187 results about "Derived Data" patented technology

Implementations are directed to developing and facilitating a data collaboration using a debug mode that permits debugging a data pipeline without exposing collaborator data. In an example implementation, collaborators that contribute production data into a data pipeline specify sample data such as mock, random, or expired data for each production dataset they contribute. When one of the collaborators triggers the data pipeline in debug mode, a modified data pipeline that substitutes the production data for the sample data is executed to generate sample derived data in a data trustee environment, without exposing the production data. Since debug mode does not use production data, a data pipeline may run in debug mode even if a governing contract has not been signed (and entitlements have not been granted), and / or diagnostic logs generated by the modified data pipeline in debug mode may be exposed to the collaborators for debugging purposes.

The invention relates to the technical field of data analysis, in particular to an intelligent water affair monitoring method and system based on digital twinning, and the method comprises the steps: obtaining the abnormal degree of all kinds of water affair data at each moment according to the historical data of all kinds of water affair data, obtaining association degrees among the various water affair data and abnormal independence of the various water affair data at various moments; derived data of each device and cause data of various water affair data, direct influence devices and indirect influence devices are obtained, and the abnormal degree, the abnormal independence and the correlation degree between the water affair data of the water affair data are combined. And obtaining the fault factor of each direct influence device and the fault factor of each indirect influence device of the water affair data at each moment so as to obtain a fault device, and marking the fault device in the digital twinborn model of the water affair system. According to the invention, by analyzing the association between different water affair data in the water affair system, the fault in the water affair system is accurately monitored and positioned.

The invention provides a vegetation remote sensing monitoring method and system based on a geographic information cloud platform, and the method comprises the steps: determining the time and region of target monitoring, and determining the geographic boundary and time range of target monitoring according to the demands of a user; screening a suitable data source, and cutting the screened remote sensing image; carrying out cloud layer interference processing on the image data by adopting a cloud removal algorithm, and removing pixels covered by a cloud layer; carrying out radiation correction, geometric correction and atmospheric correction standardization preprocessing on the image; calculating a vegetation ecological index, a vegetation drought index and a meteorological factor of the target area based on the preprocessed remote sensing image; and displaying the processed vegetation growth time sequence monitoring data on a user interface in an image form, and supporting exported data of the data for subsequent analysis. According to the method, through an efficient processing algorithm, the precision and timeliness of vegetation growth state monitoring are remarkably improved, and important technical support is provided for vegetation management, ecological protection and scientific research.

The invention provides a ship pipeline leakage detection network training method, an application method and a pipeline detector, and belongs to the field of industrial equipment detection. The training method comprises the following steps: performing physical modeling analysis on acquired ship pipeline sensing data to obtain physical derived data, and fusing the physical derived data and the ship pipeline sensing data to obtain fused training data; performing multilayer graph convolution on the fusion training data to obtain spatial information features, performing spatio-temporal information attention enhancement to obtain spatio-temporal information features, fusing the spatial information features and the spatio-temporal information features to obtain spatio-temporal fusion features, predicting the spatio-temporal fusion features to obtain pipeline leakage prediction output, and determining prediction loss according to the pipeline leakage prediction output. And iteratively training to obtain a ship pipeline leakage detection network. According to the ship pipeline leakage detection method, the data dimension and the data volume are enriched through the physical derived data, the space-time relevance in the mined data is enhanced through multilayer graph convolution and space-time information attention, and the accuracy of ship pipeline leakage detection is effectively improved.

Provided herein are methods, apparatuses, computer program products, and systems for anomaly detection using machine learning models. One method can include obtaining X-ray computed tomography (CT) scan data for a scan object of a predetermined object type; producing derived data from the X-ray CT scan data, wherein the derived data reveals at least one interior structure usable for anomaly detection in objects of the predetermined object type; inputting at least the derived data into a machine learning model, which has been trained using at least derived data produced from prior X-ray CT scan data for objects of the predetermined object type; receiving an output from the machine learning model that indicates that at least one anomaly has been detected for the scan object; and providing an output to a physical device based on the at least one anomaly having been detected for the scan object.

Systems and methods for assessing user engagement with digital materials combining a variety of data sources, including video images, user device activity, and derived data, to estimate user attention recovery time. User engagement can be proactively monitored, analyzed and optimized, thereby improving the learning experience for student users.

