340 results about "Multiple site" patented technology

The invention relates to a hydrological flow prediction method and system based on multi-site time-space association. The prediction method comprises the following steps: carrying out dimension reduction and feature reconstruction on original multi-site hydrological data through an auto-encoder; space-time correlation modeling and adjacency matrix dynamic construction are carried out, a multi-dimensional Euclidean distance matrix between stations is calculated based on a multivariable dynamic time warping (MDTW) algorithm, a similarity matrix is generated in combination with dynamic programming, and a dynamic adjacency matrix is constructed by fusing a geographic space adjacency relation; extracting spatial features of a GCN (Graphics Convolutional Network); carrying out adaptive time sequence decomposition and trend-period modeling; and carrying out multi-stage fusion prediction and result output, and generating a final prediction result through a decoder in combination with the decomposed trend item and periodic item. According to the method, accurate extraction and dynamic correlation modeling of spatial-temporal characteristics of multi-site hydrological data are realized, the accuracy and robustness of single-site flow prediction are improved, and the problems that multi-site spatial-temporal correlation modeling is insufficient, non-linear time sequence alignment is difficult, and single-site prediction precision is limited are solved.

The invention relates to a multi-site water quality prediction method based on spatio-temporal feature fusion. Compared with the prior art, the multi-site water quality prediction method solves the defect of low water quality prediction accuracy. The method comprises the following steps: acquiring and preprocessing multi-site water quality basic data; performing feature screening on the water area water quality data; a # imgabs0 # model is constructed; a # imgabs 1 # model is trained; and obtaining a water quality prediction result. According to the method, modeling is carried out on multi-site water quality index data from three angles of causality, space and semantics, and more related factors are considered for a complex water area scene; on the basis of the Shenchang differential equation, a more interpretable modeling scheme is provided for the water quality propagation process, and compared with a traditional Shenchang differential modeling and multi-hop propagation mode, the utilization rate and the propagation rate of information are improved.

A technique monitors input / output (I / O) and storage ownership takeover activity patterns to detect degraded performance of a highly available and fault tolerant application executing in a multi-site environment. Multiple instances of the application execute in different containers or pods running on virtual machines (VMs) of a compute layer within a containerized (e.g., Kubernetes) clustering configuration that extends across clusters of the sites. A storage layer of the clusters provides shared storage to the pods running on the VMs across the multiple sites. One of the sites is configured as an active storage site configured to receive and service I / O requests from the compute layer. A single instance of the application is active at a time and configured as a “compute owner” of the shared storage to issue the I / O requests to the shared storage. The compute owner and active storage site may not be co-located on the same site, leading to excessive I / O and storage ownership takeover activity patterns indicative of degraded performance. Upon detecting such patterns, the technique automatically triggers a storage failover to ensure that compute owner and active storage site are co-located at the same site.

The invention relates to the technical field of intelligent charging, in particular to an edge intelligent calculation enabling type peak-valley energy storage charging system which comprises a power grid frequency judgment module, an energy storage dispatching output module, a load trend recognition module, a multi-station matching sorting module and a load dynamic distribution module. According to the invention, through the continuous data analysis of the power grid frequency, the energy storage unit power and residual energy, and the load current, the system can accurately identify the linkage relationship of various parameters during the frequency abnormal response period, refine the condition judgment of peak-valley energy storage triggering and scheduling, optimize the energy storage output decision path, and improve the reliability of the system. Through dynamic screening of load trends and changing stations, the real-time performance and flexibility of energy allocation among multiple stations are enhanced, the adaptive capacity of the system to complex supply and demand changes is further improved in station matching and priority ranking, energy storage resources and loads are dynamically allocated, the electric energy utilization rate and the cooperative regulation and control level of the energy storage system are effectively improved, and the energy utilization rate and the cooperative regulation and control level of the energy storage system are improved. And intelligent upgrading of energy management is promoted.

The invention relates to a regional pollutant concentration prediction method and device and a storage medium, and is applied to the technical field of pollution monitoring. The method specifically comprises the following steps: acquiring historical pollutant concentration monitoring data and meteorological data of each single site, and predicting future fine particulate matter concentration and pollutant chemical components of the single site based on an EnvMulti-KAN model fused by KAN and Transform architectures; according to the prediction result of each single station in the target area, particle diffusion and convergence are simulated by adopting a multi-station fine particle diffusion model in combination with geographic information and interaction among the single stations, the pollutant area concentration is constructed, the area fine particle concentration is accurately predicted, and the view limitation of the single station is broken; according to the method, the single-site model and the multi-site model are cooperated, the concentration of the regional fine particulate matter is accurately predicted, the main components of the pollutants are synchronously and accurately predicted, and a demonstration is provided for atmospheric chemistry theoretical research.

