499 results about "Computer appliance" patented technology

The invention relates to the technical field of equipment collaboration, and discloses a multi-equipment collaboration control method and device, computer equipment and a storage medium, and the method comprises the steps: building a session channel with collaboration equipment according to the authentication information of main control equipment; acquiring operation state parameters of the collaborative devices based on the session channels, and generating a permission allocation matrix according to the operation state parameters of the collaborative devices; and processing task requirements of the main control equipment by utilizing the permission allocation matrix, generating a differential instruction set, and executing synchronous control on the collaborative equipment according to the differential instruction set. According to the invention, the problems of authority conflict, inflexible task allocation and incapability of fast and automatic switching after the main control equipment fails when multiple devices cooperatively control the cloud mobile phone instance in the prior art are solved.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention relates to a concurrent task processing method and device based on an event-driven architecture, computer equipment, a storage medium and a program product. The method comprises the following steps: receiving concurrent service events which are fed back by an event generation service component and respectively correspond to different service operations, publishing the plurality of concurrent service events to a message queue, and respectively carrying out event classification processing on the plurality of concurrent service events in the message queue according to a trained event classification model, and obtaining an event type corresponding to each concurrent service event, and determining the event type as a service theme corresponding to the concurrent service event. And in response to subscription requests triggered by different event consumption service components for the business theme, asynchronously feeding back at least one concurrent business event associated with the business theme to the event consumption service components, and asynchronously executing task operations corresponding to the concurrent business events through the different event consumption service components to obtain task execution results. By adopting the method, the concurrent task processing efficiency can be improved.

An autonomous driving system test method, comprising: sampling scenario data of a plurality of scenarios (S10), and sending the scenario data of the plurality of scenarios to a plurality of simulation emulators (200) for distributed testing, so as to obtain test result data of the plurality of scenarios, the plurality of simulation emulators (200) being each integrated with an autonomous driving domain controller; and determining a dangerous scenario boundary for an autonomous driving system on the basis of the test result data of the plurality of scenarios (S20). The method is an improvement on the a scenario-based simulation test technology for autonomous driving systems, and using a server and a plurality of simulation emulators integrated with autonomous driving system domain controllers to perform joint testing can greatly improve the simulation test efficiency and the test effect for autonomous driving systems.

The invention provides a robust optimization method and system for a flexible power distribution network, computer equipment and a storage medium, and belongs to the field of power grid planning, and the method comprises the steps: obtaining a network topology structure and line electrical parameters of a to-be-optimized flexible power distribution network, and constructing a line model corresponding to the to-be-optimized flexible power distribution network according to the network topology structure and the line electrical parameters; acquiring historical operation data of each node in the line model; performing clustering analysis on the historical operation data of each node based on a fuzzy C-means clustering algorithm to obtain a typical scene of the to-be-optimized flexible power distribution network; constructing a polyhedral uncertainty set according to the typical model; taking the limited extreme scene as input, and minimizing the operation cost as an optimization target to construct a main problem; and taking a decision scheme for solving the main problem and the polyhedral uncertainty set as input, taking a scene with the maximum search operation cost as a target to construct a sub-problem, and iterating to obtain a robust optimization result of the to-be-optimized flexible power distribution network. According to the method, reliable optimization of the flexible power distribution network in a real uncertain environment is realized.

The invention relates to a cross-platform data sharing method and device, computer equipment and a readable storage medium. The method comprises the steps that in response to a data sharing request, data transmission delay and data transmission energy consumption generated when data sharing is achieved at the current moment and storage loads of all data storage platforms are obtained, and data sharing data are obtained according to a pre-built platform attribute model, a pre-built platform demand model and a pre-cross-platform data flow model; acquiring a corresponding constraint condition, inputting the data transmission delay, the data transmission energy consumption and the storage load into a pre-constructed data transmission path optimization model, acquiring a target transmission path meeting the constraint condition, and acquiring a corresponding target synchronization path and a target storage path based on the target transmission path, and realizing data sharing of each data storage platform by using each target path. By adopting the method, various factors can be considered during path optimization, and data sharing between platforms is realized by utilizing the optimized paths, so that the data sharing efficiency is improved.

