270results about How to "Accurate judgment" patented technology

The invention belongs to the technical field of brain-computer interfaces, and particularly relates to a post-stroke dysphagia rehabilitation recognition system, feedback stimulation equipment and a storage medium. The system comprises a signal acquisition module, an electroencephalogram signal preprocessing module, a neural network feature extraction module, a statistical feature extraction module and a swallowing intention recognition module, and can recognize whether a patient has a swallowing intention or not through a Mama deep learning model based on space-time statistical features. The invention also constructs brain-computer interface equipment for rehabilitation of dysphagia after stroke in combination with electroencephalogram signal acquisition equipment and electrical stimulation equipment. The technical scheme of the invention has the advantages of being accurate in recognition, good in rehabilitation treatment effect and capable of forming center-periphery closed loop feedback, and has a very good application prospect.

The invention relates to the technical field of roadbed detection, and discloses a roadbed compaction quality detection method based on ultrasonic wave collaboration, and the roadbed compaction quality detection method comprises the following steps: collecting full-wave band ultrasonic echo signals, and obtaining current environment data; calculating a longitudinal wave speed, a transverse wave speed and a wave speed ratio, and extracting an attenuation coefficient and a frequency dispersion characteristic of the ultrasonic echo signal to obtain an acoustic characteristic set; inputting the acoustic feature set and the environmental data into an environmental compensation model, outputting a standard acoustic parameter set subjected to temperature, humidity and water content correction, analyzing the standard acoustic parameter set through a soil texture classification algorithm, and judging the current soil texture type; and combining the standard acoustic parameter set and the current soil texture type into a multi-dimensional feature vector, inputting the multi-dimensional feature vector into a compactness calculation model, and calculating to obtain a roadbed compactness value of the current measuring point. According to the method, high-precision compaction degree numerical calculation is realized, and intelligent and visual whole-process support is provided for roadbed construction quality.

The application belongs to the technical field of mechanical processing, and particularly relates to a mechanism for detecting the axial arc surface center point size of an interlocking rod. The mechanism mainly comprises a measurement standard base (1), the axial center of the measurement standard base (1) is consistent with the center size of each arc surface of the standard interlocking rod, each measurement standard base (1) comprises a center through hole, the measurement standard base (1) has a lateral positioning hole with a through hole on the side edge, which is used for installing a first wedge block (2), and has a top edge positioning hole with a through hole on the top edge, which is used for installing a second wedge block (3). The interlocking rod installed in the center through hole is adjusted through the two wedge blocks, the end of the interlocking rod is pressed against a touch screen instrument (4), and the size of the arc surface center of the interlocking rod (5) deviating from the axial center of the measurement standard base (1) is determined through the touch screen instrument. The application can quickly and accurately determine the axial size deviation of the four arc surface center points of the interlocking rod.

The application discloses a network-constructed photovoltaic station precise active reserve control method and system, relates to the photovoltaic power generation and power system control technical field, and the method comprises the following steps: in the MPPT mode, a global iterative search is carried out on the power-voltage curve of the photovoltaic array by using an intelligent optimization algorithm, and the maximum power point of the photovoltaic array is located; whether the intelligent optimization algorithm converges to the maximum power point is judged by using a multi-index convergence monitoring condition, and the target reserve power is calculated in the case that the intelligent optimization algorithm has converged; the network-constructed photovoltaic station enters a weighted soft switching transition period, and the duty cycle is adjusted based on a dynamic weight; after the weighted soft switching transition period ends, the network-constructed photovoltaic station is switched to an active reserve mode, and in the case that the environment mutates, the network-constructed photovoltaic station is switched back to the MPPT mode. The application realizes precise reservation, stable tracking and fast frequency modulation response of the active reserve of the network-constructed photovoltaic unit.

The utility model discloses a photovoltaic cell EL defect sorter, and relates to the technical field of sorters. Comprising a sorter main body, the sorter main body is provided with a photovoltaic cell EL defect detection mechanism, the detection mechanism comprises a moving assembly, the moving assembly comprises an opening groove formed in the top end of the sorter main body, a positioning rod is fixed to the inner wall of the opening groove, and a sliding rail support is slidably connected to the outer wall of the positioning rod; after the camera lens is detected, the protective shell is screwed on the bolt rod on the outer side of the camera lens, the camera lens is protected through the protective shell, external physical collision and scraping can be effectively resisted, tools, parts and other hard objects are prevented from directly impacting the lens under the daily operation, maintenance or unexpected conditions of equipment, and the camera lens is protected. The surface of the lens is prevented from being scratched and damaged, the optical structure integrity of the lens is protected, the definition and accuracy of a detection image are guaranteed, and pollution of impurities and dust is prevented.

