21 results about "Data degradation" patented technology

The invention discloses a multi-level cache optimization method and system based on data popularity self-adaptive adjustment, relates to the technical field of data storage, and aims to solve the problems that a cache mechanism in an existing desktop cloud system is static, initialization is slow, and cold and hot data are difficult to recognize. The adopted scheme comprises the steps that a three-level cache structure of a local cache L1, a distributed cache L2 and a database L3 is set; constructing a data popularity scoring model based on the access frequency, the time decay and the user priority; dividing the data into hot data, temperature data and cold data according to the popularity score, and dynamically determining migration and elimination strategies of the data in L1, L2 and L3; the hot data are loaded to the upper-level cache T in advance through a prediction mechanism, and container-level cache preheating is supported; a cache preheating strategy is integrated to a Kubernetes life cycle, and hot data loading and cold data degradation are achieved; visual strategy configuration and monitoring during operation are supported, and cache strategies are managed in real time. The method is suitable for an access optimization scene of a large-scale desktop cloud system.

The invention discloses a distributed photovoltaic user power behavior monitoring method and system. The method comprises the following steps: acquiring multi-source time sequence data of a distributed photovoltaic user side; based on the multi-source time sequence data, the self-supervised time sequence model generates an abnormal feature sample through a multi-channel time sequence data degradation strategy; meanwhile, the power behavior feature extraction model extracts power behavior features from the multi-source time sequence data to obtain a power behavior feature sample; carrying out weighted fusion on the abnormal feature sample and the power behavior feature sample by adopting a cross-layer feature fusion strategy to obtain a fusion feature; the fusion features are used for optimizing supervised learning model parameters based on different tasks, and an optimized supervised learning model is used for monitoring. According to the method, the power utilization characteristics and the power generation behaviors of the photovoltaic users are described more accurately, and the potential abnormal operation state is identified in time.

The invention particularly relates to a dynamic form data binding method. According to the dynamic form data binding method, the dynamic association between the field and the control is realized through visual configuration, the control type is automatically adapted when the field is changed, and code logic does not need to be manually intervened; a global binding relation matrix is introduced based on graph structure storage, the mapping relation between fields and controls is managed in a centralized mode, and when the fields are added, deleted and modified, a control adaptation and data degradation strategy is automatically triggered; and establishing bidirectional binding of a data model and a control based on a responsive framework, and dynamically generating an API response format in combination with JSON Schema to realize real-time synchronization of field addition, deletion, modification and check. According to the dynamic form data binding method, the data binding code can be automatically generated, so that the code writing workload of a developer is greatly reduced, the project development period is shortened, the business demand change can be quickly responded, real-time data interaction can be realized, and the fluency and convenience of filling the form by a user are improved.

The invention discloses an enterprise information intelligent management method and system based on big data, and relates to the technical field of big data analysis, and the method comprises the steps: carrying out the data degradation analysis processing of to-be-managed enterprise information; and carrying out comparative analysis processing on the data degradation difference value. According to the method and the device, the data degradation difference value is determined by performing data degradation analysis on the enterprise information to be managed, the data degradation difference value can display the access times and the access frequency of the enterprise information, and the calling condition of the enterprise information can be determined by performing comparative analysis on the data degradation difference value subsequently; in addition, even if the number of calling times of some enterprise information is small, the enterprise information cannot be directly deleted, data or blood relationship analysis needs to be carried out on the enterprise information, whether the enterprise information has relevance with other enterprise information which does not reach the retention period or not is determined, and if the enterprise information has relevance, the enterprise information which does not reach the retention period needs to be deleted. And the data are also reserved, so that the integrity of the data during calling is ensured.

The application discloses a control method and system suitable for close-range desktop AR interaction, and relates to the technical field of artificial intelligence. The method comprises the following steps: monitoring the spatial correlation quality index of a binocular perception module in real time, and switching between a first interaction mode and a second interaction mode according to the index; in both modes, the spatial distance parameter of a target interaction object is calculated based on binocular spatial correlation calculation, and an interaction control instruction is generated according to the parameter to update an AR display terminal, wherein the first parameter configuration is used to preferentially ensure spatial positioning accuracy at a normal distance, and the second parameter configuration is used to enhance the adaptability to spatial data degradation in a close-range interaction area, so as to maintain the reliability of spatial positioning and suppress the jitter of the interaction control instruction. The application guarantees the interaction continuity, availability and immersion of close-range desktop AR interaction in a complete working distance range.

