85 results about "Decorrelation" patented technology

The invention discloses a bearing fault diagnosis method based on a causal decoupling network, and relates to the technical field of computers. The method comprises the following steps: extracting overall characteristics of a sample bearing vibration signal; extracting cross-domain consistent causal factors of the overall features, and carrying out bearing fault classification on the causal factors; an extractor extracts non-causal factors of the overall features, performs fault classification on the non-causal factors, and performs domain classification on the non-causal factors; and determining the loss of the causal decoupling model based on the cross entropy loss of the causal branch and the non-causal branch, the cross-branch decorrelation loss, the maximum entropy loss and the branch specialization loss, and adjusting the parameters of the causal decoupling model until the loss of the causal decoupling model is minimum. The false correlation between the non-causal factors and the fault classification labels is blocked, and a trained causal decoupling model is obtained; and carrying out fault diagnosis on the to-be-classified bearing vibration signals through the trained causal decoupling model. The method can improve the diagnosis precision of the bearing fault.

The invention discloses a subcarrier-level diversity combining method and subcarrier-level diversity combining equipment for multi-frequency diversity, and relates to the technical field of wireless communication. The method comprises the following steps: receiving a plurality of frequency points bearing the same service, and respectively carrying out orthogonal frequency division multiplexing demodulation on each frequency point to obtain a symbol estimation value; calculating a soft decision log-likelihood ratio and a reliability vector according to the symbol estimation value; aligning the time deviation, the carrier frequency deviation and the sampling rate deviation generated by frequency crossing of each frequency point based on each symbol estimation value; performing decorrelation processing on the aligned soft decision log-likelihood ratio to obtain a decorrelated soft decision log-likelihood ratio, and performing merging operation on the decorrelated soft decision log-likelihood ratio according to the aligned reliability vector to obtain a merged soft decision log-likelihood ratio; and decoding the merged soft decision log-likelihood ratio to obtain a decoding result, and adjusting the merging operation according to the decoding result. By implementing the technical scheme provided by the invention, the diversity combining accuracy can be improved.

The invention relates to a double-antenna BDS-R real-time water level monitoring method and an integrated device, and belongs to the technical field of GNSS remote sensing. The method comprises the following steps: data acquisition; data processing: firstly decoding the received data, and extracting a double-frequency pseudo range and a carrier phase observation value; a second step of performing quality control on the decoded data, including constraint of an elevation angle and an azimuth angle, cycle slip detection and the like, and eliminating non-water-surface reflection signals and abnormal data; a third step of establishing a double-difference observation equation based on the pseudo-range and carrier phase observation value, introducing a horizontal baseline constraint into the observation equation, and enhancing the resolving stability through virtual observation quantity; fourthly, Kalman filtering is used for achieving per-epoch baseline estimation, and a least square ambiguity decorrelation adjustment (LAMBDA) method is used for solving a baseline fixed solution; and finally calculating the water level of the current epoch by using the baseline vector. And transmitting data. According to the invention, the problems of poor real-time performance, complex deployment and high cost in the existing water level monitoring technology are solved.

The invention relates to the technical field of satellite navigation signal processing, in particular to an integer ambiguity resolving method based on a hybrid adaptive differential evolution grey wolf algorithm, which comprises the following steps of: loading GNSS (Global Navigation Satellite System) original data to carry out decorrelation, and calculating an adaptive integer search space boundary; calculating the solution of each individual, storing, comparing the fitness of each individual, and updating and recording globally optimal, suboptimal and third optimal solutions; executing a variation-crossover-selection process of differential evolution to generate a new generation of population; parameter self-adaption and Levy flight are executed; checking whether the current number of iterations reaches a preset maximum number of iterations, if not, adding one to the counter, returning and repeating the steps of comparing the fitness of each individual, updating and recording the global optimal solution, the suboptimal solution and the third optimal solution, and if yes, starting local search to find a better global optimal wolf; and the final calculation result finally recorded in the global optimal wolf variable is output, so that the premature convergence problem in the traditional algorithm solution is solved, and the solution success rate is improved.

