogeneous data and establishing an information model, constructing an index dependency relation directed acyclic graph, establishing an incremental calculation engine and configuring a calculation rule and a triggering mode. And data driving and timing triggering calculation are supported, and real-time automatic calculation of indexes is realized. According to the method, the index calculation efficiency can be improved, the calculation resource consumption is reduced, the data real-time performance is ensured, and the system expandability and the index management capability are improved.

The invention relates to the technical field of data processing, in particular to a real-time data aggregation optimization method and system based on incremental calculation. The invention discloses a real-time data aggregation optimization method and system based on incremental calculation. The method comprises the following steps of data receiving and preprocessing, incremental calculation model construction, data aggregation and storage, data updating and maintenance, and query and analysis. Data receiving and preprocessing: receiving data streams from a plurality of data sources in real time, performing cleaning operation on the data streams to remove invalid data, performing format conversion to unify a data format, and allocating a unique identifier and a record timestamp to each processed data record; according to the method, only the change part of the data needs to be calculated through the incremental calculation model, so that the calculation amount is greatly reduced, and the efficiency of real-time data aggregation is improved; compared with a traditional method and system, the calculation time can be shortened by more than 50% when large-scale real-time data are processed.

The invention discloses a GPS oscillator taming system and method based on PID parameter adaptive adjustment, and the system comprises a forgetting factor dynamic updating module which is used for dynamically updating a forgetting factor with the variance of the frequency error of VC-TCXO as a negative index; the system identification module is used for estimating ARX model parameters of the VC-TCXO by adopting a recursive least square algorithm with the updated forgetting factor; the model parameter mapping module is used for mapping the estimated ARX model parameters to obtain PID control parameters; the incremental PID controller is used for calculating a frequency control word increment based on the frequency error; the digital-to-analog converter is used for calculating according to the frequency control word increment to obtain a frequency control word and converting the frequency control word into analog voltage; and the VC-TCXO is used for obtaining the frequency of the GPS reference signal based on the analog voltage corresponding to the frequency control word so as to complete frequency taming. The method is high in calculation efficiency and robustness, and can adapt to the dynamic change and external disturbance of the controlled object in real time.

The invention relates to the field of data processing, and provides a parallel computing performance optimization method and device, equipment and a medium. The method comprises the following steps: initializing a CostAware Splitter, receiving a data list and a user-defined cost calculator, determining an initial processing interval, and associating the initial processing interval with the user-defined cost calculator; in the initial processing interval, element processing cost is obtained based on a self-defined cost calculator, and interval processing cost is dynamically obtained through incremental calculation and a cache mechanism; dynamic task splitting is carried out according to the total interval cost, and sub-task load balancing is achieved; the sub-tasks are executed in parallel, elements in the intervals corresponding to the sub-tasks are processed respectively, interval dynamic contraction is carried out based on element processing conditions, and the interval total cost after dynamic contraction is updated; and circularly executing the steps of dynamic splitting and parallel execution of the subtasks based on the total interval cost after dynamic updating, and automatically recycling all resources until splitting conditions cannot be met. According to the method, the parallel processing efficiency of large-scale non-uniform cost data is improved.

The invention relates to the technical field of distributed rendering synchronization, and discloses a distributed rendering node display picture synchronization method. The method comprises the following steps: acquiring real-time display picture data and timestamp information of a plurality of nodes, extracting picture display delay characteristics of each node and determining a delay change period through time domain statistical analysis of timestamps, and further constructing a synchronous fluctuation degree of each node; calculating the current overall network interference degree by combining the relevance of the synchronization fluctuation degree in the historical data and the historical mean value, and analyzing the delay growth mode of the display timestamp in the current continuous time point sequence to obtain the synchronization lag trend degree; and combining the two to obtain a synchronization offset index, calculating a feedback synchronization adjustment amount by using the variation of the synchronization offset index, the current synchronization control intensity and a preset adjustment increment, and finally performing dynamic synchronization adjustment on a display image of the distributed rendering node based on the adjustment amount and an actual image synchronization measurement value by applying a neural network controller.

The invention relates to a dynamic increment calculation method for a power flow Jacobian matrix and power unbalance, which comprises the following steps of: redesigning a calculation process of the Jacobian matrix, storing a lower triangular admittance value of a target column into a circular queue, and dynamically updating a power accumulated value into a diagonal register at the same time; and the time for searching the admittance matrix during Jacobian lower triangular matrix calculation is greatly shortened. According to the method, the parallel pipeline dynamic increment calculation of the Jacobian matrix and the power unbalance is realized, and the calculation time and storage resource consumption of the Jacobian matrix of a large-scale network are reduced.