The application discloses a fraud-related short message identification method, system, device and medium based on a pruned large model, which comprises the following steps: performing data degradation on short message data; searching for a vector short message similar to the degraded short message from a fraud short message vector knowledge base; selecting a vector short message with a required similarity from the similar vector short messages; inputting the degraded short message and the vector short message with the required similarity into a pruned large model to obtain a final identification result. Through the degradation algorithm, the application reduces the data pressure of the large model reasoning, improves the model identification capability through the way of searching for similar short messages, compresses the model parameters by using the pruning technology, and reduces the influence caused by the model compression by using the post-training mode, so that the fraud-related short message can be quickly and accurately identified, and the recall rate and accuracy of the fraud-related short message are improved.

The application relates to the field of automatic driving point cloud attack, and discloses an automatic driving point cloud attack method, system, medium and equipment based on data degradation feature constraint, which comprises the following steps: constructing a vehicle point cloud sample library to be injected based on vehicle point cloud data under ideal acquisition conditions; screening a candidate region, combining a sensor field of view and ground constraints to determine an initial injection pose of the vehicle point cloud sample to be injected; extracting a degradation scene style feature of a target scene point cloud and mapping the degradation scene style feature into a degradation control parameter used for controlling a point cloud degradation mode; performing degradation processing on the injected vehicle point cloud according to the degradation control parameter to generate an adversarial point cloud sample consistent with the target scene degradation style; constructing an optimization objective function containing attack effectiveness, style consistency and point number budget constraints; and through a two-stage iterative optimization process, adjusting the selection of injection points, space pose parameters and the degradation control parameter, and feeding back optimization updates until an adversarial sample meeting the constraint conditions is generated.

The invention relates to the technical field of solid state disk testing, and discloses a method and a system for testing data retention of a solid state disk. The testing method comprises the following steps: selecting a to-be-tested solid state disk, and initializing a testing environment of the solid state disk; writing test data into the solid state disk, and performing whole disk data verification and bad block scanning to obtain a first verification result and first bad block information; performing high-temperature processing on the solid state disk, and then performing cooling processing on the solid state disk; performing secondary full-disk data verification and bad block scanning on the solid state disk to obtain a second verification result and second bad block information; evaluating the data retention according to the first verification result, the second verification result, the first bad block information and the second bad block information; a data degradation process is accelerated through a high-temperature environment, the data retention of the solid state disk under a long-term high-temperature storage condition is simulated, and data errors and bad block growth conditions are quantified through full-disk data verification and bad block scanning, so that the data retention is comprehensively evaluated.

An information processing system capable of suppressing data degradation caused by the generation of unprocessed data and the passage of time is provided. The information processing system (1) includes a buffer unit (5), a processing unit (6), and a control unit (9). The buffer unit (5) sequentially receives data acquired by the observation terminal (2) from the observation terminal (2). The processing unit (6) sequentially processes the data received by the buffer unit (5). The control unit (9) controls an index value representing information freshness by adjusting the processing resources of the processing unit (6). The index value representing information freshness is calculated based on the elapsed time since the time when the data was acquired by the observation terminal (2).

Systems, methods, and instrumentalities are disclosed herein associated with transmission of demodulation reference signals (DMRS) in wireless systems. DMRS symbols may be placed in a slot for channel estimation operations. For example, channel estimation performance may be enhanced based on using an increased number of DMRS symbols. Channel estimation enhancements may be performed by transmitting DMRS symbols in an extended slot, for example, to avoid data degradation (e.g., which may result from replacing data transmission symbols with DMRS symbols). An extended slot may be a slot that includes symbols spanning across multiple slots (e.g., two slots, consecutive slots), for example, such as a slot n and a slot n+1.

