33 results about "Natural gradient" patented technology

The invention relates to discrete gradually color-changed chromatography constructed by coupling and mixing trichromatic fibers and spinning of gradually color-changed yarns. Based on the mass of thetrichromatic fibers, increasing and decreasing mass sequences are constructed, a combination pair is used as a trichromatic fiber mass coupling and mixing and gradient mixing model. Based on the model, the discrete gradually color-changed chromatography of the trichromatic fibers and a draft ratio discrete algorithm for spinning of discrete gradually color-changed yarns are constructed. The discrete gradual change effect of the color of the yarns based on coupling and mixing of three primary colors is achieved, the yarns which have discrete gradually changed colors and have color serializationcharacteristics are spun, or gradually color-changed yarns with a natural gradient color effect on one yarn are spun. Therefore, based on the coupling mass ratio among the trichromatic fibers, secondary color matching among the multi-primary color fibers can be efficiently realized, discrete gradually changed serialized colors and discrete gradually changed colors in a full-color domain space canbe obtained, and colored yarns with different discrete gradually changed colors corresponding to fibers in three colors are obtained accurately.

The invention discloses a glass cover plate surface color gradient printing process and relates to the technical field of cover plate surface processing. The printing process comprises the steps of (1) manufacturing a five-layer screen printing plate; (2) conducting screen printing through A-color printing ink; (3) conducting screen printing through B-color printing ink; (4) conducting screen printing through white semi-transparent ink; (5) conducting screen printing through white ink; and (6) conducting screen printing through black ink, wherein the color A and the color B are different colors. According to the glass cover plate surface color gradient printing process, the structure of the screen printing plate is designed, and multiple layers of ink in colors is printed on the surface ofa cover plate through the screen printing plate; through mutual overlapping and permeation of the multiple layers of the ink, the natural gradient effect of the colors in a printing region is achieved; the gradient colors are uniform, natural, clear and not dull, and the appearance visual effect is good. Through the glass cover plate surface color gradient printing process, there is no need to purchase expensive equipment, the cost is low, the preparation process is simple, and the process meets the requirements for industrial production.

The invention discloses a blind source separation method based on an improved artificial bee colony algorithm. The method includes the following steps that an observation signal X(k)=[x[1](k), x[2](k),..., x[M](k)]<T> is obtained by a source signal S(k)=[s[1](k), s[2](k),..., s[M](k)]<T> through a nonsingular hybrid matrix A; the obtained observation signal X (k) is sent to a preprocessing filter, and an output signal Z(k) of the preprocessing filter is obtained; the output signal Z(k) of the preprocessing filter is sent to a separation matrix W(k), and a separation signal Y(k) is obtained; an initial optimized separation matrix Wopt (0) of the W(k) is obtained through the improved artificial colony algorithm; after the initial optimized separation matrix Wopt (0) of the W(k) is obtained, the W(k) is updated. According to the method, the improved artificial bee colony algorithm is adopted for optimizing the blind source separation method NGA based on natural gradient so that the initial optimized separation matrix can be obtained, and then signal separation is performed through the initial optimized separation matrix. The method is low in convergence speed and small in crosstalk error and has wide application prospect in the aspects of wireless communication, image processing, voice signal processing and the like.

Electrochemical cells of the present disclosure may include one or more multilayered electrodes. Each multilayered electrode may be configured such that active materials of the layer closest to the current collector have a lower energy to lithiate per mole, a higher energy to delithiate per mole, a different solid state diffusivity, and / or a different average particle size. This arrangement counteracts, for example, natural gradient fields and undesirable polarization found in standard lithium-ion batteries.

A natural gradient, single well, tracer dilution non-continuous mixing apparatus and method for measuring flow of liquids through porous media are described. The method has been applied to concentrations of Light Nonaqueous Phase Liquid (LNAPL) in monitoring wells to measure the rate of LNAPL flow through the wells and through the adjacent formation. Similar measurements were made for concentrations of water in other formations. A LNAPL-soluble fluorescing tracer was added to LNAPL in the wells, and a water-soluble fluorescing tracer was added to water in the wells. The tracer is initially uniformly-mixed into the LNAPL or water in the well, and the tracer concentration is measured using a fiber optic cable and a spectrometer. The LNAPL or water with dissolved tracer is then allowed to flow from the well without any mixing. At a later time, the LNAPL or water and tracer in the well are mixed to a uniform tracer concentration, and the tracer concentration is remeasured. Using the initial tracer concentration, the subsequently mixed tracer concentration, the elapsed time, and the well diameter, a LNAPL flow rate is calculated.