The invention discloses a method and a device for realizing quasi-real-time and quasi-incremental scheduling control by using big data ETL (Extract Transform and Load), and the method comprises the following steps: 1, carrying out task starting control by adopting automatic starting or manual starting, and outputting starting parameters; 2, performing asynchronous execution control on sub-theme tasks with inconsistent subordinate scheduling periods based on the starting parameters, and sending a sub-theme task execution instruction; 3, when a sub-theme task execution instruction is received, execution of each task in the sequence is controlled, and a table task execution task instruction is sent; 4, after a table task execution instruction is received, task execution is started according to the received input parameters; and step 5, monitoring the execution process of the task, recording execution steps and states, and meanwhile, giving an alarm for an abnormal execution condition. According to the method, the problem that high-frequency scheduling is difficult to control during quasi-real-time and quasi-incremental calculation of big data ETL is solved.

The invention discloses an intelligent electric energy meter error data processing method based on edge calculation, and belongs to the technical field of error data processing methods. The method comprises the following steps: S1, calculating a mean value and a standard deviation in a total electric energy increment sequence and a sliding window, defining a first-order autoregressive lag correction item, setting an adaptive threshold, defining a state machine system state, and finding out a computer increment sequence of a user in an inactive period by constructing an inactive period criterion; s2, storing the screened effective data increment, calculating the electric energy increment of each sub-meter, defining a continuous zero increment detection function, and constructing an observation matrix X and an observation vector Y by setting an ammeter failure detection window and parameters and collecting effective rearranged data points; s3, calculating a characteristic value and a condition number through an improved genetic optimization algorithm framework, and carrying out iteration to obtain optimal and different individuals; and S4, determining an optimal regularization parameter through an L curve method, and calculating an error coefficient of each electric energy meter through improved Tikhonov regularization.

The invention discloses a QT-based 3D imaging parameter real-time monitoring visualization method and system, relates to the technical field of computer vision, and solves the problem that a real-time visualization means for parameter adjustment is not provided in the prior art. Receiving input parameters through a graphical user interface input control of the QT framework; 3D imaging related parameters are calculated in a layered mode according to the input parameters, and parameter dependency tree data structure storage parameters and dependency relationships thereof are constructed; a one-way dependency relationship is formed among nodes in the parameter dependency tree; when the parameters of the root node are updated, positioning associated nodes according to the parameter dependence tree and triggering an incremental calculation process; and S2, generating a 2D imaging effect preview image in real time through a graphic rendering control QGraphics View of a QT framework in combination with a hierarchical calculation result in the step S2. According to the invention, through dynamic parameter adjustment, real-time preview and parameter bidirectional synchronous feedback closed-loop mechanisms, the efficiency and accuracy of 3D imaging parameter setting are significantly improved.

The invention provides an incremental data processing method, electronic equipment and a storage medium, relates to the field of aviation data, and is used for improving the data processing efficiency and ensuring the accuracy of an event time sequence. The method comprises the following steps: acquiring change logs of a plurality of data sources; aggregating the change logs which are the same as the incremental event metadata to obtain first data; performing out-of-order correction on incremental data in the first data based on the incremental event metadata to obtain second data; and respectively carrying out incremental calculation and lake and reservoir integrated incremental storage on the second data.

The invention belongs to the technical field related to information analysis, and discloses a flow graph processing method and processing system based on sub-graph hybrid computation.The processing method comprises the steps that an original flow graph in a graph database is divided into a plurality of sub-graphs with the vertex total weight distributed in a balanced mode; executing a graph updating operation on the sub-graphs; matching a corresponding graph calculation mode for each updated sub-graph, matching the corresponding updated sub-graph with a full-amount calculation mode when the proportion of the vertexes influenced by the updating operation in the updated sub-graphs exceeds a preset proportion, and otherwise, matching with an incremental calculation mode; extracting a dependency relationship between the sub-graphs; performing graph calculation on each updated sub-graph in parallel by adopting the matched graph calculation mode to respectively obtain an intermediate result of each sub-graph; and transmitting and updating the intermediate result of each sub-graph based on the dependency relationship, and outputting a global processing result of the flow graph. According to the processing mode, the locality characteristic of the graph can be fully utilized, the calculation overhead is reduced, and the accuracy of a calculation result is ensured.

Methods, systems, and devices for storing and retrieving checkpoint values using delta computation and compression are described. Techniques described herein may enable a memory system may compute deltas between initial checkpoint values and updated checkpoint values associated with values of weights of an artificial intelligence (AI) model. The memory system may compress and store the deltas. In some examples, the memory system may perform delta computation and compression simultaneously while receiving the updated checkpoint values (e.g., without storing the updated checkpoint values in a volatile memory of the memory system in accordance with a first operation mode) or after receiving the updated checkpoint values (e.g., after storing the updated checkpoint values in a volatile memory of the memory system in accordance with a second operation mode). In some examples, the memory system may switch between the first operation mode and the second operation mode.

