111 results about "Derived Data" patented technology

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 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.

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.

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.

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 .

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.

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.

The registration machine is configured to derive a data key by using a plaintext word representing a word which is not encrypted, create encrypted data obtained by encrypting, by using the derived data key, plaintext data representing data which is not encrypted, distribute the data key to a plurality of shares, and encrypt the plaintext word and the shares with searchable encryption to create an encrypted word. The analyzer is configured to encrypt, with the searchable encryption, a plaintext query representing a query which is not encrypted to create an encrypted query. The provision server is configured to acquire the created encrypted word and the created encrypted data to register the encrypted word and the created encrypted data in a database, acquire the created encrypted query to compare the created encrypted query with the registered encrypted word, and acquire the shares if a comparison result indicates a match.

The invention discloses a data desensitization method and device, a storage medium, electronic equipment and a program product, and relates to the field of financial science and technology and the field of information security, and the method comprises the steps: constructing a blood relationship dependence graph based on a data processing link; determining the blood relationship dependence intensity of the derivative data on the original sensitive data according to the blood relationship dependence map, wherein the blood relationship dependence intensity represents the probability of obtaining the original sensitive data after leakage of the derivative data; determining the desensitization intensity of the target business data according to the sensitive field in the target business data, the scene identifier and the sensitivity level of the target business data and the blood relationship dependence intensity, and then determining a desensitization strategy according to the desensitization intensity; and performing desensitization processing on the target business data according to the desensitization strategy, and outputting the desensitized target business data. According to the method and the device, the technical problem of high desensitization leakage risk caused by incapability of effectively tracking and identifying derivative sensitive data in a desensitization technology in the prior art is solved.

Some embodiments provide a non-transitory machine-readable medium that stores a program. The program retrieves a data object associated with a defined category. The program further determines a subcategory of the defined category associated with the data object. The program also determines a set of machine learning models based on the subcategory of the defined category associated with the data object. The program further uses the set of machine learning models to determine a first set of data values. Based on the set of data values, the program also derives a second set of data values associated with the data object.

This invention discloses an AI-based data management system and method for production machinery operation, belonging to the field of industrial data management technology. The method includes: real-time acquisition of raw time-series data and derived data during the operation of production machinery, and extraction of data feature information; construction of a data value assessment model to score the total value of the data and predict access popularity, generating storage tags; construction of a data hierarchical decision model based on the storage tags, comprehensively analyzing real-time data access patterns, storage costs, and lifecycle stages, generating dynamic hierarchical instructions to dynamically allocate data to corresponding storage levels; real-time monitoring of data access patterns, dynamically adjusting storage tags, and triggering data migration operations to optimize storage performance and access efficiency. This invention improves the access speed of critical data and reduces the storage cost of low-value data through AI-driven dynamic data hierarchical storage.

Provided herein are methods of training a model for cardiac phenotyping using cardiac ultrasonography images and videos adaptable to point-of-care acquisition. The method includes providing an echocardiogram dataset; labeling the echocardiogram dataset with at least one condition of interest; splitting the echocardiogram dataset into a derivation dataset and a testing dataset; initializing a deep neural network (DNN); automating the extraction of echocardiographic view quality metrics; generating natural and synthetic augmentations of cardiac images and videos; and implementing a loss function that accounts for variations in view quality to train noise-adjusted computer vision models for phenotyping at the point-of-care. Also provided herein is a method of cardiac phenotyping employing the model.

A method for restoring missing sampling points in an array spectrometer leverages the advantages of Fourier interpolation. By adding high-frequency zero values ​​in the frequency domain, it reduces the sampling interval of spectra without missing points, thus increasing the amount of sampled data. In principle, if the original data has sufficient sampling density, Fourier interpolation offers higher accuracy compared to local interpolation. This invention proposes utilizing the distribution characteristics of spectral peaks to reset the sampling density of the original data. After meeting the interpolation prerequisites, it extracts higher-density Fourier interpolations to compensate for the sampling deficiencies in the original data, obtaining estimates with higher accuracy than linear interpolation and spline interpolation. The benefits of this invention are twofold: firstly, it obtains higher-accuracy completed values; secondly, because it does not alter the frequency distribution characteristics of the spectrum, it ensures the processing accuracy of the derived data.

