4889 results about "Central processing unit" patented technology

The invention discloses a mobile phone anti-theft system and method based on a camera and a GPS (Global Position System) of a mobile phone. The mobile phone anti-theft system comprises a mobile phone CPU (Central Processing Unit), a microprocessor, a camera driving module, an input module and an unlocking detection module, wherein the input module is connected with the unlocking detection module; the unlocking detection module is connected with the mobile phone CPU; and the mobile phone CPU is connected with the camera driving module through the microprocessor. Compared with the prior art, the mobile phone anti-theft system and method provided by the invention have the advantages as follows: an unlocking operation is carried out when the mobile phone is used after the mobile phone is lost or stolen, and a background operation program which is installed in the mobile phone is triggered and started when the unlocking operation is inaccurate; and a user who currently uses the mobile phone is positioned and photographed through a GPS module and the camera of the mobile phone; meanwhile, obtained information is sent to a mailbox or other addresses which are specified by the user in time, so that the user is convenient to alarm and can find the mobile phone back accurately in time, and the problems of the mobile phone loss and personnel information release of the user can be solved.

The invention relates to the technical field of coal mine safety monitoring, and discloses a coal mine safety data comprehensive analysis and early warning system which comprises a data integration module, a three-dimensional visualization module, a risk assessment module, a linkage control module, a model training module and a central processing unit and can further comprise a decision support module, a storage cluster and a communication gateway. The data integration module constructs a multi-source heterogeneous data acquisition channel and performs dynamic topology modeling; the three-dimensional visualization module dynamically renders the monitoring data based on the space-time reference axis; the risk assessment module generates a danger situation map through space-time correlation analysis; the linkage control module establishes a multi-level response mechanism; the model training module optimizes the risk prediction model; and the central processing unit schedules each module to operate. According to the system, integrated analysis, dynamic visualization, risk prediction and cross-system linkage disposal of coal mine safety data are achieved, and the intelligent level and emergency capacity of coal mine safety monitoring are improved.

The invention provides an intelligent toy injection molding process optimization method which comprises the following steps: aiming at a key temperature control area, calculating an optimized cooling time value and a cooling water temperature set value through a correlation model of thermodynamic attribute parameters of toy materials, cooling time and cooling water temperature, and issuing the cooling time value and the cooling water temperature set value to an injection molding machine control system, meanwhile, according to the temperature fluctuation amplitude of the key area, the forming pressure value is finely adjusted, so that the temperature fluctuation of the key area is reduced; in the injection molding process, temperature data of multiple positions on the surface of the mold are collected in real time and transmitted to the central processing unit, the central processing unit conducts filtering and denoising on the collected temperature data and then judges whether the temperature data exceed the optimized temperature control range or not, if yes, an alarm mechanism is triggered, and meanwhile operation parameters of the injection molding machine are automatically adjusted. And the time for deviating from the temperature control range is shortened to the minimum.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention discloses a hybrid expert model reasoning method based on cooperation of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit), and belongs to the field of deep learning. According to the method, a CPU-GPU computing framework of a hybrid expert model is constructed, heterogeneous computing resource loads are effectively balanced, and the hardware utilization rate is remarkably increased; an intelligent cache management mechanism based on dynamic priority scores is provided, high-demand experts are reserved preferentially, and the transmission overhead caused by cache missing is reduced; through pipeline parallel design for separating calculation and transmission tasks, CPU calculation and PCIe transmission are overlapped in the GPU execution period, and delay is effectively hidden. In addition, in combination with a multi-layer expert activation prediction prospective prefetching mechanism, the expert cache hit rate is improved. The method is compatible with hybrid expert models of different scales and structures, and stable and efficient reasoning acceleration is realized on a resource-limited heterogeneous platform.

