4960results about "Redundant data error correction" patented technology

A nonvolatile memory device includes a plurality of latch groups, an address controller, an encoder, and a buffer. The address controller controls an input address and an output address to indicate one of the plurality of latch groups. The encoder receives sector data from a latch group corresponding to the output address among the plurality of latch groups and also compresses the received sector data. The buffer stores the compressed sector data. Among the plurality of latch groups, the compressed sector data stored in the buffer is overwritten in a latch group corresponding to the input address.

Apparatuses, systems, and methods for alternate memory die metadata storage. A memory module includes a number of memory devices. A controller writes data and metadata to the module. The data is stored in the memory devices, while the metadata is stored in a selected portion of the memory devices. The selected portion of the memory devices may use separate write enable signals to protect bit lines which the metadata is not being written to.

Apparatuses, systems, and methods for shared codeword in two-pass access operations. The memory may use a read read modify write write (RRMWW) cycle to write data and metadata to the array. Metadata and a data codeword are read out as part of two read access passes and combined into a shared codeword. Error correction is performed on the shared codeword, and then the corrected shared codeword is modified with write data and metadata. Updated parity is generated based on the modified shared codeword and the modified data and updated parity and the metadata are written as two write access passes.

Apparatuses, systems, and methods for scalable 1-pass error correction code operations. A memory device includes an error correction code (ECC) circuit which generates a number of parity bits based on a plurality of data bits during a write operation. The number of parity bits may be selected based on a setting in a mode register. The data and parity are written to the memory array as part of a single access pass. The data may be written to a selected portion of the data column planes, while the parity is written to one or more column planes of the extra column plane or a non-selected portion of the data column planes.

Apparatuses, systems, and methods for read / modify / write single-pass metadata access operations. During a write a memory receives data bits and at least one metadata bit and a column address which includes column select bits and column sub-select bits. A column decoder selects a set of bit lines in an extra column plane based on the column select bits and a set of bits is read out. A subset of that set of bits is selected based on the column sub-select bits and overwritten with the at least one metadata bit. The modified set of bits is written back to the extra column plane.

The invention relates to the technical field of big data, in particular to a cloud computing-based tumor early screening data sharing platform construction method and system. The sharing platform construction method comprises the following steps: carrying out association modeling on multi-dimensional early screening data by adopting a knowledge graph technology; encrypting the sensitive field of the metadata index database, and synchronously generating an irreversible anonymous identifier to replace the original identifier; partitioning and arranging the metadata index database in a distributed storage system corresponding to a cloud platform according to tumor types, and performing cross-region redundant backup by adopting an erasure code technology; and constructing an access control smart contract based on a block chain technology, verifying the qualification of a requester through a digital identity authentication module, and after verifying the validity of a digital certificate of the requester, dynamically decrypting and transmitting a data copy conforming to an authority level. Through organic fusion of the knowledge graph, the encryption technology, distributed storage and the block chain, a safe, efficient and reliable tumor early screening data sharing platform is constructed.

The invention relates to the field of data backup, in particular to an intelligent data backup method and system based on an AI large model. The method comprises the following steps: acquiring an enterprise global data list, performing intelligent data structure deconstruction and dynamic attribute mapping modeling, and constructing a holographic data semantic perception model; performing real-time transient risk mutation detection on the holographic data semantic perception model, and constructing an intelligent backup triggering mechanism; carrying out storage resource demand prediction based on an intelligent backup trigger mechanism, carrying out multi-storage cloud environment resource dynamic scheduling, and constructing an elastic backup storage resource pool; carrying out incremental backup analysis and self-adaptive compression coding to obtain an incremental backup coding packet; and performing dynamic backup sequence adjustment and intelligent incremental backup decision on the incremental backup coding packet based on the elastic backup storage resource pool, and constructing an intelligent incremental backup execution engine. According to the method, the reliability, the accuracy and the traceability of a backup result are improved through self-adaptive intelligent incremental backup.