A method and apparatus is disclosed for performing blind source separation using frequency-domain normalized multichannel blind deconvolution. The multichannel mixed signals are formed as frames of N samples, which consist of r consecutive blocks of M samples. The frames of mixed signals are separated using separating filters in the frequency domain in an overlap-save manner using a discrete Fourier transform(DFT). The separated signals are then converted back into the time domain using the inverse DFT to be applied to a nonlinear function. The cross-power spectra between separated signals and nonlinear-transformed signals are computed and are normalized by the power spectra of separated signals and the power spectra of nonlinear-transformed signals to have flat spectra. The invention then applies the time domain constraint to preserve the first L cross-correlations. These alias-free normalized cross-power spectra are further constrained by nonholonomic constraints. The invention then computes natural gradient by convolving alias-free normalized cross-power spectra with separating filters. After the length of separating filters is constrained to L, separating filters are updated using the natural gradient and normalized to have unit norm. The terminating conditions are checked to see if separating filters converged.

The invention belongs to the technical field of blind source separation and specifically relates to an improved natural gradient variable step-size blind source separation algorithm. The algorithm mainly comprises the following steps of randomly generating a hybrid matrix, carrying out linear mixing on source signals, and obtaining three hybrid signals; initializing a separation matrix, an autocorrelation matrix and a learning step, adding an orthogonal constraint, and controlling a variable step size by instantaneous errors and carrying out iteration; calculating an estimation signal; and drawing a recovered source signal waveform according to the estimation signal. The invention provides the natural gradient blind source separation algorithm based on the orthogonal constraint, the algorithm constrains the strength of recovered signals, and the stability of a separation process under an unstable environment is ensured; in addition, the convergence is accelerated, the separation precision is improved, the signal amplitude under the unstable environment constantly changes, the change frequency is relatively high, and a conventional separation method can not perform the separation, so that the related technology of the invention is advanced and prospective.

The present invention belongs to the wireless communication method technical field and relates to a spectrum sensing method based on the alpha-divergence of information geometry. The method includes the following steps that: a cognitive radio model is established, a noise environment is simulated, so that N noise covariance matrices Rk are generated, and one of the N noise covariance matrices Rk is adopted as a unit to be detected R; the Riemann mean overlineR of the N noise covariance matrices Rk is calculated through using a natural gradient descent algorithm, a geometric distance L betweenthe detection unit R and the Riemann mean overlineR is calculated; n geometric distances L are sequenced in a descending order, and a threshold value T is calculated; simulation is performed, so thata plurality of unknown signal covariance matrices are generated, and the alpha-divergence of the information geometry is adopted to solve distances D between the signal covariance matrices and a knownsignal matrix in manifold; and the threshold value T is compared with the distances D, if D is larger than T, signals to be sensed exist, otherwise, only noises exist. The method of the invention hasthe advantages of simple and practical calculation process, high spectrum sensing efficiency and high spectrum sensing stability.

A natural gradient Blind Multi User Detection (BMUD) network system and method adaptively estimates a set of matrices to counter a linear convolutive environment model. Feedforward and feedback network structures may be implemented, with or without matrix inversion. In other aspects, an adaptive weighting matrix is introduced into a RAKE structure, and the matrix is adaptively estimated using Principal Component Analysis (PCA) computational techniques and / or static Blind Source Recovery (BSR) computational techniques based on Independent Component Analysis (ICA).

The invention relates to a blind source mixed signal accelerated separation method in engineering. Different kinds of signals have different inherent kurtosis values, so that when a mixed signal reaches the complete separation, each signal reaches the inherent kurtosis value; the kurtosis accumulation quantity of each separation signal reaches a stable value; by aiming at the defect of a blind source signal separation fixed step length natural gradient algorithm that the convergence speed and the steady state error characteristics cannot be met at the same time, the kurtosis accumulation quantity relevant to the separation signal is used for forming a proportional-differential control self-adaption variable step length algorithm; as long as in an allowed initial step length selection range, the acceleration step length can be regulated by the algorithm in a self-adaption way by freely selecting one initial step length value; and as the result, the separation speed can be accelerated, and the separation precision is ensured. The method provided by the invention only involves with the value of the selected step length initial value eta 0; the separation complete speed by the algorithm is higher than the fixed step length separation speed, and is almost identical; the ideal stable babbling errors are realized; and therefore the engineering use values are realized.

