116 results about "Wavelet noise" patented technology

The invention discloses a bearing fault diagnosis method based on improved EMD decomposition and sensitive characteristic selection. The method comprises steps of: performing wavelet noise reduction and EMD decomposition on the original vibration signals of a bearing in different fault states to obtain a plurality of IMF components; selecting, by quantitatively computing the correlation of each IMF component and the corresponding original vibration signal, the first h IMF components including the main fault information of the bearing as an object from which fault characteristic information is extracted, and extracting the characteristic parameters from the IMF components to form a original characteristic set; determining the sensitivity factor of each characteristic in the original characteristic set according to a distance evaluation method and constructing a sensitive characteristic set; inputting the sensitive characteristic vector of a training sample in the fault sample of the bearing into a SVM to be trained, optimizing the kernel function parameter g and the penalty factor c of the SVM according to a genetic algorithm, and identifying the fault of a tested sample. The method may reduce the dimensionality of the fault characteristic vector and the computational scale of a classifier, and increasing fault diagnosis accuracy of the antifriction bearing.

The invention discloses a milling cutter wear prediction method and a state recognition method. The wear prediction method comprises the following steps that firstly, wavelet noise reduction processing is performed on milling vibration data, feature extraction is performed on vibration signals from three aspects including time domain, frequency domain and time domain, after an initial feature vector set is obtained, a correlation coefficient method is used for calculating the correlation between feature vectors and wear amount, and an optimal feature vector set is obtained by screening; then,an average relative error predicted by a least squares support vector machine is defined as a fitness function of an adaptive step size cuckoo search algorithm, and by searching for a nest position, input parameters of the least squares support vector machine are optimized; finally, the wear amount is predicted by using the optimal least square support vector machine. Through comparison with two other hybrid intelligent algorithms, the superiority of an ASCS- LSSVR algorithm is verified.

The invention discloses a bearing fault diagnosis method based on an LS-SVM and the D-S evidence theory. Through a multi-level information fusion method, the least squares support vector machine (LS-SVM) is adopted in a feature layer, and the D-S evidence theory is adopted in a decision-making layer to solve the problems that the single sensor fault diagnosis accuracy is low and the sensitive features are difficult to extract. Firstly, the wavelet noise reduction technology is used to improve the signal-to-noise ratio of a rolling bearing vibration signal, and eight parameters in the time domain and the frequency domain are introduced as feature parameters of the bearing vibration. Secondly, the bearing is fault identified by the LS-SVM. Thirdly, the LS-SVM feature output is used as the D-S evidence theory input, and the D-S evidence theory is used for fault decision making. The method can effectively improve the accuracy of rolling bearing fault diagnosis. The invention has certain significance for improving the fault diagnosis accuracy of the rolling bearing and the reliability of the diagnostic system.

The invention discloses a loosening detection device and method for a fastening bolt of a high voltage breaker, and belongs to the fault detection field of a power system. The detection device comprises a vibration accelerometer laid on the fastening bolt to be monitored in the high voltage breaker and a signal collection module connected to the output end of the vibration accelerometer, and the output end of the signal collection module is connected to a signal processing module. The accelerometer is not limited by the narrow internal space of the breaker due to the fact that the accelerometer is small and light compared with other intelligent detection means, and safe and reliable operation of the detection device is ensured. According to the detection method, violent vibration excitation in the opening / closing process of the breaker is utilized, the vibration accelerometer collects vibration signals near the target bolt, sensitive vibration waveform extraction, wavelet noise reduction, blind source separation and wavelet packet energy entropy analysis are carried out on the vibration signals, and whether the fastening bolt of the high voltage breaker is loosened is determined bytaking an established bolt loosening detection database as reference.

The invention discloses an urban gas buried pipeline leakage detection and locating method and applications thereof. Based on an acoustic emission technology and combined with the current gas pipeline laying situation of Changzhou city, layout schemes and relevant parameters of sensors are designed, and the leakage detection method under the situation of guaranteeing normal gas transmission of buried pipelines and not excavating pipelines is determined. Simultaneously, wavelet noise reduction and relevant analysis methods are introduced, and accordingly, the locating accuracy is improved, and objective and credible results are obtained. According to the urban gas buried pipeline leakage detection and locating method and applications thereof, the application of the acoustic emission detection technology in gas buried pipeline leakage detection is facilitated and improved to a large extent, and hence, while the method is effective and accurate in detection and cost saving, popularization and use values are increased, and the method has great significances in enhancement of urban gas buried pipeline risk control.

