326 results about "Ratio method" patented technology

A wavelet-based band difference-sum ratio method reduces the computation cost of classification and feature extraction (identification) tasks. A Generalized Difference Feature Index (GDFI), computed using wavelets such as Daubechies wavelets, is employed in a method to automatically generate a large sequence of generalized band ratio images. In select embodiments of the present invention, judicious data mining of the large set of GDFI bands produces a small subset of GDFI bands suitable to identify specific Terrain Category / Classification (TERCAT) features. Other wavelets, such as Vaidyanathan, Coiflet, Beylkin, and Symmlet and the like may be employed in select embodiments. The classification and feature extraction (identification) performance of the band ratio method of the present invention is comparable to that obtained with the same or similar data sets using much more sophisticated methods such as discriminants, neural net classification, endmember Gibbs-based partitioning, and genetic algorithms.

The invention relates to a precise single point positioning method of a single-frequency GPS receiver, which comprises GPS data reading and pretreating steps, error correcting and resolving steps and a result output step, wherein the pretreating step comprises the cycle slip exploration and restoration which comprises the following steps: (1) m non-cycle-slip carrier phase observed values Phi are substituted into a formula and the polynomial fitting is then carried out; (2) a calculated polynomial coefficient is used for extrapolating a carrier phase observed value Phi of the next epoch which is then compared with the actual observed value Phi; (3) the carrier phase observed values of the first epoch and the i-1 epoch are utilized to obtain the variance ratio of the carrier phase observed values. The invention comprehensively utilizes the polynomial fitting method and the carrier phase variance ratio method to explore the cycle slip, combines the gross error correction to realize the precise single point positioning of a single-frequency GPS receiver, thereby effectively realizing the high-precision positioning and navigation of a GPS system.

Fiber optic grating sensor demodulation systems are described that offer high speed and multiplexing options for both single and multiple parameter fiber optic grating sensors. To attain very high speeds for single parameter fiber grating sensors ratio techniques are used that allow a series of sensors to be placed in a single fiber while retaining high speed capability. These methods can be extended to multiparameter fiber grating sensors. Optimization of speeds can be obtained by minimizing the number of spectral peaks that must be processed and it is shown that two or three spectral peak measurements may in specific multiparameter applications offer comparable or better performance than processing four spectral peaks. Combining the ratio methods with minimization of peak measurements allows very high speed measurement of such important environmental effects as transverse strain and pressure.

A digital eddy current proximity system including a digital impedance measuring device for digitally measuring the proximity probes impedance correlative to displacement motion and position of a metallic target object being monitored. The system further including a cable-length calibration method, an automatic material identification and calibration method, a material insensitive method, an inductive ratio method and advanced sensing characteristics.

The invention relates to a power transformer fault diagnosis method which combines the extension theory and the rough set theory, belonging to the electric power main equipment fault diagnosis technical field. The invention completes primary reduction classification of the attribute condition needed by various fault types according to a rough set attribute reduction method and then establishes a matter element model of transformer fault diagnosis; DGA testing data is taken as a transformer fault diagnosis attribute set; a transformer standard fault mode is taken as a transformer fault diagnosis decision-making set; various fault degrees are calculated by means of an extension correlation function; moreover, fault inclusion-exclusion rule is defined to determine a transformer fault. The power transformer fault diagnosis method carries out analysis through taking a certain transformer as an example with the diagnosis result according with the practical situation; seventy six pieces of transformer DGA information are collected and fault diagnosis is carried out by means of the method, thereby obtaining higher diagnosis correct rate as compared with IEC three-ratio method.

The invention relates to a transformer fault diagnosis method based on a Bayesian network. According to the method, gas dissolved in oil of a transformer is analyzed by adopting a three-ratio method; data about gas is obtained in a real operation environment; study of structures and parameters of the Bayesian network is accomplished by adopting a TAN (Tree Augmented Naive) algorithm; a fault diagnostic model is established, and an expert system is utilized for correcting the fault diagnostic model; and the fault diagnostic model is used for diagnosing real-time operation states of the transformer. The method has the benefits that the problem about fault diagnosis for the transformer under the condition of uncertainty and lacking given information is solved, and meanwhile, an importance analytical method based on the Bayesian network is introduced to play a certain assistant role in analysis of the fault mechanism. The method can quickly and accurately diagnose the fault of the transformer, provide support for establishment of a maintenance decision for the transformer, effectively improve the maintenance efficiency, and lower the operation cost of a power system.