The invention discloses a data security management method and system based on AI drive, and relates to the technical field of artificial intelligence and data security, and the method comprises the steps: generating a fusion feature vector through a multi-modal feature extraction and semantic alignment mechanism; constructing a blood relationship knowledge graph, performing graph embedding calculation on the blood relationship knowledge graph to obtain a graph embedding vector, and calculating a blood relationship propagation sensitivity score; generating a sensitivity evaluation result through a dynamic decision neural network in combination with context features of the data access request; generating and executing an access control strategy according to a sensitivity evaluation result, and triggering cascade label evaluation on downstream derivative data along a blood relationship knowledge graph; and selecting samples based on confidence, submitting the samples to manual auditing, and executing incremental learning updating on model parameters according to auditing feedback. According to the method, the sensitivity identification accuracy of cross-modal data is improved through multi-modal semantic fusion, and automatic cascade updating of sensitive tags is realized through a blood relationship propagation mechanism.

The invention discloses an intelligent ticket business recommendation method and system based on dynamic flight data, and relates to the technical field of intelligent ticket business recommendation, the intelligent ticket business recommendation method based on the dynamic flight data comprises the following specific steps: step 1, based on basic data, associated data and derivative data contained in a dynamic flight, establishing a dynamic ticket business recommendation model; the method comprises the steps of collecting multi-dimensional dynamic flight data and completing data preprocessing, 2, constructing user portrait data by integrating user real-time preferences and basic demands, and dynamically updating the user portrait data based on fixed duration or behavior recognition, and 3, based on the real-time user portrait and flight dynamic data as input, adopting a random forest algorithm to identify the flight data in real time. And step 4, setting a comprehensive recommendation score based on the feature scores, determining flight ticket business data to be matched, and screening data with high matching degree with the user as recommended ticket business data, and by capturing user demand changes in real time, it is ensured that recommended contents are highly matched with user demands.

Systems and methods for encrypting and streaming AV sensor data and AV derived data. In particular, systems and methods are provided for receiving AV sensor data at a streaming platform and providing the received data to multiple endpoints. In some examples, the multiple endpoints are multiple viewers viewing the AV sensor data. In various examples, an AV transmits the AV sensor data to the streaming platform, and the AV sensor data can be transmitted from the streaming platform to multiple endpoints without any effect on AV central processing unit or bandwidth constraints. The AV sensor data can include multiple data streams that are layered and compressed for efficient streaming, and the streaming platform can receive real-time streamed data from the AV even as bandwidth and connection constraints vary. In various examples, the bit rate of the video ingestion from the AV can be changed based on bandwidth availability.

Empirical data fitting with Ordered Statistic Constant False Alarm Rate (CFAR) detection is described. An empirical approach is used to derive data for indicated expected target responses to provide a CFAR in a variety of different noise distributions. Multiple (e.g., at least two) ordered-statistics are extracted from radar data, which are then used identify a ratio for mapping to an appropriate CFAR multiplier of quantile function for a distribution at hand. Empirical data fitting evaluates an ordered-statistic ration (OSR) against expected OSR values. From evaluating the expected OSR values derived from multiple test frames, a mapping between measured OSR values and their appropriate CFAR multiplier is derived. Through this empirical data fitting, a radar system can perform CFAR detection to account for shape shifts or other variations in a noise distribution beyond just fluctuations in noise strength.

The invention discloses an image transmission system for visual communication display, and relates to the technical field of data management.The image transmission system collects and manages multi-dimensional information data of all credit enterprises through a data management unit, and a behavior analysis unit analyzes behaviors of authorized users; the data management unit generates a reading strategy of each dimension, and the enterprise portrait unit generates portrait analysis of each credit enterprise based on different dimensions. In the process of comparing and generating the reading strategy of each dimension, a plurality of character data are intercepted from the dimension data table corresponding to the dimension with relatively high click frequency and relatively large data capacity; according to the method, the high-frequency access data is stored in the independent table to generate the derivative data table of the corresponding dimension, in this way, the database cache can be more effectively utilized, and more common data can be ensured to be reserved in the cache by separating the high-frequency data, so that the performance is improved, the contention for a large table can be reduced, and the possibility of lock contention is reduced.