This invention discloses a radar embedded communication method based on OTFS modulation. The implementation steps are: 1. Generating an integrated signal based on OTFS modulation; 2. Estimating the relative speed and distance of the RF tag by the integrated signal receiver; 3. Modeling the time-domain channel based on the speed and distance measurement results of the RF tag by the integrated signal receiver; 4. Equalizing the received signal using the constructed time-domain channel matrix and performing decorrelation decision on the equalized signal. This invention utilizes the characteristic that OTFS signals can obtain full channel diversity in both time and frequency, achieving a more accurate estimate of the time-varying frequency-selective bidispersive wireless channel, and equalizing the fading signal, reducing the communication symbol error rate and effectively improving the communication reliability of the radar embedded communication system.

The invention discloses a data processing method and system based on Lie algebraic dynamics and an entropy modulation mechanism, and relates to the technical field of artificial intelligence and data processing, and the method comprises the steps: constructing a group action operator on a physical manifold, and carrying out the manifold enhancement of input data; the Hilbert subspace decomposition and decoupling of the feature channel are completed in parallel through a learnable decorrelation kernel and tensor contraction operation by using a matrix parallel elastic orbit layer; mapping the features to a symplectic geometric phase space, and introducing an adaptive symplectic respiration operator and a Hamiltonian kinetic equation to carry out physical conservation evolution; constructing a Lie algebra classifier, and determining a data category by calculating a Lie bracket transpose norm of a feature generator and a category base; and guiding model training by using an entropy modulation action quantity loss function containing physical potential energy. The method can solve the problems of poor generalization and weak anti-noise capability of a traditional deep learning model, and can be widely applied to the fields of power equipment monitoring, computer vision, natural language processing and the like.

This disclosure provides an audio encoding method, an audio decoding method and apparatus therefor, an electronic device, a storage medium, a computer program product, and a computer program. [Solution] The method includes the steps of: grouping a channel sequence to obtain a plurality of channel groups, each channel group containing a plurality of consecutive channels in the channel sequence, with one or more identical channels between adjacent channel groups; performing a frequency domain transformation on each audio signal of each channel in the channel sequence frame by frame to obtain frequency domain coefficients for each frame of each channel; determining a target transformation matrix for each frequency band in a frequency band set corresponding to the channel group from a set of transformation matrices based on the frequency domain coefficients of each channel; performing same-band decorrelation on the frequency domain coefficients of the channels in the channel group based on the target transformation matrix for each frequency band to obtain coding information for the channel group; and obtaining a coded stream based on the coding information for the channel group and sending the coded stream to a decoder for decoding.

This disclosure provides systems, methods, and devices for wireless communications that support operation with an antenna array, such as multiple-in-multiple-out (MIMO) and / or beamforming. In a first aspect, a method for wireless communications includes exciting elements of an antenna array simultaneously using a plurality of encoded signals; determining a correlation matrix indicating a response of the antenna array based on responses of the elements of the antenna array captured during simultaneous excitation of the antenna array; and transmitting signals from at least some elements of the antenna array simultaneously based on the correlation matrix. Other aspects and features are also claimed and described.

The invention provides a conventional beam forming method and system and a water depth detection method and system based on array data decoherence, and the beam forming method comprises the steps: carrying out the frequency mixing and filtering of a signal received by an array element, obtaining a baseband signal, calculating an array baseband signal covariance matrix, and introducing a tiny regularization factor; performing eigenvalue decomposition on the array baseband signal covariance matrix, and calculating an inverse square root matrix of the array baseband signal covariance matrix to obtain a solution coherence matrix for eliminating spatial correlation; multiplying the array baseband signal by the decoherence matrix to realize elimination of correlation data between array elements and obtain a decoherence array baseband signal; and carrying out total energy normalization processing on the array baseband signal after decoherence, and then inputting the array baseband signal into a conventional beam former to carry out beam forming. The method has the advantages that space correlation noise is effectively suppressed, and the integrity of signal energy is kept; not only is the resolving power of beam forming improved, but also the robustness in a complex noise environment is enhanced.

