889 results about "Data export" patented technology

The embodiment of the invention provides a distributed server cluster log processing method and device, and the method comprises the steps: constructing a distributed server cluster log collection network, and dynamically distributing collection tasks through a load balancing scheduling center. An incremental data acquisition channel is designed, efficient transmission is realized by using a websocket long connection pool, and the transmission efficiency is optimized in combination with real-time compression coding and a repeated data detection mechanism. An intelligent log analysis model is constructed, the intelligent log analysis model comprises an anomaly detection sub-model, a mode recognition sub-model and an event association analysis sub-model, anomaly score calculation, log classification labeling and multi-dimensional association analysis are achieved based on a deep learning method, and visual display and data export functions are provided. According to the method, the defects of the traditional technology in the aspects of distributed acquisition, data transmission, intelligent analysis and the like are effectively overcome, and the performance and practicability of a log processing system are remarkably improved.

The invention discloses an automatic three-dimensional reconstruction method and system based on power transmission corridor point cloud. The method comprises the following steps: acquiring data and exporting point cloud data; noise points, outliers and ground points are removed from the acquired point cloud data of the power transmission corridor, and down-sampling is performed on the point cloud with a large data volume; performing clustering segmentation on the preprocessed point cloud of the power transmission corridor, positioning the position of a tower, determining a point cloud range of a power line, and removing point clouds of other objects on the ground; aiming at the structural characteristics of a power line and an insulator in the power transmission line, extracting point clouds of the power line, the insulator and a tower by adopting a geometric constraint principal component analysis (PCA) algorithm; performing automatic geometric model reconstruction on the tower, insulator and power line point clouds according to different reconstruction rules to generate each module grid model; and transforming the grid model of each module, restoring the grid model to the original position of the point cloud, and generating a power transmission line digital twin model conforming to the real size. According to the method, automatic segmentation, classification and real-time reconstruction can be accurately carried out on the point cloud of the power transmission corridor.

The invention relates to the field of Web application, in particular to a tree structure data dynamic management method based on node hierarchical attributes and recursive updating. Comprising the following steps: S1, constructing a tree structure data system, and endowing each node with a hierarchical attribute which is used for identifying position information of the node in a tree structure; s2, receiving an operation instruction of a user for the tree structure data, wherein the operation instruction comprises node adding, node batch deleting, node moving, data exporting and importing, node querying and node quantity counting; and S3, according to different operation instructions, executing corresponding processing logic: based on node hierarchy attributes, applying a recursive update mechanism to realize dynamic management of tree structure data, and ensuring data consistency and accuracy.

Systems and methods are disclosed for implementing cloud network service management. In certain embodiments, a method may comprise operating a cloud native application (CnApp) custom operator in a containerized software environment to dynamically manage cloud native network service on a target application pod via a persistent network interface to an external network. The method may include obtaining a first resource definition data, for a first custom resource, to define attributes for a bundle of resources used to implement the cloud native network service, and creating the first custom resource based on the first resource definition data, including initializing the target application pod. The method may include generating a second resource definition data, derived from the first resource definition data, to define attributes for a virtual network interface to associate with the target application pod, and applying the second resource definition data to initialize creation of a second custom resource.

The invention belongs to the technical field of automatic driving automobile testing, and particularly relates to an intelligent automobile key scene optimization generation method based on large model knowledge guidance. Comprising the following steps: S1, constructing a knowledge expression module, and designing a knowledge result, a large language model application mode and a dynamic updating process from scene theoretical knowledge and scene exploration knowledge; s2, constructing a parameter optimization module, designing a multi-stage optimization strategy including global exploration, local optimization and spatial pruning, and fusing the multi-stage optimization strategy with the knowledge model; s3, establishing a simulation test module, and based on a Carla simulation platform, establishing a scene establishment component, a to-be-tested system component, a simulation execution component and a data export component through LLM-Agent; s4, selecting a model and pre-training a large language model, designing a model selection index and selecting the model; and S5, constructing an evaluation and memory module, evaluating a test result, and storing all test and knowledge data. According to the method, the key scene of the intelligent automobile can be quickly generated through mutual combination of the knowledge model, the large language model and optimization search.