The invention provides a method for evaluating a set of input data, the input data comprising at least one of: clinical data of a subject; genomic data of a subject; clinical data of a plurality of subjects; and genomic data of a plurality of subjects, using a deep learning algorithm. The method includes obtaining a set of input data, wherein the set of input data comprises raw data arranged into a plurality of data clusters and tuning the deep learning algorithm based on the plurality of data clusters. The deep learning algorithm comprises: an input layer; an output layer; and a plurality of hidden layers. The method further includes performing statistical clustering on the raw data using the deep learning algorithm, thereby generating statistical clusters and obtaining a marker from each statistical cluster. Finally, the set of input data is evaluated based on the markers to derive data of medical relevance in respect of the subject or subjects.

Encrypting a verifiable credential (VC) and generating one or more instructions, at least one of which grants a scope of permission associated with the VC to the relying entity. The scope of permission includes permission to access a subset of data contained in the VC or a portion of data that can be derived from data contained in the VC. The encrypted VC and the one or more instructions are sent to the credential issuer or the relying entity to cause the credential issuer to generate a response containing the subset of data or the derived data and a proof code. The proof code is configured to prove the validity of the subset of data or the derived data.

Systems and methods for artificial intelligence (“AI”) bidirectional monitoring with a quantum-computing-powered system as a flexible guardrail to AI are provided. The systems and methods may include a quantum processor and a classical processor. The systems and methods may include requesting data elements pertaining to boundaries. The systems and methods may include controlling boundary rules and creating classical boundary rules via a classical processor. The systems and methods may include interfacing classical boundary rules with a quantum processor. The systems and methods may include running Grover's conversions in parallel over the boundary rules. The systems and methods may include pulling dynamic market data through a legacy transformation platform including dynamically derived data values and a machine learning model (“MLM”) thereby monitoring and controlling an AI and machine learning (“ML”) processor.

A storage product manufactured as a standalone computer component and installed in a computing system to implement an internet application. The storage product includes a network interface, a host interface, computing circuits, and a local storage device having a storage capacity accessible via the network interface. A data generator is connected to the network interface. A local host system is connected to the host interface to control access, made via the network interface. The data generator can send bulk data to the network interface. The computing circuits can generate derived data from the bulk data and store the derived data and / or the bulk data in the local storage device. A central server and / or a user device can connect over internet via to the network interface of the storage product to access the derived data and / or the bulk data.

The invention provides an automatic state evaluation method for hydropower main equipment, and the method comprehensively considers the operation data, defect data and test data of the equipment at the same time, constructs the state monitoring data of the equipment, derivative data of big data mining, and other characteristic index quantities which are closely related to the health state of the equipment. And an evaluation model and a score weight of a single characteristic index quantity are formed. And an evaluation model is constructed from index quantity-component-equipment category-equipment hierarchically, so that the health value of the whole equipment is formed. The method is composed of information configuration, state evaluation, evaluation result release and diagnosis analysis report, realizes automatic scoring of the state of the hydroelectric main equipment, and improves the accuracy and efficiency of scoring. And through intelligent calculation of the index data and the evaluation standard, the requirement of manual intervention is reduced, and the misjudgment rate is reduced.

The present disclosure relates to deriving cryptographic keys for use in encrypting data based on a plaintext to be encrypted. An example method generally includes receiving, from a querying device, a request for a cryptographic key. The request generally includes data derived from a plaintext value to be encrypted and an indication of a type of the plaintext value to be encrypted. A cryptographic key is generated based, at least in part, on the derived data and the type of the plaintext value to be encrypted. The key deriver transmits the generated cryptographic key to the querying device.

There is provided networks, systems and displays for providing derived data and predictive information for use in emergencies; and in particular for use in wildfire emergencies for a EFMS having three fire suppression medium sub-systems. More particularly, there is provided systems, equipment and networks having a control system having an operation control command plan for performing an operation plan. in embodiments the operation plan can be a hydration plan, a lockout plan, a low line pressure plan, an adjacent structure based plan, an auto-activation notice with default activation plan for use with EFMS having three fire suppression medium sub-systems. In embodiments there is provided a fire retardant fire suppression medium sub-system. In embodiments there is provided three fire suppression medium sub-systems including a foam fire suppression medium sub-system, a water fire suppression medium sub-system and a fire retardant fire suppression medium sub-system. In an embodiment there is provided a parcel by parcel control and optimization of the EFMS having three fire suppression medium sub-systems for a particular area.

