482results about How to "Accurate capture" patented technology

The invention relates to the technical field of semantic segmentation, in particular to a night semantic segmentation method based on low illumination enhancement and edge optimization, and the method comprises the steps: inputting an image into a low-light enhancement repair network based on the Retinex theory, local contrast enhancement and adaptive feature fusion, obtaining a denoised and enhanced intermediate image, and carrying out the edge optimization of the intermediate image; inputting the intermediate image into a semantic segmentation network to obtain a category distribution diagram of each pixel; inputting a discriminator embedded with a channel attention module according to the category distribution diagram of each pixel, and optimizing a generator composed of a low light enhancement repair network and a semantic segmentation network through a multi-task joint optimization loss function; and inputting a night image to be detected and segmented into the optimized generator, and outputting a segmentation result. By adopting the method, the low-light enhancement repair network is combined with a local contrast enhancement and channel feature fusion mechanism, so that the overall brightness of the image is improved, the details and edge structures of the image are reserved, and the perception capability and robustness of the model are improved.

The invention belongs to the technical field of optical signal collection and imaging, and particularly relates to a multi-path nonlinear optical signal collection device. The adjusting table is used for bearing a to-be-detected sample and adjusting the spatial pose of the to-be-detected sample, the laser emitting module is used for emitting laser, the to-be-detected sample is excited by the laser to obtain a nonlinear optical signal, the nonlinear optical signal is incident to the overturning mirror through the reflection transmission unit or the transmission transmission unit, and the overturning mirror is used for adjusting the spatial pose of the to-be-detected sample through angle adjustment. The nonlinear optical signal is reflected to a first signal processing module or a second signal processing module, and the first signal processing module processes the nonlinear optical signal from the reflection transmission unit or the transmission transmission unit to obtain a spectral analysis processing result; and the second signal processing module processes the nonlinear optical signal from the reflection transmission unit or the transmission transmission unit to obtain an electric signal processing result. The device is used for collecting non-linear optical signals of far-end detection optical paths of reflection, transmission, oblique incidence and the like.

The invention discloses a short-term traffic flow prediction method based on a dynamic multi-dimensional global perception graph convolutional network, and belongs to the field of traffic flow prediction. The method comprises the following steps: acquiring an input dynamic graph feature sequence, and generating space-time element parameters; carrying out feature enhancement on the dynamic graph feature sequence, fusing the dynamic graph feature sequence with the space-time element parameters, and then respectively carrying out space-time feature coding; fusing the spatio-temporal characteristics obtained by coding the spatio-temporal characteristics; and carrying out space-time trend alignment decoding on the fused space-time features. According to the method, the defects of limited modeling precision, insufficient dynamic adaptability, incomplete spatial-temporal feature capture and the like in short-term traffic flow prediction are overcome, and the accuracy and the applicability of short-term traffic flow prediction are improved.

The embodiment of the invention provides an audio and video alignment method, an audio and video understanding method, electronic equipment, a storage medium and a computer program product. The alignment method comprises the following steps: performing feature extraction and coding processing on a target audio to obtain a first audio feature sequence, and performing block feature extraction and coding processing on a target video to obtain a first video feature sequence; performing visual dynamic enhancement coding processing on the first video feature sequence based on the visual difference of the cross-frame image blocks to obtain a second video feature sequence; performing time sequence information injection on the second video feature sequence and the first audio feature sequence to obtain a third video feature sequence and a second audio feature sequence; and performing cross-modal alignment fusion processing on the third video feature sequence and the second audio feature sequence to obtain an aligned video feature sequence and an aligned audio feature sequence. According to the method, the visual dynamic change corresponding to the audio event can be accurately captured, and the alignment accuracy of the audio and the video is improved.

The invention relates to the technical field of three-dimensional point cloud data processing, and provides a subsidence-water-area-oriented point cloud stripe noise LTCF elimination method and system, and the method comprises the steps: obtaining point cloud data, preprocessing, filtering, and optimizing point cloud output. The filtering processing comprises the following steps: local covariance feature extraction: aiming at each target point in the preliminarily optimized point cloud obtained by preprocessing, constructing a k-nearest neighbor domain, and calculating local covariance contribution values of each target point in X, Y and Z spatial directions; compared with the prior art, the method has the following beneficial effects that the industrial pain point of a subsidence water area scene is solved, a three-direction covariance joint judgment mechanism is innovatively introduced, exclusive recognition logic is designed for specific low-elevation strip noise only containing XY-direction covariance in the subsidence water area, the lamellar strip noise which cannot be recognized completely by a traditional algorithm can be accurately captured, and the recognition accuracy of the subsidence water area scene is improved. And the problem of eliminating the point cloud core noise of the subsidence water area is solved fundamentally.