The invention discloses a tunnel face back break visual analysis method based on laser point cloud, and the method comprises the steps: carrying out the scanning operation of a tunnel through a laser scanner, and collecting the specific original rock three-dimensional point cloud model data in a tunnel construction circulation process; exporting the acquired original rock mass three-dimensional point cloud model data, and performing denoising preprocessing on the original rock mass three-dimensional point cloud model data to obtain tunnel rock mass three-dimensional point cloud model data; the rock mass three-dimensional point cloud model data also need to be subjected to space compression processing, so that the quality of the rock mass three-dimensional point cloud model data is improved; based on the rock mass three-dimensional point cloud model data, a tunnel actually-measured excavation section after construction is obtained; and weighting and calculating tunnel over-excavation and under-excavation values based on the point-to-pole distance between the tunnel design section and the tunnel actually-measured excavation section. According to the method, the back-break depth is directly calculated through the point cloud data and is bound as the point cloud attribute, so that the analysis efficiency and the result intuition are remarkably improved.

The invention discloses a slope radar early warning system and method based on a domain network, and relates to the technical field of slope safety monitoring. The system comprises a data exporting and obtaining module, a domain network communication connection module, a receiving server remote connection module and an early warning information exporting module, it is ensured that radar data is uploaded to a public network server in real time, and private network communication, multi-user simultaneous access and management and timely and accurate pushing of early warning information of domain networks among multiple regions are achieved. The method comprises the steps of measuring slope operation parameters in real time, calculating a stability index and a comprehensive index, carrying out fault early warning and recognition, and dynamically adjusting a mechanism. Data are collected through various sensors, a stability index and a comprehensive index are calculated, fault identification is carried out based on a historical database and feature vectors, and a monitoring period and threshold setting are optimized through a dynamic adjustment mechanism. According to the invention, the accuracy and timeliness of early warning are improved, the self-adaptive capability of the system is optimized, potential risks can be quickly positioned and processed, and the safety of the slope is ensured.

A communication system includes a server operable to communicate with vehicles located remote from the server. The server is operable to receive an indication of an event that occurs remote from the server. The indication identifies a location associated with the event. The server, responsive to receiving the indication of the event, establishes a geofence that bounds the location associated with the event. The server transmits information pertaining to the established geofence to at least one of the group consisting of (i) a vehicle within the established geofence and (ii) a vehicle within a threshold distance of the established geofence. When a vehicle that received the transmitted information is within the established geofence, the server receives, from the respective vehicle within the established geofence, information derived from sensor data captured by at least one sensor of the vehicle within the established geofence.

The embodiment of the invention relates to the technical field of information, and discloses an intelligent data acquisition method and device, a medium and a product, the intelligent data acquisition method comprises the following steps: acquiring artificial demonstration data through an exoskeleton system, the artificial demonstration data comprising video stream data, motion data and force sense data; importing the video stream data into a first large language-vision model, segmenting the video stream data into a plurality of independent task fragments, and generating independent task fragment descriptions; importing the independent task fragment description into a large language model, and generating a task label based on natural language description; and exporting the independent task segments, the task labels and the corresponding motion data and force sense data to generate intelligent data. Therefore, a low-cost, high-efficiency and large-scale intelligent data acquisition and processing method is established, and automatic conversion from human operation data to high-quality VLA training data is realized.

The invention relates to a dynamic data column table exporting method and device, computer equipment and a storage medium. The method comprises the following steps: in response to a header information design operation input in a user interaction interface by a target user, acquiring customized header information corresponding to the target user; constructing a data source information export template according to the custom header information; based on the data source interface, retrieving data from the data source according to the data source information export template to obtain data source self-defined retrieval data; according to the assembly format of the data source information export template, the data source self-defined retrieval data is assembled to the data source information export template, and user-defined assembly data is obtained; and according to the user-defined assembly data, generating a user-defined data file corresponding to a file format specified in the header information design operation. By adopting the method, the workload of processing the exported data format and content can be reduced, and the convenience and accuracy of data export and format conversion are greatly improved.

