1460 results about "Cluster based" patented technology

The invention discloses an intelligent power grid optimal scheduling method and system based on multivariate energy storage cooperative scheduling, and relates to the technical field of power grid optimal scheduling, and the method comprises the following steps: building a prediction model based on first data, generating prediction data, coupling energy storage characteristic parameters of different types of energy storage equipment with the prediction data, and obtaining a prediction model; establishing a multi-energy collaborative scheduling model; dynamically screening the energy storage scheduling strategy set based on a preset real-time performance evaluation index to generate an optimal strategy subset; according to the optimal strategy subset, performing differentiated charging and discharging control instructions on the energy storage equipment cluster; and collecting second data in the charge and discharge control process, calculating a deviation value between the second data and the prediction data, converting the deviation value into a feature vector, inputting the feature vector into a preset incremental learning algorithm, and optimizing parameters of the multi-energy collaborative scheduling model. Layered screening is implemented in combination with real-time performance evaluation indexes, and it is ensured that the optimal scheduling scheme can be rapidly selected in different time periods and under the uncertain disturbance condition.

The invention relates to the technical field of equipment fault prediction, in particular to a cup labeling equipment fault prediction method and system based on machine learning. According to the method, equipment operation state parameters are converted into a multi-mode pulse sequence with a timestamp synchronization characteristic; loading the purified pulse flow to a quantum bit array for entanglement state evolution, and extracting a three-mode entanglement association tensor; carrying out dimensionality reduction projection on the three-mode correlation tensor to an equipment degradation manifold space, and determining quantum tunneling probability density distribution; constructing a time-varying Hamiltonian of an equipment degradation state based on quantum tunneling probability density distribution, and generating a degradation track cluster according to the time-varying Hamiltonian; and performing time sequence convolution processing on the degradation track cluster, performing probability amplitude amplification on a fault critical point in the track cluster by using an energy level splitting characteristic of a time-varying Hamiltonian, and generating a space-time probability cloud picture. The fault evolution law can be visually presented, the accuracy and timeliness of early fault early warning are improved, and a reliable basis is provided for predictive maintenance.

A method, system, and device for wind speed prediction and layout optimization in wind power generation are provided. The method includes: obtaining a basic wind resource dataset of a target region; constructing a physics-informed neural network model based on the basic wind resource dataset; obtaining wind speeds data at a specific location in a velocity field based on the physics-informed neural networks and constructing a training dataset; training the physics-informed neural network model based on the training dataset; reconstructing a wind speed distribution within the velocity field and predicting wind speeds for a next time period with a wind farm using the trained physics-informed neural network model; and optimizing a layout of a wind turbine cluster based on a reconstructed wind speed distribution within the velocity field. The present application reconstructs a two-dimensional velocity field of the wind farm by training the PINN and enables accurate ultra-short-term wind speed prediction.

The invention relates to the technical field of deep learning, in particular to an AI chip test parameter adaptive optimization method based on deep learning, which comprises the following steps: acquiring historical test data of an AI chip, and calculating correlation strength among different failure modes based on the historical test data; identifying a failure coupling matrix according to the edge weight, and converting a preset static detection parameter constraint boundary into a dynamic constraint space changing along with a failure detection state; a multi-level optimization framework is constructed, the upper layer executes failure type correlation analysis and generates constraint propagation information, the middle layer optimizes a parameter cluster based on the constraint propagation information, and the lower layer adjusts a single detection parameter and outputs a parameter optimization result; establishing a neural network mapping model, and obtaining a nonlinear mapping relationship between the detection parameters and the failure types; based on the physical state parameters, the nonlinear mapping relation is adjusted, the dynamic constraint space is updated, parameter optimization is executed again, a parameter optimization result is output, and an optimal test parameter combination is output.