The invention discloses a method for acquiring the seismic velocity in a thin reservoir layer through well control. The method comprises the following steps of: selecting a standard single well velocity model and acquiring a corresponding logging acoustic time difference; acquiring a stacking velocity spectrum for seismic data imaging treatment and generating a stacking velocity data volume; selecting a time step length of 15-20 ms and carrying out velocity conversion on the logging acoustic time difference to obtain the velocity of a logging layer; converting the stacking velocity data volume into a logging-seismic joint forecast interval-velocity data volume; carrying out hierarchic and segmental error correction calculation on the velocity of the logging layer and the logging-seismic joint forecast interval-velocity data volume by adopting a ratio method to generate planar correction coefficients corresponding to various layers in different depths; and hierarchically and segmentally applying the planar correction coefficients to the logging-seismic joint forecast interval-velocity data volume of each layer correspondingly from top to bottom so as to obtain a high-accuracy seismic interval velocity data volume. The embodiment of the invention verifies that the method and the device for acquiring the seismic velocity in the thin reservoir layer through well control are beneficial to improvement of the forecast accuracy of the seismic velocity in the thin reservoir layer and can be used for obtaining velocity data with higher reliability.

The invention relates to the field of evaluation on a regional intelligent distribution network. A comprehensive evaluation method for low-voltage transformer areas of the regional intelligent distribution network comprises the following steps: establishing an intelligent low-voltage distribution network evaluation index model for a plurality of low-voltage transformer areas of the regional intelligent distribution network; acquiring an intelligent low-voltage distribution network actual data value corresponding each intelligent low-voltage distribution network evaluation index in the intelligent low-voltage distribution network evaluation index model; calling a score, which is obtained by rating according to the importance degree of each intelligent low-voltage distribution network evaluation index, in an expert score database, calculating according to the scores to obtain a weight value of each intelligent low-voltage distribution network evaluation index, and giving the corresponding weight value to each intelligent low-voltage distribution network evaluation index in the intelligent low-voltage distribution network evaluation index model; according to the intelligent low-voltage distribution network actual data value of each intelligent low-voltage distribution network evaluation index and the weight of each intelligent low-voltage distribution network evaluation index, carrying out sorting and grading on the plurality of low-voltage transformer areas by adopting a rank sum ratio method. The power distribution transformer areas are comprehensively evaluated, sorted and graded, so that the power distribution transformer areas can be invested and built in a targeted manner.

Methods of forming dielectric layers are described. The method may include the steps of mixing a silicon-containing precursor with a radical-nitrogen precursor, and depositing a dielectric layer on a substrate. The radical-nitrogen precursor is formed in a remote plasma by flowing hydrogen (H2) and nitrogen (N2) into the plasma in order to allow adjustment of the nitrogen / hydrogen ratio. The dielectric layer is initially a silicon-and-nitrogen-containing layer which may be converted to a silicon-and-oxygen-containing layer by curing and / or annealing the film in an oxygen-containing environment.

The invention discloses a super-short-term prediction method of photovoltaic power station irradiance. The method includes the steps that irradiance data are extracted from a history database, data of a night time quantum are removed, corresponding extraterrestrial theoretical irradiance is calculated, data abnormal detection is carried out based on the preceding operations, and the data are normalized in the difference value ratio method of an extraterrestrial irradiance theoretical value and practical irradiance; a training sample set is extracted according to input and output dimensionality of a model; a model of an irradiance time sequence is built through an ANFIS, a the rule quantity and an initial parameter of the ANFIS model are determined in a subtractive clustering method, and a fuzzy model parameter is optimized in a counter propagation algorithm and a least square method; a prediction sample is input, and a prediction value is obtained through calculation; the prediction value is added to form a new sample set, and multiple steps of prediction are achieved in a cycling mode; counter normalization processing is carried out on the prediction value. Super-short-term prediction of the irradiance can be achieved only by means of a history irradiance time sequence, prediction accuracy is good and the method is easy to carry out.