The embodiment of the invention discloses a method and device for identifying overlapped digital signals. Two-order, four-order and six-order cumulants of standard signals in different modes are calculated in advance; characteristic values of the standard signals in various modes are calculated and stored according to the cumulant result; and the identification process comprises the following steps of calculating a separate matrix by using a natural gradient ICA algorithm according to a received overlapped signal matrix; calculating the overlapped signal matrix by using the separate matrix to obtain a final estimated value matrix of source signals, wherein each component, which is not zero, in the final estimated value matrix of the source signals is a separated source signal; calculating the two-order, four-order and six-order cumulants of each component, which is not zero, and calculating the characteristic value of each component, which is not zero; and determining the modulation manner of each separated source signal according to the characteristic value of each component, which is not zero, and the characteristic values of the standard signals in various modes. By means of the embodiment of the invention, the overlapped signals can be separated; the identification performance of the overlapped signals is improved; and the overlapped signals are demodulated easily.

The invention discloses an MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) signal blind separation method for increasing the natural gradient algorithm convergence speed. The method comprises the following steps: firstly, making improvements on a Flexible ICA algorithm under the constraint of ICA preprocessing in combination with an ICA principle to obtain an improved iterative algorithm; secondly, giving a method for adaptively feeding back and adjusting an iteration step size based on a separation error in combination with a BP neural network feedback thought with the separation error; and lastly, giving a comprehensive ending criterion of algorithm iteration from the aspects of the convergence of a separation matrix, iteration times and the separation error. Compared with the prior art, the method has the advantages that the requirements on the convergence speed and the separation accuracy are met on the basis of combining the MIMO-OFDM system features, and an effective separation effect is achieved.

A natural gradient, single well, tracer dilution non-continuous mixing apparatus and method for measuring flow of liquids through porous media are described. The method has been applied to concentrations of Light Nonaqueous Phase Liquid (LNAPL) in monitoring wells to measure the rate of LNAPL flow through the wells and through the adjacent formation. Similar measurements were made for concentrations of water in other formations. A LNAPL-soluble fluorescing tracer was added to LNAPL in the wells, and a water-soluble fluorescing tracer was added to water in the wells. The tracer is initially uniformly-mixed into the LNAPL or water in the well, and the tracer concentration is measured using a fiber optic cable and a spectrometer. The LNAPL or water with dissolved tracer is then allowed to flow from the well without any mixing. At a later time, the LNAPL or water and tracer in the well are mixed to a uniform tracer concentration, and the tracer concentration is remeasured. Using the initial tracer concentration, the subsequently mixed tracer concentration, the elapsed time, and the well diameter, a LNAPL flow rate is calculated.

The invention provides a speech signal blind separating method based on a variable step size natural gradient algorithm. (1) A speech signal collecting device is used for collecting mixed speech singles of speeches of multiple speakers, and the number of microphones is larger than or equal to that of the speakers; (2) the collected mixed speech signals are preprocessed, and then mixed speech signals which have zero mean, irrelevant signal sources and the high signal to noise ratio are obtained, wherein the preprocessing comprises the steps of adopting an LMS digital filter, removing the mean and removing related whitening processing; (3) the estimation of the speech of each speech source is restored and obtained from the mixed human speeches by using the variable step size natural gradient algorithm for regulating step size based on gradient factors. The speech signal blind separating method based on the variable step size natural gradient algorithm is capable of separating real mixed speech signals, high in separating speed and accurate and stable in separating effect.

The invention relates to an irrigation method and system for a desertified river bank wind prevention and sand fixation forest belt. The system comprises a water taking facility which is arranged in the upstream river channel of a river and formed by multiple water taking permeable pipes, the water taking facility is connected with at least one water collecting facility arranged on the lower portion of the water taking facility through pipelines, the water collecting facility is connected with irrigation facilities arranged on the lower portions of the water collecting facilities, and the irrigation facilities are arranged on the outer side of the wind prevention and sand fixation forest belt. Accordingly, the mode of infiltration water taking under a river bed is adopted, blocking of silt and sundries is avoided, and excellent clean water is improved for irrigation. Meanwhile, the water taking facility, the water collecting facilities and the irrigation facilities are arranged on the river from the upper portion to the lower portion according to the homeopathy of the terrain, gravity-flowing irrigation is formed by means of the natural gradient, irrigation is conducted by completely replying on the natural gravity without using any manual power facility, and the operation cost is greatly lowered. Due to the fact that no mechanical power device is arranged, loss in daily operation is very low, regular maintenance is not needed in general, and the operation cost is lowered.

