86 results about "Data combination" patented technology

The present disclosure relates to communication methods, communication devices, a communication system, a storage medium and a program product, and belongs to the technical field of communications. A method comprises: a first device receives second data corresponding to first data transmitted by a second device, wherein the first data and the second data form training data pairs, and the training data pairs are used for training a first model. In the method provided in the present disclosure, while transmitted data of a transmitting end is unknown, the transmitted data of the transmitting end and received data of a receiving end are combined as training data pairs on the basis of the received data, so as to train a model, thereby improving the model training performance and accuracy.

A computer-implemented method of performing robotic simulation and design may include performing a simulation where a robot interacts with a user, and generating sensor data indicative of the robot interaction with the user during the simulation. The computer-implemented method may include generating discussion data indicative of user feedback from the simulation, generating form data associated with the user feedback from the simulation, and combining the sensor data, the discussion data, and the form data in a multimodal signal. The computer-implemented method may include determining a design characteristic of the robot based on the multimodal signal, and augmenting a robot model to incorporate the design characteristic using a machine learning algorithm.

The embodiment of the present application discloses a digital certificate installation method and equipment, in which, when a terminal device opens and installs a first certificate file and a second certificate file, first certificate data in the first certificate file and second certificate data in the second certificate file are combined as a set of target certificate data for installation, and a user only needs to perform a naming operation once for the set of target certificate data, thereby greatly simplifying the naming operation required to be performed by the user in the digital certificate installation process. In addition, when the terminal device is connected to a network subsequently, the user can simultaneously call the first certificate data and the second certificate data in the set of certificate data by selecting the name of the set of certificate data.

This invention discloses an adaptive maximum power point tracking method, system, and storage medium for solar cells, belonging to the field of photovoltaic power generation technology. The method includes: extracting mechanical correction data and temperature regulation data of the solar cell for the current day according to a preset time slot, and constructing a multi-dimensional feature vector; combining the multi-dimensional feature vector and the corresponding recorded electrical operation data to form the daily operation record; based on the daily operation record, clustering and grouping the multi-dimensional feature vector by fusing the feature distance and time-series distance to obtain multiple control intervals; calculating corresponding control parameters based on the multi-dimensional feature vector and electrical operation data respectively; and upon arrival of the next day, reading the corresponding control parameters according to the control interval to which the current time belongs to complete the tracking of the solar cell's maximum power point. This invention can improve the accuracy of maximum power point tracking.

This specification discloses a method for air-to-ground transmission of flight record data with high temporal integrity, comprising: encapsulating target data to obtain a data assembly containing a header information portion, a data content portion, and a tail information portion; enabling the ground subject to perform data verification operations based on the header information portion and / or the tail information portion after receiving the data assembly; comprising: decomposing the target data into data packets; calculating the feature value of any data packet and determining the flag bit of the data packet for each data packet; setting the header information portion of the first data packet to preset information for the first data packet; using the feature value and flag bit of the previous data packet as the header information portion for any non-first data packet; generating a check code corresponding to the combination of the header information portion and the data content portion as the tail information portion; and forming a data assembly containing the header information portion, the data content portion, and the tail information portion.

This invention discloses a method for image data augmentation based on dynamic gating and large-model collaborative modeling, comprising: processing original image samples through a target model to obtain feature representations and predicted class probability distributions; calculating the difficulty score of the original image samples relative to the current learning state of the target model through a dynamic gating network; generating routing instructions to adaptively route the original image samples to the feature space augmentation path corresponding to the lightweight augmentation module, or the image synthesis augmentation path corresponding to the multimodal large-model augmentation module, generating augmented data; and combining the original image samples and the augmented data to train the target model. The method utilizes a dynamic gating network to perform real-time difficulty assessment of samples, achieving an intelligent routing strategy and reducing computational costs and overhead. Through large-model collaborative augmentation, the target model simultaneously possesses robustness to intra-class variations and strong generalization ability for complex and unknown scenarios.

