10813results about How to "Reduce power consumption" patented technology

The invention relates to a display screen backlight brightness adjusting method and system based on a Mini QLED technology. The method comprises the following steps: dividing a display image signal into a brightness signal, a chrominance signal and a saturation signal, and generating image feature data; performing sub-pixel-level boundary detection to obtain a plurality of initial backlight sub-regions, and calculating region brightness distribution data; executing region clustering operation and region merging to obtain a plurality of target backlight sub-regions and calculating backlight partition mapping data; carrying out nonlinear feature transformation, and calculating backlight unit control data of the Mini LED backlight unit; outputting the backlight compensation amount of each target backlight sub-region by adopting a deep Q network; and performing real-time feedback correction, calculating a deviation value between an actual display parameter and a target parameter of each target backlight sub-region, and outputting a backlight optimization control signal. According to the invention, the backlight brightness is accurately adjusted, and the consistency of the display effect is improved.

The invention relates to the technical field of low-power-consumption AI processors, and discloses a load balancing method of a low-power-consumption AI processor, a chip and a storage medium, the method comprises the following steps: performing cache resource division on a plurality of neural mimicry cores in the low-power-consumption AI processor to obtain a double-layer cache structure; performing characteristic analysis on the target calculation task, and constructing a calculation task directed acyclic graph and a dependency intensity matrix; performing task segmentation to obtain a subtask division scheme; creating a task-core mapping table; organizing the neural mimicry core into a plurality of concentric ring layers based on the task-core mapping table to obtain a multi-layer ring cooperative computing network; and calculating a plurality of performance indexes of each neural mimicry core based on the multilayer annular cooperative computing network, and performing dynamic load optimization to obtain a dynamic load optimization strategy. According to the invention, the load balancing of the low-power AI processor is realized, and the calculation efficiency and the energy utilization rate are improved.

The invention relates to the technical field of intelligent spectrum regulation and control, and discloses an intelligent spectrum regulation and control system based on multi-modal data fusion and dynamic optimization, which comprises a multi-modal data acquisition module, a real-time data processing module, a dynamic weight distribution algorithm, a deep reinforcement learning regulation and control algorithm and a distributed collaborative optimization module. According to the system, real-time fusion and dynamic priority adjustment of multi-dimensional data are realized through acquisition of multi-modal data (environment spectrum, temperature and humidity, carbon dioxide concentration and user behavior) and dynamic weight distribution based on LSTM (Long Short Term Memory); dynamic optimization of a spectrum regulation and control strategy is realized by combining a deep reinforcement learning model and utilizing a weighted reward function of a spectrum output error and energy consumption, and efficient light supplement and low energy consumption requirements are balanced; a distributed optimization algorithm with edge computing and cloud collaboration is adopted, computing resources are dynamically allocated, task processing efficiency is greatly improved, and system power consumption is reduced.

The invention discloses a coal mine ventilation optimization and air quality regulation and control method based on adaptive learning, and relates to the technical field of coal mine ventilation optimization, and the method comprises the following steps: arranging a plurality of sensors in a mine, carrying out the real-time collection and monitoring of key environment parameters, and uploading the collected data to a central control system; and comparing the collected real-time data with historical data, and dividing the collected real-time data into conventional working condition data and abnormal working condition data according to environment states. Through real-time monitoring of various sensors and a deep learning algorithm, accurate ventilation control is achieved, a problem area is rapidly positioned in an emergency, airflow is dynamically adjusted, and safety of miners is guaranteed. According to the system, an LSTM model is adopted to predict an event trend in advance, preventive regulation and control measures are taken in time, real-time adjustment is supported, and the strain capacity is improved. Through a multi-stage ventilation mode, the system optimizes energy consumption and equipment operation efficiency, reduces unnecessary energy waste and maintenance cost, and realizes balance between safety and economy.