The invention relates to the technical field of deep learning, in particular to a new energy photovoltaic power prediction system and method based on deep learning. Comprising the steps of obtaining historical power data and meteorological data of a photovoltaic station, and performing power prediction of a current time sequence through a single-station power prediction model; building a multi-site joint prediction model to capture the spatial correlation between sites so as to predict the power of the photovoltaic sites; establishing a weather mode identification method according to the meteorological data to distinguish a normal weather mode and a sudden weather mode; and establishing an adversarial weather-power combined prediction model to process multi-site power collaborative prediction. According to the method, single-station power prediction and multi-station joint prediction are carried out through online learning and transfer learning, so that the station power prediction efficiency is improved; by simulating extreme and violently changing weather conditions, adversarial training is carried out to establish an adversarial weather prediction network, and then weather and power combined prediction is carried out, so that the accuracy of the prediction model in a severe environment is improved.

Multi-site distributed storage systems and computer-implemented methods are described for improving a resumption time of input / output (I / O) operations during a common snapshot procedure for storage objects. A computer-implemented method includes initiating a snapshot multi create operation to selectively form a batch of first and second synchronous replicated datasets that belong to a first group of storage disks at the primary storage site and corresponding second group of storage disks at the secondary storage site, performing a batch snapshot create operation on the primary storage site by executing snapshots of storage objects on the primary storage site of the batch of first and second synchronous replicated datasets in parallel multiple threads to effectively utilize processing resources on the primary storage site, and initiating an independent workflow and state machine for each storage object of the batch of first and second synchronous replicated datasets.

The invention relates to the technical field of medical image processing and artificial intelligence, in particular to a multi-part fracture parallel recognition system and method based on a medical image, and the system comprises an image preprocessing module which is used for obtaining an original CT image sequence, carrying out the standardization processing of the original CT image sequence, and outputting a standardized three-dimensional image data body; the basic recognition enhancement module is used for processing the three-dimensional image data body and generating an enhanced feature map; the texture complexity evaluation module is used for processing the three-dimensional image data volume and generating a control signal; the self-adaptive recognition module is used for combining the three-dimensional image data body and the enhanced feature map, responding to the control signal, executing parallel recognition and outputting a parallel recognition result; the diagnosis report generation module is used for summarizing the parallel recognition results and generating a structured fracture diagnosis report, one-time and efficient parallel screening and diagnosis of multi-part fractures are achieved, and the clinical diagnosis efficiency and accuracy are improved.

A method and system are disclosed, for building segmented SPB network comprising at least two sites (102i) and a IS-IS for SPB network topology, wherein each one of the at least two sites comprises at least one Site Border Node, SBN (105il), and at least one node, the node being an end-point node (BEB 104ij) or an intermediate node (BCB 103ik), connected to the at least one SBN through a IS-IS for SPB Level 1 interface to form an intra-site network that operates in IS-IS for SPB Level 1, wherein each one of the at least one SBN and the at least one node is identified with a unique first System-id; and all SBNs are connected through a IS-IS for SPB Level 2 interface to form an inter-site network that operates in IS-IS for SPB Level 2.

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 present invention discloses a fast charging station planning method, system, device and medium applicable to multiple scenarios. The area to be planned is divided into multiple scenarios to be planned, and the number of fast charging piles in each scenario is quantified according to the charging demand data of multiple types of electric vehicles in each scenario to be planned. The fast charging station demand data in each scenario is quantified by combining the capacities of the fast charging piles in various scenarios. The points with the conditions for building charging stations in various scenarios are used as candidate sites to construct candidate site sets in various scenarios, and several candidate sites are selected from them to form multiple site combinations, so as to construct site combination optimization models in various scenarios based on these site combinations. The optimization algorithm is used to solve these site combination optimization models to obtain the best site combination and combine them to generate a fast charging station planning scheme for the area to be planned for execution. By distinguishing the charging demands in different scenarios and the points with the conditions for building fast charging stations, the layout of fast charging stations can be realized reasonably and scientifically.