An on-line wind alignment angle calibration system of a wind direction instrument, comprising a wind direction instrument on-line calibration system, a cabin, a tower base, a monitoring room, and a server; the wind direction instrument on-line calibration system comprises: a wind direction instrument (1), a housing upper cover (2), a transmission gear (3), a transmission shaft (4), an integrated control module (5), a housing (6), a base (7), a speed reducer (8), a drive motor (9), and an angle encoder (10). Further provided is a calibration method based on the calibration system. The calibration method comprises: preprocessing wind power data; performing data processing by means of a DBSCAN clustering analysis method; performing data processing by means of a quartile algorithm, and determining a data interval; calculating a blade tip speed ratio interval; extracting wind direction interval power; and calculating a wind alignment angle. The wind alignment angle deviation of a wind turbine is obtained by means of calculation using an intelligent algorithm, then a reference zero position of the wind direction instrument is adjusted by means of an intelligent precise hollow drive module, thus on-line adjustment and calibration of the reference zero position of the wind direction instrument can be achieved, and safety hazards due to manual operation, frequent adjustment and calibration, high-altitude operation, and the like are reduced. Also provided are a computer-readable storage medium and a computer device for implementing the calibration method.

The invention discloses a GPU resource scheduling method based on data awareness and computer equipment, and the method comprises the steps: carrying out the real-time monitoring of a cluster node, obtaining a current cluster load state, and predicting the cluster load state in a future time period; creating services according to an input service definition description file, adding the services into a to-be-scheduled service queue, periodically selecting the services from the to-be-scheduled queue, and allocating computing nodes for the services; distributing a target service for the user request according to the current cluster load state, establishing a session, and analyzing the intention of the user; dynamically adjusting and allocating static resources through deep reinforcement learning based on a hybrid expert model according to a user intention analysis result and a cluster load state; required resources are loaded to a GPU through a multi-level cache architecture, and after calculation is completed, a calculation result is returned to a user through a session. According to the method, the utilization rate and the performance of GPU resources in an interactive GPU acceleration service scene can be improved, and the energy consumption and the cost are reduced.

The present application relates to a method and apparatus for controlling a wireless electrical energy transmission system, and a computer device. The method comprises: determining a real-time load resistance of a wireless electrical energy transmission system, and obtaining a trained value table, the trained value table comprising a sample load resistor, a sample primary resonant capacitor, and a sample value of correspondence between each sample load resistor and each sample primary resonant capacitor; in the trained value table, determining a real-time primary resonant capacitor of the wireless electrical energy transmission system on the basis of the real-time load resistance and the sample value; and controlling the wireless electrical energy transmission system on the basis of the real-time primary resonant capacitor. By adopting the present method, the convenience of control can be improved.

Telecommunications systems, computer devices and systems, and methods are provided. One example method, performed by a data abstraction layer of a telecommunications system for a network, includes accessing data elements associated with a network function of the network, performing abstraction on the data elements to generate one or more data products, receiving a customer request for a service on the network, identifying the data product related the service, and provisioning the identified data product to the customer. The method may further include identifying proprietary data and private data of the data elements based on a predefined policy and removing proprietary data and private data before performing abstraction.

A computer device and method are provided for detecting anomalies in a monitored computer system by classifying detected events using a machine learning model trained based on an activity log of events detected during an initial activity period. The machine learning model embeds logged events by generating a vector based on a tokenization of the logged event and a categorization of the logged event by a large language model. Events detected during the initial activity period are used to generate a profile of the monitored computer system. Events detected after the initial activity period are compared to the generated profile by a classifier of the machine learning model to classify each detected event as anomalous or normal.

The invention relates to the field of antenna calibration, and discloses a phased-array antenna calibration method and device, computer equipment and a phased-array antenna, and the method comprises the steps: obtaining an array element clustering result associated with a to-be-calibrated phased-array antenna; dividing the phased-array antenna to be calibrated into a plurality of array element groups according to an array element clustering result; obtaining measurement data of each array element group; and calibrating the phased-array antenna to be calibrated according to the measurement data. Through a grouping calibration strategy, the calibration efficiency of the phased array antenna is greatly improved, and the method is particularly suitable for a phased array system in a large-scale, high-density and dynamic environment.

The invention relates to a view-driven subgraph matching load balancing method and system, computer equipment, a storage medium and a computer program product. Candidate points of part of subgraphs are acquired and screened by starting a kernel function; collecting calculation data; calculating a prefix sum and a total candidate vertex number; dividing a calculation range according to the total number of candidate vertexes; screening candidate points, and recording the number of the candidate points; and counting results and generating a matching result array. The problems of waste of computing resources and low efficiency in the prior art are solved; through the load balancing method, the computing resources of the GPU are fully utilized, and the overall computing performance is remarkably improved.

