1163 results about "Wave crest" patented technology

The invention relates to the technical field of cable fault detection, in particular to a cable fault analysis system based on big data, which comprises a disturbance feature extraction module, a time sequence hierarchy judgment module, a reflection characteristic analysis module, a topological section identification module and a fault path mapping module. According to the method, gradient inflection points are extracted by calculating voltage and current increment included angles at adjacent moments, spatial-temporal characteristics of disturbance signals are correlated, interference of noise to a starting point mark is reduced, time difference is fitted in combination with cable length, deviation over-threshold abnormal points are dynamically filtered, influences of synchronization errors on sorting are eliminated, and fault path mapping stability is improved; the method comprises the following steps: generating characteristic parameters by fusing amplitude values and wave crest intervals of main waves and reflection waves, quantifying amplitude attenuation and phase offset coupling, enhancing the multi-path superposed signal separation capability, judging the reflection characteristic similarity of a physical section based on a Manhattan distance and a support vector machine, and dynamically dividing a fault section in combination with line topology. The problem of fuzzy positioning in a branch parallel scene in a traditional method is solved.

The invention relates to the technical field of steam turbine testing, in particular to a testing system for mismatch correction of a wide-load-interval thermal power generating unit speed regulation model, which comprises a thermodynamic system component, a speed regulation system device, a dynamic signal acquisition device and a control device, a dynamic signal processing module analyzes the relevance between main steam pressure sudden change and control valve opening oscillation in real time, a dynamic response database including a time sequence is constructed, and a control valve opening feed-forward compensation instruction and a combustor fuel distribution gradient adjustment signal are generated according to correction parameters. Power over-modulation wave crests caused by sudden load change are suppressed, and primary frequency modulation action delay is reduced; and the simulation verification module compares measured data with model output through an error feedback mechanism, dynamically adjusts high-pressure cylinder flow characteristic curve calibration parameters, optimizes a control strategy in combination with a network-related safety margin evaluation result, and realizes improvement of dynamic response precision in a full peak regulation interval and enhancement of power grid frequency stability.

The invention relates to the technical field of disaster monitoring, in particular to a mine inrush water image processing system based on AI recognition, which comprises a fissure water flow tracking module, a voiceprint disturbance extraction module, an acceleration mapping module, a form analysis module and a risk output module. According to the method, a fine voiceprint perturbation sequence is formed by extracting crack water flow boundary pixel coordinates from continuous frame images, accurately capturing water flow micro-displacement and generating a deviation track in combination with an inter-frame difference between a gray gradient amplitude and a direction angle, and synchronously extracting a liquid level perturbation wave crest position and a voiceprint primary and secondary frequency amplitude difference; the acceleration is calculated according to the pixel displacement speed and mapped to a time axis, so that the boundary movement trend and morphological change are synchronously analyzed in the time dimension, risk frame locking is completed by multi-source signals in the same time period under double-threshold judgment, and three-dimensional presentation of the water flow dynamic state is achieved. The accuracy and reliability of sudden water gushing risk judgment in a complex mine environment are remarkably improved, and the probability of missing detection and misjudgment is reduced.

The invention discloses a large-section long-footage mechanical blasting mixed excavation method suitable for a subway station, and relates to the technical field of underground rail transit engineering, and the method comprises the following steps: obtaining a tunnel section contour, a geologic structure and blasting parameters, building a three-dimensional blasting wave propagation inversion model, and combining geology and material heterogeneity to obtain a three-dimensional blasting wave propagation inversion model; and identifying a closed space region forming reflection interference, and predicting a space point location where blasting wave energy is abnormally accumulated. Based on the three-dimensional blasting wave propagation inversion model, the abnormal wave energy accumulation area is predicted in combination with geological heterogeneity, the high-risk blasting section is recognized, parameters are reconstructed, vibration real-time monitoring and model feedback correction are assisted, a closed-loop control system is constructed, local wave crest abnormity is effectively restrained, and the safety and controllability of large-section tunnel blasting construction are improved.

