43 results about "Data description" patented technology

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for indexing actions for resources. In an aspect, a method receiving, for each resource a plurality of resources that are each accessible by a user device, action data describing actions supported by each resource, each action being an action that can be performed by the user device processing the resource; for each resource: determining, from the action data, an action type for each action supported by the resource, determining, from the action data, action parameters for each action supported by the resource, and indexing the action data, action types and action parameters for the resource in an index that is searchable by a search engine.

This invention discloses an intelligent prediction and process optimization system for the corrosion resistance of laser-treated stainless steel based on a grain boundary feature database, relating to the fields of metal surface engineering and intelligent manufacturing technology. The invention includes a grain boundary modeling module, which performs grain boundary-related microstructure modeling on laser-treated stainless steel samples to form grain boundary feature data. Based on grain boundary-related microstructure modeling and parametric encoding with unified data description rules, this invention forms grain boundary feature data containing grain boundary types, orientation difference distributions, and special grain boundary proportions. Using this grain boundary feature data as the core master index, the corresponding laser-treated process parameters and corrosion resistance test results are associated and stored, constructing a systematic grain boundary feature database. This provides stable data support for the quantitative characterization of corrosion resistance and data-driven prediction, avoiding the problem of scattered grain boundary information that is difficult to use for decision-making.

This application discloses a data-aware intelligent analysis method, system, storage medium, and computer device. The method includes: receiving a target problem and initializing state information, wherein the state information includes key sub-conclusions, a reasoning state logic diagram, and key data descriptions; a language model-based agent iteratively performs the following operations: obtaining input information for the current step; updating the state information of the current step based on the action and environmental feedback information of the previous step, and making decisions based on the state information of the current step to obtain the action and tools to be invoked for the current step; invoking the tools to execute the action of the current step, and obtaining environmental feedback information for the current step after the action is completed; checking whether the target problem has been analyzed; if not, proceeding to the next loop; if completed, generating analysis results. This method can improve the reasoning ability, response consistency, and interpretability of deep reasoning models.

Provided is a method and apparatus for describing data using a language model. The method includes executing a language model based on non-text-based input data to generate a first key for describing the input data in a text-based key-value format, executing a first expert model, selected from among expert models, based on the first key to generate a first value corresponding to the first key, executing the language model based on the first key and the first value to generate a second key, executing a second expert model, selected from among the expert models, based on the second key to generate a second value corresponding to the second key, and generating output data in the text-based key-value format by executing the language model and the expert models based on a first key-value pair comprising the first key and the first value, and a second key-value pair comprising the second key and the second value.

The application discloses a data processing method, device and equipment and a storage medium. The data processing method comprises the following steps: obtaining a running instruction of a program; in a trusted execution environment, obtaining a data description node and a preset structure tree according to file content comprising data required by the program to execute the running instruction, the data description node storing description information of the file content, encrypting a node key of a root node in the preset structure tree and encrypting a data key of a data node corresponding to the root node; encrypting the file content based on the preset structure tree to obtain an encryption result; the root node stores the node key and the data key, the node key encrypts a child node having a parent-child relationship with the root node in the preset structure tree, the data key encrypts a data node corresponding to the child node, and the data node stores the file content. The problem that data cannot be provided with encryption protection while being calculated and data for persistent storage cannot be provided with encryption protection in the prior art is solved.

The application discloses a sea surface weak target detection method based on a residual network and a hypersphere constraint. The method can solve the problems of training underfitting or overfitting caused by the fact that the target sample is much smaller than the sea clutter sample under the sea clutter background, and the specific steps include: 1, performing short-time Fourier transform on each piece of data obtained after dividing the radar echo signal sample to obtain a time-frequency spectrum; 2, constructing an anomaly detection network model combined with a support vector data description based on a variational autoencoder of a residual network; 3, inputting the time-frequency spectrum into the anomaly detection network model for end-to-end training; 4, performing online detection of the target based on the trained model; and 5, evaluating the performance of the application from the constructed multi-dimensional index. The application can effectively enhance the separability between the target and the sea clutter, and improve the weak target detection precision under the condition of a real small sample.

The Internet of Things big data analysis method and the server provided by the embodiment of the application can efficiently and accurately classify Internet of Things data sets through an Internet of Things data classification model, and overcome the defects of low efficiency and roughness in the prior art. When the Internet of Things data classification model is adjusted, the Internet of Things data classification model is optimized together with multiple selected pre-optimization models associated with the categories of the Internet of Things data, wherein the first selected Internet of Things data description carrier obtained by the operation of different selected pre-optimization models is different, the corresponding focused data classification carrier information is different, the adjusted Internet of Things data classification model can distinguish different classification carriers, and the analysis of the Internet of Things data classification is more comprehensive, so that the reliability of the obtained data classification estimation information can be improved.