The invention discloses an event and RGB image fused SNN target tracking method and system, and the method comprises the steps: converting an event flow from an event camera into an event frame image through a representation method based on time stacking, and enabling the event frame image to be aligned with an RGB frame image from an RGB camera in time and space; performing target detection on the event frame image # imgabs0 # and the RGB frame image # imgabs1 # after alignment by using a spiking neural network SNN, wherein the spiking neural network SNN comprises a feature extraction module, a feature fusion module and a detection head; and learning the similarity of the targets by using the anchor frame extracted by the spiking neural network SNN and the fusion feature map # imgabs2 # to perform multi-target tracking. The invention aims to realize fusion of the fusion event and the RGB image so as to ensure high precision and high efficiency of multi-target tracking, and keep stable detection performance and tracking performance in static and high-motion scenes.

The invention provides a ternary logic gate circuit, a calculation circuit, a chip and electronic equipment. The ternary logic gate circuit provided by the invention can realize addition and subtraction of 1 to an input logic value. Based on the ternary logic gate circuit, 27 univariate functions of ternary logic are utilized, the ternary logic gate circuit is applied to the ternary logic circuit, the purpose of simplifying the structure of the ternary logic circuit can be achieved, the number of transistors in the ternary logic circuit can be reduced, the power consumption of the ternary logic circuit can be reduced, and the reliability of the ternary logic circuit is improved. And the calculation efficiency of the ternary logic circuit is improved.

The invention relates to the technical field of data packet transmission, and discloses a data packet transmission control method and device for a low-power-consumption dual-mode Bluetooth chip, and the method comprises the steps: obtaining a signal strength parameter, a power consumption parameter, a transmission rate parameter and an environment parameter of the dual-mode Bluetooth chip, inputting the parameters into a deep time convolution network, and carrying out the time sequence feature extraction, obtaining a transmission mode switching judgment result; calculating transmission state evaluation data; the chip transmitting power is adjusted in a grading mode, standby power consumption is controlled through a nanoamp LDO circuit, and a dynamic power control parameter is obtained; performing compression coding on the data packet to obtain a dual-mode transmission protocol configuration parameter; executing time sequence sampling, and constructing a dual-mode state feature sequence; the data packet delivery success rate, the end-to-end transmission time delay and the total power consumption of the system are calculated, transmission control parameter optimization is carried out through double-layer deep reinforcement learning, target transmission control parameters are obtained, and the optimal balance of the transmission performance and the power consumption of the dual-mode Bluetooth chip is achieved.

Embodiments of the invention disclose a method, a device and a system for processing a wake-up lock, and belong to the field of computer technology. The method comprises the following steps: detecting whether a wake-up lock is in a working state, and if so, obtaining a wake-up parameter corresponding to the wake-up lock, wherein the wake-up parameter comprises an arithmetic operation corresponding to the wake-up lock and an application program corresponding to the wake-up lock; judging whether the arithmetic operation corresponding to the wake-up lock requires a sensor of a terminal; if not, generating a wake-up transfer request according to the wake-up parameter, sending the wake-up transfer request to a server, and releasing the wake-up lock, wherein the server is used for executing the arithmetic operation corresponding to the wake-up lock; and receiving a wake-up message sent by the server, and waking up the terminal according to the wake-up message. By adopting the method disclosed by the invention, after a display screen of the terminal is closed, the system time occupancy of the application program can be reduced, the power consumption of the terminal is reduced in the case that the display screen of the terminal is closed, and the standby time of the terminal is prolonged.

The invention discloses a GIS partial discharge fault detection device and method based on D-S evidence multi-source information fusion, and relates to the technical field of power system control, and the device comprises a signal collection module which is used for collecting an electromagnetic wave signal, a mechanical vibration signal, an ultrasonic signal and a partial discharge optical radiation signal generated by the partial discharge in GIS equipment; the GIS partial discharge fault detection module is used for processing, storing and sending the signals acquired by the signal acquisition module; the coordinator is used for realizing data transmission between the GIS partial discharge fault detection module and the background terminal; and the background terminal is used for receiving the processing information of the GIS partial discharge fault detection module and judging the fault type of the GIS equipment. According to the GIS partial discharge fault diagnosis system, various signals generated by partial discharge in the GIS equipment are detected through the information acquisition module, and the relationship between the characteristic data and the GIS partial discharge fault is established, so that diagnosis of the GIS partial discharge fault is realized, and the fault judgment efficiency of the background terminal is improved.