An apparatus for decoding a signal is provided. The apparatus comprises a transient separator (410) for separating an apparatus input signal into a first signal component and into a second signal component such that the first signal component comprises transient signal portions of the input signal and such that the second signal component comprises non-transient signal portions of the input signal. Moreover, the apparatus comprises a transient decorrelator (420) for decorrelating the first signal component according to a first decorrelation method to obtain a first decorrelated signal component; wherein the transient decorrelator (420) is configured to decorrelate the first signal component according to the first decorrelation method by multiplying a phase term with the first signal component. Furthermore, the apparatus comprises a further second decorrelator (430) for decorrelating the second signal component according to a second decorrelation method to obtain a second decorrelated signal component, wherein the second decorrelation method is different from the first decorrelation method. Moreover, the apparatus comprises a combining unit (440) for combining the first decorrelated signal component and the second decorrelated signal component to obtain a decorrelated combination signal. Furthermore, the apparatus comprises a mixer (450), being adapted to receive mixer input signals and being adapted to generate output signals based on the mixer input signals and a mixing rule. The mixer (450) is adapted to receive correlation / coherence parameter data indicating a correlation or coherence between two signals and is adapted to generate the output signals based on the correlation / coherence parameter data.

The audio device comprises a receiver (101) arranged to receive a set of input audio signals segmented by a sectionalizer (103). An output signal generator (105) generates an output audio signal by performing, for each segment, the following steps: generating (201) a frequency bin representation of the input audio signal; generating (203) a frequency bin representation of the output audio signal as a weighted combination of frequency bin values of the input audio signal of the frequency bin; the frequency bin representation is converted to the time domain. The adapter (107) updates a weight for a given weight in response to a correlation metric between an output frequency bin value of the output audio signal being generated and the output audio signal linked to the input signal weighted by the weight. The approach may generally provide improved frequency domain adapted spatial decorrelation of audio signals.

The audio device includes a receiver 101 configured to receive an audio signal from which an audio beamformer 103 generates a beamformed output audio signal. The beamformer 103 includes a signal processor 201 that applies signal processing to the audio signal. Signal processing includes spatial beamforming and spatial decorrelation, which is often adapted spatial decorrelation. A decorrelation adapter 203 adapts decorrelation parameters depending on the audio signal, and a beamform adapter 205 adapts beamform parameters depending on the beamformed output audio signal. An equalizer 105 applies frequency equalization to the beamformed output audio signal to generate an output signal, and an equalization adapter 107 adapts frequency equalization depending on the beamform parameters, typically also depending on the decorrelation parameters. This approach provides improved fidelity to the desired sound source and attenuates interfering sound sources.

The application discloses a kind of parallel random number generation devices based on pulse driving micro-ring resonator cavity, by adopting pulse mode driving micro-ring resonator cavity, improve the instantaneous power level in cavity, enhance nonlinear effect, to effectively expand the effective bandwidth of chaotic entropy source, improve the complexity of entropy source.Meanwhile, using the characteristics that micro-ring resonator cavity naturally supports multi-wavelength output, and combining with filter demultiplexing structure, the parallel output of multiple chaotic entropy sources is realized, which is beneficial to improve the parallel generation ability of random number.Finally, by combining post-processing methods such as difference, quantization and decorrelation, the overall generation rate of random bits is improved while ensuring randomness quality.The application has relatively simple structure, is suitable for integrated implementation, and can meet the application requirements of high-speed parallel random number generation.

This invention discloses an optimization prediction method for software testing based on big data management, specifically including: Step 1, collecting test data and performing time index alignment and numerical processing to form a test feature set; Step 2, constructing a static Markov blanket and a dynamic Markov blanket, and determining the target feature subset by switching input boundaries; Step 3, constructing a neural prediction model based on the target feature subset and outputting predicted values; Step 4, dividing the target feature subset into relevant feature clusters and updating the model parameters using the decorrelation-based isoangular minimum angle regression rule; Step 5, outputting test results, defect risk, and resource consumption prediction values; Step 6, generating test optimization configuration results based on the predicted values. This invention completes the prediction through isoangular regression using a two-layer Markov blanket.

The invention provides a physical layer key generation method of a hidden key construction path, which comprises the following steps: two communication parties use a shared partitioning algorithm to block an original key to obtain a non-repeated and ordered key fragment sequence; the method comprises the following steps: a first communication party samples a secret key fragment sequence of the first communication party, disturbs unsampled secret key fragments by using a disturbance device, and then splices the unsampled secret key fragments with the sampled secret key fragments according to the sequence of original secret key fragments to obtain a disturbed secret key fragment sequence; the second communication party carries out information coordination on a secret key fragment sequence of the second communication party on the basis of the sequence to obtain an error-corrected secret key fragment sequence; converting the error correction result into a sampling index; and the two communication parties perform sampling according to the sampling index, and then perform compression by adopting a privacy amplification technology to obtain a final key. According to the method, a secret key path is hidden as a means, a stronger security protection choice is provided for a PKG task in a spatial decorrelation or even active attack scene, and the security boundary of the PKG is greatly widened.