The invention discloses a solar cell, a photovoltaic module and a manufacturing method of the solar cell, relates to the technical field of solar cells, and aims to solve the problem of poor quality of a patterned film layer in the prior art. The solar cell includes a semiconductor substrate and a first doped semiconductor layer. The semiconductor substrate comprises a first surface and a second surface which are opposite, and the first surface is provided with first areas and second areas which are alternately distributed along a first direction. The first doped semiconductor layer is arranged in the first region, and the first doped semiconductor layer is not arranged on the second region. The side surface of the first doped semiconductor layer close to the second region is wave-shaped along a second direction, and the first direction is different from the second direction. In the first direction, the distance between a wave crest and a wave trough of the wave shape is greater than or equal to 1 mu m and less than or equal to 4 mu m; and / or the average roughness of the wave shape extending in the second direction is smaller than or equal to 2.4 microns.

The invention relates to the technical field of vibration monitoring, in particular to a self-powered transmission line fitting aeolian vibration damage diagnosis system and method, and the system comprises a sensing collection module, a signal decoupling module, a damage identification module, a damage association module and a risk assessment module. According to the method, stress wave velocity and acceleration data are synchronously collected, time alignment is implemented, feature coupling precision is enhanced, wave crest offset and energy density are respectively extracted by using moving average filtering and wavelet transform, effective data segments are dynamically screened, and environmental noise interference is suppressed. A stress wave propagation change rate is quantified based on a path attenuation model, a continuous energy abnormal node is matched to realize damage positioning, a breeze response abnormal region is identified by combining vibration direction change and a signal envelope offset degree, multi-dimensional features are coded and subjected to risk judgment through a neural network, a damage positioning and risk assessment closed-loop framework is formed, and the risk assessment accuracy is improved. And the spatial resolution and evaluation precision of aeolian vibration damage identification under complex working conditions are significantly improved.

The invention discloses an electrocardiosignal classification method based on a multi-threshold pulse neural network, which comprises the following steps: acquiring an original electrocardiosignal, and denoising the original electrocardiosignal by using wavelet transform; segmenting the denoised electrocardiosignals into cardiac beat segments according to R wave crest positions, and dividing the cardiac beat segments into a training set and a test set; based on the discharge characteristics of the biological neurons, multi-threshold integral pulse distribution neurons are constructed, and a multi-threshold pulse neural network is constructed; training and testing are carried out, a gradient agent method is used for adjusting parameters of the pulse neural network, an optimal configuration network is obtained, and classification of electrocardiosignals is achieved. According to the method, the extraction capability of the pulse neural network on the electrocardiosignal time sequence characteristics in the training process is improved; and meanwhile, the original sparsity and biological inspiration characteristics are reserved, so that the method not only has excellent performance in the aspect of processing time sequence data, but also can remarkably reduce the calculation energy consumption, and is suitable for portable medical equipment and resource-limited edge equipment.

The invention discloses an ultrasonic-assisted broaching machine tool based on a resonance broach and a design method. The machine tool comprises a broaching machine structure; a groove of the ultrasonic broaching tool is located in the position corresponding to the vibration mode wave crest of the broaching tool; the main piezoelectric actuator device is arranged in the groove; the compensation piezoelectric actuator device is arranged at the position, corresponding to the radial vibration mode, of the ultrasonic broach; the frequency of the ultrasonic power supply and the main power supply is the corresponding frequency when the axial ultrasonic vibration amplitude is maximum and the radial ultrasonic vibration amplitude is minimum under different power supply frequencies, the main driving voltage is determined according to the frequency of the main power supply, the preset broaching maximum speed of the broach and an ultrasonic auxiliary broaching effectiveness coefficient, and the frequency of the compensation power supply is the same as the frequency of the main power supply; the phase difference is 180 degrees, and the compensation driving voltage is the corresponding driving voltage when the ratio of the radial ultrasonic vibration amplitude to the axial ultrasonic vibration amplitude in different driving voltages is smaller than or equal to a preset threshold value. The ultrasonic assistance of the broach which needs to be frequently clamped can be realized.

