185 results about "Data domain" patented technology

The invention relates to the field of computer information security and access control, and discloses a block chain power data security sharing method. The method comprises the following steps: acquiring a data field list, a measuring point list and a configuration mapping table, executing naming specification, time anchor registration and de-identification processing, and forming a fixed field sequence and an on-chain index record structure; based on context legality verification and policy mapping, generating an access constraint policy and completing generation and binding of a one-time session token, solidifying a policy snapshot on a contract side and registering a validity period; zero-knowledge proof is constructed, witness materials are assembled, and dual-channel checking, event account falling and failure evidence registration are carried out through a verifiable session smart contract; and an end-to-end key negotiation and control channel is established, so that safe data minimum access and result sealing are realized. According to the method, credible sharing and traceable access of the power data among multiple subjects are realized, and the data security, compliance and chain governance capability are improved.

The invention relates to the technical field of visual recognition, and particularly provides a bottle sorting-oriented three-dimensional visual posture recognition method and related equipment, and the method comprises the steps: obtaining three-dimensional point cloud data and RGB image data of bottles on a conveyor belt, carrying out the preprocessing, feature extraction and geometric analysis of the three-dimensional point cloud data, and obtaining six-degree-of-freedom posture parameters; and the six-degree-of-freedom attitude parameters are projected to RGB image data, the overlap ratio of the projection contour and the actual bottle contour is calculated, and when the overlap ratio is larger than a preset threshold value, it is confirmed that the attitude is effective, and the corresponding attitude parameters are output to a mechanical arm sorting control system. Six-degree-of-freedom posture parameters of the bottle body can be obtained through posture alignment and template point cloud registration, reliability confirmation of a posture recognition result is achieved through overlap ratio verification in an RGB image data domain, and the defect that the posture recognition result is not reliable under the conditions that the posture change of the bottle body is large, the appearance of the bottle body is diversified and the bottle body is inclined or shielded and interfered is overcome. The problems of insufficient recognition precision and incomplete attitude parameters exist.

The invention discloses a multi-objective collaborative optimization pipe network automatic transmission and distribution control method, and belongs to the technical field of process control and system automation, and the method comprises the steps: obtaining pipe network operation data, generating robust features through adversarial domain adaptation training, monitoring environment mutation in real time, building a dynamic multi-objective optimization model through the robust features, and carrying out the optimization of the dynamic multi-objective optimization model. And generating a Pareto solution set by adopting a dynamic multi-objective evolutionary algorithm, and finally selecting an optimal control solution in combination with a collaborative balance index and a preference function. According to the method, data field differences are eliminated by adopting an antagonistic domain adaptation technology, robust features insensitive to working condition changes are obtained, and an optimal equilibrium solution among energy consumption, pressure safety and service quality can be tracked and determined in real time when a pipe network load and an operating environment dynamically change in combination with a dynamic multi-objective evolutionary algorithm responding to environment mutation, so that the service quality of the pipe network is improved. And the real-time performance, the robustness and the multi-target collaborative optimization level of automatic transmission and distribution control of the pipe network are improved.

A system and method for neural network confidence regularization is disclosed. A classification system uses a neural network training model to generate prediction. The confidence of the neural network training model is adjusted based on feature prevalence. The system processes mixed data types (binary, categorical, continuous, and date) through type-specific transformations and tensor construction. A tempering factor is calculated from the unweighted sum of features and applied to intermediate neural network outputs. This tempering mechanism reduces model confidence when several low-weight features are present, enabling faster convergence, better generalization, and improved classification accuracy compared to standard neural networks, particularly for complex non-linear relationships in tabular data domains.

The invention discloses an HSI and ranging data cross-domain classification method and system based on domain expansion and feature decoupling, and aims to solve the problems of single mode, insufficient domain offset simulation and weak generalization ability in the prior art. Preprocessing the HSI and LiDAR data to obtain a source domain sample, and generating an expansion domain sample through frequency domain amplitude disturbance and adversarial domain expansion; extracting basic features by using a multi-stream convolutional neural network, and separating domain specific features and cross-modal and cross-domain domain invariant features; and adaptively fusing the three types of features through an attention network, and inputting the preprocessed target domain data into a trained model to obtain a classification result. Through multi-modal fusion, mixed domain expansion and feature decoupling, the cross-domain classification precision and generalization ability are improved, fine adjustment of target domain data is not needed, application is flexible, and the method is suitable for multi-scene remote sensing ground feature classification.

