101 results about "Centralized computing" patented technology

According to the DMA controller applied to audio data carrying, the control method and the I2S system provided by the invention, the bottleneck that multi-channel audio data and 24-bit audio data cannot be transmitted through general DMA is effectively solved by introducing the DMA controller special for audio. The method supports various audio data formats, and can intelligently and automatically select the optimal DMA data transmission strategy according to specific configuration information such as the number of sound channels, the width of the audio data and the width of a groove. According to the innovation, the audio data transmission efficiency and flexibility are remarkably improved, the burden of a CPU in the data carrying process is greatly relieved, the CPU can process more complex tasks in a centralized computing power mode, and therefore the overall system performance is improved.

The invention relates to the technical field of virtual power plants, and discloses a virtual power plant optimal scheduling system and method, and the system comprises a data obtaining module, an edge calculation module, a prediction module, a scheduling controller, a topology reconstruction module, and an intelligent terminal device cluster. According to the invention, the edge computing module carries out localization processing and prediction on the sensing data, so that rapid generation and issuing of a scheduling scheme are realized, and the problem of response delay caused by network transmission and centralized computing of a traditional centralized architecture is avoided, thereby supporting millisecond scheduling feedback and improving the scheduling efficiency. The real-time response capability under the sudden load fluctuation or fault condition is remarkably improved, a multi-dimensional perception and prediction mechanism is constructed based on an LSTM neural network prediction model, the recognition and trend prediction capability of the system on meteorological disturbance, equipment aging and operation abnormity is enhanced, the intelligent level of the virtual power plant system is improved, and the real-time performance of the virtual power plant system is improved. The system can dynamically generate an optimal scheduling strategy to ensure stable operation of the virtual power plant under various working conditions.

A computing system including one or more processing devices configured to receive an image generation prompt and a reference image. Over a plurality of denoising timesteps, the one or more processing devices compute a guided image by applying denoising updates to a generated image at a denoising diffusion model. At a subset of the denoising timesteps, computing the guided image further includes applying guidance updates to the generated image based on the image generation prompt, the reference image, and a generated image set. The one or more processing devices compute each guidance update by performing a forward pass and a backward pass in first and second integration timesteps. A size of the generated image set and numbers of the first and second integration timesteps are each equal to a predefined integration timestep count. The one or more processing devices output a final generated image computed in a final denoising timestep.

The invention relates to a low-delay low-coupling multi-channel analog video signal processing system and method, electronic equipment and a storage medium, aiming at the limitation of the existing multi-channel video processing technology, hardware such as an analog signal receiving board, a controller, a video processing development board, a computing power development board and a serial port receiver and software technologies such as Python and Gstreamer are adopted. The method comprises the steps of signal receiving, frequency point screening, video processing and transmission, intelligent identification, layout customization and the like. The system achieves the positive effects of centralized computing power processing, accelerated calculation, rapid frequency point scanning, low-delay picture display, user-defined layout, convenient access to an intelligent identification module and the like, and is suitable for various actual scenes under analog video signals.

The invention discloses an intelligent electric energy meter data acquisition device, method and equipment for electric carbon metering and a storage medium, and relates to the technical field of electric power systems and carbon emission management, and the device comprises a carbon emission hardware calculation circuit module which verifies frozen data and regional-level electric carbon factors and line loss power of a superior node, and generates carbon emission data; a first non-volatile processor storing carbon emission data; and the second nonvolatile processor and the first nonvolatile processor are backups for each other and store electric quantity freezing data. The device expands a carbon emission metering function at low cost through an idle RS485 port of an existing intelligent electric energy meter, generates a dynamic electric carbon factor through local calculation, accurately calculates carbon emission data, and improves the accuracy of the carbon emission data. The data accuracy, real-time performance and reliability are improved, the centralized calculation burden is relieved, the system modification cost is reduced, large-scale popularization and application are supported, and low-carbon transformation and green power management are effectively promoted.

The invention discloses a global cooperative control method and system for unbalanced fault ride-through of a multi-terminal DC power grid, and the method comprises the steps: constructing a fault detection model based on the positive sequence voltage drop depth and the negative sequence voltage size of the AC side of each converter station, automatically judging an unbalanced fault, and carrying out the fault detection when the fault is detected. Calculating the maximum allowable active current based on a fault station unbalanced current control model to carry out current limiting control on the converter station; and carrying out output power adjustment on the DC voltage deviation of the non-fault station based on the non-fault station power cooperation model. Asymmetric fault ride-through is carried out by using a constrained and optimized current instruction generation algorithm, and the capacity of a converter is dynamically and accurately distributed and utilized in a limited manner, so that the voltage support capability of a fault alternating-current power grid is maximized, and globally optimized adaptive power distribution is realized; the power of the non-fault station is calculated and distributed in a centralized manner and is dynamically adjusted according to the real-time available capacity, so that the self-adaption and global optimization of the running state change of the power grid are realized, and the fault handling toughness and reliability of the power grid are improved.