The invention is suitable for the technical field of flight ability training, and provides a flight balance ability training optimization method based on multi-source data analysis, and the method comprises the steps: obtaining a current manipulation input fine tuning index of a pilot, judging whether a corresponding attitude perception conflict index in a change trend exceeds a reference conflict index, and if yes, executing the step 1; and adjusting the manipulation input sensitivity of the manipulation input stage based on the difference between the current manipulation input fine tuning index and the manipulation input fine tuning index corresponding to the turning point. According to the method, the cross-stage correlation model of the'manipulation input stage-balance maintenance stage 'is constructed, so that the foresight evaluation and the active training intervention on the balance ability of the pilot are realized, and compared with the mode that the training strategy is passively adjusted only after the attitude is unstable in the balance maintenance stage in the prior art; according to the method, the risk can be recognized in advance and the manipulation input sensitivity can be dynamically adjusted before the problem is explicit.

The invention relates to the technical field of medical informatics, in particular to a pain management-oriented physiological signal real-time monitoring and intervention system, which comprises a determination module for determining the abnormal tendency of a stimulation window updated in real time at each updating moment to obtain an abnormal tendency sequence; the determination module is also used for determining similar fluctuation segments in the abnormal tendency sequence; the determination module is also used for determining an abnormal process factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation section; the determination module is also used for determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal process factors of each physiological sensing signal at the current moment and the previous moment, and performing normalization processing on the trigger factor to obtain a normalized trigger factor; and the intervention module is used for carrying out visualization and intervention on the pain degree of the target user based on the normalized trigger factor. According to the invention, the physiological signal abnormity identification accuracy and sensitivity are improved.

The application discloses a vehicle seat occupancy detection method and system and a storage medium, and relates to the technical field of vehicle intelligent detection. The method comprises the following steps: in response to an opening operation of a target vehicle door, camera sensing data in a preset time period is acquired, wherein the camera sensing data is used to represent vital sign object information sensed by a surround-view camera of a target vehicle within a preset range of the target vehicle door; and based on the camera sensing data and seat pressure sensor data, the occupancy of a vehicle seat corresponding to the target vehicle door is determined, wherein the seat pressure sensor data is used to represent force information of the vehicle seat sensed by a seat pressure sensor of the target vehicle. The application solves the technical problem that the accuracy of vehicle seat occupancy detection in the related art is low, and thus the vehicle safety reminder function cannot be effectively triggered to ensure passenger safety.

The invention relates to the technical field of battery safety management, and particularly discloses a water system battery thermal runaway early warning method and system based on multiple sensors, and the method comprises the steps: continuously analyzing the real-time cross correlation among sensor data flows, such as voltage, temperature, pressure and hydrogen concentration, and comparing the real-time cross correlation with a preset health state reference model; and isolated abnormal signals which are caused by single-point instantaneous interference and are inconsistent with other data streams can be accurately identified. Besides, a dynamic trust score with historical inertia is established for each sensor, so that the system can judiciously adjust the data weight of the sensor according to the long-term performance of the sensor, and the defect that the early warning model is polluted and missed in report due to the fact that the continuous slow failure of the sensor cannot be identified is overcome. Through the self-adaptive weight-descending fusion of the sensor data with low reliability, a judgment which is more robust and more accurate to the battery state can be finally generated, and the reliability of the early warning system in a complex real environment is improved.

The invention provides a rapid pollutant detection method based on spectral feature analysis, and relates to the technical field of environmental pollutant spectral detection, and the method comprises the following steps: obtaining an original absorption spectrum of a sample, and preprocessing and reconstructing an intrinsic spectral signal; extracting three types of features of morphology, frequency domain energy distribution and phase disturbance, and splicing to obtain a multi-dimensional joint feature tensor; pollutant classification and confidence judgment are completed through an improved CNN, and concentration is predicted through a linear regression model synchronously; in combination with a Mahalanobis distance verification result, deep residual network secondary feature mining is triggered for a low-confidence sample, a final type is determined according to a fusion result, and the concentration is corrected; and generating a detection report and dynamically updating the spectral feature intrinsic library. The method is adaptive to a portable ultraviolet-visible spectrophotometer, the single sample detection time consumption is less than 10min, the classification accuracy is greater than or equal to 90%, the unknown pollutant judgment accuracy is greater than or equal to 89%, and the method is suitable for on-site rapid detection.