The invention provides a multi-modal knowledge graph generation method and system for hardware design analysis, which break through design, verification and physics, realize information barriers among the three traditional EDA (Electronic Design Automation) fields, perform semantic fusion and association on information from different data fields, create a unified and queriable hardware design knowledge representation, and improve the reliability of hardware design analysis. And a coherent and operable unified knowledge model is formed. A multi-modal design knowledge graph is created by integrating a simulation result (dynamic data) and a physical implementation feature (performance data) into a static structure model, and a comprehensive design model with structure, behavior and performance features is analyzed in a unified framework. An engineer does not need to manually switch between different tools and data formats, but can access all-dimensional information about a static structure, a dynamic behavior and a physical property of a design through a single query interface.

This invention relates to the field of multi-source data processing technology, specifically disclosing a data governance method and system for multi-source heterogeneous data fusion. The method includes receiving unstructured data fed back by a data acquisition agent, performing virtual mapping on the unstructured data, recursively adjusting the access parameters corresponding to the data source node, and generating simulation data based on the virtual mapping when a data source node experiences a data upload anomaly. It also includes receiving data increment density and data hotspot distribution fed back by the data acquisition agent, planning the scheduling path of the unstructured data parsing end based on the data hotspot distribution, and receiving the parsing results fed back by the parsing end. This invention acquires unstructured data by deploying edge nodes, identifies the unstructured data, constructs a data domain model, synchronously determines the data state of the unstructured data, marks hotspot areas in the data domain model, and then determines the parsing order, enabling important data to be parsed faster.

The application discloses a STCA-MIMO radar anti-interference method based on nonlinear time delay, which comprises the following steps: calculating target echo signals based on nonlinear time delay of transmitting array elements; carrying out digital mixing and matching filtering on the target echo signals to obtain preprocessed signals; determining a first transmitting steering vector and a first receiving steering vector according to the preprocessed signals; calculating a covariance matrix of STCA-MIMO radar system receiving data; calculating an adaptive weight vector by using a robust direct data domain beam forming technology based on the covariance matrix and an actual receiving-transmitting joint steering vector of a target; calculating a first receiving-transmitting joint steering vector of the STCA-MIMO radar system; and carrying out beam forming by using the first receiving-transmitting joint steering vector and the adaptive weight vector. The application can effectively suppress interference signals in the main lobe of the STCA-MIMO radar, and solves the problem of the decline of interference suppression performance in the prior art when the interference is located near the distance grating lobe of the main lobe of the real target.

The invention relates to the technical field of data transmission, and discloses a cross-domain model communication method, device and equipment and a computer readable storage medium. The method is applied to a cross-domain model communication system, the system comprises a user domain, a data domain and a gatekeeper, and the method comprises the following steps: sending request information of a model in the data domain to the gatekeeper through the user domain, and performing security auditing on the request information through the gatekeeper; when the security check is passed, sending the request information to a data domain through a gatekeeper, and generating response information based on the request information through a model; sending the response information to a gatekeeper through the data domain, and performing security auditing on the response information through the gatekeeper; and when the security check is passed, the response information is sent to the user domain through the gatekeeper. When the model carries out data transmission between the user domain and the data domain, the transmitted data is subjected to security auditing through the gatekeeper, and the data transmission is carried out only when the security auditing is passed, so that the security of cross-domain communication of the model is improved.

The system inputs at least one security log file from a first data domain into a first machine learning (ML) model. The system compute, using an output from the first ML model, a weighted sentiment value for one or more of the multiple cybersecurity events. The system detects, using a second ML model, an anomaly in a first cybersecurity event of the multiple cybersecurity events. The system correlates, using a third ML model, the anomaly in the first cybersecurity event to a different anomaly in a second cybersecurity event associated with a second data domain. The system determines, using a fourth machine learning model, a cybersecurity action to minimize a cyberattack. The system determines, using the fourth model, a predicted impact to the telecommunication network associated. The system generates a prioritization ranking of every cybersecurity action and executes each cybersecurity action based on the prioritization ranking.