The invention discloses an impedance measurement method for a power electronic power system. The method comprises the steps of setting a frequency point set based on a frequency range of preset impedance measurement; constructing a disturbance signal set comprising a plurality of sinusoidal signals; with the initial phases of all the sinusoidal signals as independent variables and the time-domain wave crests after the superposition of the plurality of sinusoidal signals as dependent variables, constructing a target function and solving to obtain a minimum time-domain wave crest superposed sinusoidal disturbance signal; for the component to be detected, based on the sine disturbance signal corresponding to the injection position, the sampled voltage and current data are analyzed by adopting fast Fourier transform to obtain impedance frequency domain information of the component under each frequency point, and the impedance frequency domain information comprises impedance amplitude and phase information; and performing fitting based on impedance frequency domain information under a certain frequency point so as to obtain a complete impedance curve of the component in impedance measurement. The method provided by the invention can ensure that the response information of the corresponding frequency can be accurately identified in the FFT process of the subsequent impedance information solving.

The invention relates to the technical field of oil and gas exploitation, and discloses an oil and gas well water production position positioning method, device and equipment based on distributed optical fiber sound waves and a medium, and acoustic signals corresponding to a plurality of production profiles can be collected through a distributed acoustic sensor arranged in an oil and gas shaft. And performing preprocessing, time-frequency conversion and normalization on the acoustic signal corresponding to each production profile to obtain a normalized acoustic spectrum corresponding to each production profile. And extracting a characteristic wave crest section from the normalized acoustic spectrum corresponding to each production profile. And carrying out reverse normalization on the characteristic wave crest segment corresponding to each production profile to obtain a reverse normalization wave crest segment. And based on the water phase response frequency band, determining a matched first wave crest section in the anti-normalized wave crest section corresponding to each production profile, and determining the production profile corresponding to each first wave crest section as a water production position. According to the invention, intelligent detection of the water production position of the oil and gas well can be realized, and the detection precision of the water production position is effectively improved.

The invention discloses a signal detrending method based on sliding window statistics and dynamic wave crest correction. The method comprises the step of realizing accurate extraction of signal trend and fluctuation characteristics through a double-window dynamic adjustment mechanism of a mean value window and a standard deviation window. A detection threshold value is dynamically adjusted by analyzing the change trend of the historical wave crest amplitude, and the real-time tracking capability of signal fluctuation is ensured; meanwhile, different parameter configurations are preset for different application scenes, the problem of missing detection or misjudgment generated when the amplitude of a fixed threshold algorithm changes suddenly is solved, and the wave crest recognition accuracy is improved. Based on a dynamic threshold algorithm, wave peak start and end points are automatically identified, a linear interpolation technology is adopted to calculate slope of each interval, fitting parameters are dynamically adjusted through residual analysis, and finally low-frequency trend terms and high-frequency characteristic components are effectively separated through difference operation of original signals and fitting signals. The problem of fitting deviation caused by non-uniform sampling can be effectively solved.

The invention relates to the technical field of tumble prediction, in particular to a tumor patient tumble prediction system based on machine learning, which comprises a stride frequency recognition module, a physiological fluctuation monitoring module, a gait coupling judgment module, a posture analysis module and a risk output regulation and control module. According to the method, by accurately positioning the wave crests and the wave troughs in the acceleration signal period and analyzing the frequency change direction of the adjacent sections, accurate marking of the mutation sections is achieved, the sensitive capture capacity of behavior state changes is effectively improved, and the synchronous extraction mode of the mutation sections and the physiological signals is improved. Through consistency comparison of attitude angle change and gravity center track direction, accuracy of motion structure abnormity and imbalance state identification is enhanced, pressure distribution and spine compression direction are extracted, corresponding pairing between structure pressure and physiological load is realized, optimization of time sequence integrity is realized, and accuracy of motion structure abnormity and imbalance state identification is improved. And multi-dimensional data fusion analysis has high pertinence and stability.

The invention relates to the technical field of equipment anomaly recognition, and particularly discloses an equipment anomaly behavior recognition method and system based on audio and video streams, and the method comprises the steps: collecting equipment operation audio decibel values, preprocessing the audio decibel values to obtain digital audio streams, converting the digital audio streams to obtain a detection spectrogram, calculating a wave crest offset ratio and a wave crest number ratio, and obtaining section anomaly values; the method comprises the following steps: acquiring a video image of an equipment connection part, calculating a position and an angle offset ratio by using an algorithm to obtain a stable value of a part, judging and generating an abnormal analysis signal, further calculating an abnormal difference ratio, a stable difference ratio and an equipment abnormal ratio based on the abnormal analysis signal, drawing a change curve, and calculating an angle offset ratio, variance and the like to obtain linear values. The system comprises an audio acquisition and analysis module, a video analysis module and an abnormity judgment module. The device abnormal behavior can be effectively identified, and the device maintenance efficiency is improved.