The invention discloses a cascaded arc fault detection method and a special device for the cascaded arc fault detection method and relates to electrical fault detection devices. The cascaded arc fault detection method is based on a voltage waveform of an electric appliance, the voltage across the two ends of the electric appliance is used as a detection object, a difference mean value method is adopted to analyze the waveform of the voltage across the two ends of the electric appliance, and signals are subjected to wavelet noise reduction; a difference mean value obtained after processing is used as a characteristic quantity of an arc, and whether an arc fault happens is judged according to the characteristic quantity. The special device for the cascaded arc fault detection method comprises a cascaded arc fault simulation test unit and a cascaded arc fault detection unit. The cascaded arc fault detection method and the special device for the cascaded arc fault detection method overcome the defects that in the prior art, by the adoption of the arc fault detection method of extracting sudden change characteristics of current signals, the reliability is not high, and a misjudgment is easily made.

The invention belongs to the field of underwater sound communication, and particularly relates to a channel estimation method for reducing gaussian noise and interpolation error in channel estimation. The channel estimation method for underwater sound orthogonal frequency division multiplexing comprises the following steps that (1) channel frequency response estimation is obtained through the channel estimation method based on a least square method; (2) noise reduction is carried out on the pilot frequency response estimation through a wavelet noise reduction method to obtain the channel frequency response subjected to noise reduction; (3) the channel frequency response of a data sub-carrier is obtained by carrying out a discrete Fourier transform interpolation method on the channel frequency response subjected to noise reduction; and (4) the received data is balanced through the channel frequency response of the data sub-carrier, the balanced data is encoded and re-modulated, comb-shaped pilot frequency is converted into block-type pilot frequency, and the received data is judged, fed back and balanced through a block-shape minimum mean square error channel balancing method. According to the channel estimation method for underwater sound orthogonal frequency division multiplexing, the impact of the channel noise on channel estimation and the interpolation error are reduced, and the judging performance and the channel estimation precision are improved.

The invention discloses a wood damage monitoring method based on an acoustic emission technique. The method specifically comprises the following steps: arranging an acoustic emission sensor on the surface of a wood stress concentration part; collecting weak acoustic emission signals generated by wood damages; amplifying and filtering the collected acoustic emission signals, carrying out analogue-to-digital conversion, and then transmitting the converted signal to an FIFO in an FPGA control module and buffering; uploading data to an upper computer through a wireless transmission module; carrying out wavelet noise reduction and reconstitution on the collected acoustic emission signals collected by the upper computer; positioning a wood-damage acoustic emission source by a linear positioning method, and monitoring the position of the wood-damage acoustic emission source in real time through a human-computer interaction interface designed by LABVIEW; and extracting energy characteristic values form the collected acoustic emission signals by the upper computer by virtue of a wavelet packet analysis system, constructing a corresponding training sample set, building a neural network, and predicting and analyzing the variation trend of the acoustic emission signals of wood stress damages through accumulated energy so as to deduce the positions of the wood damages.

The invention discloses a method of fault diagnosis on a ball socketed bearing of a steel-making converter by comprehensive analysis. The method carries out fault diagnosis on the ball socketed bearing of the steel-making converter by using vibration signal analysis and grease oil analysis. The method comprises the following steps of: firstly, selecting evenly distributed measuring points on the periphery of the outer ring of the bearing, and then punching bearing seats at the measuring points; carrying out resonance demodulation and wavelet noise reduction analysis on acquired bearing vibration signals; firstly, selecting centre frequency and bandwidth according to frequency bands generated in resonance peaks in a frequency spectrogram to carry out bandpass filtering, and then carrying out wavelet noise reduction and Hilbert demodulation treatment on the filtered signals; on the basis of detecting the conventional typical properties, carrying out qualitative and quantitative analysis on the components and the content of metal wearers in oil samples by using spectrum analysis and ferrographic analysis, and judging according to the detection result; and comparing and synthesizing two analysis results to make a fault diagnosis report. The invention can effectively increase the accuracy of fault diagnosis, thereby being beneficial to discovering fault hidden troubles as early as possible and avoiding major accidents happening.

