361 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 embodiment of the invention provides a big data bucket query method and device, electronic equipment and a storage medium, and belongs to the technical field of digital medical treatment. The method comprises the steps that query information is obtained, and the query information comprises an index name, user data and a query condition; when the query condition is discrete query, performing statistical processing on the user data to obtain a dimension value of an index name; when the dimension value is greater than a preset dimension threshold value, performing bucket dividing processing on the query condition, and updating the query condition; generating bucket query statements according to the query conditions, and sending the bucket query statements to a database; and receiving a data query result returned by the database. According to the embodiment of the invention, the bucket query statements meeting the dimension threshold can be automatically generated according to different query conditions and dimension values thereof, the reference value of a big data query result is improved, the situation that data statistics is too dispersed is avoided, and the accuracy and timeliness of data query are improved.

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.

The invention discloses a structural plane network sequence modeling method and system suitable for layered rock mass, and relates to the technical field of rock mass engineering, and the method comprises the steps: obtaining layered rock mass outcrop data as basic data, and obtaining basic data interpretation representation after processing; determining a rock mass outcrop homogeneous region division result according to the type attribute of the basic data; according to the interpreted occurrence data in combination with a rock mass outcrop homogeneous region division result, correcting the occurrence data in each homogeneous region by using a weighting function, and grouping the structural surfaces by using a K-mean particle swarm algorithm to obtain grouped structural surfaces; determining the density of the distribution space of each group of structural surfaces according to the spacing, occurrence, size and survey line trend data of each group of structural surfaces; determining the position and size of a layer in the layer structure space according to the determined distribution position of the structural surface; and randomly combining parameters of each group of structural surfaces, and constructing a stratified rock mass structure spatial network model in combination with sequence modeling and Monte Carlo simulation methods.

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 discloses a fire investigation and survey system based on cloud computing, which comprises an edge computing module, a cloud computing module, a storage module, a data analysis and processing module, an investigation data fusion module and a display processing module, the second investigation analysis module is used for performing second investigation analysis according to the obtained real-time collected fire scene data and the first investigation analysis result to obtain a second investigation analysis result; according to the data analysis and processing module, data acquisition and analysis are performed by deploying a plurality of deep learning models at a cloud computing node for parallel processing, and a survey analysis result is recorded as a third survey analysis result; the survey data fusion module is used for performing data fusion on the second survey analysis result and the third survey analysis result and outputting a final survey analysis result; according to the invention, different deep learning model analysis and fusion algorithm analysis are carried out on the acquired fire occurrence data at different stages, so that the accuracy of fire investigation is improved.

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.

A method for determining a category responsive to a user query is disclosed. The method includes receiving a training data set comprising a plurality of data pairs, each data pair including: (i) a query; and (ii) an associated one or more categories that are responsive to the query, wherein the one or more categories in the training data set defines a plurality of categories. The method includes training a machine learning algorithm, according to the training data set, to create a trained model, wherein training the machine learning algorithm includes: creating a first co-occurrence data structure defining co-occurrence of respective word representations of the queries with the plurality of categories, and creating a second co-occurrence data structure defining co-occurrence of respective categories in respective data pairs. The method also includes deploying the trained model to return one or more categories in response to a new query input.

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 provides a multi-type lost data recovery model construction method and system, and the method comprises the steps: obtaining data of a target sensor in a structural health monitoring (SHM) system, carrying out the preprocessing of the data of the target sensor, and obtaining a recovery data set; inputting the recovery data set into a pre-constructed data recovery model for training, and adjusting the weight of the pre-constructed data recovery model by using a loss function to obtain a final multi-type lost data recovery model; the method further comprises the steps of performing data recovery on data of a missing area in a structural health monitoring (SHM) system through the final multi-type lost data recovery model, and restoring the obtained data to obtain a final data result. According to the method, random point data missing, continuous fragment data missing and multi-channel and even full-channel data missing can be processed at the same time when the structural vibration data is recovered, and the method has relatively high comprehensive recovery capability and relatively high precision.

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 invention discloses a data search method and device, electronic equipment and a computer readable medium, and relates to the technical field of data search, one specific implementation mode comprises the steps that in response to a data search request, corresponding source code data is obtained, word segmentation is executed on the source code data, and all vocabularies are obtained; determining the length of each vocabulary, and counting the word frequency of each vocabulary in the source code data according to the length; determining the importance score of each vocabulary according to the word frequency; determining a target keyword based on the importance score, and determining a target database and a target data fragment according to the target keyword; calling a feature extraction model to extract feature values of the target data fragments, obtaining a feature vector library corresponding to the target database, searching in the feature vector library based on the feature values, and determining target search result data according to feature vectors with similarity higher than a preset threshold value. And the searching efficiency of the leaked data when the data leakage occurs is improved.

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.