837 results about "Data fragment" patented technology

The invention relates to a remote education data processing system which comprises the following steps: under a remote teaching task, pre-defining a task intention and a data expectation point; a semantic timestamp and a task binding label are printed on each data fragment; mapping the collected confusion data including silence, eye movement drift and prediction into a unified learning semantic vector; a micro-expression + interactive behavior + time sequence decision path ternary modeling mode is introduced, and a potential cognitive intention corresponding to the feature combination is recognized; teaching context information is fused; constructing a cognitive state mapping model; reasoning a current cognitive state label from multi-modal sensing data; searching intervention track VS effect feedback data in a historical database; generating a predicted intervention behavior sequence by using a sequence modeling algorithm; a dynamic combination suggestion chain including light prompt, content reconstruction, personalized practice and tutoring invitation is adopted; and superposing the cognitive state sequences of all students into a group cognitive trajectory map.

The invention discloses a financial data privacy protection system based on block chain security multi-party computing, and belongs to the technical field of data protection, and the system specifically comprises the steps that a data preprocessing module segments original financial data, and adds a unique watermark identifier; the distributed account book stores the Hash abstract and watermark mapping relation of the encrypted logic data fragment, and the digital signature and the timestamp are fused to ensure reliability; the computing node cluster receives data through a special interface, dynamically builds a computing group according to a service rule and outputs an encrypted computing result; bidirectional authentication is carried out on the zero-knowledge proof verifier and the computing node cluster to verify the data holding right; the encryption proxy gateway processes an output result of the computing node cluster to complete format conversion and ciphertext superposition; the cross-link routing module constructs a virtual coverage layer, cross-link transmission of encryption results is achieved through protocol conversion and three-way handshake verification, and a heterogeneous chain protocol converter and a verification assembly guarantee transmission safety; the financial data privacy security is comprehensively guaranteed, and the data processing efficiency and reliability are improved.

The invention provides a performance optimization method and system for cross-data-source paging query, and relates to the technical field of data processing, and the method comprises the steps: calculating a data distribution weight and a data source routing table, determining an optimal data access path, and splitting a paging query request into sub-query tasks; dynamically adjusting the number of parallel query threads based on the load state and distributing sub-query tasks; establishing a local index and adaptively adjusting an index structure; dynamically adjusting the connection number of the connection pool and acquiring data in batches; performing feature analysis according to a query result, determining the size of a data fragment, and selecting an optimal merging strategy to merge the data; constructing a cache access mode graph, and aggregating and storing related data based on the access association degree; and optimizing a performance strategy set through reinforcement learning, adjusting a cache updating interval, a data access path and a feature matrix, and finally obtaining a query result. According to the method, the cross-data-source paging query efficiency can be effectively improved, the query delay is reduced, and the overall performance of the system is improved.

The invention provides a data optimization processing method and system for edge calculation in architectural design. According to the method, geometric deviation and attribute change characteristics between building design models are obtained, and a version conflict identification graph for marking high-conflict areas is generated through topological relation comparison; deploying an incremental synchronous controller at an edge computing node of a physical layer, dynamically intercepting a high-conflict area association difference data fragment and compressing the high-conflict area association difference data fragment into an incremental data packet; analyzing a dependency topological relation between the models to predict a conflict propagation path; converting the path into a combined operation instruction conforming to a design rule through a collaborative modeling engine; and executing the instruction to integrate the difference, and outputting a conflict-free multi-building design integrated model after the models are fused. According to the method, second-level dynamic resolution and automatic fusion of building model conflicts are realized, the design iteration efficiency is improved, and the construction rework risk is eliminated.

The invention discloses a data fragment storage index optimization method and system, and relates to the technical field of data fragment storage, and the method comprises the steps: determining an observation time period according to a data access mode and short-term fluctuation characteristics; monitoring data performance, and performing log data analysis; and performing data resource adjustment, and adjusting task allocation. According to the method, data loading is efficiently managed and adjusted, the user experience and reliability of a system are improved, the importance of a dynamic data management method based on the observation time period is highlighted, hot spot fragments can be recognized in time when the load is too high through setting of a dynamic threshold value, and therefore effective measures are taken, and the workload of a user is reduced. According to the method and the device, the waste of system resources is reduced, the use efficiency of the resources is improved, data operation can be completed under the condition that a main thread is not interfered, the data transmission efficiency and the storage utilization rate are improved, and the overall processing capacity and fluency of the system are improved.