The present application relates to the field of intelligent fault diagnosis, especially the intelligent fault diagnosis of rotating machinery, and discloses a rotating machinery generalized zero-shot coupled fault diagnosis method based on a double convolution network and dynamic semantic fusion and a medium, aiming to solve the problem that it is difficult to simultaneously identify known single faults and unknown coupled faults in the test stage when only a single fault sample is used for training. The method comprises the following steps: collecting a vibration signal and converting it into a time-frequency image; using a first convolution network to extract discriminative features, and realizing single / coupled fault separation through an improved support vector data description model; using a second convolution network to learn single fault semantics, and constructing coupled fault pre-defined semantics based on dynamic fusion of signal complexity; and finally mapping coupled fault samples to a semantic space for distance matching identification. Through the "two-stage separation-semantic reasoning" architecture and dynamic semantic fusion, the present application improves the accuracy and robustness of coupled fault diagnosis, and has great engineering application value.

The application discloses an industrial process fault detection method and system based on double subspace division, which firstly performs normality evaluation on process variables, divides a data space into Gaussian subspace and non-Gaussian subspace; a fault detection model based on principal component analysis is established in the Gaussian subspace, and a fault detection model based on support vector data description is established in the non-Gaussian subspace; current test data variables are divided into the Gaussian subspace and the non-Gaussian subspace, and are respectively input into the trained models, so that fault detection statistics corresponding to each subspace are calculated; each subspace statistic is converted into a fault probability by using Bayesian inference and is weightedly fused to construct a comprehensive monitoring statistic, and fault detection is realized. The application effectively solves the problem that a traditional method has poor adaptability to Gaussian and non-Gaussian mixed distribution data, improves the fault detection rate while significantly reducing the false alarm rate, and is suitable for real-time monitoring requirements of complex industrial processes.

The present application relates to a kind of underwater robot control model training method, control method and system thereof, edge control model is deployed in underwater robot. Training method includes: control decision and the first data description of the first data of multi-source sensor acquisition input first pre-training large model as conditional context obtains the first output probability that each target word element outputs according to instruction rule;Control instruction label includes the multiple preset control fields arranged based on instruction rule, multiple target word elements in preset control field are arranged based on instruction rule, and multiple target word elements include control field keyword;According to each first output probability, determine first loss, and according to the first output probability of each control field keyword, determine second loss;According to the first total loss determined by first loss and second loss, first pre-training large model is updated to obtain edge control model.The above method can obtain the edge control model that accurately outputs control instruction based on instruction rule.

An online system trains a machine-learning model to generate an embedding in real time for a current session of a user with the online system. The machine-learning model is trained by applying a masked language modeling algorithm to training data including a training sequence of actions and a masked action to predict a user’s action that follows the training sequence of actions. The online system captures current session data describing a sequence of actions of the user performed during the current session. The online system applies the trained machine-learning model to predict a next user’s action and generate a session embedding that encodes information about the sequence of actions and the next action. Using the session embedding, the online system ranks a list of objects. The online system generates a user interface signal causing a user’s device to display a user interface with the ranked list of objects.

The application discloses a kind of seismic remote sensing data management method and system, it is related to earth science information technology field, including, access multi-source seismic remote sensing data, define the standardized metadata description format to each type of multi-source seismic remote sensing data, store multi-source seismic remote sensing data and corresponding standardized metadata description into data resource library;Real-time monitoring of earthquake event information, when target earthquake event is captured, the epicenter of target earthquake event is generated as dynamic space-time range, according to dynamic space-time range, aggregate multi-source seismic remote sensing data from data resource library, and construct dynamic data cube for target earthquake event;Based on dynamic data cube, generate data product through data processing workflow, record the complete processing process log of data product.The application forms integrated management scheme from data intelligent aggregation, product quality controllable to problem accurate traceability and closed-loop correction.

A real-time threat prediction method based on a GAN-SVDD fusion model is proposed. This method maps each process step in the entire production process into a behavioral profile of a control operation. Based on the behavioral profile, a raw data matrix of control operation behaviors is constructed, and a threat prediction engine based on the GAN-SVDD fusion model is built. By tracking changes in control operation data, the likelihood of an impending threat is predicted. This invention, through the above method, abstracts all control operations in the entire production process into a systematic behavioral pattern. It fully utilizes the high-quality data generation capability of Generative Adversarial Networks (GANs) and the efficient classification capability of Support Vector Data Description (SVDD) to predict anomalies in control operation behavior caused by malicious attacks or misoperations, perceive threat development trends in industrial production, and ensure the security of industrial control systems.

