2896 results about "Data class" patented technology

The invention provides a Beidou electric power data intelligent acquisition and dynamic communication scheduling method and system. The method comprises the steps that a master station constructs a database and trains a data priority classification model, a channel quality prediction model and an intelligent compression model; a substation collects power data through an edge calculation unit, constructs a skyline candidate set to perform data screening, and performs intelligent classification marking by using a data priority classification model; performing reliability evaluation on the data stream by using a Gaussian mixture model, selecting a compression strategy according to a reliability score, a data type and a priority, and packaging into a data frame; determining a transmission strategy in combination with a channel quality prediction result and a context-aware intelligent switching protocol, and sending a data frame; the master station receives the data frame, performs integrity verification, decompresses and reconstructs the data frame, and feeds back a communication state for model updating; and monitoring the operation state, performing early warning based on the anomaly detection model, and triggering a self-healing strategy. According to the invention, the Beidou communication resource utilization rate and the system reliability are improved.

The invention discloses a mineral resource intelligent prediction method and system based on multi-source heterogeneous data fusion and deep learning, and the method comprises the steps: collecting and preprocessing multi-source heterogeneous data, and carrying out the standardization processing to form a structured data set; multi-source heterogeneous data fusion: realizing data layer space registration and feature layer weight dynamic allocation through an attention mechanism multi-modal fusion module, and outputting a high-dimensional metallogenic feature vector; constructing a CNN-LSTM mixed deep learning model and completing initialization training, and outputting an initial mineralization probability graph; and establishing a dynamic updating engine, performing model increment training based on transfer learning, correcting the mineralization probability through positive and negative sample reinforcement learning in combination with a newly added data type, and outputting a time sequence dynamic mineralization probability graph. According to the method, mineralization probability dynamic evaluation and risk quantitative updating are realized, the prediction precision and the model updating efficiency are improved, the method is adaptive to a multi-stage exploration scene, and accurate real-time support is provided for exploration decision making.

The invention discloses a method for simulating and forecasting flood in a cold highland area based on hydrological and hydrodynamic coupling, and belongs to the technical field of disaster forecasting. The method specifically comprises the following steps: S1, multi-source basic data collection and preprocessing: collecting multi-type and multi-scale basic data for a target cold and cold mountainous area drainage basin; and S2, deep learning correction and fusion of the satellite rainfall data: aiming at the local overestimation and underestimation problems of the satellite rainfall data, a deep learning algorithm is adopted to carry out hour scale correction and fusion. Four types of core data of satellite remote sensing, reanalysis, ground observation and geographic space are collected, total factors of'rainfall-runoff-terrain-underlying surface 'required by flood simulation in the cold and cold mountainous area are covered, simulation one-sidedness caused by lack of data types in traditional modeling is avoided, rainfall and runoff abnormal values are eliminated by adopting a 3-sigma criterion, data formats and spatial-temporal scales are unified, and the modeling efficiency is improved. A standardized data set is formed, and interference of abnormal values, format incompatibility and space-time mismatching on subsequent model input is avoided.

The invention discloses a privacy protection data sharing and cooperative training method and system based on federated learning. The method comprises the steps of receiving software development log data, adaptively judging the sensitivity degree according to a data type, dynamically adjusting noise disturbance intensity according to the sensitivity degree to perform data desensitization, and generating a sensitivity index; selecting a feature extraction strategy, extracting time sequence correlation features from the desensitization data, constructing a dynamic graph structure with a weight, and obtaining a time sequence feature vector through iterative fusion; calculating the time sequence correlation of the time sequence feature vector to obtain a data quality score, and setting a contribution weight based on the quality score to perform parameter aggregation; combining sensitivity indexes with data quality scores to construct a security sharing domain, decoupling global training parameters into knowledge fragments in the domain, formulating a recombination rule, and selectively acquiring the required knowledge fragments by all parties for local training. According to the method, deep collaboration is realized on the premise of protecting data privacy, and the collaboration training effect is improved.

