57 results about "Data abstraction" patented technology

The invention relates to the technical field of underground pipe gallery intelligent monitoring, and discloses an underground pipe gallery data real-time processing system based on edge computing, which comprises a dynamic sensing primitive library abstracting pipeline pressure and video monitoring multi-source data into primitive units containing associated weights, the primitive recombination module dynamically adjusts the coupling relation between primitives through a function according to the rainfall environmental parameters, so that the association weight of the video texture and the pressure data is adaptively enhanced; according to the system, through a dynamic coupling mechanism driven by environment feedback, leakage gradual change characteristics which are difficult to capture by a traditional fixed threshold are effectively identified; the edge collaborative network realizes cross-node knowledge migration, and automatically triggers high-precision sampling of adjacent nodes when local abnormality occurs, so that a self-organizing diagnosis cluster is formed. According to the invention, through a composite architecture of dynamic primitive recombination and edge collaboration, the problems of response lag and high false alarm rate of a traditional monitoring system are avoided, and the technical span from passive monitoring to active prediction of the underground pipe gallery is realized.

The present application relates to a digital base fusion system and electronic equipment. The system includes: a data source access module, which is used to obtain raw data from design tools and data sources in different fields; a data abstraction module, which is used to manage raw data and provide a unified data access interface for upper-level modules; a semantic modeling module, which is used to build a unified semantic model; a semantic alignment module, which is used to perform semantic analysis and matching on raw data from different fields; a data fusion module, which is used to fuse semantically aligned data of corresponding types based on different fusion methods to build a unified data view; a knowledge graph construction module, which is used to build a knowledge graph based on the semantic model and the fused data; an application service module, which is used to provide data services and knowledge services for upper-level applications. The present application solves the problem that the existing digital base architecture lacks a unified semantic conversion and expression mechanism, resulting in difficulties in cross-domain data integration.

The invention discloses an internet of things multi-protocol fusion limo intelligent control system, which relates to the field of limo intelligent control and comprises a hardware irrelevant layer, a multi-protocol adaptation layer, a data abstraction layer, a programmable logic layer, a security and redundancy mechanism and an edge AI module. According to the invention, a physical bus is packaged into an independent module, an MCU / SoC and a protocol transceiver are built in, hot plugging is realized through a standardized PC I e / EdgeConn backboard, a core processing board only exposes a unified backboard interface, module drive localization execution is realized, master control firmware does not need to sense protocol details, only the module needs to be replaced when a protocol is newly added, and the master control firmware or upper software does not need to be modified. The defect that the whole vehicle firmware needs to be refreshed when a bus is replaced in a traditional system is overcome, each protocol corresponds to an independent drive micro-service deployed in a containerization mode, the drive micro-service analyzes a message into a unified data model and uploads the unified data model to a data abstraction layer, a downlink control instruction is packaged and issued according to a target protocol, and it is guaranteed that the protocols are diversified and interfaces are consistent.

A system, method, and device for generating data visualizations are disclosed. The method includes (i) obtaining a natural language query, (ii) determining an intent for the natural language query, (iii) generating one or more data requests to one or more selected data sources, the one or more data requests being based at least in part on the intent, (iv) abstracting result data to obtain a data abstraction, the result data being responsive to the one or more data requests, and (v) generating a visualization for the result data based at least in part on the data abstraction.

The invention provides a multi-platform collaborative data acquisition and analysis method, and relates to the technical field of data processing and analysis. The method comprises the following steps: receiving and converting data from different platforms into a unified intermediate format through a data adaptation intermediate layer, and processing a data structure and an interface of a specific platform by using a plurality of adapters; the data abstraction layer defines a standardized interface, so that multiple platforms request data through a unified interface, and a mapping rule converts the request into calling of the adaptation layer; the data integration engine preprocesses the data to ensure data consistency and accuracy; the data warehouse adopts a distributed architecture to ensure the service continuity through load balancing, data partitioning, a caching mechanism and a redundant storage and fault transfer mechanism; and the data processing and analysis module converts the data into decision suggestions by using a visual tool and a data mining algorithm. According to the method, manual intervention is reduced, the data processing speed and accuracy are improved, the data mode and trend are deeply mined, and service decision is promoted.