The invention relates to the technical field of hard disk testing and verification, in particular to a multi-thread-based solid state disk reliability automatic testing method and system.The method comprises the steps that firstly, SMART information is deeply analyzed through microsecond-level high-granularity continuous performance monitoring, and multi-thread parallel processing is assisted; according to the method, fine performance fluctuation of the solid state disk under the concurrent load can be quickly captured, a fault mode can be identified, then early warning is realized by utilizing the extracted multi-dimensional features and a machine learning model, and a detailed fault diagnosis report is generated; and through dynamic error correction code strength verification and data integrity verification under pressure, an internal error correction mechanism of the solid state disk is actively detected and optimized. And finally, in combination with prediction reliability modeling, the system can estimate the remaining service life and predict faults, and provides product optimization suggestions for design, manufacturing and firmware optimization of the solid state disk, so that automation, intelligence and full life cycle management of the fault detection reliability of the solid state disk are realized.

The invention discloses a cost optimization method for resource scheduling management of a cloud data center, and relates to the technical field of cloud computing, and the method comprises the following steps: S1, collecting and modeling a multi-dimensional resource state of the cloud data center, and generating a resource change trend based on a sliding time window and a prediction model; and S2, constructing a multi-target game scheduling model taking calculation, storage, bandwidth and energy consumption as participants, outputting a scheduling game solution in combination with task modal adaptability parameters, and forming task-resource optimal matching. According to the method, through multi-dimensional resource state collection, a sliding time window and an advanced prediction model, resource dynamic changes and future trends can be captured more accurately, more reliable input is provided for scheduling decisions, resource waste or performance bottlenecks caused by information lag are avoided, calculation, storage, bandwidth and energy consumption are modeled as multi-party game participants, and the game efficiency is improved. Nash equilibrium is solved in combination with task modal adaptability parameters, and an optimal scheduling scheme giving consideration to resource utilization rate, performance and cost can be found.

The invention discloses a food safety sampling inspection data verification method and system and a computer storage medium thereof, particularly relates to the technical field of food safety supervision informatization, and is used for solving the problem of false consistency caused by silent failure when an external data source fails in an existing verification system. Field-level binding is realized by establishing a dependency mapping table of a sampling inspection system and an external data source; constructing a service response causal graph model, and dynamically marking a failure data source based on the deviation between the anti-fact response and the actual response; analyzing the verification field set and positioning a key verification field; degrading the weight of the key field to be below a threshold value and activating a manual auditing flag bit; scanning a preset coupling relationship between fields, freezing associated fields which do not trigger rules, verifying and constructing a conflict topology; when the check engine is executed, dynamically skipping the weight reduction field, suspending the freezing field, and generating a to-be-rechecked report in combination with the artificial flag bit and the topological graph; error data transmission is blocked from the source, and the food safety risk missed judgment rate is remarkably reduced.

Apparatuses, systems, and methods for bounded fault compliant metadata storage. Memory devices include a first data terminal and a second data terminal. As part of an access operation a first set of data and a first set of metadata may be sent / received across the first terminal and a second set of data and a second set of metadata may be sent / received across the second terminal. The first set of metadata may be stored in a first location and the second set of metadata may be stored in a second location in the memory array, such as a first and second column plane. The two locations may be remote from each other.

The invention discloses a computer memory bank fault prediction method and system based on deep learning, and relates to the technical field of computer hardware fault diagnosis, and the system comprises a multi-source time sequence data collection module which is used for obtaining memory bank operation state data in real time; the dynamic feature enhancement module is based on a composite architecture of a generative adversarial network and transfer learning, comprises a fault mode generator, and generates synthetic data consistent with real fault distribution by using an LSTM network; aligning feature spaces of different hardware platforms through a maximum mean difference loss function; the multi-modal fusion deep learning model comprises a space-time convolutional network, a graph attention network and an adaptive weight adjustment mechanism; and the fault early warning analysis module is used for analyzing a fault probability predicted value, an interpretable thermodynamic diagram and a maintenance suggestion. According to the invention, passive maintenance is changed into active prevention and control, and preposition and precision of fault management are realized through dual mechanisms of long-term trend prediction and short-term risk early warning.

