317 results about "Occurrence data" patented technology

Augmented reality systems and methods for user health analysis. Methods for user health analysis may include collecting data for an initial prediction model and continuing to collect additional data based on one or more data criteria. The methods may further include updating the initial prediction model based on the additional data to produce a revised prediction model or causing an intervention to occur based on the additional data. The data may be collected by a display system including one or more sensors configured to collect user-specific data and a display device configured to present virtual content to a user. The display device may be configured to output light with variable wavefront divergence.

In a method of forming a target error model to facilitate correcting or suggesting corrections to misspelled input text related to a target data collection, a source query log containing user queries to at least one source data collection is provided. Next, target relational data is generated based on the source query log including corrective substring suggestions that relate to the target data collection and corresponding misspelled substrings extracted from the source query log. A target error model is then built using the target relational data. The target error model includes target statistical occurrence data for the substrings of the target relational data derived from the source query log. Finally, the target error model is stored on a computer readable medium. Additional embodiments of the invention are directed to a system configured to implement the method.

A data processing system stores encrypted data. Object identifiers are assigned to storage objects, and data encryption keys are assigned to the storage objects. When performing an operation upon a storage object, data encryption key failure may occur due to a corrupt or incorrect key. In this case, a copy of the data encryption key is fetched from a key server. It is possible for the association of the object identifiers with the data encryption keys to become lost or confused, so that the key server may fail to provide the correct key for a specified object identifier. Therefore, an absolute key identifier that is unique across the key server namespace also is stored in association with the object identifier in the storage system and in the key store of the key server, and the absolute key identifier is used as a failsafe for recovery of encrypted data.

A method for managing sample selection bias is disclosed. Embodiments of the method can include automatically determining an unbiased estimate of a distribution from occurrence data via a special purpose processor. The occurrence data that is utilized in determining the estimate may have an occurrence data sample selection bias that is substantially equivalent to a background data sample selection bias associated with background data. Additionally, the occurrence data may be related to the background data, and the background data may be chosen with the background data sample selection bias. Furthermore, the occurrence data may represent a physically-measurable variable associated with one or more physical and tangible objects or substances.

A method and structure converts a document corpus containing an ordered plurality of documents into a compact representation in memory of occurrence data, where the representation is to be based on a dictionary previously developed for the document corpus and where each term in the dictionary has associated therewith a corresponding unique integer. The method includes developing a first vector for the entire document corpus, the first vector being a sequential listing of the unique integers such that each document in the document corpus is sequentially represented in the listing according to the occurrence in the document of the corresponding dictionary terms. A second vector is also developed for the entire document corpus and indicates the location of each of the document's representation in the first vector.

Disclosed is a method of predicting crime occurrence in a prediction target region using big data, the method including: collecting crime prediction data of the prediction target region from a plurality of data domains; collecting crime occurrence data of crime that occurred in the prediction target region during a preset period of time from a crime occurrence record domain; analyzing the crime prediction data and the crime occurrence data of each of the data domains according to a statistical analysis, and extracting meaningful data of the crime prediction data as available data by each of the data domains; and predicting crime occurrence by applying the available data to a pre-registered deep learning algorithm, wherein the available data is classified into a plurality of data groups according to a data type, and the deep learning algorithm includes: a first deep neural network; a second deep neural network; and an output.

The invention provides a data race detection and evidence generation method based on multithreaded program constraint building. The data race detection problem is converted into a constraint solving problem by building a constraint expression according to the multithreaded program semanteme, a constraint solver is used for detecting possible data races and program execution paths triggering the data races are generated; the data race detection and evidence generation method comprises the steps of firstly, carrying out the instrumentation of the detected program, executing the program and obtaining an execution path, secondly, converting the execution path into a quantifier-free first-order logic expression covering all feasible thread interleaving according to the multithreaded program execution semanteme, thirdly, building a data race candidate set according to the sequential relationship of statements when a data race happens and generating candidate conditions for races, and finally, traversing the candidate set to determine whether the data race exists, and if so, generating a corresponding evidence sequence. The method is capable of finding out all data races in once operation without misinformation; for each data race, the evidence sequence indicating the trigger process of the data race is generated.