The invention provides a centralized computing power scheduling method applied to a computing power network, and the method comprises the steps: for a service deployment scheduling task, obtaining computing power resource information in the computing power network, and determining computing power resources needed by the service deployment scheduling task; filtering all computing nodes to obtain a plurality of candidate computing nodes meeting computing power resources; all the candidate computing nodes are scored, and the candidate computing node with the highest score is selected from scores to deploy the service instance; for a service request scheduling task, acquiring computing power resource information and network resource information of a computing power network, determining computing power resources and network resources required by the service request scheduling task, filtering all service instances which do not meet the computing power resources and the network resources, and taking the remaining service instances as candidate service instances; and scoring all the candidate service instances, and selecting the candidate service instance with the highest score from scores to execute the service request.

The invention discloses a new energy grid-connected line fault section positioning system based on an ant colony algorithm, and relates to the technical field of power grid fault diagnosis, and the system comprises a wire signal collection module and a fog end intelligent analysis module. According to a distributed architecture, an optimal section where a fault occurs in a grid-connected line is analyzed. The method has the advantages that real-time positioning is high, the network pressure of data centralized transmission and the complexity of centralized calculation are reduced, the fault analysis precision is improved, and the criterion reliability is improved.

The invention discloses an Internet of Things equipment linkage control method and system based on cloud computing and a storage medium, and relates to the technical field of intelligent control. The method comprises the following steps: constructing a home coverage network; executing network partition aggregation, establishing a network partition aggregation result, and configuring a distributed computing center; synchronizing monitoring data of the sensor group to a distributed computing center; calling and positioning the home equipment by using the distributed computing center, reading the equipment state of the corresponding home equipment, performing linkage control optimization of the equipment in the region, and establishing a first linkage control result; if the probability evaluation result of the partition linkage meets a preset threshold value, synchronizing the first linkage control result and the monitoring data to a centralized computing center; and performing linkage control on the home equipment by using the second linkage control result. The technical problems of slow response and low efficiency of linkage control of the Internet of Things equipment in the prior art are solved, and the technical effect of improving the real-time performance and efficiency of linkage control of the Internet of Things equipment is achieved.

The invention discloses an FPSO (floating production storage and offloading) riser support structure modeling method, which belongs to the field of simulation modeling and comprises the following specific steps: I, acquiring basic parameters through data acquisition equipment and field experiments, and analyzing an application scene and working conditions of an FPSO riser support to determine modeling requirements; iI, on the basis of a demand analysis result, constructing a preliminary stand pipe supporting structure model of the stand pipe supporting structure; according to the method, design and operation decisions can be dynamically adjusted, long-term benefits are optimized, it is ensured that the structure can keep high reliability and benefits in the long-term operation process, and the flexibility and adaptability of structural design and maintenance schemes can be improved; the safety and privacy of the method are enhanced, real-time adaptation to the multi-field coupling effect in long-term operation is achieved, the performance of the riser supporting structure can be improved from different angles, the calculation cost is reduced, the calculation efficiency is improved, and delay possibly caused by concentrated calculation is avoided.

The invention provides a wireless network load dynamic adjustment method and device, a training method and device and electronic equipment. The adjustment method comprises the following steps: acquiring model parameters of a machine learning model of at least one cell of a centralized computing node sending guarantee area; loading the model parameters to obtain a machine learning model of the corresponding cell; processing the telephone traffic load data of the corresponding cell in real time by using a machine learning model to obtain a telephone traffic decision point feature value; converting the telephone traffic decision point characteristic value into a base station parameter adjustment command; sending the base station parameter adjustment command to the base station, so that the base station adjusts a corresponding base station parameter according to the base station parameter adjustment command; evaluating the real-time data obtained after the base station parameters are adjusted, and determining a threshold adjustment instruction; and sending a threshold adjustment instruction to the centralized computing node to enable the centralized computing node to perform corresponding threshold adjustment, retraining the machine learning model, and sending model parameters of the retrained machine learning model to the edge computing node.

