17 results about "Block detection" patented technology

The invention relates to a blocking detection method and device for a structured query statement, equipment, a readable storage medium and a program product. A first thread identifier and a second thread identifier are determined according to an active service table of a target database, a first service identifier of a first service requesting a lock and a second service identifier of a second service holding the lock, and a first structured query statement and a second structured query statement corresponding to the first thread identifier and the second thread identifier respectively are used for querying the first structured query statement and the second structured query statement. And structuring the time corresponding relationship between the query statements to obtain a blocking detection result. Compared with a traditional method of searching according to manual experience, the method has the advantages that the first thread identifier and the second thread identifier are determined by using the first service identifier of the request lock and the second service identifier of the holding lock, each structured query statement is determined by using each thread identifier, and the blocking detection result is determined by combining the corresponding relationship between the query statements; and the blocking detection efficiency of the structured query statement is improved.

The application discloses a kind of non-contact heart rate detection methods for face has sheltered scene, steps are as follows: input training video, face picture sequence is intercepted by face recognition;Training is based on the three-dimensional residual neural network of block;Extract the block weight based on correlation coefficient;Extract the block frequency domain feature clustering center;Input real video, face picture sequence is intercepted by face recognition;Extract the block depth feature by the three-dimensional residual neural network of block based on the training;Extract the block weight based on clustering;Extract block detection heart rate, the block weight based on correlation coefficient and the block weight based on clustering are weighted summation, obtain final detection heart rate.The application assigns different weights to the heart rate detection results of different facial regions, overcoming the problem of accuracy decline in non-contact heart rate detection method under the scene of partially occluded face.

The invention discloses a database fault self-recovery method, system and device and a storage medium, and relates to the field of computers.The method is applied to the database fault self-recovery system, a fault processing module is deployed between an application layer of the system and a database, and the method comprises the steps that in response to a data processing request, the state of a main database is detected through the fault processing module; and when the main database is in a fault state, controlling the fuse to be switched to a fusing state so as to transfer the data processing request to the standby database for processing. According to the application, the fault processing module is deployed between the application layer and the database, when the fault processing module detects that the main database is in the fault state, the fuse is controlled to be switched to the fusing state, so that the request is transferred and sent to the standby database to be processed, a complex switching process of a server cluster is not needed, and the processing efficiency is improved. Instead, the deployed module directly detects the fault, triggers the fusing and transfers the request, so that the switching time consumption can be shortened, and the service can still run continuously when the main database fails.

The application relates to an IO blocking detection method and device, computer equipment, a storage medium and a product. The method comprises the following steps: obtaining a current IO request number and a current IO request completion number from a system; the current IO request number is used for representing the number of IO requests to be processed at the current time; the current IO request completion number is used for representing the number of IO requests processed within a first preset time period before the current time; according to the current IO request number and the current IO request completion number, it is determined whether the system has IO blocking; if the system has IO blocking, an alarm information is output. By using the method, whether the system has IO blocking caused by software and hardware faults can be determined through the current IO request number, the current IO request completion number and a counting variable, the problem that the IO blocking detection accuracy is low in the traditional method can be avoided, and thus accurate IO blocking detection can be realized.

Data digitization via custom integrated machine learning ensembles is provided. For example, a system integrates multiple trained machine learning ensembles to identify, extract, and map data. The system receives a data set from sources. The system identifies ensembles can include machine learning models that can determine an outcome. The system filters a subset of data from the data set. The system identifies a layout for the data set based on a vendor type, data type, and the data set. The system executes a block detection module to identify blocks of the layout. The system executes a header detection module. The system executes a policy detection module to identify the headers as policies. The system transforms, based on the headers, the layout, the blocks, and the policies, the data set into a second file type, and presents the transformed data set for integration into a capital management system.