The invention discloses a low-orbit satellite communication link switching method based on deep learning, relates to the technical field of satellite communication link switching, and aims to solve the problem that the switching effect is poor when a low-orbit satellite communication link is switched. According to the method, switching strategy reasoning is carried out by utilizing the powerful computing and learning capabilities of the neural network model, different link environments can be automatically adapted, the accuracy and flexibility of switching decision making are improved, a switching strategy comprises link selection, switching opportunity and switching conditions, and the switching strategy is expressed through a probability value, a classification label or a numerical value, so that the accuracy and the flexibility of switching decision making are improved. According to the technical scheme, various possible switching schemes can be provided, if the operation process or performance of link switching does not reach the expected standard, link switching is carried out again according to the switching strategy, the link switching can be ensured to be in the optimal state all the time through the continuous optimization and adjustment process, and the overall performance and stability of a communication system are improved.

The invention discloses an imaging method and system based on cosmic ray muon transmission, which are applied to a control mechanism, and the control mechanism is electrically connected with at least one detection module. The imaging method based on cosmic ray muon transmission comprises the steps that the response state of at least one detection module is obtained, the detection module comprises a plurality of sensor units, and the sensor units are arranged in a string shape. And preprocessing the response state to obtain muon flux in unit area, unit time and unit solid angle. Based on the topographic data, the landform data and the muon flux, the density distribution in the muon ray irradiation range is reversely deduced through a preset algorithm. And performing visual imaging based on a density distribution result. According to the method, the working efficiency of muon geological imaging is improved, the system complexity is reduced, the adaptability in a narrow working environment is remarkably enhanced, and an efficient and convenient solution is provided for the fields of geological exploration, engineering detection and the like.

A mechanical intelligent fault diagnosis method based on neuromorphic calculation and multi-modal data comprises the steps that firstly, a vibration, current and torque multi-modal data set is preprocessed, and a multi-modal sample set is constructed; then establishing a multi-modal bionic pulse neural network model, realizing bionic coding of original multi-modal information, establishing a bionic fusion layer to realize bionic fusion of different sensor signal features, inputting a training set of a multi-modal sample set into the model for training, performing bionic extraction on personalized information in the multi-modal sample set, and fusing advanced representation; calculating classification loss, iteratively updating and optimizing model parameters until training is completed, and obtaining a final multi-modal bionic spiking neural network model; deploying the final multi-modal bionic spiking neural network model to a neuromorphic hardware system, inputting a test set in the multi-modal sample set into the neuromorphic hardware system, and obtaining a tag of a category corresponding to test set data; according to the invention, low-power-consumption and high-precision mechanical intelligent fault diagnosis based on multi-modal data is realized.

The invention relates to the technical field of intelligent water affair management, and discloses a method and system for dynamically adjusting the reporting moment of an NB water meter, and the system comprises NB water meter terminal equipment which is responsible for collecting water meter data in real time and uploading the data to a data management platform through an NB-IoT network; the data management platform is responsible for receiving, processing and storing data from the plurality of NB water meter terminal devices; the reporting moment adjusting module is used for dynamically adjusting the reporting moment of the NB water meter terminal equipment according to the strategy generated by the data management platform; the communication management module is responsible for communication between the NB water meter terminal equipment and the data management platform; the intelligent alarm and event processing module is responsible for monitoring the running state of the system in real time; and an equipment remote control module. By dynamically adjusting the reporting time, the system can reduce unnecessary frequent reporting, especially in a time period when the water consumption is relatively small or relatively stable, so that the power consumption of the equipment is remarkably reduced. Therefore, the battery life of the NB water meter terminal equipment can be prolonged, and the maintenance cost is reduced.