The application discloses an airborne radar downburst optimized detection method and belongs to the technical field of radar signal processing. An airborne Doppler radar is used to emit a radar waveform to a possible wind shear dangerous area in front of a route, and wind speed estimation in a transmission direction is obtained by analyzing radar echo IQ data. The application sets a pitch angle of radar scanning according to a take-off or landing stage of an airplane. After receiving the IQ signal, frequency domain correction is performed, data is preprocessed according to spectrum width information, and a wild value area is eliminated. After the preprocessing is completed, a frequency domain clutter suppression algorithm is used to suppress a ground clutter area near zero frequency, and then wind shear wind speed estimation is performed. The application proposes a novel vertical wind speed estimation model, establishes a connection between a downburst core area range and a vertical wind speed gradient, forms a detection and sensing capability for a three-dimensional area of the downburst, and proposes an airplane scanning strategy for optimizing the model.

The invention belongs to the technical field of trace detection and intelligent early warning, and particularly discloses an intelligent sensing fire early warning system based on pyrolysis particulate matter detection. The system comprises a multi-angle polarized light scattering spectrum acquisition device, a physical information neural network inference engine, an airflow interference filtering unit, a pyrolysis dynamics constraint embedding module and a fire trend early warning output unit. A pyrolysis dynamic rule in an Arrhenius equation form is used as a physical constraint to be embedded into a neural network loss function, air flow disturbance is filtered out by combining topological data analysis and computational fluid mechanics, and a pyrolysis reaction stage and a fire behavior increasing trend are reversely deduced by using a multi-angle polarization scattering spectrum. And graded early warning is realized before the particulate matter concentration does not reach a traditional threshold value. According to the technical scheme, the accuracy, the robustness and the response lead of early fire recognition are improved.

The application discloses a liquid crystal screen data optimization management system and method based on big data analysis, and relates to the technical field of electric digital data processing. The liquid crystal screen data optimization management system based on big data analysis comprises a standby power consumption data acquisition management module, a sleep power consumption data acquisition management module and a power consumption data storage management module. The standby power consumption data acquisition management module is used for judging whether a sleep mode switching instruction is sent according to the standby power consumption data acquisition influence result, if yes, the sleep power consumption data acquisition influence result is obtained, otherwise, the standby power consumption data acquisition optimization management is executed, and finally, whether the power consumption data uploading storage management is executed is judged according to the obtained sleep power consumption data acquisition influence result, so that the liquid crystal screen power consumption data management is more accurate, and the problem that the influence of the switch from the standby mode to the sleep mode is neglected when the power consumption data of the liquid crystal screen is managed in the prior art is solved.

This invention relates to a laundry drying apparatus and its control method that provides a water shortage notification while minimizing residual water in a storage tank for storing water used to generate steam. The laundry drying apparatus includes: a housing forming the outer body; a drum into which hot air and steam are supplied; a steam unit for generating steam; a storage tank containing a storage space for storing water to be supplied to the steam unit; a supply pump for transferring water stored in the storage tank to the steam unit; and a steam control unit that, upon receiving a steam supply command, supplies steam to the drum by activating the steam unit. If it is determined that the water level in either the steam unit or the storage tank is below a predetermined level, the steam control unit provides a water shortage notification for the user to recognize. Thus, by providing a water shortage notification while minimizing residual water in the storage tank, the frequency of refilling water by the user is reduced, thereby improving ease of use.

The present application relates to the technical field of medical apparatus and instruments, in particular to a ventilation trigger recognition method and system of an anesthesia machine and the anesthesia machine. The method comprises the following steps: step 1: collecting airway pressure waveform data and flow waveform data of the anesthesia machine; step 2: converting the collected airway pressure waveform data and flow waveform data into matrix data; step 3: inputting the matrix data into a trained convolutional neural network, and the convolutional neural network outputs a probability value based on the input matrix data; step 4: comparing the probability value with a threshold value, and determining whether the ventilation trigger condition is met; if the trigger condition is met, the anesthesia machine starts ventilation support. The present application can more accurately determine the trigger condition by analyzing the shape of the ventilation curve through the convolutional neural network, so as to realize more accurate and safe anesthesia ventilation control.