The invention provides a complex scene-oriented AI large model lightweight deployment method, and relates to the technical field of edge computing, and the method comprises the steps: carrying out the structured pruning of a pre-trained Transform network based on the attention head importance score, carrying out the dynamic sparsification of the activation state of a feedforward network according to the input tensor entropy value, employing the dynamic mixing precision quantization, and carrying out the reconstruction of an AI large model. Obtaining network parameters after pruning quantization; deploying the pruned and quantized network parameters to an edge computing device, distributing a feature extraction operator to a neural network processor through a heterogeneous computing scheduler, and unloading a classification operator to a multi-core central processing unit; and managing an on-chip memory in combination with a virtual memory paging mechanism, realizing zero-copy data transmission by utilizing a direct memory access controller, and outputting a reasoning result tensor. According to the method, efficient and reliable operation of the large model at the resource-constrained edge node is realized.

An autonomous environmental regulation and behavioral monitoring system is disclosed, configured to adapt temperature, lighting, and acoustic conditions based on real-time emotional and physiological data. The system includes a dual-redundant central processor, hierarchical communication networks, multi-angle visual acquisition units, infrared thermometers, and modular environmental subsystems. It detects posture, gestures, facial expressions, and thermal signals to classify user states and apply individualized airflow, light, and sound modulation without relying on external internet connectivity. The system also monitors connected appliances using voltage-based pressure analysis to forecast device degradation. With integrated gesture recognition, privacy-preserving data handling, and predictive adaptation, the invention enables multi-user personalization, long-term learning, and uninterrupted operation within residential, administrative, or healthcare infrastructures.

The invention discloses an extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention relates to the technical field of vacuum extraction control, and discloses a multi-stage vacuum extraction control system which comprises a central processing unit, a multi-stage vacuum pump set control module, a vacuum degree dynamic monitoring module and the like. The multi-stage vacuum pump set control module coordinates all stages of pumps to work according to a preset vacuum degree target value, and the vacuum degree dynamic monitoring module collects pressure data in real time and judges abnormity. The pressure gradient adjusting module adjusts the extraction rate of the adjacent pump to maintain the vacuum gradient stable when the pressure is abnormal, and the abnormity response module controls starting and stopping or rate correction of the pump according to the abnormity level. In addition, the system further has the functions of vacuum pump efficiency evaluation, pressure feedback compensation, leakage detection, redundancy switching, historical data backtracking, collaborative optimization and the like. The system can accurately control the vacuum degree, efficiently deal with abnormities and realize equipment state monitoring and optimization, and is widely applied to the fields with high requirements on the vacuum degree, such as semiconductor manufacturing and vacuum coating.

The invention discloses a hardware fault real-time detection method and system based on cooperation of a CPU (Central Processing Unit) and a BMC (Baseboard Management Controller), and the method comprises the following steps: respectively collecting high-frequency state data and tendency indexes by establishing a communication mechanism between the CPU and the BMC; the processor uses a CUSUM algorithm to carry out abrupt change analysis on the periodically collected operation state to generate an abrupt change event; and the management controller uses an EWMA algorithm to model the trend data, and extracts abnormal changes. And the system performs fusion analysis on the mutation event and the trend anomaly to form a fusion anomaly vector. The risk assessment module calculates a risk score based on a preset rule, determines a fault level, positions a target component, and outputs a processing strategy. And triggering a response action according to the strategy and recording an execution state. And the fusion data and the response record are input into the adaptive module together for dynamically adjusting CUSUM and EWMA parameters, so that adaptive updating of the algorithm is realized, and finally a fault detection signal is generated.

The invention discloses a reservoir dam safety monitoring method and system based on edge calculation, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the steps: obtaining monitoring data by each edge calculation node, carrying out the preprocessing, and generating a standardized data matrix; establishing a reference database, executing anomaly detection, and generating a labeling time sequence data matrix; performing multi-scale decomposition on the labeled time sequence data matrix, and constructing a node response feature matrix; the central processing unit receives data of each edge computing node, analyzes a multi-parameter spatial propagation mode by constructing a parameter-spatial correlation matrix, and obtains a spatial correlation feature matrix; establishing a Bayesian risk assessment model, predicting a dam risk level and outputting a risk evolution trend; and issuing a differentiated early warning instruction according to the risk assessment result. Through a distributed architecture combining edge calculation and central processing, real-time monitoring, intelligent analysis and accurate early warning of the dam safety state are realized, and the monitoring efficiency and the risk identification accuracy are improved.