The invention discloses a road user track and behavior prediction method in a dense heterogeneous traffic environment, and the method comprises the steps: carrying out the target detection and recognition of each road user in a video image sequence frame based on a deep convolutional neural network, and taking a recognition result as a road user type constraint; taking the detected and identified track coordinates and timestamp information of the road user as historical track information of the road user; according to the method, on one hand, a traffic map sequence of a target road user per second within a tf time period after a timestamp t is obtained through prediction, the traffic map sequence is subjected to spectral clustering and then serves as a map regularization constraint in a regularization loss function form to be used for correcting future trajectory coordinates of the road target user output by semi-supervised pipeline prediction; and on the other hand, entering a logistic regression function network after passing through a full-connection network, and outputting the behavior prediction type of the road user. According to the method, the track and behavior of the road user can be accurately predicted in a dense heterogeneous traffic environment.

The invention discloses an electrocardiosignal compressed sensing method based on Transform. The electrocardiosignal compressed sensing method comprises the following steps: S1) making a data set; s2), a compressed sensing network based on a Transform is constructed; s3) constructing a final loss function; s4), a compressed sensing network based on a Transform is trained; and S5) evaluating a compressed sensing reconstruction effect, cutting a test data set into data blocks with the same size as the training blocks, then sending the data blocks into the neural network trained in the step S4) for prediction, and verifying on the test set by adopting quantitative indexes such as a percentage mean square error PRD and a signal-to-noise ratio SNR. Through the capability of capturing global characteristics and the advantage of processing sequence-to-sequence tasks of the Transform network, the compressed and sampled low-dimensional electrocardiosignals can be quickly and accurately reconstructed, the power consumption of the system is reduced, and high-quality reconstructed electrocardiosignals can be obtained.

The invention belongs to the technical field of display driving, and particularly relates to a display panel, a display driving method and a display device.The display panel comprises N rows of scanning lines, M columns of data lines and 2N rows * 2M columns of pixel circuits; each pixel circuit comprises a pixel unit and a control unit, and when the nth row of scanning line outputs a first scanning signal, a data signal on the mth row of data line controls any one of the control units in the (2m-1) th row and the (2n-1) th row and the control units in the (2m) th row and the (2n-1) th row to be switched on or switched on alternately in a time-sharing manner; when the nth scanning line outputs a second scanning signal, the data signal on the mth data line controls any one of the (2m-1) th and 2nth control units and the 2m and 2nth control units to be switched on or switched on alternately in a time-sharing manner; according to the display panel, the number of the scanning lines and the number of the data lines are reduced by at least half under the display condition of the same resolution, the power consumption and the cost of the panel are reduced, and the refresh rate and the aperture opening rate are improved.

The utility model discloses assembly welding equipment for a flange connecting pipe of a pressure vessel, which belongs to the technical field of pressure vessel manufacturing equipment and comprises a first workpiece clamping component fixedly arranged on a lathe bed and a second workpiece clamping component arranged on the lathe bed through a moving component, and the second workpiece clamping component can be close to or far away from the first workpiece clamping component. The first workpiece clamping assembly and the second workpiece clamping assembly each comprise a chuck fixing base, and a rotating chuck is installed on one side of each chuck fixing base. Three clamping jaws are arranged on the rotary chuck; the clamping jaws can move in the radial direction of the rotary chuck. The clamping jaw is driven by a first pneumatic motor; the rotary chucks are driven by a second pneumatic motor to rotate, and the center lines of the two rotary chucks are coaxial; the device further comprises a welding mechanism and a ground wire contact block. The welding mechanism is arranged corresponding to a welded junction of the flange and the connecting pipe. The ground wire contact block is arranged on a chuck fixing seat of the first workpiece clamping assembly; according to the utility model, the assembly precision can be improved, the clamping time can be shortened, and the processing efficiency can be improved.