The invention provides a micro seismic event recognition method based on morphological filtering. The method comprises the steps of conducting spectral analysis on micro seismic data; rectifying a demodulator used for receiving micro seismic signals; selecting structural elements, combining opening operation and closing operation to form a morphological noise filter, and conducting the morphological filtering on the micro seismic data going through spectral analysis; calculating the energy ratio of a long-time window and a short-time window according to the data going through the morphological filtering, and comparing the energy ratio and triggering threshold to identify whether the demodulator has micro seismic events; and conducting microseism pickup according to the micro seismic events. According to the method, the signal to noise ratio of the data can be improved through preprocessing and further filtering treatment; the micro seismic events can be detected quickly and accurately by utilization of the energy ratio method, the fluctuation type of the micro seismic events can be identified through polarization analysis, and first arrival can be picked up accurately by utilization of self-adaptation threshold and the long-time and short-time window energy ratio method.

Disclosed in the invention is a three-stage-diagnosis-based large-scale photovoltaic array fault diagnosis and positioning method. The method is characterized in that faults of the photovoltaic array are classified into four types including an assembly short-circuit type, an assembly open-circuit type, a shadow type, and a multi-fault type under the circumstances that the photovoltaic array is in a formation mode of series connection and parallel connection. During a first-stage fault diagnosis execution process, whether a fault occurs at a branch circuit where a photovoltaic grid-connected inverter as well as the type of the fault is determined by using a power incremental ratio method; during a second-stage fault diagnosis execution process, whether faults occur at all photovoltaic assembly strings of the branch circuit of the photovoltaic grid-connected inverter is determined by using a current and voltage similarity method; and during a third-stage fault diagnosis execution process, positioning of an internal fault assembly of the fault photovoltaic assembly string is carried out by using a trained three-layer BP fault diagnosis neural network. According to the invention, on-line real-time detection of the operation state of the large-scale photovoltaic array is carried out based on the three-stage diagnoses, thereby realizing accurate and rapid fault type identification and fault positioning.

The invention relates to a method for diagnosing a transformer fault in a transformer station. The method comprises the steps of: (1), data acquisition, i.e., an oil chromatogram sensor installed on the transformer acquiring an oil sample and performing chromatography to obtain the concentration values of hydrogen, methane, ethane ,ethane and acetylene; (2), data processing, i.e., selecting concentration values three times of standard deviations; (3), health assessment; (4), three-ratio method diagnosis; (5), characteristic gas method diagnosis; (6), Duval's triangle method diagnosis; and (7), diagnosis result, i.e., performing comprehensive determination on a transformer first fault diagnosis result, a transformer second fault diagnosis result and a transformer third fault diagnosis result, and then selecting a conclusion with the highest confidence as a transformer fault diagnosis result according to a confidence rule. According to the invention, by taking current operation condition of electrical network equipment as a basis, the equipment operation state is analyzed in real time and fault diagnosis is carried out on abnormity equipment so that power supply reliability is ensured, maintenance cost is lowered, and maintenance risks are reduced.

The invention provides a method and device for analyzing online oil chromatogram fault in real time, which comprises the following steps of: acquiring oil chromatogram data in real time; if the quantity of oil chromatogram gas components in the acquired data is beyond a preset warning value, giving an early warning; performing three-ratio-method analysis to the early warned oil chromatogram gas component data, and detecting equipment fault; and generating a quick report of exception situation of a gas-in-oil online monitoring system in real time for the equipment with the fault. The method and the device provided by the invention can perform analytic statistics quickly and effectively by unified standards without lots of manual operations, so that unnecessary human factor influence is avoided, thereby being capable of guaranteeing timeliness and accuracy of analytic statistics results in handling large amounts of online monitoring data.

The invention discloses a kit for detecting pig pseudorabies virus antibodies and a block enzyme-linked immuno sorbent assay (ELISA) method. The kit for detecting pig pseudorabies virus antibodies comprises pig pseudorabies virus monoclonal antibodies which are labelled by horseradish peroxidase, wherein the pig pseudorabies virus monoclonal antibodies are monoclonal antibodies obtained by pig pseudorabies viruses as immunogens and the pig pseudorabies viruses are pseudorabies virus strain Ea. The kit for detecting pig pseudorabies virus antibodies also comprises an enzyme label plate, a sample diluent, negative and positive contrasts, a coloured solution, a washing solution, and a stopping solution. The block ELISA method comprises the following steps of 1, taking out a detection plate pre-coated with virus antigens from the kit for detecting pig pseudorabies virus antibodies, adding diluted blood serum needing to be detected into the detection plate pre-coated with the virus antigens, and simultaneously, setting negative and positive contrast apertures, 2, shaking up the diluted blood serum in the negative and the positive contrast apertures, shaking off a solution in the negative and the positive contrast apertures, and washing the detection plate by the washing solution, and 3, adding the pig pseudorabies virus monoclonal antibodies labelled by horseradish peroxidase into the negative and the positive contrast apertures, washing, adding the colored solution into the negative and the positive contrast apertures to carry out room-temperature coloration in the dark, adding the stopping solution into the negative and the positive contrast apertures, and determining OD630nm values of the negative and the positive contrast apertures by an ELISA apparatus. The block ELISA method has the advantages of good singularity, high sensitivity, short detection time, and high accuracy because of utilization of an S / N ratio method in result determination.