The invention discloses a power transmission line fault positioning method and system based on multi-dimensional magnetic field measurement. At least two multi-dimensional magnetic field measurement devices are arranged on power transmission towers at different positions of the same power transmission line; based on the multi-dimensional magnetic field measuring device, determining components of magnetic field intensity in the x, y and z directions of the space, and monitoring dynamic magnetic field distribution data in the space of the power transmission line in real time; determining a sensitive directional component based on the dynamic magnetic field distribution data; performing data preprocessing on the determined sensitive direction component, and establishing a space-time magnetic field intensity matrix based on the preprocessed sensitive direction component; based on the space-time magnetic field intensity matrix, establishing a magnetic field intensity time-varying oscillogram, and based on the magnetic field intensity time-varying oscillogram, determining a wave crest of sudden change of the magnetic field intensity and a moment corresponding to the wave crest; and determining a fault position based on the wave crest and the moment corresponding to the wave crest.

The invention provides a three-dimensional skin waviness fitting analysis method. The method comprises the following steps: performing boundary analysis on skin point cloud data obtained by laser measurement, performing interpolation sampling from the point cloud data according to a specified area and resolution to obtain point cloud coordinates which can reflect the real condition of the skin and are regularly arranged, performing bicubic spline surface least square approximation based on a sampling point set, and calculating the skin point cloud data according to the bicubic spline surface least square approximation. The method comprises the following steps: acquiring a reference curved surface reflecting the overall distribution trend of an analysis area, calculating distance deviation from a sampling point to the reference curved surface, carrying out filtering smoothing processing, forming a smooth deviation curved surface capable of reflecting the deviation condition of a measurement curved surface, extracting information such as wave crest and wave trough positions, wave amplitude, wavelength and slope in the deviation curved surface, and carrying out skin waviness characteristic analysis. According to the method, the tiny waviness characteristics of the irregular curvature surface of the aircraft can be extracted, the skin waviness distribution condition can be visually displayed, and the method has the advantages of being wide in applicability, small in error, high in reliability, low in time cost and the like.

The invention relates to the technical field of sediment monitoring equipment, and discloses an online pipeline particle monitoring system based on ultrasonic detection, which solves the problem of low monitoring accuracy, and comprises a to-be-detected pipeline, the outside of the to-be-detected pipeline is slidably connected with a front fixing belt and a rear fixing belt, and the top of the front fixing belt is provided with a monitor lower shell. A plurality of ultrasonic detectors and a processing module are arranged at the top of the monitor lower shell, an alarm is arranged at the top of the monitor lower shell, a supporting column is arranged in the to-be-detected pipeline, a cleaning knife is arranged at the left end of the supporting column, a cleaning disc is arranged at the rear end of the cleaning knife, and moving wheels are arranged outside the supporting column; through a non-contact monitoring method, the ultrasonic signals generated when silt impacts the pipe wall are obtained, the granularity and the wave crest and wave trough density are judged according to the amplitude of the amplified frequency domain electric signals to judge the number, the characteristics of the silt in the pipeline are well reflected, and therefore the monitoring efficiency is improved.

The invention discloses a characteristic value calculation method based on electrocardio data, belongs to the field of medical data analysis, and provides an electrocardio data analysis and processing method. Comprising the following steps: decomposing different frequency components of an electrocardiosignal by using wavelet transform, and analyzing on different time scales. Wavelet transformation is relatively suitable for detecting QRS wave groups, and due to the fact that the wavelet transformation can highlight transient characteristics of signals on different frequency bands, complex electrocardiosignal processing is more efficient. In addition, wavelet transform is not only suitable for wave crest detection, but also can help to remove signal noise and improve signal definition. The deep learning technology is used for automatically extracting the features from the electrocardiosignals, a feature extractor does not need to be manually designed, related medical professional knowledge and the artificial intelligence technology are combined, wave crests in the electrocardiosignals can be more accurately detected, and meanwhile anomaly detection can be carried out, such as early signs of heart diseases such as sinus arrhythmia and the like.

