260 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 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.

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.

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 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.

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.

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.

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.

The invention relates to the related technical field of pollution early warning, in particular to a multi-site multi-pollutant prediction method and device based on GATv2 and time feature extraction. The method comprises the following steps: acquiring historical data of multi-site pollutant monitoring; generating an adjacent matrix among multiple sites based on the historical data; the adjacent matrix is used for representing the relationship and dependency between different sites; extracting a first feature based on the adjacent matrix and the historical data through a GATv2 model; based on a time feature extraction module, capturing time features in the historical data to obtain second features; splicing the first feature and the second feature to obtain a spatial-temporal feature; and inputting the spatial-temporal characteristics into a preset full connection layer to obtain a prediction result. Through the arrangement, more accurate prediction can be carried out on multiple pollutants at multiple sites.

The invention discloses a migratable surface water quality prediction method based on representation learning, and the method comprises the steps: 1, obtaining historical monitoring data, so as to construct a data set in a multi-site sequence format, carrying out the mask processing of the historical monitoring data in the data set, so as to obtain mask data, forming a training test set by the historical monitoring data and the mask data; 2, selecting a Transform model framework with the same total number of source domains to construct a prediction network; step 3, training the prediction network to obtain a prediction model; and step 4, inputting the water quality data collected in the station and the meteorological data of the current day into the prediction model to obtain the water quality data of the source domain where the station is located. The invention further provides a device for predicting the water quality of the transferable surface water. According to the method provided by the invention, the key water quality index of the source domain river monitoring section is predicted through the change of the water quality information of each station in the source domain, and more accurate and comprehensive reference is provided for subsequent water quality management.

The invention relates to a wind power prediction method, system and device based on a multi-site dual-space-time network and a medium. The method comprises the following steps: acquiring historical data of a multi-site wind power plant, and preprocessing the historical data of the multi-site wind power plant to obtain preprocessed data; performing feature selection processing on the preprocessed data to obtain an optimized feature set; inputting the optimized feature set into a preset multi-site dual-time-space network to obtain a first future predicted value of each site and a second future predicted value of each site; the multi-site dual-time-space network comprises a long short-term memory network branch and a time convolution network branch; and carrying out weighted fusion on the first future predicted value and the second future predicted value to obtain a multi-station wind power predicted value. By adopting the method, the precision and robustness of multi-station wind power prediction can be improved, the clustering development requirement of the wind power plant can be better adapted, and powerful support is provided for power grid dispatching strategy optimization and safe and stable operation.

The invention relates to a method and a system for combining a panel industry site technological process. The invention relates to the technical field of photoetching machine production line processing, and provides an automatic station crossing processing method and system for station merging. A file change notification mechanism is configured between the feeding and discharging machine and the equipment; wherein the file change notification mechanism is used for notifying the equipment to locally store multi-site information when a file is changed, and the equipment is configured with a multi-site detection mechanism based on a Recipe mechanism; the method comprises the following steps: receiving a multi-site specific event of a target product detected by a multi-site detection mechanism through equipment, and generating a log file; and receiving the log file through the MES to execute automatic station crossing of the station. In the application, after the product enters the production line, the equipment locally stores the multi-site information, the detection parameters of different products can be quickly switched, and the method is particularly suitable for the flexible production requirements of multiple varieties and small batches. Accurate positioning of abnormal events or accident faults and the like can be realized through multi-site specific time.

The invention provides a distributed multi-site operation cloud storage system, which comprises a data management module used for being responsible for storage, retrieval and copy management of data and ensuring the integrity and availability of the data; the storage pool management module is used for managing resources of a storage pool; the copy distribution module is used for automatically calculating a placement rule of a data copy and realizing distributed storage of data; the network scheduling module is used for transmitting and scheduling data in a cross-machine-room network; the fault detection module is used for monitoring the running state of the storage system, detecting faults and abnormal conditions, and giving an alarm and processing in time; and the arbitration module is used for being responsible for decision judgment and brain split prevention in a fault scene, and ensuring data consistency and system stability. The invention provides a distributed multi-site operation cloud storage system which solves the problem that once a machine room goes wrong, the whole cloud platform and a business application system borne by the cloud platform are both in an unavailable state.

This invention discloses an automatic update system for website article, tag, and category matching models, relating to the field of model update technology. It includes a data input module, a recommendation query unit, and a training unit. The data input module includes inputting query data and matching keyword data with data obtained from the database by the recommendation query unit to obtain matching data and corresponding articles. The database used is the Milvus database. This application optimizes the model and its performance, accelerating the training speed. Furthermore, this application uses the Milvus database, the Roformer model, and the prompt technique to solve the problem of requiring multiple manual training and restarts for a single site. Model updates are convenient and do not affect business operations. The same model can be adapted to data groups from multiple sites, and the problem of repeated training across multiple sites is solved.

