521 results about "Data slice" patented technology

The invention discloses an intelligent report generation method and system based on multi-source heterogeneous data fusion, and particularly relates to the technical field of data fusion, and the method comprises the steps: constructing a fact anchor point containing a stable identifier in a fusion layer, and solidifying an aperture version signature and a time interval; packaging a screening condition, a sorting rule, an access plan, time window limitation and the like into a controlled context packet and freezing the controlled context packet into a unique fact source; in the range, generating sentences by using a limited template and writing sentence-level reference marks, so that each sentence can be traced back to a data slice and an evidence fragment; performing consistency verification according to a source, an aperture and time, performing conservative degradation on the attribution expression according to a consistency threshold value, and outputting a degradation strategy description; and finally, page two-way pointer and cross-sentence consistency recording and audit playback are realized through an evidence chain pointer table and a published version signature, and the problems of caliber drift, cross-window access and difficulty in evidence tracing are solved.

The invention discloses a data slice recombination order-preserving method based on a Beidou short message, which relates to the technical field of data transmission and recombination optimization, and ensures the stability and low error rate of data in the transmission process by dynamically optimizing the size and quantity of slices according to channel bandwidth and delay through a self-adaptive slice adjustment mechanism. A unique serial number identifier and a first check code attached to each fragment are beneficial to identifying data under a complex channel condition, and errors and chaos in a recombination process are reduced. At a receiving end, a conflict analysis model is constructed by analyzing sequence offset and delay conditions of fragment arrival in real time, and a multi-layer correction instruction is provided according to an overall conflict rate and a time sequence consistency coefficient. The order-preserving mechanism ensures that the original order of the data can be maintained even in a complex network environment by preferentially processing the unlocked fragment and generating a retransmission instruction of the lost fragment. The combination of dynamic priority adjustment and a retransmission mechanism promotes the structure of the original data after recombination.

The invention discloses a data processing method and device. The method comprises the following steps: any computing node in a set communication cluster receives a first data slice sent by a previous computing node, executes a computing task according to the received first data slice, and splits to-be-sent task data independent of the computing task into a plurality of second data slices without a dependency relationship; the second data slices are sent to the next computing node in sequence, so that the next computing node receives the subsequent second data slices while executing the computing task according to the received second data slices; after the calculation task is executed according to all the task data in the calculation node, a target calculation result is obtained. According to the scheme, the overall efficiency of data transmission and calculation of the set communication cluster is effectively improved.

The invention relates to a large model data protection method and device based on a trusted environment, equipment and a medium. In the scheme, after a server side obtains original large model data, the original large model data needs to be encrypted and signed, and then the original large model data and an encrypted information table are issued to a target application side; when the target application end calls the large model data, performing exception check according to a credible strategy; if the check is passed, performing signature verification on each encrypted data slice; if the signature verification succeeds, decrypting the encrypted slice data by using the encrypted information table to obtain original large model data; wherein the server side and the target application side are both in a trusted environment. It can be seen that the trusted environment and the key system are combined, and a dual protection mechanism for large model data is achieved; moreover, before the large model data is called, the operation environment of the target application end needs to be subjected to exception check, so that an attacker is prevented from acquiring or tampering the sensitive data, and all-around data protection is provided for the large model data.

The invention discloses an intelligent monitoring and adjusting method and system for a steel structure, and the method comprises the steps: deploying a plurality of types of sensors at key parts of the steel structure, continuously collecting various types of physical quantity data, recognizing potential abnormal events in real time through a dynamic threshold detection mechanism, and responding to the abnormality of a certain sensor. The system automatically associates and extracts the time sequence data of other sensors associated with the spatial position of the sensor before and after the exception to form a multi-dimensional and time-space coupled data slice, and then performs time-space feature extraction on the data to extract the mode representation of the complex exception state; and based on this, accurate matching and diagnosis of the causes of the abnormal events are realized. Through the mode, the intelligent sensing and analysis capability of the health state of the complex, multi-source and multi-dimensional steel structure is improved, solid data support and decision basis are provided for subsequent scientific adjustment measures, and the operation safety and management intelligence level of large-scale steel structure engineering are better guaranteed.