The invention provides a gross error detection method, gross error detection equipment, a readable storage medium and a product, and relates to the technical field of positioning. The method comprises the following steps: inputting a satellite observation value into an RTK filter, and obtaining a residual error output by the RTK filter at the current moment and a prediction variance covariance matrix of the residual error; determining an error in a unit weight according to the residual error and the predicted variance covariance matrix, and calculating a dynamic threshold value according to the error in the unit weight; according to a preset experience threshold value and a dynamic threshold value, performing first gross error elimination on the residual error to obtain a first residual error sample; according to the condition number of the predicted variance covariance matrix, performing second gross error elimination on the first residual error sample to obtain a second residual error sample; and performing de-correlation calculation on the second residual error sample construction, and performing adaptive robust filtering on a de-correlation residual error subset. According to the method, gross error elimination is carried out from a data layer and a model layer, and more accurate and efficient RTK gross error detection capability is achieved.

The application provides a kind of missing distribution offset based on the prediction method and system of incomplete data, comprising: with incomplete feature as input, construct the prediction function network based on multilayer perceptron, predict complete label;Construct the prediction function parameter network based on multilayer perceptron, learn the relevance between prediction function parameter and missing condition;Using random Fourier feature, the relevance between incomplete feature and missing condition is measured;According to random Fourier feature, the weight of incomplete sample in data set is redistributed;By minimizing the prediction reconstruction error, the prediction function parameter network and the prediction function network are jointly trained, and the overall predictability of the combined two networks is optimized.The application uses a prediction framework based on decorrelation, which can effectively learn the underlying relevance between complete labels and incomplete features, thereby achieving good generalization performance when the missing distribution is offset.

This invention proposes a stable target recognition method and apparatus based on visual feature random partitioning and decorrelation. The method includes: acquiring a visual feature set of a training dataset consisting of industrial images and their defect labels, and performing multiple random partitions; after each partition, all dimensions of visual features are divided into two mutually exclusive feature segments; under each feature partitioning scheme, using the training dataset, learning a set of sample weights to decorrelate the two feature segments in a weighted distribution; weighting the training data using the sample weights, and using the weighted training data to train a base recognition model; inputting the image to be recognized into the trained base recognition model to obtain the corresponding recognition result; and integrating the recognition results from all models to obtain the final stable recognition result. This invention can effectively alleviate the environmental bias problem in industrial production lines and improve the stability and generalization performance of defect detection models with limited training data.

The application provides a self-adaptive signal beam forming method, device, equipment and storage medium. The method is based on a 2N+1 element uniform linear array receiving signal. The method comprises the following steps: a multiple Toeplitz matrix is constructed, and a covariance matrix is obtained by merging; a signal direction vector is calculated according to an eigenvalue and an eigenvector, and an expected signal direction vector is obtained by optimization; a noise power is estimated by using a minimum eigenvalue, a corresponding relationship between a signal power and the eigenvector is established, and an expected signal power is determined; a covariance matrix of mutually independent noise is reconstructed according to the noise power, the corresponding relationship and the expected signal power; and the expected signal direction vector and the reconstructed covariance matrix are brought into a Cap on beam former, and an optimal weight vector is calculated to control beam forming. The method improves the performance and reliability of the beam forming system in a complex signal environment by constructing a multiple Toeplitz matrix and using the characteristics of the multiple Toeplitz matrix for signal decorrelation and covariance matrix reconstruction.

Techniques to handle the Spherical Harmonic (SH) coefficients associated with 3DGS towards effective compression are described herein. SH coefficients are used to represent view dependent RGB color values, which takes a significant amount of memory—48 out of 59 parameters per Gaussian. By applying suitable transformation to SH coefficients, the inter-channel redundancies are exploited, thereby effectively reducing the memory requirements by 50%.