The invention relates to the technical field of intelligent building energy management, and particularly discloses an air conditioner load cluster optimization regulation and control method and system based on building thermal inertia modeling, and the method comprises the steps: collecting building structure parameters, building material thermophysical parameters, indoor and outdoor temperature and humidity historical data, air conditioner operation data and real-time electricity price data of a building; building a building thermal inertia model based on the collected data; inputting the collected air conditioner operation data, indoor and outdoor temperature and humidity historical data into a building thermal inertia model, and predicting air conditioner loads under different working conditions; constructing an optimized objective function, and solving the optimized objective function by adopting a genetic algorithm to obtain an optimal air conditioner load regulation and control strategy; and according to the optimal air conditioner load regulation and control strategy, the air conditioner load cluster is regulated and controlled in real time. The energy utilization efficiency can be effectively improved, the operation cost is reduced, and intelligent and refined regulation and control of the air conditioner load cluster are achieved.

A method, system, and readable storage medium for fault line selection in distribution networks is provided. The method includes: obtaining the zero-sequence current of each feeder and the zero-sequence voltage of the busbar within a preset time window after a fault occurs; using these to process the feeder's short-time window zero-sequence instantaneous power curve cluster in the distribution network through KPCA (Kernel Principal Component Analysis) for dimensionality reduction, determining the principal component scores; and performing BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies) clustering based on these scores to identify whether a feeder is faulted. This clustering process allows for precise and rapid identification of the faulted feeder, even when the current is small, improving detection accuracy. This solves the problem of quickly identifying the faulted feeder in a small current grounding distribution network during single-phase grounding faults.

The invention relates to the technical field of transformer substation intelligent patrol, and provides a transformer substation three-dimensional fusion patrol method and system based on digital twinborn and autonomous identification. According to the method, a fused three-dimensional model is constructed through multi-source data acquisition and a three-dimensional Gaussian splash algorithm, and in combination with deep learning-based point cloud semantic segmentation and clustering, an equipment-patrol means coverage relationship is generated. Creating a virtual inspection proxy object based on a three-dimensional virtual environment, and controlling terminals such as an unmanned aerial vehicle to collect real-time video image data; the system carries out automatic identification on pictures, automatically completes equipment level alignment and standard point location identification, generates fine control of camera zooming, horizontal rotation, pitching and the like, and realizes standardized view finding and acquisition. By combining an enhanced recognition algorithm, traditional image processing and a deep learning model are fused, model self-evolution is realized through incremental learning, flexible expansion and collaboration of various patrol terminals are supported through a unified interface, and refined, real-time and intelligent patrol operation and maintenance requirements of an intelligent substation are met.

The system and method for processing a digital pathology image using a machine learning model that includes a self-supervised hierarchical Vision Transformer (ViT) configured to perform unsupervised clustering with multiple classification tokens. The method includes receiving a digital pathology image that depicts a tissue slice stained with histological dyes. The digital pathology image may be processed to generate a result comprising multiple predicted classifications of individual patches of the digital pathology image. The result is generated by a machine-learning model using a self-supervised hierarchical Vision Transformer (ViT) that may further comprise a multi-head self-attention module configured to predict a crosspatch relevance metric using an attention mechanism for each individual patch in the digital pathology image thereby assigning the individual patches to a cluster based on the crosspatch relevance metrics.

The invention provides an intelligent decision-making method based on multi-dimensional risk coupling modeling of new energy ships and related equipment. The method comprises the following steps: fusing multi-modal data of each new energy ship to obtain a target multi-modal feature vector; mining risk implicit association strength by using a large language model, and constructing a risk knowledge graph; performing risk time sequence evolution prediction by adopting a dynamic Bayesian network based on the atlas to obtain a single-ship risk prediction result; constructing a graph structure according to the single ship risk and the operation parameters, and carrying out space coupling modeling by adopting a graph convolutional network to obtain a cluster risk prediction result; and generating an optimal operation and maintenance strategy by adopting reinforcement learning based on a cluster risk prediction result and a reward function by taking a high-fidelity digital twinborn body as a virtual environment. Therefore, according to the method, the problem of closed-loop adaptive control from risk deduction to decision response in a complex navigation scene is effectively solved by constructing a unified modeling mechanism of multi-dimensional risk coupling and linking real-time control strategy generation.

