89 results about "Incremental computing" patented technology

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 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.

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 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 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 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.

This application proposes a method for storing plaintext data on a cloud server and a data access method, including the following steps: generating an access policy based on attribute information; using the access policy to perform attribute encryption on the plaintext data to obtain ciphertext data; uploading the ciphertext data to the cloud server; obtaining all duplicate ciphertext data corresponding to the same plaintext data in the cloud server, and integrating and reconstructing the access policy of each duplicate ciphertext data to obtain a reconstructed access policy; obtaining any duplicate ciphertext data as saved ciphertext data, and using the reconstructed access policy to re-encrypt the saved ciphertext data to obtain the plaintext saving result. This scheme introduces a re-encryption vector to generate a reconstructed linear share, and incrementally calculates the reconstructed ciphertext component and the reconstructed policy component to deduplicatize multiple duplicate ciphertexts, thereby achieving the goal of not needing to fully decrypt and re-encrypt the duplicate ciphertexts, significantly reducing the computational overhead of the re-encryption process.

This application relates to the field of data processing and provides a method, apparatus, device, and medium for optimizing parallel computing performance. The method includes: initializing a CostAwareSpliterator, receiving a data list and a custom cost calculator, determining an initial processing interval and associating it with the custom cost calculator; within the initial processing interval, obtaining the element processing cost based on the custom cost calculator, and dynamically obtaining the interval processing cost through incremental calculation and caching mechanisms; dynamically splitting tasks based on the total interval cost to achieve subtask load balancing; executing multiple subtasks in parallel, processing elements within the interval corresponding to each subtask, and dynamically shrinking the interval based on the element processing status, updating the total interval cost after dynamic shrinkage; and iteratively executing the steps of dynamic splitting and parallel execution of subtasks based on the dynamically updated total interval cost, until the splitting conditions can no longer be met, automatically reclaiming all resources. This method improves the parallel processing efficiency of large-scale, uneven cost data.

The embodiment of the invention provides a magnetic disk device and method capable of efficiently executing rewriting related to ATI. A controller of an embodiment sets an increment for each of a plurality of counts corresponding to a plurality of storage regions arranged in a radial direction of a disk. The controller increments the value of the count related to the nearby storage area by a set increment in accordance with a write operation of writing data to the disk. The controller executes, for each of the memory regions, an operation of calculating a remaining write count on the basis of an increment related to one memory region, the remaining write count being the number of write operations that can be executed in the vicinity of the one memory region until the value of the count corresponding to the one memory region reaches a first threshold value. In the idle state, the controller performs a rewrite operation on the plurality of memory regions in an order corresponding to the number of remaining writes.

The present application relates to heating disc temperature control technology field, disclose a kind of heating disc multi-partition temperature control system, comprising: steady gain calculation module, obtain edge partition steady gain, inner ring partition steady gain and lateral coupling coefficient;Inner ring heat back threshold point estimation module, obtain the estimated value of inner ring heat back threshold point;Edge set temperature difference calculation module, obtain the proportion close to threshold point;Power increment calculation module, obtain edge power increment and inner ring power increment;Rolling update module, update parameter;Power instruction issuing module, issue power instruction.The present application quantifies the strength of thermal coupling by applying small power step to edge and inner ring partition, avoids temperature deviation caused by coupling interference;With the help of trace scanning edge set temperature difference and linear interpolation, accurately locate the inner ring heat back threshold point;According to fixed interval rolling update parameter, it can adapt to material aging, environmental fluctuation and other working condition changes in real time, avoid local overheating or underheating, greatly improve the reliability of temperature control.

The invention relates to a solvable network coding retransmission method based on an improved Harris eagle optimization algorithm. The method comprises the following steps: an information source node transmits a data packet set to each information sink node through a wireless channel in a downlink time slot; each sink node generates feedback information according to the receiving condition of the data packet; the expected packet loss rate of each sink node is calculated, and the accumulated decoding time delay increment of each sink node is counted; calculating the expected retransmission times of each sink node, and determining the sink node with the maximum expected retransmission times; setting a critical condition according to the sink node with the maximum expected retransmission frequency, and dividing each sink node into different levels; and based on the constructed target fitness function, solving by using an improved Harris eagle optimization algorithm to obtain an optimal coding packet of the current downlink time slot. According to the method, local optimization of the algorithm can be avoided, the expected retransmission times and the overall decoding time delay are remarkably reduced, the transmission efficiency and reliability are improved, and the low-time-delay and high-reliability transmission requirements of scenes such as the Internet of Things and the Internet of Vehicles are met.

Embodiments of the present invention provide a method, apparatus, electronic device, medium, and chip for determining data association increments. The method includes: acquiring current state fingerprints corresponding to multiple data tables in a target database; determining a state change feature vector corresponding to each data table based on the current state fingerprints and local state data; determining at least one first-type data table and at least one second-type data table; determining a detailed data feature set based on the at least one first-type data table; acquiring a historical key data feature set corresponding to each second-type data table from locally cached data; determining a candidate association set based on the detailed data feature set and the historical key data feature set; and determining the association increment result corresponding to the first-type data table based on the candidate association set. Through the solution of the present invention, accurate perception and incremental calculation of data changes are achieved, thereby significantly improving the efficiency of association discovery.

A system includes at least one hardware processor and at least one memory storing instructions that cause the at least one hardware processor to perform operations, such as configuring, at a first computing node, a feature registry based on a plurality of feature definitions received via a user interface associated with an application programming interface. The operations include obtaining, at a second computing node, decoded raw data comprising streaming data and batch data, and performing incremental computation of features using a dependency graph comprising dynamic table objects and using configuration data from the feature registry. The operations include maintaining, as a feature store, a low-latency serving storage for computed feature values and a versioned historical storage. The operations include updating the low-latency serving storage using an event-driven task, controlling retrieval by a computing node based on a retrieval application programming interface, and backfilling historic results using a version-aware query.

The application relates to a dynamic incremental calculation method of a power flow Jacobian matrix and a power imbalance, which redesigns a calculation flow of the Jacobian matrix, saves lower triangular admittance values of a target column to a circular queue, and dynamically updates power accumulation values to a diagonal line register, so that the time for searching an admittance matrix during calculation of a lower triangular matrix of the Jacobian is greatly shortened. The application realizes parallel pipeline dynamic incremental calculation of the Jacobian matrix and the power imbalance, and reduces the calculation time of a large-scale network Jacobian matrix and consumption of storage resources.