The invention discloses an energy-saving illumination control system and method for an urban illumination LED lamp group, and relates to the technical field of illumination control, and the system comprises an ambient light detection module, a traffic flow monitoring module, a time period division module, a weather information acquisition module, a weather information control module, an LED lamp group driving module, a wireless communication module, a high-precision photosensitive sensor for the ambient light detection module, a filtering and temperature compensation module, and an intensity measurement accurate data transmission module. The traffic flow monitoring module depends on geomagnetism or camera shooting, is high in precision and strong in recognition, and facilitates regulation and control through data; the time period division module accurately times and corrects time according to a clock chip, and sets illumination measures according to the time; the weather information acquisition module is networked to acquire meteorological data, and lamps are adjusted according to day conditions; and the control module multi-core microprocessor calculates illumination levels and brightness according to multi-source data to control lamp groups. According to multi-factor intelligent lamp control, energy saving is remarkable, lighting quality is good, comfort and safety are improved, remote monitoring and management can be achieved, troubleshooting is accurate and efficient, maintenance is convenient and fast, intelligent and green development of urban lighting is promoted, and the intelligent city construction requirement is met.

The invention discloses a two-dimensional magnetic material device and a preparation method and a regulation and control method thereof, and the regulation and control method comprises the steps: enabling ionic liquid to cover the surface of a two-dimensional magnetic material, enabling the ionic liquid to be in contact with a grid electrode, a source electrode, a drain electrode and the two-dimensional magnetic material, and continuously applying current from the grid electrode to the source electrode and to the drain electrode, and the regulation and control of the two-dimensional magnetic material device are completed. According to the two-dimensional magnetic material device and the preparation method and the regulation and control method thereof provided by the invention, the ionic liquid containing the cationic molecular chains with different lengths is selected to perform intercalation on the two-dimensional magnetic material device, so that regulation and control on the two-dimensional magnetic material device are realized, and the regulation and control flexibility of the two-dimensional magnetic material is greatly improved; and the coercive field, the magnetic anisotropy energy and the carrier concentration of the two-dimensional magnetic material can be changed.

The invention discloses an intelligent henhouse environment concentration monitoring system, and relates to the technical field of henhouse environment concentration monitoring. The influence of changes of environmental parameters in a henhouse in different seasons on chicken raising is solved. The system specifically comprises a sensor module, a data acquisition module, a data transmission module, a data processing module and a remote monitoring module, the sensor module acquires various environmental parameters in a henhouse and transmits the acquired data to the data acquisition module, and the data acquisition module receives the data transmitted by the sensor module, pre-processes the data and transmits the pre-processed data to the remote monitoring module. The preprocessed data is uploaded to the data processing module through the data transmission module, the data processing module analyzes and processes the data, and the collected original data is converted into useful information understood by a breeder. Parameters of the model can be obtained by fitting the model into historical data, and the parameters are used for predicting the influence of changes of environmental parameters in the henhouse on chicken raising in different seasons in the future.

The invention relates to a high-speed data synchronous acquisition and emission system suitable for a satellite communication MIMO (Multiple Input Multiple Output) system. The system comprises two acquisition and emission daughter cards and a clock management unit, the acquisition and emission daughter card comprises a multi-channel ADC acquisition unit and a multi-channel DAC refreshing unit; a multi-channel ADC acquisition unit of the acquisition and emission daughter card sends acquired data to an FPGA for processing through a JESD204B interface, and then the data is forwarded through an optical module; a multi-channel DAC refreshing unit of the acquisition and emission daughter card sends data received by the optical module to an FPGA for processing, and then the data is sent to a DAC through a JESD204B interface for interpolation filtering and IQ conversion, refreshed and output to a radio frequency front end; and the clock management unit realigns the phase of the device clock and the phase of the reference clock in each power-on process by using a trigger signal of the clock chip so as to realize inter-board synchronization. The system can improve the synchronization performance and reduce the power consumption.