The invention discloses a code coverage rate data exporting method and device, equipment and a medium, relates to the technical field of software testing, is applied to NVMe equipment, and comprises the steps that when a data exporting command sent by a target host is obtained, the data exporting command is analyzed to obtain address information of a memory buffer area pre-applied in the target host and the size of the buffer area; transmitting and writing pre-collected code coverage rate data into a memory buffer area; the code coverage rate data is data which is obtained by the NVMe equipment after establishing a one-way linked list for a target data structure used for storing code coverage rate information in each source file and counting executed code segments by using a preset instrumentation code and is added to the one-way linked list; and after writing of the code coverage rate data in the one-way linked list is completed, sending a target command used for representing that the execution of the writing operation is completed to the target host, so that the target host generates a coverage rate data file for the obtained code coverage rate data by using a local file system.

The invention provides a method and system for dynamically exporting Excel based on asynchronous streaming block data, and belongs to the technical field of large data volume export. Comprising the following steps: receiving a user export request, and generating a unique request ID; an export task is generated based on the export request, a task ID is returned, and an asynchronous response is made; submitting the task to a thread pool queue, and obtaining block data from a database in batches through streaming query; wherein the core thread count and the maximum thread count of the thread pool are adjusted based on a dynamic resource allocation method, and load change is smoothed in combination with a sliding window algorithm; writing the block data into an Excel file batch by batch by adopting a streaming writing technology; and after the task is completed, pushing a notification to the user side. According to the method, the rapidity, the availability and the safety during data export can be ensured when large data volume export is performed.

The invention provides a storage network planning method and a logistics simulation system for energy enterprise materials, and relates to the field of energy enterprise material planning, user data is received through an interactive interface, and the user data is pre-processed and stored in a database; after a user triggers a solving instruction, a back end calls a solver to construct a mixed integer programming model, a warehouse network layout scheme is iteratively optimized based on a constraint satisfaction algorithm, and a result is stored back to a database; the support visualization module generates a multi-dimensional chart to display data before and after optimization, provides a data export function to output a portable document according to a preset format, integrates business rules, algorithm solution and visual interaction, supports dynamic optimization and rapid verification of an energy storage network, realizes dynamic collaborative optimization of storage site selection, inventory decision and transportation planning, and improves the efficiency of the energy storage network. And through introduction of multi-scene and multi-dimensional simulation analysis capability, an integrated optimization and simulation platform and a human-computer interaction friendly visual decision-making interface are constructed.

The invention discloses a method for constructing a 3D Tiles data structure. The method comprises the following steps: acquiring data of different sources and different types; constructing a corresponding data storage table for each type of data, reading information of each type of data, and storing the information in the corresponding data storage table; converting the coordinate system of each type of data into the same coordinate system; exporting each type of data after coordinate system conversion as a glTF model; the method comprises the following steps: constructing a 3D Tiles extension data structure consisting of an index file and a data file, adding schema attributes in the index file, and defining a plurality of data types in the schema attributes; adding a minimum unit attribute field in the root attribute of the index file to record a corresponding data type and an attribute value; and based on the constructed 3D Tiles extension data structure and the glTF model of each type of data, integrating each type of data into 3D Tiles format data.

In various examples, occupant assessment using multi-modal sensor fusion for monitoring systems and applications are provided. In some embodiments, an occupant monitoring system comprises an occupant evaluation function that may predict at least one characteristic representative of a size of the occupant. The occupant evaluation function may include a first processing path that generates a representation of features corresponding to the occupant based on optical image data, and a second processing path that performs operations to determine a depth corresponding to the one or more features based on depth data derived from the optical image data and the point cloud depth data. In some embodiments, a three-dimensional pose detection model generates a three-dimensional pose estimate of the occupant using the optical image data, and the three-dimensional pose estimate is scaled to an absolute pose based on the point cloud depth data.