The invention relates to the technical field of environment monitoring, in particular to an intelligent environment monitoring system based on the Internet of Things, which comprises a data quality checking module, a data exception correction module, a data association analysis module, an exception monitoring marking module and an environment early warning transmitting module. According to the method, the checking of data equipment numbers and time labels and the filtering of abnormal or discontinuous data fragments are emphasized, so that the integration of the data is improved, the calling and mode analysis of historical data are carried out, and the abnormity is identified and corrected, and the data better meet the requirements of a time sequence mode; according to the method, key monitoring item combinations can be accurately identified and recorded in data correlation analysis, so that the real-time analysis and response capability of monitoring data is remarkably improved, the adaptive capacity to environmental changes and the early warning accuracy are greatly enhanced through timely marking of abnormal states and rapid release of early warning information, and the early warning efficiency is improved. And the efficiency of environment monitoring and decision support is optimized.

The invention relates to the technical field of equipment anomaly detection, and discloses an anomaly detection method and system for an intelligent motor, and the method comprises the steps: collecting multi-source heterogeneous data when the intelligent motor works, carrying out the feature-level fusion, and carrying out the potential local learning, and obtaining optimized data features; performing data fragment segmentation on the optimized data features to obtain segmentation features, and determining a time-frequency feature spectrum of the segmentation features under multiple resolutions; mining a feature association relationship of the multi-dimensional operation features of the intelligent motor to construct a reinforcement learning strategy, and performing target feature screening on the multi-dimensional operation features of the intelligent motor to obtain a screened feature subset; and carrying out confrontation generation processing on the screening feature subset to obtain a simulation sample, carrying out feature ensemble learning processing on the simulation sample and the screening feature subset to obtain a judgment feature, and carrying out anomaly analysis on the intelligent motor to obtain an anomaly detection report. According to the invention, the detection precision of non-obvious or weak-feature anomalies in the intelligent motor can be improved.

The invention relates to the technical field of mobile communication transmission, and discloses a heterogeneous network multilink parallel mobile communication transmission method and system. A dynamic link aggregation module of the system collects link state parameters including signal strength, delay jitter and packet loss rate from a plurality of heterogeneous network interfaces in real time; a flow distribution decision module calculates available bandwidth weight of each link according to the data and generates a dynamic flow distribution strategy; the data fragment packaging module splits to-be-transmitted data into a plurality of data fragments according to the strategy, and adds a sequence identifier and a link identifier to each fragment; the multi-channel concurrent transmission module sends corresponding data fragments in parallel through a specified heterogeneous network link; and the receiving recombination verification module sorts the recombination data fragments according to the sequence identifiers, and detects the data integrity through a verification mechanism. According to the system, a set of complete multi-link parallel transmission solution is formed, and the problems existing in a traditional single link and an existing multi-link transmission technology can be effectively solved.

The invention belongs to the technical field of data management, and discloses a research and development data traceability and collaborative management system based on a block chain, and the system comprises a sensing layer which is used for collecting multi-source research and development data on various research and development tool chains through a multi-protocol collection agent, carrying out the structural packaging of the data to generate metadata, and embedding a data blood relationship tracking mark in the metadata; false data filtering is carried out on the metadata by deploying a trusted execution environment; the chain network layer is used for dividing the filtered metadata into data fragments and respectively storing the data fragments in a distributed storage platform; applying a Hash algorithm to each data fragment to generate a corresponding Hash value; taking the hash values corresponding to all the data fragments as leaf nodes, constructing a fragment hash tree, and further obtaining global root hash; the security of research and development data is ensured, and the cooperation efficiency is improved.

The invention discloses a system fault diagnosis method and device, a medium and a program product, and relates to the technical field of computers, and the method comprises the steps: extracting fault data fragments for system fault diagnosis from a system database based on fault description information, reducing the influence of huge and redundant information amount on useful information extraction, and improving the system fault diagnosis efficiency. According to the system fault diagnosis method and device, the fault knowledge fragments used for system fault diagnosis are extracted from the system knowledge base based on the fault description information, fault diagnosis is conducted according to the fault data fragments and the fault knowledge fragments, experience dependence is reduced, the technical problems that the system fault diagnosis process is tedious and low in efficiency are solved, and the technical effect of accurate fault diagnosis is achieved.