The invention relates to a noise removal method with noise IMF (intrinsic mode function) components and electrocardiography signals. According to the noise removal method, by the aid of an EEMD (ensemble empirical mode decomposition) algorithm and a new wavelet threshold value noise removal method, the shortcomings of a soft threshold function and a hard threshold function are overcome relative to a traditional wavelet noise removal algorithm, traditional threshold values are improved, and used flexibility and used practicability of the threshold values are improved. By the aid of a combined mode of dual threshold values and a single threshold value, calculated amount is decreased, and efficiency is improved.

The invention discloses an improved operating condition transfer path analysis method capable of increasing precision. The method comprises the following steps of step1, collecting operating condition excitation and response signals; step2, using a wavelet noise reduction technology to carry out noise reduction processing on the collected signals; step3, using the signals after noise reduction processing to calculate a frequency response function; step4, calculating a load; step5, calculating a transfer path contribution amount. The method possesses advantages that transfer path contribution amount calculating precision is effectively increased and so on.

The invention relates to an adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method, belonging to the technical field of failure recognition of a rolling bearing. The technical scheme is that the method comprises the following steps of: (1) performing adaptive redundant lifting wavelet transformation of a bearing vibration signal; (2) performing variable-size threshold noise reduction processing on a high-frequency detail signal obtained by each decomposition process; (3) performing complete reverse reconstruction on a low-frequency approximation signal obtained by final decomposition and a high-frequency detail signal subjected to wavelet threshold noise reduction; and (4) performing Hilbert demodulation processing on a reconstructed signal to obtain an envelope spectrogram of an initial vibration signal, extracting and recognizing a frequency component in the spectrogram, and judging that a bearing fails if frequency conversion or failure characteristic frequency and even corresponding frequency multiplication occurs. The adaptive redundant lifting wavelet noise reduction analysis-based bearing failure recognition method has the beneficial effects that a threshold can be flexibly selected according to the characteristic of change of noise in a wavelet region, so that noise can be filtered better, and meanwhile, the completeness of a real signal can also be guaranteed as much as possible.

The invention provides a temperature compensation method for an accelerometer based on wavelet noise elimination, comprising the following three steps: step 1: designing an experiment scheme to carry out fixed point high-temperature and low-temperature measurement experiments; and utilizing collection software to collect data; step 2: pre-processing the data collected by the step 1, eliminating abnormal values and carrying out noise elimination treatment; and step 3: through the analysis, utilizing the data processed by the wavelet noise elimination to identify a temperature model structure of the accelerometer and solve identified model parameters. According to the temperature compensation method disclosed by the invention, the data obtained by a lot of experiments are processed by the wavelet noise elimination; and the identifications of the model structure and the parameters are carried out on the base, so as to establish a static temperature model of the accelerometer. The model established by the method completely accords with real-time compensation requirements of an engineering. Therefore, the temperature compensation method has better practical value and wide application prospect in technical fields of aviation and aerospace navigation.

The invention provides a method for eliminating signal online outliers. Firstly, the elimination of outlier energy in signals is finished by performing detection and linear compression on a wavelet function after signal wavelet transformation, and simultaneously, a method for linear elimination of the outliers protects signal characteristics nearby an outlier point and avoids a concussion phenomenon caused by the elimination of the outliers; secondly, aiming at the working characteristics of a micro-machined gyroscope, a method for performing a wavelet noise reduction threshold value calculation by using a virtual outlier concept is put forward to realize the noise reduction of signals of a ship micro-machined gyroscope; and finally, micro-machined gyroscope actually-measured signals containing the outliers and noises are filtered by using the method provided by the invention to ensure that a signal-to-noise ratio higher than that obtained by a universal threshold value noise reduction method is obtained while reducing the concussion nearby the outlier point. The method used for filtering has a small influence on a signal value nearby the outlier point, and the filtering effect is smoother.

A method and a system of predicting an electric system load based on wavelet noise reduction and empirical mode decomposition-autoregressive integrated moving average (EMD-ARIMA) are provided. The method and the system belong to a field of electric system load prediction. The method includes the following steps. Raw load data of an electric system is obtained first. Next, noise reduction processing is performed on the load data through wavelet analysis. The noise-reduced load data is further processed through an EMD method to obtain different load components. Finally, ARIMA models corresponding to the different load components are built. Further, the ARIMA models are optimized through an Akaike information criterion (AIC) and a Bayesian information criterion (BIC). The load components obtained through predicting the different ARIMA models are reconstructed to obtain a final prediction result, and accuracy of load prediction is therefore effectively improved.

