123 results about "Occurrence data" patented technology

The invention discloses an altered rock mass engineering geologic feature rapid identification method, and belongs to the technical field of engineering rock mass identification. Comprising the steps of obtaining topographic data of a rock mass feature region to be recognized, obtaining an earth surface plane model by fitting the topographic data, and generating a three-dimensional route based on the obtained earth surface plane model; controlling the unmanned aerial vehicle to fly close to the surface of the rock mass through the three-dimensional route, and shooting an image according to a preset position; performing data preprocessing on the shot image, performing image segmentation on the preprocessed image, and constructing a structural plane occurrence data set; and performing data processing based on the structural plane occurrence data set, and obtaining a rock mass feature recognition result through an FCM-WOA algorithm of structural plane occurrence automatic clustering and grouping. According to the method, the normal vector robust estimation method is optimized to improve the region growth efficiency and accuracy of the rock mass structural plane, the region growth algorithm is improved to optimize the initial structural plane recognition result, and the rock mass structural plane is quickly and accurately recognized from mass point cloud data of the rock mass outcrop.

The invention relates to the technical field of document management, and relates to a legal document key point automatic extraction and association method and system.The new legal document data of a target case is obtained, a preset document fingerprint algorithm is used for automatically comparing the new legal document data with a historical reference document, differential text data fragments are extracted, and the text data fragments are extracted; the method comprises the following steps of: performing deep semantic analysis on different text data fragments to generate candidate triple data containing entities and relationships, intelligently mapping the candidate triple data into a pre-constructed case knowledge graph, quickly positioning graph anchor nodes with data change, and then, performing data change on the basis of change types of the different text data fragments. And executing atomic updating operation on the map anchor node, including updating the dynamic mark of the node state attribute, and finally generating the change source node data with the latest state identifier. The problems that the current legal case data updating depends on manpower, is low in efficiency and is easy to make mistakes are effectively solved.

A method for performing attribute constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. The method further includes determining singleton events in the dataset, where each singleton event is associated with an attribute. The method also includes generating event pattern sets corresponding to different distinct singleton events and that maintain a fixed positional order for each event sequence. The event pattern sets include pattern occurrence data for each event sequence positioned according to the fixed positional order. The method also includes identifying the fixed positional order maintained by each event pattern set, and generating an attribute constraint search structure, such as a B-tree, B+tree, or table, that includes keys and pointer values. The method may also include searching the search structure to identify key values satisfying a received attribute constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention relates to a watermark tracing method and device, electronic equipment and a computer storage medium, and belongs to the technical field of data security, and the watermark tracing method comprises the steps: obtaining the identity feature information of each data receiver and the data feature information of to-be-transmitted source data when a data transmission request of the to-be-transmitted source data is received; adding watermark data in the to-be-transmitted source data based on the identity feature information and the data feature information of each data receiver, and generating to-be-transmitted data corresponding to each data receiver; and when the to-be-transmitted data leaks, leakage tracing is carried out based on the watermark data in the to-be-transmitted data. According to the method, the watermark data is generated by combining the feature information of the data and the identity feature information of different data receivers, the identity features of the data receivers are reserved, meanwhile, the confidentiality of the watermark data is improved, and when the data leaks, the data receivers with data leakage can be determined through the identity feature information in the watermark data.

The invention discloses a social group member dynamic management method and system, and relates to the field of social network analysis, and the method comprises the steps: obtaining online interaction data and offline co-occurrence data of social group members, cleaning the online interaction data and the offline co-occurrence data, and converting the cleaned online interaction data and offline co-occurrence data into a structured data set; importing the structured data set into a graph database to obtain an initial weight, calculating a dynamic attenuation weight, and constructing a basic relation network; and converting an edge weight matrix in the basic relation network into a quantum bit entanglement state, measuring the quantum bit entanglement state to form a quantum probability amplitude, setting a quantum entanglement judgment threshold, and generating a quantum relation thermodynamic diagram through a Grover algorithm. Through the technical scheme of combining quantum calculation and dynamic entropy analysis, accurate management and risk prevention and control of the social group membership are realized, an edge weight matrix of a basic relation network is converted into a quantum bit entanglement state, and a quantum relation thermodynamic diagram is generated by using quantum measurement and a Grover algorithm.