The present application discloses a data management method, apparatus and system for a distributed storage system, and an electronic device. The method comprises: traversing all storage paths in a target distributed storage cluster, and acquiring file storage information of the storage paths during traversal; on the basis of the file storage information, calculating a redundancy policy score of each storage path, and on the basis of the redundancy policy score and a first redundancy policy score threshold, determining a storage policy adapted to the storage path; when it is determined that the storage policy adapted to the storage path is an erasure code storage policy, determining a data recovery mode of the storage path on the basis of the architecture information of the storage path; and when it is determined that the storage policy adapted to the storage path is a multi-copy storage policy, determining the number of copies of the storage path on the basis of a service importance degree of the storage path and the redundancy policy score.

Apparatuses, systems, and methods for an enhanced ECC mode. The memory array includes a number of data column planes and an extra column plane. When the memory device is set in an Enhanced ECC mode, data is stored in a subset of the data column planes, and an error correction code circuit (ECC) stores corresponding parity data in one of a column plane other than one of the subset of data column planes or the extra column plane. In this manner, memory may be capable of performing single error correction or single error correction with double error detection (SECDED) depending on the mode selected.

The invention discloses a data dynamic migration and storage method, device and equipment and a medium, and relates to the technical field of computers. The method comprises the steps that environment parameters of a current mechanical hard disk in a distributed cluster are collected; wherein the environment parameters comprise a vibration parameter, a temperature parameter and a noise parameter; quantifying the input and output delay performance of the current mechanical hard disk by using the environmental parameters of the current mechanical hard disk; if the input and output delay performance of the current mechanical hard disk is greater than a preset threshold value, searching a target mechanical hard disk combination in the distributed cluster; and migrating and storing the target data block of the current mechanical hard disk into the target mechanical hard disk combination based on an erasure code and a multi-copy mechanism. Through the scheme, the reliability of storing the data in the mechanical hard disk in the distributed cluster is remarkably improved.

Methods and apparatus for application aware memory patrol scrubbing techniques. The method may be performed on a computing system including one or more memory devices and running multiple applications with associated processes. The computer system may be implemented in a multi-tenant environment, where virtual instances of physical resources provided by the system are allocated to separate tenants, such as through virtualization schemes employing virtual machines or containers. Quality of Service (QoS) scrubbing logic and novel interfaces are provided to enable memory scrubbing QoS policies to be applied at the tenant, application, and / or process level. This QoS policies may include memory ranges for which specific policies are applied, as well as bandwidth allocations for performing scrubbing operations. A pattern generator is also provided for generating scrubbing patterns based on observed or predicted memory access patterns and / or predefined patterns.

The invention discloses a full-stack NPU system supporting a multistage fault mitigation mechanism, and the system comprises a plurality of NPU functional blocks which are used for executing a neural network calculation task; the plurality of hardware protection modules are used for providing error detection and correction capabilities for at least one NPU functional block in the NPU or a data path in the NPU; and a control mechanism for coordinating operation of the plurality of hardware protection modules. By means of the scheme, NPU functional block calculation errors or data path transmission errors caused by hardware faults and the like can be detected and corrected in time, wrong calculation results are prevented from being continuously used or output, and the accuracy of final results is guaranteed. Meanwhile, even if part of hardware breaks down, an error correction mechanism can also cover errors to a large extent, normal operation of the system is maintained, the overall availability and task success rate of the system are improved, and therefore the reliability and robustness of the NPU are remarkably improved.

The invention discloses a large model generation content traceability technology based on model copyright ID watermark embedding, which comprises the following steps that: firstly, a large model content generator compiles a dynamic watermark embedding process into an arithmetic circuit, and generates a watermark text and a proof by using a zero-knowledge proof algorithm; then the large model content generator publishes the text with the watermark and the proof together, and hides the copyright ID and the secret key; a large model content user verifies the proof and the watermarked text through a smart contract by adopting a zero-knowledge verification algorithm; after verification is passed, an adversarial sample corresponding to the text with the watermark is generated, an adversarial training method is used for optimizing the text with the watermark, and parameters of the Viterbi balance algorithm are dynamically adjusted and improved. According to the method, the concealment and the generation quality are balanced, high-capacity watermark embedding is realized, the tamper resistance and the accurate traceability of multi-model copyright disputes are improved, and the privacy protection intensity is improved.