The invention relates to the technical field of sugarcane spica monitoring, and discloses a sugarcane spica flowering intelligent monitoring system, which is characterized in that a hyperspectral image and an infrared image are acquired, the hyperspectral image and the infrared image are acquired and registered into a multi-source image data set, and a spica area binary mask image is segmented; extracting morphological structure characteristics, spectral reflection characteristics and thermal radiation characteristics, calculating a visibility index KJ, a wave band reflectivity BS and a radiation temperature value FP based on the extracted characteristics, calculating a determination value P, and comparing the determination value P with a hybridization threshold ZY; according to the method, the hyperspectral image and the infrared image of the sugarcane spica are collected, the hyperspectral image obtains the fine spectral reflection characteristics of the sugarcane spica so as to analyze the physiological state of the sugarcane spica, and the infrared image is used for assisting in obtaining the overall shape, contour and thermal radiation information of the spica, and especially, the image characteristics are enhanced in a weak light environment; therefore, the sugarcane spica area can be accurately identified, and the effective distinguishing between the spica and the background can be realized.

The application relates to the technical field of aerial video analysis, and discloses a large-scene multi-modal analysis system and method based on a UAV. The method acquires an original video frame sequence from aerial equipment, extracts a scene change period and an object motion trend by calculating an inter-frame motion feature vector, and judges whether a scene is in a stable or dynamic state by means of a state classification model. In the dynamic state, a tracking target position is calculated according to preset parameters, and an analysis target area is adjusted in combination with the motion trend; a camera parameter adjustment instruction is generated according to a deviation amount of the analysis target area from an actual object position, and is output to an executing mechanism; a complexity value is acquired through a scene complexity feedback signal, and when the complexity value exceeds a preset range, an adaptive algorithm is used to update an analysis strategy and output an adjustment signal; the output timing of control and strategy adjustment signals is optimized, the frame sequence and analysis parameter change trend are monitored in real time, preset parameters are dynamically updated to match user requirements, and efficient video analysis processing is realized.

The application discloses a novel full-line fast distance protection method under a power system, which comprises the following steps: S1, continuously monitoring the voltage and current on one side of the line, and starting the protection when detecting that the current mutation variable exceeds a set threshold; S2, establishing an accurate line differential equation based on a pi type equivalent model; S3, constructing the differential equation in the time domain by using the collected voltage and current data, and directly and real-timely solving the equivalent transition resistance of the fault point; S4, establishing a relationship model of the additional measurement impedance and the equivalent transition resistance, accurately compensating the apparent impedance measured by the protection device, and accurately calculating the real distance from the protection installation to the fault point; and S5, comparing the calculated fault distance with a preset protection section, and realizing the fast isolation of the fault; the application has the advantages of weak feed adaptability improvement, strong anti-transition resistance capability, long line anti-maloperation optimization and fast action speed, and can provide effective technical support for the main protection configuration of new energy sending-out lines and high-proportion power electronic power grids.

The invention provides a rapid screening method and system for retired power batteries, and relates to the technical field of battery detection, and the method comprises the steps: firstly obtaining the circulation historical data of a to-be-detected power battery, and generating a battery aging index; meanwhile, surface temperature data of the battery pack are collected, and a heat distribution diagram is generated through multi-scale fusion processing; then identifying an abnormal heating area in the heat distribution diagram by adopting a convolutional neural network algorithm and generating battery cell position information; then associating a battery aging index with battery cell position information, and carrying out collaborative analysis through a logistic regression algorithm; and finally, on the basis of the analysis result, combining a pre-trained classification decision model to screen out a bad battery cell, and determining the power battery containing the bad battery cell as a retired power battery. According to the invention, the identification efficiency and accuracy of the thermal defect battery cell in the retired power battery are improved.