The invention belongs to the field of memory control, and provides a memory control method and a storage device.The method comprises the steps that a maintenance strategy setting command from a host system is received, and the command is used for configuring maintenance strategy data for a specified logic area; updating a maintenance record in a key data maintenance table according to the command, wherein the maintenance record comprises maintenance strategy data and dynamic metadata of a logic region; dynamically selecting a target logic area from the logic areas associated with the key data maintenance table, and executing background health examination on the target logic area; and according to a health examination result, triggering a grading repair action on the target logic region. According to the method, the reliability strategy is flexibly defined by the host, closed-loop management of health monitoring and hierarchical repair is autonomously executed by the storage device, the data degradation trend can be actively found and processed, and the reliability and maintainability of the storage device are effectively improved.

This invention proposes a multi-feature adaptive fatigue detection method and system for inland waterway crew members in complex environments, relating to the fields of computer vision and intelligent navigation safety. It includes a video enhancement preprocessing module, a spatiotemporal fatigue feature extraction module, an individual adaptive judgment module, and a system integration output module. On a shipborne edge computing device, a "Collaborative Reflectance Perception Low-Light Enhancement Network CRL-Net" is designed to address the data degradation problem in low-light and strong reflective environments. An "End-to-End Spatiotemporal Fatigue Feature Learning Network RMT-3DCNN+LSTM" is designed to achieve the fusion extraction of three types of features: pupil movement, eye state, and fatigue expression. Combining AIS system data and individual crew member alertness benchmarks, a dual-branch adaptive weighting mechanism is used to determine and warn of fatigue status. This invention achieves accurate adaptive detection of crew member fatigue status in complex inland waterway navigation scenarios, providing an efficient and reliable intelligent monitoring solution for improving the navigation safety of inland waterway vessels.

The application discloses an energy consumption optimization scheduling method and system for automobile stamping production lines, relates to the technical field of automobile production line scheduling, and a scheduling instruction generation module calculates a recommended moving path that makes the adjustable domain of the equipment completely cover the energy consumption scope, extracts the maximum change trend and the maximum change rate of history and real time, dynamically compensates data degradation, generates a hierarchical scheduling instruction according to a threat level parameter when the real-time change rate or trend deviates from the historical benchmark to trigger an abnormal condition, an optimization module constructs a fitness function based on the trend and abnormal analysis result, optimizes the scheduling parameters, makes the fitness function correspond to the optimal parameter configuration when the fitness function is at the minimum value, and feeds back the optimal parameter configuration to a production line energy consumption operation situation model. The scheduling system forms a complete technical chain from spatial perception, situation modeling to dynamic scheduling and closed-loop optimization, effectively reduces the energy consumption management misjudgment and missed judgment risk under the premise of ensuring the safety of the equipment.

The application discloses a metadata backup method and device, equipment and medium, and relates to the field of data storage. The method comprises the following steps: after target data is stored into a mechanical hard disk in a preset operating system by using a first block device in a preset object storage engine, target metadata corresponding to the target data and stored in a preset database is stored into a preset metadata storage partition in a target disk array based on RAID10 in the preset operating system by using a second block device in the preset object storage engine; and the target metadata is backed up into a target backup partition corresponding to the preset metadata storage partition and based on RAID1 in the target disk array. In this way, the application can reduce the data reconstruction amount when the disk is damaged, reduce the data degradation time, maximize the guarantee of data safety and reliability, and reduce the influence of the failure stage on normal business.

A system and method for for determining a degree of point cloud data degradation of a LiDAR sensor of a Self-Driving Car (SDC) using a machine-learning algorithm (MLA) are provided. The method comprises: determining, based on a training point cloud generated by the LiDAR sensor representative of surroundings of the SDC, a plurality of LiDAR features; determining, for each training object in the surroundings, based on statistical data of coverage of training objects with LiDAR points, a plurality of enrichment features; receiving a respective label indicative of a degradation degree of the training point cloud; generating, based on the plurality of LiDAR features, the plurality of enrichment features, and the respective label, a given feature vector of a plurality of feature vectors; training, based on the plurality of feature vectors, the MLA to determine an in-use degree of degradation of in-use sensed data further generated by the LiDAR sensor.