The invention relates to a cross-system data synchronization method and system for a cake electronic brokerage city docking system, and the method comprises the steps: capturing a heterogeneous data flow through a local operation monitoring module disposed in a shopping mall and an interface adapter of each partner system, and transmitting the data flow to message-oriented middleware; performing intelligent analysis on the data stream by using a pre-trained analysis model, identifying a static template and a dynamic parameter through the deep learning ability of the analysis model, and outputting a structured data stream in a unified format; performing staged incremental calculation on the structured data based on a distributed stream calculation framework, including performing non-blocking merging compression on DML operation and fusion calculation with DDL operation, and generating an incremental data snapshot; and the snapshot is routed and synchronized to the target system according to the data source, so that stable and efficient bidirectional data synchronization between the cake electronic brokerage city and the partner system is realized, the cross-system data collaboration capability is effectively improved, and data support is provided for core services such as state synchronization of electronic coupons and inventory update.

The present application relates to the technical field of computer model generation, and is a concrete parameter optimization model generation method and system, specifically comprising: establishing a digital twin evolution model fusing prestressed vector field and heat conduction gradient; extracting effective axial force prediction value and interface debonding displacement increment changing with fire duration based on the digital twin evolution model, calculating and obtaining dynamic debonding sensitivity coefficient, synergy factor and topological redundancy response coefficient; generating structure topological robustness maintenance rate; online evaluating deviation tolerance of maintenance rate generated by the current model and target maintenance rate, and adjusting concrete aggregate gradation parameter combination. The present application solves the problem in the prior art that the dynamic collapse risk of the effective contact network of aggregate cannot be characterized, it is difficult to give a gradation optimization boundary with thermal and mechanical robustness, and a technical blind area exists in the safe operation and maintenance of the structure.

The invention relates to the technical field of big data processing and information security, and discloses a cloud architecture-oriented multi-dimensional credit data processing system. Comprising a multi-source data acquisition and standardization module, a multi-dimensional space-time topology mapping engine, a query intention analysis and topology preheating module, a cold and hot layered encryption storage scheduler, a path integrity verification and cache management module and an event driving and incremental calculation module. According to the method, a multi-dimensional spatio-temporal topological graph containing entities and field domain nodes is constructed, and the influence of spatial attributes on credit is quantified; performing cold and hot layered encryption storage by utilizing query mode recognition; constructing a cache fingerprint based on the encrypted object to verify the path integrity; and in response to data change, performing dynamic incremental calculation by using a credit potential energy conduction model in combination with a consistency verification result. According to the method, on the premise of ensuring data privacy and consistency, the calculation efficiency and evaluation accuracy of the large-scale credit graph are improved.

The invention discloses a feature warehouse updating method and device, electronic equipment and a storage medium. The method comprises the following steps: detecting each feature item in a feature warehouse through a preset detection mode; if at least one feature item in the feature warehouse is changed, performing incremental calculation on each changed feature item to obtain an incremental calculation result corresponding to each changed feature item; according to the incremental calculation result corresponding to each changed feature item, dividing each changed feature item into a corresponding feature cluster; and according to the feature cluster corresponding to each changed feature item, updating each changed feature item into a new feature item in the feature warehouse. According to the embodiment of the invention, computing resources and storage resources can be effectively saved, the updating process of the feature warehouse is accelerated, and the real-time or quasi-real-time business requirements are met.

The application provides a vehicle multi-sensor fusion positioning method and device based on extended Kalman filtering, equipment and medium, comprising: caching DR data to a first data buffer and caching VO data to a second data buffer; performing relative pose increment calculation on the DR data and the VO data in the cache area to determine the relative pose increments of the DR data and the VO data at adjacent time points; adaptively adjusting a VO observation noise covariance matrix based on the relative pose increments; and performing fusion processing on the adjusted VO observation noise covariance matrix and DR observation data based on sequential update extended Kalman filtering to determine the final fusion positioning result of the vehicle. Through the closed-loop fusion architecture of "relative pose increment calculation-adaptive observation noise adjustment-sequential update EKF", the positioning accuracy, robustness, real-time performance and engineering reliability are synergistically improved.

The invention relates to an FPGA power flow Jacobian matrix bidirectional skip list incremental calculation method. The method comprises the steps that a Jacobian matrix non-zero element structure skip list is designed; reading a binary power grid branch data sequence, and splicing the read binary power grid branch data sequence by adopting a shift register to obtain binary power grid branch data; and calculating the non-zero element structure of the Jacobian matrix according to the binary power grid branch data and the Jacobian matrix non-zero element structure skip list dynamic increment. According to the method, the calculation efficiency of the Jacobian matrix can be improved.