The invention discloses a self-adaptive narrowband interference suppression method and device for power line communication, and the method comprises the following steps: S1, receiving a mixed signal, and carrying out the decorrelation processing of the mixed signal, so as to obtain a decorrelation signal; s2, inputting the decorrelated signal into a filter, and estimating the narrowband interference signal at the current moment by the filter according to the filter coefficient at the current moment to obtain a narrowband interference estimation signal; s3, calculating the energy of the decorrelation signal; s4, subtracting the received signal at the current moment from the narrowband interference estimation signal to obtain an error signal; and S5, according to the filter coefficient at the current moment, the error signal and the energy of the decorrelation signal, calculating the filter coefficient at the next moment by using a power normalized least mean square algorithm, so that the algorithm maintains the characteristic of insensitivity to the power change of the input signal, the calculation resource consumption is greatly reduced, and the calculation efficiency is improved. And the feasibility and efficiency of the algorithm in practical application are improved.

The present application relates to a kind of unsupervised image representation learning method based on clustering space decorrelation, and the input of one batch image is twice random enhancement and obtains two batch data input twin network;Similarity input to each cluster is calculated based on the output and cluster center;Based on the intercorrelation matrix between the input in a batch based on clustering space feature, the unitization loss is calculated;The membership of input to each cluster is obtained by normalizing clustering space feature, the membership of one image is used as the target distribution of another relevant image, and the alignment loss between the prediction of model to input and its target distribution is calculated;Unitization loss and alignment loss are added as total loss for model training;The minimization of unitization loss realizes the consistency and decorrelation of cluster level, and each other target loss realizes the consistency of sample level to input disturbance, finally effectively unsupervised image representation learning is carried out under the condition of no negative sample pair.

The invention relates to the technical field of data processing, in particular to a cross-modal feature alignment underground facility monitoring data analysis method and system, and the method comprises the steps: obtaining underground facility multi-source heterogeneous monitoring original data, and mapping the data into an original modal feature flow; extracting the power spectrum distribution of the original modal characteristic flow, calculating a channel attenuation coefficient by combining a background reference, and generating an environment background embedded vector; splitting the original modal characteristic flow into a task-related characteristic flow and an environment clutter component flow through mutual orthogonal projection operation of a potential space; performing de-correlation constraint on the task related feature flow by using the environment clutter component flow, and outputting standardized cross-domain invariant representation based on gradient flipping mapping and domain distribution discrimination criteria; and loading the representation into a gradient evolution processing flow, reconstructing an internal control parameter to generate a weight parameter, and outputting an anomaly monitoring result. Through the feature space mapping and alignment process, the distribution difference among the multi-source data is reduced, and the data processing precision is improved.

The invention belongs to the technical field of radar maneuvering target tracking, and particularly relates to an interactive multi-model radar maneuvering target tracking algorithm based on random Kalman filter (RKF), which is suitable for effective tracking of a maneuvering target under the condition of polar / spherical coordinate measurement. According to the method, a plurality of RKF filters with different variances are introduced under an interactive multimode framework, so that the coverage of the motion state of a maneuvering target with multiple unknown motion modes is realized, and meanwhile, non-linear measurement conversion of depolarization and decorrelation is realized based on a predicted value under the filtering framework; finally, a radar maneuvering target tracking algorithm (IMM-RKF-DUCM) based on multiple RKF models is provided. Compared with the traditional IMM-REKF, IMM-DUCM and single-model RKF-DUCM, the method can maintain high stability and high precision in an unknown or rapidly changing maneuvering scene, significantly reduces the probability of failure, and is a radar target tracking algorithm with higher robustness and better tracking performance.

The invention relates to the technical field of data transmission, in particular to a 5G network-oriented data transmission encryption method and system. The data transmission encryption system for the 5G network comprises a channel measurement module, a filtering module, a real value vector construction module, a bit quantization module, a bit unification module and a secret key construction module. Based on uplink and downlink reciprocity under TDD same frequency and same wave beam, according to correlation of adjacent CSI-RS and SRS in coherence time and corresponding channel response, decorrelation real value features are extracted through amplitude / phase adjacent frequency difference, additional round trip does not need to be added, and the method is simple and convenient to implement. Error correction is carried out through bit quantization and an error correction test feature transmitted by embedding a control instruction, so that the base station completes consistency correction of a bit string sequence under the condition of not increasing any round-trip and new signaling; and finally, based on the corrected uplink bit string sequence and the uplink bit string sequence, constructing a key seed and deriving a key required by data encryption transmission.