The invention relates to an electromyography signal noise reducing and aliasing removing method based on combination of second-generation wavelet transform and ICA and aims to overcome some revealed defects when the current signal aliasing removing methods are applied to electromyography signals. The method includes: a second-generation wavelet noise reducing algorithm is first used to filter noises in the electromyography signals, the second-generation wavelet noise reducing algorithm is applied to the electromyography signals after noise reduction is smooth, unnecessary vibration in waveforms is restrained, and the electromyography signal features are evident; ICA separation is performed on the vague signals after noise reduction so as to fast remove the aliasing components in the signals. The method has the advantages that by the pre-treatment, interference in the signals can be removed greatly, and convenience is brought to subsequent researches such as electromyography signal feature extraction and action identification.

The invention provides a power microwave communication system wavelet noise reduction method. The method comprises the steps that b, according to different communication signals, a wavelet primary function is selected and wavelet decomposition level is determined; c, the signals are filtered to acquire a wavelet coefficient; d, according to the preset wavelet decomposition level, threshold quantization processing is carried out on the wavelet coefficient to acquire a quantized wavelet coefficient; and e, a reconstructed signal is acquired through wavelet reconstruction filtering. According to the invention, the original signals are decomposed into a series of approximate components and detail components through wavelet decomposition; detail component processing and wavelet reconstruction are carried out to extract useful communication signals; weak signal characteristics are extracted in a strong noise background; noise in the signals can be effectively reduced and even eliminated; wireless communication demodulation signal to noise ratio and signal relevance can be significantly improved; phase distortion and bit error rate are reduced; the power microwave communication performance is improved; and the problems of large electromagnetic background noise and many signal harmonic components of power communication are solved.

A more effective noise reduction method is provided. In the method, when mass spectrum information having a spatial distribution is processed, the whole data is taken as three-dimensional data (positional information is stored in an xy plane, and spectral information is stored along a z-axis direction), and three-dimensional wavelet noise reduction is performed by applying preferable basis functions to a spectral direction and a peak distribution direction (in-plane direction).

The invention relates to a hydraulic cylinder leakage analysis diagnosis method comprising the following steps: combining wavelet decomposition with wavelet noise reduction, using wavelet analysis todecompose hydraulic cylinder two-chamber pressure signals into four layers, extracting segment pressure signal time domain characteristics, combining a root mean square and a root mean square error ofwavelet decomposition coefficients cA4 and cD2 to evaluate the hydraulic cylinder leakages, thus diagnosing the hydraulic cylinder internal and external small leakage rates. The hydraulic cylinder leakage analysis diagnosis method uses two-chamber pressure sensors arranged in hydraulic cylinder without adding extra sensor, thus effectively detecting the common hydraulic cylinder internal and external leakages, and preventing accident hidden troubles caused by hydraulic cylinder leakage factors.

The invention discloses an information entropy theory noise canceling method in hydrological sequence analysis, which comprises the following steps: firstly, selecting wavelet function and wavelet decomposition layer number according to the basic property of a hydrological time sequence to be analyzed, and then carrying out discrete wavelet transform for the hydrological sequence to acquire wavelet coefficients d(j,k) on different time scale levels; determining a wavelet coefficient threshold value of each layer by using the optimized entropy rule of the wavelet coefficient threshold value; and carrying out hard or soft threshold value quantizing treatment for the high-frequency wavelet coefficient of each layer, and then reconstructing the treated wavelet coefficient to acquire a reconstructed main sequence in a real measured hydrological sequence, wherein the difference of the really-measured hydrological sequence and the reconstructed main sequence is a noise component, namely the separation of the noise component is realized. Based on the information entropy theory and the wavelet noise canceling thought, the method establishes the optimized entropy rule of the wavelet coefficient threshold value so as to effectively separate the noise component in the hydrological sequence by using a wavelet analysis method, and improves the reality and accuracy of hydrological data.