A method for modifying engine files, the method performed by at least one processor in one or more servers at a cloud based location, the method comprising receiving, by the one or more servers at the cloud based location from at least one host device, a derived data cache (DDC) generated by the at least one host device; distributing the DDC from the one or more servers at the cloud based location to one or more client locations, the one or more client locations performing at least one modification to the DDC; and receiving the modified DDC, by the one or more servers at the cloud based location from the one or more client locations.

The application relates to the technical field of data storage, and discloses a multi-user storage docking method of an AI platform and an AI system. The method comprises the following steps: monitoring data access of an AI task to generate an I / O image; combining context quantization of the AI task to generate a future access intention; when the intention meets a preset optimization condition, generating an asynchronous optimization task of a specified source physical storage system and a target physical storage system; and executing the task, performing in-transit calculation in the process of data transmission from the source system to the target system to generate derived data and write the derived data into the target system. The system comprises a monitoring unit, a prediction unit, a task generation unit and an execution unit. Through prediction and response of the future access intention, the application realizes data self-adaptive optimization and seamless docking transparent to the AI task, can significantly reduce data access delay on the premise of not interrupting business, and improves system resource utilization efficiency.

Embodiments of the present application provide a data usage updating method, device and system, which can improve the accuracy of data usage statistical results. The method comprises: a user plane function entity receiving a derived data packet of a first downlink data packet from an access network device, wherein the derived data packet is obtained according to the first downlink data packet, and the derived data packet comprises a first identifier; the user plane function entity determining, according to the first identifier, that the first downlink data packet is a discarded data packet; and the user plane function entity sending data usage updating information to a session management entity, wherein the data usage updating information is used to deduct the data usage of the first downlink data packet.

The application discloses an intelligent selection and calculation method of a current and voltage transformer of a power station, and particularly relates to the field of power electronics, and comprises basic data acquisition and input, derived data calculation, index modeling and intelligent selection decision. The application realizes adaptive adjustment of selection parameters of the transformer by dynamic data acquisition and real-time calculation model, and effectively improves equipment matching precision under complex working conditions. Based on a multi-dimensional index fusion evaluation system, the application breaks the isolated decision limit of traditional item selection, achieves optimal control of the whole life cycle cost of the equipment, adopts an intelligent scoring model and an automatic decision scheme driven by a rule engine, replaces artificial experience judgment by standardized evaluation indexes, significantly improves selection efficiency and objectivity, and ensures the reliability of protection action while taking into account the three targets of measurement precision maintenance and economic optimization.

A data transformation system for implementing reproducible and consistent data transformations in multiple execution contexts (batch, streaming, etc.) where the transformation function / logic initially acts on historical raw data to produce derived data to train a machine learning model. When the model is trained and deployed to handle streaming event data, the same transformation is reused to transform streaming data into the appropriate derived data for the model scoring, and later for a refit of the model.

One or more processors store rules for performing rules-based cleaning operations on a plurality of datasets, wherein each rule comprises one or more functions to be executed against a dataset during the rules-based cleaning operations, the one or more functions each having one or more associated conditions and actions, wherein the one or more actions are performed on the dataset responsive to the one or more associated conditions being satisfied. The one or more processors further apply the rules to each of the plurality of datasets to perform the rules-based cleaning operations. To apply the rules to a given dataset, the one or more processors identify an ordered list of the one or more functions to be executed with respect to the given dataset during the rules-based cleaning operations and determine, for each of the one or more functions, whether the given dataset satisfies one or more conditions associated with a respective function of the one or more functions. Responsive to the given dataset satisfying the one or more conditions associated with the respective function, the one or more processors perform, on the given dataset, one or more actions associated with the respective function and provide a derived dataset comprising at least one modification to the given dataset resulting from the one or more actions associated with the respective function.

The present disclosure relates to a system, method and non-transitory computer-readable media designed for decision support in treatment using a medical device at a treatment site. A medical device collects (S702) and transmits (S704) healthcare data to a first server of one or more servers. This server runs (S706) an application providing a first graphical user interface (GUI) based on the healthcare data. The system includes a portable device equipped with a sensor to retrieve (S708) identification data of the medical device and transmit (S710) derived data to the servers. The servers compare this data with stored data to determine (S712) that the healthcare data can be displayed on the portable device. The servers generate (S714) a URL to the server application, which is then sent to the portable device. The portable device uses this link to connect (S716) to the server application and display the first GUI.