The present invention provides a natural gradient ramp anti-fake method and a product. The method comprises the steps of: printing codes onto a printing stock, printing B-color ink liquid to A-color ink without drying to allow the A-color and B-color ink to be intersected, fused, diffused and mixed so as to form a random pattern with the gradient ramp trending to C color; and collecting personality characteristic information on the random pattern to combine and store the personality characteristic information and the codes into a cloud database to take as anti-fake files for checking true andfalse by customers. The natural gradient ramp anti-fake method and the product are zero in device investment, simple in manufacturing process and low in production cost, cannot be copied and are easyto identify.

The invention relates to a mixed signal adaptive fast separation method based on proportional-derivative control, including the steps: firstly, the initial separation signal is calculated; the initialvalue of the step length is determined, and the first transformation matrix and the separation signal are calculated by using a natural gradient blind source separation algorithm; then, the next variable step size related to the separated output state is calculated by the transformation of the difference of the kurtosis cumulants of the separated signals between the separated iterative calculations, and a new separation matrix is calculated from the next variable step size; it is judged whether the new separation matrix meets the control precision requirement; and if so, the separation signalis calculated by using the separation matrix which meets the control precision requirement. As the initial step length is within a certain value range, and the maximum convergence speed is close to the uniform order of magnitude under the same stable error. In particular, the technical parameters of the transformation are related physical quantities output from the separation process, and the composition of the separation algorithm has self-adaptability and practical use value.

The invention belongs to the field of SAR image speckle noise reduction processing, and particularly relates to a polarized SAR image speckle noise reduction method based on the natural gradient of a PID. The polarized SAR image speckle noise reduction method based on the natural gradient of the PID includes the steps that n polarized SAR images collected at the same moment are converted to be a one-dimensional vector image; the one-dimensional vector image is preprocessed; iteration solution is carried out on the preprocessed image signal through a natural gradient algorithm of the PID to obtain a separation matrix, and one-dimensional signal output of each image is obtained by multiplying the image signal by a whitened signal; the images are reconstructed; a one-dimensional signal with the maximum equivalent number of looks is found out; an image with the maximum equivalent number of looks is the SAR image with reduced noise, the one-dimensional signal with the maximum equivalent number of looks is converted into a two-dimensional image, and the two-dimensional image is converted to be a gray level image with N*M pixel dots according to a two-dimension to one-dimension inverse conversion method. The method with the PID applied adjusts the separation step length of the natural gradient algorithm, so that the processing result image is clearer than that obtained according to a traditional method, and speediness is achieved.

The invention discloses a neural network noisy vocal print recognition method based on residual error and batch normalization, which comprises the following steps: preparing a data set in a noisy environment, and respectively extracting corresponding Mel-frequency cepstral coefficients from voice signals; constructing a ResTDNN network structure: after the construction is completed, training the network by using a natural gradient random descent method, after the training is completed, capturing vocal print information of a speaker by using voice information about 10 seconds through x-vector extracted by dimension reduction, and adopting a cross entropy loss function; and performing dimension reduction by using linear discriminant analysis, performing length normalization on an x-vector after the dimension reduction, training linear probability analysis, respectively calculating likelihood functions of two voices from different spaces, and evaluating a recognition system by adopting equal error rate and minimum detection cost. According to the method, the robustness of the time delay neural network in the noise environment can be improved, the neural network degeneration and gradient disappearance phenomenon requirements are relieved, and the vocal print recognition rate is improved.