This invention relates to the field of communication data processing, specifically to a method and system for ship identification in multi-channel communication. It includes: acquiring historical multi-channel communication data; determining batch camouflage verification data based on camouflage verification data; acquiring a batch ensemble classifier and a local ensemble classifier; determining first derived data and second derived data based on the batch camouflage verification data and normal verification data; determining the batch camouflage verification data with updated weights based on the first and second derived data; determining an overall ensemble classifier; acquiring target multi-channel communication data; determining target batch data from the batch camouflage verification data with updated weights based on the target feature vector; and determining the detection result corresponding to the target multi-channel communication data based on the target batch data and the overall ensemble classifier. This invention can improve the accuracy of ship identification.

Disclosed are methods comprising determining experimental data associated with one or more monoclonal antibodies (mAbs), determining computationally-derived data associated with the one or more mAbs, wherein the computationally-derived data comprises one or more computational parameters weighted based on accessible surfaces (ASAs) of one or more residues of the one or more mAbs, determining, based on the experimental data and the computationally-derived data, a plurality of candidate predictive models, determining an optimal predictive model from the plurality of candidate predictive models, and outputting the optimal predictive model. 20 26 20 53 60 07 J ul 2 02 6 A B S T R A C T D i s c l o s e d a r e m e t h o d s c o m p r i s i n g d e t e r m i n i n g e x p e r i m e n t a l d a t a a s s o c i a t e d w i t h o n e o r 2 0 2 6 2 0 5 3 6 0 0 7 J u l 2 0 2 6 m o r e m o n o c l o n a l a n t i b o d i e s ( m A b s ) , d e t e r m i n i n g c o m p u t a t i o n a l l y - d e r i v e d d a t a a s s o c i a t e d w i t h t h e o n e o r m o r e m A b s , w h e r e i n t h e c o m p u t a t i o n a l l y - d e r i v e d d a t a c o m p r i s e s o n e o r m o r e c o m p u t a t i o n a l p a r a m e t e r s w e i g h t e d b a s e d o n a c c e s s i b l e s u r f a c e s ( A S A s ) o f o n e o r m o r e r e s i d u e s o f t h e o n e o r m o r e m A b s , d e t e r m i n i n g , b a s e d o n t h e e x p e r i m e n t a l d a t a a n d t h e c o m p u t a t i o n a l l y - d e r i v e d d a t a , a p l u r a l i t y o f c a n d i d a t e p r e d i c t i v e m o d e l s , d e t e r m i n i n g a n o p t i m a l p r e d i c t i v e m o d e l f r o m t h e p l u r a l i t y o f c a n d i d a t e p r e d i c t i v e m o d e l s , a n d o u t p u t t i n g t h e o p t i m a l p r e d i c t i v e m o d e l .

A method of enabling propagated deletion in a distributed database system comprises receiving a request to delete data in a distributed database system; causing a display of a relevant dataset and a switch between applying propagated deletion or not; receiving a first selection of a subset of records from the relevant dataset using one or more filter functions and a second selection of applying propagated deletion to the subset of records; and applying propagated deletion to the subset of records to generate a new dataset.

The invention provides a configuration parameter storage method, electronic equipment and a storage medium, and relates to the field of distributed technologies. The method is applied to a configuration center, and comprises the following steps: receiving pre-stored key derived data and a configuration parameter plaintext sent by an application server side; extracting central key derived data from a parameter database according to the pre-stored data storage identifier; determining a target key according to the central key derived data and the pre-stored key derived data; encrypting the configuration parameter plaintext according to the target key to obtain a configuration parameter ciphertext; randomly selecting an unused target unique identifier, and storing the target unique identifier and the configuration parameter ciphertext into a parameter database; and sending the target unique identifier to the application server side, so that the application server side stores the target unique identifier. According to the method, the security of the configuration parameters in the storage stage is effectively improved, and an attacker is prevented from acquiring the configuration parameters by attacking the configuration center.

This invention discloses a method for processing multimodal data acquisition in intelligent manufacturing systems, relating to the field of industrial data governance technology. The method includes: acquiring multimodal data streams; segmenting raw data blocks and standardizing and encapsulating them to generate metadata records; establishing time indexes, batch indexes, and process indexes; hashing the metadata records to generate content addresses and writing them to object storage; recalculating the hash and comparing the content addresses to complete consistency verification and writing the results to a commit log table; generating operator records for successfully committed data blocks and hashing them to obtain operator addresses; registering lineage edges to form directed acyclic lineage relationships; generating leaf lists according to a preset range and aggregating them to obtain root digests; verifying content addresses object by object based on the leaf lists and root digests; replaying and recalculating to generate replay-derived data blocks; comparing them to generate audit records and storing them. This invention ensures continuous monitoring of data quality during big data acquisition through the replay and recalculation processing of directed acyclic lineage relationships.

