671 results about "Data consolidation" patented technology

The provided are a price elasticity analysis and prediction method and model based on deep learning. The model consists of a CNN layer and an RNN layer. The method comprises the following steps: S1, collecting historical data and merging the historical data into a multi-dimensional time series dataset; S2, extracting sentiment data and trend data from market news and social media; S3, inputting the data obtained into CNN for data preprocessing and feature extraction; S4, inputting the feature extracted by CNN into RNN for time series analysis; S5, training and optimizing model: using Adam algorithm to adjust the learning rate adaptively, and combining the momentum method and RMSProp algorithm to improve the generalization ability and prediction accuracy of the model. The provided combines the advantages of CNN and RNN, which can understand and predict the complex relationship between price and market behavior more comprehensively and accurately.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

An AI-driven centralized orchestration system (100) with MCP protocol for real-time compliance and risk data management, including: a. a centralized integration and orchestration platform configured to connect heterogeneous enterprise IT systems through standardized API connections and execute automated workflows; b. a variety of domain-specific artificial intelligence (AI) agents that interface with IT service management (ITSM), vulnerability management, and endpoint and asset management platforms for data collection, analysis, and assessment; c. a Multi-Agent Communication Protocol (MCP) layer that enables structured communication, negotiation and coordination between AI agents in real time; d. a data consolidation module configured to aggregate, standardise and align heterogeneous data into a unified repository using machine learning models; e. a compliance reporting module for dynamically generating and distributing audit-ready compliance reports based on AI-analyzed data and triggered workflows; f. an alerting module configured to deliver context-dependent predictive notifications of compliance violations or risk anomalies through various communication channels; g. a predictive dashboard module capable of displaying compliance status, risk metrics, and proactive insights in real time via AI-driven visualization tools; i.e., where the orchestration platform, AI agents, and MCP layer together enable automated, scalable, and proactive compliance and risk actions.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

In various examples, a framework is or provides an end-to-end solution that includes multi-sensor capture, data processing, inferencing, synchronization, alignment, and 3D rendering for multi-modal perception fusion pipelines. A multi-modal perception fusion pipeline may include a mixer, an aligner, an inference environment, and a multi-view renderer. The mixer may merge sensor data from different data sources into a single HashMap frame. The aligner may use calibration data for sensor-to-sensor coordinate transformations. The inference environment may receive multi-modality data and use custom preprocessing and custom postprocessing to generate inference results. The renderer may generate different sensor data renderings. The framework may include an application that uses configuration data to generate or configure a custom multi-modal perception fusion pipeline. The inference environment may access inference models using a uniform inference interface and support remote inference, allowing the pipeline to become an API client of the inference models.

The invention discloses a precise advertisement putting optimization method and system, and relates to the technical field of advertisement putting, and the method comprises the following steps: collecting and recording key parameter information of different data sources in a merging process in real time, providing basic data for the subsequent anomaly detection, and providing an optimization result for guaranteeing the comprehensiveness of the data; recording each step of operation in all merging processes; and carrying out anomaly analysis on the key parameter information subjected to the standardized processing, and identifying abnormal data generated in the merging process by analyzing multi-dimensional information. According to the invention, by monitoring the cross-session persistence information and the platform situation consistency information in real time, the system can effectively prevent the problem of data merging errors or segmentation, improve the accuracy of user portraits, and accurately push advertisement contents, thereby improving the click rate and the conversion rate. A multi-level anomaly detection and grading processing mechanism ensures that risks are timely handled, the safety of data integration and the stability of advertisement putting are improved, and the data integrity is guaranteed.

The invention discloses an intelligent construction method and system for a knowledge graph in the field of power marketing based on a large model Agent. The method comprises the following steps: constructing a to-be-trained small model and a pre-trained large model Agent; obtaining initial annotation data and performing supervised training on the small model to obtain a trained small model; constructing a performance evaluation function to evaluate the relation extraction performance of the trained small model; reviewing and correcting results generated by the trained small models to obtain corrected data; combining the corrected data with the initial annotation data to obtain an expanded data pool, and extracting training data to retrain the trained small model; according to a performance evaluation result, the large model Agent repeatedly expands the data pool and trains the small model until a preset standard is reached; and constructing a knowledge graph in the field of power marketing based on the trained small model. According to the method, the accuracy, generalization ability and optimization efficiency of the model in the relation extraction task are improved, and the method is particularly suitable for high-precision relation extraction requirements in the field of power marketing.