The invention relates to the field of network security, and discloses a network abnormal traffic monitoring and attack defense system based on artificial intelligence, which comprises a data preprocessing module used for receiving original traffic data, executing cleaning, duplicate removal and normalization processing, and dividing a processing result into data fragments according to a preset time window; the network flow analysis module is used for receiving the data fragments, performing feature extraction and mode analysis and outputting an analysis result representing the flow abnormal degree, and the analysis result at least comprises a score value representing the overall abnormal degree and a feature vector representing the flow mode feature; the attack judgment module is used for comparing the score value with a preset abnormal threshold value so as to judge whether network abnormal traffic exists or not, if yes, the feature vector is further matched with a pre-constructed abnormal traffic type feature library, and a specific abnormal traffic type is determined according to a matching result; and the attack defense module is used for automatically triggering and executing a corresponding defense strategy.

The invention relates to the field of quantum storage and cloud computing fusion, and discloses a big data distributed storage method and system based on cloud computing, and the method comprises the steps: revealing a data intrinsic geometric structure through manifold embedding, and generating a manifold coordinate; secondly, constructing an Ising physical model based on the geometric structure, and solving a model ground state through quantum annealing to determine an optimal data dynamic fragmentation scheme; thirdly, homomorphic encryption is carried out on the data fragments, a Lie group optimization algorithm is used for solving on a special orthogonal group manifold, and a resource mapping matrix considering safety and efficiency is generated; and finally, instructing a fragmentation scheme, constructing a transmission tensor load fusing manifold information, data content and security mapping for each fragment, and optimizing transmission configuration. According to the method, multiple frontier technologies are fused, dynamic fragmentation of data structure sensing and safe and efficient resource mapping are achieved, and the overall performance and safety of the system are remarkably improved.

The invention relates to a distributed data security storage and encryption system based on an AI technology. The system comprises a data acquisition and preprocessing layer used for acquiring original data and preprocessing the original data to obtain an identifiable data object; the AI identification and strategy decision layer is used for identifying data features of identifiable data objects based on an interpretable AI model to obtain an encrypted instruction packet; the encryption execution and distributed storage layer is used for performing encryption storage processing on the original data content according to an encryption instruction packet to obtain data access interface information; and the dynamic access control layer is used for obtaining user information in response to a data access request instruction of a user, recombining and decrypting the data by using the data distribution mapping index based on the access control strategy table, and returning a plaintext data fragment. By adopting the system, a high-strength, differentiated and multi-level security mechanism can be realized through the AI, intelligent encryption strategy decision is carried out, and the security requirements of multiple scenes and multiple types of data are met.

The invention relates to the technical field of network security of a power monitoring system, and discloses an attack event prediction method and system based on low-intensity abnormal mode data, and the method comprises the steps: intercepting abnormal data fragments from historical multi-source heterogeneous data based on the low-intensity abnormal mode data selected from the historical multi-source heterogeneous data; analyzing to obtain a target abnormal data fragment; training the gating loop network model based on target key time sequence abnormal features obtained by performing feature extraction on the target abnormal data fragments to obtain a future time sequence feature prediction model; performing structured processing on the low-intensity abnormal event to obtain a spatial correlation feature topological structure; obtaining future time sequence features based on the future time sequence feature prediction model; and obtaining an attack event prediction result based on an abnormal event evolution graph constructed by fusing the future time sequence feature and the spatial correlation feature topological structure. According to the method provided by the invention, accurate identification can be carried out in the preparation stage of the attack event.

The invention discloses a building construction intelligent safety monitoring method based on the Internet of Things, and the method comprises the following steps: obtaining construction environment data, structure state data and operation behavior image data, and carrying out the preprocessing; environment state modeling, local structure strain and behavior recognition and operation scene segmentation are carried out through the edge intelligent processing unit; feature fusion is carried out, and a fusion situation vector is constructed; performing high-frequency anomaly identification and emergency preliminary screening, and generating an edge preliminary early warning result and a high-risk data fragment; constructing a safety evolution trajectory crossing a time window, and fusing historical data to generate a risk semantic map; cloud semantic reasoning operation is executed, and a final risk level judgment result and a corresponding trigger source identifier are generated; and automatically triggering a safety response instruction according to a risk level judgment result. According to the method, the Internet of Things and intelligent semantic analysis are fused, multi-source safety monitoring and self-adaptive response are realized, and the method has the advantages of high real-time performance, global perception and continuous optimization.