The utility model discloses a metal belt material and a honeycomb heating body with the same, and the metal belt material comprises a characteristic belt which is provided with a plurality of wave crest parts and wave trough parts, the wave crest parts and the wave trough parts are alternately arranged, first fire holes are formed between the adjacent wave crest parts, and second fire holes are formed between the adjacent wave trough parts; the partition structure comprises a first partition piece and / or a second partition piece, the first partition piece is arranged at the lower end of each first fire hole, and the first partition piece divides the lower end of each first fire hole into at least two small holes; a second partition piece is arranged at the lower end of each second fire hole and divides the lower end of each second fire hole into at least two small holes. The burner is simple in structure, convenient to manufacture, high in porosity and capable of reducing the tempering probability and improving the heat intensity of the fire holes.

The invention relates to the technical field of partial discharge detection, in particular to a partial discharge detection method and system for a high-low voltage switch cabinet, and the method comprises the steps: obtaining electromagnetic wave signals and power system voltage data of each sensor position in real time; acquiring a partial discharge period in each detection window according to the electromagnetic wave maximum value of all the sensors in each power frequency period in each detection window, the distance between the electromagnetic wave maximum value and the voltage wave crest and the voltage wave trough of the power system, and the electromagnetic wave signal difference between the sensors; and according to the dispersion degree of the electromagnetic wave maximum values of all the sensors under each partial discharge period in each detection window, the electromagnetic wave signal frequency domain energy of each partial discharge period and the proportion of the partial discharge period in each detection window, obtaining the existence confidence of each detection window, and judging whether partial discharge exists under the current detection window or not. According to the invention, by analyzing the distinguishing characteristics of the partial discharge and the electromagnetic interference in the electromagnetic wave signals at multiple positions, the detection precision of the partial discharge is improved.

The invention provides an aviation nacelle part waviness automatic detection and evaluation method, and belongs to the field of product production detection, and the method comprises the steps: carrying out the protocol scanning of part surface contour features, and obtaining the undistorted coordinate point discrete point cloud data of the actual surface contour; filtering the point cloud data; obtaining a piece of flattened deviation value discrete point cloud data; obtaining a flattened deviation value continuous curve by adopting a general standard curve reduction method; extracting waviness components, and obtaining a curve center line; obtaining a crest value and a trough value participating in calculation; and calculating the maximum wave amplitude, the waviness and the slope for evaluating the waviness by using the wave peak value and the wave trough value. According to the method, the relation between the minimum cut-off wavelength of the waviness and the sampling interval is stipulated, the undistorted reduction of the surface profile curve is ensured, the influence of non-waviness profile components in the digital evaluation of the waviness is eliminated through a combined calculation method, and the evaluation accuracy of the waviness of the aviation nacelle part is improved.

The invention discloses an ion chromatography wave crest boundary extraction method based on fitting residual and tangent judgment, and belongs to the technical field of ion chromatography analysis. Comprising the following steps: S1, reading an original waveform data sequence, and executing frequency reduction sampling according to a set step length; s2, calculating the slope of the data after underclocking sampling by using a difference method, and searching positive and negative slope sections by performing first-order derivation on the waveform after underclocking sampling; s3, noise interference is eliminated, slope areas are grouped, then a chromatographic peak detection limit value is set, and an effective peak left section and an effective peak right section are screened out; s4, combining the effective wave crest left side section and the effective wave crest right side section to obtain an effective wave crest and a preliminary left boundary point and a preliminary right boundary point of the effective wave crest; s5, fitting a straight line through the preliminary left boundary point and the preliminary right boundary point to position an error integral region; and S6, updating the optimal boundary point by traversing the evaluation indexes of all points in the error integral region. The boundary search range is more accurate, and the calculation efficiency is higher.