The invention relates to the technical field of power distribution network monitoring, and discloses a power distribution network load monitoring system and method, and the system comprises a load data collection module which can carry out the hierarchical division of an operation region to obtain basic monitoring units and extract a collection interval; the multi-source data integration processing module collects multi-category data such as load power and voltage fluctuation according to a sampling period and generates time sequence integration data; the state feature construction module abstracts a load state according to time sequence data to obtain a model, and extracts multi-dimensional features such as capacity bearing and operation stability; the abnormal working condition discrimination and evaluation module identifies local anomalies according to the multi-dimensional features and determines overall anomalies; and the monitoring strategy regulation and control module dynamically adjusts monitoring parameters and monitors the real-time load state of the power distribution network in real time. According to the system, layered monitoring and abnormity identification of the load of the power distribution network are realized, and the monitoring accuracy and the dynamic regulation and control capability are improved.

The embodiment of the invention provides a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark in a generative model. Black box watermarks are first embedded into probability density functions of data abstraction in different layers of the generative model. The method also includes embedding the white box watermarks into different layers for generative model output after the embedding of the black box watermarks is completed. Model data is generated by the generative model based on predetermined trigger data. The predetermined trigger data comprises a predetermined trigger text or a predetermined trigger image. An identity associated with the generative model is determined based on the model data. By using the method, white box and black box attacks can be resisted by embedding two complementary and independent watermarks, so that double-layer protection is provided for the generative model.

The invention provides an undercarriage system scheme design method based on heterogeneous design simulation data management, which comprises the following steps of: firstly, analyzing, abstracting and standardizing different types of heterogeneous design and simulation data in a development process of an undercarriage system, and constructing a uniform abstract data model; secondly, according to the defined semantic rules, an undercarriage semantic ontology model is constructed, then the semantic rules are loaded through a semantic reasoning engine, and processed data are associated; according to the research and development process of the undercarriage system and the semantic association relationship between the data, data storage and display are realized; after data abstract analysis, semantic association and unified storage are completed, functional information, design information and simulation information are further derived based on an undercarriage semantic ontology and a reasoning result, so that an undercarriage system development scheme driven by requirements is constructed. According to the method, multi-source heterogeneous design and simulation data generated in the research and development process of the undercarriage system can be effectively managed, and a design scheme of the undercarriage system is constructed on the basis of the multi-source heterogeneous design and simulation data.

The invention discloses an equipment manufacturing energy consumption and state visual monitoring method based on an industrial internet of things, solves the problem of passive fixation of an industrial monitoring visualization technology in the prior art, and realizes autonomous and intelligent generation of an optimal visual view matched with a real-time working condition. The method comprises the following steps: collecting industrial field multi-source heterogeneous data in real time, abstracting a monitoring object as an entity node, and constructing a feature vector containing static and dynamic attributes; constructing and updating a dynamic knowledge graph based on nodes and features, wherein relation edges of the dynamic knowledge graph comprise dynamic edges generated by static connection and real-time analysis; inputting the atlas into a pre-trained multi-attention graph neural network to obtain a display priority score and a visual affinity matrix of each node; calculating coordinates of all nodes on an interface through a layout optimization algorithm in combination with the priority and the affinity matrix; and generating a visual drawing instruction according to the coordinates and the node attributes, and driving a display device to render a monitoring interface.

The invention discloses a nuclear power platform data edge processing method and a storage medium, the nuclear power platform comprises a platform layer and an edge layer, and the method comprises the following steps: data standardization: obtaining data and carrying out data standardization processing; data structuring: abstracting the standardized data into structured data; data sinking: sinking the structured data to the edge layer according to a sinking decision, and processing the structured data by the edge layer; and data synchronization and feedback: synchronizing the data processed by the edge layer so as to realize nuclear power platform data edge processing. Through edge processing of the nuclear power platform data, the delay of data transmission is reduced, the burden of the central server is reduced, the response speed and reliability of the system are improved, and the operation efficiency and safety of the nuclear power station are effectively improved.

A method and system for performing a task requiring a plurality of sequential decision-making operations by an autonomous agent are disclosed. The method includes acquiring a portion of offline data from a database, and discovering a set of separate skills. The method further includes segmenting the offline data and segmenting the offline data according to skill, and abstracting the segmented offline data into reusable functions. From the abstracted reusable functions, distilling primitives for each skill and distilling reusable tips in various functions. Modifying skill based text-based and code-based policies for augmenting the autonomous agent, and processing a task requiring a set of sequential decision-making operations using the augmented autonomous agents.

