441 results about "Data domain" patented technology

The invention discloses an energy consumption prediction and scheduling control method based on machine learning. According to the method, operation parameters, energy consumption curves and environment disturbance data of multiple devices are collected through a distributed sensing terminal, the data are input into a pre-trained machine learning model, and a probability prediction result of future energy consumption distribution is generated. On the basis of prediction, an intervention signal is applied in an equipment safety boundary, equipment response characteristics are obtained according to the difference before and after intervention, and an energy consumption risk map is constructed by combining the equipment response characteristics with a probability prediction result. And based on the energy consumption risk map, generating an extreme disturbance scene by using digital twinning, performing consistency check on a probability prediction result and a scheduling scheme in a data domain and a physical domain, and performing multi-stage scheduling in combination with task delays to generate a scheduling result. And finally, issuing the scheduling result to the equipment. The method can improve the accuracy of energy consumption prediction and the reliability of scheduling decision making, and is suitable for intelligent management of data centers, industrial production and high-energy-consumption scenes.

The present application is suitable for the technical field of fault prediction, and in particular relates to a transfer learning-based cross-server fault prediction system and method. The method comprises: carrying out data collection, collecting server data, and preprocessing the server data to obtain preprocessed data; constructing a neural network model, constructing a data set, and, on the basis of the data set, pre-training the neural network model to obtain a pre-trained model; on the basis of a deep domain confusion mechanism, aligning feature distributions of data domains of different servers to obtain a deep domain confusion neural network model; collecting sample data, and tuning the neural network model to obtain a prediction neural network model; and performing data information testing on a server requiring prediction to obtain a test result, and, on the basis of the test result, performing visualization processing. The present application takes preventive measures to improve the stability and reliability of servers, shortening shutdown time and reducing maintenance costs, thus improving the overall performance of systems and user experience.

The invention relates to the technical field of information and data processing, in particular to an enterprise digitalization-oriented data management and application method, which comprises the following steps of: acquiring multi-source heterogeneous data and recording metadata; establishing a cross-department management system to standardize the treatment process; building an asset catalog based on the business section and the data field classification; formulating unified data description of a multi-level data standard system; designing a quality control rule to filter low-quality data; constructing a business index system and embedding the business index system into a business system; identifying a cross-system and cross-type association mode through a metadata association technology and a rule engine; and dynamically adjusting the treatment process by adopting a multi-objective optimization algorithm in combination with service feedback and treatment parameters. According to the method, the defects of existing data governance in comprehensiveness, depth and business adaptability are overcome, standardized governance and potential value mining of multi-source data are achieved, and effective data support is provided for intelligent decision making of enterprises.

A system and method for cross-domain knowledge transfer in federated compression networks. The system enables efficient lossless data compression across diverse data types by intelligently sharing compression strategies between domains. A cross-domain knowledge transfer system identifies relationships between different data domains, adapts compression parameters accordingly, and optimizes learning processes to maximize knowledge reuse. The architecture may include a knowledge repository for storing domain features and compression patterns, domain mapping components that identify similarities, and transfer learning optimization that enables efficient adaptation with minimal examples. This approach significantly accelerates model training for new domains while improving compression performance. Applications include satellite telemetry systems where efficient compression is critical for transmitting large information sets between distant locations. The system may employ probability prediction driven arithmetic coding paired with long short-term memory networks, enhanced by cross-domain knowledge sharing that adapts successful compression strategies from one domain to another while preserving domain-specific optimization.

Systems and methods are configured to generate a set of potential responses to a prompt using one or more data models with data from at least a plurality of data domains of an enterprise information environment that includes access controls. A deterministic response is selected from the set of potential responses based on scoring of the validation data and restricting based on access controls in view profile information associated with the prompt. These enterprise generative AI systems and methods support granular enterprise access controls, privacy, and security requirements. enterprise generative AI providing traceable references and links to source information underlying the generative AI insights. These systems and methods enable dramatically increased utility for enterprise users to information, analyses, and predictive analytics associated with and derived from a combination of enterprise and external information systems.