The invention relates to the technical field of full-link monitoring, and discloses a Skwalking-based micro-service system full-link monitoring method, which comprises the following steps of: 1, automatically collecting full-link tracking data without invasion, loading an Agent probe through a bytecode enhancement technology when a micro-service instance is started, and loading the Agent probe through a bytecode enhancement technology; and the Agent probe automatically intercepts service calling under the condition of not modifying a service code. According to the technical scheme, a Kafka cluster is introduced between a data acquisition end and a processing platform to serve as a buffer channel, and an L1 and L2 distributed OAP processing architecture capable of being horizontally expanded is designed, so that the technical effect of guaranteeing the integrity and real-time performance of mass monitoring data in a super-large-scale micro-service environment is achieved; compared with a transmission architecture in which a monitoring probe is directly connected with a central server in the prior art, the transmission architecture solves the problem that the accuracy of a monitoring result is damaged due to the fact that data congestion, delay and even packet loss are easy to occur during data peak flood due to the lack of an effective buffering and peak clipping mechanism.

The invention discloses a data asset management method and device based on a knowledge graph, terminal equipment and a storage medium, and belongs to the field of knowledge graphs. The method comprises the steps that all data sources serve as federated learning nodes, initial local models are constructed, and all initial local model parameters are aggregated through a federated aggregation algorithm; generating initial global model parameters, repeatedly carrying out iterative updating operation on the initial local model, generating a global knowledge graph, and obtaining change data when determining that a target data source with a data change event exists by monitoring the data change event of the data source; and according to the change data, updating the current global knowledge graph, and generating an updated target global knowledge graph. Therefore, by implementing the method and the device, the problem of data leakage when the multi-source knowledge graph is constructed at present can be solved.

A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.

The invention discloses an electric energy meter abnormity monitoring and early warning method based on real-time electrical logging data change and a terminal. The method comprises the following steps: analyzing a master station configuration command to determine a target electric energy meter and an electrical logging data item and an anomaly type thereof, and setting a data acquisition frequency and an anomaly judgment parameter; periodically collecting real-time data of an electrical logging data item of the target electric energy meter; the method comprises the following steps of: (1) comparing real-time data with historical data, and if a difference value or a change rate exceeds a threshold range, judging that data jump abnormity occurs; (2) counting the continuous communication failure times of the target electric energy meter in the acquisition period, judging whether the continuous communication failure times exceed a time threshold value in the abnormal judgment parameters or not, and if yes, determining that communication fault abnormity occurs; and generating a corresponding early warning message and reporting the message to a master station in real time. According to the invention, the first-time field abnormal state sensing capability is provided for the main station, and the active operation and maintenance level and the risk early warning capability of the intelligent power utilization information acquisition system are remarkably improved.

This invention discloses a deep learning-based method for predicting the phenological synchronization of specialized butterflies and their host plants, belonging to the interdisciplinary field of biodiversity conservation, ecological prediction, and deep learning technology. The method includes: collecting environmental climate variables, butterfly occurrence data, and phenological time-series data of their host plants; preprocessing the data to create training and validation sets; constructing a dual-label system with the day of year (DOY) of butterfly occurrence peaks (e.g., emergence peak, egg-laying peak) as the primary label and the normalized value of synchronization deviation as the secondary label; building a deep learning model with ecological weights, including an embedding layer, an LSTM layer, and a fusion layer; inputting a vector concatenated from daily or weekly climate time-series features and ecological dynamic features; and outputting the primary and secondary labels through a dual-output layer; designing a hybrid loss function that integrates the main regression loss, ecological synchronization constraint loss, and small-sample regularization loss; training the model using the Adam optimizer and implementing an early stopping mechanism; and validating and evaluating the model using regression accuracy and ecological synchronization accuracy indicators. The method includes modules for data acquisition, preprocessing, label construction, model training, prediction evaluation, and storage. This invention improves the accuracy of phenological synchronous prediction by embedding ecological dynamic feature weights and dual-label constraints, adapts to small sample scenarios, and provides technical support for the monitoring and protection management of rare butterfly species (such as the Golden Birdwing and the Chinese Tiger Swallowtail).

The present disclosure relates to a data analysis method, device, storage medium and electronic equipment, so as to reduce the calculation amount of data analysis request and improve the data analysis efficiency. The method comprises: receiving a data analysis request; in response to the data analysis request, updating the data analysis result of a pre-stored first business model according to the changed data corresponding to the target pheromone, to obtain the updated data analysis result corresponding to the first business model, the target pheromone being the pheromone recording the data change in response to the business data change, the first business model being a business model including the target pheromone, and the business data change including changing the data in part of the pheromones; and outputting at least the updated data analysis result corresponding to the first business model.