The objective is to provide a mechanism that enables printing devices to perform appropriate printing even with information processing equipment that has low processing power. [Solution] A printing system that enables communication between a mobile terminal 103 and a printing device 102, wherein the mobile terminal 103 makes a capability acquisition request to the printing device 102 to request information indicating whether partial acquisition of data combining conditions that can be set by the printing device 102 is possible, and when the printing device 102 responds with information indicating that partial acquisition is possible, the mobile terminal 103 makes a partial acquisition request to the printing device 102 to request partial acquisition, and the printing device 102 responds to the capability acquisition request with a capability acquisition response indicating whether partial acquisition is possible to the mobile terminal 103, and responds to the partial acquisition request with a portion of the data combining conditions that can be set by the printing device 102 to the mobile terminal 103.

The application provides a kind of city road network data resource directory generation method and system combined with service atlas, it is related to city traffic data management technical field, first access city road network service association conduction source and road network basic data generate service data association conduction pool;Then, based on the service association conduction logic deduces the road network data association derivative relationship, forms data association derivative network;According to the dynamic adaptation of data association derivative network, the dynamic directory unit is constructed by combining road network data;Based on the service conduction attribute of dynamic directory unit, the association conduction link is generated;Finally, the dynamic directory unit and the association conduction link are integrated to form a closed-loop city road network data resource directory that can be dynamically updated, which effectively improves the management efficiency and utilization value of city road network data resources.

The application discloses a hardware forming defect prediction method and system based on stamping parameters, and particularly relates to the technical field of hardware forming defect prediction, and comprises the following steps: acquiring stamping parameters corresponding to each stroke in a hardware forming process and forming response data corresponding to the stamping parameters, combining the stamping parameters and the forming response data to form a stroke data entry, and writing the stroke data entry into a cache queue of edge calculation; reading a plurality of continuous stroke data entries in the order of strokes from the cache queue of edge calculation, and calculating the difference value of the stamping parameters in adjacent stroke data entries to obtain parameter change data; collecting the stamping parameters and the forming response data in real time in continuous strokes, constructing a separability index reflecting the sensitivity of the material to the parameter change, and judging whether the deformation freedom of the material is contracted according to the continuous downward trend of the separability index, so as to identify the state of the material being forced to enter an irreversible failure path before the appearance of the explicit defect.

This disclosure provides a data shifting operation method, apparatus, electronic device, and storage medium, relating to the field of data operation technology. The method includes: splitting a source operand into multiple sub-operands according to its bit width, determining the shift direction based on the shift type, and determining the segmented shift processing method based on the target shift amount, so that multiple shifting operation units perform shift operations on each sub-operand respectively; further, combining and concatenating the shift results across segments to obtain the target shift result. This technical solution, by transforming the shifting of high-bit-width data into a collaborative processing process of multiple low-bit-width data, and employing differentiated processing paths under different shift ranges, can reduce circuit implementation complexity, reduce hardware resource consumption, improve the reusability of shifting operation units, and enhance the flexibility and adaptability of the system structure while ensuring the consistency of shift semantics.

This invention discloses a Skyline query method for multidimensional missing data combined with preferences for intelligent recommendation. The invention utilizes a weighted classification tree for weighted classification preprocessing of multidimensional missing data, classifying data with similar missing patterns, and combines bucket optimization and incremental generation strategies to achieve efficient querying. The method includes: performing weighted preprocessing and partitioning of data using a multidimensional missing data classification algorithm based on a weighted classification tree to form a set of leaf node buckets; optimizing and merging the classified data into buckets to construct a WCT-R*-Tree index; performing preference Skyline querying and pruning based on the index structure; and performing bidirectional dominance checks using a combined preference Skyline generation and dominance algorithm to output a result set. This invention can significantly reduce the number of tuple comparisons, lower computational overhead, and improve pruning efficiency, solving the problems of low efficiency and insufficient accuracy of existing methods when processing multidimensional missing data and complex combined preferences. It can be widely applied in the fields of data query and intelligent recommendation technology, such as intelligent housing recommendation and product matching.

This program causes a computer to perform the processing of: acquiring a tabular file that includes tabular data associated with a layout specifying the relationships among a plurality of cells; extracting layout-defined data included in the tabular data and defined as a layout target; and generating an integrated table by combining a plurality of items of the extracted layout-defined data.