The invention provides a distributed data transmission optimization method, device and equipment, and belongs to the technical field of wireless communication, and the method comprises the steps: carrying out the strategy optimization of each node of a Mesh network based on a near-end strategy optimization algorithm, and obtaining an optimization strategy result, the optimization strategy result comprises a route selection optimization strategy and a node dormancy and wake-up optimization strategy of the Mesh network; performing fragmentation and routing planning on the to-be-transmitted data according to an optimization strategy result to obtain fragmentation information and a routing path; performing data transmission on the to-be-transmitted data according to the fragment information and the routing path; and in the data transmission process, optimizing the routing path based on a preset routing optimization strategy until the data transmission is finished. According to the method, intelligent decisions of routing selection, node dormancy and wakeup can be realized, the fragment size and the transmission path are dynamically adjusted, uniform distribution and efficient scheduling of data fragments in the network are ensured, single-point overload is avoided, and the data storage and transmission efficiency of the whole network is improved.

Systems and techniques are described for tracking and providing access reports for individual pieces of data managed by a data storage service. A service may generate and store a record of operations performed on a piece of data, such that may be classified as containing sensitive or important data, in a data store. The record may link representations of users and the operations performed by those users to instances of the piece of data, as it is found in one or more data objects within the data store. The data store may link other instances of the piece of data and other operations performed on the piece of data to the first instance of the piece of data. The service may access the data store to produce a history record of the various instances of the piece of data and operations performed on those instances of the piece of data.

The invention discloses a data quality treatment method and system based on an AI Agent, and belongs to the technical field of artificial intelligence and data treatment. An initial data semantic distribution map is constructed, cross-dimension correlation feature factors are extracted, a multi-scale quality anomaly sensitive factor matrix is constructed, time sequence evolution weights are embedded, and a dynamic feature evolution trajectory is formed; performing perception modeling on the evolution trajectory by using an AI Agent, generating a multi-level quality risk thermodynamic diagram, extracting a deviation dense region and constructing an anomaly propagation path set; in combination with upstream and downstream data links and task flow information, calculating a potential impact factor weight, constructing a causal traceability map, and injecting a correction strategy label for a key field node; the AI Agent autonomously selects an adaptive strategy combination according to the target data segment, and performs online intervention on the target data segment; according to the method, closed-loop treatment of the data quality problem from perception and judgment to intervention and feedback is realized, and the method has the advantages of self-adaption, high interpretability and the like.

The invention provides an intelligent decision-making method and system for a job progress based on big data, and the method comprises the steps: obtaining a real-time data stream of a job site, and carrying out the unified format processing of the real-time data stream; segmenting and cleaning the data in the unified format to generate cleaned data fragments; performing aggregation processing on the cleaned data fragment set to generate an aggregated data set, and generating an early warning signal and a scheduling instruction according to the aggregated data set; carrying out data packaging on the early warning signal and the scheduling instruction, and generating a preliminary scheduling scheme in combination with historical records; and dynamically comparing the preliminary scheduling scheme with the dynamic input parameter sequence, and if a deviation value is detected to exceed a preset threshold value, optimizing the scheduling scheme based on the deviation value to obtain a final optimized scheduling scheme. The scheduling strategy is continuously optimized through dynamic comparison and a feedback mechanism, intelligent management and continuous optimization of the production process are achieved, and the production efficiency and the resource utilization rate are improved.

The invention relates to the technical field of underground coal mine safety, and discloses an underground coal mine early warning system and method. The method comprises the following steps: collecting multidimensional monitoring data of an underground coal mine environment; performing dynamic window division on the multi-dimensional monitoring data to generate a data fragment set; performing abnormal mode recognition based on the data fragment set, and extracting abnormal feature points exceeding a dynamic threshold in each data fragment; inputting the abnormal feature points into a pre-constructed space-time correlation model, and analyzing diffusion paths of the abnormal feature points in a time dimension and a space dimension; according to the diffusion path, generating an abnormal propagation map containing the propagation direction and the intensity attenuation gradient of the abnormal feature points; hierarchical clustering is carried out on the abnormal propagation atlas, and abnormal clusters with similar propagation characteristics are divided; generating an early warning signal containing a priority label by using the spatial and temporal distribution characteristics of the abnormal cluster; performing synchronous verification on the early warning signal and underground equipment state data, and screening out a target early warning signal needing to be responded; and triggering a corresponding emergency control instruction according to the target early warning signal.