Disclosed in the embodiments of the present application are a data storage method and apparatus, a device, and a non-transitory readable storage medium. According to the embodiments of the present application, F parity blocks occupying blank storage areas are added to a RAID system, which not only increases error tolerance but also eliminates the need for additional parity disks. During data storage, corresponding original check codes are solved according to an original encoding method, and additional check codes located on the parity blocks are solved simultaneously. Thus, it is possible to recover more erroneous data based on the original check codes and the additional check codes. When an error occurs in a disk in which any block in a stripe is located and data loss occurs in other blocks of the same stripe, the corresponding data can still be recovered.

The application discloses a kind of magnetic disk array data leakage detection method, product, equipment and medium, it is related to computer technical field, including obtaining audit log content information, data leakage detection of operating behavior is carried out to audit log content information, obtain first detection result;In the case where first detection result is no data leakage risk, the outgoing data packet generated by disk array system is acquired;Data leakage detection of data feature is carried out to outgoing data packet, and second detection result is obtained;In the case where second detection result is no data leakage risk, it is determined that data leakage does not occur in disk array system;In the case where first detection result is there is data leakage risk, and / or second detection result is there is data leakage risk, it is determined that data leakage occurs in disk array system, solve the technical problem that disk array data leakage detection accuracy is low, resource overhead is too large, reach the technical effect of improving detection accuracy, reduce resource overhead.

According to the privacy computing data processing method in the data element market provided by the invention, the collected data is encrypted, so that the security of the data in the transmission and storage process is ensured; by adopting a safe storage mode, the safety of the data in the storage process is ensured; according to characteristics and requirements of data, privacy protection algorithms including homomorphic encryption and differential privacy are selected, and calculation and analysis of encrypted data are achieved. Healthy development of the data element market is promoted through data sharing and utilization; when data leakage occurs, measures including stopping using related data and informing a data source party are taken in time so as to reduce loss; in a data processing process, data security risk assessment is carried out regularly, and potential risk points and data leakage risks in risk assessment are identified; and when the data life cycle is ended, the data are safely deleted, so that the data are ensured not to be leaked and abused any more.

The invention discloses an important data error correction method suitable for an aerospace power supply and distribution single machine. According to the method, the Hamming code principle and the binary coding principle are utilized, and the characteristics of a microcontroller of the aerospace power supply and distribution stand-alone are combined, so that an error correction coding and decoding method is designed. According to the method, the single-machine micro-control system can correct a certain degree of data bit flipping errors under the condition that the data bit flipping errors occur under the influence of space environments such as space single particles, errors of important data caused by the space environments are reduced, and then part of autonomous control operations threatening spacecraft safety are prevented from occurring.

Embodiments of the present application disclose a data processing method, device, equipment, medium and program product. The method comprises: in the case where it is detected that a second window has data update, acquiring first data information sent by the second window, the first data information comprising data updated by the second window; comparing the first data information with subscription data information of each subscription information in a plurality of subscription information set in advance to determine target subscription information, the subscription information comprising subscription data information and identification information of a subscription object, the subscription object comprising a subscription component and / or a subscription page; determining a target subscription object according to the identification information of the subscription object in the target subscription information; and sending the first data information to the target subscription object for data update by the target subscription object according to the first data information. The embodiments of the present application can reduce the risk of page lagging under the premise of realizing data synchronization between windows, thereby improving user experience.

This invention relates to the field of data synchronization technology, specifically to a method, device, and storage medium for efficient data synchronization in an office system. The invention collects several changed fields and their attribute data, time-series data, and concurrent operation data of the current business process within the current synchronization cycle, as well as the data of the current business process itself. Based on the changed field attribute data and concurrent operation data, the business weight of each changed field in the current business process is obtained. Atomic synchronization units are constructed by analyzing the closeness of business linkage between each changed field and other changed fields. The exclusivity and overlap rate of atomic synchronization units containing the same changed fields are analyzed to select conflict-free synchronization units. Based on the numerical distribution of the changed field business weights within the conflict-free synchronization units and the timeliness characteristics of the time-series data, the priority scheduling coefficient of the conflict-free synchronization units is obtained. Combined with the basic risk characteristics in the changed field attribute data, this guides the orderly execution of the final update synchronization.

The application relates to the technical field of document management, and relates to a legal document gist automatic extraction and association method and system. Newly added legal document data of a target case is acquired, preset document fingerprint algorithms are used to automatically compare the newly added legal document data with historical benchmark documents, difference text data segments are extracted, deep semantic analysis is performed on the difference text data segments, candidate triple data containing entities and relations are generated, the candidate triple data is intelligently mapped to a pre-constructed case knowledge graph, graph anchor point nodes with changed data are quickly located, and then, based on the change type of the difference text data segments, atomic update operations are performed on the graph anchor point nodes, including dynamic marking update of node state attributes, and finally, change source node data with the latest state identification is generated. The method effectively solves the problems of dependence on manual work, low efficiency and errors in current legal case data updating.