The invention relates to the technical field of computer security, in particular to a security isolation method for an edge computing node of the Internet of Things, which comprises the following steps of: identifying a trust domain in the edge computing node of the Internet of Things, defining a sensitive data source, and generating a sensitive data identifier set, and based on the sensitive data identifier set, planning minimum central processing unit time and maximum memory bandwidth for each isolation domain to form an isolation domain resource limit, and establishing initial security isolation configuration. According to the method, the identification mechanism based on the sensitive data identification set is introduced into the edge computing node of the Internet of Things, the definition of the trust domain can be realized, and the resource limit is constructed in combination with the minimum central processing unit time and the maximum memory bandwidth of each isolation domain, so that the basic distribution mode of computing resources is restrained, and the computing efficiency is improved. And resources are prevented from being occupied by high-frequency low-priority tasks. After a data entry is positioned on a system level and stain marks are applied according to sensitive attributes, sensitive data images have identifiable features on a memory level.

The invention relates to the technical field of charging device detection, and discloses a charging device intelligent detection system and method based on dynamic load simulation. The system comprises a load configuration module, a charging parameter acquisition module, a load fluctuation calculation module, an abnormity identification module, a central processing unit and the like. The load configuration module configures dynamic load parameters according to the type of the charging device and generates a load simulation rule; the charging parameter acquisition module acquires an operation parameter set according to a rule; the load fluctuation calculation module generates a load fluctuation curve; the anomaly identification module calculates an anomaly fluctuation index; and the central processing unit fuses and processes the detection dimension parameters to generate state evaluation parameters. In addition, the system is further provided with a fault tracing module and a storage module. The system can simulate actual load change, collect and analyze parameters in multiple dimensions, realize fault intelligent diagnosis and traceability, can be widely applied to detection of various charging devices, improves the detection accuracy, reliability and efficiency, and reduces the detection cost.

The invention discloses an output conversion system and method for a UPS (Uninterrupted Power Supply), and the system comprises a main power input end which is connected with a bridge rectifier through a first electromagnetic relay; the energy storage module comprises a super capacitor group and a parallel lithium battery group, and is coupled with the bus capacitor through a bidirectional DC / DC converter; the input end of the multi-path regulation and control module is connected with the bus voltage sensor, and the output end is connected with the switching execution unit through a second electromagnetic relay group; the dynamic switching unit comprises an I-GBT matrix and a reverse blocking diode array which are respectively connected with the inverter and the bypass circuit in parallel; and the real-time monitoring module is provided with a current harmonic analysis unit and a load break variable detection unit, and the output end of the real-time monitoring module is connected with the ADC port of the central processor. According to the invention, systematic improvement is realized in three dimensions of electric energy quality guarantee, equipment reliability and dynamic response capability through an original multi-path regulation and control architecture and a deep cooperative intelligent control strategy.

The present invention relates to the field of automated apparatuses. Disclosed is a robot capable of automatically climbing, wherein the robot capable of automatically climbing comprises a main body and climbing mechanisms, each of the climbing mechanisms comprising a telescopic linkage. An inner lifting sliding rail moves vertically along a fixed sliding rail, a middle lifting sliding rail moves vertically along the inner lifting sliding rail, and an outer lifting sliding rail moves vertically along the middle lifting sliding rail. An instant adhesive storage tank and a numerical control nozzle are provided at the top end of each of the inner, middle and outer lifting sliding rails, the numerical control nozzle spraying an instant adhesive to a climbing surface to form a load-bearing plate. A climbing assembly is provided on each of the inner, middle and outer lifting sliding rails, and the climbing assembly moves vertically along its corresponding inner, middle and outer lifting sliding rails and is configured to be supported on the load-bearing plate. A central processing unit is in communication connection with the telescopic linkage, the inner, middle and outer lifting sliding rails, the climbing assembly and the numerical control nozzle.