The invention provides a legal long text abstract generation method based on a large language model, and belongs to the technical field of crossing of artificial intelligence and legal text processing. The technical problems that a traditional legal long text abstract generation method is insufficient in semantic understanding depth, weak in field adaptability, poor in cross-scene generalization ability and high in labor cost are solved. The method comprises the following steps: firstly, carrying out cleaning, logic segmentation and crime determination and sentencing core element pre-labeling on a legal text; secondly, constructing a criminal law four-key modular prompt template; then, strengthening the learning of key legal elements; and finally, quantifying the abstract quality through dynamic verification-optimization closed loop, automatically generating a supplementary prompt guide model for iterative correction when the abstract quality exceeds a threshold value, and outputting an abstract conforming to a criminal law theory framework. According to the method, the abstract can be automatically generated, the labor cost is reduced, and efficient support is provided for scenes such as legal document processing and judicial aid decision making.

The invention discloses a high-performance cast cold roll evaluation method based on stress of a working layer and a bonding layer, and belongs to the technical field of metallurgical mechanical equipment design and life evaluation, and the method comprises the following steps: calculating the maximum contact pressure between a working roll and a supporting roll and the flattening width of a contact surface; further determining the maximum shear stress in the working roll and the depth position corresponding to the shear stress; according to radius changes of the working roller in different wear stages, the maximum contact pressure and the contact surface flattening width in different wear stages are calculated for multiple times, and then normal stress at the position of a bonding layer in each stage and shear stress derived from the normal stress are calculated; performing simulation verification on calculation data based on a stress field of finite element simulation; comparing the shear stress of the bonding layer obtained after calculation with the total stress level after the residual stress is considered, and the allowable shear stress of the same material; and the risk is judged. The service life of the roller can be prolonged, and the equipment loss cost of cold-rolled strip steel production is reduced.

The application provides a quality improvement method and system for hysteroscopic surgery image data, and relates to the technical field of image processing. The method comprises the following steps: acquiring a hysteroscope image of a target patient at a specific time point, and acquiring multi-modal physiological time series data of the target patient before and after the specific time point; performing time series feature fusion analysis on the multi-modal physiological time series data to obtain individual physiological cycle phase information corresponding to the specific time point; inputting the individual physiological cycle phase information into a pre-trained healthy uterine cavity image generation intelligent agent to output a standard healthy uterine cavity reference image corresponding to the specific time point; comparing the hysteroscope image with the standard healthy uterine cavity reference image, generating a pathological residual feature map, and enhancing the display of the pathological residual feature map. The technical problem that abnormal features are not prominent in the existing hysteroscope image is solved.

The application belongs to the field of cutting tools, and provides a turning force measuring tool with embedded flexible sensors and a machine tool, which effectively measures the turning force without affecting the structure of the turning process, and ensures the accuracy and reliability of the signal, and the technical scheme comprises a tool body, the tool body comprises a blade, a positioning assembly and a clamping assembly for clamping the blade; the positioning assembly comprises a freedom degree limiting assembly for limiting the movement of the blade along the x-axis, y-axis and z-axis directions; a flexible sensor is arranged on the lower surface of the blade, a flexible circuit is arranged at the front end of the tool body, the pressure sensor and the flexible circuit are connected through flexible wires; the flexible sensor is used for converting the deformation of the blade under the action of the turning force into a voltage signal; and the flexible circuit is used for calculating the size of the turning force according to the relationship between the voltage signal and the turning force.