The invention discloses a neural network extension execution computer system based on RISC-V. The neural network extension execution computer system comprises an RISC-V processor based on an out-of-order execution system structure, a neural network hardware accelerator, an instruction memory, a data memory and a dynamic scheduler. The dynamic scheduler is used for switching different working modes for the computer system according to the current calculation load, and distributing a calculation task to be executed to the RISC-V processor or the neural network hardware accelerator for execution; the working modes comprise a standard calculation mode, a neural network acceleration mode and a low power consumption mode; the computing tasks comprise a general computing task and a neural network computing task. According to the technical scheme, the intelligence of the computer system is improved, and the calculation efficiency, the resource consumption and the power consumption are well balanced.

The utility model discloses a multi-battery pack energy storage system water cooling unit and an energy storage cabinet applied by the same, a plurality of circulating water cooling pipelines connected in parallel are adopted, and each battery pack is correspondingly distributed with one independently controlled circulating water cooling pipeline; a circulating pump, a flow regulating valve, a heater and a heat exchanger are arranged on each circulating water cooling pipeline in series; a water inlet and a water outlet of each circulating water cooling pipeline are respectively butted to the liquid cooling unit of the corresponding battery pack; each battery pack is provided with a temperature sensor, the liquid cooling unit corresponding to each battery pack is provided with a pressure sensor, the water cooling unit further comprises a liquid cooling controller, and the input end of the liquid cooling controller is in communication connection with the temperature sensors and the pressure sensors of all the battery packs and the liquid cooling units. And the output end is in communication connection with the circulating pump, the flow regulating valve and the heater on the circulating water cooling pipeline corresponding to each battery pack. According to the utility model, balanced temperature control among the multiple battery packs of the energy storage system is realized, and the reliability of the whole system is improved.

The invention discloses an unmanned aerial vehicle patrol route optimization method fusing weather conditions and power consumption prediction, and the method comprises the steps: (1), according to the historical flight data of a patrol unmanned aerial vehicle, combining a random forest and a linear regression method; establishing an unmanned aerial vehicle power consumption prediction model considering the coupling influence of environmental factors and task factors, and drawing a time-power-power curved surface; (2) inputting a patrol object digital map, a required patrol point set, a selectable patrol point set, unmanned aerial vehicle performance data, a route starting point and a route ending point, and obtaining an initial flight route without considering real-time weather conditions; and (3) optimizing the initial flight route in combination with the real-time weather condition and the time-power-electric quantity curve, and when the predicted consumed electric quantity is greater than the acceptable consumed electric quantity, sequentially giving up the selectable patrol points according to the importance sequence and re-drawing the route until the predicted consumed electric quantity is less than or equal to the acceptable consumed electric quantity. The method has a remarkable optimization effect on precise patrol task execution of the unmanned aerial vehicle.

The invention discloses a cooling system and a server cabinet, and relates to the technical field of server cabinets, and the cooling system comprises a cooling module and at least two cooling branches. The cooling branch is provided with a control valve, a detection assembly and a cold plate module used for cooling working devices in the server. The cooling module is connected with each cooling branch, the cooling module is used for providing a cooling medium for each cooling branch, the cooling module comprises a control module, and the control module is used for being in communication connection with a server, a control valve and a detection assembly in each cooling branch; the control module is further used for receiving the working performance parameters of the working devices in the servers in the cooling branches and the cooling medium performance parameters detected by the detection assembly so as to control the opening degree of the control valves in the corresponding cooling branches. The cooling system solves the problem that the server node flow in the server cabinet cannot be adjusted in real time.