The invention relates to the technical field of industrial communication and FPGA virtualization, in particular to an FPGA-based virtual Ethercat slave station function implementation method, which comprises the following steps of: pre-storing a plurality of sets of independent virtual slave station parameter configuration tables in a BRAM (Border Random Access Memory) of an FPGA, switching responses according to time slices in a sequence of the configuration tables during topology scanning of a master station, and simultaneously injecting programmable transmission delay to simulate physical link difference; in the operation stage, a data frame logic address is extracted through a hardware analysis engine, multi-slave-station data field modification is completed in a single cycle through a multi-channel parallel processing unit, protocol compatibility is guaranteed through a mailbox command agent, and finally a CRC check code is updated and a response is returned according to a virtual topological sequence. According to the method, a single FPGA supports multiple virtual slave stations, the deployment cost of the multiple slave stations is remarkably reduced, the hard real-time performance and the protocol compatibility are guaranteed, the resource utilization rate is improved, the slave stations can be dynamically added and deleted by updating the configuration table, and the core problems that an existing scheme is high in cost, poor in real-time performance and weak in expansibility are effectively solved.

The invention discloses a jungle scene illumination and clustering degree dual-adaptive animal detection method, and relates to the technical field of image recognition, and the method comprises the following five steps: obtaining a balanced training set with a source mark through data field reforming and rare class condition synthesis in combination with triple quality inspection and progressive matching; implementing scene consistency enhancement of label synchronization, linkage of geometry and illumination disturbance, border-crossing repair and failure rollback; an illumination adaptive converter is embedded in the middle layer of the trunk in parallel, gating residual error fusion is carried out, and a brightness agent amount is exported; according to the brightness proxy amount and the target scale, monotone mapping of a target confidence threshold and an initial intersection-to-union ratio threshold is established, and neighborhood consistency screening is combined; and softening non-maximum suppression by combined scheduling of the clustering degree and the brightness proxy amount, and outputting a detection result. The link gives consideration to low illumination, small target and cluster shielding, improves sample availability and candidate cleanliness, and realizes cooperation from data to a cluster level.

The application relates to the field of data asset management, and discloses a data asset right confirmation management method and system based on a unified data standard. The method comprises the following steps: standard ownership information and data domain verification information are respectively constructed for a plurality of data assets of a plurality of asset nodes, the standard ownership information comprises a plurality of right confirmation field information, and the data domain verification information comprises a plurality of data domain field information; the number of data assets in each data domain is acquired; when the number of first data assets in a first data domain is greater than a preset number, the first data domain is divided into a plurality of sub-data domains each comprising a plurality of first data assets; each sub-data domain corresponds to a plurality of sub-asset nodes; and the data domain field information in the data domain verification information of the sub-data assets is updated according to the ownership relationship between the sub-data assets and the sub-asset nodes. The application realizes standard and normative management of data and facilitates clear property right definition of data assets.

A multilinear domain-specific domain generalization (MDSDG) approach that utilizes information stored in multilinear indices of data domains to improve machine learning. In particular—based on limited sample size(s) in observed scenarios—an array of models is jointly trained, which advantageously are generalized to a new, unseen scenario, where only domain descriptions in the form of multilinear indices are available.

The application discloses a data and model heterogeneous federated learning method of global decision boundary distillation learning, and solves two key problems of data heterogeneity and heterogeneous model performance difference through two sub-methods. The first sub-method is aimed at the data heterogeneity problem, private data domain distribution information is fully condensed through local clustering of multiple prototypes, and irrelevant class distillation is introduced in the local supervised learning stage to alleviate local knowledge forgetting. The second sub-method is aimed at the problem of heterogeneous model performance difference, and a global decision boundary distillation learning is proposed, a global decision boundary learner is maintained and updated on the server, the local model is optimized from the perspective of the global decision boundary, and the influence of conflict information on the local model is reduced. The performance of the application is better than that of the most advanced multiple federated learning methods, and the application can better adapt to challenging scenarios, and has higher communication efficiency and better privacy reliability.