The invention relates to an intelligent network connection vehicle formation centralized control method based on deep reinforcement learning, and belongs to the technical field of intelligent traffic. A communication network architecture in a vehicle-road-cloud set is constructed, a vehicle-mounted computing task is unloaded to an edge cloud node, and global optimization control is realized in combination with a DDPG algorithm. The vehicle node obtains state information through a vehicle-mounted sensor and transmits the state information to the edge cloud node through the roadside node; and the edge cloud integrates multi-vehicle data collected by the RSU into formation global state information, generates a control instruction in real time by using a pre-trained DDPG intelligent agent, and broadcasts the control instruction to formation vehicles for execution through C-V2X communication. And the DDPG intelligent agent is guided to train through a reward function and is combined with a fixed time interval strategy and a second-order kinematics model to realize rapid convergence of a vehicle distance and a speed error. According to the invention, the load of vehicle-mounted hardware is reduced through a centralized computing architecture, and the real-time performance and safety of formation control are improved; complex algorithm global optimization is supported by using cloud computing power.

The invention discloses a free airship cluster autonomous coverage method based on distributed learning and model predictive control, and belongs to the technical field of automatic control. The method comprises the following steps: firstly, establishing a six-degree-of-freedom dynamic model considering quality, buoyancy characteristics and wind field disturbance, and ensuring the physical accuracy of airship motion modeling; secondly, acquiring local wind field data by each airship through a sensor, performing local estimation by using a distributed Gaussian process regression algorithm, and realizing global wind field prediction through induction point approximation and hyper-parameter iteration; then, carrying out Voronoi division based on a global wind field sensitivity function, and determining a coverage area and a weighted centroid target of each airship; and finally, constructing an optimization problem based on model predictive control, constraining thrust and torque input, and carrying out rolling optimization by adopting a sequential quadratic programming method to realize stable track tracking and autonomous region coverage. The sensing, planning and cooperative control capabilities of an airship cluster in a complex non-uniform wind field environment are effectively improved, the centralized calculation overhead is reduced, and the robustness and adaptability of the system are enhanced.

A system receives ETL specification for processing stream data, including a transform operation represented using a database query specification. The system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. The system executes the sequence of database queries for performing the transform operation on stream data received from the source. When receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.

The invention discloses a federated learning-based power grid line fault inspection method, equipment and a medium. The method comprises the following steps: each client obtains operation data of a local power grid line as a local data set; the central server generates and distributes global model parameters to each client; each client establishes and uses a local data set to train a local model according to the global model parameters, calculates generated gradient loss and uploads the gradient loss to the central server; the central server carries out aggregation processing on the gradient loss uploaded by each client to obtain a new round of global model parameters to continue a federated learning cycle until the global model parameters converge and / or the number of cycles reaches an upper limit; and each client uses the latest local model to carry out power grid line fault inspection work. According to the method, local computing resources of all participants are utilized, dependence on centralized computing power is reduced, model training efficiency is improved, it is ensured that data do not leave the local environment, and sensitive information is effectively protected.

The invention discloses a retrieval question and answer method and system combined with a time sequence context, a medium and equipment, and belongs to the crossing field of artificial intelligence and intelligent operation and maintenance of an electric power system. Extracting time sequence semantic elements in the event sequence, and retrieving a corresponding event sub-graph in a time sequence directed graph constructed based on an event sequence, a time sequence and a logic link of the power equipment; performing feature aggregation on the sub-graph nodes to obtain context vectors; performing weighted fusion on the query semantic vector and the context vector to obtain a joint vector; calculating the similarity between the knowledge fragment vector set and each knowledge fragment in the knowledge fragment vector set, and screening out a plurality of most relevant knowledge fragments; and inputting the original query and the knowledge fragments into a text generation model, outputting answers, and completing questions and answers. By implementing the method and the device, the technical problem that an intelligent question and answer retrieval method in the prior art lacks pertinence and professional traceability of retrieval results in a complex event sequence scene in the power field can be solved.

Methods and computer systems for providing access providing access to representations of computing events. Identifying, from a plurality of computing events, a set of computing events corresponding to a computing event category. The identification of the set of computing events including identifying a respective first category of each particular computing event in the set of computing events; and determining that each respective first category matches with a respective category defined in a list of categories maintained by a third-party server. Transmitting, to a computing device associated with a second category of a particular computing event of the set of computing events, an interface providing access to representations of computing events in the set of computing events.