The present invention relates to the field of energy storage scheduling technology, and in particular, to an energy storage scheduling system for multi-site complementarity. It includes a prediction calculation unit, a steady state unit, a prediction judgment unit, and a site complementarity unit. The load state module of the present invention obtains a steady grid load from the load steady stage, calculates the grid target load electrical state and the grid dispatching parameters based on the monitored internal status data of the multi-site energy storage devices, and utilizes the near-point energy storage device to dispatch the grid according to the grid dispatching parameters, so that the grid is dispatched to the target load state in advance when the grid reaches the peak at time t. By accurately calculating the grid target load electrical state, the fluctuation of the grid load can be better absorbed and released, the power fluctuation of the grid can be reduced, and the voltage and frequency of the grid can be kept stable, thereby improving the overall stability of the grid. At the same time, by considering the dispatch of the grid by the energy storage device during the steady stage of the grid, the dispatching error can be reduced, and the grid dispatching accuracy can be 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.

The application relates to the field of 5G communication technology, in particular to a 5G base station site selection method and system taking a convergence device as a center, which comprises setting candidate base stations, convergence device and link related parameters and decision variables; enabling the candidate base stations and connecting the nearest convergence device in a high demand area, calculating the total cost, the time delay of each service and the utilization rate at this time; using the evaluation function of the simulated annealing algorithm to calculate the total cost, the time delay and the utilization rate to obtain the current solution; randomly exchanging the convergence device allocation of the base station or adjusting the link path until the constraint condition is met, recalculating the total cost, the time delay and the utilization rate and substituting them into the evaluation function to obtain a new solution; judging whether to accept the new solution according to the simulated annealing algorithm criterion, constantly updating the temperature and iterating until the maximum running number is reached, and finally forming a convergence device deployment scheme and a link planning. The application is used for reasonably planning the base station site under the conditions of multi-site, multi-convergence device and multi-target optimization.

The invention discloses a multi-site collaborative energy storage centralized control scheduling method, which relates to the technical field of multi-site collaborative energy storage, and comprises the following steps of: after an ambiguity condition of a scheduling strategy is identified, constructing a response mode vector based on a site historical scheduling behavior, and utilizing a response prediction model fusing a multi-head attention mechanism and a behavior trajectory transformation network to obtain a multi-site collaborative energy storage centralized control scheduling strategy; determining a site response behavior difference under the condition that the scheduling strategy has ambiguity; according to a site response behavior difference result, a strategy analysis prompt instruction is generated through a behavior deviation value mapping mode, the strategy analysis prompt instruction and an original scheduling strategy are combined to construct a structured scheduling strategy with a consistent analysis path, and unified analysis and constraint control of scheduling strategy execution logic are achieved. According to the method, the problem of multi-site response behavior difference caused by an ambiguous scheduling strategy is solved, unified analysis and closed-loop control of the scheduling strategy are realized, and the consistency and stability of cooperative scheduling are guaranteed.

Storage volumes of a remote data forwarding group are mirrored on a leg of a data replication facility from a production site in a primary region of the data replication facility to a cyber vault using asynchronous remote data forwarding. A multi-site consistency application is used to bring and maintain the data of the storage volumes on the cyber vault into a consistent state. Once the storage volumes in the cyber vault have achieved a consistent state, a snapset including a snapshot of each of the storage volumes is created from the storage volumes contained in the cyber vault on a determined rhythm. The snapset is optionally linked to a set of target devices at the cyber vault. If the production site is not in the primary region, the absence of consistency of the data at the cyber vault halts creation of snapsets at the cyber vault.

The present disclosure relates to methods, systems, and apparatus for monitoring temperatures at multiple sites within a substrate processing chamber. A system for processing a substrate includes: a processing chamber including a processing volume; a first window at a first periphery of the processing space; a substrate support member, wherein the substrate support member is arranged in the processing space; and a first multi-wavelength pyrometer configured to measure: a first temperature at a first site proximate the first window; and a second temperature at a second site proximate the substrate support.

The invention discloses a service request allocation method and system, electronic equipment and a computer program product. Relates to the field of distribution, and the method comprises the steps: receiving a target service request which is used for requesting a service unit in a first site to provide a service; l target service units are screened out from S service units in the first site, the target service request is allocated to the L target service units, the target service units are service units containing effective application nodes, S and L are positive integers, and L is smaller than or equal to S; and receiving service results returned by the L target service units, and sending the service results to the initiating node of the target service request. Through application of the method and the device, the problem of service interruption of the service request of the multi-site cluster caused by node faults in the cross-site service unit in the related technology is solved.

This invention provides various schemes related to minimizing contention based on Target Wake-up Time (TWT) in mesh networks. A device acting as a broker compiles TWT durations requested by multiple stations (STAs) to generate a total TWT duration. The device sends this total TWT duration information to a controller. In response to this transmission, the device receives time slot allocations from the controller. The device then allows multiple STAs to compete for media access during the period of that time slot.

Some embodiments of the invention provide a method for implementing an SD-WAN connecting multiple sites at multiple physical locations. The method is performed at a first route reflector for a first region of the SD-WAN. The method receives, from a hub router of the first region, a peer-connection notification regarding a newly connected first edge router located at a first site in a second region. The method determines that a routing table maintained by the first route reflector does not include routes of the first edge router and that the first route reflector does not have a direct connection to the first edge router. Based on said determining, the method requests routes of the first edge router from the hub router. After receiving from the hub router the requested routes of the first edge router, the method updates the routing table to include the routes of the first edge router.