The application relates to the field of data analysis, and provides a data sampling method based on data modeling, a device and a storage medium.The method comprises the following steps: obtaining a data sampling requirement, the data sampling requirement comprising data attributes and a sampling quantity; comparing the data attributes with preset statistical attributes to obtain target attributes and non-statistical attributes; extracting target data corresponding to the sampling quantity from a preset bitmap index according to the target attributes; randomly generating virtual data of the sampling quantity according to the non-statistical attributes; and combining the target data and the virtual data to obtain sampling data, each piece of the sampling data comprising target data corresponding to the target attributes and virtual data corresponding to the non-statistical attributes. The target attribute data meeting the data sampling requirement is quickly generated in the form of the bitmap index, and the data of the non-statistical attributes is generated in a random manner, so that the data requirement and the quantity requirement of the data sampling are met, and the sampling efficiency is effectively improved, and the reliability of the sampling data is ensured.

This disclosure relates to the field of bridge engineering, specifically to a design method, system, and medium for HPC-RC combined eccentrically compressed columns. The method includes: defining a design space; selecting optimal design data combinations based on engineering specification constraints, bearing capacity constraints, and cost-effectiveness values ​​to construct a design data sample set; constructing a two-branch heterogeneous neural network model, the model including an input layer, a shared feature extraction layer, a diameter prediction classification branch, and a reinforcement area prediction regression branch; constructing a loss function composed of focal loss and mean square error loss; training the two-branch heterogeneous neural network model using the design data sample set; and using the trained two-branch heterogeneous neural network model to predict the diameter and total reinforcement area of ​​the eccentrically compressed column. This disclosure improves the stability and accuracy of the design, reduces the computational burden, increases computational speed, and reduces computational complexity.

The present invention provides a robot motion path generation method and a robot motion path generation apparatus that enable the user to understand the timing in the motion path where the gap between at least one of the robot and the workpiece on the motion path within the derived robot placement area and the placement area is relatively small. [Solution] In this method for generating the operating path of the substrate transport robot 10, the timing 221a in the operating path OP where the size of the gap G between at least one of the robot 10 and the workpiece W on the operating path OP and the placement area 20 is smaller than a predetermined distance is displayed on the display unit 220 in an identifiable manner. This is done using an integrated data ID which is an integrated data ID obtained by combining robot CAD data CD1, placement area CAD data CD2, and workpiece CAD data CD3, used to derive the operating path OP of the substrate transport robot 10 in the placement area 20 where the substrate transport robot 10 and the workpiece W are placed.

The application discloses an injection mold human-computer interaction optimization system and particularly relates to the field of fault prediction and health management of injection mold monitoring, and comprises a data normalization module, a trend extraction module, a tensor construction module, a score identification module, a risk generation module and an early warning triggering module; the data normalization module collects cavity pressure curve, guide pillar displacement, mold locking force fluctuation curve and mold surface temperature state data in an injection cycle, performs cycle segmentation and synchronous normalization processing, and constructs a state data window; the application constructs a behavior mutual exclusion relationship tensor based on state data combination under the condition of edge calculation, dynamically identifies and predicts deviation areas in combination with confidence change trends, realizes the perception response of the conflict relationship of multiple state parameters, and thus solves the problems that the existing fault prediction and health management system cannot identify the conflict between state variables, causes the distortion of health determination and the delay of intervention.

The invention discloses a chronic disease whole-process digital medical treatment cloud service and medical insurance real-time supervision unified large model. The model comprises a technical architecture and a combination engine; the technical architecture comprises an infrastructure layer, a data base layer, a core technology layer, a business application layer and an ecological collaboration layer. The combination engine comprises federated learning and block chain combination, digital twinning and AI and big data combination, edge calculation and 5G and Internet of Things combination, NLP and knowledge graph and AR combination, quantum encryption and zero knowledge proof combination, and Web3.0 and DAO combination. Through cross-layer cooperation of a five-layer technical architecture and six technology combination engines, balance between data sharing and privacy protection is realized, cross-mechanism data islands are broken, data security compliance is guaranteed through technologies such as federal learning block chain quantum encryption, and the contradiction between data intercommunication and privacy protection in the prior art is solved; according to the digital twinborn AI big data combination, a full-process risk control system is constructed, and risk pre-prediction in-event intervention and post-event optimization are realized.