This application provides a data access method, a vehicle, and a computer-readable storage medium, relating to the field of electronic digital data processing technology. The method includes: acquiring a data access instruction; wherein the data access instruction carries access path information; determining a target data node corresponding to the access path information in a target database; wherein the target database includes multiple data nodes, each data node including identification information and data content, the data content including data collected by sensors in the vehicle; and performing a target access operation on the target data node. The technical solution of this application can centrally manage data collected by sensors in the vehicle in a unified manner. For example, the database has a unified data description and data format, and for the same data, the data access instruction can carry unified access path information, thereby eliminating the need for repeated development and code refactoring for applications in the vehicle on different hardware platforms, improving R&D efficiency.

The application discloses a cognitive value evaluation method of medical record data technology based on network science, and particularly relates to the technical field of cognitive value evaluation, and the method comprises the following steps: constructing a multi-scale medical data description set and establishing a medical cognitive relationship network, extracting a node structure stability coefficient and a knowledge propagation influence coefficient to form a node comprehensive stability index, combining a node centrality change to obtain a node cognitive contribution fluctuation index, and jointly constructing a cognitive value index with the node time scale weight, so that stable quantitative evaluation of the cognitive value of the medical record data is realized, and the accuracy and reliability of the cognitive value evaluation result in the multi-time scale medical data environment are improved.

The application discloses a kind of based on vehicle rear axle carrying jig processing method, device, equipment and medium.Judge whether the type of vehicle can be identified according to vehicle description characteristic information when detecting target vehicle reaches installation station;If yes, determine the type of vehicle identification information;According to the type of vehicle identification information, data matching is carried out in the pre-set vehicle type program database, and if at least one jig description type is determined, the current jig data description information is obtained to determine the target jig description type;The target jig position is obtained by analyzing the current jig data description information, and the robot is instructed according to the target jig description type to grasp the jig, so that the rear axle of the vehicle is carried after the current jig is removed.The problem of low efficiency and high difficulty in carrying the rear axle of the vehicle by the staff to judge and remove the jig is solved, the efficiency of removing the vehicle jig is improved, and the labor cost is reduced.

The application discloses a kind of drop lightning arrester multi-source signal discrimination and alarm method, it is related to the on-line monitoring and fault early warning technical field of power system equipment, the application can effectively separate high-frequency sub-band signal by wavelet packet decomposition technology, adapt to non-stationary vibration environment, ensure the robustness of feature extraction;The introduction of approximate entropy and peak factor and other characteristics, quantifies the randomness and impact of signal, can sensitively capture the subtle changes in the fatigue accumulation process of lead wire;Single-class classification model trained only by healthy sample is used, such as support vector data description, decision boundary is constructed, so that the model has high sensitivity to abnormal state, and can early warning before lead wire fracture occurs;This kind of evaluation mode based on healthy baseline avoids the false alarm problem of traditional threshold method in complex environment.

The application discloses a data transmission method and device, equipment and storage medium, and is applied to a high-speed serial point-to-point double-channel PCIE system. The PCIE system comprises a sending end, a direct memory access (DMA) control end and a receiving end. The method is executed by the sending end and comprises the following steps: when it is determined that the current sending condition is met based on a preset sending buffer queue, current to-be-sent data in the sending buffer queue is determined by a woken sending thread; data description information corresponding to the current to-be-sent data is filled into a chain buffer table of the DMA control end, so that the current to-be-sent data is transmitted to the receiving end by the DMA control end; and a blocking state is entered until a receiving feedback signal sent by the receiving end is received, and then the determination operation of the current to-be-sent data is returned to be executed again. The above technical scheme guarantees the safety and reliability of data transmission, improves the efficiency of data transmission, and can be applied to various data transmission scenes.

The application provides an ultra-coupling cross-domain trusted data space implementation method, an electronic device and a storage medium, and relates to the field of data processing. The method comprises the following steps: a first data providing terminal accesses a first data source, obtains first data and data description information, and sends a data access registration application to a first data hub; a first data hub platform allocates a first data identifier to the first data; the first data hub generates an initial access registration credential of the first data according to the data description information and the first data identifier; a first data demand terminal sends a data acquisition application; the first data hub generates a transmission registration credential of the first data; the first data hub verifies the data by using the initial access registration credential, generates a transmission certificate of the first data in the case of passing the verification, and transmits the first data to the first data demand terminal according to the transmission registration credential of the first data, the initial access registration credential and the transmission certificate, so as to complete the data transmission of the cross-domain trusted data space.