An automated, closed-loop gravity infusion system (10) includes a fluid source (14), a drop counter (20) operatively engaged with a drip chamber (16) of the fluid source (14), a roller clamp (24) functionally linked with the drop counter (20), a processor (26a) operably engaged with the roller clamp (24), and a HMI (28) functionally linked with the processor (26a). Patient data is accessed by the HMI (28). The system utilizes the patient data and data from the drop counter (20) to determine an appropriate roller clamp position. The drop counter (20) continuously monitors the drip chamber's drip rate and feeds the data to the processor (26a) which then analyzes the data and automatically adjusts the roller clamp's position to provide an appropriate fluid dosage to the patient. The HMI (28) is functionally linked to a remote centralized computing system configured to simultaneously monitor several identical systems, each of which is being used to infuse a different patient.

Virtual Machine (VM) creation based on a dynamically-calculated feature set. A plurality of feature sets are identified. Each feature set in the plurality of feature sets corresponds to a different VM host node of a plurality of VM host nodes that are part of a common VM migration group, and indicates a set of features available at a corresponding VM host node. From the plurality of feature sets, a group feature set is calculated. The group feature set includes a subset of features that are common among the plurality of feature sets. A VM created within the plurality of VM host nodes is configured to use the group feature set.

The present invention discloses a method for optimizing the weight of a conical spring based on the Harris hawk algorithm with random unit permutation. First, the objective function for optimizing the weight of the conical spring and the key parameters to be solved are determined. Then, the structure of the original Harris hawk algorithm is optimized. The specific optimization measures are as follows: First, cancel the steps of centrally calculating the objective function value in the original Harris hawk algorithm except for the step of initializing the population, but record the results while the algorithm obtains a better solution. Second, introduce a random unit permutation mechanism at the position before the end of each iteration optimization in the original Harris hawk algorithm. Third, delete the search steps in the original Harris hawk algorithm where the absolute value of the energy factor E is greater than or equal to 1. The optimized Harris hawk algorithm is called the Harris hawk algorithm based on random unit permutation, and the key parameters are solved using this Harris hawk algorithm based on random unit permutation to obtain the key parameters for optimizing the weight of the conical spring. The advantages are fast convergence speed and high solution accuracy.

The invention provides a handwriting elimination method, a model training method and a related device. An electronic device obtains a to-be-processed image and a color mask of the to-be-processed image; wherein a handwriting area in the image to be processed is marked in the color mask, the handwriting elimination model of the image to be processed comprises an encoder and a decoder, and the encoder comprises a feature extraction layer; the feature extraction layer processes various image features in the to-be-processed image by using the attention information in the color mask to obtain output features of the feature extraction layer; and processing the output characteristics of the encoder through a decoder to obtain a clean image after handwriting elimination. Therefore, the attention information in the color mask is directly utilized to guide the feature extraction process, so that the model can concentrate computing resources in a local range, the unnecessary global computing burden is reduced, and the overall computing efficiency in the handwriting elimination process is improved.

Systems and devices for network data collection and processing are provided. An example system includes a first networked device and a centralized computing device communicably coupled with the at least one networked device. The first networked device operates to generate event-driven data entries associated with the first networked device and generate first data packets including the event-driven data entries and / or manipulated outputs generated based on manipulations to the event-driven data entries. The centralized computing device receives the first data packets from the first networked device and determines configuration updates based on the first data packets. The configuration updates are generated locally by the centralized computing device, and the centralized computing device transmits the one or more configuration updates to the first networked device.

This invention discloses a smart energy meter data acquisition device, method, equipment, and storage medium for carbon emission metering, relating to the fields of power system and carbon emission management technology. It includes a carbon emission hardware calculation circuit module that verifies frozen data and regional-level carbon emission factors and line loss power from the upper-level node to generate carbon emission data; a first non-volatile processor that stores the carbon emission data; and a second non-volatile processor that serves as a backup for the first non-volatile processor and stores frozen electricity data. The device of this invention expands the carbon emission metering function at low cost through the idle RS485 ports of existing smart energy meters, generates dynamic carbon emission factors through local calculation, accurately calculates carbon emission data, improves data accuracy, real-time performance, and reliability, reduces the burden of centralized calculation, lowers system transformation costs, supports large-scale application, and effectively promotes low-carbon transformation and green power management.

A method for deterministic execution of an instruction set (I) containing instructions (M) to be carried out by a processing unit of the computing device includes the steps of executing a predefined number of pre-branch instructions (A) up to a conditional branch (C) of the instruction set (I) containing at least one branch condition determining a subsequent instruction which is to be executed after the conditional branch (C); predicting at least one of the pre-branch instructions (A) and / or a target instruction (T) to be executed as the subsequent instruction if the at least one condition is met; and inserting the at least one predicted pre-branch instruction (A) and / or the target instruction (T) into the instruction set (I) after the conditional branch (C). A computer program, computer-readable data-carrier, computing device, and apparatus, such as a vehicle, in particular an aircraft may be configured to perform the method.

