19 results about "Soft data" patented technology

The invention discloses a flash memory error correction method based on adaptive learning and a readable storage medium. According to the method, offset step lengths under different bias voltage types are determined according to historical error correction data, then data reading is performed according to reference voltage and the offset step lengths to obtain soft data, and finally the offset step lengths are optimized in real time according to deviation data generated by error correction of the reference data and the soft data, so that dynamic adjustment of the offset step lengths and voltage offset is realized. Compared with a preset fixed step length in a traditional error correction method, the offset step length can be set according to the actual condition of the data, so that the error correction process can flexibly adjust the bias condition according to the actual states of different flash memory units, different application scenes are effectively adapted, and the probability of flash memory data reading errors is effectively reduced.

The application relates to the technical field of new energy charging pile fault prediction and maintenance, in particular to a new energy charging pile fault prediction and maintenance system and method based on Internet of Things, which comprises data collection and generation of charging pile multidimensional information, processing of the charging pile multidimensional information, obtaining of charging pile operation data based on Internet of Things, comparative analysis of the charging pile operation data, determination of a charging pile fault prediction method based on Internet of Things, and implementation of alarm decision based on the charging pile fault prediction method. The application discards high-frequency hard data collection, is based on relevant soft data collection, reduces the cost and difficulty of data acquisition required for charging pile fault prediction, can process and analyze soft data when the charging pile is in standby state and working state, realizes early prediction and alarm of charging pile faults, improves the freedom of self-fault prediction of the charging pile, and is favorable for early elimination of hidden dangers of the charging pile.

The invention relates to the technical field of oil field reservoir geological modeling, in particular to a reservoir modeling method and device based on automatic calculation of a Kriging variation function. The method comprises the following steps: acquiring work area logging data and corresponding hard data thereof, and seismic data and corresponding soft data thereof, and performing grid division on the hard data and the soft data; dividing the data into training data and verification data; inputting the training data into an established reservoir geologic model based on a Kriging variation function, and training the model; and if the error between the trained model and the well point corresponding part of the verification data is greater than a first predetermined percentage, optimizing the trained model by using an optimization algorithm. The problems that an existing reservoir geologic model establishing method seriously depends on prior cognition of geologic experts, and the subjectivity of an interpretation result is high are solved; the problems that the uncertainty of model feature distribution performance is too large and reliability evaluation is difficult due to the fact that a trained model lacks effective verification are solved.

The invention relates to the technical field of new energy charging pile fault prediction and maintenance, in particular to a new energy charging pile fault prediction and maintenance system and method based on the Internet of Things, and the method comprises the steps: collecting data, generating multi-dimensional information of a charging pile, processing the multi-dimensional information of the charging pile, obtaining the operation data of the charging pile based on the Internet of Things, and carrying out the fault prediction and maintenance of the charging pile. And comparing and analyzing the operation data of the charging pile, determining a charging pile fault prediction method based on the Internet of Things, and implementing an alarm decision based on the charging pile fault prediction method. According to the method, high-frequency hard data acquisition is abandoned, and the cost and difficulty of acquiring data required for fault prediction of the charging pile are reduced on the basis of acquisition of related soft data; when the charging pile is in the standby state and the working state, soft data are processed and analyzed, advanced prediction and warning of the fault of the charging pile are achieved, the freedom of self-fault prediction of the charging pile is improved, and hidden dangers of the charging pile can be eliminated in advance.

The embodiment of the present disclosure provides a decoder, a decoding method, a memory controller and a memory system. The decoder comprises a cache module and a flip indication module. The cache module is configured to cache soft data related to a to-be-decoded codeword. The flip indication module is configured to obtain the soft data from the cache module, and use the soft data to assist in determining the number of bits in the to-be-decoded codeword that do not satisfy a check when performing hard decision decoding on the to-be-decoded codeword for the first time.

The present disclosure includes apparatuses and methods for monitoring error correction operations performed in memory. Several embodiments include a memory and a circuit configured to determine a quantity of error data corrected during an error correction operation performed on soft data associated with sensed data states of several memory cells of the memory, determine a quality of soft information associated with the error data corrected during the error correction operation performed on the soft data, and determine whether to take a corrective action on the sensed data based on the quantity of error data corrected during the error correction operation and the quality of soft information associated with the error data corrected during the error correction operation.

A system and method for performing error recovery using LLRs generated from multiple read operations is provided. The method may include selecting a set of decoding factors for multiple read operations of a non-volatile memory device. The set of decoding factors may include, but is not limited to, a total number of reads, an aggregation mode for aggregating the results of the multiple reads, and whether the read results include soft data. The method may also include issuing a command to read user data to the non-volatile memory device based on the set of decoding factors, generating a plurality of log-likelihood ratio (LLR) values ​​from a set of preselected LLR magnitudes based on the set of decoding factors using a mapping engine, obtaining an aggregated read result according to the aggregation mode, and obtaining an LLR value from the plurality of LLR values ​​using the aggregated read result as an index.