Techniques and systems are provided for user interactions. For instance, a process can include generating perception data based on received sensor data; generating normalized sensor data based on an application of one or more transformations to the received sensor data; combining the perception data and normalized sensor data to generate output data; and predicting, based on the output data, a user interaction with an interactable element in a virtual environment.

The application discloses a kind of communication tunnel test data generation method and its device, electronic equipment, it is related to automatic test technical field, wherein, the test data generation method includes: determining initial configuration item;Based on target strategy dependency graph, configuration item extension is carried out to initial configuration item, obtains multiple target extension configuration items and at least one target extension configuration item value corresponding to each target extension configuration item, wherein, target strategy dependency graph is constructed by multiple configuration items and multiple constraint relations;Based on initial configuration item, all target extension configuration items and all target extension configuration item values, at least one combination test data for communication tunnel is generated.The present application solves the technical problem that different test data combinations are difficult to cover in related technologies, and there is a risk of missing measurement.

This document proposes a color selecting method for lighting equipment, that enables an operator to perform an intended selection of color by intuitive steps of operation. Lighting device includes full color light source, such as RGB LED. The lighting device can process data, to control colors and brightness, can collect data from a connected computing devices via communication interface. The software on the computing device can generate a data to control lighting device, such as colors, timing, brightness, or other data related to the user such as time, or weather forecasts. The software on the computing device can send a combination of such data to lighting device which user prefer to use for lighting. The lighting device emits the light, based on the received data, may aid users willing to select the lighting color and control with ease. The software may aid users selecting the intended color for lighting.

This application discloses a self-binding anti-copying method, system, and storage medium. The method includes: reading hardware features from a target chip; generating a first hardware fingerprint based on at least two types of the hardware features using a preset hybrid algorithm; reading locking information from non-volatile memory; determining that the first run is occurring when the locking information is empty or invalid; generating integrity verification data based on the first hardware fingerprint and binding data; combining the first hardware fingerprint, the binding data, and the integrity verification data to construct locking information; and writing it into the non-volatile memory. By reading the chip's multi-dimensional hardware features to generate a unique fingerprint, and automatically completing the binding process on the first run, the fingerprint is dynamically verified upon subsequent startups. This method achieves high-strength anti-copying protection without requiring an external server or dedicated security hardware, while greatly simplifying the mass production process.

This application discloses a primary-backup fault-tolerant storage architecture for spaceborne computers, relating to the field of spaceborne solid-state memory technology. It includes a physical storage module and a fault-tolerant management module. The physical storage module comprises: a primary boot partition for storing the computer system's bootloader; a primary system root partition for storing system files; a backup partition comprising three independent sub-storage areas—a first sub-storage area, a second sub-storage area, and a third sub-storage area—each storing a mirrored data packet formed by combining data from the primary boot partition and data from the primary system root partition; and a data partition for storing user data. This architecture enables the primary boot partition and primary system root partition to operate in single-copy mode during normal task execution, completely avoiding the additional latency, bandwidth consumption, and CPU overhead caused by real-time data synchronization and online voting, thereby maximizing the core performance and efficiency of spaceborne task processing.

This invention discloses a method and system for constructing an ophthalmological knowledge graph. The invention relates to the technical field of knowledge graphs, and involves labeling a patient's multimodal data in the ophthalmological dimension, combining this data with a corresponding standard ontology for deep learning to output corresponding ontology association paths. Multiple key data points are determined by parsing these ontology association paths. Furthermore, an adaptive optimization mechanism is used to construct an ophthalmological twin, improving its accuracy. The ophthalmological twin is loaded with corresponding ophthalmological constraint relationships, and a corresponding ophthalmological relationship network is constructed using a graph neural network. Ophthalmological anomaly data is simultaneously acquired, and dynamic deduction is performed using the ophthalmological relationship network to construct an N-gram knowledge graph containing temporal relationships. A semantic search mechanism is then used to determine the corresponding target data. Based on the parsing of this target data, multiple factors to be pre-analyzed in different dimensions are determined to determine the updated data combination, thus realizing the dynamic updating of the N-gram knowledge graph.