The invention provides an unstructured data compression method and device, equipment and a storage medium, and the method comprises the steps: carrying out the feature extraction and clustering processing of a to-be-compressed unstructured data set, and obtaining a data segment clustering group; performing semantic region segmentation processing on each data fragment in the data fragment clustering group to obtain a semantic important region and a semantic non-important region; performing differential coding processing on the semantic non-important regions in the same clustering group to obtain differential coding data; and performing high-quality compression processing on the semantic important regions in the same clustering group, and performing merging processing on the compressed important semantic data and the difference coding data to obtain an unstructured data compression result. According to the method, semantic region segmentation and differential compression processing are performed on the unstructured data, so that the data compression efficiency can be effectively improved on the premise of ensuring the quality of important information, and an efficient solution is provided for storage and transmission of large-scale unstructured data.

The invention relates to the technical field of data transmission, and particularly discloses a block chain-based data security storage and verification method, which comprises the following steps: collecting biological characteristics, generating a first dynamic key, the first dynamic key being used for encrypting first data, and updating during each access to ensure the key timeliness; dividing the first data into a plurality of segments according to logic, generating segmented hash values, and storing the segmented hash values on a chain; dynamically loading the corresponding fragment according to the user permission, and realizing anonymous verification through a homomorphic encryption technology; based on the Internet of Things technology, the first positioning parameter and the first state parameter are combined, the access permission is dynamically adjusted, and a self-destruction mark is set to avoid the overtime access risk; and in the storage process of the data fragments, dynamic security scoring and path adjustment are performed through distributed storage. According to the method, the static problem in data storage and access control is solved, and a more flexible and reliable security guarantee can be provided in a dynamic access scene.

A model training method includes obtaining first and second modal data sets including pieces of data, wherein the pieces of data include sub-modal data pieces, and wherein pieces of the first modal data correspond to pieces of the second modal data; obtaining masked data sets by masking a piece of the first and second sub-modal data, respectively; performing feature prediction on the first masked data set and the second modal data set and the second masked data set and the first modal data set, based on ta feature extraction model, to obtain first and second global features and first and second global recovery features; and generating a trained feature extraction model by optimizing the feature extraction model based on the first global recovery features, the first global features, the second global recovery features, and the second global features.

The invention belongs to the technical field of machine room power distribution, and provides a machine room power distribution equipment remote monitoring method and system, and the method comprises the steps: collecting operation data in real time through deploying edge equipment, fusing basic operation data, transient feature data and historical abnormal data, and constructing a state scoring model; utilizing a neural network training weight coefficient to realize equipment health degree dynamic evaluation; the sampling frequency is adaptively adjusted based on a scoring result, abnormal data are screened by combining multiple characteristic thresholds such as current sudden change intensity, energy sudden change quantity and temperature gradient, and only high-frequency data fragments in an abnormal time period are reserved; through an edge-cloud collaborative storage mechanism, abnormal data are locally cached after RAID redundancy check, normal data are uploaded to the cloud according to storage duration and access frequency in a grading manner, full-life-cycle traceability of the data is realized through tagging processing, anomaly detection sensitivity is improved, and storage efficiency and fault diagnosis reliability are both considered.

The invention relates to the technical field of intelligent monitoring, and particularly discloses an intelligent water meter metering data abnormity monitoring system and method.Metered data of an intelligent water meter is sent to a remote monitoring center through a built-in communication module of the intelligent water meter, and meanwhile a data processing algorithm is deployed in the remote monitoring center; the method comprises the following steps: segmenting intelligent water meter measurement data into continuous time sequence fragments according to a preset time step length, taking a measurement data fragment at the tail end as a verification reference, and carrying out secondary stacking time sequence coding on the intelligent water meter measurement data fragments in a historical period so as to capture deep time sequence characteristics of the intelligent water meter measurement data; and performing water meter measurement data reasoning under the current time step, and further realizing abnormality judgment on the measurement data based on comparison between a reasoning result and a verification reference. The method can effectively reduce dependence on preset rules, dynamically adapt to diversified water use scenes, and reduce misinformation and missed detection risks.