According to one aspect of the present disclosure, a decoder is provided. The decoder may include a cache module. The cache module may be configured to cache soft data related to a codeword to be decoded. The decoder may include a flip-indication module. The flip-indication module may be configured to obtain the soft data from the cache module. The flip-indication module may be configured to, when performing hard-decision decoding on the codeword to be decoded, determine, at least in a first flip iteration and with assistance of the soft data, a number of parity checks not satisfied by bits in the codeword to be decoded.

The invention discloses an LDPC soft decoding method, a computer readable storage medium and a solid state disk, the decoding method is used for the solid state disk, and the decoding method comprises the following steps: receiving a reading command sent by a HOST end, and reading data in a chip according to the reading command; if the read data is wrong data, executing hard decoding; if hard decoding fails, reading the threshold voltage data, and forming a threshold voltage oscillogram according to the threshold voltage data; analyzing and calculating a threshold voltage waveform in the threshold voltage oscillogram to obtain a target voltage range; acquiring soft data of a preset number of times in a target voltage range and performing LDPC soft decoding; wherein the size of the target voltage range is used for representing the offset degree of the threshold voltage data. According to the method, soft data are obtained without using a preset voltage range, the threshold voltage data are read for analysis, the target voltage range is dynamically obtained, the accuracy of LDPC soft decoding is increased, the calculated amount is reduced, and time is saved.

The application belongs to the technical field of data flow, and discloses a data flow method and system based on a high-speed data network. The method comprises the following steps: returning a soft data gateway download address to a supplier and a demander based on their subscription requests; obtaining a transaction request initiated by the demander to the supplier to generate a service identification ID; sending a configuration instruction to the soft data gateway corresponding to the demander and the supplier based on the service identification ID to realize the interconnection of the demander connector and the supplier connector and the setting of data transmission quality; allocating an independent VPN instance for the service identification ID, and binding the demand network quality to construct an isolated transmission tunnel; when the data transaction is completed, sequentially closing the isolated transmission tunnel, deleting the QoS setting, deleting the white list, and closing the intranet network; and obtaining the unsubscription request of the supplier and the demander to allow them to unload the corresponding soft data gateway. The application has high data flow security and high transmission quality.

An embodiment of the present invention provides a method, device, equipment and storage medium for continuous variable modeling of meandering rivers. The method includes: establishing a training image of continuous variables of meandering rivers according to simulation grids; scanning the training image of continuous variables of meandering rivers through a data template to obtain a plurality of training patterns; for each empty grid node in the simulation grids, scanning the grid node through the data template, and under the constraint of soft data trend, obtaining the training pattern of the grid node, determining the training pattern corresponding to the training pattern of the grid node among the plurality of training patterns of the meandering river training image, and referring the determined training pattern to the grid node to complete the simulation of the grid node. This solution can characterize complex spatial structures and geometric forms, and can better reflect the geological sedimentation law of meandering rivers; it is realized under the constraint of soft data trend, which is conducive to overcoming the disadvantage of poor channel continuity of multi-point geostatistics without trend constraint.

The invention discloses a virtual drill hole generation method based on a fuzzy membership degree, electronic equipment and a medium. The method comprises the steps that an actually-measured drill hole is discretized into a plurality of depth units in the depth direction, the stratum type of the depth units is defined as hard data, and the occurrence probability of the stratum type in the drill hole is defined as soft data; parameters of a membership function are calculated based on actually measured drilling data, and the membership is obtained; determining an adjacent actual measurement drill hole based on the target virtual point; and based on the membership degree, for each depth unit, fusing the hard data and the soft data of the adjacent actually measured drill holes to generate a virtual drill hole histogram. According to the method, the influence of the hard data and the soft data is comprehensively considered, the correlation degree between the actually-measured drill hole and the virtual drill hole can be comprehensively and accurately reflected, spatial correlation quantification based on the membership model is achieved, and the precision of drilling the virtual drill hole is improved.

The present disclosure includes apparatuses and methods for determining soft data. A number of embodiments include determining soft data associated with a data state of a memory cell. In a number of embodiments, the soft data may be determined by performing a single stepped sense operation on the memory cell.