The invention mainly relates to a three-dimensional hierarchical site selection method for a sea-related project, and the method comprises the steps: selecting site selection factors and constructing a site selection factor library according to the management and control requirements of a sea area and the actual sea use demands from the consideration of site selection types in four aspects of administrative regions, matching, compliance and suitability; collecting related data information of site selection types and site selection factors, and preprocessing the related data information; and acquiring pattern spots meeting the administrative region and project function requirements, and obtaining an alternative site selection scheme in combination with the ecological red line region requirement, the island region requirement, the offshore buffer region requirement, the water depth requirement and the area requirement. According to the method, multiple site selection types and multiple site selection factors are comprehensively considered, and sea area use fund grade analysis is combined, so that an alternative site selection scheme is finally obtained, support is provided for sea use project site selection analysis decision, the site selection accuracy is improved, the site selection time is shortened, and the site selection cost is reduced.

The invention discloses a personalized prediction system and device for temporal lobe epilepsy based on multi-site frequency-crossing coupling, and the system comprises a signal collection module which is used for synchronously collecting electroencephalogram signals at a plurality of sites of the brain; the signal preprocessing module is used for preprocessing the collected electroencephalogram signals, drawing a cross-frequency coupling common modulation graph and segmenting frequency bands; the feature extraction module is used for extracting phase amplitude coupling features including average coupling center phase frequency, amplitude frequency and average coupling center intensity; the prediction module is used for inputting the phase amplitude coupling characteristics into a pre-trained machine learning model and outputting a prediction result of the temporal lobe epilepsy; and the early warning module is used for generating an early warning signal according to the prediction result. According to the method, a traditional fixed frequency band analysis normal form is broken through, and phase / amplitude migration characteristics are captured through energy distribution weighting; constructing a personalized adaptation mechanism to identify epilepsy atypical cross-frequency coupling features; and the interpretability of a neural oscillation desynchronization mechanism is higher.

The invention discloses an optical power prediction method based on multi-site spatio-temporal characteristics and dynamic optimization. The method comprises the following steps: collecting data of a plurality of monitoring sites and carrying out characteristic selection by using a selection-deletion characteristic selection method; carrying out weighted fusion by adopting a multi-site data weighted fusion strategy; constructing an interval prediction model, inputting data subjected to feature selection and weighted fusion into an STG-Mamb-neural network model, and then performing LUBE interval prediction on output to obtain upper and lower boundaries of optical power; introducing an improved weighted average optimization algorithm, and optimizing the interval prediction model in combination with random disturbance and a boundary constraint strategy; and evaluating the optimized interval prediction model by adopting a cross validation method, and predicting the optical power of the multiple sites after the optimal interval prediction model is obtained. According to the invention, stable prediction performance can be maintained in a changeable environment, so that the reliability and stability of optical power prediction are improved.

The invention relates to the technical field of photovoltaic power generation cloud platforms, and discloses a multi-site energy storage cluster cloud scheduling platform based on photovoltaic power generation prediction, and the platform employs a real-time data collection and space-time synchronization module to carry out the timestamp alignment and space grid processing of multi-source heterogeneous data. In combination with a dynamic priority scheduling mechanism of a prediction task distribution module based on volatility and a power grid security situation, the effects of cooperative execution of prediction tasks and priority guarantee of key tasks are achieved; through containerized parallel reasoning and consistency Hash load balancing technology of a distributed prediction calculation module, time consistency of a multi-node prediction result is ensured, and a rolling optimization algorithm and a time sequence constraint embedding strategy of a unified scheduling decision module are adopted. Accurate calculation of power generation unbalanced power and time sequence consistency generation of an energy storage strategy are achieved, and through instruction verification and conflict detection and filtering frequency reduction processing of a smoothing module, the effects of prolonging the service life of equipment and guaranteeing stable operation of a power grid are achieved.

The invention relates to the technical field of intelligent water conservancy and automatic control, in particular to a polder area multi-station water conservancy data real-time collection and intelligent fusion processing method and system, and the method comprises the steps: a multi-dimensional collection system construction step: collecting time sequence state, fluid field and environment boundary data, and executing time base unified cleaning; an ideal model construction step: constructing an ideal water tank model based on the law of conservation of mass and generating an ideal water level change curve; a parameter injection simulation step: generating a simulation fault water level curve according to injection efficiency attenuation and medium leakage parameters; a double-track difference calculation step: respectively carrying out difference on actual measurement and simulation data and an ideal curve to generate real and theoretical residual vectors; a similarity judgment and control step: calculating sequence similarity to judge a real physical fault or sensor data noise, and generating a fusion control instruction; according to the invention, the technical problem of distinguishing the noise of the sensor from the real physical fault is effectively solved, and false alarm or missing alarm is avoided.