The invention relates to the technical field of battery detection, and discloses an abnormality diagnosis and traceability method and system for battery detection data, and the method comprises the steps: carrying out the normalization processing of the real-time detection data of a battery, and obtaining a preprocessed data set of the real-time detection data; on the basis of pre-stored historical data reference distribution and a historical reference threshold value, comparing the real-time data distribution of the preprocessed data set to obtain an adaptability detection threshold value of the preprocessed data set; performing abnormal state judgment on the preprocessed data set by using the adaptability detection threshold to obtain an abnormal data fragment of the preprocessed data set; tracing change events of associated parameters from historical working states of the battery based on the abnormal data fragments, and constructing an abnormal conduction chain according to a time sequence relation of the change events; according to the invention, the accuracy of a diagnosis result, the depth of traceability analysis and the operability of operation and maintenance guidance can be improved.

The invention discloses a test case generation method and system for security and futures software. The method comprises the following steps: converting data in an original file into an embedded vector through an information analysis agent, and storing the embedded vector in a vector database; generating an initial test case set by using the plurality of test points and the data fragments corresponding to each test point through a case generation agent; constructing a business rule atlas to perform rule similarity scoring on each test case, deleting the test cases with low scores, and forming a high-score test case set by the test cases with high scores; semantic similarity scoring is carried out according to the historical test cases, sorting is carried out according to scores from high to low to form an optimal test case set, and the test case with the highest score is marked as the historical test case to be stored in the vector database. According to the method, the test cases of the security futures software can be automatically generated, redundant cases are reduced by evaluating the test cases, the case quality is improved, the workload of testers is greatly reduced, and the test efficiency is greatly improved.

The invention relates to the technical field of blockchain data management, and discloses a blockchain-based cleaning and spraying data management system, which comprises a cleaning parameter acquisition module, a data fragment encryption module, a cross-chain feature verification module, a dynamic decision reconstruction module and an execution instruction generation module. The system obtains the operation state data flow of the spraying equipment through distributed nodes, generates a data verification feature matrix set through space-time dimension division and feature fragmentation encryption, obtains an optimized verification feature matrix set through consensus mechanism screening and feature relevance verification, generates a data chain type decision feature map based on Hash time lock fusion, and obtains a data chain type decision feature map. And finally, an equipment control instruction set is generated. The system realizes data security storage, cross-chain verification and dynamic decision by means of a block chain technology, improves the cleaning and spraying data management efficiency and decision accuracy, is suitable for intelligent data management and equipment control in an industrial scene, and solves the problems of weak data security, decision lag and the like of a traditional system.

The invention discloses a customer information storage management method and system based on a block chain, and relates to the technical field of distributed data evidence storage, and the method comprises the steps: collecting terminal customer information, generating an encrypted data packet, carrying out erasure coding, and generating an encrypted data fragment set; carrying out distributed storage based on the encrypted data fragment set, and calculating a data availability commitment at the same time; executing availability sampling according to the data availability commitment, and generating a fragment validity proof; based on the fragment validity proof, performing format and validity verification to obtain a to-be-processed proof set; and generating a recursive aggregation proof according to the to-be-processed proof set, and submitting the recursive aggregation proof to the block chain to verify and update a proof storage state. And cryptographic authentication is carried out on the persistent evidence storage state of the data assets, so that the dual beneficial effects of ensuring data privacy and credible evidence storage are achieved.

The invention discloses a load migration method and device and electronic equipment, and relates to the technical field of cluster resource management and intelligent operation and maintenance, and the load migration method comprises the steps: obtaining historical time sequence data corresponding to each node in a cluster, the historical time sequence data corresponding to the node comprises running state information corresponding to the node at each time point of the historical time sequence; predicting node load information of each node in a future predetermined time period according to the historical time sequence data corresponding to each node; loads of nodes in the cluster comprise data fragments; determining a load migration decision for the nodes in the cluster according to the node load information of each node in a future predetermined time period; migrating partial data fragments of the first node in the cluster to at least one second node according to the load migration decision; wherein the node load of the first node in the future predetermined time period is higher than the node load of the second node in the future predetermined time period.