The invention provides a covered stent coated with an electrostatic spinning membrane and a preparation method thereof.The covered stent comprises a metal stent and a covering membrane, the metal stent is composed of a spiral metal wire, the metal wire is provided with a plurality of zigzag parts which are evenly arranged and connected end to end, every two adjacent zigzag parts are in a wave crest shape and a wave trough shape respectively, and the multiple zigzag parts enable the metal wire to be in a wave shape; the covering film covers the surface of the metal support to form a tubular structure, and the tubular structure can be kept shaped after bending deformation at the lesion position so that the tubular structure can be matched with the lesion position in shape. The covered stent has a high specific surface area and an adjustable porous structure, the micron-sized aperture of the covered stent can effectively prevent blood permeation, the high porosity allows a proper amount of tissue or cells to penetrate, vascularization and tissue integration of the implanted stent are promoted, and therefore the risk of stent displacement or instability is reduced.

The invention discloses a dry-type air-core reactor turn-to-turn short circuit fault detection method based on the average offset of resonance wave crests, and the method comprises the steps: constructing a circuit model of a dry-type air-core reactor to simulate normal operation and turn-to-turn short circuits at different fault degrees and fault positions, and obtaining a corresponding impedance logarithmic amplitude spectrum; respectively calculating the resonance wave crest offset and the characteristic value of the impedance logarithmic amplitude spectrum corresponding to each turn-to-turn short circuit; obtaining an impedance logarithmic amplitude spectrum of the dry-type air-core reactor to be detected, and calculating a resonance wave crest offset and a characteristic value; and if the resonance wave crest offset is greater than the preset threshold value, matching the resonance wave crest offset and the characteristic value with the resonance wave crest offset and the characteristic value of the impedance logarithmic amplitude spectrum corresponding to each turn-to-turn short circuit, and determining the turn-to-turn short circuit fault position and fault degree of the to-be-detected dry-type air-core reactor according to the matching result. According to the invention, the accuracy of turn-to-turn short circuit fault detection of the dry-type reactor is improved, and the safe operation of the dry-type air-core reactor is ensured.

The invention provides an automatic digging method of an excavator based on image recognition, which is realized based on a material pool and the excavator, a laser radar and a binocular camera are arranged on the upper edge of the material pool, and the excavator comprises a digging arm, a double-bucket assembly and a control unit. The method comprises the following steps: determining a peak initial position by a laser radar material pile three-dimensional point cloud, and performing secondary calibration by a camera to obtain a peak final position and a slope angle; the control unit switches a first / second digging mode according to the distance between the wave crest and the excavator, the first mode controls the excavator bucket to conduct wall-attached digging and conduct bottoming detection through the pressure sensor, and the second mode adjusts the angle of the excavator bucket to conduct digging according to the slope angle; when no wave crest exists, a corresponding digging scheme exists, and the point cloud is updated after digging each time; the wave crest positioning precision and the environmental adaptability are improved, the digging efficiency is optimized, manual intervention is reduced, and the device is suitable for digging of materials such as cereals and gravels.

The invention discloses a corrugated steel web bridge deformation monitoring temperature lag compensation method and system, and the method comprises the steps: constructing time sequence data of a corrugated steel web, the time sequence data comprises a time sequence composed of a wave crest temperature, a wave trough temperature, a strain measurement value, a wind speed and solar radiation intensity in any fold period of the corrugated steel web at any moment; according to the time sequence data, calculating the temperature lag time of the temperature to the structure response by using a sliding cross correlation coefficient algorithm of the frequency domain energy weight; time sequence data are reconstructed according to the temperature lag time, and the reconstructed time sequence data are obtained to serve as state vectors of the corrugated steel web; the state vector of the corrugated steel web is input into the strategy network of reinforcement learning, an optimal action instruction of the next step is output and obtained, and the optimal action instruction comprises the step that a compensation signal is applied to time sequence data; according to the optimal action instruction, the predicted state vector of the corrugated steel web at the next moment is obtained; and obtaining a strain prediction value of the corrugated steel web according to the state vector of the corrugated steel web at the next moment. Dynamic and high-precision compensation of the corrugated web bridge temperature hysteresis effect is achieved for the first time.