The embodiment of the invention provides a data processing method and a hardware fault prediction method. The method comprises the steps that when it is monitored that a preset trigger event occurs, a data export module obtains first data from a target register; wherein the preset trigger event is used for triggering the data exporting module to execute data exporting operation; the target register is a register which needs to export data in the plurality of registers; and the data export module stores the first data into the static memory, so that the processor reads the target data from the static memory and sends the target data to firmware in communication connection with the system-on-chip. Obtaining to-be-analyzed data of the target system-on-chip; and inputting the to-be-analyzed data into a preset fault prediction model to obtain fault prediction information corresponding to the target system-on-chip. The method is used for achieving the effects of completely storing real-time changing data in the register and carrying out hardware fault prediction according to the changing data in the register.

The invention discloses a space-air-ground integrated railway ground disaster monitoring and early warning method, which comprises the following steps of: selecting a satellite model and wavelength according to the ground surface characteristics of an object area, and determining the satellite shooting scene number of the object area by combining a satellite flight route according to the object area; planning a satellite shooting period; acquiring satellite data of an object area according to a plan; using professional software to analyze satellite data, and analyzing terrain deformation; exporting the deformation data as vector data; carrying out on-site checking on a region (or a specified region) with severe terrain deformation; determining whether the GNSS deformation monitoring device is installed according to the checking result; performing on-site survey on a deformation area, and determining positions of reference points and monitoring points; gNSS equipment is installed on site; the GNSS equipment is adjusted and calibrated; the software platform accesses GNSS equipment data; according to the GNSS equipment data, setting an alarm threshold value of a monitoring point position; early warning information is sent out after the GNSS monitoring data exceed a threshold value; the early warning information is directly sent to a manager through a short message; the manager arranges a work order to the disposer; a disposer arrives at the site to carry out checking processing, and on-site checking is carried out through a matched APP system; uploading the on-site photos before checking and after initial value by a disposal major through a matched APP system; the early warning information is closed, and the train driving safety is cooperatively guaranteed from multiple levels and multiple aspects of'air, space and ground '.

The invention relates to the technical field of data processing, and particularly provides a data exporting method, system and device based on dynamic batching and a storage medium, and the method comprises the following steps: receiving a retrieval request, and determining metadata information matched with the retrieval request; monitoring a memory vacancy rate, and dynamically updating a data volume threshold value based on the memory vacancy rate; evaluating a data volume based on the metadata information, and if the data volume exceeds the data volume threshold, dividing an entity class data export task based on the metadata information into a plurality of sub-tasks; and executing the subtasks one by one to export all entity class data corresponding to the metadata information in batches. According to the method, the memory is dynamically limited, and the data volume processed at a time is limited based on the memory allocation amount, so that the memory resources are fully utilized, and meanwhile, the memory can be prevented from being impacted, and normal operation of other services can be prevented from being influenced.

The invention discloses a data exporting method and device based on EasyExcel annotations and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: acquiring a data export template, wherein the data export template defines a data export structure; under the condition that the data export template meets a preset condition, generating a receiving object comprising the annotation according to the data export template; the receiving object comprises an object class bearing data, and the annotation is used for controlling the export behavior of the data; assembling the data to be exported into a receiving object; and exporting the to-be-exported data in the receiving object into the data exporting template based on the annotation. Through the predefined data export template, when the data export template meets the preset condition, the receiving object containing the annotation can be automatically generated, a developer only needs to pay attention to data assembly and business logic, data export can be achieved, a large number of tedious redundant codes do not need to be written, the workload of manual coding is reduced, and the data export efficiency is improved. And the data export efficiency is improved.