The application provides a multi-domain extremely narrow pulse radar echo target fusion recognition method based on scattering center matching degree constraint and structural feature auxiliary constraint, constructs an end-to-end classification model for multi-frequency and multi-polarization fusion recognition of an extremely narrow pulse radar echo, can perform feature extraction on input multi-frequency and multi-polarization extremely narrow pulse radar echoes and fusion of frequency dimension and polarization dimension information, and then directly recognizes the fusion features, realizes end-to-end fusion recognition integration, adopts deep learning technology based on feature level fusion, can more fully utilize feature information contained in samples, makes the model make more accurate and stable decisions in the classification task. The method fully mines the correlation between multi-frequency and multi-polarization information, fuses the frequency dimension and polarization dimension information mined, utilizes rich scattering information of multi-frequency and multi-polarization extremely narrow pulse radar echoes, and achieves a relatively ideal target recognition effect.

The problem to be solved by the present invention is to provide a spatter detection method that can detect the generation of spatter using existing sensors without resetting the voltage detection line near the electrode chip. To solve the above problem, in a spot welding method using a spot welding apparatus, pressure is applied to a clamped workpiece using an electrode chip, and a pulsed welding current generated by repeated cycles of alternating energization control and energization pause implemented by a welding power supply circuit is provided to the workpiece. Simultaneously, during energization control, the welding current is maintained within a set peak current range during the peak maintenance interval. The spatter detection method includes: a step of acquiring the average value of the voltage detection value Vpv of the voltage sensor during the peak maintenance interval of each of the aforementioned cycles; and a step of determining the presence or absence of spatter generation based on the difference between the average value of the voltage detection value Vpv during the peak maintenance interval of the Nth cycle and the average value of the voltage detection value Vpv during the peak maintenance interval of the (N-1)th cycle.

The invention provides a method and system for accurately controlling the temperature of an adhesive production reaction kettle, and relates to the technical field of adhesive production, and the method comprises the steps: applying a transient heating pulse to the reaction kettle in adhesive production, collecting the surface temperature data of a container wall, processing the data, calculating a temperature decline curve, and extracting key information of heat transfer characteristics. A heat abnormal area caused by uneven mixing is identified based on spatial distribution of key information, and the thermal state change of the adhesive reaction degree is estimated in combination with a time change mode. And adjusting a heating pulse mode and a working state of the stirring device according to the abnormal area and the change mode, and introducing a pause detection stage to collect surface temperature feedback under weak heating pulses so as to confirm an adjustment effect. And finally, associating the key information with the reaction degree based on feedback to generate a temperature adjusting command, and adjusting the heating or cooling intensity, so that accurate control on the temperature of the reaction kettle can be realized, and the production quality and efficiency are improved.

The present application belongs to the technical field of port safety monitoring, and specifically relates to a method and system for identifying personnel approaching a cable launching area in a port, a terminal and a storage medium, which comprises selecting a fixed cable column of a port berth as a physical reference, using a high-resolution network camera to collect panoramic images of the port berth, using a pre-trained YOLOv8 deep learning model for multi-target segmentation, and performing secondary focusing identification to quickly and accurately identify personnel, cables, cable columns and ship outline targets, and by obtaining dynamic factors such as cable tension and ship docking angle, the reference radius of the dangerous area is corrected by combining the corresponding calculation formula, and a dynamic dangerous area polygon is generated, fully considering the dynamic changes of the port operation environment, which can update the dangerous area in real time, making the division of the dangerous area more in line with the actual situation, avoiding misjudgment and missed judgment due to the fixed dangerous area, and improving the accuracy and effectiveness of safety management.

This invention provides a novel biomarker for determining the pathogenesis of urinary calculi. The biomarker for determining the pathogenesis of urinary calculi disclosed herein is characterized by comprising at least one of calcium phosphate crystals and amorphous calcium phosphate in urine.

The utility model discloses a mixed power control device of telescopic boom crawler crane, include: engine, clutch, motor, integration unit, control unit, battery system, direct current charging mouth and mains interface, hydraulic pump, integration unit includes OBC, PDU, AC DC rectifier module and DC DC conversion module, and battery system is integratively equipped with BMS module, TMS module, and direct current charging mouth and mains interface are electrically connected with battery system and integration unit respectively through central swivel joint, and control unit can acquire charging interface state, battery capacity, clutch pressure and load power in real time, and according to the automatic switching engine mode and motor mode of working condition, through the pressure value of closed loop control hydraulic clutch dynamic adjustment between setting connection point and disconnection point, be applicable to various complex working conditions, through control unit real -time detection and transmission signal control, guarantee that vehicle has sufficient power performance. The utility model is simple to operate, and quick and stable, and the energy utilization rate is high, safe, environmental protection.