A system receives ETL specification for processing stream data, including a transform operation represented using a database query specification. The system generates a dataflow graph of a sequence of database queries by decomposing the database query into a first database query that generates an intermediate results table, and a second database query that receives as input the intermediate results table and outputs data used for performing the transform operation. The system executes the sequence of database queries for performing the transform operation on stream data received from the source. When receiving an incremental data set, the system determines an output change set based on the received incremental data set by traversing an execution plan and processing each operator in the execution plan, and computing a change set of a particular operator from the change sets output by the one or more other operators based on the incremental data set.

Systems and methods for a patient monitoring system enable a hub (e.g., centralized computing device) to automatically receive data from varying different types of patient monitoring devices. In particular, the patient monitoring system may utilize polarity selection circuitry to automatically select (e.g., swap, switch) polarity of one or more input ports of the hub of the patient monitoring system. As such, the hub may include one or more universal ports to successfully receive or accept multiple different connectors of the varying patient monitoring devices that may include varying pin definitions.

A centralized control device based on a thermal power plant is mainly composed of a centralized computing and processing mainboard, a spare centralized computing and processing mainboard and a baseboard; wherein the centralized computing and processing mainboard and the spare centralized computing and processing mainboard are respectively equipped with a multi-core low-voltage processor, and a hot standby mode of one in use and one in standby is adopted to process real-time data; the hardware watchdog and forwarding control module carried by the baseboard jointly ensure the data consistency and millisecond-level disturbance-free switching of the centralized computing and processing mainboard and the spare centralized computing and processing mainboard; the four four-port 10G SFP network cards carried by the baseboard meet the real-time communication needs of a large number of field equipment data that are safe and reliable; the present invention utilizes the powerful concurrent processing capability of the multi-core processor to integrate the original distributed control devices into one, thereby improving resource utilization and reducing maintenance costs.

A comprehensive and centralized system for managing and tracking communications being delivered through both digital communications channels and physical communication channels. A centralized computing hub receives communication requests from originating systems of record or via user input. In response to receiving a communication request, content is generated or otherwise obtained and quality assurance checks are performed to assure that the content meets the requirements of the originating system of record and is applicable to the intended communication recipient. Once the quality checks have been performed and the content is deemed verified, delivery of the communications is orchestrated in accordance with the chosen communication channel. The timeliness of the communications is tracked throughout the system and alerts are generated in response to the timeliness exceeding thresholds. Thresholds are intelligently determined on a continuous basis using rolling averages of previous communications that result in predictions of future communication averages.

The invention provides a centralized computing power network system aiming at transmission scheduling, which comprises a transmission scheduling subsystem, a computing power scheduling subsystem and a plurality of computing power clusters, and is characterized in that the transmission scheduling subsystem is configured to divide the computing power network system into different levels of regional networks, sense and manage the network resource state of each regional network in a level-to-level manner, and transmit the network resource state to the computing power clusters; a user terminal is accessed to each regional network; receiving a resource request message sent for the application by the user terminal or the computing power cluster, and determining a resource demand of the application according to the request message; the computing power scheduling subsystem and the computing power scheduling subsystem cooperatively execute resource scheduling, and the method comprises the steps that the computing power scheduling subsystem schedules computing power resource instances meeting resource requirements from other computing power clusters according to the network resource state of the regional network and the sensed computing power resource state of the computing power clusters, and a scheduling result is obtained; the transmission scheduling subsystem generates a routing strategy for accessing the computing power resource instance when the scheduling is successful; and the user terminal or the computing power cluster communicates with the computing power resource instance based on the routing policy.

The present invention relates to the technical field of shared energy storage scheduling, and discloses a distributed control method for a microgrid group considering dynamic trading of shared energy storage space, comprising the following steps: step one: obtaining relevant data and processing the relevant data; the relevant data processing includes synthetic sampling, outlier processing and sliding average; step two: constructing a microgrid user optimization scheduling model considering dynamic allocation of shared energy storage space and electric energy trading; step three: using a consensus algorithm based on combined adaptation to solve the shared energy storage space trading problem between users to achieve the optimal effect; the present invention adopts a consensus algorithm based on combined adaptation, which reduces the dependence on centralized communication equipment and centralized computing equipment on the basis of effectively solving the user's free trading energy storage space, and protects the user's privacy to a certain extent.