A computer-implemented method, according to one approach, includes performing a predetermined first data merge process in response to a determination that a version conflict exists between a plurality of versions of a data file resulting from editing performed on different cloud devices. The predetermined first data merge process includes determining a first merging policy of the data file from a plurality of potential merging policies, determining a plugin associated with the data file, and calling the plugin. The first merging policy is used as an input for the plugin, and the plugin includes predetermined conditions for determining first contents of the versions of the data file to exclude from a merge operation performed on the data file and second contents of the versions of the data file to include in the merge operation.

The invention provides a table data generation method and device for collaborative enhancement of a small model and a language model, and the method is characterized in that the method comprises the steps: S2-1, constructing a table data generation model, and taking an original training data set as a training data set; step S2-2, generating training synthetic data corresponding to the training data set through the table data generation model, and optimizing the table data generation model according to the training synthetic data and the training data set; step S2-3, judging whether a preset termination condition is met or not, if so, obtaining a trained table data generation model, and if not, entering step S2-4; and S2-4, auxiliary training data corresponding to the training synthesis data is generated through the trained small model, the original training data set and the auxiliary training data are merged to serve as a training data set, and the step S2-2 is executed. In a word, the method can generate more real synthetic table data.

The invention discloses a mobile terminal offline bidirectional data synchronization method and system, and relates to the technical field of data synchronization processing, and the method comprises the following steps: initializing a whole table synchronization mechanism, and establishing a file mapping structure; a user performs data operation in an offline state, records data operation details and stores the data operation details in a local cache; when the network connection is recovered, a whole table synchronization mechanism is started, data compression and partitioning are performed, a synchronization request is sent to a server, and segmented data transmission is performed; after receiving the data, the server performs data storage and management by using a file mapping mechanism; detecting potential conflicts by utilizing a multi-version parallel management mechanism, carrying out conflict resolution, merging data with local data after the conflict resolution, and confirming that synchronization is completed to a server; after it is confirmed that all data are synchronized, local and global timestamps are updated, and meanwhile, the optimal thread count is dynamically determined by adopting a multi-thread technology. According to the invention, the efficiency, reliability and accuracy of data synchronization of the mobile terminal are remarkably improved.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

The invention relates to the technical field of data acquisition, in particular to a multi-source heterogeneous bus data parallel acquisition recording method and system, and the method comprises the steps: initializing a task period and a maximum sampling period; scheduling the tasks in the task queue based on the task cycle, setting a time step length and marking schedulable tasks; setting sampling channels according to the number of schedulable tasks and distributing the sampling channels to sampling threads; packaging the acquired data into an acquired data packet and aligning by taking a timestamp as a reference; offset calculation and compensation are carried out on the collected data packet; controlling a data acquisition thread to start according to the data capacity and the sampling frequency; the time difference and the content similarity of adjacent data packets are detected, and intelligent data merging is executed; storing the processed data packet in real time; through the adaptive task scheduling and precise time alignment technology, efficient parallel acquisition and precise recording of heterogeneous bus data are realized, and the synchronism, integrity and processing efficiency of data acquisition are remarkably improved.

The invention discloses an MQTT message transmission optimization method and system. The method comprises the steps of analyzing a theme, extracting a device type, a data feature and a geographic position triple, calculating a hash value, and mapping a device to a specified Broker fragment cluster node to generate a fragment mapping table; processing the equipment data in the edge domain in the fragment mapping table through an edge calculation layer, removing invalid data according to a preset rule, merging the equipment data in the same fragment node, and embedding a fragment node ID for an aggregation message generated after merging; identifying a fragment node ID and routing to a target fragment node, positioning a corresponding shared memory pool, and writing the message into the shared memory pool; and responding to a direct access request of the client, so that the client directly accesses the data from the shared memory pool through the user mode network stack. According to the method, the problem of uneven load is effectively solved, multiple times of state switching in the data transmission process is avoided, and the MQTT message transmission efficiency is improved while the transmission cost is reduced.