The invention relates to the technical field of supply chain management, in particular to an electronic component supply chain supply and demand management method, which comprises the following steps: S1, abstractly tagging acquired electronic data; s2, selecting an LLM model, inputting the abstract tagging data in the S1 into the LLM model, analyzing the input data text by the LLM model, and outputting the result, the output result including further explanation of the supply and demand condition and prediction of the future supply and demand trend; s3, performing fine tuning on the LLM model by using a fine tuning module; s4, after system building is completed, vectorization representation is carried out on the labeled supply and demand data and the material data stored in the system, and rapid matching and query are carried out through a vector search technology; data at the supply end and the demand end are regularized through abstract labels, the consistency and efficiency of data processing are remarkably improved, and discrete and complex original data are converted into a unified label format by adopting an abstract label regularization technology.

Telecommunications systems, computer devices and systems, and methods are provided. One example method, performed by a data abstraction layer of a telecommunications system for a network, includes accessing data elements associated with a network function of the network, performing abstraction on the data elements to generate one or more data products, receiving a customer request for a service on the network, identifying the data product related the service, and provisioning the identified data product to the customer. The method may further include identifying proprietary data and private data of the data elements based on a predefined policy and removing proprietary data and private data before performing abstraction.

The invention discloses a hybrid expert model deployment method and device based on a heterogeneous hardware equipment cluster, equipment and a medium, and relates to the technical field of artificial intelligence, and the method comprises the steps: carrying out the data abstraction processing of all equipment in a target heterogeneous hardware equipment cluster, so as to obtain an initial equipment description vector, compiling the target expert model file into a binary intermediate representation; generating a hardware probe for the target equipment based on the hardware type of the target equipment in the target heterogeneous hardware equipment cluster and the binary intermediate representation, and obtaining a real-time equipment description vector based on hardware; determining a hardware sensing routing weight of each device in the target heterogeneous hardware device cluster based on the real-time device description vector, and determining a to-be-activated expert model file from the target expert model file based on the sensing routing weight; and distributing the to-be-activated expert model file to the target heterogeneous hardware equipment cluster for deployment and installation. Therefore, heterogeneous deployment of the hybrid expert model can be realized through unified arrangement.

The present invention discloses a network behavior analysis method based on big data, which relates to the field of big data processing technology. The present invention utilizes a server to obtain user behavior data, identifies users, and generates useful trajectory segments based on user access; performs frequent sequence mining to obtain user frequent behavior sequences, uses frequent behavior sequences as user features, distinguishes users' abnormal behaviors from normal behaviors, abstracts the obtained abnormal behavior data into labels, constructs a user portrait model, establishes an abnormal behavior pattern feature library, matches the user behavior sequence to be tested with the abnormal behavior pattern feature library, determines whether the user is an abnormal user based on whether the match is successful, and extracts abnormal features of the user behavior data accordingly. The present invention completes the judgment and extraction of abnormal features of user behavior data, improves the accuracy of abnormal detection of user behavior data, and rates the detection accuracy.

Embodiments of the present disclosure provide a method for determining a generative model. The method includes embedding a white box watermark and a black box watermark into a generative model. The black box watermark is first embedded into a probability density function of data abstractions in respective layers of the generative model. The method further includes embedding, after the embedding of the black box watermark is completed, the white box watermark into respective layers for outputs of the generative model. Model data is generated by the generative model based on predetermined triggering data. The predetermined triggering data includes a predetermined triggering text or a predetermined triggering image. An identity associated with the generative model is determined based on the model data. Advantageously, the illustrative method is capable of providing double-layer protection for a generative model by embedding two complementary and independent watermarks to resist white box and black box attacks.

The application provides a kind of detection method and system of Internet of Things equipment credential trigger type backdoor, comprising: the binary code library is formed by decompressing device firmware;Executable file in code library is analyzed reversely, and backdoor verification key code is positioned using string comparison function and backdoor model feature;The code segment related to backdoor verification key code is stripped from original program, and complete credential generation process code is obtained;Each credential generation process code is subjected to data abstraction and process modeling processing, forming formal description of verification credential, and the semantics and uncertainty of each input variable in formal description are calculated;According to the semantics and uncertainty of each input variable, it is judged whether the verification credential is predictable, and whether the device has a backdoor that can be triggered by predictable credential.The application can automatically detect whether there is a backdoor triggered by credential in the device through static analysis of binary program in Internet of Things firmware.