The invention provides an underground cable position detection method and system based on a ground penetrating radar, relates to the technical field of ground penetrating radar imaging, and provides a fast search method of an optimal migration speed combined with F-FK, which does not need to traverse all speed values in a scanning interval, and only needs to determine a small number of key search points based on a specific sequence. The search interval is dynamically reduced by comparing the information entropy values of the search points, the number of times of calculation of the imaging entropy values is greatly reduced, and the cable data processing efficiency is greatly improved; secondly, the algorithm does not need a fixed scanning interval, the search point determined through the golden section proportion can adaptively focus the optimal speed area, even if the cable penetrates through the concrete-wet soil mixed medium section, the speed sudden change point can be accurately captured, the problem that the real speed is missed at the fixed interval is avoided, and finally, the algorithm is high in convergence speed and high in accuracy. Compared with a traditional method, the processing time of GPR data of a long measuring line and a high-density sampled cable is greatly shortened, and the method can be applied to emergency troubleshooting.

The present application relates to the technical field of power distribution control, and particularly relates to a power intelligent dynamic distribution method for a skid steer loader based on multi-working condition identification. The method comprises the following steps: performing skid steer loader multi-modal operation characteristic analysis on power states of skid steer loader multi-source operation data to generate skid steer loader multi-modal operation characteristic data; performing skid steer loader working condition characteristic analysis on the skid steer loader multi-modal operation characteristic data to generate skid steer loader working condition characteristic data; performing power demand characteristic analysis on working condition dynamic change trends based on skid steer loader real-time operation data and the skid steer loader working condition characteristic data to generate working condition dynamic change trend power demand characteristic data; and performing skid steer loader power distribution and dynamic optimization processing based on the working condition dynamic change trend power demand characteristic data to generate skid steer loader power distribution dynamic optimization data. The present application realizes efficient and energy-saving intelligent distribution of power under multi-working conditions of a skid steer loader.

The present application provides a kind of forest tree species classification method, device, electronic equipment and storage medium, belong to artificial intelligence technical field, method includes: forest remote sensing image is input to forest tree species classification model, obtains the output tree species classification result;Forest tree species classification model includes preliminary feature processing module, high-order feature extraction module and tree species classification result output module, high-order feature extraction module is sequentially connected first residual block stack layer, first down-sampling layer, second residual block stack layer, second down-sampling layer, third residual block stack layer, third down-sampling layer and fourth residual block stack layer, stacked residual block embedding dimension rearrangement layer, dimension recovery layer and convolution block attention module.The present application improves the dimension compatibility of CBAM and convolution architecture by embedding explicit dimension rearrangement layer and dimension recovery layer in the residual block embedded with CBAM, effectively improves the classification accuracy of forest remote sensing image for forest tree species classification task.

This application relates to a method for identifying hourly-scale extreme high temperatures using temperature series data. The method includes: preprocessing hourly temperature data to obtain an hourly-scale temperature series, and calculating a daily-scale average temperature series based on a meteorological daily window. Based on the hourly-scale temperature series and historical reference periods, determining hourly-scale extreme high temperature thresholds for different hours and identifying hourly-scale extreme high temperatures. Based on the daily-scale average temperature series and historical reference periods, determining daily-scale extreme high temperature thresholds for different dates and identifying daily-scale extreme high temperature days and non-daily-scale extreme high temperature days. Extracting the corresponding times that meet the hourly-scale extreme high temperature criteria from both daily-scale and non-daily-scale extreme high temperature days to obtain the corresponding hourly high temperature identifier sequences. This method enables accurate identification of the specific time periods and distribution characteristics of extreme high temperature events, avoiding the omission of high-temperature events.

The application discloses a kind of emotion evaluation and intervention method and system based on multi-modal data.Through terminal synchronous acquisition voice, face, screen tremor and so on multi-modal data;Reaction mode vector is generated based on context information and data verification fusion;The composite emotional state of user is determined using emotional emergence model.At the same time, personalized emotional causal diagram is constructed and updated, and the enhanced state vector is generated by extracting key causal path features.The vector is input into the intervention agent to output intervention actions;After execution, the emotional state is evaluated again and the reward is calculated, and the system model is updated online.The application realizes accurate emotion evaluation and closed-loop adaptive intervention that strikes the root.

The invention discloses a distributed optical fiber-grating-based rockburst test system and method, solves the problem that in the prior art, only strain parameters are simply obtained in a rockburst test, and has the beneficial effects of accurately simulating rockburst and obtaining related parameters of the rockburst. According to the specific scheme, the rockburst test system based on the distributed optical fiber-grating comprises a test device, the test device comprises a vibration table, and the vibration table supports a supporting box body; the rock sample is provided with an optical fiber channel, and the distributed optical fiber penetrates into the optical fiber channel; the vibration table, the stress applying component and the drilling disturbance component are respectively connected with the control unit; the monitoring unit is connected with the distributed optical fiber and the grating optical fiber; the monitoring unit sends monitored data to the control unit, and the control unit judges when rockburst is triggered and obtains stress change, elastic strain energy release amount and strain abrupt change time difference at the moment of rockburst when the rockburst occurs.