The invention provides a VTS radar station multi-station collaboration method, device and equipment and a storage medium, and the method comprises the steps: constructing a collaboration decision matrix containing a real-time coverage capability weight, and generating a station task distribution instruction set; during construction, historical and real-time data of each radar station are obtained, the weight is optimized through a reinforcement learning model, and the model is guided to learn by a reward function containing a regulation coefficient (,), an actual coverage capability (and) and an estimated weight (). And finally, outputting the optimized weight to the collaborative decision matrix. According to the method, reasonable allocation and efficient utilization of resources are realized, the target tracking precision and the system robustness are improved, and a complex water traffic environment is effectively dealt with.

The invention provides a central intelligent kitchen full life cycle operation optimization method based on digital twinning, and relates to the technical field of intelligent kitchens, and the method comprises the steps: building a self-correction twinning model, and achieving the dynamic self-adaption of model parameters and structures; based on a multi-objective collaborative optimization algorithm, performing joint optimization on energy consumption, productivity and quality indexes; setting a trigger control logic in combination with the prediction deviation, the constraint conflict and the energy price change; a federal aggregation mechanism is adopted to realize unified updating of parameters and strategies among multiple sites, a global optimal strategy model is finally formed, and a collaborative optimal control scheme throughout construction, operation and maintenance stages is generated, so that dynamic optimal operation and full-life-cycle energy efficiency improvement of the central intelligent kitchen are realized. According to the method, the problems of staticizing of a digital twin model, simplification of an optimization target and insufficient multi-site collaboration in the prior art are solved.

The invention discloses a multi-site photovoltaic power generation power prediction method and system based on a graph attention network. The method comprises the following steps: obtaining a spatial dependency model containing a site spatial relationship according to a site dependency relationship based on the graph attention network; constructing a time dependence model containing a site time relationship based on an autoregression long-short-term memory network in combination with a probability framework; constructing a first missing data filling layer according to a spatial feature compensation strategy, constructing a second missing data filling layer according to a time feature compensation strategy, and constructing a third missing data filling layer according to the spatial-temporal correlation of the neighbor site set so as to construct a spatial-temporal data processing layer; and constructing a photovoltaic power generation power prediction model according to the spatial dependence model, the time dependence model and the spatio-temporal data processing layer by utilizing historical operation data of a photovoltaic station. The method has the beneficial effects that the requirement on training data is reduced, the pertinence of photovoltaic power generation power prediction is improved, and the accuracy of photovoltaic power generation power prediction and the applicability of the model are improved.

An illustrative method includes a multi-site storage system determining performance data associated with the multi-site storage system; determining, based on the performance data, a replication lag threshold value; and providing the replication lag threshold value to an application. In some embodiments, the application uses the replication lag threshold value to set a configuration parameter for the application, such as a replication timeout value of the application.

An enterprise engineering and configuration system includes a common configuration database and support services stored in and executed in a compute fabric of an enterprise. The configuration database and support services use and implement a common configuration data schema to support the configuration of hardware and software in the compute fabric and at multiple different sites or physical locations of the enterprise even when different control and automation systems are used at these different sites or physical locations. The configuration system enables implementing hardware or software configuration changes to various different sites or locations of an enterprise either centrally from a configuration device connected directly to the compute fabric of the enterprise or locally from any physical location or site of the enterprise, while maintaining a single integrated enterprise configuration database that stores configuration data for each of the multiple sites of the enterprise. This configuration system is flexible as it enables engineering and configuration changes to be made by users anywhere in the enterprise for any of the sites of the enterprise and across different sites of the enterprise.

Techniques for a Software-Defined Networking (SDN) controller associated with a multisite network to implement jurisdictional data sovereignty polices in a multisite network, route network traffic flows between user sites and destination services over one or more provider sites, and / or perform a routing operation on the network traffic flow(s) based on the jurisdictional data sovereignty policies. The jurisdictional data sovereignty polices may be implemented using destination group tags (DGTs) and / or source group tags (SGTs). A secure access service edge (SASE) associated with the network controller may generate, store, and distribute the DGTs to provider sites and / or the SGTs to user sites. Based on the SGT and / or DGT associated with a network traffic flow, one or more services may be applied to the network traffic flow, and the network traffic flow may be routed through a particular region of a software-defined access (SDA) transit.

Multi-site distributed storage systems and computer-implemented methods are described for improving a resumption time of input / output (I / O) operations during an automatic unplanned failover (AUFO). A computer-implemented method includes determining, with a second storage cluster, whether heartbeat information from one or more storage objects of a CG of a first set of CGs is received during a time period, determining an out of sync state for a data replication relationship between the CG of the first set of CGs and a mirrored CG of a second set of CGs when the heartbeat information is not received during the time period and sending a single bulk role change call with a cluster identifier from the second cluster to an external mediator to provide a role change from follower to leader in the second set of CGs.