The invention discloses an optical remote sensing image change detection method based on image fusion, and mainly solves the problem of low detection result precision of the existing change detection technology. The optical remote sensing image change detection method comprises the following steps: respectively constructing a difference method difference graph and a ratio method difference graph after two optical remote sensing images which are acquired in the same region at different time points are preprocessed; respectively performing N-layer wavelet decomposition on the two difference graphs to acquire wavelet coefficients of a high-frequency band and a low-frequency band of each decomposition layer; performing fusion processing on the wavelet coefficients of the high-frequency band and the low-frequency band by using different fusion operators to obtain fusion wavelet coefficients of the high-frequency band and the low-frequency band; performing inverse transformation on the fusion wavelet coefficients of the high-frequency band and the low-frequency band to obtain a fused difference graph; and partitioning the fused difference graph by using a blurring partial C-mean-value clustering method to obtain a change detection result. According to the invention, the difference graph performance of the optical remote sensing image is improved, so that the precision of the detection result is improved; and the method can be used for natural disaster evaluation and environment detection.

The invention provides a quality factor spectrum of stratum and a solving method thereof. The quality factor value is solved by using a frequency spectrum ratio method based on zero offset VSP (Vertical Seismic Profiling) data. The method specifically comprises the steps of: selecting a reasonable first arrival and time window parameter, carrying out spectrum analysis on the original section; picking a peak frequency in a frequency spectrum effective band to obtain the smoothed trend; carrying out normalization on the original frequency spectrum, then correcting the frequency spectrum by using the smoothed trend; comparing by using adjacent channels of frequency spectrums to obtain frequency spectrum ratio data for the combination; and aiming at the combined frequency spectrum ratio data, solving a plurality of quality factor Q values or carrying out Q scanning by using a Q value attenuated channel compensating method to obtain coefficients similar to a reference channel frequency spectrum to form a Q spectrum, and obtaining a stable Q value through explanation of the Q spectrums. The invention has the advantages of eliminating the influence of the frequency spectrum yield value on the linear fitting as far as possible, increasing the utilization ratio of the frequency spectrum information, and obtaining a more reasonable Q value.

Methods of increasing the loft of a multi-ply thermoplastic film include stretching thermoplastic films with different rebound ratios, non-continuously laminating the films together, and releasing the films to create puckers in one or more of the films. The puckers can comprise billowing of one of the films. The non-continuous laminated areas can maintain the puckers in the films. The puckers can increase the loft of the film, improve the feel of the film, and / or modify the look of the film. The loft of such multi-ply puckered thermoplastics films vary independent of the basis weight of the films. Thus, multi-ply puckered thermoplastics films with increased loft of one or more implementations can look and feel thicker while using reduced amounts of thermoplastic material.

The embodiment of the invention provides a method for picking a seismic wave first break. The method comprises the following steps of: forming a new seismic attribute trace based on a complex seismic trace noise-adding method; and picking the seismic wave first break by using an energy ratio method. Compared with the conventional energy ratio method of the seismic attribute trace (an amplitude absolute value or an amplitude envelope), the method for picking the seismic wave first break has the characteristics of high anti-noise capacity and high picking accuracy.