The embodiment of the invention discloses a multi-microgrid regulation and control method and device based on federal hierarchical reinforcement learning, and a medium, belongs to the technical field of smart grids, and solves the problem of low microgrid dispatching precision. Each microgrid uploads a model gradient parameter corresponding to the local scheduling model to a central server; the central server determines an aggregation weight according to the similarity between the micro-grid data distribution characteristics so as to update a global model; the central server dynamically divides each micro-grid into a plurality of micro-grid clusters according to model gradient similarity, and constructs a leader-follower game model in each micro-grid cluster; the central server outputs a first scheduling strategy corresponding to each micro-grid based on the updated global model, the plurality of micro-grid clusters and the leader-follower game model; and each micro-grid periodically performs secondary optimization on the first scheduling strategy based on the local feature data to obtain a second scheduling strategy corresponding to the current micro-grid.

The invention discloses a complex search scene-oriented unmanned cluster distributed collaborative decision-making method, which comprises the following steps of: modeling a search area into a three-dimensional space-time grid, establishing an unmanned aerial vehicle cluster comprehensive hit rate model and a cost function, and solving approximate optimal path distribution; establishing a target consistency judgment model based on multi-view space overlapping, and judging whether observation targets are the same target or not; target encircle collaborative decision-making is realized by improving reinforcement learning; forming a consensus unmanned aerial vehicle cluster based on group consensus, and performing position and speed estimation on a target in combination with an unmanned cluster observation error and target steering; cooperative network graph relation construction is carried out on non-consensus unmanned aerial vehicles through a dynamic graph theory, so that space-time scheduling is carried out, and surrounding point distribution and conflict resolution are optimized. According to the method, through path optimization, target consistency judgment, reinforcement learning of a hunting strategy, consensus target state collaborative prediction and a dynamic graph scheduling mechanism, efficient collaborative hunting and task allocation of multiple unmanned aerial vehicles in a complex environment are realized.

Various embodiments include a wireless communication network that comprises resource allocation circuitry. The resource allocation circuitry hosts a traffic forecasting machine learning model, a resource forecasting machine learning model, and a resource allocation machine learning model. The resource allocation circuitry obtains traffic data for a provisioning engine cluster and provides the traffic data to the traffic forecasting model. The resource allocation circuitry obtains an output that comprises a traffic prediction for the provisioning engine cluster and provides the prediction to the resource forecasting model. The resource allocation circuitry obtains an output that comprises a hardware requirement prediction for the provisioning engine cluster and provides the hardware requirement prediction to the resource allocation model. The resource allocation circuitry obtains an output that comprises a hardware allocation recommendation for the network provisioning engine cluster. The resource allocation circuitry allocates hardware resources to the cluster based on the hardware allocation recommendation.

This disclosure relates to a privacy preserving machine learning platform. In one aspect, a method includes identifying a request for processing an input feature vector by a machine learning model using a multiple multi-party computation (MPC) cluster including a plurality of MPC computing systems. Each feature of the input feature vector is encoded to generate an encoded weight vector. A respective share of the encoded weight vectors is generated for each computing system and provided to a corresponding computing system to generate a partial prediction for the respective share. The MPC cluster collects modified partial predictions for the input feature vector from the rest of the multiple MPC computing systems, where each of the modified partial predications is generated based on the partial prediction by a corresponding MPC computing system. A final prediction is generated by the MPC cluster based on the respective partial predictions.

The invention provides a task scheduling method and system for big data, and relates to the technical field of big data, and the method comprises the steps: obtaining task parameters and a dependency relationship between tasks, and calculating a task priority score; according to the data locality coefficient of the task, the cluster parameter and the cluster relevance correction coefficient, determining the fitness of the task and the cluster; determining a target cluster of the tasks and a task sequence according to the task priority score and the fitness of the tasks and the cluster; when the number of failure times of the task exceeds a time threshold value, fusing is triggered, if cross-cluster dependence fails, the dependent task is migrated to the current cluster, the fitness is recalculated, and the priority of the task is recovered after continuous success; by means of dynamically adjusting task priorities, optimizing cluster resource allocation, establishing fusing and migration mechanisms and the like, the core problems of thread waste, priority disability, stability risk and the like in the traditional technology are solved, so that the service stability and the processing efficiency of a data intermediate station in a complex scene are improved.

