87 results about "Markov transition" patented technology

The invention discloses a regional water environment bearing capacity change evaluation and prediction method and system, and the method comprises the steps: obtaining water resource data, water environment data and social economic data in a target region, and constructing a water environment bearing capacity index system; the index system comprises a plurality of subsystems; evaluating the subsystems through a projection pursuit model based on a sparrow algorithm to obtain subsystem evaluation results; obtaining a comprehensive evaluation result of the water environment bearing capacity through an entropy weight approximation ideal solution sorting algorithm according to the subsystem evaluation result; constructing a Markov transition probability matrix according to the comprehensive evaluation result; and predicting the change probability of the water environment bearing capacity according to the Markov transition probability matrix. The method can improve the evaluation and prediction accuracy of the water environment bearing capacity change, and can be widely applied to the technical field of water environment evaluation and prediction.

The invention discloses an autism detection method based on multi-mode collaborative embedding, and belongs to the technical field of medical image analysis and artificial intelligence. The method comprises the following steps: firstly, obtaining resting state functional magnetic resonance imaging data and non-imaging data of a subject; a Markov transition field is utilized to encode the time sequence into an image so as to retain dynamic features, and feature extraction is carried out through an efficient multi-scale attention module; then realizing effective fusion and semantic alignment of multi-modal information by adopting a three-level fusion architecture and a joint loss function; and then adaptively constructing a graph structure based on the fusion features, dynamically learning a node relationship by using a graph attention network, and completing a classification decision. According to the method, the defects of a traditional method in the aspects of dynamic feature modeling, multi-modal fusion and heterogeneous graph structure processing are effectively overcome, the autism detection accuracy and robustness are remarkably improved, and a reliable tool is provided for clinical intelligent diagnosis.

The invention discloses a power quality disturbance identification method based on a Markov transition field and a lightweight dense connection network, and the method comprises the steps: constructing a PQDs standard signal set, converting a complex PQDs one-dimensional signal set into a two-dimensional feature image data set based on an MTF visual conversion method, and dividing the two-dimensional feature image data set into a training set, a verification set and a test set; the method comprises the following steps: developing a DenseNet-L lightweight identification network model based on a dense connection mechanism; a CBAM attention mechanism model combining CAM and SAM is constructed; the DenseNet-L model and the CBAM model are combined through module fusion, and a DenseNet-LC lightweight identification model is constructed and obtained; the DenseNet-LC model is trained through the training set, and an optimal performance model is stored; and performing identification performance test on the DenseNet-LC optimal performance model by using the test set to obtain a final complex PQDs identification result. According to the method, the classification precision and noise immunity of the complex PQDs can be effectively improved, meanwhile, real-time performance is achieved in the efficiency performance aspect, and technical support can be provided for rapid sensing of the operation health situation of a power distribution network system.

The invention relates to the technical field of target positioning and tracking, and provides a passive multi-station time difference target positioning and tracking method. Comprising the following steps: performing initialization assignment on an initial algorithm by using an initial state of initialized data and a covariance matrix of a positioning target, inputting a model into an initialized interactive algorithm, and performing model interaction to obtain mixed state estimation and a covariance matrix of the model after interaction; performing state prediction updating by using an improved volume Kalman filter to obtain a model likelihood function; updating the model probability according to the model likelihood function, and obtaining an updated Markov transition probability matrix through an improved IMM algorithm; and calculating state estimation and a covariance matrix which are finally fused by the interactive multi-model to obtain a positioning and tracking result of the target. According to the method, the QS-CKF algorithm is introduced, the IMM algorithm is improved, and the Markov transition probability matrix is dynamically adjusted, so that the problem of filtering divergence or tracking lag is avoided, and the target positioning and tracking performance is improved.

The invention provides an optical access network PON port fault prediction and operation and maintenance support method and system, and the method comprises the steps: collecting real-time multi-index sequences of optical power, bit error rate, temperature, frame loss rate, business load and the like, automatically extracting a long-term trend based on variational mode decomposition, constructing a residual sequence, generating a state transition map through employing a Markov transition field algorithm, and carrying out the fault prediction and operation and maintenance support of the PON port. And future operation state distribution prediction is realized through the LSTM neural network. And after an abnormal threshold is dynamically adjusted in combination with quantile regression, a time sequence prediction result and a threshold response are fused to output a comprehensive fault probability score, and an early warning signal is triggered through exponential smoothing and continuity check, so that the progressive fault detection accuracy and advancement can be improved, and an efficient and steady prediction and early warning basis is provided for intelligent operation and maintenance of the PON port.