The invention discloses a railway tunnel foreign matter detection method based on radar point cloud and RGB image fusion. The method comprises the following steps: acquiring a laser radar point cloud and an RGB image in a railway tunnel; obtaining an RGB image by using an adversarial network model GAN to judge the abnormal occurrence state of the foreign matter in the railway tunnel, and if the abnormal state of the foreign matter occurs, projecting the obtained laser radar point cloud to an image plane to obtain a dense depth map; processing through a sliding window to obtain data slices which are approximately square; and carrying out data feature extraction, fusion and foreign object target detection on the data slices of the RGB image and the dense depth map by using a fusion target detection network based on the OfficientNet-FPN, and outputting a foreign object target information detection result containing a target category, a position, a confidence coefficient and a distance. According to the railway tunnel foreign matter detection method based on radar point cloud and RGB image fusion, high precision, high efficiency and high reliability of railway tunnel foreign matter detection are realized.

The embodiment of the invention provides a data retransmission method, device and system. According to the embodiment of the invention, retransmission is carried out on a physical layer based on a data slice, the data slice comprises a code block carried by at least one FEC code word, and a corresponding serial number is allocated to each data slice in data. A sending device can store sent data slices and corresponding serial numbers in a buffer area while sending data. And after receiving the data, the receiving equipment distributes a corresponding serial number for each data slice according to the same way as the sending equipment. And furthermore, the receiving equipment sends a retransmission request carrying the serial number corresponding to the data slice after finding the uncorrectable error, and the sending equipment extracts the corresponding data slice from the buffer area according to the retransmission request and starts retransmission. It can be seen that the number of code blocks borne by the data slices is small, and compared with message-based retransmission, retransmission based on the data slices is more beneficial to reducing the transmission delay of retransmission and reducing the space of a buffer area.

The present invention relates to the field of health information processing technology, and specifically discloses an artificial intelligence-based student psychological data analysis and early warning system. The system sets a data set complexity determination module, a data slice determination module, a GPU compatible matching module, and a psychological data analysis and early warning module. The psychological health monitoring platform collects multi-source student psychological data sets, evaluates their comprehensive complexity, and determines the number of psychological data slices and their respective complexities based on this. The platform monitors the execution data of each GPU in real time, evaluates its initial execution quality indicators and compatible complexity. Subsequently, through data processing, the comprehensive complexity of the psychological data slices is matched with the compatible complexity of each GPU, and the allocation of psychological data slices to GPUs is completed. Finally, the platform uses this allocation relationship to analyze the students' psychological data, and ultimately achieves analysis and early warning of the students' psychological data.

The invention discloses an archive information processing method and system based on digital security, and relates to the technical field of computer application, and the method comprises the following steps: S1, constructing a time sequence consistency profile, and collecting the actual arrival time and the corresponding position index of each archive data fragment, a network jitter fingerprint model reflecting instantaneous delay fluctuation characteristics is generated based on multi-round transmission data; s2, in a data inbound process, injecting a unique position label for each archive data fragment, and enabling the position labels and fragment contents to jointly participate in Hash calculation to generate a content Hash and position Hash parallel dual verification baseline; according to the method, sequence verification and correction are realized by constructing a time sequence consistency profile and a dual Hash mechanism, the Hash collision risk is prevented in combination with disturbance verification, result write-back and model updating are introduced, a closed-loop control link is formed, and the safety, accuracy and traceability of file synchronization are remarkably improved.

The invention discloses a cloud storage service implementation method and system for unified management of multiple types of storage, and belongs to the technical field of cloud storage, and the method specifically comprises the steps: building a unified abstract model oriented to heterogeneous storage media, mapping distributed files, object storage and block storage devices into standardized storage units, deploying a cloud block chain, and carrying out uplink, a storage index table under a chain is constructed, an index state is dynamically updated through a loose coupling consensus mechanism, the loose coupling consensus mechanism comprises a weighted voting algorithm based on a storage node reputation value and an asynchronous verification mechanism based on space-time certification, and data is divided into data pieces and verification pieces; fragmentation information is written into a storage metadata registration chain and a service quality proof chain at the same time, cross-chain storage is carried out on data, and cloud storage resources are dynamically distributed; according to the method, the storage management function is decoupled through the block chain, so that the retrieval accuracy is improved, the retrieval response time is effectively shortened, and the data transmission cost is reduced.

