382 results about "Data consolidation" patented technology

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 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 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.

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 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 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 discloses a short burst SOQPSK carrier synchronization method based on an RPT-Net network. The short burst SOQPSK carrier synchronization method comprises the following steps: preprocessing a signal, and merging time domain data of the signal and quartic data into input data; a multi-level residual convolutional network is adopted to extract cross-symbol local phase features, fine phase changes in SOQPSK signals are captured through layer-by-layer stacked convolution operation, the gradient degradation problem of a deep network is relieved in combination with a residual connection mechanism, and the local characterization capacity for phase jump and frequency offset disturbance is enhanced; through a phase perception position coding module, the time domain signal is converted into a complex number form, an instantaneous phase is extracted, orthogonal basis coding is constructed to represent phase change, and deep fusion with position coding is carried out; a Transform coding layer is introduced, a multi-head self-attention mechanism is utilized to capture a long-distance symbol dependency relationship, global phase consistency constraint is optimized, long-term influence of frequency offset on signals is captured, and time sequence error propagation is effectively inhibited.

The invention provides a knowledge question-answering method and device based on a large model and a medium, and relates to the technical field of knowledge questions.The method comprises the steps that a data query period corresponding to a question text is obtained through a first large language model; if the duration of the data query time period is smaller than the preset duration, determining a target operation and maintenance tool from a plurality of preset operation and maintenance tools through a second large language model; controlling the target operation and maintenance tool to perform data acquisition, and taking the acquired data as intermediate data corresponding to the target operation and maintenance tool; performing data compression on the intermediate data based on the character number of the intermediate data to obtain target data; merging the target data into the context information, and obtaining an answer text according to the context information and a specified large language model after merging is completed; according to the method, input overflow, model reasoning failure or response delay caused by too large data volume or exceeding of the context length limit of the large language model can be avoided, the stability of answer generation is ensured, and the user experience is improved.

The invention relates to the technical field of data processing, in particular to a cloud platform data analyzing and processing system based on front-end segmentation, which comprises the steps of synchronously displaying fields and paging information to construct export parameters, cloning rendering data to establish a running state, monitoring page turning change to generate buffer content, and segmenting and packaging buffer data according to a memory state. And pushing the data to the local, rolling back the page structure, and completing the export of the process ending identifier. According to the method, export parameters are constructed by synchronously searching fields, header structures and paging numbers; a data clone structure is established in combination with field mapping and paging tracks; field changes are monitored to generate buffer contents; paging data are split according to memory occupation and packaged into local caches; and pushing data to the local and rolling back a display structure to support key links of export processes such as field tracking, memory control, data merging and page recovery.

There is provided a method for automatically generating a cyber crisis scenario, performed by a computing system. The method may comprise classifying types of content included in a cyber crisis report and extracting image data and text data from the cyber crisis report, inputting the image data into an image processing model and extracting attack procedure / behavior information included in the image data, inputting the text data into a text processing model and generating structured attack procedure-related data from each attack procedure included in the text data, generating threat behavior data corresponding to the attack procedure-related data, and merging the threat behavior data with the attack procedure-related data, matching the attack procedure-related data to the attack procedure / behavior information and generating a cyber crisis scenario template and automatically generating a cyber crisis scenario based on the attack procedure-related data and the cyber crisis scenario template.

The invention discloses a Tranformer network-oriented hybrid precision multiplier, which is applied to an FPGA (Field Programmable Gate Array), and is used for allocating a target high-resolution image according to the weight precision of the weight data of the current layer of the target Tranformer network, the feature map precision of the feature map data of the target high-resolution image and a preset data allocation rule. Determining a first quantity of weight data and a second quantity of feature map data of a single calculation cycle; according to the first quantity of weight data and the second quantity of feature map data, recoding the weight data and the feature map data after determining a target recoding algorithm to obtain weight coded data and feature map coded data; performing multiplication calculation on the weight coding data and the feature map coding data in a single calculation period to obtain an initial product result of the single calculation period; and decoding the initial product result, and merging the multiple sub-feature map data of all the calculation periods corresponding to the current layer of the target Transform network to obtain target feature map data. According to the method, the resource utilization rate is improved on the basis of ensuring the model precision.