The invention provides a multi-source heterogeneous data dynamic updating and agent knowledge base construction method, and relates to the technical field of data processing, and the method comprises the steps: organizing heterogeneous data through a three-dimensional feature matrix, constructing feature mapping through singular value decomposition and cross decomposition, and executing recursive tensor completion to generate a fusion feature space; extracting multi-scale features and determining a stable knowledge entity based on comprehensive measurement; constructing a network structure and dividing knowledge clusters; and performing differentiation fusion of the knowledge clusters based on the life cycle parameters. According to the method, efficient integration of heterogeneous data, accurate extraction of knowledge entities and dynamic optimization of knowledge structures are realized, and the intelligent level of knowledge management is improved.

The invention provides a virtual power plant group resource scene adaptive scheduling method and system, and a storage medium, and the method comprises the steps: building a typical external feature model of a virtual power plant based on the resource characteristics and core parameters of different types of distributed resources; generating a feasible region of the single equipment based on power constraint, electric quantity constraint and climbing constraint of the single equipment in the virtual power plant, and aggregating the feasible region of the single equipment to form an aggregated feasible region of the virtual power plant; based on a typical external feature model of the virtual power plant and different service scene requirements, dynamically adjusting response capability index weights in different service scenes, and based on an aggregation feasible region of the virtual power plant, constructing a virtual power plant dynamic aggregation model adapted to multiple scenes; and solving the dynamic aggregation model of the virtual power plant by taking minimization of the power generation cost of the virtual power plant as a target to obtain an optimal scheduling scheme of the virtual power plant.

The invention belongs to the technical field of computer software, and particularly relates to a heterogeneous database unified deployment management method based on a cluster management platform, which comprises the following steps: predefining metadata of various database services; the method comprises the following steps: pre-constructing a multi-version program package warehouse, and establishing a structured warehouse directory system; obtaining a chip architecture and an operating system type of the target node, and generating environment fingerprint information; dynamically selecting a database installation package matched with the current environment and a dependency item set of the database installation package by mapping a warehouse directory system; through a unified database adaptation layer, a standardized operation interface is called, and installation, configuration and start-stop operation of the database are executed; a visual deployment and arrangement function based on component roles is provided by expanding a Web service interface of the management platform; and according to a deployment arrangement result and the environment fingerprint information, calling a deployment script to complete automatic deployment and configuration of the database cluster. According to the invention, unified adaptation, deployment and full-life-cycle management of various domestic databases are realized.

The invention discloses a spinning machine cluster pressure cooperative control method based on multi-source evidence fusion, and the method comprises the steps: obtaining the real-time state parameters of each spinning machine, and calculating the derived state parameters of each spinning machine and a connection region thereof through the real-time state parameters; forming an abnormal evidence according to the abnormal state parameters of each spinning machine, and fusing the abnormal evidences of all the spinning machines through a DS evidence theory to obtain a global abnormal confidence coefficient; dynamically generating a cluster security envelope and a dynamic security threshold of the real-time state parameters according to the global abnormal confidence and the state parameters of each section; and real-time state judgment is carried out through the dynamic safety threshold and the global abnormal confidence coefficient, and the spinning machine cluster is controlled to work.

The invention relates to the technical field of power system operation control, and discloses a power grid time-sharing margin-based charging pile cluster edge collaborative scheduling method, which comprises the following steps of: establishing a dynamic projection matrix and a constraint model by constructing a power grid spatial-temporal feature tensor and combining charging pile spatial distribution and health states, so as to obtain a power grid time-sharing margin-based charging pile cluster edge collaborative scheduling model; time-sharing margin-driven charging pile cluster collaborative scheduling is realized; the system comprises a measurement acquisition module, a dynamic margin analysis module, a collaborative scheduling module, a health state evaluation module, an optimization solution module, an instruction control module and a communication interface module. According to the method, the dynamic margin of the power grid is captured through the spatio-temporal feature tensor, power space coordination distribution is realized in combination with the projection matrix, the battery health constraint model is established to prolong the service life of equipment, the edge calculation load is reduced by adopting alternate optimization, a 5G-A and OPC-UA converged communication architecture is constructed to improve the system reliability, and the charging pile cluster coordination scheduling efficiency is comprehensively improved.

