541 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.

The invention relates to an impedance modeling method and device of a grid-connected converter system, a grid-connected system, computer equipment and a computer readable storage medium. The grid-connected converter system comprises a converter and a filtering module which are connected, the converter is used for connecting a direct-current power supply end, and the filtering module is used for connecting a power grid; the method comprises the steps of injecting a disturbance signal corresponding to a target operation parameter into a grid-connected point in a process that a grid-connected converter system operates at the target operation parameter; the grid-connected point is a connection point of the filtering module and a power grid; obtaining measurement electrical parameters of the grid-connected converter system; determining a transfer function matrix of the converter according to the measured electrical parameters; and determining an impedance model of the grid-connected converter system based on the transfer function matrix of the converter. In the process of constructing the impedance model by adopting the method, the transfer function matrix of the converter is obtained by collecting the real electrical parameters of the grid-connected converter system during operation, and the impedance model of the grid-connected converter system obtained based on the transfer function matrix is more accurate and reliable.

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 embodiment of the invention provides a protein stability prediction method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring a first to-be-detected sequence corresponding to an original protein and a second to-be-detected sequence corresponding to a mutant protein; determining a plurality of first target residue features from the first to-be-detected sequence according to an attention mechanism of a protein language model, and extracting global features of the first target residue features to obtain first virtual structure microenvironment features; similarly, obtaining second virtual structure microenvironment features corresponding to a plurality of second target residue features; determining a first difference value based on the first virtual structure microenvironment feature and the second virtual structure microenvironment feature, and determining a second difference value based on a first global sequence feature corresponding to the first to-be-detected sequence and a second global sequence feature corresponding to the second to-be-detected sequence; and performing prediction according to the first difference value and the second difference value to obtain a protein stability prediction result. Therefore, the accuracy of predicting the stability of the protein can be improved.

Systems, apparatuses, methods, and computer-readable media, are provided for offloading computationally intensive tasks from one computer device to another computer device taking into account, inter alia, energy consumption and latency budgets for both computation and communication. Embodiments may also exploit multiple radio access technologies (RATs) in order to find opportunities to offload computational tasks by taking into account, for example, network / RAT functionalities, processing, offloading coding / encoding mechanisms, and / or differentiating traffic between different RATs. Other embodiments may be described and / or claimed.

The embodiment of the invention discloses an air conditioner control method and device, computer equipment and a storage medium. The method is applied to the air conditioner control system, the air conditioner control system is used for controlling a plurality of air conditioners installed in a target area, the different air conditioners are installed in different sub-areas in the target area correspondingly, and the method comprises the steps that when it is detected that target crowds exist in the target sub-areas, the crowd trend of the target crowds is determined according to a preset crowd trend algorithm; obtaining the crowd density of the target crowd, and determining a target temperature; the air conditioners located in the directions corresponding to the crowd trends are determined as associated air conditioners; according to the temperature of the environment where each associated air conditioner is located, the target temperature and the distance between each associated air conditioner and the target air conditioner, the target temperature adjusting gradient is determined, and the target air conditioner is the air conditioner corresponding to the target sub-area; and temperature control is conducted on the target air conditioner and all the related air conditioners according to the target temperature and the target temperature adjusting gradient. According to the method, the energy consumption of the air conditioner can be reduced, and resources are saved.

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.

The invention relates to a self-adaptive inter-chip current sharing circuit for linear LED driving and a control method of the self-adaptive inter-chip current sharing circuit. The current sharing circuit comprises a reference circuit module, an operational amplifier, a power tube, a plurality of current mirrors and a sampling port, current flowing through each chip is adjusted through reference voltage and a reference current source, and current balancing of multiple chips is achieved. And the operational amplifier controls the power tube to maintain constant current output according to a sampling resistor feedback signal. The current mirror group forms a proportional current feedback mechanism to ensure that the ratio of the sampling current to the reference current is constant, so that the driving current is stabilized. Under the condition that multiple chips are connected in parallel, a current sampling tube is combined with a current mirror, overload current is adjusted, and it is ensured that driving current of each chip is within a rated range. The self-adaptive current-sharing control method is suitable for a multi-chip current-sharing control device and computer equipment, accurate distribution and adjustment of the LED driving current are achieved, and the stable lighting effect and efficient energy utilization are ensured.

The invention relates to the technical field of grinding wheel manufacturing, and discloses a grinding wheel wear resistance detection device which comprises a feeding station a, a fixed station b, a rotating station c and a discharging station d, and further comprises a main body frame, the main body frame comprises a mounting table arranged on the main body frame, and a fixed table is fixedly mounted on the surface of the mounting table; the supporting device comprises four T-shaped rotating discs installed on the fixing table, grinding wheels are placed on the surfaces of the T-shaped rotating discs, and the supporting device further comprises a plurality of pressing blocks installed on the fixing table. The thickness of the grinding wheel after unit time loss is measured at the discharging station d under the action of the measuring mechanism, the difference between the thickness of the feeding station a and the thickness of the discharging station d is obtained through external computer equipment, and therefore the loss amount of the grinding wheel in unit time is obtained; and new grinding wheel feeding operation is carried out at the feeding station a, so that the purposes of automatically and circularly detecting and improving the detection efficiency are achieved.

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.

To provide a solar array failure detection method, an apparatus, a computer device, and a memory medium, that solve a problem that detection accuracy and detection efficiency of the conventional solar array failure detection method are low.SOLUTION: A solar array failure detection method contains: a step of acquiring a solar array state information library; a step of transmitting a step signal to a solar array by using a signal transmission system, and acquiring a state reflection signal of a solar module; and a step of obtaining a failure detection result of the solar array by transmitting and filtering the state reflection signal to a filter system, obtaining a target state reflection signal waveform figure, combining the solar array state information library, performing failure detection against the solar array. The state reflection signal in each state of each solar module in the solar array is matched to the solar array state information library, and the failure state of each solar module in the solar array is determined, and thus, a failure detection result of the solar array is obtained.SELECTED DRAWING: Figure 1

The present disclosure discloses a controllable line-commutated converter forced commutation test circuit and method, a computer device, and a storage medium. The controllable line-commutated converter forced commutation test circuit comprises: a steady-state power supply and a multi-wave harmonic current source. The steady-state power supply and the controllable line-commutated converter are connected in parallel, and the steady-state power supply is used to provide a voltage and a current that meet an actual stable operating condition of the controllable line-commutated converter. The multi-wave harmonic current source and the controllable line-commutated converter are connected in parallel, and the multi-wave harmonic current source is configured to periodically inject a fault current into the controllable line-commutated converter when a primary branch and an auxiliary branch in the controllable line-commutated converter are continuously alternately turned on and off over a plurality of cycles, so as to perform periodic forced commutation testing on the controllable line-commutated converter. In the present disclosure, the test circuit for synthesizing multi-source injection is used, and by means of using the multi-wave harmonic current source, an adjustable half-wave multi-wave sub-current is injected and is superposed on a test current of the steady-state power supply, to form a test current capable of verifying a controllable turning-off function of the converter.

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.