The application belongs to the technical field of positioning and measurement, and particularly relates to a target positioning method and device combining CSI coarse positioning and Y-type CCD fine correction, which comprises the following steps: S1, CSI acquisition equipment is arranged in a target area, and CSI data between a sending end and a receiving end is acquired; S2, a Y-type CCD sensor is geometrically calibrated and a coordinate system is corrected, and coordinate reconstruction is completed; S3, the CSI data is preprocessed, noise is removed, and input is normalized; S4, time domain features of the CSI are extracted by using a time domain branch; S5, frequency domain features of the CSI are extracted by using a frequency domain branch; S6, a gating fusion module is used to fuse the time domain and frequency domain features, and the approximate position coordinates of a target are output; S7, the Y-type CCD sensor is controlled to optically acquire a target area, and point light source coordinate information is acquired; and S8, error correction is performed on the data acquired by the Y-type CCD, and the accurate position coordinates of the target are output.

The application provides a fiber-optic Fabry-Perot sensor based on light beam convergence and a manufacturing method thereof, and relates to the technical field of semiconductors. The sensor comprises a diaphragm layer, an intermediate layer and a bottom layer which are arranged in layers; the diaphragm layer is provided with a first groove, the intermediate layer is attached to the diaphragm layer in a manner that a first layer surface thereof faces the first groove, so as to form a Fabry-Perot cavity based on the first groove; the bottom layer is provided with a second groove, the intermediate layer is attached to the bottom layer in a manner that a second layer surface thereof faces the second groove, so as to form a nano-containing cavity based on the second groove; the second layer surface of the intermediate layer is formed with a micro-nano anti-reflection structure comprising array-distributed anti-reflection units located in the nano-containing cavity; and the bottom layer is formed with a micro-nano collimation structure comprising array-distributed collimation units located in the nano-containing cavity and in a symmetrical positional relationship with the micro-nano anti-reflection structure. The application at least reduces the manufacturing complexity.

The invention relates to the technical field of high-speed aircraft health management, and particularly discloses an interpretable aircraft state sensing and predicting system and method. The method comprises the following steps: constructing and training a lightweight anomaly diagnosis network with an expansion perception layer; aircraft dynamics, kinematics and fault mechanism formulas are used as soft constraints to be embedded into the future state estimation network; an SHAP interpretation framework is used for sorting the importance of the input features, and a network structure is optimized accordingly; and finally, accurate diagnosis of abnormal types and positions and rapid prediction of aircraft states at a plurality of moments in the future are realized. According to the method, by introducing three innovation points of an expansion perception layer, physical information embedding and feature importance sorting, the diagnosis accuracy and the model interpretability are remarkably improved, and the high-performance calculation requirement in a scene with limited calculation power is met.

This application provides a method and apparatus for detecting covert communication behavior, relating to the field of network security technology. On one hand, the method uses a dynamic time warping algorithm to determine the minimum cumulative distance between the packet length-timestamp sequence and the standard response traffic template sequence in the packet length dimension. This quantifies temporal misalignment differences, thus solving the problem of missed detection caused by the lack of temporal analysis capabilities in traditional methods. On the other hand, based on the arrival timestamp of the packet length-timestamp sequence, the method determines the differential entropy and the maximum time span of sudden events. The differential entropy can amplify the minute temporal fluctuations caused by the mechanical packet sending of tunnel traffic, while the maximum time span can identify deliberately lengthened idle periods in tunnel traffic. Combining these two methods can accurately capture gradual covert communication such as slow tunnels. Therefore, this method can improve the accuracy of detecting covert communication behavior.