The invention discloses an intelligent computing power cluster task allocation method and system based on cloud edge collaboration, and relates to the technical field of computing power task allocation, and the method comprises the steps: extracting a first task in to-be-allocated tasks, collecting and obtaining a first multi-dimensional feature parameter of the first task, and obtaining a first feature curve; performing clustering analysis on the to-be-allocated tasks to obtain a clustering result; judging whether distributed edge computing power in a cloud edge computing power cluster meets the first resource requirement or not; if yes, performing comparative analysis to obtain first fitness of the first clustering cluster; descending the computing power nodes to obtain a target computing power node; and performing task processing of the first clustering cluster. According to the method and the device, the technical problems of low task allocation efficiency and poor task and computing power node adaptability in the cloud-side collaborative environment in the prior art are solved, and the technical effects of realizing efficient allocation of the tasks in the intelligent computing power cluster in the cloud-side collaborative environment and improving the task processing adaptability and efficiency are achieved.

The invention discloses an end-cloud collaboration large model secret state operation method and system, the method is applied to an end-cloud collaboration trusted execution environment, and the application-end cloud collaboration trusted execution environment comprises an end-side security access layer and a cloud-side secret calculation layer for deploying a TEE cluster. The method comprises the following steps: terminal equipment negotiates with a cloud side encryption layer through an end side security access layer to generate a session key, and sends a first ciphertext generated based on the session key to a TEE cluster; and receiving a second ciphertext formed by the TEE cluster based on the first ciphertext, and decrypting the second ciphertext to obtain an interaction result. According to the method, large model secret state operation is carried out by utilizing a two-stage framework of an end side security access layer and a cloud side secret calculation layer, and cloud large model service privacy protection is realized. Meanwhile, the trusted execution environment constructed by the TEE and the heterogeneous AI expansion TEE is adopted, so that the large-model secret-state operation can be operated on a multi-architecture server and a multi-type GPU, and the requirements of diversified deployment scenes are met.

A system identifying outlier data and generating corrective action, wherein the system includes at least a processor and a memory communicatively connected to the at least a processor and containing instructions. The instructions may configure the at least a processor to obtain a dataset, apply a clustering model to the dataset to generate a set of clusters based on the inherent relationships between the data points, determine the distance metric for each data point relative to its corresponding cluster centroid, define an outlier threshold based on the distance metrics of the data points, identify the data points that exceed the outlier threshold as one or more outliers, classify the one or more outliers across one or more axes, and output a report of the one or more identified outliers, wherein the report suggests corrective actions and insights for each of the one or more identified outliers.

The invention relates to the field of power distribution system simulation, in particular to a power distribution and micro-grid coordination wide-area balance regulation and control method and system in a multi-micro-grid access scene, and the method comprises the steps: carrying out the cluster division of a plurality of micro-grids based on the electrical coupling degree and net power between the micro-grids; using a pre-constructed multi-energy coupling source load prediction model to cluster resources of an area where the micro-grid is located, and performing load prediction on the divided clusters based on the clustered resources to obtain a group composed of the clusters, the resources and corresponding loads; based on the group, a target cascade method is adopted to solve a pre-constructed zone area or micro-grid layer optimization model, a feeder line level optimization model and a region layer optimization model to realize dispatching optimization of the power distribution network and the micro-grid; by constructing the multi-layer optimization model, the power exchange cost, the power generation cost and the environment cost between the micro-grid and the power distribution network can be optimized, and economic operation of the system is achieved.

Systems and methods include obtaining well log data and core sample data of a subsurface formation; generating, based on the well log data and the core sample data, an unconfined compressive strength log for the subsurface formation; using an unsupervised machine learning model to form rock type clusters based on the unconfined compressive strength log and the well log data; forming a training dataset including the well log data, the training dataset labeled based on the rock type clusters; training a supervised machine learning model using the training dataset. While drilling a well in the subsurface formation, logging-while-drilling data is obtained from drilling equipment used to drill the well; and rock types in the subsurface formation are determined using the supervised machine learning model and the logging-while-drilling data.

