243 results about "Occurrence data" patented technology

The invention discloses a dynamic key threshold fragmentation privacy tracing method based on block chain collaborative verification, and belongs to the field of information security. According to the method, a dynamic key is taken as a hub, decentralized identity, zero-knowledge verification, deep watermark and threshold collaborative decryption are deeply coupled, a temporary key generated by each conference is split into a plurality of fragments, the fragments are stored in a distributed manner by a block chain node and a tracing unit, only a hash fingerprint combined by the fragments is subjected to chaining and evidence storage, and the dynamic key is subjected to zero-knowledge verification and deep watermark and threshold collaborative decryption. A light evidence chain which cannot be tampered but has no privacy exposure is formed; participants are anonymously accessed through a verifiable certificate without leaking specific information, and truth of anonymous identity is ensured through signature of a trusted unit; when data leakage occurs, the tracing unit extracts a watermark, compares a zero knowledge proof, verifies the relevance between a leaked ciphertext and a chain fingerprint, triggers a multi-party threshold decryption recovery key, and finally accurately positions a person in charge in combination with the encrypted and stored DID mapping table without exposing irrelevant information in the whole process.

A computer generates an accident risk definition model to estimate a probability of hazard occurrence as an accident risk by inputting first in-vehicle sensor data collected in the past and hazard occurrence data having information on hazard occurrence from the first in-vehicle sensor data preset therein, generates accident risk estimation data by inputting second in-vehicle sensor data collected in the past to the accident risk definition model and estimating the probability of the hazard occurrence, generates an accident risk prediction model to predict the accident risk after a predetermined time by inputting first biological index data corresponding to the second in-vehicle sensor data and the accident risk estimation data, calculates second biological index data from second biological sensor data by acquiring the second biological sensor data of a driver, and predicts the accident risk after the predetermined time by inputting second biological index data to the accident risk prediction model.

The invention relates to the technical field of computers, and discloses a multi-database deployment method based on a server cluster, which comprises the following steps of: distributing transaction state information for each database node, and realizing data consistency among the nodes in the database cluster by utilizing a consistency operator; a data synchronization path is optimized according to the topological structure between the nodes, and an optimal path is selected to reduce data transmission delay; the synchronization data volume is reduced through an increment synchronization and redundancy elimination method; designing and implementing a fault recovery strategy to ensure that the normal operation of the system is rapidly recovered when the database node has a fault; and dynamically adjusting a synchronization strategy and a consistency level of the database cluster according to a system load condition. By adopting the technical scheme based on transaction state vector allocation and consistency operator mapping, the technical effect of efficiently realizing data consistency in a distributed database environment is achieved. The problems that the consistency maintenance efficiency is low and data conflicts are prone to occurring in a high-concurrency scene are solved.

Techniques for correcting data drift of a language model are disclosed. A model is built, and this model is designed to solve a same task for which the language model has been trained. The model is applied to new input data. This application results in generation of a prediction comprising predicted label data. Context is stored in a context management structure (CMS). The context includes a prompt template, a prediction, and labeled input data used to train the language model. The data drift is determined to have occurred. This determination is performed by determining that the context is within a threshold level of similarity to a previously stored context. In response to determining that the data drift has occurred, an operation is performed to correct the data drift.

The invention discloses a coastal region storm surge disaster prediction method based on multiple modes, which combines multi-source meteorological environment data, three-dimensional geographic space data and storm surge historical occurrence data, and comprises the following steps: on one hand, performing long-term prediction and trend fitting on future storm surge conditions by using a multi-mode network; fitting to obtain a linear component and a nonlinear component related to storm surge prediction, combining the linear component and the nonlinear component, and converting the combined component into a storm surge prediction probability P1 through an activation function; on the other hand, for the three-dimensional geographic space data, respectively obtaining a predicted value of supervised learning of a labeled scene and a predicted value of hierarchical supervised learning of an unlabeled scene of the three-dimensional geographic space data; averaging the predicted values of the two, and converting the averaged predicted values into a storm surge prediction probability P2 through an activation function; and finally, weighting the storm surge prediction probability P1 and the storm surge prediction probability P2 to obtain a final storm surge prediction value.

The invention discloses a database migration breakpoint resuming method and device, equipment and a storage medium, and belongs to the technical field of databases. According to the database migration breakpoint resuming method, a unique identifier is given to each piece of data according to the transaction of each piece of data, the sequence of the data in the transaction and the sequence of the data in the rowevent, and in the collection process, the collected data and collection sites of the collected data are cached respectively instead of being directly sent to a target end database, so that the data collection efficiency is improved, and the data collection cost is reduced. When a data migration interruption fault occurs, data collection continues from the cached latest collection site, and repeated data filtering is performed by using the data unique identifier, so that the problem of data repeated collection during restarting of the data migration interruption fault is solved, and compared with the prior art, data migration continuing is more accurate and more reliable.