Apparatuses, systems, and methods for enhanced metadata information. The memory array includes a number of column planes and an extra column plane. A memory device is set in an x4 single-pass operational mode. In this mode, the memory may store a data codeword in a selected ones of the column planes, and metadata may be stored in a non-selected ones of the column planes and in the extra column plane. An error correction code circuit (ECC) may store parity bits associated with the data and metadata in the non-selected ones of the column planes. In this manner, the data, metadata, and parity may be accessed as part of a single access of the memory array.

The invention provides a remote intelligent operation monitoring method and system for an intelligent substation, relates to the technical field of intelligent operation and maintenance of substations, and relates to multi-source heterogeneous sensing, depth feature modeling, fault prediction evaluation and model self-optimization. According to the method, electrical, environmental and meteorological data are collected through heterogeneous sensors, a structured original data set is constructed, time sequence prediction is carried out in combination with a convolution-LSTM model, a Transform fusion network is utilized to output a fault probability and a confidence interval, online early warning and response control are realized, and the method has a federated learning driven adaptive updating capability.

The present invention discloses an SSD solid state drive data storage method, which relates to the technical field of solid state drives, and comprises the following steps: a data receiving and classification step: receiving data to be stored from a host system, classifying the data based on a predefined data feature library through a data identification module, and dividing the data into system key data, application data and user common data, wherein the predefined data feature library contains typical file formats, data header information and access frequency feature information of each type of data; a storage area planning step: dividing the SSD solid state drive into a plurality of storage areas according to its internal architecture, including at least one high-speed and high-reliability core storage area for storing system key data, a plurality of medium-performance storage areas for storing application data and a large-capacity storage area for storing user common data; achieving significant beneficial effects such as efficient use of storage resources and safe and reliable data.

The invention discloses a power grid material sampling inspection management method for realizing whole-course tracing based on intelligent sample sealing, and relates to the technical field of electric power material management and intelligent perception, and the method comprises the following steps: S1, an initialization stage: executing a sample sealing coding segmentation pre-configuration process, distributing independent numbered segments to each sample sealing terminal, and the central control module synchronizes the use state of the terminal code to realize distributed isolation of code generation. According to the invention, coding uniqueness is realized through segmented pre-configuration and global duplicate checking, identity information security is guaranteed by adopting chip one-time write protection and tamper-proof technologies, and sample packet switching and data counterfeiting are effectively prevented by combining automatic acquisition and multi-round consistency comparison. Abnormal recognition and closed-loop processing ensure that abnormal events are frozen in time and responsibility is traced, and irregular samples are prevented from flowing into subsequent links. The whole-process auditing and dynamic optimization enable the management process to be self-perfected, and the intelligence, standardization and reliability of sampling inspection of the power grid materials are remarkably improved.

Apparatuses, systems, and methods for per row error correct and scrub (pRECS) information. There may be pRECS information associated with each row, and it may reflect a number of codewords stored along that row which were determined to include an error during error correct and scrub (ECS) operations. The memory may store the pRECS information in the memory array, for example, each row may store the pRECS information associated with that row.

The invention relates to the technical field of edge computing, and discloses an intelligent edge computing co-processing system based on an integrated circuit, which comprises a heterogeneous computing cluster module, a hardware computing unit set, a multi-core control processor based on RISC-V, a programmable pulse tensor computing array and a reconfigurable engine oriented to streaming processing, according to the intelligent edge computing cooperative processing system based on the integrated circuit, zero-delay data exchange is realized through silicon intermediate layer integration of the heterogeneous computing cluster module and a snakelike data channel, and traditional bus arbitration delay is eliminated through real-time operation code analysis and optimal optical communication path mapping of the hardware task routing matrix (TRF) module; the priority path distribution of the task scheduling subsystem is synchronously coordinated with the time-sensitive task, so that the collaborative efficiency of the computing unit is improved in multiple dimensions, the non-blocking transmission of the high-priority task is ensured, and the effect of enhancing the overall processing capability and response speed of the intelligent edge computing is achieved.