The present disclosure provides a method, device, terminal and non-transitory storage medium for power grid service data domain division, comprising: judging a feature of power grid service data; wherein the feature comprises a descriptive feature and a discrete feature; determining a category of first data of the descriptive feature by a first model; determining a category of second data of the discrete feature by a second model; and dividing the power grid service data domain by using the category of the first data and the category of the second data. The method for power grid service data domain division can solve the classification problem of different types of data.

The invention belongs to the technical field of industrial automation, and discloses a file cabinet control method and device. The method comprises the steps that the touch screen receives a touch operation instruction of a user; through a self-defined data interaction protocol, the touch screen determines an operation instruction frame according to the touch operation instruction and issues the operation instruction frame to the PLC; after receiving the operation instruction frame, the PLC analyzes the operation instruction frame based on the data interaction protocol to obtain a first instruction code and a first data field corresponding to the operation instruction frame; and the PLC controls an execution unit in the file cabinet body to execute the touch operation instruction according to the first instruction code and the first data field. By the adoption of the method and device, C language programming can be built in the touch screen, service logic processing is achieved, the operation burden of the PLC is reduced, and the PLC is made to concentrate on accurately executing instructions of the touch screen and conduct real-time feedback of data.

The application belongs to the technical field of bioinformatics, and discloses a multi-modal cell deconvolution method and system based on priori driving. The method comprises the following steps: uniformly naming, deduplicating and intersecting filtering genes / proteins of reference single cell data and target data, and performing scale standardization in respective data domains; generating a pseudo bulk training set based on the scale-standardized reference single cell data; mapping sample expression to a pathway space based on a GMT file of an external knowledge base, the pseudo bulk training set and the target data; constructing a deconvolution model, and respectively performing forward processing on double-view data of a source domain or a target domain by using a double-branch encoder with shared weights, so as to complete cell deconvolution.

The application provides an automatic parking method, comprising the following steps: a domain controller receives an externally input parking instruction; the domain controller acquires 4D point cloud data, image data and positioning data according to the parking instruction; the domain controller performs fusion processing on the 4D point cloud data, the image data and the positioning data to obtain environmental data of a surrounding area of an automatic driving vehicle; the domain controller performs judgment according to the environmental data and a preset judgment condition; when the domain controller determines that the environmental data meets the preset judgment condition, target parking space data is determined according to the environmental data; the domain controller calls a preset simultaneous localization and mapping (SLAM) algorithm to perform route planning processing on the 4D point cloud data, the image data, the positioning data and the target parking space data to obtain parking route planning data; and the domain controller controls the vehicle to enter a parking space corresponding to the target parking space data according to the parking route planning data.

This invention discloses a personalized AI avatar construction and group digital experience asset inheritance system based on a private data domain, relating to the fields of artificial intelligence and digital asset management. This invention uses a user-isolated private data domain as its operating platform to construct a personalized AI avatar that highly matches the user's personality, thinking, and behavioral habits for an individual user or corresponding group. Through continuous collection and structured processing of user interaction and decision-making data, it accumulates storable, iterative, and transferable digital experience assets within the group. The system supports the authorized integration and scheduling of external compliant experience assets under secure isolation, and employs a dual-drive engine of "general knowledge + personalized experience" for intelligent decision-making and scenario-based responses. Based on anticipating potential user needs, it outputs decision references and adaptive services, while relying on the private data domain to achieve end-to-end data isolation, encrypted storage, and controllable permissions. This invention effectively solves the technical problems of insufficient personalization in existing AI avatars, the inability to scalably inherit digital experience, and the single, passive interaction mode. It can be widely applied to scenarios such as personal intelligent assistants, family management, team collaboration, experience inheritance, and digital experience circulation, demonstrating strong technological innovation and rich application scenarios.