The invention relates to the technical field of communication quality, and particularly discloses an acquisition terminal for communication quality monitoring, which comprises an electric energy meter interface module for reading an instrument value from an electric energy meter; the time synchronization module is used for aligning the terminal clock to the coordinated world time and generating a time frame with 1s as granularity; the communication quality monitoring module is used for measuring a communication quality index and calculating a value at the 95% position within a preset statistical time period as a statistical result preset statistical time period; the data slicing and counting module is used for forming 10T0 fragments according to 10 fundamental wave periods in each time frame; the caching and out-of-order correction module is used for performing grouping, out-of-order rearrangement and missing compensation on instrument values and communication quality indexes according to the same time frame before uploading and completing framing in a waiting window; and the alarm and uploading module is used for outputting a local alarm by taking the value of the 95% position as a statistical result. According to the invention, the overall end-to-end time delay of the acquisition terminal is reduced.

The invention discloses a high-speed rendering method and system for a three-dimensional large-scale dynamic net rack of a one-graph of a power grid. The method comprises the following steps: generating three-dimensional model parameters of a power transmission line according to tower account data; dynamically scheduling rendering of the three-dimensional model of the power transmission line according to the visible area range; slicing the power transformation multi-source heterogeneous three-dimensional data; optimizing a three-dimensional rendering pipeline of the power grid graph; and three-dimensional net rack high-speed rendering is carried out through visual cone removal, LOD and instantiation rendering technologies. According to the invention, based on the machine account data of the power transmission line, accurate rendering of the three-dimensional line on one graph of the power grid is realized through parametric modeling; for a multi-source heterogeneous transformer substation model, three-dimensional slices in a unified format are formed through a scene division technology, and support of a power grid graph on a three-dimensional slice rendering pipeline is expanded; in order to improve the rendering performance, dynamic rendering scheduling is carried out on the three-dimensional net rack in the viewport, repeated drawing of a large number of same devices is avoided, the performance overhead is reduced, and the performance bottleneck of loading the large-scale three-dimensional net rack is effectively solved.

The invention relates to a cross-network domain security data interaction method and device based on dynamic multi-channel and intelligent verification, computer equipment, a computer readable storage medium and a computer program product, and is applied to an external network domain. The method comprises the following steps: acquiring network state data of each available transmission channel between an internal network and an external network, and determining a transmission priority score of each available transmission channel so as to screen out a main transmission channel and a standby transmission channel; dynamically distributing to-be-transmitted data fragments to the main transmission channel and the standby transmission channel through a reinforcement learning algorithm; transmitting the data fragments distributed to the main transmission channel and the standby transmission channel to an isolation domain; the isolation domain is used for filtering the received data fragments through a hardware filtering engine and sending the filtered data fragments to the internal network domain; the internal network domain is used for verifying the integrity of the data fragments according to the fragment hash values corresponding to the data fragments. By adopting the method, the intelligence of data transmission in the internal and external network interaction process can be improved.

The invention belongs to the technical field of digital monitoring, and provides a three-party environmental protection management system and method suitable for an industrial park, environmental protection monitoring equipment is utilized to collect monitoring data in the park in real time, a blockchain network based on an alliance chain is established, Hash processing is performed before all monitoring data are transmitted, Hash values are recorded to the blockchain, and the monitoring data are stored in the blockchain. A secure transmission protocol and an encryption technology are adopted, the security and integrity of monitoring data in the transmission process are optimized, a large monitoring data block is divided into a plurality of small data fragments at a sending end to be transmitted, and a unique identifier and verification information are added to each monitoring data fragment. Verifying the integrity of each monitoring data fragment through verification information at a receiving end, reassembling the monitoring data, processing and analyzing the collected monitoring data by using the edge computing node, generating a monitoring data abstract, processing the monitoring data processed and analyzed by the edge computing node by using the cloud computing node, and sending the monitoring data abstract to the cloud computing node. And verifying the integrity of the monitoring data.