A wild animal species identification method based on incremental learning belongs to the field of target detection, and comprises the following steps: constructing a known category species detection data set, training a known category species detection model, selecting representative samples of each category species, and storing expression characteristics of each category species; constructing a species discrimination data set, and training a species discrimination model; manually checking new species sample data; and constructing a species detection fine tuning data set, updating a known category species detection model based on a feature playback class increment strategy, and updating a species discrimination model based on a feature-level sample playback class increment strategy. The method has the capability of identifying whether species belong to known categories in a training set; according to the method, after the data of the newly-added species category is acquired, the ability of identifying the new species can be learned at the minimum calculation cost; according to the method, the intermediate expression features of partial representative samples of old data are stored locally and then merged with the new category of data, and then the network is finely adjusted, so that the computing resource consumption is low.

A decision support system and a computer-implemented method of enterprise decision support include a data consolidation module, domain-specific machine learning models, an enterprise decision intelligence model, and a user interface layer. The data consolidation module collects data from both internal and external data sources. The domain-specific machine learning models generate decisions based on the collected data. The enterprise decision intelligence model integrates real-time trends and the decisions to provide context-aware recommendations. The enterprise decision intelligence model maintains a decision graph that connects decision variables of the domain-specific machine learning models in a causal relationship, with the domain-specific machine learning models interacting as an interconnected network. The decisions are influenced by affects of decision variables from other domain-specific machine learning models. The user interface layer facilitates interactive decision-making by way of the enterprise decision intelligence model and visualizing the recommendations.

The invention discloses a data processing method, device and system, and relates to the technical field of video or image processing. And the encoder obtains the basic layer data, the enhancement layer data and the metadata of the first image, and encodes the basic layer data, the enhancement layer data and the metadata to obtain a code stream. And the decoder receives and analyzes the code stream to obtain the basic layer data, the enhancement layer data and the metadata of the first image. And the decoder determines and converts target data (namely basic layer data and enhancement layer data or an image obtained by combining the basic layer data and the enhancement layer data) according to conversion information carried by the metadata. Therefore, the decoder determines and converts the target data according to the conversion information carried in the metadata, so that the conversion performed on the target data is matched with the target data, and the decoder converts the target data by adopting the conversion information matched with the target data. Therefore, the dynamic range of the image obtained through conversion can better adapt to the dynamic range supported by the display device, and the display effect is improved.

The embodiment of the invention provides a data conflict automatic solution method and device, electronic equipment and a medium, and the method comprises the steps: automatically recognizing a data conflict through text comparison and semantic analysis; based on a rule base and a reinforcement learning algorithm, dynamically selecting an optimal conflict resolution rule based on the identified data conflict, and executing the conflict resolution rule; selecting an optimal version to be executed according to the conflict resolution rule, carrying out data merging, and storing key information of conflict data; under the condition that the integrity of AI language large model training data is guaranteed, redundant data is removed from the merged conflict data, and incremental updating is carried out.

Embodiments are directed to a file system that include object stores. An object store for write requests may be provided. Write ahead log (WAL) entries that include data blocks may be generated. A WAL object may be generated based on the WAL entries and stored in the object store. An in-memory overlay may be updated to associate the data blocks with the WAL object. A checkpoint operation may be executed to: generate an index object that includes index entries that associate other data blocks with data objects stored in the object store; update the index object to include index entries that associate the data blocks with the WAL object; store the updated index object in the object store; update the in-memory overlay to remove the association of the data blocks and the WAL object and update the in memory WAL to remove records of successfully checkpointed WAL objects.

The invention discloses an airport terminal security check passenger number hourly prediction method comprising the following steps: 1) preprocessing security check passenger flow characteristic data and terminal historical security check passenger data, and merging the preprocessed security check passenger flow characteristic data and terminal historical security check passenger data into a passenger security check number prediction data set; 2) constructing an NBMT-OneNet-based hourly security check passenger number prediction model; 3) training the hourly security check passenger number prediction model by using the passenger security check number prediction data set; and 4) obtaining security check passenger data and security check passenger flow characteristic data of the terminal to be predicted in the past T time period, and inputting the data into the hourly security check passenger quantity prediction model to obtain security check passenger data in the future T'time period. According to the characteristics of hourly airport security check data, the N-BEATS model and the MTGNN model are used for capturing the complex correlation between the time sequence trend and the variable, and the accuracy of the prediction result is effectively improved.