The invention discloses a highway bridge engineering construction safety monitoring method. The highway bridge engineering construction safety monitoring method comprises the steps of obtaining highway bridge construction historical accident data; extracting structured information from the accident report text to determine an accident label and risk factors associated with the accident label; processing co-occurrence data of the risk factors in the accident case by using a preset association rule mining algorithm, and generating a risk factor association rule set representing a potential causal relationship among the risk factors according to the co-occurrence data; constructing a probability graph model of the risk factors based on causal semantics of the risk factor association rule set, wherein the probability graph model represents a dependency relationship among the risk factors; based on the probability graph model, determining sensitivity values of at least part of risk factors by using a preset importance degree measurement algorithm; determining a key risk factor according to the sensitivity value of the risk factor and a preset sensitivity threshold; and determining safety monitoring indexes of highway bridge engineering construction according to the key risk factors.

This invention relates to the field of performance data management technology, specifically disclosing a performance data management method and system based on a mini-program. The method includes: when a high-concurrency scenario is identified, real-time monitoring and analysis of data interaction parameters between the mini-program and the server are performed to determine whether the high-frequency data interaction process is stable; the mini-program receives raw performance data returned by the server; if the interaction process is unstable, the raw data is preprocessed and the interaction process is adjusted synchronously; if the interaction process is stable, preliminary processing is performed directly to improve processing efficiency and obtain the final performance data; a performance data form is generated based on the final performance data, and the form generation process parameters are parsed and obtained to determine whether data corruption has occurred. This method enables dynamic adjustment of the interaction process in a high-concurrency environment, ensuring data processing stability, improving the generation efficiency and accuracy of performance data forms, and enhancing the performance and reliability of the mini-program in complex scenarios.

Systems, computer-implemented methods, and computer program products to facilitate modeling bi-directional effects between events and system variables are provided. According to an embodiment, a system can comprise a processor that executes components stored in memory. The computer executable components comprise a machine learning component that learns mutual dependencies jointly over event occurrence data and transition data, wherein the transition data comprises state variable transitions observed over a multivariate state variable set.

A method for performing time constrained queries for event sequence visualization may include receiving an event sequence dataset including event sequences. Each event sequence includes at least two sequentially occurring events. The method further includes generating a forward index having event pattern sets that include pattern occurrence data for each event sequence positioned according to a fixed positional order. The method includes generating a time constraint search structure, such as a B−tree, B+tree, or table, that includes keys and pointer values. The method also includes inserting, into the search structure, a sequence identifier corresponding to each event sequence based on the fixed positional order and time interval data for the at least two sequentially occurring events of each event sequence. The method may also include searching the search structure to identify key values satisfying a received time interval constraint, and outputting the sequence identifiers corresponding to the identified key values.

The application provides a data dynamic caching method and system, which comprises the following steps: determining the data display content in a visualization large screen corresponding to a target data source; in the case that the target data source is determined to have data changes, obtaining target changed data in the target data source according to data change state information corresponding to the target data source and a target data change strategy set bound to the target data source, wherein the target data change strategy set at least comprises a data change strategy, the data change state information is determined based on change conditions of data having data changes in the target data source, and the target changed data is data having data changes in the target data source and satisfying the data change strategy; and performing data caching processing on the target changed data, so as to refresh the data display content in the visualization large screen. The application improves the efficiency and flexibility of data refreshing of the visualization large screen.

The application provides a database for RFID collision identification and a construction method thereof, an RFID collision identification method and system. The construction method of the database for RFID collision identification comprises the following steps: S1, obtaining tag IDs and tag data of any two tags, wherein the tag ID comprises a binary sequence with a preset number of bits; S2, calculating superimposed data of the superimposed tag IDs of the two tags according to the overlapping range from one-bit overlap to complete overlap; S3, merging the superimposed data associated with the two tags into collision data and assigning a collision ID, wherein the collision ID is associated with the tag IDs and tag data of the two tags. Through retrieval from the database, the computational difficulty of directly analyzing the tag data from the collision data is avoided, thereby improving the efficiency and accuracy of the identification system in reading the RFID tags after data collision.