The invention relates to an electric power multi-source data slicing processing method and system based on reinforcement learning, and the method comprises the following steps: S1, obtaining electric power multi-source data, and carrying out the preprocessing of the electric power multi-source data, and obtaining a structured original data stream; s2, obtaining a multi-dimensional feature vector through feature extraction; s3, adopting a clustering algorithm to identify a behavior pattern to obtain a clustering label, calculating the distance from a sample to each clustering center, and when the distance is obviously higher than the average value plus threshold value beta times standard deviation of the cluster, additionally marking as abnormal; s4, intelligent slicing decision making is carried out based on reinforcement learning, and a slicing strategy, data and labels are obtained; s5, according to the obtained slicing strategy, data and label, through knowledge graph service rule verification, it is ensured that domain constraints are met; and S6, sending the verified strategy to distributed slice storage and management, and carrying out data slice storage and index construction according to the strategy. According to the method, the structured governance level and the high-value utilization capability of the power data assets are effectively improved.

The invention provides a key frame extraction method for a multi-modal large model. The multi-modal large model video data processing efficiency and generation quality are improved through cooperative computing of a user side and a cloud side. According to the technical scheme, the method comprises the following steps: 1) after a client application submits data, an interface service performs primary processing on the data, and video frames and audio frames are divided and merged into data pieces according to a time sequence; 2) dynamically extracting n key video frames in each data piece of the user side, and compressing, packaging and transmitting the n key video frames and corresponding audio frames to the cloud side; 3) the cloud performs coding fusion on the multi-modal data and then inputs a large model generation result; and 4) the cloud evaluates the generation quality, and the user side adaptively adjusts data preprocessing and key frame extraction parameter configuration according to the generation quality and the response time delay. By reducing redundant data transmission and dynamically optimizing calculation and transmission resources, the response speed and the output quality of the multi-mode large-model cloud service are remarkably improved.

The invention provides a UCIe standard protocol layer data transmission circuit, a related method and a chip, and the circuit comprises a slicing circuit which is configured to segment a data packet from a service port; sending the slice frame and slice description information associated with the slice frame to a framing device; the framing device is configured to fill a flow control unit Flit with the slice frames from the slice devices and slice description information associated with the slice frames, and send the Flit to the deframing device; the frame deframer is configured to determine slice frames belonging to the same service port from the Flit, and send the slice frames and slice description information associated with the slice frames to a packing circuit corresponding to the service port; and the packing circuit is configured to splice the data slices belonging to the same data packet in the slice frame based on the slice description information and output the data packet. The technical problem that extra delay is introduced due to beat insertion due to the fact that large multiplexers need to be introduced in existing protocol layer data transmission of the UCIe standard is solved.

The invention provides an environment monitoring method and system based on big data analysis, and belongs to the technical field of data processing, and the method comprises the steps: obtaining an original data flow corresponding to at least one monitoring node in an environment monitoring region, determining a target storage node of each data fragment according to the data type of the monitoring data in each data stream in the original data stream, and storing each data fragment based on each target storage node; processing the monitoring data in all the target storage nodes to obtain a data cube corresponding to the original data stream; determining a monitoring result of each environment state in the environment monitoring area according to the space-time grid data in the data cube; the monitoring result comprises distribution information corresponding to each environment state and pollution source positioning information, and the environment states comprise a normal state and an abnormal state. According to the environment monitoring method and system based on big data analysis provided by the invention, real-time and accurate monitoring of the environment can be realized.

The invention discloses an RAG knowledge base construction method and device and a medium, and the method comprises the steps: carrying out the analysis of the format, structure and semantic features of input data through a multi-level priority judgment logic, and automatically recognizing the specific data type. And calling a corresponding adaptive segmentation strategy for processing according to the identified data type, and generating data slices with semantic coherence. And asynchronously calling the large language model to generate a keyword set for each data slice, and uniformly storing the data slices, the keyword sets and associated multi-modal resource links by adopting a mixed storage mode to construct an enhanced RAG knowledge base. In the retrieval stage, query keywords are generated for user query according to the same logic, and related results are accurately retrieved from the knowledge base through a keyword matching mechanism. According to the method, the semantic segmentation problem is effectively avoided through intelligent segmentation with adaptive types, multi-modal data access is supported, and unified management and efficient retrieval of heterogeneous resources are realized.