The application discloses a remote supervision quality alarm method and system based on a large language model, and the method comprises the following steps: S1, uploading and analyzing technical specification documents, converting the documents into quality specification requirements in natural language expression; S2, performing block processing to obtain multiple technical specification items, converting the technical specification items into embedding vectors by using a large language model, and constructing a knowledge base; S3, collecting structured supervision data of a production environment in real time, constructing a data description question and a question embedding vector; S4, calculating the similarity between the question embedding vector and each embedding vector in the knowledge base, and screening out relevant technical specification items based on a similarity threshold; and S5, combining the data description question and the relevant technical specification items into a prompt, inputting the prompt into the large language model, and performing alarm according to the output result of the large language model. According to the application, whether the data collected from the production environment meets the requirements of the specification file can be judged based on the bidding technical specification book and the bid file specified by a user, and alarm can be performed.

This invention discloses a multi-channel high-performance data storage and processing method based on a structured file system, belonging to the field of data processing technology. It is applicable to application scenarios such as testing benches and metallurgy where data acquisition density is high, storage frequency is fast, continuous storage time is long, and single file data volume is large. This invention constructs a structured file system for big data storage, which consists of a data index file, a data description file, and binary data files. Only one data index file and one data description file are generated in a single storage process, while one or more binary data files can be generated as needed. This invention also provides an efficient method for reading this file system, enabling fast and accurate access to large-scale data.

An information processing apparatus acquires structured data having items and data elements belonging to the items and text information including a description of the structured data, determines whether the description includes an explanation about the items, outputs, to a user, a question regarding a target item that is an item the explanation about which is determined not to be included, acquires a first user response to the question, as learning data for a language model that outputs a response when an inquiry regarding the structured data is input, and updates the language model to adapt to the learning data.

An online concierge system uses a findability machine-learning model to predict the findability of items within a physical area. The findability model is a machine-learning model that is trained to compute findability scores, which are scores that represent the ease or difficulty of finding items within a physical area. The findability model computes findability scores for items based on an item map describing the locations of items within a physical area. The findability model is trained based on data describing pickers that collect items to service orders for the online concierge system. The online concierge system aggregates this information across a set of pickers to generate training examples to train the findability model. These training examples include item data for an item, an item map data describing an item map for the physical area, and a label that indicates a findability score for that item / item map pair.

Methods, systems, and computer-readable storage media for receiving data and data schema metadata, the data schema metadata being descriptive of a data structure and the data being stored in accordance with the data structure, processing the data and the data schema metadata using a set of rules to generate a first data query, prompting a first LLM using a first prompt that includes at least a portion of the first data query to generate a first natural language query, prompting a second LLM using a second prompt that includes at least a portion of the first natural language query to generate a second data query, selectively storing the first natural language query and the second data query as a query pair, and evaluating performance of a ML model using the query pair.

The present invention relates to a method, program product, control unit, and forming machine for controlling a percussion forming machine. The method includes: a) providing a real dataset (D) having actual operating data of the forming machine (1), wherein the actual operating data (D) describes the actual operating state of the forming machine (1) at or immediately following the start of the execution of the forming stroke by a control or adjustment unit (15); b) transmitting the real dataset (D) as input data (E) to a trained neural network (N), wherein the neural network (N) models the execution of the forming stroke of the forming machine and generates at least one target dataset (SD) having target operating data as output data (A) based on the input data (E); c) transmitting the output data (A) to the control or adjustment unit (15); and d) controlling or adjusting the further execution of the forming stroke by the control or adjustment unit (15) based on the target operating data of the target dataset (SD).

Methods and apparatus are provided for encoding and decoding a data stream carrying data representative of a three-dimensional scene, the data stream comprising color pictures packed in color images; depth pictures packed in depth images; and a set of patch data items comprising de-projection data; for retrieving the color pictures and geometry data in the color images. Two types of geometry data are possible. The first type of data describes how to retrieve the depth pictures in the depth images. The second type of data comprises constant depth values.

A device stacking detection computing system receives single-provider data from a service provider computing system associated with a telecommunications service provider. The single-provider data describes a request for a digital device, such as a consumer request to receive a digital device for a new account. Based on the single-provider data, the device stacking detection computing system determines multi-provider data associated with additional telecommunications service providers. Based on a combination of the single-provider data and the multi-provider data, the device stacking detection computing system identifies a risk level for the request, e.g., a risk of device stacking fraud. The device stacking detection computing system provides, to the service provider computing system, data indicating the risk level. In some cases, the device stacking detection computing system provides alert data to additional service provider computing systems associated with the additional telecommunications service providers.