The invention discloses a working surface outburst risk evaluation index system analysis method, which comprises the following steps of: acquiring gas occurrence data of a mine stope face; comprehensively analyzing the data to form risk evaluation indexes for judging working surface outburst risk, and storing the risk evaluation indexes in a criterion index library; processing the risk evaluation indexes and preset evaluation index critical values to determine the risk level of a working surface region range; and pre-warning according to the risk level. According to the method, working surface outburst risk dynamic evaluation indexes are classified into a gas index, a geology index, a stress index and an outburst prevention measure index on the basis of researching the gas occurrence rule of the mine stope face, so that the change rule of each evaluation index is grasped in time and analyzed intelligently, a working surface outburst evaluation result is formed comprehensively, outburst risk pre-warning of different levels is generated automatically, and real-time change update of each evaluation index can be predicted along with outburst; and the blindness of outburst prevention work is reduced, outburst prevention engineering cost is saved, and safe production of a mine is ensured.

A method and computer program product for testing a software application program. Randomized input is provided into a logging facility and automated testing of the logging facility is performed in a multi-processing environment. Self-verification of data retrieved from the logging facility is performed to ensure no data loss occurs due to external system events or failures driven by a test tool. Real-time analysis of results received is performed by the test tool based on the randomized input into the logging facility. First failure data is captured and self-verification of results of a data recovery operation of the logging facility is performed in a disaster recovery scenario.

A video processing system configured with DMA latency compensation is disclosed. This compensation helps minimize or otherwise mitigate shortages of data to the display, thereby improving the quality of displayed video. A relatively small line buffer is used to stage data for video processing. Should an underflow of data occur (where the buffer reading process is ahead of the buffer writing process), data is read from the previous line buffer. This not only prevents shortages of data to the display, but also provides data that is more likely to be relevant to the actual scene being displayed (as compared to random data).

The invention provides an HAC (High Availability Cluster) system, a warning method based on the HAC system and a warning system based on the HAC system. When the HAC system, the warning method and the HAC system are used, warning information is simultaneously stored in a first main database and a first auxiliary database of a master node and second auxiliary databases of each slave node. Under the condition that a database connection fault does not occur, the warning information stored in each database is consistent. The warning information can be obtained from the database of the currently accessed node no matter whether the node currently accessed by a user is a master node or a slave node. Even when the database of the master node or the slave node fails, the user can also obtain the warning information from the second auxiliary databases of any one slave node, so that corresponding dealing measures can be taken in time according to the obtained warning information; the normal operation of the HAC system is ensured; the loss since the dealing measures are not taken in time is effectively avoided; and the system performance of the HAC system is greatly improved.

The invention discloses a real-time data synchronization method and system, and the method comprises the steps: obtaining a data change sequence of a source database when detecting that the source database has data change, and storing the data change sequence in a data change log table; When it is detected that the data change occurs in the data change log table, obtaining change data in the datachange log table, packaging the change data into message queue information, and sending the message queue information to a message queue; Informing a target database needing to synchronize data, so that after receiving the notification message, the target database reads the message queue information from the message queue and then performs data updating operation according to the message queue information; And after judging that the message queue information in the message queue is successfully sent, sending a synchronization completion message notification to the target database. The synchronization mode is simple, program development difficulty is low, synchronization efficiency is high, data synchronization can be carried out in time, the period is short, and the method can be widely applied to the software technology industry.

The invention relates to a driver risk assessment model training method and device and a risk assessment method and device. According to the invention, data corresponding to dimensions required by a driver model is obtained from various data resources, wherein the data sources comprise GPS data, vehicle-mounted box data, traffic data, weather data, driver data, map data and insurance accident occurrence data, the data sources are very rich, the data for assessment is more comprehensive based on the data sources, and the driver type and score outputted by a trained machine learning classification model can reflect the level of each type of drivers. In addition, the data sources comprise the insurance accident occurrence data, the driver score is comprehensively calculated by giving a weightto the data of each dimension of the driver model, so that the obtained driver type and score are more accurate, and the vehicle insurance price set according to the model is more accurate.