The present application relates to the technical field of data processing, and more particularly to a kind of electric energy meter fault detection method based on multi-sensor data fusion, the method according to the data variation trend of each monitoring index in current period, judge whether electric energy meter occurs data drift anomaly in current period;If electric energy meter occurs data drift anomaly in current period, the historical abnormal data of each monitoring index that occurs data drift anomaly is obtained, according to the performance of each historical abnormal data itself, the historical abnormal data of each monitoring index under the influence of temperature is obtained;Comprehensive temperature and the relationship curve of each monitoring index under the influence of temperature historical abnormal data, and the real-time data performance of each monitoring index in current period, determine the reason of electric energy meter occurs data drift anomaly in current period, and then carry out fault detection to electric energy meter, improve the maintenance efficiency of electric energy meter.

Various embodiments of the present invention provide methods, apparatus, systems, computing devices, computing entities, and / or the like for performing predictive structural analysis. Certain embodiments of the present invention utilize systems, methods, and computer program products that perform predictive structural analysis using at least one of techniques using time bound code transition likelihood data objects, techniques using cross-code relationship values, techniques using augmented entity-code occurrence data objects, techniques using per-pathway text representations of inferred occurrence pathways of a one or more individual historic code occurrences, techniques using polygenic risk score (PRS) measures, and / or the like.

The invention provides a snapshot optimization method and device, electronic equipment and a storage medium. According to the method, the judgment identification value is configured for each snapshot to indicate whether the snapshot is allowed to be directly deleted or not, so that when the target snapshot is deleted subsequently, if the judgment identification value of the target snapshot is the first value, the snapshot is directly deleted, time does not need to be consumed for analyzing a snapshot dependency relationship, and snapshot deletion is accelerated. Furthermore, whether the data block represented by the LBA corresponding to the bit in the snapshot time period is subjected to data change or not is indicated through the numerical value on each bit on the bitmap of each snapshot, so that when the target snapshot is deleted subsequently, even if the judgment identification value of the target snapshot is the second value, the data can be quickly checked by comparing the bitmap of the target snapshot, and the data processing efficiency is improved. Compared with the prior art that the operation records are checked one by one, the snapshot deletion speed can be remarkably increased.

This invention provides a data recovery method, apparatus, storage medium, and electronic device. The method includes: upon detecting data corruption in a first node of a target storage cluster, locating a target node in the target storage cluster associated with a target switch; if a target node exists in the target storage cluster, locating a second data block based on a first data block, and performing a data recovery operation based on the second data block to generate a third data block, wherein the third data block is considered to be a data block generated after recovering the first data block; and storing the third data block to the target node. This invention solves the technical problem of low data recovery efficiency due to complex business processes in related technologies.

The invention discloses a Dioryctria diorycis propagation and diffusion trend prediction method based on TabNet, and belongs to the technical field of forestry disease and pest prediction. The problem of improving the accuracy of dioryctria dioryctria occurrence risk prediction is solved. The method comprises the following steps: collecting historical occurrence data, meteorological factor data, forest stand factor data, geographic factor data and human interference factor data of dioryctria dioryctria to obtain a multi-source data set, and preprocessing the multi-source data set to obtain a data set for training a TabNet model; constructing a Dioryctria diorycis propagation and diffusion trend prediction model based on TabNet, wherein the model comprises a feature transformation layer, an attention selector and a multi-step decision mechanism; a weighted binary cross entropy is adopted as a loss function for training, and a trained Dioryctria diorycis propagation and diffusion trend prediction model based on TabNet is obtained; and using the trained TabNet-based dioryctria dioryctria propagation and diffusion trend prediction model to predict future dioryctria dioryctria nationwide county scale occurrence conditions based on historical occurrence conditions.

The invention relates to a static recovery method of intelligent video equipment, the intelligent video equipment and an intelligent door lock, and the static recovery method comprises the steps: S1, collecting a historical static correlation factor set and correspondingly generated static generation data, training an FMEA failure model, obtaining a static correlation factor feature contribution rate model, and obtaining a static correlation factor feature contribution rate model; outputting the contribution rate of each electrostatic correlation factor to different electrostatic types; s2, collecting a historical electrostatic correlation factor set, correspondingly generated electrostatic generation data and contribution rates of all electrostatic correlation factors to different electrostatic types, and training the ETA evolution model to obtain an electrostatic event evolution model; s3, video data are collected, a current electrostatic correlation factor set is obtained through video data analysis, the current electrostatic correlation factor set is input into the electrostatic event evolution model, and the occurrence probability of each electrostatic type is obtained; and S4, starting module-level monitoring, and resetting an abnormal module.