Certain aspects of the disclosure provide techniques for access control policy management. A method generally includes factorizing a user access co-occurrence data element to generate two data sub-elements, wherein: the user access co-occurrence data element represents co-occurrences between users of a system and resources of the system, a product of the two data sub-elements approximates the user access co-occurrence data element, and each of the two data sub-elements has reduced dimensionality compared to the user access co-occurrence data element; generating an approximated user access co-occurrence data element based on the product of the two data sub-elements; comparing the user access co-occurrence data element and the approximated user access co-occurrence data element to determine one or more anomalies, wherein each of the one or more anomalies relates to access for a user to a resource of the system; and taking one or more actions to rectify the one or more anomalies.

The invention relates to the technical field of data processing, in particular to an electric energy meter fault detection method based on multi-sensor data fusion, and the method comprises the steps: judging whether an electric energy meter generates data drift abnormity or not in a current time period according to the data change trend of each monitoring index in the current time period; if the data drift abnormity of the electric energy meter occurs in the current time period, acquiring historical abnormal data of the data drift abnormity of each monitoring index, and acquiring the historical abnormal data influenced by the temperature under each monitoring index according to the own performance of each piece of historical abnormal data; according to the method, the relation curve of the temperature and the historical abnormal data influenced by the temperature under each monitoring index and the real-time data performance of each monitoring index in the current time period are integrated, the reason of the data drift abnormity of the electric energy meter in the current time period is determined, then fault detection is carried out on the electric energy meter, and the maintenance efficiency of the electric energy meter is improved.

The invention provides an avalanche disaster risk method, system and device and a medium, and relates to the technical field of avalanche disaster response and prevention. The method comprises the following steps: acquiring avalanche occurrence data and disaster-pregnancy background data of a target area, and determining avalanche disaster-pregnancy factors according to the disaster-pregnancy background data; based on the avalanche occurrence data of the target area, taking an avalanche point as a positive sample, collecting a non-avalanche point as a negative sample, constructing training data, constructing a random forest model, and training the random forest model by using the training data to obtain an avalanche dynamic evaluation model; and based on the avalanche dynamic evaluation model, evaluating the avalanche disaster risk of the target area. The problem of how to obtain a dynamic avalanche risk assessment model by focusing on the change of meteorological factors in the snow season avalanche high-incidence period and provide key factors needing to be monitored for avalanche early warning in different months of the snow season is solved.

The present application provides a data processing method, device, equipment and storage medium, which relates to the field of data protection and aims to solve the problem of data being unable to be restored due to accidental deletion of data when performing backup, recovery and garbage collection concurrently. The method includes: in the current round of garbage collection process, all available data fingerprints that are filtered out and belong to expired snapshots are converted into forgettable data fingerprints, and all backup processes that occurred during the current round of garbage collection process are recorded; in the next round of garbage collection process, wait until all recorded backup processes have ended, check whether any of the recorded backup processes has used the forgettable data fingerprint, and if a forgettable data fingerprint has been used by one or more recorded backup processes, convert the forgettable data fingerprint into an available data fingerprint; in the recovery process, use the available data fingerprint and the forgettable data fingerprint to restore the snapshot of the data.

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 field of rock mass structural surface recognition, in particular to a high and steep slope rock mass structural surface intelligent recognition method based on double clustering. According to the method, high-precision image data of a high and steep slope is obtained through an unmanned aerial vehicle approaching photogrammetry technology, and a three-dimensional digital model of the high and steep slope is constructed by utilizing a three-dimensional reconstruction and calculation technology. And calculating normal vectors of all triangular surfaces based on the triangular surface vertex information of the model, and performing clustering analysis to divide structural surface groups with similar occurrence. And further performing clustering analysis according to the surface center coordinates of the triangular surface, and dividing to obtain the structural surface. And finally, calculating the inclination angle, inclination direction and trend data of the structural surface through an accurate mathematical model. According to the method, the flexibility and the safety of high and steep slope structural plane measurement operation are enhanced, a measurement area of any scale can be completely covered, objective, accurate, rapid and low-cost structural plane intelligent identification and attitude data acquisition are realized, and the method has important significance in engineering application related to high and steep slopes.

The invention discloses a file marking method and device, equipment and a storage medium, and relates to the technical field of financial science and technology. The method comprises the following steps: acquiring watermark information of a to-be-marked file, wherein the watermark information at least comprises time information; the watermark information is processed, and the watermark information is converted into invisible characters; and inserting the invisible character corresponding to the watermark information into the to-be-marked file to obtain a marked file corresponding to the to-be-marked file. According to the technical scheme, the invisible characters corresponding to the watermark information are inserted into the to-be-marked file to obtain the marked file corresponding to the to-be-marked file, marking of the to-be-marked file is achieved under the condition that data in the to-be-marked file is kept unchanged, and invisible marking of the to-be-marked file is achieved; therefore, a tracing basis is established for the data in the file, once data leakage occurs, the operation time of the data can be determined by converting invisible characters into watermark information, a leakage channel is positioned through the operation time, and the security and traceability of the data are improved.