The invention belongs to the field of medical image processing, particularly relates to a general medical image segmentation method and system based on semantic aggregation, and aims to solve the problem that an existing model is poor in generalization ability when facing a new data field. The core of the method is that an expectation maximization semantic aggregation mechanism is adopted, and iterative purification is performed on visual features extracted from an image and language features extracted from a text so as to enhance semantic compactness of the features and form stable text semantic anchor points; then, through a depth alignment strategy, carrying out cross-attention fusion on the text semantic anchor point and the most abstract feature of the visual network; and finally, accurately guiding pixel-level segmentation decoding by using the fused features. According to the method, the segmentation performance and robustness of the model on unseen data are remarkably improved by forcing the model to learn an invariant internal semantic structure in the field, and the method can be widely applied to multi-center and cross-equipment clinical image intelligent analysis.

This invention relates to the field of cardiovascular disease auxiliary diagnosis technology, specifically to a method and system for auxiliary diagnosis of high-altitude hypertension based on electrocardiogram (ECG). It uses 12-lead ECG images as input, constructs a multi-branch adaptive classification model that integrates regional priors, sequentially extracts shallow general features through a shared feature extraction module, combines independent feature branches from multiple leads with a paired spatial attention module to capture relative potential differences between leads, and a multi-path adaptive fusion module dynamically fuses differential features between high-altitude and plain areas using regional labels as gating signals. Finally, a hybrid classifier composed of capsule networks and DenseNet cascades the features to complete a four-class prediction: high-altitude hypertension, high-altitude normal, plain hypertension, and plain normal. This invention effectively overcomes the problem of cross-regional data domain shift, improves the ability to capture weak pathological features in ECG images, and has strong clinical transferability and robustness, making it suitable for auxiliary screening of hypertension across high-altitude and plain areas.

The application discloses a kind of equipment health degree evaluation methods, the data field of object equipment is established in this method, the structure tree of object equipment is determined according to different levels;Download the historical data of all data items;According to the fault number determined by the historical alarm number or maintenance record statistics of different data, the initial weight is determined in proportion according to the same parent node under the child node;Build benchmark data feature matrix, calculate score scale, download the data of each data item, calculate the KPI score of data item, calculate KPI dynamic weight, respectively calculate the KPI score of part, equipment, system and the corresponding dynamic weight.This method takes equipment structure as model input, and is associated with data item, quantifies the initial weight, calculates dynamic weight, uses unlabeled data modeling, effectively improves the reliability of equipment health degree evaluation.

The invention discloses an auditable structural consistency receipt system and a single-step repair method for an edge AI reasoning pipeline, and belongs to the technical field of control and data auditing during operation of artificial intelligence equipment, and the auditable structural consistency receipt system comprises a sequence exchangeable rectangle and a loop conservative triangle (structure), and according to a predetermined level (geometry-framework-data field), a single correction adapter is selected to restore inconsistency. Each repair strictly ensures that the inconsistent measure mu is reduced by 1 (delta mu = 1), and single-step convergence is realized. The system serializes and records each step operation as an additional plain text receipt, and performs integrity protection on a receipt chain through a deterministic natural number fence value without depending on an encryption algorithm. And finally, result judgment is carried out through six Boolean condition'judgment gate 'outside the assembly line, wherein the six Boolean conditions comprise check in the aspects of parity check, phase alignment, sequence exchange (torsion), label consistency, scale benchmark, gluing closing and the like.

The invention provides an imaginary number hybrid simulation consistency data interaction method and system based on puppet entities, and the method comprises the steps: determining virtual simulator force resources participating in simulation, and generating a virtual force-puppet entity four-layer element index mapping table; according to the four-layer element index mapping table, exclusive data field intervals are divided for puppet entities of the same kind, data field identifiers are further dynamically distributed to members in the intervals, and data field isolation between the puppet entities in the digital space is completed; configuring transmission strategies for different theme protocol types according to different types of protocol data of different puppet entities in respective data interaction exclusive domains; the puppet entity carries out frequency division on the synchronization time macro-beat to the micro-beat capable of being scheduled and calculated in the simulator according to the simulation period requirement of the mapped simulator; and formulating a set of full life cycle management mechanism for the puppet entity, and performing unified management on the states of stages such as creation, initialization, operation, pause / recovery, termination and destruction of the puppet entity in the simulation process. According to the method, the data transmission overhead is effectively reduced, and the space-time consistency and situation consistency of imaginary number hybrid simulation are ensured.