The invention relates to a cross-domain voice classification method and device based on feature decoupling and multi-task learning. The method comprises the following steps: firstly, acquiring a multi-data-domain voice file, and preprocessing the multi-data-domain voice file to obtain a cross-domain voice classification data set; then, constructing a cross-domain voice classification model which comprises a voice feature encoder module, a data domain classification module, a supervised comparative learning module and a multi-task classification module; then, a joint optimization loss function is constructed based on data field classification loss, supervised contrast learning loss and task classification loss, and a gradient descent algorithm is adopted to train and optimize the cross-domain voice classification model based on the cross-domain voice classification data set and the joint optimization loss function; and finally, inputting to-be-classified voice into the trained cross-domain voice classification model to obtain a voice corresponding category. The discrimination ability and generalization ability of the model in a cross-domain scene are significantly improved, so that the model still maintains high classification precision in a complex multi-source voice environment.

The invention discloses a firmware-level security protection system and method for a hard disk of industrial automation equipment, particularly relates to the technical field of storage security protection, and is used for solving the problem of abnormal tampering of firmware. According to the method, instruction calling, interface access and interrupt timestamps are collected in a sliding window, a behavior sequence set containing an instruction sequence, calling frequency and sensitive marks is constructed and compared with a reference sequence library, firmware-level judgment on operation abnormity, access border crossing and tampering risks is triggered based on a transfer relation and an offset index, and the operation safety is improved. The method comprises the following steps of: dividing firmware into a control domain, a data domain and a configuration domain, realizing minimum permission and dynamic isolation by relying on state and permission mapping and permission register and interface shielding, forming a flow from detection to isolation to closed loop recovery by combining protected segment integrity verification and local copy hot switching, writing an exception into a protected loop log area for threshold updating, and performing protection on the protected loop log area. And the tamper-proof performance, the operation safety and the maintainability of the firmware are integrally improved.

The invention provides an automatic data management method and system based on a large model, and the method comprises the steps: obtaining a native data set corresponding to a to-be-managed data field, carrying out the cross-domain feature association mining of the native data set based on a pre-trained large model, generating a semantic feature map of data entities and an implicit association link between the data entities, and carrying out the data management through the implicit association link. Calling a pre-trained large model, performing quality dimension evaluation based on the semantic characteristic spectrum and the implicit association link, outputting a quality defect vector of a data entity, performing source tracing analysis based on the quality defect vector, and determining a data quality defect type existing in a to-be-treated data field and a cross-entity propagation path of the data quality defect; a strategy evolution module in the pre-training large model is driven to generate a self-adaptive governance strategy containing defect positioning anchor points, and the self-adaptive governance strategy is deployed to a data governance execution environment to complete automatic data governance, so that the governance strategy can better adapt to different data quality scenes; and the pertinence and effectiveness of data management operation are improved.

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 provides a grain pile surface pest detection model training method, a detection method and equipment, and relates to the technical field of image recognition, and the training method comprises the steps: respectively generating different types of data samples based on grain pile surface image data marked with pest bounding box labels in a plurality of data fields in advance, respectively inputting the two model branches, so that the two model branches sequentially carry out backbone network feature extraction and backdoor adjustment on the data sample to obtain a target feature map and pest detection result data; and determining the causal feature alignment total loss corresponding to the current iteration round according to the target feature maps obtained by the two model branches so as to optimize the two model branches. According to the method, the domain generalization ability of the grain pile surface pest detection model and the adaptability to complex and changeable actual detection scenes can be effectively improved, the application convenience and effectiveness of the grain pile surface pest detection model can be improved, and then the accuracy and reliability of a grain pile surface pest detection result can be improved.

The invention provides an ore grinding grading intelligent decision-making method and system based on machine learning, and the method comprises the steps: S1, collecting the manual control data of a sensor data field, and constructing a training data set and a test data set; s2, analyzing and decoupling a plurality of groups of manual control data; and training a decision tree model according to the training data set, and predicting parameters of the ore grinding classification equipment through the decision tree model to obtain a prediction result. According to the method, the multivariable coupling nonlinear dynamic optimization problem in the ore grinding and grading process can be effectively solved, and the operation efficiency, the product quality and the production efficiency of ore grinding and grading are comprehensively improved.