The invention relates to a dynamic heterogeneous graph neural network reasoning device and method based on an FPGA. The device comprises a hierarchical data loading module and a collaborative feature aggregation module. The hierarchical data loading module senses and captures changes of a vertex topological structure between continuous snapshots in the dynamic heterogeneous graph in real time; meanwhile, neighbors of different target vertexes under each timestamp are combined into neighbor set information, and corresponding feature vectors are sequentially loaded according to the occurrence frequency from high to low; the collaborative feature aggregation module receives neighbor set information, reads feature vectors corresponding to the neighbor set information, decouples an incremental calculation process into two parts of matrix vector multiplication and element calculation of corresponding positions, and completes an operation process of feature vector multiplication between a target vertex and a neighbor based on a local aggregation unit formed by a systolic array; and organizing the calculation units into a single instruction stream and multiple data streams based on the feature collection unit so as to complete normalization of calculation results of the same vertex.

The application discloses a kind of based on expert statistical feature incremental calculation updating method, electronic equipment, medium, the method of the present application is divided into offline calculation and online calculation, wherein offline calculation first reads the configuration information of expert statistical feature;Then extract the user statistical association information of the statistical feature of historical data;Finally, the statistical association information of historical data is persisted.Online calculation first reads the configuration information of expert statistical feature;Then extract the user statistical association information of the statistical feature of new data;While reading the user statistical association information persisted by historical data;Next, the statistical association information generated based on historical and new data extraction is updated offline data statistical association information and executes persistence operation;Finally, the corresponding statistical feature is generated based on the statistical association information of updated offline data.The method of the present application can still efficiently output statistical features for low server resources.

The invention discloses a variable synchronization ratio free curve motion interpolation method based on NURBS, and the method comprises the steps: segmenting track data points based on curvature features, and generating an ordered sub-data point set; performing NURBS curve fitting on each sub data point set, and constructing a segmented NURBS free curve; calculating a unit parameter increment of each interpolation period according to the starting position, the ending position and the NURBS parameter of the curve to be interpolated; creating a virtual axis M as a guide axis of synchronous movement, and taking a physical axis X and an axis Y as following axes; calculating the synchronization ratio of each interpolation period by using the unit parameter increment; the virtual axis M is activated to move, the synchronization ratio of the current interpolation period is updated according to the movement track of the virtual axis M, the physical axis X and the axis Y move according to the updated synchronization ratio until the virtual axis M moves to the end point of the NURBS curve parameter corresponding to the end point, and interpolation is completed. According to the method, the interpolation error is remarkably reduced, and high-precision free curve interpolation is realized.

The present application relates to the technical field of data processing, in particular to a gyro angle data adaptive calibration method and system; when calibrating the gyro angle, the motion state of the gyro is first determined, the state of the gyro is detected by an accelerometer, and the gyro is divided into a static state and a dynamic state; then different calibration methods are adopted for different states, and the gyro angle data is calibrated through acceleration data, which can effectively improve the accuracy of calibration; at the same time, when the gyro is in a dynamic state, the gyro and acceleration constraints are directly fused in the angle integral increment calculation stage of the gyro, the integral trajectory of each step is corrected from the source, and the error is avoided.

The invention relates to the technical field of unmanned aerial vehicles, and provides an unmanned aerial vehicle task interactive deduction method and system based on historical flight data, and the method comprises the steps: a data playback module loads and plays back historical flight data, and outputs a synchronous data stream; the data injection module injects new data into the synchronous data stream and outputs an updated data stream; the calculation engine module calculates a speculative flight state through increment according to the updated data flow and outputs a speculative result flow; and the branch comparison module generates result branches according to the speculation result flow and performs comparison display on the result branches. Through close cooperation of all the modules, continuity and consistency of data streams from loading to final comparison are ensured, the data playback module provides basic data streams, the data injection module intervenes in modification and updates the data streams, the calculation engine module receives and processes the data streams to generate speculation results, and the branch comparison module summarizes multiple outputs for analysis. A complete interactive deduction chain is formed, and high interactivity and prediction accuracy are achieved.

This invention discloses a method and system for classifying and grading table-level data based on big data, belonging to the field of table-level data classification and grading technology. This solution achieves second-level sensitive table grading response through streaming incremental computation and dynamic semantic coupling; finite state machine and hash fingerprint technology accurately capture table structure and content mutations, avoiding the latency of traditional batch processing; multimodal fusion model synchronously parses topological features and semantic vectors, solving the problem of field-level sensitivity misjudgment; timeliness weight mechanism ensures real-time evaluation of dynamic relational table relationships, which is particularly suitable for high-frequency change scenarios during promotional periods; asynchronous architecture design isolates computational load, effectively ensuring the processing priority of core price tables.