The invention discloses an accelerator for accelerating the reasoning process of an unstructured sparse large language model and an operation method of the accelerator. The accelerator comprises a processor module, a direct memory access module, a memory controller module, a DDR memory module, an interconnection bus module, a calculation core module and a router module. The interconnection bus module is connected with the direct memory access module, the memory controller module and the computing core module; the processor module is connected with the direct memory access module; the DDR memory module is connected with the memory controller module; the router module is connected with the computing core module and the adjacent router module; and the calculation core module obtains to-be-calculated data from the off-chip DDR memory module through the interconnection bus module. According to the method, the limit that data reuse is carried out only along a single row or a single column on a traditional systolic array is broken through, cross-row and cross-column data reuse is achieved, the pressure of hardware equipment is relieved, the positions of non-zero elements are coded through bit masks, and the calculation efficiency is improved.

The invention relates to the technical field of angle steel tower welding, in particular to an angle steel tower base automatic welding system and method.The angle steel tower base automatic welding system comprises a base and a storage table and further comprises a controller, a welding mechanism, a splicing mechanism and a limiting mechanism; the splicing mechanism comprises a driving assembly, four pushing assemblies and eight electric clamping jaws, the limiting mechanism comprises a tightening block, an abutting assembly and a lifting assembly, an L-shaped rod is fixedly arranged at the top of the workbench, and the tightening block is fixedly arranged at the bottom of the lifting assembly. Through multi-directional abutting limiting, the shapes and the relative positions of the bottom plate and the four rib plates can be effectively controlled, internal stress generated by heating in the welding process of the four rib plates and the bottom plate is balanced, welding deformation is prevented, and then the welding effect is improved.

The invention discloses a training method and device of a distributed computing system, a program product and a medium, which are used for solving the problems of performance difference and low communication efficiency of heterogeneous computing nodes. According to the scheme, the calculation capability of each calculation node is fully considered, so that dynamic allocation and iterative optimization of tasks are realized; specifically, the problems of idle resources and low efficiency are avoided by acquiring the computing power of each computing node and allocating the training task matched with the computing power to the computing node; the time length of single iteration is determined, and the number of iterations in the communication interval is adjusted according to the time length, so that each computing node can perform data exchange at a proper time, network bandwidth occupation and delay caused by frequent communication are reduced, the overall efficiency of the training process is optimized, mutual waiting between the computing nodes is avoided, and the network performance is improved. When the computing node runs the training process, the power consumption and heat generation are reduced, and the communication overhead is also reduced, so that the long-term stability and reliability of the computing node are improved.

The invention discloses an intelligent deicing method and system based on ultrasonic detection, a medium and equipment, and relates to the technical field of deicing. The intelligent deicing method based on ultrasonic detection is used for the intelligent deicing system and comprises the following steps that the surface temperature of an airplane is detected based on an infrared sensor, and the surface temperature value of the airplane is obtained; comparing the airplane surface temperature value with a temperature threshold value; if the aircraft surface temperature value is greater than the temperature threshold value, continuing to detect the aircraft surface temperature based on the infrared sensor; if the aircraft surface temperature value is smaller than or equal to the temperature threshold value, an ultrasonic pulse echo icing sensor is started to detect the icing thickness of the aircraft surface, and icing signal information is obtained; and based on the icing signal information, whether the automatic deicing system of the airplane is started for deicing treatment is judged. Through the steps, the deicing efficiency of the aircraft during flight is improved.

The technical scheme of the invention discloses an epilepsy diagnosis system based on electroencephalogram data channel selection and a KAN convolutional spiking neural network, which is characterized by comprising a data processing module, a channel feature extraction module, a self-adaptive channel selection module and a feature classification module. According to the method, an effective solution is provided by introducing KAN convolution, channel selection and a spiking neural network. The introduced attention mechanism allows the model to dynamically pay attention to the most relevant EEG channel and signal characteristics, so that personalized channel selection is realized. Through the mechanism, the model can distribute different weights for different individuals, only key signals most related to epileptic seizure are selected, the calculation burden of redundant channels is avoided, and meanwhile the classification precision is improved. Finally, the artificial neural network is converted into the pulse neural network, an event-driven calculation mode is adopted, energy consumption is effectively reduced, and the method is particularly suitable for being applied to portable equipment.