The invention is suitable for the technical field of radar signal processing, and provides a tomographic SAR super-resolution imaging method based on a structured sparse network, and the method comprises the steps: firstly obtaining multi-channel SAR observation data, constructing multi-channel observation vector MMV data in a data domain, and constructing a multi-pixel signal model corresponding to the MMV data based on a neighborhood pixel elevation consistency hypothesis. The method comprises the following steps: designing a kernel principal component analysis KPCA expansion network model, carrying out dimension reduction and enhancement on MMV data, introducing a norm compressed sensing model on the basis of the MMV data after dimension reduction and enhancement, solving the compressed sensing model by using an ADMM iterative algorithm, expanding the ADMM algorithm into a deep network, reconstructing an elevation spectrum, and obtaining an SAR image super-resolution three-dimensional reconstruction result. According to the method, the super-resolution performance and the solving efficiency of three-dimensional reconstruction can be effectively improved, and high-resolution three-dimensional reconstruction of a large-scale scene can be efficiently realized.

The invention discloses a data and model heterogeneous federated learning method of global decision boundary distillation learning. Two key problems of data heterogeneity and heterogeneous model performance difference are respectively solved through two sub-methods. According to the first sub-method, aiming at the problem of data heterogeneity, multiple prototypes are locally clustered, private data field distribution information is fully condensed, irrelevant distillation is introduced in a local supervised learning stage, and local knowledge forgetting is relieved. According to the second sub-method, global decision boundary distillation learning is provided for the problem of performance difference of the heterogeneous model, a global decision boundary learner is maintained and updated in a 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 method is superior to that of various most advanced federal learning methods, the method can better adapt to challenging scenes, and meanwhile, the method has higher communication efficiency and better privacy reliability.

The invention belongs to the technical field of seismic exploration and artificial intelligence, and relates to a physical perception collaborative fusion-based full waveform inversion neural network construction method, which comprises the steps of required model data construction, full waveform inversion neural network construction, loss function construction, network pre-training, and network fine tuning and application. Physical feature perception and collaborative fusion are carried out through a U-Net network and an adaptive residual learning module, so that the problem that traditional full-waveform inversion depends on synthetic data training and is not matched with an actually measured data field is solved. The designed pre-training strategy not only can get rid of dependence on a large-scale synthetic data set, but also can effectively relieve the domain offset problem through fine adjustment of measured data, thereby improving the generalization ability of the model. And the adaptive residual learning module enables the neural network to adaptively and dynamically optimize feature expression, and improves inversion precision through collaborative fusion of a physical perception mechanism. According to the method, the physical consistency and imaging reliability of speed model prediction can be remarkably improved while the labor cost is reduced.

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 belongs to the technical field related to data processing, and particularly relates to a data authority evaluation method under a multi-source social governance data fusion scene, which comprises the following steps: extracting metadata of to-be-evaluated data; inputting the metadata into the fine-tuned large model to obtain a matching score of the to-be-evaluated data and all preset social governance business fields, a professional authority score of a to-be-evaluated data source department or institution and weights of representation form standardization degree evaluation, data field association degree evaluation and data source department level evaluation; calculating a data field association degree evaluation score based on the matching scores of the to-be-evaluated data and all preset social governance business fields; determining a data source department level evaluation score based on the professional authority score; calculating a representation form standardization degree evaluation score by comparing the to-be-evaluated data with an industry data specification; and weighting each score to obtain an authority quantitative evaluation score. According to the invention, authority of multi-source social governance fusion data can be guaranteed.

The invention provides a change analysis method and device based on cross-temporal frequency domain information interaction and a readable storage medium thereof. The method comprises the following steps: acquiring a dual-temporal remote sensing image and partitioning the dual-temporal remote sensing image; extracting spatial features through a shared encoder; fourier transform is carried out on the double-tense features in a feature domain, high and low frequency components are divided according to a step length r, low frequency information is exchanged, and after inverse transform, splicing is carried out to obtain a fusion feature F; performing same frequency domain interaction on an original image in a data domain, and modeling through a video encoder to obtain a feature E; and finally fusing F and E, and decoding and outputting a change prediction map. According to the method, the high and low frequency information proportion is dynamically regulated and controlled through the frequency domain step length, dual interaction of the data domain and the feature domain is combined, structural difference perception is enhanced, pseudo change interference is suppressed, and change detection precision and robustness are remarkably improved. Experiments show that the method is superior to the prior art in F1 score, intersection-union ratio and other indexes, and is suitable for the fields of smart cities, disaster monitoring and the like.