The invention relates to the technical field of pest control, in particular to a forest pest prediction system based on data analysis, which comprises a data preprocessing module, a dynamic weight distribution module, a pest feature recognition module, a prediction model training module and a prediction result output module. According to the method, the feature weight is dynamically adjusted through error distribution analysis, the influence of each feature on a pest event is accurately quantified, the adaptability of weight distribution is improved, multi-feature correlation analysis is combined, key features highly related to pest and disease damage are screened, interference of irrelevant factors is reduced, the model input quality is enhanced, and the weight error change trend is dynamically calculated. According to the method, time sequence optimal weight configuration is formed, high-precision prediction of the occurrence trend of diseases and insect pests is realized, the risk distribution trend is quantified and risk areas are divided based on comparison of prediction data and actual occurrence data, a scientific space-time evaluation result is provided for prevention and control of diseases and insect pests, and accurate prevention and control resource configuration is facilitated.

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.

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.

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 provides a congestion control method, a communication device and a storage medium, relates to the field of communication, and is used for performing congestion control on data transmission between communication nodes to avoid data congestion. The method is applied to a first node and comprises the following steps: acquiring flow configuration information of a data flow; and under the condition that the transmission scheduling of the data flow cannot meet the requirement of the flow configuration information, congestion control information is sent to the second node, and the congestion control information is used for indicating congestion control of the data flow.

The invention discloses a power equipment test control terminal online or offline data intelligent synchronization method and system. The intelligent synchronization method comprises the following steps: two data synchronization parties subscribe to data updating events mutually to establish an event subscription relationship; monitoring whether any data source of the two data synchronization parties changes or not; when any data source of the two data synchronization parties changes, the party with the changed data source adopts event driving to publish a data change event; receiving the published data change event by the party which does not have the data source change; and performing data synchronization on the data sources of the two data synchronization parties according to the data change event and a preset event subscription relationship. According to the method, data synchronization is performed in real time only when the update event occurs, and most events are asynchronously processed due to the fact that the occurrence time points of most events are different, so that resource consumption is reduced, real-time performance is effectively improved, and use performance is improved. The technical problems of resource consumption, poor real-time performance and influence on use performance are solved.

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 present disclosure relates to a data processing method for a spreadsheet, a medium, an electronic device and a program product, which relate to the field of computer technology. In the method, a first data amount of target table data for which a data change occurs in a spreadsheet is determined, and the target table data is sent to a client according to a data sending mode corresponding to the first data amount of the target table data, so that the client performs data update on the spreadsheet according to the target table data. In this way, the target table data can be sent to the client in different scenarios using different data sending modes, so as to ensure the reliable transmission of the table data between a server and the client, thereby ensuring the timeliness and integrity of the table 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 provides an industrial park safety supervision method and system based on a digital twinborn model, and belongs to the technical field of digital twinborn, and the method specifically comprises the steps: determining whether the identification accuracy of the potential safety hazard of the digital twinborn model meets the requirement or not based on the deviation condition of identification deviation data and an identification deviation date; based on an analysis result of the digital twinborn model, determining model matching conditions of the digital twinborn model and different areas in the industrial park, and determining model updating demand coefficients of the different areas in combination with historical occurrence data and identification deviation data of potential safety hazards of the different areas; and determining historical occurrence data of potential safety hazards of the industrial park at different dates, determining an updating processing strategy of a digital twin model of the industrial park in combination with model updating demand coefficients of different regions, and performing safety supervision processing on the industrial park by using the updated digital twin model. And the identification processing efficiency of the security risk is improved.

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.

The invention relates to a real-time feature data processing method and device, computer equipment and a storage medium. The method comprises the following steps: synchronizing business data of a plurality of business libraries to a business data table, calculating a pre-calculation result of a real-time feature according to the business data in the business data table, and writing the pre-calculation result into a pre-calculation feature result table; the method comprises the steps of synchronously updating a service data table when data change occurs in a service library, capturing corresponding change data when the service data table is updated, converting the change data into a Kafka message, sending the Kafka message to a specified theme of a Kafka message system, pulling a new Kafka message from the specified theme by using a stream processing engine Flink, processing the new Kafka message to obtain real-time feature update information, and sending the real-time feature update information to the service library. A pre-calculation characteristic result table is updated based on the information; and in response to a feature query request of the application program, obtaining feature data matched with the request from the pre-calculation feature result table and returning the feature data to the application program. The real-time performance of the feature data can be improved, the complexity and the maintenance cost of the system are reduced, and the feature access efficiency is improved.

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.

The embodiment of the invention provides a data synchronization method and device, electronic equipment, a storage medium and a program product. Relates to the computer field. The method comprises the steps of determining whether changed data meet a synchronization requirement of a target database or not in response to monitoring that data change occurs in a source database; if it is determined that the synchronization requirement is met, generating a corresponding change record according to the change data and storing the change data in a change record table; determining a corresponding target synchronization strategy based on the change records which are not synchronized in the change record table and preset target data synchronization time; the target synchronization strategy is a single-thread execution data synchronization strategy or a multi-thread parallel execution data synchronization strategy; and synchronizing the change data into the target database according to the target synchronization strategy and based on the change record. According to the method, the change data is synchronized into the target database through different target synchronization strategies and based on the change records, and real-time synchronization of the change data can be achieved.

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).