The invention provides a semi-open type reclamation sludge mudflat and a mudflat building method thereof. The mudflat structurally comprises a built seawall, a newly-built siltation-promotion submerged dyke, a hydraulic reclamation block and a hydraulic reclamation pipeline, wherein the built seawall is connected with the newly-built siltation-promotion submerged dyke, the hydraulic reclamation block is arranged between the built seawall and the newly-built siltation-promotion submerged dyke, and the hydraulic reclamation pipeline penetrates through one side of the built seawall to be connected with the hydraulic reclamation block. The newly-built siltation-promotion submerged dyke comprises a siltation-promotion submerged dyke body, a levee crown, a levee slope and a water-permeable structure. Compared with a traditional technology, under the conditions that a closure zone has not been closed, one side or two sides are opened, and water inside the closure zone mutually exchanges withwater outside the closure zone, on the dependence of an existing shoreline or dyke, the submerged dyke for reducing water power is properly built so as to achieve semi-open type hydraulic reclamationor naked blowing, and therefore, the aim of increasing mudflat resources is achieved. Meanwhile, through the natural gradient of dredger fill, the good ecological mudflat with high, middle and low beaches is formed. The technology is simple, the mudflat building speed is high, and the cost is economical; dredged spoil matter can be utilized, and the mudflat meets ecological green building requirements.

The invention discloses a digital printing process for a chenille floor mat with a natural gradual effect. The process comprises the steps that pretreatment is carried out; a pretreatment solution isprepared, wherein a hydrophilic agent is dissolved in water to obtain the pretreatment solution; the side, with shorter wool, of the chenille floor mat is placed upward, and the pretreatment solutionis atomized and sprayed onto the side, with the shorter wool, of the chenille floor mat; the pretreatment solution diffuses and moves from the end with the shorter wool to the end with longer wool under the action of gravity; inkjet printing is carried out on the back surface of a grey cloth; drying, baking, reduction cleaning and cleaning with hot water are carried out; then softening, washing, spin-drying and drying are carried out to obtain a product. According to the process, natural diffusion of the pretreatment solution in each wool is promoted under the action of gravity, and gradient dyeing of the wool is achieved in cooperation with inkjet printing.

The invention provides a voice signal blind separation method based on a variable step size natural gradient algorithm. (1) collect the mixed voice signal of multiple voices with the voice signal acquisition device, and the number of microphones is more than or equal to the number of speakers; (2) preprocess the mixed voice signal collected, including: adopting LMS digital filter, removing the mean value Whitening processing with decorrelation, and then get a mixed speech signal with zero mean value and high signal-to-noise ratio that is irrelevant to each source; (3) Using the variable step size natural gradient algorithm based on the gradient factor to adjust the step size to recover from the mixed human voice Estimation of the sound of each sound source. The invention not only can separate real mixed voice signals, but also has fast separation speed, accurate and stable separation effect.

The invention belongs to the field of SAR image speckle noise reduction processing, and particularly relates to a polarized SAR image speckle noise reduction method based on the natural gradient of a PID. The polarized SAR image speckle noise reduction method based on the natural gradient of the PID includes the steps that n polarized SAR images collected at the same moment are converted to be a one-dimensional vector image; the one-dimensional vector image is preprocessed; iteration solution is carried out on the preprocessed image signal through a natural gradient algorithm of the PID to obtain a separation matrix, and one-dimensional signal output of each image is obtained by multiplying the image signal by a whitened signal; the images are reconstructed; a one-dimensional signal with the maximum equivalent number of looks is found out; an image with the maximum equivalent number of looks is the SAR image with reduced noise, the one-dimensional signal with the maximum equivalent number of looks is converted into a two-dimensional image, and the two-dimensional image is converted to be a gray level image with N*M pixel dots according to a two-dimension to one-dimension inverse conversion method. The method with the PID applied adjusts the separation step length of the natural gradient algorithm, so that the processing result image is clearer than that obtained according to a traditional method, and speediness is achieved.

The invention provides a deep neural network image recognition method based on structured natural gradient optimization, and belongs to the field of image recognition based on machine learning and neural network technologies. According to the method, a structured natural gradient descent (SNGD) method is provided, a normalization layer is added, a deep network mode of restructuring image recognition is adopted, the correlation calculation of a global Fisher matrix is decomposed, and finally, the decomposition is converted into optimization by using traditional GD, so that the NGD effect can be achieved. Meanwhile, the invention provides a new local Fisher layer and an implementation scheme thereof. Second-order information is introduced into the local Fisher layer, different attributes of parameters in an image recognition network at different positions are considered, constraints are added to recognition model parameter transformation, and gradient updating can be conducted stably and rapidly. By adopting the method, the image recognition network training has higher convergence speed, and the trained model also has better recognition performance.