The invention discloses an oil-gas pipeline leakage monitoring system based on an optical fiber. The system involves a DAS system based on a time-gated optical frequency domain reflectometer, a signal processor and an optical fiber to be tested, wherein the optical fiber to be tested is arranged at an input end of the DAS system, and the signal processor is arranged at an output end of the DAS system. According to the system, a high-performance distributed optical fiber acoustic wave sensing technology and a wavelet noise reduction algorithm are applied to detection of gas pipeline leakage, the spatial resolution is determined by the frequency sweeping range of a detection optical signal and is independent of the duration of a optical pulse, so that the contradiction between the spatial resolution and the detection distance in the prior art is solved, the meter-scale high-spatial resolution, high sensitivity and long sensing distance can be realized, and meanwhile, the influence of ambient noise is eliminated, so that the detection sensitivity to weak leakage signals is remarkably improved.

The invention aims to provide a method for detecting small targets of a sea surface by utilizing a least squares support vector machine (LS-SVM) on the basis of wavelet noise reduction. The method comprises the steps that: a section of original sea clutter data is selected, is de-noised and is then subjected to chaos judgment, and embedded dimensions and time delays are extracted; another sectionof original sea clutter data is selected, is subjected to wavelet decomposition and denoising and is normalized; the best regression parameters of the SVM are searched; obtained data are trained by using the LS-SVM as a tool; another group of sea clutter data is selected, is preprocessed and is then predicted by using an obtained sea clutter model, so short-term prediction data corresponding to the data are obtained; the obtained short-term prediction data are subtracted from a data value which is only preprocessed, and absolute values of the difference values are obtained, so as to obtain absolute errors; the absolute errors are classified by using the LS-SVM which is optimized by using particle swarms, two-dimensional information classification is equivalent to threshold limitation, andsmall target detection in a strong sea clutter background is finally realized. By adopting the method, the false-alarm probability is greatly reduced, and thereby the detection performance is improved.

The invention relates to a reciprocating compressor air valve fault diagnosis method based on a time-varying singular spectrum, and the method specifically comprises the steps: collecting an air valvevibration acceleration signal and a pressure signal, carrying out the wavelet noise reduction and decomposition of an initial vibration acceleration signal, and removing a pseudo-component; selectinga proper time delay to construct a window function, and introducing time information into the multi-fractal analysis of the vibration signal, thereby obtaining a time-varying singular spectrum; and enabling the drawn time-varying singular spectrogram of each working process to describe the whole and detail information of the vibration signal more finely, so that parameters with large influence difference for a certain fault type are captured, fault feature vectors are convenient to construct, an SVM classifier is convenient to establish, parameters are optimized and set, and finally, the fault diagnosis of the air valve is successfully realized. The problem of fault diagnosis of the air valve of the reciprocating compressor is solved.

The invention relates to a pore throat recognition method with consideration of a pore throat tail end and in particular to a method for recognizing pores and throats in microscopic visualization model oil displacement experiment images in the field of oilfield development. The method comprises the following steps: carrying out binaryzation treatment on images; carrying out length transformation on space of pores and throats; extracting a center line by using a turning point algorithm; cancelling inner paths in the pores to obtain path curves of the rest of paths; carrying out wavelet noise reduction on the path curves; finding bottleneck positions of the pores and the throats and carrying out pore throat recognition without consideration of the tail end; extracting a center line by using a combustion algorithm and finding the pore throat tail end; extracting a center line of the tail end by using the turning point algorithm by means of an endpoint of a combustion center line of the tail end; and combining the center line of the turning point algorithm and carrying out the pore throat recognition method with consideration of the pore throat tail end. With combination of the combustion algorithm and the turning point algorithm, the tail end part can be independently recognized; the extracted center line is good in centering effect; the relative errors of recognition of the pore throat length are reduced.

The invention relates to the technical field of generalities of batteries, and particularly discloses a battery matching method. The method specifically comprises the following steps: acquiring a timeseries curve of a battery voltage of sample battery formation; completing a missing value of the time series curve by using a KNN (K-Nearest Neighbor) algorithm; eliminating interference informationin a process of obtaining the time series curve by adopting a wavelet noise elimination method; aligning a time series of a time series curve to be compared by adopting a dynamic time wrapping method;performing primary screening on a sample battery by adopting voltage and capacitance standards; clustering the sample battery according to a static final voltage of the sample battery formation and acapacitance measured by discharging by adopting a Gaussian mixed model; and matching the clustered sample battery according to a characteristic distribution density of the sample battery. The Gaussian mixture model is used to cluster the sample battery, and then the clustered sample battery is matched according to the characteristic distribution density of the sample battery, so that a battery pack is higher in consistency due to the adoption of the voltage and capacitance dual process data.