The invention relates to a sleep staging method based on Markov chain dynamic loss, and relates to the field of data processing. The method comprises the steps that electroencephalogram signals are preprocessed to obtain training samples, a sleep staging model is constructed and trained, sleep staging is conducted through the trained model, and training comprises the steps that basic classification loss is calculated; when the sleep stage of the training sample is transferred to different stages, obtaining a real physiological transition probability through a Markov transition probability matrix, and if the probability is smaller than a threshold value, calculating a loss weight factor of the training sample according to whether the model correctly predicts the sleep stage of the current training sample; calculating an average value of the sequence sensing loss according to the loss weight factor and the basic classification loss; and calculating the gradient of the average value to the parameters of the sleep staging model, and updating the model parameters of the sleep staging model. According to the method and the device, the physiological interpretability of understanding and prediction of the sleep staging model on the overall sleep structure is improved, and then the sleep staging accuracy is improved.

The invention provides a river trajectory data protection method based on differential privacy, and belongs to the field of environmental data privacy protection. According to the method, river flow velocity and flow direction data are discretized into a two-dimensional grid, spatial-temporal characteristics are extracted by using a graph convolutional network (GCN), and a Markov transfer matrix is established; and according to the grid access frequency and the spatial density, privacy budget is adaptively allocated through a dual attenuation factor model, and differential privacy protection is realized. In the trajectory generation stage, dynamic privacy budget is combined, noise is injected into direction and time features by adopting an index mechanism and a Laplace mechanism, and a synthetic trajectory conforming to physical constraints is generated; and finally, evaluating and optimizing the track quality by using Savitzky-Golay filtering and multiple indexes (such as Frechet distance, access frequency error and KL divergence). According to the method, the privacy leakage risk is effectively reduced while the space-time continuity of the trajectory is kept, the data privacy and availability are considered, and the method is suitable for hydrological monitoring, ecological analysis and environmental data sharing.

This invention discloses a Wi-Fi visualization perception method based on Markov transition fields, primarily addressing the existing issues of poor channel state information (CSI) feature extraction and low recognition accuracy. Its implementation involves: utilizing Markov state transition characteristics to characterize the temporal variations of Wi-Fi signals and transforming CSI data into CSI images; adaptively correcting the CSI images based on a channel attention mechanism to focus on the channel feature information of the CSI images and synthesize RGB three-channel CSI images; dividing the CSI images into training and test sets according to a set ratio; constructing a Visual Transformer (ViT) network model and training it using the training set; and inputting the test set into the trained ViT network model to obtain gesture recognition results. This invention can effectively extract channel features from CSI images, improve the accuracy of indoor human gesture recognition, and can be used for visualizing and recognizing dynamic human gestures indoors.

The invention relates to a multi-mode Transform distribution network power frequency signal fault identification method based on a Markov transfer field, and the method comprises the steps: obtaining three-phase power frequency data, and obtaining an input data set; performing Markov transfer field conversion on the continuous power frequency signal to obtain Markov transfer field image features; performing feature extraction on the continuous power frequency signal to obtain a time sequence feature; carrying out feature fusion on the Markov transfer field image features and the time sequence features to obtain fusion features; inputting the fusion features into a model for training to obtain a power frequency signal fault identification model; and performing fault identification by using the model. According to the method, the robustness and adaptability of fault recognition are enhanced through multi-modal data fusion, so that the system can analyze the health state of the power network more comprehensively, the accuracy of fault recognition is improved, and the method is particularly prominent in a complex fault mode.

The present invention discloses a multi-sensor signal fusion method for tool wear prediction. By using the method of average segmented aggregation, the data volume can be reduced while preserving the original data trend, thereby shortening the operation time. The present invention proposes a method using Markov transition fields to encode the time series data generated during the machining process into two-dimensional images, and realizes the fusion of multi-channel pictures through the method of Laplacian image pyramid, achieving the fusion of multi-sensor information at the data level and avoiding the loss of the time relationship between sensing data. The present invention encodes the multi-sensor signals during the machining process into two-dimensional images through the method of Markov transition fields, and uses the method of Laplacian image pyramid to realize the fusion of multi-channel pictures, and then realizes the encoding of multi-sensor signals and the fusion into multi-channel two-dimensional images, which can maximize the preservation of the time relationship inside the original signals.