The application relates to a message exchange method and system, which is based on an 8583 protocol format, configures message composition content, and has fields containing a purpose, a data field number, a maximum length, a length type, a data format, a padding type, a padding position, padding content, an existence identifier, original data, and a general basic packet format of packet data after formatting; transaction data dynamically generated according to a transaction process is encrypted through the configured message composition content to generate a data set; the data set format is converted into a required format of a transaction message to generate message format data; a message header is added to the message format data, and a MAC is composed to complete the message, which is uploaded to a transaction processing party transaction system. The method takes into account the standard difference of data fields of different transaction parties, avoids risks caused by cognitive bias, strips the deep association of the system with software and hardware, standardizes the data to simplify integration, does not depend on external resources for original code development, avoids transplantation problems, and improves compatibility and stability.

The invention discloses a CCSDS (Consultative Committee for Space Data System) protocol security enhancement method based on an application layer feedback chain, which comprises the following steps: setting a packet auxiliary guide header of a CCSDS source packet as a security metadata area to obtain a security extension packet structure, the security metadata area at least comprising a reset flag bit field and an integrity check code field; based on the CCSDS source packet, obtaining an initial calculation sequence by extracting a specific field comprising an application process identifier, a packet sequence count and packet data field total load data; based on the initial calculation sequence, performing stream reading and iterative calculation by taking 32-bit as a single processing step length to obtain a hash operation result; based on the Hash operation result, historical feedback factors are injected and confused, and an intermediate processing result is obtained; and based on the intermediate processing result, acquiring an updated integrity check code and filling the security metadata area with the updated integrity check code.

The application discloses a kind of based on honeypot system's industrial control network attack message response method and response system, wherein, the method includes the following steps: receiving attack message and screening out the valid industrial control protocol message in the attack message;From the valid industrial control protocol message, corresponding industrial control application data unit function code field and industrial control application data unit request data domain field are extracted, and the corresponding field of the data in the request response table in the honeypot system is matched, and according to different matching results, the response message corresponding to the result is replied.The based on honeypot system's industrial control network attack message response method and response system, by collecting and processing specific power industrial control network flow data, request response table can be generated, using the request response table, reasonable response with high fraud can be returned to the sniffer behavior of attacker.

The invention relates to the technical field of graph neural networks, in particular to a multi-domain-oriented graph model distribution external prediction method, device and system and a medium, and the method comprises the steps: obtaining a plurality of heterogeneous pre-training models on different graph data domains; generating composite graph data for the corresponding pre-training model through a generator under a passive data access condition; inserting a learnable Adapter module at the rear end of a classifier of each pre-training model, adjusting classifier parameters through the Adapter module to obtain adjusted parameter representation, and performing weighted fusion on a prediction result of each pre-training model through globally learnable fusion weight parameters to obtain a final fusion prediction result; simultaneously optimizing Adapter parameters and fusion weight parameters through a back propagation algorithm to obtain a trained fusion model; predicting the distribution exterior graph data from the target domain; according to the invention, accurate prediction of cross-domain distribution exterior graph data is realized.

The application discloses a method and device for adjusting MCS level, equipment and storage medium, and relates to the field of mobile communication. The specific implementation scheme is as follows: obtaining a target data amount to be transmitted by a target user to be scheduled; determining a position mark of the target user in a scheduling process based on non-data domain resource occupation adjustment MCS level according to the target data amount and a preset data amount threshold value; and adjusting the MCS level of the target user according to a preset strategy based on the position mark. Through the method, whether the scheduling MCS level needs to be further adjusted can be adaptively judged based on the time-frequency domain density information occupied by the non-data domain resource in the service channel resource allocation process, so that the efficiency and accuracy of the MCS level adjustment are improved.