The invention discloses an MIMO-OFDM (Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing) signal blind separation method for increasing the natural gradient algorithm convergence speed. The method comprises the following steps: firstly, making improvements on a Flexible ICA algorithm under the constraint of ICA preprocessing in combination with an ICA principle to obtain an improved iterative algorithm; secondly, giving a method for adaptively feeding back and adjusting an iteration step size based on a separation error in combination with a BP neural network feedback thought with the separation error; and lastly, giving a comprehensive ending criterion of algorithm iteration from the aspects of the convergence of a separation matrix, iteration times and the separation error. Compared with the prior art, the method has the advantages that the requirements on the convergence speed and the separation accuracy are met on the basis of combining the MIMO-OFDM system features, and an effective separation effect is achieved.

A computer-implemented method for training a normalizing flow. The normalizing flow predicts a first density value based on a first input image. The first density value characterizes a likelihood of the first input image to occur. The first density value is predicted based on an intermediate output of a first convolutional layer of the normalizing flow. The intermediate output is determined based on a plurality of weights of the first convolutional layer. The method for training includes: determining a second input image; determining an output, wherein the output is determined by providing the second input image to the normalizing flow and providing an output of the normalizing flow as output; determining a second density value based on the output tensor and on the plurality of weights; determining a natural gradient of the plurality of weights with respect to the second density value; adapting the weights according to the natural gradient.

The embodiment of the invention discloses a method and device for identifying overlapped digital signals. Two-order, four-order and six-order cumulants of standard signals in different modes are calculated in advance; characteristic values of the standard signals in various modes are calculated and stored according to the cumulant result; and the identification process comprises the following steps of calculating a separate matrix by using a natural gradient ICA algorithm according to a received overlapped signal matrix; calculating the overlapped signal matrix by using the separate matrix to obtain a final estimated value matrix of source signals, wherein each component, which is not zero, in the final estimated value matrix of the source signals is a separated source signal; calculating the two-order, four-order and six-order cumulants of each component, which is not zero, and calculating the characteristic value of each component, which is not zero; and determining the modulation manner of each separated source signal according to the characteristic value of each component, which is not zero, and the characteristic values of the standard signals in various modes. By means of the embodiment of the invention, the overlapped signals can be separated; the identification performance of the overlapped signals is improved; and the overlapped signals are demodulated easily.

The invention discloses a device for using a stable isothermal layer to testing structure dynamic properties. The isothermal layer exists in a temperature box. After the starting of a heat source, the temperature in the box forms a natural gradient along the vertical direction due to a gas heat-expansion and cold-contraction characteristic and the natural adjustment of gravity. The temperature at the same horizontal height is identical and varies little with time. According to the invention, a temperature field of the stable isothermal layer is used, the uniform temperature of a test-piece is ensured and the dynamic property test of the test-piece is completed. According to the invention, the isothermal layer is formed through the natural convection of gas, the temperature control difficulty is lowered, the reliability is high, the power dissipation is low, the space is saved; the rapid switching of a temperature point can be realized through moving the test-piece into different isothermal layers; the device is simple, and the disassembly and assembly are convenient; in addition, micro-adjustment is performed on the temperature gradient by means of thermal insulation, heat reflection and heat dissipation processing of the box, and the temperature control is flexible.

The invention relates to an efficient and accurate detachable runoff plot which comprises a sand separating plate, a primary screen, a tertiary screen, a funnel type water inlet hole, a sand settlingtank, a communicating vessel, a water collecting bottle, a secondary screen, a U-shaped water collecting pool, an air inlet pipe, a water collecting device, a number reading device, a valve, a Markovbottle, non-woven fabric and the like. Surface runoff is collected in the U-shaped water inlet pool under influence of the natural gradient, enters the water collecting device after particles with different particle sizes are intercepted through the different screens, enters the Markov bottle through the funnel type water inlet hole, and is concentrated in the Markov bottle when the water quantityis small; when the water quantity is too large, water flow can enter the water collecting bottle, and it is only required to read water level in the Markov bottle according to the principle of the communicating vessel. The runoff plot has the beneficial effects that manual monitoring is not needed, the testing device can meet the requirements for automatic water collection and sand collection, different particle sizes can be controlled through the different screens, the water level can be accurately and efficiently obtained in the water collection device, and then the water yield is calculated.