The invention discloses a method and device for automatic roasting and process intelligent control of a belt type roasting machine, and relates to the technical field of pellet roasting of the belt type roasting machine in the metallurgical iron and steel industry. The method comprises the four steps of historical data slice acquisition and modeling, target process parameter determination, feedback verification and online correction and rolling prediction control, a model historical library is established through service slices and time slices, modeling is performed based on a three-layer feed-forward neural network, flexible weighted deviation calculation combined with dynamic weight and rolling optimization of a softening coefficient are combined, and the rolling prediction control is performed. And accurate regulation and control of equipment parameters are realized. The automatic control system is mainly used for automatically controlling the pellet production process of the belt type roasting machine, the product quality stability is improved, the energy consumption is reduced, the labor intensity of workers is relieved, and support is provided for unmanned and intelligent production.

The invention provides a data updating method and device, equipment and a storage medium, relates to the field of artificial intelligence, in particular to the technical fields of big data, machine learning and the like, and can be applied to the fields of smart finance and the like. According to the specific implementation scheme, to-be-updated data is obtained, and the to-be-updated data is fragmented to obtain a plurality of first data fragments; the first data piece represents a part of the data to be updated; storing the first data piece into a preset database; initial data is stored in the preset database, and the initial data is data before the to-be-updated data is updated; if it is determined that all the first data pieces are stored in the preset database, switching the data query version from the version of the initial data to the version of the to-be-updated data; the data query version represents the version of the data to be acquired when the preset database is subjected to data query. According to the invention, mixed use of new and old versions of data in the database can be avoided, and the data updating efficiency and the data query precision are improved.

The invention relates to the technical field of health information processing, and particularly discloses a student psychological data analysis and early warning system based on artificial intelligence, which is provided with a data set complexity judgment module, a data slice judgment module, a GPU compatible matching module and a psychological data analysis and early warning module. The psychological health monitoring platform evaluates the comprehensive complexity by collecting a multi-source psychological data set of a student, and determines the number of psychological data slices and the respective complexity according to the comprehensive complexity. The platform monitors execution data of each GPU in real time, and evaluates an execution quality initial index and compatible complexity of the GPU. Then, through data processing, the comprehensive complexity of the psychological data slices is matched with the compatible complexity of each GPU, and distribution of the psychological data slices and the GPUs is completed. And finally, the platform analyzes the psychological data of the students by using the distribution relation, and finally realizes analysis and early warning of the psychological data of the students.

The invention relates to a time sequence cross calibration method and system of a synthetic aperture radar. The method comprises the following steps: screening out a target area for time sequence cross calibration; sAR image data pairs are generated one by one in the time range; adopting a window median method to extract a median matrix of a data slice at a single moment, obtaining a median matrix array, carrying out robust regression fitting of abnormal value elimination, and generating a radiometric calibration coefficient array; and processing the radiometric calibration coefficient array according to the final radiometric calibration coefficient reference value to obtain a final radiometric calibration coefficient. Interference of abnormal values is avoided by performing robust regression fitting of abnormal value elimination on a median matrix array; the radiation calibration coefficient corresponding to each single moment is calculated, the correlation of the radiation calibration coefficients at different moments is taken as a reference, the radiation calibration coefficient value measured at the to-be-calibrated moment and the radiation calibration coefficient value at the previous moment are combined, the final reference value of the radiation calibration coefficient is determined, and the calibration accuracy is improved.

The present invention relates to a satellite-based method and system for processing ocean observation data. The method includes: obtaining ocean observation data of an ocean observation device within a set time; formatting the ocean observation data to obtain a plurality of data slices; arranging the plurality of data slices based on timestamps, and for each data slice, calculating the difference between this data slice and an adjacent data slice with a smaller timestamp, and defining the difference as the differential slice data corresponding to this data slice; for each differential slice data, performing: preprocessing the differential slice data to obtain preprocessed data, and determining whether the preprocessed data meets the similarity requirement. If so, defining this differential slice data as data to be processed, otherwise defining the preprocessed data as data to be processed; performing compression, encryption, and channel coding on the data to be processed to obtain data to be sent; and sending the data to be sent to a land observation station. The present invention can simplify ocean observation data and improve the security of transmitting ocean observation data.