The invention provides a component-level collaborative editing control method, which is applied to a multi-user collaborative editing control system, and the system comprises a plurality of front-end engines and a back-end server. A front-end engine obtains user editing operation data and a rear-end real-time version snapshot, component structure difference comparison is carried out, and a component operation list is generated; and performing component conflict judgment based on the operation list, merging and editing operation data according to a conflict judgment result and a preset conflict resolution strategy, generating processed version snapshots, sending the processed version snapshots to a rear end, storing the processed version snapshots by a rear-end server, and sending the processed version snapshots to each front-end engine. Compared with the prior art, the method has the advantages that the data transmission quantity and the load of the rear-end server are reduced by a lightweight architecture through the front-end component level difference comparison and the preset conflict resolution strategy, the rear-end server does not need to be frequently requested for conflict resolution and data merging, the response speed and the collaborative editing efficiency are remarkably improved, and the user experience is improved. And better user experience is brought to the user. The system has the same beneficial effects.

The invention relates to a downlink data transmission method and device, equipment and a storage medium. According to the invention, the base station sends the first indication information to the terminal to indicate whether the terminal performs coverage enhancement. And under the condition that coverage enhancement needs to be carried out, sending second indication information to the terminal, and sending downlink data to the terminal according to the second indication information. According to the embodiment of the invention, the terminal receives the downlink data from the corresponding time slot on the basis of the repetition times of the downlink data indicated in the second indication information and / or the time slot number for transmitting the downlink data, and the same downlink data is transmitted in multiple time slots, so that the demodulation performance is improved by reducing the code rate. Besides, the terminal merges the repeated downlink data received on different time slots, so that the success rate of receiving the downlink data by the terminal is improved, the transmission delay caused by retransmission is avoided, and the downlink data transmission efficiency of the satellite communication system is improved.

A replication policy management and scheduling technique of a Files service is configured for deployment in multi-site data replication environments. The technique involves policy management for data distribution and / or data consolidation (concentration) where multiple sources, e.g., file system (FS) clusters or sites, replicate the data to a central target FS site, e.g., in a spoke and hub arrangement typical of remote office / branch office environments. The technique also involves creation and configuration of a main replication policy by a customer at a central resource manager configured to interact and manage the FS sites, each of which includes one or more file server virtual machines.

The invention discloses a data merging optimization method and device for incremental data migration, equipment and a storage medium, and relates to the technical field of databases, and the method comprises the steps: carrying out the data collection of a source end database, and sequentially sending the collected data to a message queue; sequentially pulling the data from the message queue, merging the data, and converting the data into a data packet; performing execution sequence number distribution on the data packets; and based on the execution sequence number, sequentially playing back the data of the data packet to a target end database. According to the method, the effect of concurrent execution of the single-table data is achieved through data merging, the problem that the single-table incremental data cannot be executed in parallel due to the time sequence problem is solved, the method does not depend on the characteristics of the database, can be suitable for data migration synchronization of different database types, and is good in compatibility.

The present invention discloses a small sample fault diagnosis method based on an improved TimeGAN model, comprising: collecting elevator operation data, including normal samples and fault samples; establishing an improved TimeGAN model; inputting fault samples into the improved TimeGAN model to generate fault data; merging the elevator operation data and the generated fault data into a sample set, and extracting the time-frequency features of the sample set by using wavelet packet decomposition; dividing the extracted time-frequency features into a training set and a test set, and inputting the training set into a CNN model for training; inputting the test set into the trained CNN model, obtaining fault diagnosis results and verifying the fault diagnosis results. The method generates fault samples with time series features, reduces the imbalance of original data distribution, and fully extracts the time-frequency features of samples to obtain efficient and accurate fault diagnosis results, thereby realizing safe maintenance of intelligent equipment.