The embodiment of the invention provides a data caching method and device and a storage medium, and relates to the technical field of artificial intelligence chips, and the method comprises the steps that a plurality of independent post-merging buffer areas are physically merged into a cache resource pool, and the cache resource pool comprises a plurality of entries. Traversing a plurality of entries based on target index information of the target data when the target data read by the thread bundle from the memory is received each time, so as to judge whether the corresponding entry is allocated for the target index information or not; if yes, caching the target data to the allocated entry, and merging and caching the target data and the received historical data corresponding to the target index information; otherwise, allocating idle entries to cache the target index information and the target data; in this way, the items in the resource cache pool can be correspondingly allocated according to the target index information of the target data received in real time, allocation of buffer area resources according to needs is achieved, and the overall resource utilization efficiency and the overall calculation performance in a fine-grained task division scene are improved.

Systems and corresponding methods are provided for moving object predictive locating, reporting, and alerting. This method includes receiving moving object data corresponding to a moving object; receiving sensor data from a sensor and merging the received moving object data and the received sensor data into a set of merged data. The example method further includes based on the set of merged data, automatically determining one or more of a predicted location or range of locations for the moving object, a potential path of travel for the moving object, an alert concerning the moving object, and providing the alert. The automatically determining may be further based on one or more historical traits concerning the object, and the geographic medium the object is moving through. The geographic medium may include one or more of terrain, air, water, and space. The object may be a soldier, vehicle, drone, or ballistic.

Systems and methods for the amalgamation of data from different sources for processing by an algorithm is provided. A synthetic data steward receives an encrypted algorithm from the algorithm developer via a core management system. Likewise, two or more data stewards may encrypt their data sets and provide them to the synthetic data steward. The sensitive data being operated upon is compiled into a unified / amalgamated dataset. Within the synthetic data steward is a sequestered computing node. This allows the algorithm and the various sensitive data sets to be decrypted within the sequestered computing node, and computations be performed on the amalgamated data, without the synthetic data steward (or any party for that matter) from having access to the algorithm and / or sensitive data sets while ‘sealed’ in the vault like sequestered computing node.

The invention discloses a method for merging road network data subjected to multi-user asynchronous modification, and relates to the technical field of traffic and transportation engineering, geographic information systems and traffic network data processing. According to the method, basic components of the road network data are abstracted, a basic data set is created, difference analysis is carried out on new data and the basic data, and then the road network data are merged; and detecting and marking data conflicts. And selecting a proper merging strategy according to a conflict detection result, and carrying out updating operation on the basic data, including newly added element integration, conflict element processing and deleted element processing. According to the invention, the efficiency and accuracy of data merging are improved, the quality and availability of road network data are ensured, and the method is suitable for multi-user cooperative road network data maintenance scenes in the fields of urban planning, traffic management and the like.

The invention provides a data transmission method, system and device, electronic equipment and a storage medium, and the method comprises the steps: determining a target cache queue from a plurality of cache queues according to the state information of a data sender and the state information of a first cache queue in the plurality of cache queues when obtaining new to-be-sent data, the state information of the data sender comprises a software system state and / or a bus state, writing new to-be-sent data into a target cache queue, merging the data in the target cache queue into a merged data packet, mounting the merged data packet to a preset first-in first-out queue, and sending the merged data packet to a data receiver, and sending a plurality of data obtained by analyzing the merged data packet to the corresponding upper-layer application by the data receiver. According to the method, the buffer queue structure is additionally arranged, the small data packets are merged into the large data packets to be sent, the transmission interaction frequency of PCIE is reduced, the bandwidth utilization rate is increased, and CPU resource waste is reduced.