The present disclosure relates to systems and methods remote memory access between systems. In particular, some implementations relate to remote memory access using data processing units that can reduce loads on central processing units or other system components. Some implementations utilize scheduling algorithms to optimize memory transfers. Some implementations relate to data processing unit hardware that includes programmable logic, which can be configured for scheduling, data processing, and the like.

The invention discloses a cache management method and device for memory access, equipment and a storage medium, and relates to the technical field of storage, and the method comprises the following steps: obtaining a data read-write message of a central processing unit; detecting a data popularity type of an access address of the data read-write message; searching cache data corresponding to the access address from a target cache region; if the cache data is not found in the target cache region, sending a corresponding read-write request to a memory so as to find memory data corresponding to the access address from the memory; by setting the preset cache regions corresponding to the cold data and the hot data respectively, the cache can be partitioned according to the heat of the data, so that cache resources are reasonably allocated, the cost is reduced, the memory access efficiency and speed are improved, finer consistency control can be realized, and the cache hit rate and the system performance are improved; therefore, the required data is searched in the corresponding preset cache region according to the heat of the data, and the memory access speed is increased.

The invention discloses an offshore platform anti-collision device and method based on multi-mode BEV perception, and the device comprises a three-dimensional fusion distance measurement module, a UWB distance measurement module, a ship navigation state monitoring module, a platform marine environment monitoring module, and a central processing unit. The three-dimensional fusion ranging module comprises a holder, a laser radar and a camera, and is used for performing 360-degree three-dimensional scanning on the periphery of the offshore platform; the UWB distance measuring modules are installed on the periphery of a ship and pile legs of an offshore platform. The ship navigation state monitoring module is used for acquiring ship position, navigational speed and course information in real time; the platform marine environment monitoring module collects wind wave data and weather data near the offshore platform and route information of passing ships through network communication. The invention discloses an offshore platform anti-collision device with real-time monitoring and collision risk assessment functions, the anti-collision safety of an offshore platform is improved, and the occurrence rate of collision accidents is reduced.

The invention relates to the technical field of construction site safety early warning, in particular to a multi-modal construction site safety early warning method and system for dynamic weight allocation, and the method comprises the following steps: deploying a multi-modal data collection module, and collecting and transmitting construction site data to a central processing unit in real time; constructing a correlation model according to the historical accident database; carrying out preprocessing and anomaly detection on real-time data, and dynamically adjusting a multi-modal weight according to a result; inputting the adjusted weight into the model, calculating a comprehensive early warning score and performing graded early warning; and correcting the model according to the early warning and accident matching degree. According to the method, the multi-modal data acquisition module is deployed to acquire sensor, visual and text data, and a dynamic weight distribution mechanism is combined, so that the weight of each modal can be adjusted in real time according to an anomaly detection result, the accuracy and comprehensiveness of potential safety hazard identification are remarkably improved, and the problems of one-sidedness and missing report of single-modal monitoring are effectively avoided.

A system for assessing the ecological sensitivity and development potential of areas within ecologically sensitive mountain towns, consisting of: a data acquisition module configured to extract various data types, including multi-source geospatial data, environmental data, built environment data, socio-economic data and community participation data, with the extracted data being validated and stored in a database; a central processing unit comprising one or more processors connected to a memory, the processor communicatively connected to the database in which the extracted data is stored, the memory storing instructions that, when executed, configure the processor to implement the following: a data input module configured to receive data corresponding to multiple indicators from different assessment dimensions, including ecology and environment, built environment, economy, heritage and culture, mobility, location, community participation, visual aspects and safety from hazards; an indicator evaluation module configured to evaluate each indicator according to predefined sensitivity categories and assign corresponding sensitivity index values in the range of 0.1 to 1; a dimension weighting module configured to apply dimension-specific weights to calculate weighted scores for each indicator; a development potential calculation module that aggregates the weighted assessments to determine an overall sensitivity assessment and assigns this overall sensitivity assessment to a development potential rating scale; and a visualization module configured to generate visual representations and reports of the assessment results; and an output module having a user interface operatively connected to the visualization module of the central processing unit, the output module configuring the user interface to display the visualized results and the final index value obtained from the central processing unit.