The invention discloses a nursing scene data enhancement and generation method for intention recognition model training, and belongs to the technical field of artificial intelligence and smart old-age care. The method aims at solving the problems that in the prior art, high-quality nursing scene training data is deficient, and the obtaining cost is high. According to the core technical scheme, the method comprises the steps that firstly, a staring sequence and other context information (such as time, place and physiological signals) of a user are processed through a multi-modal information fusion model, and a structured initial situation vector is generated; secondly, inputting the vector into a scene generation model combined with a nursing knowledge base, and automatically generating a batch of basic nursing scene data with intention labels; key points are that a data enhancement module is introduced, and a plurality of innovative strategies such as situation element disturbance, virtual physiological data synthesis, gaze path variation and virtual scene deduction are adopted to deeply process basic data, so that the diversity and complexity of a data set are greatly enriched; and finally, combining the basic data with the enhanced data to construct a final comprehensive training data set. According to the method, large-scale and high-fidelity training data can be generated in a low-cost and high-efficiency manner, and the accuracy and robustness of the intention recognition model in a real nursing environment are remarkably improved.

The invention provides a data acquisition component optimization method based on a distributed storage all-in-one machine, a medium and equipment. A distributed lock and version control combined method is adopted. Specifically, the distributed lock mechanism can ensure that only one node performs write operation on certain data at the same moment, so that data conflict and write competition are avoided. In the version control, a version number is added to each piece of data, so that the system can detect and solve conflicts occurring in the data synchronization process. When one node prepares to write data, whether the version number of the data is consistent with the latest version or not is checked firstly, if not, the data of the latest version needs to be acquired again, and the data is written after being merged and changed. Therefore, the data consistency is ensured, and the stability and reliability of the system are improved. Through the method, the problem of data consistency in the optimization process of the data acquisition component can be effectively solved, so that the overall performance of the distributed storage all-in-one machine is improved.

The invention provides a multi-modal evolutionary knowledge injection method and system, and the method comprises the steps: obtaining basic dynamic multi-modal evolutionary knowledge through automatically and continuously collecting network dynamic update news events and object entity knowledge, generating an evolutionary knowledge text description abstract data set, and eliminating abnormal image data through clustering operation. Dynamic clustering multi-modal evolution knowledge is obtained; constructing a multi-modal evolution knowledge data set, and carrying out analysis and evaluation on the multi-modal evolution knowledge data set; training the multi-modal intelligent model through the target evolutionary knowledge, verifying the task performance improvement degree of the multi-modal intelligent model and the learning ability of the multi-modal intelligent model to the target evolutionary knowledge, and constructing a knowledge injection tuning multi-modal intelligent model; and merging the data after knowledge enhancement with the data before knowledge enhancement, performing knowledge enhancement on the knowledge injection adjustment and optimization multi-modal intelligent model, merging the knowledge enhancement and injection into collaborative adjustment and optimization, performing collaborative enhancement on the knowledge injection adjustment and optimization multi-modal intelligent model, and performing network dynamic data knowledge enhancement collaborative accurate reasoning.

The invention discloses a multi-source data merging method and merging equipment based on rule chain configuration, and relates to the technical field of data fusion. In the method, merging equipment generates a structured configuration file through a visual configuration interface, and decouples a variable data source association relationship and a field mapping rule from a program code, so that a user can dynamically define a data merging task in a visual manner without modifying and redeploying a bottom program code, and the user experience is improved. And the execution triggering condition of each data source node is determined through the directed acyclic graph execution plan, so that the data processing tasks with the dependency relationship can be executed in series according to the dependency relationship, and the data processing tasks without the dependency relationship can be executed in parallel, thereby improving the merging efficiency of the multi-source data.

The invention relates to an intermediate result storage and transparent replacement method and device for an asynchronous materialized view and electronic equipment, and the method comprises the steps: designing a storage structure of the materialized view, and obtaining an asynchronous incremental materialized view with a prepolymerization storage structure; when new data are inserted into the basic table, the asynchronous materialized incremental view is updated by using increment, and preset specific data are merged in the updating process to obtain a merged view; and rewriting query logic, and transparently rewriting the original query into a materialized view in combination with the asynchronous incremental materialized view. According to the method, through the design of a storage structure of the asynchronous incremental materialized view, a rewriting method for keeping query semantics and optimization of incremental data merging in the asynchronous incremental materialized view, the real-time query performance is effectively improved, and meanwhile the expandability and flexibility of the system are kept.