The invention relates to the technical field of optical fiber sensing, in particular to an MOMES and FBG fusion sensing system based on a tunable laser array. A multi-wavelength tunable laser array light source is adopted to generate fast broadband frequency sweep laser, the fast broadband frequency sweep laser is split to M paths to be output through a multi-path planar optical waveguide shunt coupler, each path of output end is connected with N FBG sensor arrays and one MOMES sensor, and sensing signal reflected light is received by a corresponding photoelectric detector. After data processing is carried out through a photoelectric converter, a logarithmic amplifier, an analog-to-digital converter and an FPGA compatible algorithm platform, wave crests and wave troughs of reflection spectrum data of each wavelength point are searched at the same time; the wave crest data is used for demodulating the FBG sensor array, and the wave trough data is used for demodulating the MOMES sensor. According to the optical path structure and algorithm design of the system, synchronous demodulation of the FBG and the MOMES sensor is realized, and the compatibility and operation efficiency of the system are remarkably improved.

According to the roof waterproof external member and the roof structure, the construction efficiency of the roof waterproof external member is higher, and the waterproof reliability of the roof structure is higher. The roof waterproof external member comprises at least two groove-shaped members and at least one prefabricated member, the groove-shaped members are the same in structure, and the prefabricated member is provided with a prefabricated member groove body. The groove inner side surface of the groove-shaped part and the groove outer side surface of the prefabricated part groove body are covered with waterproof films; when the two groove-shaped pieces are arranged in parallel, the top end of the second groove wall of the first groove-shaped piece can be connected with the top end of the first groove wall of the second groove-shaped piece, so that the first groove wall and the second groove wall form a wave crest; the shape of the groove body of the prefabricated part is matched with the shape of the wave crest, so that the groove body of the prefabricated part can be buckled on the wave crest, and the waterproof film of the groove-shaped part and the waterproof film of the prefabricated part form a continuous waterproof film layer in the buckled state.

The invention discloses a method for automatically calculating the waviness of an aircraft profile in an MATLAB environment, and the method comprises the steps: 1, obtaining the plane section point location data of the waviness of the aircraft profile based on the reverse point cloud data of the aircraft profile; and step 2, automatically calculating the airplane profile waviness in the MATLAB environment, including plane classification of point location data, three-dimensional coordinate system conversion, two-dimensional wave crest and wave trough point location calculation, wave crest and wave trough point coordinate system reverse conversion, waviness calculation and form report output on the point location data of the plane cross section obtained in the step 1 through a program in the MATLAB environment. Therefore, automatic calculation of the airplane appearance waviness is completed. According to the invention, the problem that the existing detection mode for the airplane profile corrugation is difficult to have convenient operability, detection precision and detection efficiency at the same time is solved.

The utility model discloses a metal belt material and a honeycomb heating body thereof, the metal belt material comprises: a characteristic belt, which is provided with a plurality of wave crest parts and wave trough parts arranged alternately, fire holes are formed between adjacent wave crest parts, and fire holes are formed between adjacent wave trough parts; the lower end of the characteristic belt is connected with the vertically-arranged separation belt, the separation belt transversely penetrates through each wave crest part, each wave trough part and each fire hole, and the lower end of each fire hole is divided into at least two small holes by the separation belt. The metal belt material is simple in structure, high in porosity and capable of reducing the tempering probability and improving the heat intensity of area fire holes.

The invention discloses a heart rate self-calibration monitoring system and method based on a deep learning model, in the system, a data acquisition module acquires an ECG signal, a multiband PPG signal and an acceleration signal; the data processing module filters and amplifies the signals acquired by the data acquisition module; the analog-to-digital conversion module performs analog-to-digital conversion on an output signal of the data processing module; the state detection module classifies user states based on the acceleration signals; the signal calibration module is used for dynamically adjusting the wave band weight of a light-emitting wave acquired by the PPG signal according to a user state to obtain a weighted and optimized multiband PPG signal, and adjusting the wave crest position of the PPG signal according to the R wave position in the ECG signal to complete calibration of the PPG signal; the heart rate calculation module performs heart rate prediction based on a deep learning model and outputs a heart rate prediction result; and the interaction module transmits the heart rate prediction result. According to the invention, stable and accurate heart rate monitoring can be provided.