The invention discloses a self-adapting wave form retrieval method through utilization of zero offset vertical seismic profile (VSP) data to estimate speed and Q value. The method includes a first step of selecting undisturbed a segment of a first-motion wave in a self-adapting mode to the maximum extent according to the signal to noise ratio of the actual VSP data and the degree of a direct wave disturbed by an upward traveling wave, and fully utilizing effective information to construct an objective function, a second step of improving the sensitiveness of the objective function to changes of the Q value through proper construction of a data weighting matrix according to the feature that the Q value is more difficult to estimate than other parameters; a third step of adopting a recently developmental self-adapting multiplication regularization method to improve the ill-conditional problem of nonlinear inversion, and limiting the value range of the unknown parameters through nonlinear transformation, and a fourth step of inferring analytical expressions of elements of a jacobian matrix so as to decrease the calculated amount of inverse problems. Compared with a spectrum ratio method and a wavelet envelope peak value instantaneous frequency method, the method is relatively smaller in influence of the upward traveling wave, stronger in anti-noise performance, and higher in accuracy of inverse parameters.

The invention discloses an intelligent transformer fault diagnostic method based on an RBF (radial basis function) neural network. The intelligent method includes (1), acquiring three ratios of five gases C2H2 / C2H4, CH4 / H2, C2H4 / C2H6 as training sample data by the utilizing IEC (international electrotechnical commission) three ratio method; (2) performing fuzzily processing on the three ratios by utilizing a membership function; (3), coding fault types; (4), training the RBF neural network according to the training sample data until the RBF neural network meets precision requirements; (5), inputting to-be-diagnosed samples after being fuzzily processed; and (6), outputting diagnosed results. The intelligent transformer fault diagnostic method has good reasoning ability and high diagnosed precision, overcomes the defects of the IEC three ratio method, and can precisely display all transformer fault problems.

The invention discloses a method for identifying an alga species by combining a Raman spectroscopic technology and a spectral peak ratio method. The method comprises the following steps of extracting a plurality of samples of the same alga species in different growth stages, and acquiring the initial Raman spectral information of each sample, wherein each sample is the living alga liquid of the current alga species; preprocessing the initial Raman spectral information to obtain a corresponding preprocessed spectrogram, and obtaining a spectral peak ratio corresponding to carotene; replacing the alga species, and obtaining corresponding spectral peak ratios of different alga species in each growth stage; establishing a BP (back propagation) neural network model by taking the spectral peak ratios of all the alga species as input and taking the alga species corresponding to each ratio as output; extracting living alga liquid to be identified, obtaining a spectral peak ratio of the living alga liquid to be identified, and inputting the obtained spectral peak ratio into the BP neural network model to obtain the alga species in the living alga liquid to be identified. According to the method for identifying the alga species, the Raman spectroscopic technology is combined with the spectral peak ratio method, so that operation steps are greatly simplified, and the detection time is shortened.

The invention discloses a site selection method for immigrant settlement areas based on a geographic information system. The method comprises the following steps of: 1) by using a spatial analysis technology, which comprises terrain analysis, buffer area analysis and superposition analysis, of the geographic information system, establishing a spatial site selection model, and thus obtaining a series of candidate points / areas; 2) establishing an evaluation index system of settlement areas, acquiring each index data of the candidate settlement areas, and entering a database; 3) performing dimensionless standardization on an original matrix of each evaluation index of the candidate settlement areas; 4) determining the weight of each evaluation index by using a composite even weighting method based on an information entropy theory; and 5) based on a fuzzy mathematical theory, performing quality sequencing on the candidate settlement areas by using a fuzzy adjacency ratio method. The method has an important practical application value in the site selection of the immigrant settlement areas of water and electricity engineering and has wider application prospect.

The invention discloses a transformer fault diagnosing method based on a neural network. The method comprises the following steps: obtaining three ratios of five kinds of gases: C2H2 / C2H4, CH4 / H2 and C2H4 / C2H6 serving as training samples by using an IEC three-ratio method; (2) fuzzifying the three ratio by using a membership function; (3) encoding a fault type; (4) training a BP (Back Propagation) neural network according to fuzzified training sample data till the BP network meets the accuracy requirement; (5) inputting the fuzzified sample data to be detected into the trained BP neural network; and (6) outputting a diagnosis result by using the BP neural network. According to the method, the neural network is trained through history data, so that an online monitoring function can be realized by using the neural network. Data information on a transformer is acquired through a computer, and faults are diagnosed in advance, so that hidden danger is eliminated.