The invention discloses a mining subsidence prediction parameter solving method based on an improved center collision optimization algorithm, and belongs to the field of mine deformation monitoring data processing. Aiming at the defects that a basic center collision optimization algorithm is prone to premature convergence and insufficient in optimization precision when solving a mining subsidence parameter inversion problem, a Huber loss function is adopted to construct a fitness evaluation model to enhance the robust capability of the algorithm, and Cubic mapping is utilized to generate a random number to improve the stability of a search process, so that the method is suitable for the mining subsidence parameter inversion problem. And a self-adaptive elite-guided Cauchy variation mechanism is introduced to enhance the global exploration capability. According to the method, firstly, a probability integral method parameter inversion problem is constructed into an optimization model, and a population is initialized; performing double-space collaborative search in an original space and a decorrelation space constructed based on principal component analysis, and iteratively updating a population in combination with a dynamic space allocation strategy; and finally outputting an optimal parameter solution. According to the method, the convergence precision, stability and robustness of the algorithm in complex nonlinear parameter inversion are effectively improved.

The application discloses a two-stage modulation-based underwater polarization ghost imaging method and device, and relates to the fields of optical imaging and ocean detection. The method comprises the following steps: acquiring basic data of a target water area and constructing a backscattering point spread function; performing statistical decorrelation modulation on an original reference speckle sequence based on a first correlation coefficient, a second correlation coefficient and the backscattering point spread function, to obtain a primary modulation reference speckle distribution sequence; performing polarization space modulation on the primary modulation speckle sequence, to obtain a secondary modulation reference speckle distribution sequence; irradiating a target to be detected according to the secondary modulation reference speckle distribution sequence, acquiring a double-polarization bucket detection signal, and constructing a polarization difference signal sequence; and performing correlation reconstruction on the polarization difference signal sequence and the secondary modulation reference speckle distribution sequence, to obtain a reconstructed image. The application solves the problems of serious backscattering interference and insufficient imaging signal-to-noise ratio in a strong turbid underwater environment, and realizes high-quality underwater polarization ghost imaging.

A point cloud attribute encoding method and apparatus, a decoding method and apparatus, and a related device are disclosed. The point cloud attribute encoding method includes sorting all point cloud data to be encoded to acquire sorted point cloud data, the point cloud data to be encoded being point cloud data with attributes to be encoded; grouping the sorted point cloud data based on correlation between the sorted point cloud data to acquire groups to be encoded; and performing point cloud attribute encoding based on the groups to be encoded. It is beneficial for enhancing the correlation between point cloud data within the group, improving the efficiency of decorrelation during intra group transform after grouping, and improving coding efficiency.

The application discloses a kind of fracturing pump multi-cylinder vibration signal processing method, device, equipment, medium and product, by obtaining vibration data sequence and key phase pulse signal, vibration data sequence is the vibration data collected by vibration sensor installed between adjacent cylinder body of fracturing pump, key phase pulse signal is the key phase pulse signal collected by phase sensor installed in crankshaft part of fracturing pump, then the correlation of vibration data sequence is eliminated by decorrelating vibration data sequence, the vibration data sequence after decorrelation is analyzed independently, output multiple vibration data sequences to be matched, finally based on key phase pulse signal, multiple vibration data sequences to be matched are matched with adjacent cylinder, the target vibration data sequence corresponding to each cylinder is obtained, can significantly reduce the amount of sensor, reduce cost and complexity while extracting the vibration data with stronger feature information, to effectively improve the accuracy and timeliness of fracturing pump fault diagnosis.

The application discloses a two-dimensional conversion method for multi-dimensional space relation among GNSS ambiguity vectors, comprising the following steps: constructing a GNSS double difference mathematical model, and obtaining a multi-dimensional ambiguity vector floating point solution and a variance-covariance matrix thereof by using a least square method; obtaining an optimal integer solution and a suboptimal integer solution of the ambiguity vector by means of a least square ambiguity decorrelation adjustment method according to the ambiguity vector floating point solution and the variance-covariance matrix thereof; taking the ambiguity vector floating point solution, the optimal integer solution and the suboptimal integer solution as end points, calculating an elliptic norm side length of an edge formed by any two end points and an A-inner product included angle between any two edges; and constructing a two-dimensional Euclidean triangle based on the elliptic norm side length and the A-inner product included angle, so as to convert a complex and abstract multi-dimensional space relation among the ambiguity vector floating point solution, the optimal integer solution and the suboptimal integer solution into a simple and direct two-dimensional triangle relation; and the application provides important method support for analyzing a GNSS ambiguity confirmation reliability influence mechanism.