A system (100) for analyzing log data for operational and security anomalies is provided. A preparation module (120) prepares log data by determining the necessary volume, evaluating suitability, and categorizing it. A pattern discovery module (125) identifies structures in the log data using a plurality of methods. A patterned-message structure module ( 130) creates a structured format for each log message and computes hashes for data integrity. A processing module (135) stores and validates the structured logs. A message pattern curation module (140) customizes patterned messages and offers suggestions. A log message grouping module (145) organizes log messages into groups based on patterns and binding variables. An error identification module (150) detects and categorizes errors, highlighting operational and security issues. Finally, a log storage module (155) stores configurations, static log data, and variable content efficiently.

The application provides an information security event alarm level evaluation method and system, the method comprises the following steps: obtaining transmission data information of data in a transmission process, wherein the transmission data information comprises transmission message data and transmission content data; obtaining turning data information of data after turning, wherein the turning data information comprises turning message data and turning content data; obtaining security evaluation information according to the transmission data information and the turning data information; and obtaining a security level by processing the security evaluation information through a neural network. The information security event alarm level evaluation method and system provided in the embodiment of the application can monitor data flow and communication data on a network, accurately match network attack behaviors and other information security events by using a large number of rules, timely discover attacks and give early warnings, accurately evaluate security levels and block detection behaviors.

Embodiments of the present application relate to the technical field of kernel security, and provide a memory exception detection method and a computing device. The method comprises: in response to receiving a memory allocation instruction, determining a first memory block corresponding to the memory allocation instruction; detecting post-release use of the first memory block based on content in an isolated block and a data block corresponding to the first memory block; if it is detected that the first memory block has post-release use, filling the isolated block and the data block of the first memory block with a preset padding value; detecting out-of-bound access of a second memory block adjacent to the first memory block; and generating an exception detection record according to a detection result of the first memory block and a detection result of the second memory block. Based on the present solution, the computing device can detect the security status of a memory block to be called and its adjacent memory block in linkage when the memory is called, implement security protection of a process of using the memory of the computing device, reduce a blind area of memory exception detection, and improve the accuracy and response timeliness of memory exception detection.

To prevent monitoring accuracy of behavior of an object existing in a predetermined area from being affected by an environment in the area.SOLUTION: The monitoring control apparatus 30 is configured to monitor a behavior of an object in a predetermined area based on a captured image in which the area is captured, divide a predetermined range of the captured image into a plurality of blocks to detect a motion vector of each block, and determine, based on a direction and a magnitude of the motion vector of each block, whether an object moving in a doorway direction of the area is present in the captured image without detecting the object captured in the captured image.SELECTED DRAWING: Figure 1

The application provides a training system, a chip and a storage medium of a parallel bus link, the system comprising: a data detection module, configured to detect the validity of data signals corresponding to different phase stages in a plurality of preset phase stages of a clock phase, and obtain binary detection results of the data signals corresponding to the different phase stages; a maximum cluster detection module, configured to obtain a plurality of groups of binary sequences based on an initial binary sequence; obtain a target phase adjustment value based on a binary sequence corresponding to a maximum value in a plurality of values corresponding to the plurality of groups of binary sequences; and a clock module, further configured to adjust the clock phase based on the target phase adjustment value, and detect the validity of data signals corresponding to the adjusted clock phase through the data detection module, and when the data signals corresponding to the adjusted clock phase are detected to be valid, training of the parallel bus link is ended. Thus, fine adjustment of the clock phase is realized through the system.

The invention belongs to the technical field of computer hardware and memories, and particularly discloses a memory bad block parallel detection and repair system based on a multi-core processor. The system comprises a memory array, a multi-core processor, a system state monitoring module, a dynamic strategy generation module and a task scheduling and executing module. The system state monitoring module collects multivariate state parameters in real time; the dynamic strategy generation module calculates optimal scheduling control parameters in real time according to the state parameters on the basis of a reinforcement learning decision model; and the task scheduling and execution module schedules the multi-core processor to execute bad block detection and repair tasks of the storage area in parallel according to the parameters. According to the invention, adaptive global optimization balance of performance, power consumption and reliability in a dynamic change environment is realized.