The invention relates to the technical field of deep sea resource development, in particular to a magnetic type deep sea ore collecting device which comprises a rigid pressure-resistant main frame, mechanical legs, mechanical arms, an ore processing module, a communication module, a pushing module and a positioning module. A sealed pressure-resistant cabin is arranged in the rigid pressure-resistant main frame; the mechanical legs are symmetrically installed on the two sides of the rigid pressure-resistant main frame. The mechanical arm is installed on the front end face of the rigid pressure-resistant main frame. The ore processing module is arranged along the center line of the rigid pressure-resistant main frame; the communication module is connected with the water surface support mother ship through an armored cable; the pushing module comprises a plurality of horizontal propellers and a plurality of vertical propellers; the positioning module is installed on the upper side face of the rigid pressure-resistant main frame. The device provided by the invention has the advantages of good terrain adaptability, high collection integrity, small disturbance and reliable operation, and is suitable for selective collection operation of resources in a deep sea complex environment.

This invention provides a method, apparatus, electronic device, and storage medium for predicting species distribution, relating to the field of ecological data analysis technology. The method includes: acquiring raw data of the target species, training multiple base models in parallel based on the raw data, and outputting the species existence prediction probability and model performance indicators of each base model; constructing an environmental state based on the model performance indicators, using the environmental state as the first reward signal, and adjusting candidate environmental variables using a proximal strategy optimization algorithm to obtain an optimal subset of environmental variables; wherein the candidate environmental variables are determined based on the raw data; determining multi-source training samples for geographical units based on the optimal subset of environmental variables; performing multiple rounds of iterative training on each base model based on the multi-source training samples to output a final weight allocation scheme; and performing weighted fusion processing on each base model based on the final weight allocation scheme to generate a species distribution prediction result. This invention can effectively improve the accuracy of species distribution prediction.

This invention proposes a control method, device, vehicle, and storage medium for an electronic lock on a vehicle-mounted cargo box. The method includes: when the tailgate of the cargo box is electrically operated, the vehicle's central control system is activated to sense whether the vehicle is within a preset sensing area to determine whether to automatically lock or unlock; if the vehicle's central control system is not activated, the tailgate is locked or unlocked via a remote control unit. When the side door of the cargo box is electrically operated, the motor is controlled by the remote control unit and the side door switch unit to lock or unlock the side door. The vehicle-mounted cargo box electronic lock control method proposed in this invention can perform locking and unlocking operations on the tailgate and side doors of the cargo box through multiple locking and unlocking modes, solving the problems of time-consuming, labor-intensive, and easily worn-out operation in existing modified cargo box partitioning schemes and locking and unlocking modes.

The embodiment of the invention discloses a screenshot method and device based on a remote application, a server and electronic equipment. The method is applied to a screenshot agent module, the screenshot agent module runs on a server where a remote application is located, and the method comprises the following steps: intercepting a screenshot instruction from the remote application; sending the screenshot instruction to a client corresponding to the remote application; receiving screenshot data returned by the client, wherein the screenshot data is obtained by the client in response to the screenshot instruction and performing screenshot on a local screen image of the client; and sending the screenshot data to the remote application. By means of the method, the screenshot instruction of the server side is intercepted and sent to the client side, then the client side captures the corresponding screenshot data and then returns the screenshot data to the remote application of the server side, and therefore the purpose of obtaining the local screenshot data of the client side in the remote application of the server side is achieved. And the use experience of the remote application of the server and the remote application of the client is consistent.

The application discloses a kind of based on regional meteorological characteristics wind farm wind speed refinement prediction method, it is related to wind power prediction technical field, including: first, using convolutional neural network (CNN) extraction wind farm in the grid meteorological data of multiple pressure layers in region, to obtain regional meteorological characteristics;Second, using long short-term memory network-multi-head self-attention mechanism (LSTM-MHSA) model processing wind farm location multiple vertical isobaric surface sequence meteorological data, to extract vertical wind profile feature;Third, using multilayer perceptron (MLP) extraction wind farm surface static meteorological elements in nonlinear feature.Finally, the above-mentioned three different dimensions, different sources of features are fused, and a coupled prediction model is constructed to output short-term wind speed prediction results for wind farms.The application significantly improves the accuracy and refinement level of short-term wind speed prediction for wind farms by comprehensively integrating regional, vertical and surface multi-source meteorological information.