The present invention provides a modulation recognition method based on Markov switching fields and deep learning, which relates to the field of cognitive radio technology and includes the following steps: S1, receiving a radio signal and generating Markov switching field features by using the in-phase / quadrature data of the radio signal; S2, reducing the dimension and reshaping the Markov switching field features, and then splicing them with the in-phase / quadrature data into hybrid data; S3, establishing and training a convolutional assisted Transformer model to obtain a trained Transformer model; S4, using the trained Transformer model to identify the modulation mode of an unknown signal, using the test set of the hybrid data as the input and the modulation mode label as the output. The present invention uses two complementary data, namely the I / Q sequence and the Markov transition graph, as the input of the model, greatly improving the recognition accuracy. The convolutional assisted Transformer model proposed by the present invention can simultaneously extract the local and global features of the data, enhancing the recognition accuracy.

The present invention discloses an intelligent diagnosis method for partial discharge of a transformer based on an improved transformation network, which relates to the technical field of transformer fault diagnosis. The method includes collecting partial discharge signals of the transformer, preprocessing the partial discharge signals to obtain a time series; using the Markov transition field (MTF) algorithm, the Gram angular field (GASF) algorithm, and the Gram angular difference field (GADF) algorithm to convert the time series into feature images respectively; processing the feature images through an improved transformation network model to obtain a fused feature vector; and inputting the fused feature vector into a fault diagnosis model to output a fault diagnosis result of the partial discharge of the transformer. By combining an interpretable graph reasoning mechanism of environmental parameters and signal features, the present invention improves the recognition rate of oil gap discharge in tests, enabling the fault diagnosis to leap from the traditional threshold-based judgment mode to an intelligent mode based on causal logic reasoning.

This invention relates to an online prediction method for trajectory tracking success rate of modular unmanned surface vessels (USVs). The method includes the following steps: S1: Establishing a relative motion model between the USV and the desired trajectory point using stochastic differential equations; S2: Dividing the feasible state space of the USV into multiple sets, and rasterizing the spatial regions within each set; S3: Calculating the Markov transition probability of any grid within the feasible state space; S4: Recursively calculating the trajectory tracking success rate of the USV using backpropagation. This invention introduces a spatial correlation function to describe the influence of wind, wave, and current disturbances on the relative pose of the USV and the desired trajectory through a stochastic relative motion model. Simultaneously, it utilizes Markov stochastic approximation theory to predict the trajectory tracking success rate online, significantly improving the accuracy of trajectory tracking success rate prediction compared to existing technologies.

The present invention discloses a lightweight malware visualization classification method based on multi-scale features, comprising the following steps: converting malware binary files into grayscale images through Markov transition matrix modeling; extracting image features by a multi-scale dilated convolution fusion attention module MDFA; performing classification using the SimMobileNetV2 network; the present invention significantly improves the classification performance and has strong practical application value.

The application belongs to the technical field of sonar data processing, and particularly relates to a small-sample sonar reverberation data enhancement and target detection method and system. The small-sample sonar reverberation data enhancement and target detection method introduced the spectral perception loss and multi-scale short-time Fourier transform feature constraint through WGAN-GP, the cosine similarity of the time-frequency domain distribution of the reverberation signal generated by the method is greater than or equal to 0.92 with the real data, the mode collapse problem of the traditional generative adversarial network model under small samples is solved, and the data diversity is improved. The time sequence structure and state transition law of the sonar detection data set are coded into a two-dimensional image through the Gram angle field and the Markov transition field, and the CBAM attention mechanism is combined to improve the feature distinction degree of the target and the reverberation. The multi-modal convolution network can improve the detection accuracy under the small sample data scene, is no longer dependent on a large sample data volume, and avoids overfitting when the sample data is small.

The invention discloses a ferromagnetic resonance fault overvoltage identification method and system, computer equipment and a medium. The method comprises the following steps: acquiring a three-phase voltage and zero-sequence voltage time sequence in a power distribution network; according to the time sequence, coding the time sequence into a two-dimensional image by using a Markov conversion field so as to reserve a time sequence dynamic characteristic; inputting the two-dimensional image into a pre-trained dual-channel graph neural network model, extracting image features by the model through a space view angle channel and a time view angle channel, and performing feature fusion by using a cross-modal attention mechanism; and outputting a classification result of the ferromagnetic resonance overvoltage according to the fused features. The method is high in identification accuracy and strong in anti-interference capability, can effectively distinguish fundamental frequency, frequency division and high-frequency ferromagnetic resonance, and has good adaptability to nonlinear and non-stationary overvoltage faults in a power distribution network.