The application provides a pipeline leakage detection method and system based on visual space-time, comprising: capturing a video using a fixed camera; adopting an adaptive block method to detect a target in a static single-frame image in the video to obtain a static target area; adopting a background difference method and an inter-frame filtering method to analyze dynamic characteristics of a space-time domain of the video to obtain a dynamic target area; and judging a position relationship based on the static target area and the dynamic target area, and simultaneously satisfying a region of space prediction and time sequence prediction, which is a real leakage area. The application comprehensively analyzes static characteristics and dynamic characteristics of a chemical pipeline video through an adaptive block detection algorithm, a background difference method and an inter-frame filtering detection method, realizes real-time dynamic detection of a small leakage, greatly reduces a false detection rate of the leakage, and simultaneously avoids manual detection of pipeline faults, thereby saving manpower and resources.

The invention provides a self-adaptive cross-scene target detection method, which comprises five core technical links: firstly, constructing a general enhanced block data set, and fusing public and scene special data sets to form a three-stage training data set; performing staged transfer learning on the pre-training model based on the training data set, and sequentially optimizing the block adaptability, the general recognition capability and the scene special detection performance; dynamically loading optimal parameters through a scene classifier; calling target scale knowledge to adaptively adjust the sizes of the sub-blocks (reducing when the target density is high and increasing when the target density is low), and respectively adjusting the detected sub-blocks; and results are integrated by adopting an area inverse proportion weighted fusion algorithm, so that the weight of a small-size sub-block detection result is higher. The method breaks through the bottleneck of small target detection through dynamic partitioning, realizes cross-scene migration in combination with a scene adapter, and remarkably improves the positioning precision of the small target.

Methods, systems, and devices for corrupt block detection for write ordering are described. A memory system may be configured to implement a sequential programming order or a reverse programming order based on dynamically reconfiguring memory dies to support storing data or parity data. For example, a memory system may receive a write command for writing data and parity data to a block stripe spanning the memory dies of the memory system. The memory system may determine whether the block stripe includes a corrupted block at a parity die of the memory system. The memory system may perform a write operation corresponding to the write command using the sequential programming order based on determining the block stripe does not include the corrupted block at the parity die, or the reverse programming order based on determining the block stripe includes the corrupted block at the parity die.

The invention discloses an NAND guide method and device based on bad block management, and provides a method for using an NAND FLASH as a guide function storage medium under the limitation of factors such as processor and FLASH model selection, single board layout, cost and the like, so that the hardware model selection range is expanded, the hardware design is facilitated, and the single board cost is reduced. Meanwhile, on the basis of the current situation of NAND FLASH, a zeroth block storage guide function core is fully used, a mechanism that the number of mirror images in multiple areas and the number of redundant blocks in each area can be dynamically set according to factors such as single board starting time, performance and ECC capacity is adopted, and a block-by-block bad block detection and bad block jumping mechanism is added when a guide function solidification version is sintered and a guide function is loaded in real time. A hardware timer and a mark function are introduced, a function of identifying an effective data area in a plurality of general transaction flow mirror image areas and a plurality of other dependent file areas is realized, effective data are dynamically loaded, and the problem that a guide function stored in NAND FLASH cannot be guided due to data damage caused by bad blocks of the NAND FLASH and bad blocks generated in a subsequent use process is solved.

The invention discloses a large language model automation unit test generation method based on segmented thinking chain driving. Firstly, a to-be-tested project is analyzed by using a tree-siter to construct an abstract syntax tree, and detailed context information of class and method levels is extracted and stored in a database. Then, a sectional thinking chain driving framework is adopted, a complex unit test generation task is decomposed into three subtasks which are executed step by step, a large language model is guided to carry out systematic test scene recognition from three dimensions of a typical scene, a boundary scene and an abnormal scene, and a detailed test case is generated based on the recognized test scene; and generating a unit test code which can be directly operated in combination with the test case information. On this basis, a multi-dimensional error repair mechanism is established, template-based local repair and large language model-based intelligent repair are performed for compiling errors and runtime errors, and meanwhile, an error recognition module is introduced to detect and terminate invalid repair cycles.