The invention provides a dynamic adaptation method and system for a multi-mode power supply compatible charging pile system, and the method comprises the steps: building a multi-protocol physical layer compatible connection between a charging pile and a power supply module through a multi-communication interface layer, and receiving a data frame inputted by the charging pile; analyzing a baud rate, a check bit and an instruction set of the data frame, and identifying a charging pile protocol type and a power module protocol type; querying a matching mapping relationship between the charging pile protocol type and the power supply module protocol type in the dynamic adaptation matrix; if the matching mapping relation exists, data field conversion is executed, and a target protocol frame is generated; and if the matching mapping relation does not exist, starting a machine learning process, creating a temporary mapping rule, and effectively solving the problems of poor compatibility and high manual configuration cost of the charging pile and the power supply module in a traditional protocol mode through the modes of protocol analysis, intelligent matching and dynamic learning.

The invention discloses an efficient CAN and DIO implementation method with a low-cost scheme, particularly relates to the field of MCU and DMA scheduling, is used for solving the problems that the CAN and DIO scheme is high in cost, large in occupied area and poor in compatibility, and comprises the steps of dividing a USB data area and a CANFD message area according to a preset proportion by utilizing an RAM embedded in an MCU, and feeding back a memory state in real time through a DMA channel and a register; constructing load sensitivity and queue burst indexes according to memory occupation and queue load, and dynamically adjusting interrupt priority and time slot distribution; meanwhile, CRC verification, frame header positioning and data field recombination are carried out on a USB data packet through a firmware module, message generation strictly matched with a CANFD protocol format is achieved, conversion time delay and successful counting are fed back to a scheduling module, a closed-loop scheduling control system is formed, and therefore hardware cost and the PCB area are reduced, and meanwhile the reliability of the CANFD protocol is improved. And stable support and flexible expansion of CANFD high-speed communication are realized.

Disclosed herein are methods, systems, and computer-readable media for prompting a machine learning model to generate answer data based on a recording. Some embodiments involve preprocessing a prompt corresponding to a query for a first system by receiving the prompt and a timestamp corresponding to a time position of the query in a recording, acquiring a text transcript based on the recording, and selecting, based on the timestamp and the text transcript, a first data domain from the text transcript. Some embodiments involve transmitting at least one of the prompt, the text transcript, and the first data domain to a second system, the second system including a machine learning model. Some embodiments involve generating answer data corresponding to the prompt by querying the machine learning model with the prompt, receiving answer data from the machine learning model, and transmitting the answer data to the first system.

Embodiments herein relate to data management and, more particularly, to collecting data from a plurality of sources, and linking the collected data to derive information and knowledge. The method includes defining at least one data model and asset by including data models, vocabulary, data quality rules, data mapping rules for at least one of, a particular data industry, a data domain, or a data subject area, importing data from a plurality of data sources, performing de-duplication of the imported data and data profiling of the imported data, and creating linked data either by semantic mapping, or by curating the data.

A fault detection apparatus and computer-implemented method for providing physically explainable fault information of a bearing built in a machine by a fault detection model is provided, including: obtaining sensor data measured at the bearing as input data relating to an input data domain and the fault detection model, mapping the measured sensor data from the input data domain to a selected data domain resulting in an augmented fault detection model which outputs augmented predicted failure value related to the selected data domain, wherein the selected data domain has a physical meaning to the fault of the bearing, performing a feature attribution on the augmented fault detection model quantifying an importance of at least one individual feature to the augmented failure value related to the selected data domain, and displaying the individual feature and the respective quantified importance in the selected data domain at a user interface.

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.

The invention provides a data cache management method and device and a medium, and relates to but is not limited to the technical field of communication, and the data cache management method comprises the following steps: obtaining data; determining a target logic channel and a data sub-header corresponding to the data according to the data type corresponding to the data; according to the target logic channel, determining a target sub-data cache region corresponding to the data in the data cache region; the data sub-header and the data are cached in the target sub-data cache region, the data sub-header is cached in a header field in the target sub-data cache region, and the data is cached in a data field in the target sub-data cache region. According to the embodiment of the invention, the copy number of data can be reduced in a scene with a high transmission rate and a low transmission delay requirement, so that the memory consumption is reduced, and the data processing capability is effectively improved.