The invention relates to the technical field of digital image processing, and discloses a video super-resolution method, device and equipment and a storage medium. The method comprises the following steps: inputting a high-quality video into a low-quality video automatic generation network for frame-by-frame processing to obtain a low-quality video; inputting the random noise image and the low-quality video into an optical flow action denoising network, training the optical flow action denoising network by using the low-quality video and the random noise image, and dynamically adjusting optical flow guidance and vector similarity guidance to obtain a trained optical flow action denoising network; and obtaining a low-quality video to be super-divided, and inputting the low-quality video to be super-divided into the trained optical flow action denoising network to obtain a super-divided high-quality video. According to the method, the video of a specific type can be pre-trained, relatively effective data degradation is generated, and optical flow guidance and vector similarity guidance are dynamically adjusted, so that a better super-resolution effect is generated.

The application relates to the technical field of solid state disk testing, and discloses a data retention force testing method and system for a solid state disk. The testing method comprises the following steps: selecting a solid state disk to be tested, initializing the testing environment of the solid state disk; writing testing data into the solid state disk and performing full-disk data checking and bad block scanning to obtain a first checking result and first bad block information; performing high-temperature treatment on the solid state disk and then cooling the solid state disk; performing secondary full-disk data checking and bad block scanning on the solid state disk to obtain a second checking result and second bad block information; evaluating the data retention force according to the first checking result, the second checking result, the first bad block information and the second bad block information; accelerating the data degradation process through a high-temperature environment, simulating the data retention force of the solid state disk under long-term high-temperature storage conditions, and quantifying the data error and the bad block growth through full-disk data checking and bad block scanning, so that the data retention force can be comprehensively evaluated.

The invention provides a detector non-uniform data set manufacturing method and device, electronic equipment and a medium, and belongs to the field of detector imaging. The method comprises the following steps: acquiring a detector response image and a scene image; determining an ideal pixel value according to the detector response image, and preprocessing the scene image to obtain an original image; and constructing a data degradation model according to the ideal pixel value, performing data degradation processing on the original image according to the data degradation model to obtain a noise image, and constructing a detector non-uniform data set according to the noise image and the original image. According to the invention, data degradation processing is carried out on the high-quality scene image by using the data degradation model constructed by the detector response image, so that the high-quality detector non-uniform data set containing various non-uniform characteristics can be generated with high efficiency and low cost.

A computer-implemented method for generating synthetic training data for training a machine learning segmentation algorithm (1) of a software module, wherein the software module is configured for generating a segmentation mask of an image acquired by a medical imaging modality, the method comprising the steps of receiving a ground truth segmentation mask (4) identifying by means of labels at least one major structure generating synthetic image data (5) by filling the synthetic image data with image patterns according to the labels of the ground truth segmentation mask (4); downgrading the image quality of the synthetic image data (5) to obtain an input training image (6); and providing the input training image (6) together with the ground truth segmentation mask (4) as a training pair for the training data.

A computer-implemented method for generating synthetic training data for training a machine learning segmentation algorithm (1) of a software module, wherein the software module is configured for generating a segmentation mask of an image acquired by a medical imaging modality, the method comprising the steps of receiving a ground truth segmentation mask (4) identifying by means of labels at least one major structure generating synthetic image data (5) by filling the synthetic image data with image patterns according to the labels of the ground truth segmentation mask (4); downgrading the image quality of the synthetic image data (5) to obtain an input training image (6); and providing the input training image (6) together with the ground truth segmentation mask (4) as a training pair for the training data.

A system identifies degraded positional information by combining data from Global Navigation Satellite System (GNSS) and Alternative Positional Sources (APS) such as inertial measurement units. GNSS sources generate both location data and associated metadata, while APS provide additional location reports. An error modeling module computes an APS error covariance correction to indicate the accuracy of APS data. A primary classifier receives GNSS and APS reports along with the covariance correction and performs a generalized likelihood ratio test to assess GNSS data degradation. Simultaneously, a secondary classifier analyzes GNSS metadata to produce an independent degradation assessment. A degradation aggregation module then validates the primary assessment using the secondary one, ultimately generating a comprehensive GNSS positional degradation report that identifies and confirms instances of degraded location accuracy.