A cloud-based edge-as-as-service (EaaS) system allows edge gateways to be easily configured and deployed on the cloud for collection, contextualization, and egress of industrial data to downstream applications, including analytic applications, work order management systems, or visualization systems. The EaaS system uses predefined device profiles to automatically discover relevant data items on plant floor devices and present these data items to a user. Model configuration interfaces served by the EaaS system allow the user to map selected data items to predefined models for organizing or contextualizing the selected data, and for egressing the contextualized data to the target applications. These model configurations can be deployed on the cloud platform as edge gateways that use the resulting information models to collect and contextualize relevant items of device data during runtime and to export the modeled data to the target application.

The invention provides a data dynamic exporting method and system based on template configuration. The method comprises the steps that a data exporting request for exporting data in a data source in a report form is obtained; verifying the user permission data in the request to generate a verification result; when the verification result is that the verification is passed, generating a dynamic SQL statement according to template metadata corresponding to the template classification data; executing paging query according to the dynamic SQL statement, and performing data processing and streaming writing on data acquired from a data source to which data to be exported belongs in batches; and when the paging query is finished and data processing and streaming writing of the data obtained in the last batch are completed, generating a temporary file, and returning a downloading link to the client for triggering downloading of the data in the temporary file and generating a report corresponding to the data export request. According to the method and the system, the template adjustment efficiency is improved, the memory consumption is reduced, and the big data export success rate is greatly improved.

The invention discloses a data outbound-oriented low-overhead multi-source risk information acquisition method and system in the technical field of data cross-border. The method comprises the following steps: S1, intelligent screening of main business risk acquisition points: based on dynamic risk entropy, business criticality and flow fluctuation are taken as screening key acquisition points; s2, linkage of communication behavior classification and risk assessment: capturing key acquisition point communication metadata based on an improved random forest algorithm, and realizing abnormal behavior detection and classification through a cooperative mechanism of feature extraction, model training, dynamic classification and feedback optimization; s3, dynamically regulating and controlling an acquisition strategy and a transmission queue, namely dynamically regulating the acquisition granularity and the transmission priority according to the risk level and the queue load, and balancing the resource overhead and the monitoring requirement; and S4, sample bias correction and closed-loop optimization: correcting sample distribution deviation through inverse probability weight, and feeding back a false alarm rate and a load state to acquisition point selection and model parameters.

The invention discloses a multi-field coupling concrete dam parameter intelligent inversion method, system and device based on deep learning and a medium, and belongs to the technical field of dam engineering safety monitoring and computational mechanics crossing. The geometry and boundary modeling unit is used for uniformly expressing a dam body-dam foundation geometric region and a boundary type; simultaneously representing a state field and a parameter field by utilizing PINN inversion, calculating a physical residual error, and implementing parameter physical feasibility constraint and prior regularization; the organization optimizer, residual drive sampling and weight self-adaption are used for training scheduling, and result review, visualization and data export are carried out through the verification and output unit. According to the method, end-to-end parameter field inversion is realized on the premise of not depending on frequent mesh generation, the calculation cost of multiple forward modeling is reduced, the non-uniqueness of inversion is inhibited, and the method has higher recognition capability for engineering concerned areas such as permeation channels and degraded zones.

The invention discloses a quick and automatic layout method for a warehouse logistics conveying system based on Unity 3D and CAD. The method comprises the following steps: extracting detail data of warehouse logistics conveying equipment; exporting and optimizing warehouse logistics conveying equipment detail data, and adjusting data in exported equipment detail table grids and a list arrangement sequence; the optimized details of the warehouse logistics conveying equipment are imported into Unity; and querying and analyzing data in the equipment details on the basis of a linear traversal algorithm in combination with matrix transformation, and automatically substituting corresponding equipment position information, rotation parameters and scaling into matrix transformation to complete spatial layout and attribute assignment of the warehouse logistics conveying system. Compared with an existing traditional conveying system layout technology, the data conversion step can be reduced, and data interaction of Unity3D and CAD is improved; the modeling cost is reduced, and the project economic benefits are improved; errors of spatial layout and attributes are reduced, and the model precision is improved; compared with the prior art, the layout efficiency of the conveying system can be improved by 70%-85%.