The present application relates to a logistics transportation path planning method and apparatus, a computer device, and a storage medium. The method comprises: determining first transportation durations from service points to a transit hub and second transportation durations among the service points (102); clustering the service points on the basis of the first transportation durations to obtain a plurality of clusters (104); for each cluster, taking the remaining shift duration of a target path being greater than or equal to a preset duration as a constraint, on the basis of the second transportation durations among the service points in the clusters, sequentially selecting service points from among the service points in the clusters to connect the service points to form a corresponding target path, wherein the target path is a path formed by sequentially passing through the selected service points and then reaching the transit hub from the last selected service point, and the remaining shift duration is the remaining duration in a transportation shift after completing transportation along the target path (106); and determining the target path as a transportation path of a transport vehicle in the transportation shift (108).

The present application relates to an energy storage system, a control method for the energy storage system, a computer device, and a storage medium. The energy storage system comprises a controller, an energy storage circuit and a switching device; the controller is connected to the energy storage circuit and the switching device; the switching device comprises a first switching circuit, a second switching circuit and a third switching circuit; the energy storage circuit comprises a first energy storage sub-circuit and a second energy storage sub-circuit which are connected in series; the two ends of the first switching circuit are respectively connected to a negative electrode of the first energy storage sub-circuit and a positive electrode of the second energy storage sub-circuit; the two ends of the second switching circuit are respectively connected to a positive electrode of the first energy storage sub-circuit and the positive electrode of the second energy storage sub-circuit; and the two ends of the third switching circuit are respectively connected to the negative electrode of the first energy storage sub-circuit and a negative electrode of the second energy storage sub-circuit. According to the present application, the mode of switching the energy storage circuit on the basis of the switching device can reduce the installation and test workload of the energy storage system.

The application relates to the technical field of communication, and provides an Activity component calling processing method and system, a computer device and a storage medium, which comprise the following steps: establishing a communication connection with a calling party and constructing a cross-process connection with a preset proxy Activity component; receiving intention information input by the calling party, sending the intention information to the proxy Activity component through the cross-process connection, and enabling the proxy Activity component to call a target Activity component corresponding to the intention information to process the intention information and obtain a return result; receiving the return result sent by the proxy Activity component, and returning the return result to the calling party. The application realizes cross-process calling of the Activity component, expands the range of target Activity components that can be called by the calling party, reduces the complexity of the calling party code, improves the logic of the calling party, ensures that the calling party can efficiently obtain data from the target component, and is beneficial to maintenance and function testing of the calling party.

The embodiment of the utility model provides a multi-path interconnection system and computer equipment, the multi-path interconnection system comprises a plurality of computing acceleration entities, the computing acceleration entities are standardized entity units at least used for integrating computing units, the computing acceleration entities comprise the computing units, and the computing units are connected with the computing acceleration entities. The computing unit comprises a plurality of first ports supporting a memory interconnection bus; a plurality of interface switching chips, each interface switching chip comprises a plurality of second ports, and the second ports are matched with the first ports; the connection channel is connected with the first port of the calculation acceleration entity and the second port of the interface switching chip; wherein any computing acceleration entity is provided with a plurality of connecting channels which are respectively connected with the plurality of interface switching chips. According to the multi-path interconnection system provided by the embodiment of the invention, the interconnection requirement among a plurality of computing units can be met.

The present invention discloses a BMC communication method, comprising the following steps: creating a server and a proxy client in a host and creating a client and a proxy server in a BMC; establishing a connection between the server and the proxy client in the host through a socket; establishing a connection between the proxy client in the host and the proxy server in the BMC through a KCS interface; establishing a connection between the proxy server and the client in the BMC through a socket; communicating between the BMC and the host through the server and the proxy client in the host and the client and the proxy server in the BMC. The present invention also discloses a system, a computer device, and a readable storage medium. The solution proposed in the present invention abstracts a layer of UDS proxy, and application developers do not need to consider how to call complex KCS drivers. They only need to implement simple UDS sockets to perform inter-process communication, improve development efficiency, and more conveniently exchange messages, tools, and logs between the BMC and the HOST.