A method for constructing an entity representation engine comprises receiving time series training data representing a plurality of discrete training event sequences. Each training event sequence is associated with one of a plurality of discrete separate data domains. The training event sequences are time-ordered sequences of sample interactions within the discrete separate data domains. Representation learning is applied to the time series training data to generate a trained representation engine that ingests entity time series data representing a plurality of discrete entity event sequences for a particular entity. Each entity event sequence is associated with one of the discrete separate data domains; the entity event sequences are time-ordered sequences of entity interactions for that entity within the respective discrete separate data domains. From the entity time series data, the representation engine generates a single unified entity representation characterizing a distribution of the entity interactions within the discrete separate data domains.

The present application relates to, but is not limited to, the technical field of communications, and provides a data cache management method, a device, and a medium. The data cache management method comprises: acquiring data; on the basis of a data type corresponding to the data, determining a target logic channel and a data sub-header corresponding to the data; on the basis of the target logic channel, determining a target sub-data cache region corresponding to the data in a data cache region; and caching the data sub-header and the data into the target sub-data cache region, the data sub-header being cached in a header field in the target sub-data cache region, and the data being cached in a data field in the target sub-data cache region.

The invention discloses a privacy protection unsupervised cross-domain image retrieval method and system, and belongs to the technical field of information security. Aiming at the problems of privacy leakage risk and difficult feature alignment in the existing unsupervised cross-domain image retrieval, the invention provides a dual-protection transformation strategy, and realizes local encryption of image features of each data domain by combining orthogonal projection transformation and a differential privacy mechanism. Furthermore, intra-domain discriminant reinforcement learning and differential privacy cross-domain alignment are respectively performed through a probability prototype optimal transmission mechanism, and an intra-domain and cross-domain loss function is constructed based on a contrast learning normal form, so that safe and effective model training is realized. And after training is completed, image similarity calculation is performed by adopting protected feature representation, so that cross-domain image retrieval under privacy protection is realized. According to the method, the accuracy and robustness of cross-domain retrieval are improved, privacy security and model performance are considered, and the method is suitable for an image retrieval task in a multi-party cooperation scene.

Disclosed is a method comprising: receiving captured seismic data and generating first velocity and first pressure data therefrom; transforming, from a first data domain to a second data domain, the first velocity and first pressure data and thereby generate second velocity and second pressure data; mapping wavenumber gather data from the second data domain to a third data domain; determining envelope ratio scaling data and threshold value data using the second velocity and second pressure data; generating, threshold envelope data using a thresholding operator associated with the threshold value data; using coefficient data associated with the threshold envelope data to estimate vertical component data; transforming, from the third data domain to the first data domain, the vertical component data to generate temporal-spatial data; and generating, based on the temporal-spatial data, noise component data comprised in the captured seismic data.

The invention discloses a water quality prediction method based on fine tuning large language model knowledge driving, and belongs to the crossing field of artificial intelligence technology and environmental discipline. According to the method, a large language model is finely adjusted according to a predefined prompt template through professional knowledge in the water environment field, a knowledge extraction model suitable for the water environment field is formed, and a quintuple is extracted from a text to construct a water environment knowledge graph. Performing graph embedding learning on entities in the knowledge graph by adopting a GloVe model and a residual image attention network, and calculating correlation among water quality indexes through cosine similarity to obtain a knowledge domain correlation weight matrix; meanwhile, the Pearson's correlation coefficient, the weighted grey correlation degree analysis and the dynamic time warping correlation similarity coefficient are used for respectively calculating linear and nonlinear correlation among the water quality indexes, and a data field correlation weight matrix is obtained; and finally, constructing a KG-DW-CLA water quality prediction model jointly driven by knowledge and data to perform water quality prediction. According to the method, the problems that a traditional model is insufficient in domain knowledge utilization and single in data correlation modeling are solved, the accuracy and scientificity of water quality prediction are improved, and the method has high generalization ability and application value.

The invention discloses a data reproduction method and device, a storage medium and computer equipment. The method comprises the steps of obtaining picture information generated based on original data in a specific data field; performing optical character recognition on the picture information, and performing structured data extraction on an optical character recognition result through a preset formatted data extraction model to obtain structured information corresponding to the picture information; and performing original data reproduction according to the structured information and the picture information through a preset visual model to obtain reproduction data corresponding to the original data. According to the method, the limitation of acquiring data from pictures traditionally is broken through, and the acquisition efficiency and accuracy are improved by means of the OCR and formatted data extraction model; the original data are completely and consistently reproduced by combining the visual model with the structured and picture information, so that the automatic processing of data reproduction is realized, and the efficiency and accuracy of data reproduction are improved.