A method of data abstraction and anonymization of video data includes identifying and estimating locations of reference points of a human subject in a sequence of video images captured of the subject over time. The method further includes improving the accuracy of the estimated locations by tracking changes in the sequence of video images using object detection and known relationships with other reference points. The method also includes abstracting and anonymizing the video data by translating the locations of the known and established reference points into a coordinate space of a coordinate system to generate time sequenced coordinates for the reference points including time sequenced change

The invention relates to a computer architecture (110) enabling data transfer between several functional units. A functional unit supply structure (111) provides several functional units. A selection structure (112) provides a selection of functional units and linking information, the selected functional units being linked to perform predefined data processing. An interface structure (113) provides an interface for data transfer between linked selected functional units, this interface being designed to transform at least a portion of the output data content of the selected functional unit into input data of the other functional unit, according to the selected functional units and the linking information. [Abstract figure] Fig. 1

The invention relates to the technical field of big data processing, in particular to a cross-platform big data sharing and collaborative intelligent processing method, which comprises the following steps of: carrying out structure standardization on data from different platforms, and ensuring that data formats among the platforms are consistent through a data mapping and conversion technology; cross-platform data abstraction and integration are realized by constructing a data virtualization layer, so that data of all platforms can be seamlessly intercommunicated, an efficient data sharing mechanism is designed based on a role access control model and an attribute access control model, the security and privacy of the data are guaranteed, and the data sharing efficiency is improved. Dynamic resource allocation is carried out on cross-platform data processing tasks by adopting an intelligent scheduling algorithm, the use of computing power and storage resources is optimized, and a data processing flow, a data sharing strategy and system performance are optimized by adopting a Q-learning reinforcement learning algorithm; according to the method, the problems of difficulty in cross-platform data integration, non-uniform resource scheduling and consistency control in the prior art are effectively solved, and the method has remarkable innovativeness and practical value.

A system, method, and apparatus for generating a data visualization are disclosed. The method includes (i) obtaining a natural language query, (ii) determining an intent of the natural language query, (iii) generating one or more data requests to selected one or more data sources, the one or more data requests based at least in part on the intent, (iv) abstracting result data to obtain a data abstraction, and (iv) generating one or more data requests to selected one or more data sources based at least in part on the data abstraction. The result data is responsive to the one or more data requests, and (v) generating a visualization for the result data based at least in part on the data abstraction.

The invention discloses a big data management method and system based on semantic analysis, and relates to the technical field of big data management. A multilayer semantic abstract data organization structure comprising a base layer, a mode layer and a statistical layer is constructed, real-time semantic analysis is performed on input data, and semantic descriptors are generated; the high-frequency data instance in the base layer is stored to a corresponding level according to a central semantic index, a semantic folding operation is triggered based on a preset rule, the high-frequency data instance in the base layer is automatically abstracted into a modeled representation of a mode layer, and similar representations in the mode layer are further aggregated into an aggregated representation of a statistical layer; when the query is responded, the system analyzes a query intention and determines a target data abstraction level, high-level aggregation or modeling representation is restored into bottom-layer detailed data as required through reverse expansion operation, and a query result is generated; through the multi-layer semantic abstract data organization structure and the data folding operation, the storage efficiency of mass complex data and the performance of multi-granularity intelligent query are effectively improved.

The invention relates to a computer implemented method for efficient access to a database via a functional model-based procedure by translating a sequence of operations that are stored in a memory and that is described in the procedure that is specified using a generalized programming language into an intermediate database abstraction layer access language, whereby the intermediate data abstraction layer not being SQL. The method comprises: generating by a processor: an initial builder corresponding to an original originating source, zero or more intermediate operations and a terminating operation; storing in the memory, the sequence of operations, and translating the builder or parts of the builder is translated to at least one operation using the intermediate database abstraction layer access language upon specifying the terminating operation.

A computer-implemented method according to one embodiment includes creating a data abstraction that includes a set of memory resources and variables associated with the memory resources. The computer-implemented method also includes implementing the data abstraction within a programming language environment. A system according to another embodiment, includes a processor. The system also includes logic that is integrated with the processor, executable by the processor, or integrated with and executable by the processor. Moreover, the logic is configured to: create a data abstraction that includes a set of memory resources and variables associated with the memory resources. The logic is further configured to: implement the data abstraction within a programming language environment.