Workloads for executing a graph neural network (GNN) may be divided among various processing units, such as a central processing unit (CPU) and a neural processing unit (NPU). The NPU may include a data processing unit (DPU) and a digital signal processor (DSP). The CPU may perform precomputation, model optimization, hardware optimization, and compilation. For example, the CPU may precompute a parameter matrix and use the parameter matrix as internal parameters of a GNN. The CPU may also perform node padding, approximation computation, or transfer of DSP operations to DPU to optimize the GNN. The CPU may also perform sparsity data compute and storage, vertical fusion of DSP operations and DPU operations, or data quantization to optimize performance of the NPU. The compiled GNN may be provided to the NPU, and the DPU and DSP may perform the operations in the compiled GNN to produce a prediction of the GNN.

The invention discloses a lens production defect detection method and equipment thereof, and relates to the technical field of visual inspection.The equipment comprises an information acquisition module, a central processing unit, a process regulation and control module and an interactive communication module.The information acquisition module acquires multi-source sensing information at regular time, so that both comprehensiveness and accuracy are improved; high accuracy of visual detection is guaranteed; through an information processing model, quantification is accurate and traceable, defect degree visualization and defect accurate classification are realized, then process defects are positioned, the interference degree of the environment on a detection result is determined, and misjudgment of the defects due to environmental factors is avoided; through linkage of defect data, process precision and environmental interference, a process optimization scheme is generated in a targeted manner, closed-loop response from defect generation to process adjustment is realized, and the production line response speed is improved; and finally, the lens production yield is remarkably improved, and the production cost of long-term detection is reduced.

The invention relates to the field of sensors, and discloses an anti-electromagnetic interference dynamic compensation method for a gas sensor of power equipment, which comprises the following steps: monitoring and collecting sensor data, uploading the sensor data to a central processing unit for data fusion, and carrying out volatility analysis on the fused sensor data, so as to obtain an anti-electromagnetic interference dynamic compensation result. The change relation of the fluctuation of the sensor data along with time is monitored in real time, and the fluctuation abnormal state of the sensor data is analyzed and detected by combining a fluctuation analysis result with real-time environmental parameters, so that the monitoring duration and the monitoring frequency of the sensor data are dynamically adjusted; analyzing the contribution degree of the electromagnetic interference to the error generated by the gas sensor, detecting whether the error interferes with the measurement state of the gas sensor, and correcting the gas concentration measurement value by using a temperature and humidity-gas response coupling correction algorithm of Kalman filtering to realize the dynamic compensation of the gas concentration measurement value. And the accuracy of the corrected gas concentration estimated value is improved.

The invention discloses a dynamic feeding speed adjusting method self-adaptive to production line takt, and relates to the technical field of production line automation control, and the method comprises the following steps: 1, constructing a production line takt sensing system, setting sensing units at all key stations of a production line, and setting a plurality of monitoring points on a material transmission path of the production line; according to the invention, various sensing assemblies are arranged at key stations, various state information of materials in the process from station entering to transmission can be captured in real time in combination with monitoring points on a transmission path, and information reliability is ensured through data screening; therefore, the central processing unit can comprehensively master the actual operation condition of the production line, and firm data support is provided for follow-up adjustment. On the basis of characteristic parameters of multi-dimensional analysis, continuous correction is carried out in combination with historical data, and the defect that a traditional fixed model cannot adapt to beat fluctuation is avoided; in this way, the feeding speed can be adjusted on the basis of the current real production line takt all the time, and timeliness and pertinence of adjustment are ensured.

An AI accelerator apparatus using in-memory compute chiplet devices. The apparatus includes a first semiconductor substrate having a plurality of chiplets, each of which includes a plurality of tiles. Each tile includes a plurality of slices, a central processing unit (CPU), and a hardware dispatch device. Each slice can include a digital in-memory compute (DIMC) device configured to perform high throughput computations. In particular, the DIMC device can be configured to accelerate the computations of attention functions for transformer-based models (a.k.a. transformers) applied to machine learning applications. The chiplets are in a full mesh connectivity configuration such that at least one of the die-to-die (D2D) interconnects of each chiplet is coupled to one of the D2D interconnects of each other chiplet using a non-diagonal link. The chiplets can also include other interfaces to facilitate communication between the chiplets, memory and a server or host system.