A system and method for real-time time series forecasting using a compound large codeword model with integrated supervisory neurons. The system processes diverse inputs through adaptive codebook generation and codeword allocation. A projection network fuses different data types, creating unified representations for a latent transformer-based machine learning core. The core contains local neural network regions of interconnected operational neurons, monitored by supervisory neurons. These supervisory neurons receive activation data from operational neurons, perform real-time statistical analysis, determine necessary structural modifications, and initiate their implementation during operation. This architecture enables efficient handling of multi-modal data, capturing complex relationships between different input types. The combination of adaptive codebook generation and the supervisory neuron system ensures responsiveness to evolving data patterns and task requirements. This approach provides more accurate and timely forecasts by leveraging diverse data types in a sophisticated, integrated manner, while continuously adapting its structure to maintain optimal performance.

The invention provides a self-adaptive semantic-driven data set field matching method and system, and the system comprises a data preprocessing module which is used for carrying out the cleaning, standardization and preliminary analysis of an input data set, and extracting a field name, a data type, a field description and a data sample; the deep semantic representation modeling module is used for constructing a field-level semantic representation vector; the multi-level similarity calculation module is used for comprehensively calculating the grammatical similarity, the semantic similarity and the statistical similarity among the fields, dynamically adjusting the weight of the similarity of each level by adopting a weighted fusion algorithm, and generating a comprehensive similarity matrix; and the matching result management and application module is used for generating a field matching mapping table and a fusion suggestion according to the comprehensive similarity matrix. According to the method, high-precision automatic matching of data set fields is realized by fusing deep semantic understanding, multi-dimensional similarity calculation and incremental adaptive learning, and the efficiency and accuracy of data set fusion are remarkably improved.

The invention belongs to the technical field of multi-modal intelligent data management, and discloses a multi-modal business data co-processing and intelligent conversion method, which comprises the steps of fusing multi-source data, coupling cross-modal time sequence association, detecting image-text semantic conflicts, and generating an RAG semantic unit and a space-time alignment semantic packet. Constructing a four-dimensional priority label to divide resource pool categories, dynamically allocating resource pool computing resources, generating a low-redundancy feature set, triggering a forced preemption strategy during resource competition, and synchronously generating a resource arbitration log; injecting a dynamic rule node according to a data type, executing confidence arbitration in combination with a conflict arbitration identifier, generating business decision content and generating a rule correction decision packet; constructing a full-link semantic thinking chain, synchronously capturing a system operation track, and aligning to generate a dual-channel tracing report; identifying residual sensitive fields, triggering hot update to load corresponding rules, quantizing effectiveness to generate compliance reports, reversely updating an industry rule base, and optimizing a resource allocation strategy.

The invention provides an urban virtual power plant operation method and device based on an intelligent agent architecture, relates to the field of artificial intelligence, and solves the problem that in the prior art, operation strategies related to a virtual power plant are mostly based on static rules and manually set optimization models. And rapid and efficient response is difficult to realize in a multi-participant, multi-constraint condition and dynamic electricity market environment. The method comprises the following steps: collecting multiple pieces of first data in an urban energy system in an urban virtual power plant; performing arrangement and task scheduling on the first data on the basis of data types required by the plurality of agents to execute tasks, determining the first data required to be processed by each agent, and calling the plurality of agents to process the first data required to be processed; and generating a regulation and control decision and a market transaction strategy of the urban virtual power plant based on the processing results output by the plurality of agents and the operation constraint conditions. The method is used in the operation process of the urban virtual power plant.

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

A system for converting a source data feed schema into a canonical data product including a memory for storing computer-executable instructions and a processor for executing the instructions stored on the memory. Execution of the instructions programs the processor to perform operations that include receiving a source data feed having a source schema, identifying a plurality of data fields of the source schema, assigning a data category from a plurality of predefined data categories to each data field of the plurality of data fields, modifying the source schema based on predefined parameters, wherein the source schema is modified to match a target schema, comparing the modified source schema to the target schema, and in response to a determination that the modified source schema matches the target schema, converting the source data feed to a canonical data product having the target schema based on the assigned data categories.