The invention relates to the field of computers, and discloses a data slicing method, system and device and a storage medium. The method comprises the following steps: receiving a knowledge operation instruction uploaded by a user; creating knowledge entries through the selected knowledge parser according to the uploaded file, and generating corresponding knowledge identifiers for the knowledge entries; receiving a knowledge slicing instruction sent by a user; according to the specified knowledge identifier and the type of the uploaded file, slicing the content of the uploaded file through the selected knowledge slicing strategy to obtain a plurality of knowledge fragments; and respectively inserting the plurality of knowledge fragments into different types of databases to finish classification. In the embodiment of the invention, intelligent segmentation can be carried out on a large batch of data, and management of the large batch of data is completed, so that the AI large language model and the reference data of multi-modal pre-training are provided, and the training efficiency and effect of the AI large model are improved.

The invention belongs to the field of computer vision, relates to a rockfall monitoring method and system based on visual identification, and aims to solve the technical problems that an existing scheme is difficult to achieve good balance among reliability, accuracy and economy and is poor in application effect. The method comprises the following steps: acquiring video stream data and vibration waveform data of a target area; monitoring according to the vibration waveform data, and when the vibration energy exceeds a preset first threshold value, generating a primary vibration event trigger and extracting video data slices from the video stream data; inputting the video data slices into a target detection network to identify candidate moving targets in the video frames and extract kinematic features; performing spectral analysis on the vibration waveform data to determine a comprehensive confidence coefficient; and counting the number of candidate targets of which the comprehensive confidence exceeds a threshold value in a specific time window, and determining a current rockfall risk level. Based on the method, video detection and vibration detection are combined, and the actual application effect is better.

The invention relates to the technical field of intelligent manufacturing, and discloses a silicon carbide smelting whole process data linkage ERP management method, which comprises the following steps: collecting multi-source data of a silicon carbide smelting whole process in real time; constructing a dynamic tensor model, and updating node features based on a dynamic graph convolutional network; embedding a silicon carbide smelting process equation as a constraint condition into the tensor decomposition process, and solving a tensor factor matrix through a constraint optimization algorithm; performing incremental tensor decomposition on the newly added data slices, and updating real-time data change; dynamically adjusting weight coefficients of the process target and the business target according to real-time business requirements; and issuing the optimization instruction to production equipment. By constructing a four-dimensional dynamic tensor model, multi-dimensional data such as equipment operation states, process parameters and business requirements are uniformly mapped into structured feature representation, the problem of local optimization caused by data islands in a traditional method is solved, and high-precision data support is provided for global decision-making in the silicon carbide smelting process.

There is provided a computer-implemented method for diagnosing machine learning model underperformance or failures, comprising: embedding validation data samples and text string phrase candidates in a joint embedding space to generate data embeddings and text embeddings; receiving a trained machine learning model; conducting inference with the validation dataset using the trained machine learning model to obtain numerical metrics per validation data sample; generating a spatio-temporal relationship representation for the validation data samples; determining a plurality of data slices; and generating a semantic description for each data slice based on the text embeddings. There is further provided a computing system for diagnosing machine learning model underperformance or failures, computer programs, machine-readable storage media, data carrier signals and use of the computer-implemented method.

The invention relates to a data backup method and device, equipment, a storage medium and a program product. The method comprises the following steps: before executing data deletion processing, detecting whether a data slice corresponding to a fingerprint marked as a to-be-repaired state meets a data integrity condition or not; if at least one data slice corresponding to the fingerprint marked as the to-be-repaired state does not meet the data integrity condition, switching the data backup mode into a full backup mode; if the data slices corresponding to the fingerprints marked as the to-be-repaired state all meet the data integrity condition, switching the data backup mode into a first backup mode; wherein in the first backup mode, incremental backup of preset backup times is executed; and in the data backup process, performing backup processing on the target data according to the data backup mode. By adopting the method, the data backup flexibility can be improved.

The invention discloses a quantum random encryption and distributed synchronization information transmission method and device, and is applied to the technical field of power systems. Acquiring initial data of the state of the power equipment; generating an unpredictable quantum true random number; encrypting the initial data by using the quantum true random number to obtain target data; synchronizing the clock synchronization precision of the plurality of nodes; generating data slices according to the target data and distributing the data slices to the plurality of nodes; and synchronizing the data slices of the plurality of nodes. According to the invention, the technical problem of safe and reliable information transmission of the power system is solved at least to a certain extent.