The embodiment of the invention provides a recursive query processing method and a related device, and relates to the technical field of big data, the method comprises the following steps: when a statement to be processed is determined to be a recursive query statement, extracting a table name and a recursive condition in the recursive query statement; according to the table name and the recursion condition, constructing a directed graph, and based on the directed graph, obtaining the maximum depth of nodes and the total number of paths; splitting the directed graph into a plurality of sub-graphs according to the maximum depth and the total number of paths; and determining a link from a starting node to a target node according to the plurality of sub-graphs, and outputting the link. According to the method, the recursive query table data is abstracted into the directed graph, so that the success rate and efficiency of recursive query can be improved.

User engagement is detected and used to control operation of a computing device. User engagement is detected by a sensor such as a camera that identifies if a user's face is oriented towards a display device. If the user is not facing the display device, the sensor determines that the user is unengaged. The computing device is thus able to perform a power-saving operation, such as dimming the display device, when the user is unengaged. The computing device includes an API that abstracts sensor data into a user engagement signal indicating that the user is either engaged or unengaged. The OS and applications running on the computing device act on the user engagement signal provided by the API without communicating directly with the sensor. The user engagement signal may be provided as an input to a state machine.

The invention relates to the technical field of processors, in particular to a tile block instruction set architecture and a processing method, and the tile block instruction set architecture comprises a tile block parallel processing system control instruction, a tile block descriptor register configuration instruction, a tile block data handling instruction, a tile block data copying instruction and a tile block arithmetic instruction. According to the tile block instruction set architecture processing method, one or more tile block operation units, one or more tile block storage management units and one or more tile block calculation task scheduling and synchronization units are combined into a tile block parallel processing system, so that tile block instructions are allowed to be processed in parallel, and tile block-based data operation is completed. The architecture aims at solving the problems that when an existing processing system processes two-dimensional data blocks, instruction overhead is too large, data abstraction is insufficient, and the coupling degree of software and hardware is too high.

The invention discloses a system and method for uniformly analyzing and rendering Gerber and ODB + + in real time, and the method comprises the following steps: S1, analyzing Gerber format data or ODB + + format data, and uniformly converting each layer graph into a polygon expression; s2, organizing the polygon according to a layer sequence, and triangularizing the polygon into mesh format data; and S3, performing real-time laminated rendering on the mesh data by using a GPU through a graphic rendering pipeline. Compared with the prior art, the method has the advantages that through the innovative unified data abstraction and GPU acceleration rendering technology, various pain points of an existing PCB data processing method in the aspects of precision, memory, efficiency and uniformity are effectively solved, and a more advanced and practical solution is provided for the field of PCB design, manufacturing and detection.

The invention relates to the technical field of code development, and discloses a business meta-model-based multilingual resource decoupling method, which comprises the following steps of: abstracting data irrelevant to languages into a metadata model by analyzing data in a business logic unit on the basis of metadata modeling and context sensing technologies; and identifying the language-related data as to-be-translated data. Then, based on the metadata model, name attributes of the interface elements are extracted and a multi-language resource file is generated. Meanwhile, according to the difference between the preset metadata model and the to-be-changed metadata model, the capacity of the multi-language resource file is adjusted. And finally, automatically determining a language resource file suitable for display through a context awareness mapping technology in combination with the historical language use data of the user, thereby realizing dynamic multi-language display. According to the invention, internationalization and localization requirements of a business system are effectively supported, and changes of language environments of different users can be flexibly coped with.

The invention discloses a user consumption behavior prediction method under cross-tenant privacy protection and a related product. After a consumption prediction request of a tenant for a target user is received, interaction data and current geographic position data of the target user are firstly obtained. Converting the interaction data into a first semantic note for describing the consumption motivation of the user by using a large language model, and abstracting the geographic position data into a regional commercial attribute tag which does not contain specific street information to obtain a first tag; vectorizing and splicing the first semantic note and the first tag to form a target feature vector; and then a reference vector with high similarity with the target feature vector is retrieved in a public vector database, group features associated with the reference vector are combined, reasoning analysis is performed through a large language model, and finally a consumption intention prediction result of the target user is obtained. According to the invention, on the premise of ensuring the privacy security of the user, the cross-brand data can be effectively utilized to carry out precision marketing.