The application relates to a method for generating wind power output simulation data according to historical data, which comprises the following steps: dividing historical wind power output data into several seasons through different date segmentation modes; calculating the expected value of wind power output of each season formed by each date segmentation mode, selecting a date segmentation mode with the maximum expected value deviation between seasons, and dividing the historical wind power output data into several seasons through the date segmentation mode; statistically obtaining the wind power output probability distribution in each season and the Markov transition matrix at each time; and simulating the wind power output at each date and each time in each season by using a Metropolis-Hastings algorithm to obtain annual simulation data. The simulation data generated by the scheme can better conform to the probability distribution of local historical data and the output characteristics of local wind power, and is suitable for Monte Carlo simulation calculation and other calculation researches which require a large amount of data.

The application discloses an omnidirectional guiding interaction multi-model positioning method and system for a motorized mother ship to recover an underwater robot and a storage medium, the method obtains the state information and distance data of the mother ship by integrating a water acoustic ranging and a water acoustic communication device, constructs an interaction multi-model singular value decomposition unscented Kalman filtering algorithm fusing a uniform straight line and a uniform bow turning model, and realizes real-time estimation of the position of the mother ship under a mouth-shaped maneuvering track. An event-triggered communication mechanism is designed, information is broadcast only when the turning state of the mother ship changes, so as to reduce the communication load and maintain real-time performance; meanwhile, a Markov transition matrix is updated, the turning model weight is enhanced, and the estimation accuracy and model matching are improved. According to the model probability and the estimation result, a LOS guiding law with an adaptive look-ahead distance is constructed, and the autonomous guiding recovery of the underwater robot to the motorized mother ship is realized. The method provided by the application can realize dynamic estimation and guiding based on the measurement data of a single water acoustic device under the conditions of the mother ship maneuvering and communication limitation.

The invention discloses an unmanned aerial vehicle radio frequency fingerprint extraction method based on multi-dimensional feature field coding, and belongs to the field of wireless signal identification. In order to solve the problem that a traditional time-frequency diagram mainly represents signal energy distribution and is difficult to explicitly express a signal state transition relation, the invention provides an F-FD-A (instantaneous frequency-frequency difference-instantaneous amplitude) multi-dimensional feature coding strategy which can effectively extract information such as a frequency hopping rule and envelope deformation of a signal. The method comprises the following steps: firstly, extracting instantaneous frequency, frequency difference and instantaneous amplitude of a radio frequency signal; further, the Markov transfer field is utilized to encode the first two sequences, and the Gramer angle difference field is utilized to encode the amplitude sequence; and finally, fusing the feature images into an RGB image, and inputting the RGB image into a ResNet-50 network for identification. Experiments show that the method has excellent recognition precision in a mixed data set covering DJI multi-series and multi-brand universal models, and fine-grained and high-robustness recognition of similar models of unmanned aerial vehicles in a complex environment is realized.

The invention relates to a trajectory tracking success rate online prediction method for a modular unmanned ship. The method comprises the following steps: S1, establishing a relative motion model of the unmanned ship and an expected trajectory point by adopting a stochastic differential equation; s2, dividing the feasible state space of the unmanned ship into a plurality of sets, and performing rasterization processing on the space region in each set; s3, calculating the Markov transition probability of any grid in the feasible state space; and S4, recursively calculating the trajectory tracking success rate of the unmanned ship by adopting a back propagation method. According to the method, a space correlation function is introduced through a relative motion random model of the unmanned ship and an expected trajectory to describe the influence of wind wave flow disturbance on the relative poses of the unmanned ship and the expected trajectory, and meanwhile, the trajectory tracking success rate is predicted online by using the Markov random approximation theory; compared with the prior art, the accuracy of predicting the trajectory tracking success rate of the unmanned ship is remarkably improved.