Systems and methods for determining uniqueness of device identifiers are provided. The uniqueness of a device identifier may be indicated by a device quality score or grade that is calculated based on a plurality of parameters associated with a device identifier as well as evaluation rules derived based on historical data. The plurality of parameters may be associated with a network event or transaction associated with the device identifier. The evaluation rules may be derived using machine learning techniques. Based on uniqueness of a device identifier, a suitable action or measure may be taken.

A data backup method and apparatus, a data export method and apparatus, a data recovery method and apparatus, and a device and a medium. The data backup method is executed by means of a server, and comprises: acquiring backup data identification information and backup configuration information by means of an AnyBackup M365 protection service, wherein the backup configuration information comprises a backup type, a backup time and a backup destination address (S110); and sending the backup data identification information and the backup configuration information to a client, such that the client acquires, from a Microsoft 365 cloud platform and according to the backup data identification information and the backup configuration information, data to be backed up corresponding to the backup data identification information, and then writes said data into a cloud storage (S120).

The invention discloses an HTTP (Hyper Text Transport Protocol) text restoration method based on DPDK (Data Platform Development Kit), which relates to the technical field of computer networks, and adopts a multi-process decoupling architecture to respectively run processes such as flow collection and protocol analysis, fragmentation recombination and text restoration, formatted message splicing, text restoration timeout waiting and the like in independent CPU (Central Processing Unit) cores; and efficient and lock-free data transmission is carried out by using a ring mechanism of the DPDK. Different from a traditional memory splicing scheme, the data offset is calculated based on the TCP serial number, the load content is directly written into the corresponding position of the file, the problems of message disorder, packet loss, retransmission and the like are effectively solved, an atomic renaming mechanism from the file ID1 (intermediate state) to the file ID2 (complete state) is adopted, and accurate migration of the file state is ensured. Besides, the invention innovatively provides a bidirectional asynchronous scheduling mechanism based on data export and text restoration state monitoring, so that the data export process can continuously keep high throughput, and the overall processing capacity of the system is ensured.

Example embodiments of the present disclosure provide a method and an Internet of Things system for gas demand information management based on smart gas. The method includes obtaining gas monitoring data from a plurality of channels; obtaining estimated demand data of a gas enterprise; and generating a monitoring adjustment instruction and a data export instruction based on the gas monitoring data and the estimated demand data; sending the data export instruction to the gas monitoring device corresponding to the at least one channel based on the gas user object platform, the gas equipment object platform, and / or the gas user platform; and causing the gas monitoring device corresponding to the at least one channel to export, based on the data export instruction, the flow log file from a log storage unit to the gas user object platform, the gas equipment object platform, and / or the gas user platform.

The invention provides an attention detection method and system based on a brain-computer interface, and particularly relates to the technical field of brain-computer interfaces, and the system integrates a hardware unit (including TGAM electroencephalogram acquisition, Bluetooth connection and LED illumination control), a software unit (including login, detection, display and data export) and an algorithm unit. According to the method, electroencephalogram collection is carried out, wavelet filtering is utilized to decompose EEG signals, an Attention value and a Meditation value are extracted after calculation, a fusion value AM of the Attention value and the Meditation value is further provided, and control over the LED illumination brightness is achieved according to the fusion value. Besides, the functions of user verification, signal processing, result display and data recording are achieved through the software unit, the problems of extraction and fusion of attention features and relaxation features of electroencephalogram signals in the man-machine interaction process are solved through the algorithm unit, the accuracy and reliability of collection and processing of electroencephalogram data are improved, and the system is suitable for popularization and application. Effective attention feature extraction and feature fusion based on the electroencephalogram signals and adjustment of the LED illumination brightness are achieved, and development of the human-computer interaction technology is promoted.