The invention discloses a transformer fault diagnosis method based on Bi-LSTM and analysis of dissolved gas in oil. The method comprises: collecting fault DGA monitoring data of each substation, carrying out normalization, sequence expansion, noise superimposing and the like on the data, and extracting fault feature information based on a non-coding ratio method; carrying out length ranking on a DGA sequence, carrying out grouping and filling, and classifying groups into a training set and a verification set; constructing a deep learning frame based on Bi-LSTM, inputting data, and carrying outtraining; and then carrying out diagnosis and network updating by combining actual test data to obtain a fault diagnosis model with the high diagnosis accuracy and portability. According to the invention, the influence of the noise and error on the diagnosis during the DGA data monitoring process is reduced effectively; and the Bi-LSTM-based transformer fault diagnosis model is constructed by considering the complex correlation between different sequences. With introduction of links of sequence sorting, grouping, filling and the like, a problem of different sampling lengths of different transformers in the actual engineering is solved by using the batch training strategy.

The invention belongs to the field of fault diagnosis methods of oil-immersed power equipment and discloses a fault diagnosis method of oil-immersed power equipment by combining a fuzzy theory and improving a genetic algorithm. Aiming to a common improved IEC three-ratio method, by combining the fuzzy theory and improving the genetic algorithm, the method realizes treatment on other gas ratio boundaries and fault codes through fuzzy treatment and obtains a fault diagnosis result through a fuzzy inference. For carrying out fuzzy treatment on the boundary values of the improved IEC three-ratio method by adopting the fuzzy theory, the invention solves the problem of poor fault diagnosis accuracy caused by too absolute code boundary condition of the improved IEC three-ratio method and takes a certain action of improving the diagnosis accuracy of the improved IEC three-ratio method. The invention adopts the genetic algorithm for self-adaptive regulating mutation probability to revise an experience fuzzy membership function to obtain an optimal parameter of the fuzzy membership function and lays a good foundation of application of the fuzzy theory in improving the IEC three-ratio method.

The invention discloses a heterologous image accurate matching method, which comprises the following steps: firstly, respectively matching an infrared grayscale image and a negative image thereof witha visible light grayscale image, namely two to-be-matched image groups; secondly, for each image of the to-be-matched image group, extracting key points with invariable scales, and calculating LPQ feature vectors of neighborhoods of the key points; thirdly, performing weighted fusion on the SIFT feature, the shape context feature based on the angular point and the LPQ feature of each image in theto-be-matched image group, and performing initial matching on the images through a nearest neighbor ratio method; fourthly, removing mismatching points; and finally, integrating the matching resultsof the infrared grayscale image and the negative image thereof with the visible light grayscale image into a final matching result. According to the method, the context descriptor is designed to obtain global information of the image, meanwhile, in order to further improve the matching performance, the LPQ descriptor is utilized to obtain image fuzzy invariant texture features, and finally, the method can obtain a heterologous image accurate matching result.

The quality measurement device for laser beam comprises: a focusing lens, an attenuation sheet of a diffuse scattering board, a CCD to collect light spot, a computer with high-speed collection card, and a light splitter contained a first plate with inner surface of 100% reflectivity and a second plate with inner surface of 90~99% reflectivity both 45Deg parallel laid in light path. Wherein, there is a malposition m between two plates and an antireflection coating on outer surface of the second plate. This invention can measure accurately for Gauss beam or non-Gauss beam with power-in-barrel ratio method, and has simple structure with well stability.

The invention claims a remote radio unit (RRU) and a remote ratio method. The RRU comprises an uplink frequency mixer, a downlink orthogonal modulator, and a local vibration generator which is connected with the uplink frequency mixer and the downlink orthogonal modulator; the uplink frequency mixer is used for receiving an uplink simulative radio frequency signal, performing down-conversion to the uplink simulative radio frequency signal according to the local vibration frequency provided by the local vibration frequency mixer, acquiring an uplink intermediate-frequency simulative signal, and sending the intermediate-frequency simulative signal to an uplink intermediate-frequency band-pass filter; the downlink orthogonal modulator is used for receiving a downlink simulative intermediate-frequency signal, performing up-conversion to the downlink simulative intermediate-frequency signal according to the local vibration frequency provided by the local vibration generator, acquiring a downlink simulative radio frequency signal, and sending the downlink simulative radio frequency signal to a downlink adjustable gain amplifier; and the local vibration generator is used for providing two frequency ranges of local vibration frequency for the uplink frequency mixer and the downlink orthogonal modulator. When the scheme provided by the invention is adopted, the multiplexing of the local vibration generator is realized, and the cost of the RRU is reduced.