The invention discloses an energy field intelligent knowledge base question answering system based on multi-model cooperation, and aims to solve the problems of data type identification, image structured extraction and cross-modal indexing in energy field multi-modal document question answering. The system comprises 13 core modules, dynamic cooperation of a multi-mode large model and a large language model is achieved through a collaborative scheduling module, data types involved in user problems can be recognized, and corresponding modules can be called; structured information of images such as charts and flow charts can be extracted from documents and coded; and cross-modal retrieval is realized through a unified semantic vector. When answers are integrated, an attached source is quoted, and traceability is ensured. The system improves the accuracy and efficiency of complex document question answering in the energy field, and is suitable for professional document question answering scenes containing multiple types of images.

The embodiment of the invention discloses a report generation method and device, computer equipment and a readable storage medium, and the method comprises the steps: displaying a graphical user interface to prompt a report requester to input report demand information; report demand information is obtained, semantic analysis is carried out on the report demand information through a large language model LLM to obtain report generation indication information, and the report generation indication information at least comprises a data range, a data type, a data statistical parameter and a report display type; searching a database through the LLM to obtain a plurality of business data matched with the data range and the data type, and obtaining business indexes of the plurality of business data based on the data statistical parameters through the LLM; and determining report basic parameters based on the report display type through the LLM, and outputting a target report based on the report basic parameters, the multiple pieces of business data and the business indexes. By adopting the method, the technical threshold of data analysis processing can be reduced, the report generation efficiency is improved, and the method is simple, efficient and high in applicability.

The invention discloses a scene and compliance fused dynamic hierarchical data encryption method and system, and the method comprises the steps: obtaining multi-source data, and carrying out the preprocessing of the multi-source data, and obtaining to-be-encrypted data; key words in the to-be-encrypted data are extracted, and weight coefficients are distributed to the key words according to semantic roles and / or industry risk coefficients; according to the data type, determining a characteristic value corresponding to each keyword; determining a scene coefficient and a compliance coefficient; based on the weight coefficient, the feature value, the scene coefficient and the compliance coefficient, determining a data sensitivity score and a data sensitivity level corresponding to the to-be-encrypted data; and based on the data sensitivity level, encrypting the to-be-encrypted data by adopting the encryption strategy of the corresponding level. According to the method provided by the embodiment of the invention, the security of the high-sensitivity data is ensured, the resource waste caused by excessive encryption is avoided, the whole process is automatic, the labor cost and the overall cost of security protection are greatly reduced, and the comprehensive efficiency of data processing and security protection is improved.

The invention relates to the technical field of system monitoring, and discloses an information system full-link monitoring method based on high-frequency index acquisition optimization, which comprises the following steps: monitoring the running state of an information management system in real time, dynamically adjusting the sampling frequency by means of a customized service key identification component and a comprehensive load prediction model, and performing real-time monitoring on the sampling frequency. Transmitting the target data to the edge computing node; a lightweight monitoring agent is deployed at an edge node, and a wavelet signal decomposition algorithm is adopted to extract features and distinguish data types; constructing an information management business knowledge graph and an entity-relation-business rule base, associating abnormal features, and generating an abnormal root cause report in combination with a time sequence prediction model and a knowledge constraint large language model; based on report and information service priorities, monitoring resources are dynamically allocated in the edge-cloud collaborative architecture, and related model parameters, service association rules and constraint weights are optimized according to operation and maintenance feedback. According to the invention, targeted monitoring requirements in the business peak period and efficient utilization of system resources can be met at the same time.

The invention provides an aero-engine remaining service life prediction method based on a space-time knowledge graph and SDCNN, and belongs to the field of aero-engine health management. According to the method, a space-time knowledge graph and SDCNN neural network architecture is constructed. According to the method, a spatio-temporal knowledge graph is innovatively constructed for an aero-engine, a BERT model is adopted to carry out data type conversion, and a multi-head graph attention network and a pooling graph attention network complete feature extraction and feature fusion to obtain fusion features; and finally, inputting the fusion features into a stacked expansion convolutional neural network to carry out regression learning on feature data, and then carrying out residual life prediction on the aero-engine. According to the method, modeling and prediction are carried out on complex spatial-temporal characteristic data, the remaining service life of the aero-engine can be effectively predicted under limited data, data support is provided for formulating an aero-engine maintenance strategy, and meanwhile a new thought is provided for predicting the remaining service life of other industrial equipment.