The invention discloses a multi-mode non-contact intelligent early warning monitoring method and system, and relates to the technical field of intelligent medical monitoring. Four core physical sign data including body temperature, heart rate, respiratory rate and body movement are collected in a non-contact mode; filtering and denoising are carried out by adopting an LMS adaptive filtering and moving average filtering combined algorithm; extracting each physical sign characteristic parameter; the early warning level is judged through the improved BP neural network intelligent early warning model; when early warning is triggered, local sound-light alarm is started, and early warning information is synchronously pushed to the multiple terminals. The system not only realizes multi-sign non-contact, real-time and accurate acquisition, strong anti-interference capability, accurate early warning judgment and multi-terminal linkage alarm, is suitable for vital sign monitoring of scenes such as hospital wards and pension institutions, but also solves the problems that traditional contact monitoring is poor in comfort level, easy to cross infection and poor in monitoring effect. And the problems of weak multi-mode fusion capability, poor anti-interference performance and low early warning precision of the existing non-contact monitoring are solved.

The application discloses a face abnormal expression recognition method and device, electronic equipment and storage medium, which can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and Internet of Vehicles, and the method comprises the steps of acquiring a face image to be recognized, performing feature extraction on the face image, performing global expression abnormality recognition processing according to the extracted face features to obtain a global expression abnormality recognition result, wherein the global expression abnormality recognition result indicates whether the expression is abnormal, performing state recognition processing on at least one key face area according to the face features to obtain a state recognition result of the at least one key face area, and determining a face abnormal expression recognition result according to the global expression abnormality recognition result and the state recognition result of the at least one key face area. The application can accurately determine the abnormal expression face, and is beneficial to improving the quality of the cover picture.

This utility model relates to the field of urinary catheter machine technology, specifically an automatic feeding mechanism. It includes a notch on the first side of a slide rail and a first elongated slot on the second side of the slide rail, the first elongated slot being opposite to the notch. A first infrared sensor and a second infrared sensor are fixedly mounted on the second side of the slide rail from top to bottom. The infrared radiation from the first infrared sensor is flush with the upper part of the cap on the test tube opening, and the infrared radiation from the second infrared sensor passes through the first elongated slot and is flush with the test tube body. The first and second infrared sensors are electrically connected to a controller. A baffle is located at the notch, and the baffle moves away from or into the notch under the drive of a switching assembly. The switching assembly is electrically connected to the controller. This utility model has advantages such as simple structure, reduced manufacturing cost, and reduced maintenance difficulty.

The application discloses a characteristic peptide of Pheretima guangxiensis and application thereof, and belongs to the technical field of biological medicines. The characteristic peptide is LLNYVDYK, EFTEEQLR, LQAELAR, ALETQVK, FVVLK, ARDDPHCCSAQDR or GILAADESTATIGK. The characteristic peptide is highly specific, and a detection method thereof is simple in operation and high in sensitivity, so that whether the Pheretima guangxiensis component is contained in the animal medicine can be accurately identified, and a scientific method is provided for guaranteeing the quality of the Pheretima guangxiensis and products thereof.

The invention discloses a grain size control method of a reclaimed material hard alloy, which comprises the following steps: S1, component analysis of the reclaimed material: the proportion of oversize products is 0-8% when the reclaimed material passes through a 110-120-mesh sieve; s2, burdening according to the use ratio of the reclaimed material being 50%-99% and the ball-to-material ratio being (2-5): 1, carrying out a ball milling test, preparing a sample, and detecting the coercive force; s3, the raw material ratio and the ball-to-material ratio are equal to those in the step S2, and a mixture is obtained through batching and ball milling; s4, the mixture is subjected to compression molding, and a pressed blank is obtained; and S5, the pressed blank is subjected to pressure sintering, and the reworked material hard alloy with the grain size being 2.0-3.0 microns is obtained. The method is simple and easy to implement, the grain size of the regenerated material can be accurately judged, the integrity and uniformity of alloy grains can be ensured, the prepared regenerated hard alloy has good physical performance, the performance very close to that of a raw material alloy can be achieved, and the application field of the regenerated hard alloy is widened.