This application provides a material recommendation method applied to a recommendation system, comprising: obtaining a first estimated data combination of candidate materials from M deep learning models, wherein the first estimated data combination includes at least two of the following: estimated click-through rate, estimated interaction rate, and estimated consumption duration, where M is an integer greater than or equal to 2; determining a first fusion score corresponding to the first estimated data combination based on the mapping relationship between the estimated data combination and the fusion score, wherein the first fusion score is used to represent the target user's degree of interest in the candidate materials; and recommending materials to the target user based on the first fusion score. This application can shorten the cycle and save online traffic.

The application discloses an intelligent control method and system for a glass production line, and relates to the technical field of intelligent control. Multiple process data combinations are determined based on detection of industrial multi-modal data. A digital twin mechanism is introduced in the identification process, so that virtual deduction is carried out along different dimensions to determine the production state of the glass product in different processes, thereby improving the accuracy of the production state of the glass product in different processes. Multiple abnormal production features are adjusted at multiple scales in the multi-objective constraint space to determine multiple optimization contents at different levels. The multiple optimization contents present multiple abnormal areas of the glass product in the cloud. The multiple abnormal areas and the corresponding optimization contents are inversely calculated, and local adjustment is simultaneously performed on the corresponding edge side nodes, thereby determining the intelligent control content of the glass production line and improving the accuracy of the intelligent control content of the glass production line.

The application relates to an artificial intelligence technology and provides an interface data monitoring method and device, computer equipment and a storage medium, which comprises the following steps: determining target historical data features corresponding to a preset interface; determining a target historical interface data set corresponding to the target historical data features; determining a historical alarm level corresponding to each target historical interface data in the target historical interface data set to obtain a historical alarm level set; combining data in the target historical interface data set and the historical alarm level set into training data, training an initial alarm level prediction model to obtain an alarm level prediction model; obtaining initial to-be-monitored interface data, preprocessing the initial to-be-monitored interface data to obtain target to-be-monitored interface data; and calling the alarm level prediction model to process the target to-be-monitored interface data to obtain a target alarm level corresponding to the target to-be-monitored interface data. The application can improve the accuracy of interface data alarm and promote the rapid development of smart cities.

Techniques are disclosed for transposing and loading 6-bit floating-point matrix data into operations registers of a processing unit. A matrix comprising 6-bit floating-point data elements is received from memory, in which the matrix is stored in either a row-major or column-major layout. A matrix transposition loading operation is performed during the loading process, rearranging the matrix elements into a transposed layout (e.g., converting column-major to row-major). The transposed matrix is stored in operations registers for use in parallel processing tasks. The process may include caching partially stored data elements from memory and combining them with subsequently retrieved data to complete the transposition.

The application relates to the technical field of non-uniform time sequence data prediction, in particular to a non-uniform time sequence data processing method and device, electronic equipment and a storage medium, which comprises the following steps: acquiring non-uniform time sequence data of a current vehicle; pre-processing the non-uniform time sequence data, and combining the pre-processed non-uniform time sequence data according to a preset combination strategy to obtain multiple data groups for prediction; inputting the multiple data groups for prediction into a pre-constructed prediction model, and predicting a future state of the current vehicle according to a prediction result. Therefore, the problems that the current non-uniform time sequence data cannot be directly used as input data of a time sequence model and the like are solved, the temperature change in a vehicle, the speed change, the light intensity change and the like within a period of time can be predicted, the influence of the driver starting the air conditioner and the air conditioner temperature setting on the driver can be known, the time when the driver starts the air conditioner and the temperature setting can be estimated, and the required air conditioner setting of the driver is recommended.

This invention relates to the field of pollution monitoring technology, specifically to a multivariate dynamic monitoring platform for groundwater in mining areas. This platform includes a data acquisition module for acquiring sample environmental hydrological disturbance data sequences and sample hydrological and water quality data sequences; an evaluation value acquisition module for acquiring complex and sensitive evaluation values ​​of environmental hydrological disturbance responses corresponding to each hydrological and water quality type; and a gain compensation and decision tree model construction module for compensating the traditional information gain of the corresponding hydrological and water quality type based on the complex and sensitive evaluation values ​​of environmental hydrological disturbance responses, obtaining a target information gain, and using the target information gain as a node splitting criterion to conduct supervised learning training on the sample hydrological and water quality data combination to generate a binary classification decision tree model for groundwater pollution identification and monitoring in mining areas. Furthermore, this invention can improve the accuracy of the decision tree model for pollution identification and monitoring.