The invention relates to the technical field of water network intelligent management and control, and discloses a water network management and control multi-source space digital intelligent fusion base construction and decision optimization method, which comprises the following steps: cooperatively obtaining multi-source space data; format conversion, coordinate system unification and systematic semantic annotation are carried out through a data preprocessing platform, and a normalized spatial data set is formed; constructing a data fusion model, quantitatively evaluating the quality of a data source by adopting a fuzzy comprehensive evaluation method so as to determine a fusion weight, and performing weighted fusion by adopting a specific algorithm aiming at different data types such as vectors, grids and three-dimensional models so as to generate a unified spatial data model; and finally, a decision optimization system performs decision scheme generation and iterative optimization based on the model in combination with a hydrodynamic model and a genetic optimization algorithm. By establishing a standardized data processing and targeted weighted fusion process, the problems that multi-source heterogeneous data is difficult to integrate and the fusion precision is not high are effectively solved, and a reliable data base is constructed.

The invention discloses a remote sensing image change detection method based on deep learning and a GIS, and relates to the technical field of image processing and geographic information system application. Comprising the steps of 1, carrying out data preparation and preprocessing, 2, constructing a deep learning network model, carrying out feature extraction on preprocessed remote sensing image data by adopting a multi-layer convolution layer and a pooling layer, and aiming at obtained GIS data, carrying out feature extraction on the remote sensing image data by adopting different feature extraction methods according to data types, 3, performing feature fusion: fusing the remote sensing image feature vector and the GIS feature vector in a deep learning network model by using a feature fusion layer to obtain a fused feature vector; and step 5, performing change detection and result output: applying the trained deep learning network model to the preprocessed to-be-detected remote sensing image data and GIS data, outputting a change probability value of each pixel point through forward propagation calculation of the model to obtain a change probability graph, and performing post-processing operation on an obtained binary change detection result to obtain a change probability graph. And finally, outputting a clear and accurate remote sensing image data change detection result, and displaying the position, range and type information of a change area in the form of image or vector data.

Embodiments of the present disclosure relate to multimodal AI-based search for digital assets via an indexing and / or search pipeline. With respect to the indexing pipeline, some embodiments obtain first data and second data associated with a first digital asset. Such data represents different data types or modalities of the same digital asset. After obtaining the first and second data, some embodiments then generate a composite index. After the composite index is built such index can then be used to execute a query via the search pipeline. To execute the query some embodiments compute a relevance score for each digital asset, of multiple digital assets, based at least in part on a measure in which each digital asset satisfies one or more parameters or conditions for two or more data types of the query. Various embodiments then rank each digital asset and present one or more associated indicators.

The invention discloses a system and method for ensuring data consistency of a low-voltage power distribution network, and the method comprises the following steps: obtaining multi-source operation data uploaded by all collection devices in the low-voltage power distribution network, and converging the multi-source operation data to a unified processing channel; a timestamp dynamic alignment and mapping mechanism is adopted, sampling frequency and time offset are detected and corrected, and a unified time axis is generated; executing unit conversion and field unification operation according to the data type label, and constructing a standard field structure; carrying out prediction compensation on missing data by adopting a time sequence sliding window interpolation model; a sliding window anomaly rate detection strategy is applied to identify numerical value abrupt change and execute anomaly replacement; and performing structured output on the data set subjected to the consistency processing. According to the method, fine alignment, standardized processing and high-reliability correction of multi-source heterogeneous data can be realized, and the data consistency level and decision support capability of the low-voltage power distribution network in operation monitoring, analysis and control scenes are improved.

The invention belongs to the technical field of electric power automation, and particularly relates to an automatic modeling method of power grid dispatching knowledge based on a large model and a related system.The electric power knowledge is decomposed into an explainable intermediate reasoning path through the thinking chain technology, and adaptive small models are selected according to the path to form a modeling link; a modeling link is represented as a triple form, edges and nodes are complemented, a complete knowledge graph is obtained, problems in the knowledge graph are further decomposed to construct a reasoning link, meanwhile, input data are monitored in real time, newly added entities are associated with the reasoning link, and unified representation and processing of heterogeneous power system data are achieved. Due to the fact that data from different sources are different in format, precision and semantics, according to the method, through thinking chain decomposition and small model link processing, sub-module optimization and dynamic adaptation aiming at different data types are achieved, and precision loss and adaptation difficulty caused by data heterogeneity are effectively solved.