The invention discloses a multi-view fusion cloud native malicious software detection method and system based on high-speed introspection of a virtual machine, and the method comprises the steps: 1), deploying a high-speed introspection module in a monitoring layer of the virtual machine, capturing an API call sequence of an internal process of a target virtual machine from the outside in a safe and low-invasion manner, and structuring the API call sequence into a runtime log; (2) the API calling sequence is regarded as a sentence, and a Word2Vec model is used for training to generate a structure embedding vector of the API; then, constructing a directed heterogeneous graph containing a file, a thread and API calling for each sample, taking the structure embedded vector as an initial feature of an API node, encoding the graph by using a graph attention network, and extracting a structure context feature vector; 3) extracting an API official function description text by utilizing the pre-training language model to generate a semantic embedding vector; constructing a directed heterogeneous graph for each sample, replacing the initial features of the API nodes in the graph with the semantic embedding vector, coding the graph by using the graph attention network again, and extracting a functional semantic feature vector; 4) firstly performing function classification on the APIs, and performing dimensionality reduction on the complete API calling sequence to obtain a limited function state sequence; constructing a Markov transition probability matrix for the state sequence of each sample, and selectively stacking a multi-order transition matrix to form a multi-channel feature tensor; inputting the feature tensor into a convolutional neural network for coding, and extracting a macroscopic behavior evolution feature vector; 5) splicing the structure context feature vector, the function semantic feature vector and the behavior evolution feature vector to form a final comprehensive feature vector; and inputting the comprehensive feature vector into a multi-layer perceptron classifier, and training the classifier in an end-to-end manner to enable an output sample of the classifier to be a classification result of malicious software or benign software.

The invention relates to the technical field of hydroelectric generating set control optimization, and discloses a hydroelectric generating set start-up mode determination method, system and device fusing multi-modal characteristics and a medium, and the method comprises the steps: collecting original vibration signals in different start-up modes; preprocessing the original vibration signal to obtain two types of time sequence vibration signals in the startup process; extracting frequency domain features of the first type of signals through a Markov conversion field, namely a structural re-parameterization large-kernel network; extracting time domain features of a second type of signals through a bidirectional gating circulation unit and a global attention mechanism; adopting a global average pooling layer and a weighted fusion mode to obtain vibration characteristics in different startup modes and vibration characteristic reference vectors in an original startup state; and obtaining an optimal starting mode based on the distance between the vibration feature vector and the reference vector. According to the method, richer and more essential characteristics are extracted from multiple dimensions, and the dynamic characteristics of the startup process are calculated, so that the judgment and determination mode is quantified more accurately, and the demand can be quickly responded.

The present application relates to the technical field of power quality disturbance classification, and particularly relates to a power quality disturbance classification method based on multi-channel separable convolution.The technical scheme comprises the following steps: a power quality disturbance mathematical simulation model is established, Gaussian white noise is added to the disturbance signal to obtain a noisy power quality disturbance signal S; the decomposition scale K and the penalty factor alpha of variational mode decomposition are optimized by using a particle swarm algorithm, the power quality disturbance signal is decomposed by using an improved variational mode decomposition model to obtain multiple intrinsic mode components; the Pearson correlation coefficient is calculated to set a threshold to screen the intrinsic mode components.The present application avoids the interference of redundant modes on classification, generates multi-channel image data in combination with a Markov transition field, retains the dynamic characteristics of the components, uses a multi-channel separable convolution residual network based on weight sharing for classification and identification, makes up for the shortcomings of traditional single-channel identification, and improves the classification accuracy.

This invention discloses a memory-based dynamic event triggering control method for a single-link robotic arm system based on spoofing attacks. The method first establishes a Markov model of the single-link robotic arm system based on Markov jump system theory, considering a system model with a general transition rate. Next, a mode-dependent memory controller is designed to overcome the influence of spoofing attacks and external disturbances on the system. A memory-based dynamic event triggering mechanism is also designed to reduce communication transmission frequency. Compared with existing memoryless event triggering schemes, this scheme utilizes a series of recently released signals and introduces a threshold function and an internal dynamic factor, which can automatically adjust according to the triggering error. Finally, vertex separator processing is introduced to address the uncertainty in the Markov transition rate. A mode-dependent state feedback controller is designed to control the stochastic stability of a single-link robotic arm system. When applied to a single-link robotic arm system, this method ensures the normal operation of the system under spoofing attacks and disturbances.

The present invention proposes a method and system for underground coal mine target retrieval, belonging to the field of image retrieval technology. The method includes: constructing a target database of underground coal mine targets based on the targets to be retrieved; obtaining high-frequency and low-frequency images of the targets to be retrieved; obtaining a Markov graph of the low-frequency image of the targets to be retrieved and a Markov graph of the low-frequency target database based on a Markov transition field; using a feature extractor to extract features of the targets to be retrieved and the target database, and performing binarization processing; based on the binarized features, calculating the similarity between the target to be retrieved and each target in the target database to obtain a similarity metric, and then sorting the similarity metrics to obtain the final retrieval results. This method effectively reduces the adverse effects of complex external environments, thereby further increasing the accuracy of target descriptions and improving retrieval efficiency.