The invention relates to the field of distributed storage, in particular to an erasure code algorithm processing method for cold and hot fragmented data in a distributed storage system in a local area network or a wide area network, and solves the problems caused by using an erasure code algorithm to process cold and hot data in the prior art. The method comprises the steps of data block partitioning, a parameter strategy of data cold and hot judgment, a parameter strategy of node cold and hot judgment, data writing, an erasure code algorithm processing mechanism of different types of data, processing after erasure code algorithm processing, data cold and hot conversion processing and the like. Meanwhile, normal read-write performance is kept not affected by erasure code processing in the EC coding processing process, and the rationality of cold and hot data division in the distributed storage system is improved; layered storage and migration are performed according to cold and hot data, the read-write performance of distributed storage is considered, and the capacity utilization rate is increased; and the reliability, fault tolerance and performance of the storage system are improved.

The invention discloses an intelligent temperature monitoring device of a computer main board and a control method thereof, and relates to the field of temperature monitoring of the computer main board, the intelligent temperature monitoring device comprises a temperature sensing module which uses a plurality of high-sensitivity sensors in a key area and is high in precision and fast in response, a data transmission module transmits data at a high speed through Bluetooth or Wi-Fi and encrypts the data, and a control unit is multi-core heterogeneous and adjusts a temperature threshold according to an algorithm. The alarm module performs over-temperature acousto-optic alarm and has multiple modes, the heat dissipation control module intelligently adjusts heat dissipation equipment, the display module visually displays the temperature condition, the power management module supplies power in a voltage-stabilized mode and has stored energy backup, and all the modules cooperate to guarantee that the temperature of the mainboard is monitored, regulated and controlled accurately and efficiently. The device accurately monitors the temperature of the mainboard, the sensor is high in precision, the response is fast, data transmission is safe and efficient, control is intelligent, heat dissipation is reasonable, display is visual, power management is good, overheating can be effectively prevented, the service life of the mainboard is prolonged, the operation stability and safety of a computer are improved, and user experience is enhanced.

An apparatus for reliable fuse-based storage in an Integrated Circuit (IC) includes a plurality of electronic fuses and processing logic. The electronic fuses store (i) data bits, (ii) parity bits that were generated from the data bits in accordance with an Error Correction Code (ECC) scheme, and (iii) access information required for accessing the data bits and the parity bits. The processing logic receives a read command for reading given data bits from the electronic fuses, based on the read command retrieves access information specifying given electronic fuses storing the given data bits and given parity bits associated with the given data bits, using the access information reads the given data bits and the given parity bits from the given electronic fuses, applies the ECC scheme to the given data bits and the given parity bits, using the ECC module to generate corrected data bits, and outputs the corrected data bits.

A bank of a memory device may be divided into column planes. Each column plane may be associated with column selects. In some examples, a portion of a column plane associated with one column select may be used to store metadata associated with data of the remaining column selects. In some examples, the metadata may be mapped to the data based on a portion of a column address. In some examples, whether the memory device provides metadata responsive to a column address may be based on a value stored in a mode register. In some examples, the portion of the column plane associated with the one column select associated with metadata may also store error correction code data associated with the data of the remaining column selects.

The invention relates to a check point reading and writing method and device in distributed training, a storage medium and a program product. The method comprises the steps that in response to check point file generation and the number of check point files in a memory of a training node cluster corresponding to a target distributed training task does not reach N, an idle target memory is determined in the memory of the training node cluster, and the check point files are written into the target memory, the memory of the training node cluster comprises the memory of each training node in the training node cluster; and in response to a fault of any training node in the training node cluster, reading the target check point file from the memory of the training node cluster according to the metadata of the target check point file corresponding to the training node, and recovering training based on the target check point file. According to the method and the device, the check point file can be quickly read from the memory of the training node for recovery when the training node fails.

The invention provides a data interaction system and method, an electronic device and a storage medium, a data management module obtains operation parameter data from a plurality of graphics processors before a substrate management controller obtains operation state parameter data from the plurality of graphics processors; the plurality of graphics processors divide the operation parameter data into a plurality of data blocks, encapsulate the plurality of data blocks into a plurality of parameter data packets, and transmit the plurality of parameter data packets to the data management module in parallel through multiple channels; after the data management module receives a query instruction which is sent by the substrate management controller and is used for obtaining the running state data of the graphics processor, the data management module extracts the running parameter data cached locally based on a dynamic prefetching algorithm and sends the cached running parameter data back to the substrate management controller. Compared with the prior art, the time for directly interacting with the graphics processor by the baseboard management controller to obtain the data can be saved.