A system and method for optimizing data compression and encryption using reinforcement learning. The system analyzes incoming data streams to extract statistical features and data characteristics, which are processed by a reinforcement learning engine to automatically configure a multi-stage compression pipeline. Each compression stage transforms data into optimized distributions, applies Huffman coding, and maintains full encryption using homomorphic operations. A performance monitor tracks compression efficiency, processing speed, and output quality in real-time, providing feedback to continuously improve the reinforcement learning model's decisions. The system can dynamically adjust between one to five compression stages and select appropriate compression methods, including traditional algorithms or neural network-based approaches, based on data characteristics and performance requirements. All processing occurs on encrypted data without requiring decryption, ensuring complete data security throughout the pipeline. The adaptive nature of the system enables optimal compression performance across diverse data types while maintaining encryption integrity.

The invention discloses a power report generation method, system and equipment based on adaptive learning and a medium, which are applied to the field of power reports, and comprise the following steps: converting professional power knowledge into prompt lexical elements by utilizing a prompt technology driven by power knowledge, and taking the prompt lexical elements as context information; through a cross-modal feature enhancement mechanism, searching historical report fragments similar to the current equipment state from a historical fault report database, and fusing the historical report fragments with the preprocessed power operation data to generate enhanced feature representation; the enhanced feature representation and prompt lexical elements are input into a report generation model, a self-adaptive reflection learning loss function is adopted, the loss weight is dynamically adjusted according to the learning state of the data category in the training process, and the report generation model is optimized; and generating a power field equipment inspection report according to the optimized model. According to the invention, the professionality and the accuracy of report contents are improved, and the intelligent level and the practicability of the power report generation system are enhanced.

The invention provides a time sequence data storage method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining time sequence data and metadata information of the time sequence data, the metadata information comprising a first point location identifier, a data source and a data type corresponding to the time sequence data; according to the data source and the data type, determining a storage strategy of the time series data, including a target storage period and a target storage position; according to the storage strategy and the first point position identification, a target storage instance corresponding to the time sequence data is matched from a storage instance set, all storage instances in the storage instance set are combined and constructed according to different storage strategies and the point position identification, and all the storage instances are parallel storage instances; and storing the time sequence data to a time sequence database through the target storage instance. By determining a dynamic storage strategy driven by metadata and a parallel storage instance, the problem of performance bottleneck during high-concurrency writing of massive time series data is solved, and the real-time performance and reliability of time series data storage are ensured.

The invention belongs to the technical field of large language models, and discloses a time sequence card generation method and device based on a large language model, and the method comprises the steps: receiving a card creation instruction, and obtaining a card data type in the card creation instruction; extracting target text data in a dialog box of the large language model based on the card data type; converting the target text data into target card data corresponding to the card data type; generating a card title corresponding to the target card data according to the target text data; combining the target card data and the corresponding card titles to obtain time sequence cards; and putting the time sequence card into a tool window adjacent to the dialog box for displaying. According to the method, the manual operation of the user can be reduced, and the co-creation work efficiency and convenience of the large language model and the user are improved.

The invention discloses a stacking-based steel hot-rolled product mechanical property prediction method, and relates to the technical field of steel product quality prediction. The method comprises the following steps: constructing a steel hot rolling actual production data set and carrying out missing value interpolation on the data set; performing normalization processing on the complete data set, and dividing a part of data from the normalized data set as a training set; establishing a stacking ensemble learning model based on a regression chain; taking the yield strength, the tensile strength and the elongation in the training set as training labels, taking other data types as training features, and training the learning model by using the training set to obtain a steel hot-rolled product mechanical property prediction model; and the steel hot-rolled product mechanical property prediction model is practically applied to perform real-time prediction on the mechanical property of the steel hot-rolled product. According to the method, the coupling relation among the multiple target variables can be effectively processed, and the accuracy and stability of mechanical property prediction of the steel hot-rolled product can be effectively improved.