The invention discloses a TBR implementation method of a desktop GPU for mobile 3D application optimization, which comprises the following steps: defining block metadata of screen blocks in a Mesa driver, establishing a block manager, when a mobile application context is detected, acquiring GPU core parameters by the block manager, calculating the size of a basic block, dividing a screen into areas corresponding to shader arrays in number, and storing the areas in a memory; establishing a mapping table of a basic block, a computing unit and an L2 cache slice, generating a resource hash table, analyzing block metadata and the mapping table after whole frame rendering is started, determining a hardware channel according to the mapping table, scheduling rendering tasks according to priorities, and configuring a GPU rendering state. Then executing a vertex shader, rasterization, a fragment shader, depth testing and color mixing to complete block rendering, updating block metadata, merging intermediate data into a final frame buffer according to geometric information after rendering of all basic blocks is completed, and submitting the final frame buffer to a window system to complete single-frame display. And the operation efficiency of the mobile operation system and the mobile terminal 3D application on the desktop platform is optimized.

Provided are a frequency-domain data merging method and apparatus, a storage medium, and an electronic apparatus. The method includes: respectively performing up-conversion on baseband data of a plurality of frequency bands in a frequency domain, so as to obtain frequency-domain received data of each frequency band from among the plurality of frequency bands that has been subjected to up-conversion; and merging the frequency-domain received data of each frequency band that has been subjected to up-conversion, so as to obtain merged frequency-domain received data.

The invention relates to a collaborative modeling method, device and system, computer equipment and a readable storage medium. The method comprises the following steps: acquiring a target model copy corresponding to each client; the target model copy is obtained by designing and processing the initial model copy of the target model in parallel by each client and feeding back to the server; the initial model copy is constructed by the server in response to a design request of the client; and according to the submission sequence of the target model copies, performing data merging on the initial model master based on each target model copy in sequence to obtain a target model master corresponding to the target model. By adopting the method, the model design efficiency can be improved.

The invention discloses a distributed storage method and device based on a time sequence database, and relates to the technical field of data storage. Obtaining original time sequence data and carrying out time clustering segmentation to obtain a time sequence data fragment set; performing data merging on the time sequence data fragments in the time sequence data fragment set to obtain a merged data fragment set; determining a storage position of each merged data fragment in the merged data fragment set; constructing a time query index for the data in each merged data segment; and for the data in each merged data segment, obtaining a storage address of the data, enabling the storage address to correspond to the time query index of the data, and storing the data in the storage address. According to the method, data of a time sequence is clustered and then segmented, then the segmented data is merged, storage fragmentation is reduced, finally, a storage address and a time query index are associated by constructing a query tree, efficient storage is still met in the face of mass data, and the data management efficiency is improved.

The application discloses a kind of photovoltaic power station cluster power synergic optimization control methods based on artificial intelligence, and the original data of operation and environment are collected by the sensor of photovoltaic power generation unit local, and the local load power data of each grid-connected point is obtained from external power grid energy management system, is combined into original data set, is formed standardized input feature vector after exception elimination and normalization, constructs model with reducing cluster total power fluctuation, improves the output balance between units as target and meets constraint, solves using particle swarm optimization algorithm of fusion simulated annealing mechanism, exports power adjustment instruction set to clarify each unit planned output target value;Through communication network, issue instruction to each unit local controller, drive photovoltaic inverter to adjust active power output, collect actual operation data after execution, calculate cluster synergic energy efficiency index, load supply-demand ratio and other indexes to evaluate effect, to dynamically adjust algorithm key parameters.

The invention relates to a data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a data sending device and comprises the steps of obtaining target data to be sent to a data receiving device; performing data interception on the target data to obtain first intercepted data and second intercepted data; generating a first key key corresponding to the first intercepted data based on the second intercepted data, and encrypting the first intercepted data based on the first key key to obtain first encrypted data; based on the first encrypted data, determining a second key key corresponding to the second intercepted data, and based on the second key key, encrypting the second intercepted data to obtain second encrypted data; and merging the first encrypted data and the second encrypted data to obtain merged data, and sending the merged data to the data receiving equipment, so that the data security is improved.