The invention discloses a self-adaptive data enhancement method based on a diffusion model, and the method comprises the steps: 1, constructing a knowledge tracking network, mapping a student interaction triple into a vector through an embedded layer, and outputting the prediction probability of a next problem; 2, constructing a conditional diffusion model, converting an original interaction sequence into a noise sequence through Markov conversion, generating a positive / negative interaction sequence in combination with a context condition extracted by a bidirectional converter, and discretizing and outputting through a rounding step; 3, constructing a comparative learning network, mixing the original sequence and the generated sequence to construct an enhanced view, and calculating an adaptive weight based on problem similarity and response consistency to mine difficult samples; and 4, jointly optimizing knowledge tracking loss, diffusion model loss and comparison loss, and training the network from end to end. According to the method, enhanced data conforming to knowledge state dependence are generated through conditional diffusion, the robustness of the model to sparse noise data is improved in combination with an adaptive weighting strategy, and prediction accuracy and representation quality can be remarkably improved.

The application discloses a kind of comprehensive evaluation model construction methods for climate economic evaluation, by obtaining the climate economic evaluation parameter of first preset area and second preset area in each preset period. For climate economic evaluation problem, according to the state whether first preset area and second preset area follow global temperature rise control convention, climate policy uncertainty evaluation model is constructed. According to climate economic evaluation parameter, by the mixed solution strategy of feedback Nash equilibrium time back iteration algorithm and Monte Carlo simulation algorithm, the feedback Nash equilibrium of climate policy uncertainty evaluation model under climate policy uncertainty is solved using Markov transition matrix, obtains the climate economic evaluation index set of first preset area and second preset area in each preset period, Markov transition matrix is related to whether first preset area and second preset area follow global temperature rise control convention, improve the accuracy of obtaining climate economic evaluation index set.

The application provides a dual-mode feature fusion power quality disturbance analysis method for a power distribution network, which comprises the following steps: 1, using a Markov transition field to perform mode transformation on a power quality time sequence signal collected and processed on site to obtain an aggregated image of dynamic transition probability; 2, using a convolutional neural network to extract features from the aggregated image of dynamic transition probability to obtain a first feature vector; 3, using a gated recurrent unit to extract features from the power quality time sequence signal collected and processed on site to obtain a second feature vector; 4, using a deep learning feature fusion-based method to fuse the first feature vector and the second feature vector to obtain a fused power quality disturbance feature of the power distribution network; and 5, classifying the power quality disturbance feature of the power distribution network through a power quality disturbance classifier.

The invention relates to a gas ultrasonic flow meter fault diagnosis method and device based on an improved artificial travel mouse algorithm and a storage medium, and the method comprises the steps that a migration model sample set is generated according to echo signal data and a Markov transition field; generating a source domain sample set according to the power quality disturbance data and the Markov transition field; pre-training a preset convolutional neural network model according to the source domain sample set to obtain a migrated convolutional neural network model; in the pre-training process, parameters of the model are optimized and adjusted by adopting an improved artificial travel mouse algorithm; constructing a first model according to the migrated convolutional neural network model; performing model training on the first model according to the migration model sample set to obtain a target model; in the model training process, parameters of the model are optimized and adjusted by adopting an improved artificial travel mouse algorithm; performing fault diagnosis according to the target model and the echo signal data to be diagnosed; and accurate distinguishing of specific faults of the gas ultrasonic flowmeter is realized.

This invention discloses a personalized location trajectory privacy protection method based on mutual information, belonging to the field of crowdsourced sensing technology. It includes: Step 1: Data preprocessing; Step 2: After generating the Markov matrix in Step 1, the matrix is ​​normalized, and a location privacy protection mechanism is established by setting a distortion threshold and regularization parameters; Step 3: Measuring the effectiveness of location privacy protection. This method generates a Markov transition matrix by merging user location trajectory data, analyzes implicit privacy information and adds distortion perturbations, and uses mutual information to measure the degree of privacy leakage, achieving a trade-off between privacy and usability. It solves the problem that existing technologies cannot effectively overcome the risk of location information privacy leakage.