The invention discloses a data transmission method and device based on quantum encryption, which are used for improving the security and effectiveness of encrypted transmission of a monitoring data stream. According to the scheme provided by the invention, the method comprises the steps: obtaining the data type of a to-be-transmitted monitoring data stream of monitoring equipment, the transmission quantity of the monitoring data stream, and a communication capability index of a communication environment where the monitoring equipment is located; determining an encryption level corresponding to the monitoring data stream based on the data type and the transmission quantity; determining the division granularity of the monitoring data flow based on the encryption level and the communication capability index; dividing the monitoring data flow into a plurality of data packets according to the division granularity; and encrypting and transmitting each data packet by using a quantum key according to the encryption level of the monitoring data stream.

The invention relates to a cold and hot data migration method and device, computer equipment, a readable storage medium and a program product, and relates to the technical field of software development. The method comprises the steps of obtaining customer information in response to a trigger operation for a front-end application; inputting the customer information into a pre-trained customer behavior prediction model to obtain a customer behavior prediction result; comparing the customer behavior prediction result with a cold and hot data knowledge graph to obtain associated data corresponding to the customer behavior prediction result and a predicted cold and hot data type of the associated data; generating a migration strategy for the associated data according to the actual cold and hot data type of the associated data and the predicted cold and hot data type; the migration strategy is used for migrating the associated data to a first memory where hot data is located or migrating the associated data to a second memory where cold data is located. By adopting the method, the identification accuracy of cold and hot data can be improved.

The invention discloses a cardiovascular lesion classification method and system based on data analysis, relates to the technical field of cardiovascular lesion classification, and aims to solve the problem of poor accuracy when cardiovascular lesions of patients are classified. According to the method, multi-dimensional features are extracted for images, time sequence signals and structured data, and fusion is realized through data layer clinical association screening, feature layer composite vector recombination and decision layer weight summation. The method breaks through the limitation of a single data dimension, enables a classification result to be more fit with a pathological mechanism, remarkably improves the recognition precision of complex lesions and complications, guarantees the reliability of hardware through precise configuration of equipment and multi-dimensional detection, customizes a preprocessing process according to the data type, and associates multi-source data through a patient ID and a timestamp, thereby improving the recognition precision of the complex lesions and complications. The problems that equipment is disordered and data formats are different in a traditional process are solved.

The invention provides a clinical test data security sharing method and device based on a block chain. The method is applied to the technical field of data processing, and comprises the following steps: extracting feature dimensions corresponding to various types of data, and calculating corresponding data sensitivity coefficients; determining a data sensitivity level of the corresponding data category; extracting corresponding associated metadata; selecting a corresponding encryption algorithm to encrypt the data, and generating corresponding encrypted data and a data abstract; uploading the associated metadata, the encrypted data and the data abstract to a block chain node, constructing a clinical test data sharing account book, and recording a timestamp and a node signature of data operation; extracting identity authentication information and historical access records of visitors, and calculating access credibility; judging whether the visitor is allowed to acquire the decryption key or not; after the visitor obtains the encrypted data, the integrity in the data transmission process is verified based on the non-tampering property of the block chain. Therefore, the security of clinical test data sharing based on the block chain is improved.

A system and method for adaptive neural network architecture with real-time neurogenesis capabilities during inference operations. The system processes data through a core neural network with integrated supervisory and neurogenesis control systems. A hierarchical supervisory network, comprising low-level, mid-level, and high-level nodes, monitors network activity patterns and information flow. The neurogenesis control system maintains continuous activity maps, detects processing bottlenecks, and determines optimal placement of new neurons using geometric optimization. A modification subsystem implements controlled neurogenesis operations while maintaining network stability. The system handles data through adaptive codeword allocation and fusion of dissimilar data types. This sophisticated approach enables neural networks to dynamically expand their processing capacity during operation, responding to detected bottlenecks while maintaining operational stability through carefully managed integration of new neurons.