The application discloses a bidirectional incremental intelligent merging method in distributed storage fault recovery, and relates to the field of distributed data management. The method comprises the following steps: receiving local and remote incremental operation sequences, mapping the operation sequences to a multi-dimensional space-time coordinate system, and generating a multi-dimensional space-time operation point set; based on the multi-dimensional space-time operation point set, constructing a geometric causal cone for each operation point, and constructing a causal interference spectrum by analyzing the interference between the geometric causal cones; according to the causal interference spectrum and the multi-dimensional space-time operation point set, searching for a space-time folding path to avoid or minimize causal conflicts, and obtaining an optimal space-time folding scheme; projecting the high-dimensional path in the optimal space-time folding scheme into a one-dimensional execution sequence, and executing to achieve a consistent data state. By improving the conflict analysis and solution to a high-dimensional geometric space, the application realizes deep semantic understanding and flexible avoidance of operation conflicts, and improves the accuracy and automation level of data merging.

A system for a website, includes a processor and a unit running on the processor to capture interactions between a user of the website and at least one third party application embedded as an instance in a page of the website and to use the interactions to update a contact record associated with a user. The unit includes a data extractor to extract contact related information from the interactions; and a data merger to merge at least two contact records where the at least two contact records have an association with the same identified user and to merge the extracted contact related information to that of an existing contact record for the identified user according to predefined merging rules.

The application relates to the technical field of data management, in particular to a battery temperature data management method and system. The method comprises the following steps: based on all battery temperature data in an original data set, at least two continuous battery temperature data of the same process are combined to obtain a pretreatment data group; based on the starting time and the ending time of each pretreatment data group, a first interval duration of each adjacent pretreatment data group is obtained; a preset duration threshold is acquired, and adjacent pretreatment data groups with a first interval duration less than the duration threshold are combined to obtain a target data group; the target data group is used for indicating the classification of the battery temperature data of the same process and continuity. The method can improve the management efficiency of the battery temperature data.

The application discloses a kind of fusion display method and device of double platform data, method includes: when user requests to merge and show data on the first platform, obtain the platform identification and serial number sent by the first platform, obtain user account and organization identity based on platform identification through serial number to the first platform request, organization identity is bound with target organization identity in the second platform of user, obtain platform data through organization identity, two platform data are merged and calculated, obtain fusion data, and feedback to the first platform for display. Visible, after two platforms are associated by user account information and organization, user can obtain the organization identity of another platform with the bound organization identity in one of the platforms, without multiple login and selection identity, the data of two platforms can be fused by data fusion center, and then the fusion data of two platforms is shown on one platform, improve the experience of simultaneous use of double platform of user with demand.

The application discloses a kind of numerical control processing surface roughness prediction method, comprising the following steps: processing experiment is carried out, and cutting and segmentation are carried out, obtain the three-dimensional cutting force signal data of each sample, and obtain the processing surface roughness numerical value;Adopt time series dimension reduction algorithm to obtain the three-dimensional cutting force data of consistent length;With the three-dimensional cutting force data of consistent length as feature vector, with the processing surface roughness numerical value as label value, data merging is carried out, and three groups of prediction data sets are formed;Convolutional neural network prediction model is built, model training and structure evaluation are carried out, and the cutting force direction and convolutional neural network prediction model with the highest prediction accuracy are screened out;Roughness prediction is carried out using the screened convolutional neural network prediction model.The application has the beneficial effects that: reduce human intervention, improve the intelligent degree of prediction process;Reduce workload, improve prediction efficiency and promote the improvement of production efficiency.

The application provides a large language model-based electronic form standardization processing method and system, and belongs to the technical field of data processing, and specifically comprises the following steps: a file acquisition module is responsible for intelligently screening a target worksheet set containing valid data; a slicing module is responsible for partitioning the content of the worksheet to obtain a plurality of data slices; the table header of each data slice and sample data are input into a large language model workflow to intelligently identify the dimension type and structural characteristics of the data; an execution module is responsible for generating management strategies and prompt words, calling a large language model to dynamically generate a targeted data processing function code, executing the generated function code through a function execution engine, and standardizing and converting the data slices; and all successfully processed data is combined and exported as a standardized electronic form, thereby improving the efficiency of the standardization processing of the electronic form.