The invention discloses a sea cucumber accurate fishing robot suitable for a shallow sea culture area, and relates to the technical field of aquaculture equipment.The sea cucumber accurate fishing robot comprises a rack, an equipment cabin, a collecting cabin, a power supply cabin and a plurality of propellers are installed on the rack, and a fishing mechanical arm matched with the collecting cabin is installed on the front side of the rack; a precise fishing framework is arranged in the equipment cabin, the precise fishing framework identifies the positions of sea cucumbers in the shallow sea culture area and controls the fishing mechanical arm to fish the sea cucumbers and then put the sea cucumbers into the collecting cabin, and a power supply unit is arranged in the power supply cabin to supply power to the precise fishing framework and the fishing mechanical arm. Through fusion perception, turbid water body image enhancement and accurate positioning, full-process autonomous accurate fishing of the sea cucumbers in the shallow sea high-turbidity environment is achieved, manual diving operation is replaced, the fishing efficiency and the product quality are greatly improved, and intelligent upgrading of the sea cucumber culture industry is promoted.

The application discloses a high-precision spectral noise elimination method and system based on TDLAS technology and relates to the technical field of gas concentration optical detection. The method obtains the center frequency and half-width parameter of a Lorentz model by fitting the standard gas sampling data, determines the adaptive cutoff frequency according to the center frequency and the half-width parameter, and performs digital low-pass filtering on the to-be-detected gas sampling data. The filtered data is divided into an absorption interval and two non-absorption intervals. The local phase information of the interference fringes in the non-absorption intervals is extracted and weighted third-order polynomial fitting is performed to obtain the baseline of the whole region. The application extracts the local phase of the interference fringes in the non-absorption intervals in real time and converts the local phase into the basis for weighted fitting. The processing link is constructed by combining adaptive filtering and constrained Lorentz fitting. The phase extrapolation compensation is used to eliminate the residual error of the interference fringes caused by temperature fluctuation, the spectral absorption area more accurately reflects the real absorption strength, and thus the precision of low-concentration gas detection is improved.

The application relates to the technical field of equipment health management, and discloses a battery replacement cabinet fault prediction and health management method and system. The method comprises the following steps: acquiring an original temperature sequence, an original humidity sequence, a battery module number and a battery replacement cabinet position number, performing abnormal value correction to obtain a corrected temperature sequence and a corrected humidity sequence; performing abnormal fluctuation point screening according to the corrected temperature sequence and the corrected humidity sequence to generate an abnormality judgment record; removing interference according to the abnormality judgment record to obtain a refined feature set; performing abnormal risk analysis according to the refined feature set to obtain an overheating risk report; generating an intervention instruction according to the overheating risk report to obtain an initial execution scheme; driving operation is performed according to the initial execution scheme to obtain an equipment operation log; and performing reliability evaluation according to the equipment operation log to obtain a reliability evaluation conclusion. The method can realize early warning and health management of battery replacement cabinet faults.

The invention discloses an image translation-based inclined view angle rotator vibration displacement measurement method. The method comprises the following steps of: performing cutting operation on an acquired rotator vibration image under a standard view angle and an acquired rotator vibration image under L random view angles; randomly extracting a preset number of random view angle cutting images and standard view angle cutting images of matched frames from the L random view angles, and taking the standard view angle cutting images as labels of the random view angle cutting images to construct a data set; training and verifying the image translation model through the training set and the verification set to obtain a trained image translation model; selecting one of the obtained rotating body vibration images of the preset time length under the L random view angles, inputting the selected rotating body vibration images into a trained image translation model, and obtaining a standard view angle image of a frame number corresponding to the preset time length; carrying out image splicing operation according to the standard view angle images with the frame number corresponding to the preset duration to obtain a spliced image; and performing edge detection on the spliced image, extracting an edge contour of the rotating body, obtaining sub-pixel-level displacement data, and converting the sub-pixel-level displacement data into a displacement value under a world coordinate system. According to the invention, the vibration displacement of the rotor can be effectively and accurately extracted from a plurality of shooting angles, and a more reliable and accurate scheme is provided for the measurement of the vibration displacement of the rotating body.

The invention discloses an automatic spring clamp forming machine, which relates to the field of spring clamp preparation and comprises a spring clamp rotary feeding bin, spring clamp reciprocating up-down forming equipment, spring clamp carrier pressure maintaining equipment and spring clamp pressure maintaining completion discharging equipment. By the adoption of the scheme, through full-automatic mechanical preparation, the spring clamp preparation efficiency is improved; and after the spring clamp is folded and bonded, the pressure of the spring clamp can be maintained for a certain time, so that the spring clamp is prevented from being bounced off due to glue failure caused by the fact that glue is not dry, and the yield is improved.