This application relates to a domain name resolution method, apparatus, computer device, storage medium, and computer program product. The method includes: in response to a domain name resolution request for a target domain name, determining the target load type corresponding to the target domain name; determining a target node identifier array corresponding to the target load type from a node identifier array corresponding to each load type; wherein, in the target node identifier array, the number of node identifiers corresponding to each service node is positively correlated with the weight of each service node for the target load type; the weight of each service node for the target load type is obtained based on the load information of the service node for the target load type; determining the target node identifier in the target node identifier array according to a pre-stored node identifier determination strategy, and generating a response message corresponding to the domain name resolution request based on the Internet Protocol address corresponding to the target node identifier. This method can achieve a relatively balanced load pressure on each service node.

The invention provides a control method of a panel satellite large-size flexible solar wing driving device. The driving device comprises a satellite body, a double-shaft SADA, a swing rod, a hinge, a pressing and releasing mechanism and an ROSA solar wing. The control method comprises the steps that a release signal is sent to the pressing and releasing mechanism, the pressing and releasing mechanism releases the pressed solar wing after receiving the release signal, and the solar wing is driven by a hinge at one end of a swing rod to be passively unfolded by a first angle; according to the target angle and the first angle, calculating a second angle that the double-shaft SADA needs to rotate; a rotation signal is sent to the double-shaft SADA, the double-shaft SADA drives the solar wing to continue to rotate by a second angle after receiving the rotation signal, and a rotation in-place signal is sent out; and an unfolding signal is sent to the solar wing, and the solar wing starts to unfold the cell array plane until the cell array plane is completely unfolded. The control method improves the energy utilization efficiency of the whole satellite platform.

A method for supervised training of a machine learning model is described. Furthermore, a method for determining the armature position of an electrical actuator is described. A method for controlling an electrical actuator is also described. A computer device (2) configured to execute such a training method is described. A control device (4) configured to execute such a method for determining the armature position and such a method for controlling the actuator is also described. Finally, a vehicle (6) equipped with such a control device (4) is described.

The application relates to a reference value determination method and device of electrocardio data, a computer device, a storage medium and a computer program product. The method comprises the following steps: obtaining target electrocardio data collected in a collection time period, the target electrocardio data containing a plurality of heartbeat data units, each heartbeat data unit being composed of a plurality of heartbeat data segments. A heartbeat data segment to be detected is determined from the plurality of heartbeat data segments, and a target parameter matched with the heartbeat data segment to be detected is determined. A calculation mode of a reference value is determined, and a target test mode corresponding to the calculation mode is determined from a plurality of preset test modes; wherein the calculation mode comprises at least one of an automatic calculation mode and a manual calculation mode. The reference value of the target electrocardio data under the target parameter is calculated based on the target test mode. In this way, the accuracy of the reference value of the electrocardio data is greatly improved.

Renewable Energy Forecasting Computer Device

This application relates to the field of computer technology, specifically to a method, apparatus, computer device, and storage medium for running an open-source engine. The method for running the open-source engine is applied to a hardware extension plugin system for an inference engine. The hardware extension system includes at least an inference engine module and a hardware extension plugin module. The method includes: determining whether the current hardware is configured with all target operators required for inference computation of the model to be computed; if missing, determining whether the inference engine interfaced with the current hardware is configured with missing hardware operators; if the inference engine interfaced with the current hardware is not configured with missing hardware operators, calling the missing hardware operators from a pre-configured CPU operator library in the CPU through the hardware extension plugin system; and registering all target operators to the inference engine and / or the current hardware. This provides a secure, reliable method for third-party hardware to access an open-source engine with complete evolvability.

Provided are a voltage adjustment method, a voltage adjustment apparatus, a computer device, and a readable medium. The voltage adjustment method includes: detecting (S11) an index parameter, and calculating (S12) a first dispersion parameter of the index parameter according to detection values of the index parameter within preset duration; and in response to the first dispersion parameter being greater than a preset first threshold, adjusting (S13) a voltage of a processor core corresponding to the index parameter.