Resource-aware dynamic bandwidth control uses information about current network state and receiver performance to avoid, minimize and/or recover from the effects of network spikes and data processing spikes. Linear models may be used to estimate a time required to process data packets in a data processing queue, and are thus useful to determine whether a data processing spike is occurring. When a data processing spike occurs, an alarm may be sent from a client to a server notifying the server that the client must drop packets. In response, the server can encode and transmit an independent packet suitable for replacing the queued data packets which can then be dropped by the client and the independent packet present to the processor instead.

The invention discloses a fault-tolerance method for a high-reliability disk array. The fault-tolerance method comprises the following steps: when a data write error occurs, confirming storage space of a disk in which data with the write error is, and carrying out bad block marking on the storage space; when a data read error occurs, judging if the disk array is complete, if so, working out current position data by checking, writing the data in a corresponding position of the disk, and otherwise, carrying out bad block marking on the storage space with the data read error; and when a next write operation is carried out, carrying out the write operation on the storage space subjected to bad block marking, if the storage space is successfully written and the subsequent read operation is also successful, removing a bad block mark of the storage space, and on the contrary, remaining the bad block mark. According to the fault-tolerance method, the technical problems that only bad block information of read-write errors is recorded and bad blocks are not repaired in a disk array system and the disk array system is limited by a fault-tolerance range in the prior art are solved; and the fault-tolerance method is in particular suitable for occasions with low data accuracy requirements.

A method for preventing read-disturb errors, a memory storage apparatus and a memory control circuit unit are provided. The method includes counting an operation numerical value when receiving an operation command from the host system, wherein a first physical erasing unit is selected for executing the operation command. The method also includes selecting a second physical erasing unit and reading data from the second erasing unit. The method further includes determining whether a data error occurs at the second physical erasing unit according to the data read from the second physical erasing unit, and if the data error occurs, selecting a third physical erasing unit, correcting the data read from the second physical erasing unit to generate corrected data and writing the corrected data into the third physical erasing unit.

The invention discloses a blockchain storage method supporting incremental updating of Internet of Things data. The method comprises the following steps of partitioning data, calculating a MD5 value after the data is processed into fragmented data, and sending the processed MD5 value to a full node domain; calling the intelligent contract by all nodes, and executing an adaptive PoW algorithm to complete an uplink operation; transmitting the fragmented data to a storage domain for storage, and sending a storage certificate to a full node domain after the storage node successfully stores the fragmented data. In the data updating stage, the edge nodes compare MD5 values of the fragmented data before and after modification, the fragments with data change are screened out and uploaded, and waste of storage space is reduced. According to the invention, incremental updating of data is realized, and consumption of storage space in the data server is effectively reduced. In addition, the difficulty-adaptive PoW algorithm is utilized to improve the transaction efficiency of the blockchain network.

The invention provides a driving risk evaluation method and device based on a static state factor, and belongs to the field of risk prediction. The method comprises that first human-vehicle static state data including first vehicle-owner personal characteristic data, first vehicle physical parameter data and first insurance accident occurrence data is obtained; the first human-vehicle static state data is input to a pre-obtained risk prediction model to implement risk prediction; and an evaluation result is obtained according to the first human-vehicle static state data and the risk prediction model. An evaluation result is high in accuracy, insurance companies can identity and screen driving risks via data output, the integrated loss ratio is reduced, and a win-win effect is achieved; technical and data support is provided for innovative and different pricing of the auto insurance industry, and fairer and more reasonable commercial insurance price is provided for vehicle owners who drives safely.

The embodiment of the invention discloses a symbolic data analysis method and a system. The symbolic data analysis method comprises recording data updated information into a log recording sheet when data update of a database sheet of a business system storing data in a row storage way is monitored by a DB trigger; reading updated data information by a row storage conversion unit in real time, reading the updated data in a corresponding position of the business system database upon preset database updating, and synchronizing the updated data information to the corresponding position in a memory database storing data of the database sheet in a row storage way. The preset database sheet is preset and synchronizes data into the memory database in real time; the data symbolic analysis unit can analyze data in the preset database sheet with updated data via a symbolic data analyzing way; and a symbolic data sheet with preset database sheet having updated data in target variable areas is generated. High efficient and real-time data analysis can be achieved by the use of the symbolic data analysis method and system.