The invention relates to the technical field of liquid cooling flow distribution, and discloses a multi-GPU server cluster liquid cooling flow distribution method based on reinforcement learning, and the method comprises the steps: S1, collecting the operation state parameters of each GPU in a multi-GPU server, enabling the operation state parameters to comprise core temperature, voltage, current and load utilization rate, carrying out the thermal behavior modeling through a sliding time window, generating a thermal dynamic feature vector representing the GPU short-time thermal trend; and S2, collecting the flow velocity, the temperature difference of inlet and outlet water and the thermal resistance of the cold plate of each channel of the liquid cooling system in real time, and representing the liquid cooling assembly as a graph structure. According to the method, the technical scheme of joint modeling based on the graph neural network and reinforcement learning is adopted, fusion coding is performed on the GPU thermal dynamic characteristics and the topological state of the liquid cooling system, and an Actor-Critic architecture is introduced to realize self-adaptive regulation and control of the liquid cooling flow, so that the technical effect of improving the heat dissipation efficiency and the energy consumption balance control capability of the multi-GPU server cluster is achieved.

The invention discloses a large language model reasoning method based on a distributed KV cache pool. According to the method, a platform receives requests which are sent by a large number of users and need a large language model service, the requests of the users and machines in a cluster are modeled correspondingly, and then corresponding strategies are used for processing. In addition, the use condition of machine resources in the cluster is also considered, and machines with more idle resources are preferentially considered. In this way, interference caused by resource competition is reduced to a certain extent. Meanwhile, by abstracting memories of numerous NPU cards into a distributed KV cache pool, elastic expansion and contraction are facilitated during request processing. Through the method, an efficient cluster based on a large language model can be constructed, and a corresponding platform can better understand the demands and intentions of users so as to provide more timely and personalized services.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The invention relates to the technical field of intelligent warehousing, and discloses an AGV cluster collaborative carrying method and system applied to intelligent warehousing, and the method comprises the steps: constructing a multi-dimensional data model of a warehousing environment; generating an initial carrying task queue of the AGV cluster based on the real-time order demand and the goods allocation distribution data in the multi-dimensional data model; distributing the tasks to target AGVs in the AGV cluster according to the priority; aiming at the allocation task of the target AGV, calculating a path weight by adopting a dynamic cost function; generating a conflict-free path of the AGV cluster based on the path weight and the obstacle sensing data updated in real time; when the AGV distance of the target AGV is detected to be smaller than a safety threshold value, performing path dynamic updating on the conflict-free path to obtain a real-time path of the target AGV; multi-vehicle position synchronization and speed matching of the AGV cluster are realized based on a distributed collaborative algorithm and a real-time path, and the minimum safety spacing of the AGV cluster is ensured. According to the invention, the efficiency of AGV cluster collaborative carrying applied to intelligent warehousing can be improved.

The embodiment of the invention discloses a data skew detection optimization method and device for a multi-cluster database and a medium, belongs to the technical field of databases, and solves the problems of low efficiency and high error rate of a mode of manually detecting the data skew condition of the database under the condition of large-scale clusters. Comprising the steps of responding to a scheduling task instruction, and determining a target cluster based on the scheduling task instruction; obtaining a table object in the target cluster, and executing a corresponding data distribution detection strategy on the table object based on the type of the table object to determine line number distribution of the table object on each Segment node; obtaining a basic slope rate based on the line number distribution, and performing multi-dimensional adjustment on the basic slope rate to obtain a data slope rate corresponding to the table object; performing line number distribution standard deviation calculation based on the line number distribution to obtain a data inclination degree corresponding to the table object based on a calculation result; and matching a corresponding database optimization strategy based on the data inclination rate and the data inclination degree.

The invention discloses an unmanned aerial vehicle cluster intelligent cooperative positioning method and system based on machine learning, relates to the technical field of artificial intelligence and unmanned aerial vehicles, and aims to solve the problems that positioning signals are attenuated and drifted and the spraying precision is affected due to spray droplets of unmanned aerial vehicles in agricultural plant protection. According to the scheme, multi-source heterogeneous sensor data of a global positioning system, an inertial measurement unit, vision, ultra wide band and the like are acquired through each unmanned aerial vehicle in a cluster and are locally estimated; the central node cooperatively aggregates data, models fogdrop interference such as fogdrops by using a deep learning model, and dynamically corrects sensor noise; according to the method, the positioning robustness, the positioning precision and the self-adaptability are remarkably improved, and the spraying precision in a complex environment is ensured.