Multi-site distributed storage systems and computer-implemented methods are described for providing common snapshot retention and automatic fanout reconfiguration for an asynchronous leg after a failure event that causes a failover from a primary storage site to a secondary storage site. A computer-implemented method comprises providing an asynchronous replication relationship with an asynchronous update schedule from one or more storage objects of the first storage node to one or more replicated storage objects of the third storage node, creating a snapshot copy of the one or more storage objects of the first storage node, transferring the snapshot copy to the third storage node based on an asynchronous mirror policy, and intercepting the snapshot create operation on the primary storage site and transferring the snapshot copy to the second storage node to provide a common snapshot between the second storage node and the third storage node.

Systems and methods for orchestrating a virtualized 5G gNB are provided. In one example, a system for orchestrating wireless service includes a scalable cloud environment configured to implement a base station using a plurality of virtualized base station entities. Each virtualized base station entity of the plurality of virtualized base station entities configured to implement at least some functions for one or more layers of a wireless interface used to communicate with UEs. The scalable cloud environment further configured to implement a service orchestration tool including one or more interfaces configured to receive common configuration parameters that are applicable to multiple sites of an operator and to receive site-specific configuration parameters that are unique to respective sites of the operator. The service orchestration tool configured to automatically generate deployment files for the plurality of virtualized base station entities based on the common configuration parameters and the site-specific configuration parameters.

The invention discloses a remote cooperative verification and communication management system and method for a metering instrument, and relates to the technical field of instrument intellectualization. The method comprises the following steps: acquiring environment data and original instrument data of a plurality of metering stations through an Internet of Things communication protocol, and transmitting the original data to a remote communication management center for protocol adaptation processing to generate standard data frames in a unified format; further performing semantic standardization on the standard data frame to obtain a data set for verification; and constructing a cross-site mutual parameter verification model based on the data set and the environmental data, calculating a deviation value of each metering instrument, determining a cooperative verification priority index of the metering instruments in combination with a historical error change trend, generating a remote cooperative verification list containing verification task distribution information, and sending the remote cooperative verification list to a server. And the information is issued to each site for execution through an Internet of Things communication protocol. The remote automatic verification scheduling and data consistency guarantee of heterogeneous instruments is realized, and the efficiency and reliability of multi-site collaborative verification are improved.

One or more techniques and / or systems are disclosed for providing for improved monitoring of an individual or a plurality of individuals as they move from one location to another or one room to another, wherein imaging data is received from a plurality of imaging sensors. The received imaging data is aggregated and patterns at targeted data segments of the aggregated imaging data are analyzed using one or more algorithms to determine one or more values. The one or more values determined from the analyzed patterns are classified to represent at least one of a physiological state determination or a positioning of the individual. Data corresponding to the classified one or more values is then output and displayed.

The embodiment of the invention provides a data synchronization method based on multiple data centers, and the method comprises the steps: taking any data center as a source data center for data synchronization, and determining a target data center corresponding to the source data center for data synchronization; setting configuration information in the source data center; establishing data synchronization connection between a source bucket of the source data center and a target bucket of the target data center according to the configuration information; setting a synchronization strategy for any source bucket of the source data center according to the data synchronization connection; and synchronizing the data in the source bucket to the target bucket according to the synchronization strategy. Through the embodiment of the invention, the condition of multi-site environment deployment is relaxed, and the usability and the practicability are improved; through establishment of data synchronization connection, a data synchronization source and a data synchronization destination are associated, so that the flexibility of data synchronization is improved; by setting a data synchronization strategy for the bucket, the diversity of data synchronization among multiple sites is increased, and more choices are brought to customers.

The invention relates to the technical field of chip function testing, and particularly discloses a multi-site testing method for TOF sensor chip function testing, which comprises the following steps: completing hardware self-inspection and software loading; the host receives a'ready 'signal fed back by the sorting machine / probe station through GPIB communication, and confirms that each slave and the corresponding peripheral are in a'to-be-tested' state through TCP communication or Pipe interaction; the host issues an independent test instruction to each slave; after each slave receives the instruction, the corresponding test resource board card is controlled to provide a test condition for the chip to be tested, and the chip output data is collected at the same time; after completing the single-station test, each slave uploads a test result to the host; the host captures the test data from the corresponding network disk and counts the yield; and the host sends the final result of the multi-station test to the sorting machine / probe station through the communication driver and automatically grabs the next batch of chips to be tested. According to the method, the overall test time is greatly shortened through the multi-site parallel test.

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.