The invention provides a submarine sound signal noise reduction method. The method comprises steps that firstly, a submarine sound signal is inputted to decompose into multiple modal components; secondly, the multiple modal components acquired through decomposition are transformed to a frequency domain for processing; thirdly, a threshold function is set according to noise distribution characteristics of the submarine sound signal to filter noise; fourthly, the multiple modal components after processing are transformed to a time domain; and lastly, reconstruction of the submarine sound signal after noise reduction is carried out. The method is advantaged in that compared with a method in the prior art, noise is effectively eliminated, through decomposing the submarine sound signal into a series of modal components, identification of noise in the modal components is facilitated, and subsequent processing is convenient; compared with a common spectral subtraction method, the method has an advantage in processing non-stable signals, the physical meaning of the signal itself can be better presented, moreover, through designing the threshold function, the modal components are transformed to the frequency domain to carry out filtering processing, and energy statistic characteristics of the noise and the signal are fully utilized; compared with a common wavelet noise reduction method, the method has higher noise reduction performance.

The invention discloses a method for acquiring real-time high-precision displacement of a dam through integration of a GNSS and a ground monitoring network. The method comprises the following steps that S1: the ground monitoring network and a satellite monitoring network are established; S2: a deformation database and a displacement satellite database are converted into the displacement of the same initial state relative to the same reference system; S3: interpolation is performed on the deformation database of the dam surface of long data acquisition interval time so that a displacement database synchronized with the dam displacement satellite database is obtained; S4: noise reduction is performed on the displacement database after interpolation expansion and then data fusion is performedthrough combination of the adaptive weighted mean method and the satellite database processed by wavelet noise reduction; and S5, the data accuracy is calculated, compared and recorded and the wavelet basis is preferably selected according to the data accuracy so that the corresponding fusion displacement sequence in the S4 is obtained. Fusion calculation is performed on the two sets of databasesafter noise reduction through combination of the adaptive weighted mean method, and the obtained database sequences after fusion have the characteristics of being real-time, smooth, continuous, high-precision and reliable.

The invention discloses a leakage positioning method based on ultrasonic wave speed change. The method comprises the following steps: a leakage positioning formula based on ultrasonic wave change of an oil conveying pipe to be detected is built; ultrasonic wave speed sensors are mounted at head and tail stations of the oil conveying pipe to be detected for acquiring ultrasonic wave speed signals;a negative-pressure wave speed from a leakage point to the ultrasonic wave speed sensors at the head and tail stations of the pipe is determined; the wavelet noise removal is performed on the wave speed signals acquired by the ultrasonic wave speed sensors; difference values between the ultrasonic wave speed signals at the head and tail stations of the pipe after the wavelet noise removal and theultrasonic wave speed signals at the head and tail stations of the pipe under normal working conditions are calculated; and ultrasonic wave speed signal change quantities at the head and tail stationsof the pipe and the negative-pressure wave speed are introduced in the leakage positioning formula based on the ultrasonic wave speed change to position the leakage point. The leakage positioning method based on the ultrasonic wave speed change prevents the problem of positioning of the leakage point by using the time difference of leaked sound waves reaching the head and tail stations of the pipe; and when the leakage point of the pipe is positioned, the leakage point is positioned by using the ultrasonic wave speed change quantities of the head and tail stations of the pipe, so that the nondestructive installation on the pipe is achieved, and the feasibility and the applicability of the leakage detection by using the ultrasonic sensors are improved.

The invention discloses a method for detecting the phosphorus content of jujube tree canopies. The method comprises the following steps: acquiring high spectral information of the jujube tree canopies in sunny, cloudless or slightly cloudy and below 3 level wind power weather; carrying out multiplicative scatter correction on the high spectral data and carrying out wavelet noise reduction and smoothing treatment; extracting the spectral reflectivity of the jujube tree canopies with the phosphorus content to be determined in every one of the following wavelengths: 404 nm, 407 nm, 411 nm, 419 nm, 430 nm, 553 nm, 670 nm, 708 nm, 939 nm, 1124 nm, 1398 nm and 1399 nm; and substituting every spectral reflectivity to a detection model, and calculating to obtain the phosphorus content of the jujube tree canopies. The method has the characteristics of no pollution, simplicity in operation, fastness and high accuracy, and is suitable for being promoted and applied.