Embodiments of the present disclosure provide an interrupt controller, a chip, a computer device, an interrupt control method and a medium. The interrupt controller comprises a configuration unit, a first selection unit and a normalization processing unit. The normalization processing unit comprises a plurality of normalization sub-units. Each normalization sub-unit is connected to the first selection unit. Different normalization sub-units adopt different normalization processing modes. The configuration unit is configured to obtain first configuration information. The first configuration information is generated in advance based on the type of an interrupt request. Each type of interrupt request corresponds to a normalization processing mode. The first selection unit is configured to send the interrupt request to one of the plurality of normalization sub-units based on the first configuration information. Each normalization sub-unit of the plurality of normalization sub-units is configured to normalize the received interrupt request by using the normalization processing mode corresponding to the sub-unit.

The invention discloses a negative electrode lithium supplement utilization rate determination method, a determination system and computer equipment. The method comprises the following steps: S1, obtaining a theoretical lithium supplement thickness H theory required for realizing a battery target; s2, obtaining the effective lithium supplement thickness H < effective > of the lithium supplement negative pole piece and the corresponding lithium supplement utilization rate eta; s3, the experience lithium supplement utilization rate eta'is obtained; s4, calculating target lithium supplement thickness H production according to the theoretical lithium supplement thickness H theory and the experience lithium supplement utilization rate eta '; s5, in the production process according to the actual H, sampling is conducted regularly, the step S2 is executed, the current utilization rate eta < current > is obtained, and the current utilization rate eta < current > is compared with eta 'to obtain a correction coefficient K; and when the K value continuously deviates from the preset range, triggering process adjustment, and selectively feeding the eta back to the step S3 to update the lithium supplementing process. According to the method, a complex electrochemical design problem is converted into executable engineering parameters, the actual lithium supplementing amount can be dynamically adjusted, and a closed-loop control system of'calculation-implementation-evaluation-feedback 'is formed, so that the lithium supplementing precision is continuously improved, battery performance degradation caused by lithium supplementing deviation is fundamentally avoided, and the lithium supplementing efficiency is improved. And the reliability and the service life of a battery product are enhanced.

The invention relates to a graph data processing method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: identifying a target node with a neighbor node from graph data; performing sub-block division according to the identification number of each neighbor node, and determining a sub-block to which each neighbor node belongs; wherein the identification numbers in the same sub-block are arranged disorderly, and the maximum identification number in the previous sub-block is smaller than or equal to the minimum identification number in the next sub-block; storing the sampling weight of each neighbor node belonging to the same sub-block to the sub-block in the form of a tree array; and on the basis of each sub-block, an index structure for the target node is constructed, and the index structure is used for carrying out weighted sampling on neighbor nodes of the target node. By adopting the method, the memory overhead of graph data processing can be reduced.

A request processing method, executed by a load balancing node in a request processing system, wherein the request processing system comprises a reverse proxy node, a virtual proxy node, and the load balancing node, and request forwarding is performed between the reverse proxy node and the virtual proxy node by means of a plurality of socket nodes. The method comprises: upon receipt of a plurality of traffic requests by the reverse proxy node, determining, from among the plurality of socket nodes, socket nodes available for processing (302); for each socket node available for processing, determining, from the virtual proxy node, a processing instance having a binding relationship with the socket node available for processing, so as to obtain a plurality of processing instances (304); determining, from among the plurality of processing instances, selected processing instances matching socket nodes available for processing (306); and on the basis of multiple pieces of instance running information respectively corresponding to the selected processing instances, respectively distributing the plurality of traffic requests to the selected processing instances, so that the selected processing instances process the distributed traffic requests (308).

Level measuring system (50) for determining the level of animal feed, in particular in an animal feed silo, the level measuring system comprising: - an animal feed silo (10) with an animal feed intake area (11) for receiving animal feed (18), - a first sensor unit (1) for acquiring first sensor data (S1) relating to the fill level of animal feed in the animal feed silo, wherein the first sensor unit is connected to the animal feed silo and comprises a first sensor (1a) designed as a fill level sensor, - a second sensor unit (2) for acquiring second sensor data (S2), wherein the second sensor unit comprises a second sensor (2a), - a cloud computer device (20) configured to receive the first sensor data (S1) acquired by the first sensor unit (1) and the second sensor data (S2) acquired by the second sensor unit (2) and to store the received first sensor data and the received second sensor data as cloud data (C) in the cloud computer device (20), wherein the cloud computer device (20) is configured to determine a fill level value, which in particular indicates the fill level of the animal feed in the animal feed silo, depending on the acquired first sensor data and the acquired second sensor data, and preferably - a display device (30) which is designed to display the fill level value determined by means of the cloud computer device.