This application discloses a method, system, device, and computer medium for generating 3D models of urban rail transit, relating to the field of digital twin modeling and 3D visualization technology for urban rail transit. The method involves acquiring diverse and heterogeneous data of urban rail lines, including 2D design drawings and derived data; processing the diverse and heterogeneous data to generate a basic dataset for the urban rail lines; acquiring user-defined configuration data for the urban rail lines; integrating the basic dataset and the user-defined configuration data to obtain a target dataset for the urban rail lines; identifying 3D model elements matching the target dataset for the urban rail lines from a pre-set model library; and rendering the 3D model elements according to the target dataset to generate a 3D model of the urban rail lines. This method ensures that the generated 3D urban rail models meet user needs, provides diversity and flexibility in model construction, improves model scalability, reduces the degree of human intervention, and enhances the efficiency of urban rail modeling.

This disclosure relates generally to method of modernizing a legacy batch based on at least one functional context. The method includes at least one of: preprocessing, a plurality of metadata associated with a plurality of batches to obtain a plurality of derived data; generating, the functional context based on the plurality of derived data; determining, an average elapsed time for at least one application from the at least one functional context; parsing, log of the at least one consistent long running job to identify step and associated file referenced in the at least one long running job; determining, a hotspot based on at least program; and recommending, at least one batch design associated with at least one batch job in a future state. The hotspot corresponds to long running job on a batch stream, high volume files, and program with an increased millions of instructions per second (MIPS) usage.

The invention provides a radio pulsar searching method, system and equipment, and the method comprises the steps: obtaining observation data of a radio telescope, and carrying out the processing of the observation data, so as to generate a plurality of groups of time series data corresponding to a plurality of dispersion quantity channels; performing staged periodic signal search on each group of time sequence data, and processing each group of time sequence data or derived data formed by each group of time sequence data according to a generated quasi-period to generate feature data of a plurality of candidate bodies; inputting the feature data of the plurality of candidate bodies into a pre-trained classification model to obtain a classification result representing the credibility of the plurality of candidate bodies as a target signal; and obtaining a comprehensive score of the plurality of candidate bodies, and sorting the plurality of candidate bodies according to the comprehensive score to generate a pulsar candidate body list.

Techniques are described for protected data restoration using confidential computing. An example method comprises receiving, by a data platform implemented by a computing system, a request to restore an encrypted chunk of data, the encrypted chunk stored with first encrypted key data and second encrypted key data, receiving, by an enclave implemented in a trusted execution environment, first encrypted key data and second encrypted key data from the storage cluster, decrypting, by the enclave, the first encrypted key data to obtain the first key data and the second encrypted key data to obtain the second key data, generating, by the storage cluster, a derived data encryption key corresponding to the data encryption key based on the first key data and the second key data, and decrypting, by the storage cluster, the encrypted chunk with the derived data encryption key to generate a decrypted chunk.

In some examples, a system having a plurality of computer nodes receives a command based on program code of a program being developed in an interactive programming session. The system distributes data items from a network-attached memory to a distributed data object having data in node memories of the plurality of computer nodes. A dataset manager performs an operation specified by the command on the distributed data object, the operation executed in parallel on the plurality of computer nodes. The dataset manager produces derived data generated by the operation on the distributed data object, the derived data accessible by a programmer in the interactive programming session.

The present invention discloses a method for constructing a composite vortex identification model based on Helmholtz decomposition and deep learning, which comprises the following steps: obtaining the east-west flow velocity U and the north-south flow velocity V data of seawater to construct a velocity vector field image; performing Helmholtz decomposition on the velocity vector field image to obtain a potential field F d Image and rotation field F c Image; According to the rotation field F c The potential function and UV data in the image are used to mark single vortex cyclonic vortex, single vortex anticyclonic vortex, compound vortex cyclonic vortex and compound vortex anticyclonic vortex, and the vortex field images before and after the marking are used as deep learning data sets; the deep learning data sets are trained using a neural network to obtain a compound vortex recognition model. The present invention uses the Helmholtz decomposition method to process the derived data of the satellite altimeter to improve the recognition accuracy of the compound vortex, and uses a deep learning method to improve the recognition efficiency of the compound vortex, and finally realizes the application of the model in the entire sea area.