This invention belongs to the field of power system optimization and energy storage operation technology, specifically involving a multi-dimensional collaborative community-shared energy storage optimization scheduling method. It includes: establishing a dual-constraint scheduling model for a community-shared energy storage system based on relative diversity factors, combining a hard upper limit on total subscriptions with a soft data-driven threshold based on relative diversity factors, and using the non-overlapping nature of user loads to define the boundary of overlapping demand; constructing a two-layer optimization scheduling architecture of outer-layer price setting and inner-layer capacity allocation, introducing a piecewise linear proxy model to achieve fair capacity allocation with the goal of maximizing the minimum user benefit-cost ratio; and establishing an evaluation method that maps user differences to expected shortage costs. This invention provides a multi-dimensional collaborative community-shared energy storage optimization scheduling method that integrates user diversity constraints, supply-demand benefit balance, and risk closed-loop control, filling a gap in existing technology and promoting the large-scale application of shared energy storage in community scenarios.

The invention provides a multi-source cross-scale satellite data adaptive greenhouse gas product fusion method, which belongs to the technical field of atmospheric environment remote sensing application, and comprises the following steps: carrying out space-time unified matching, resampling and reprojection on observation data of different satellite platforms; performing error correction by using high-resolution OCO-2 data as hard data, performing system deviation correction on low-resolution GOSAT data based on an XGBoost algorithm, and constructing soft data; the hard data and the soft data are fused through a Bayesian maximum entropy framework, the fusion weight is optimized by utilizing a space-time covariance model and soft data constraints, and a posterior probability density mean value is obtained to serve as a final fusion result. And a high-precision CO2 concentration field in a global range is obtained by combining a space-time large-scale trend. According to the method, the contribution of low-uncertainty observation can be enhanced, high noise and geometric mismatch interference are effectively suppressed, a global greenhouse gas concentration fusion product with multi-source information and an uncertainty field is generated, and the precision and space continuity of greenhouse gas monitoring are remarkably improved.

The present invention discloses a multi-target tracking filtering method and device, comprising the following steps: obtaining measurement data of a target, inputting the measurement data into a trained multi-target tracking model, extracting features from the measurement data based on an encoder, and generating an improved representation of the measurement data; cascading tracking queries and detection queries into a decoder, processing the improved representation of the measurement data, and obtaining a query vector; performing soft data association on the query vector based on the decoder to obtain a decoding result; inputting the decoding result into a predictor and a classifier for processing, and generating a state prediction result for each time window; processing the state prediction results of all time windows under a cross-frame target alignment mechanism based on a QTM module, obtaining multi-target tracking query results for all time windows, and generating target motion trajectories. The multi-target tracking filtering method of the present invention improves the long-term tracking accuracy of multiple targets and can generate predicted trajectories.

The application discloses a method and device for reservoir geological modeling based on a full-space variation function. The method comprises the following steps: performing grid division on single-well reservoir attribute interpretation hard data and inter-well reservoir attribute trend soft data through well site coordinates to obtain well data and divide the well data into a training data set and a test data set; training an initial full-space variation function model through cross-validation of the training data set to obtain a target full-space variation function model; judging the accuracy of the target full-space variation function model according to the test data set; in the case that the accuracy is less than or equal to a preset accuracy, continuously training the target full-space variation function model until the accuracy is greater than the preset accuracy; and in the case that the accuracy is greater than the preset accuracy, using the target full-space variation function model for target reservoir geological model construction. The application effectively improves the accuracy of reservoir geological modeling of continuous and discrete reservoir attribute spatial distribution by establishing a full-space variation function model.

Embodiments of the present application provide a memory system error correction method, a memory system and a memory controller. The error correction method comprises: obtaining corresponding first soft data according to first hard read data and buffered second hard read data after hard decision decoding of the first hard read data fails; the first hard read data is read by the memory system according to a first hard read voltage; the second hard read data is read by the memory system according to a second hard read voltage before the first hard read data is read; performing first soft decision decoding according to the first soft data and the first hard read data, or performing first soft decision decoding according to the first soft data and the second hard read data; the first hard read voltage is one of a plurality of re-read voltages corresponding to a read command; and the second hard read voltage is an initial read voltage or a re-read voltage other than the first hard read voltage in the plurality of re-read voltages.

The application discloses a flash memory error correction method based on adaptive learning and a readable storage medium. The method determines offset steps under different bias types according to historical error correction data, then performs data reading according to a reference voltage and the offset steps to obtain soft data, and finally optimizes the offset steps in real time according to deviation data generated by error correction of the reference data and the soft data, so as to realize dynamic adjustment of the offset steps and voltage offset. Compared with a preset fixed step in a traditional error correction method, the offset steps of the application can be set according to actual conditions of data, so that the error correction process can flexibly adjust bias conditions according to actual states of different flash memory units, effectively adapt to different application scenarios, and effectively reduce the probability of flash memory data reading errors.