Disclosed in the present application are a resource adjustment method and apparatus, an electronic device, a storage medium and a training platform, which are applied to the technical field of artificial intelligence. The method comprises: acquiring a container set of computing nodes executing a task to be trained and a container associated with same; on the basis of resource configuration information of the container set, resource occupation information of the computing nodes and user resource requirements, and in a mode that target graphics processing unit and target central processing unit cores executing said task are preferentially located in the same non-uniform memory access group, selecting target central processing unit cores needing to be bound for the container, and updating configuration information of a process group corresponding to the container, so as to complete binding of the central processing unit cores and the container. The present application can solve the problem that AI training tasks cannot be efficiently completed in the related art, and can maximally ensure the efficient completion of AI training tasks by means of resource adjustment during the execution of the AI training tasks.

The invention relates to the technical field of indoor environment monitoring, and discloses an indoor temperature and humidity acquisition system based on a distributed architecture, which comprises a distributed sensor node cluster, a data aggregation gateway and a central processing unit. The distributed sensor node cluster is deployed in different indoor areas to collect data, the data aggregation gateway preprocesses the data, and the central processing unit divides the areas according to spatial and temporal distribution characteristics of the data, constructs various data collection strategies, calculates node energy consumption estimation, data redundancy and transmission delay, and screens an optimal strategy to control node sampling and communication. The system is further provided with a fault-tolerant module, an edge calculation unit and the like, and can detect faults, predict trends, adjust strategy weights and the like. The system is comprehensive and accurate in data acquisition, good in data processing and transmission performance, intelligent in node management and high in environmental adaptability, has expansibility and collaboration, and can be widely applied to temperature and humidity monitoring of various indoor places.

The invention relates to a robot system based on multi-mode perception cooperation and an autonomous navigation method, and the robot system comprises a main body frame which is internally integrated with a central processing unit; the multi-mode sensing module comprises a binocular vision camera, a pressure-sensitive touch array, a microphone array and a gas sensor and is used for collecting environmental data; the flexible joint mechanical arm is composed of at least three bionic joints connected in series, and a shape memory alloy driver and an air pressure feedback device are arranged in each joint. The edge computing unit is in communication connection with the central processing unit and used for processing sensing data in real time and generating action instructions, and the core problems that a traditional robot system is insufficient in sensing capacity in a complex environment, mechanical arm rigid-flexible control is unbalanced, and dynamic scene response lags are solved. And a high-adaptability and high-reliability solution is provided for industrial automation, dangerous environment inspection and other scenes.

The invention relates to the technical field of robot navigation and artificial intelligence, in particular to an autonomous navigation robot system based on multi-modal perception, which comprises a visual perception module, a laser radar module, a voice instruction module, a central processing unit and an execution control unit, the visual perception module is used for collecting an environment RGB-D image; the laser radar module is used for acquiring an environment distance point cloud; the voice instruction module is used for receiving and analyzing a user voice instruction; the central processing unit generates a navigation path and an obstacle avoidance strategy through a multi-modal data fusion algorithm and a path planning algorithm; the execution control unit drives the robot chassis to move according to the navigation path and the obstacle avoidance strategy.

Embodiments of the present application provide a network interface card control method and apparatus for a server, a non-volatile readable storage medium, and an electronic device. The method comprises: when a target logic device on a server is powered on and starts, identifying a network interface card type of a target network interface card on the server by means of the target logic device; according to the network interface card type of the target network interface card, controlling a first power supply controller on the server to supply power to the target network interface card in a power supply mode matching the network interface card type of the target network interface card; when it is detected that the the target network interface card has been started, by means of the target logic device, controlling a second power supply controller on the server to supply power to a central processing unit of the server. By means of the present application, the efficiency of identifying a network interface card of a server is improved.