323 results about "Transition matrices" patented technology

The present invention relates to the related technical field of natural language inference. Disclosed is a large language model joint inference method based on a knowledge graph-enhanced chain-of-thought prompt, comprising: constructing a local knowledge subgraph; decomposing an original question text into S sub-question texts and concatenating the original question text and the S sub-question texts; inputting a concatenated question text into a graph inference model to obtain a weighted entity distribution; from the weighted entity distribution, extracting first G answer entities having the highest confidence level, using an inference transition matrix to trace an inference process of each answer entity, and generating an inference path from a question entity to the corresponding answer entity; and using the inference path to assist a large language model in predicting an answer to the original question text. By means of the method, the large language model can quickly and accurately find an answer to a question text.

The invention discloses an unmanned aerial vehicle cluster patrol path decision-making method under resource constraint, and relates to the technical field of unmanned aerial vehicle path planning, and the method comprises the steps: carrying out the discretization of an actual to-be-patrolled physical position, and constructing an undirected topological graph; generating steady-state distribution of each patrol node according to topological constraints and importance degrees of the nodes; generating a plurality of transfer matrixes with the same steady-state distribution and different transfer characteristics according to a multi-stage entropy driving random matrix optimization algorithm; initializing the position of a navigator according to the transfer matrix and determining a selected path; according to the reference path of the navigator, the self-adaptive active positioning decision is realized under the positioning constraint to ensure the path tracking effect; according to path selection and tracking of the navigator, the follower and the navigator form a humanoid marshalling cluster through a reward function; and according to the multi-state transfer matrix and the humanoid marshalling, automatically switching to the next transfer matrix when a transfer frequency threshold value is reached, and realizing unmanned aerial vehicle cluster intelligent patrol path decision under resource constraint.

The invention provides a power transmission corridor forest fire spreading prediction and risk assessment method based on a satellite internet and a related device, and relates to the technical field of disaster prevention and early warning of a power system. Building a fire risk factor database based on multi-modal data of a satellite-ground fusion network mode; applying a random forest algorithm to screen out key factors and determining fire risk contribution degrees corresponding to the key factors; based on the power forest fire hidden danger factors, similar features of a power transmission corridor neighborhood are extracted, and sub-regions are divided by adopting a clustering algorithm; constructing a fire risk weighted scoring model to identify high, medium and low risk areas; a mountain fire spreading model is constructed based on meteorological observation data driving, a fire risk state transition matrix is established, and a fire spreading path is predicted in real time in combination with a dynamic Bayesian network; evaluating the damage risk of the forest fire to the power transmission line, and generating a damage risk grading graph of the power transmission corridor. The technical effects of dynamic assessment of the forest fire risk and accurate prediction of the spreading trend are achieved.

The invention discloses a high-precision continuous tracking method for a strong-maneuvering infrared weak and small target under a space-based detection visual angle, and the method comprises the steps: obtaining the uncertainty measurement of a target at a non-maximum suppression stage of a detection result, and enabling the uncertainty measurement to act on a target state updating and data association stage; therefore, the experience distribution of the detector is fully transmitted to the tracking process, and the tracking accuracy is improved. In order to cope with a complex target maneuvering state, an interactive multi-model technical route is adopted to design a tracking algorithm; in order to reduce the dependence on a prior motion model, a dynamic Markov transfer matrix construction method is designed, and the model transfer probability is updated in a mode of comprehensively modulating historical dynamic information and current static information; in a data association stage, targets with different scales are associated by integrating advantages of IoU and NWD, uncertainty is transmitted to a cost calculation process, and tracks and targets which are indefinitely matched are processed based on scale invariant and energy invariant hypotheses, so that high-precision continuous tracking of the targets is realized.

The invention provides a multi-view clustering method based on uncertainty and a graph neural network, and relates to the technical field of multi-view clustering. The method comprises the following steps: obtaining a multi-view data set, and preprocessing to obtain an original feature matrix and an adjacent matrix; performing feature extraction and fusion on the input original feature matrix and the adjacent matrix to obtain a consistent embedded matrix and a transfer matrix; based on a Dempster-Shafer evidence theory and Dirichlet distribution, obtaining reliable consistent embedding by reducing the uncertainty of an embedding space in the consistent embedding matrix; mapping the reliable consistent embedding and the transfer matrix into final embedding; and performing decoding reconstruction and clustering analysis on the final embedding to obtain a clustering result. According to the method, potential information in the views can be fully mined, the uncertainty in the model is reduced, the noise problem existing in a multi-view clustering task is solved, and the clustering result of the multi-view data can be quickly, efficiently and accurately obtained.

The invention belongs to the field of unmanned aerial vehicle control, and particularly relates to a complex task-oriented unmanned aerial vehicle modular integrated control platform, which comprises an acquisition module for acquiring and processing information such as a real-time flight state of an unmanned aerial vehicle group, a complex task text, meteorological data and the like; the task analysis module decomposes a complex task into a single subtask set and an incidence matrix, and evaluates the task complexity for resource allocation; the atmospheric analysis module is used for inverting a real-time flight association adjustment graph based on the corrected meteorological data, flight states and the like in combination with a target optimization algorithm and an optimization function taking resource quantity minimization and task completion probability maximization as targets; the record analysis module generates a performance transfer matrix by using a hidden Markov algorithm; and the flight control module responds to the adjustment graph to correct the flight state, and ensures that the task completion probability meets a preset safety threshold. According to the platform, efficient cooperative control and resource optimization allocation of the unmanned aerial vehicle group under complex tasks are realized through modular integration.

The invention discloses a time-frequency analysis method for impact signal positioning based on transient scale extraction transformation, and belongs to the technical field of mechanical vibration signal processing. The method comprises the following steps: collecting a rotating machine fault vibration signal, and reconstructing a signal model through Hilbert transform and a Dirac function; a transition matrix is generated by using a Gaussian window function traversal model, and matching and Fourier transform are carried out in combination with a discretized scale basis function; solving a frequency partial derivative of a transformation result to generate a time redistribution operator, and redefining by a Dirac function to obtain a transient scale extraction operator; based on the sub-time-frequency representation of scale-based rotation discretization, screening optimal matching results of each time center through a maximum kurtosis value, and integrating the optimal matching results into a complete time-frequency representation; and finally, redistributing a time-frequency coefficient by using a transient extraction operator to realize accurate positioning of the impact component. According to the method, the problems of serious impact energy diffusion and insufficient positioning precision in existing time-frequency analysis are solved, and the time-frequency representation readability and the impact positioning reliability are remarkably improved.

The invention discloses a lightweight malicious software visual classification method based on multi-scale characteristics. The method comprises the following steps: converting a binary file of malicious software into a grayscale image through Markov transfer matrix modeling; image features are extracted through a multi-scale cavity convolution fusion attention module MDFA; a SimMobileNetV2 network is utilized to carry out classification; according to the method, the classification performance is remarkably improved, and the method has high practical application value.

The invention discloses a complex network importance node evaluation method based on group interaction. The method comprises the following specific steps: step 1, carrying out community division on a network through a Louvain algorithm; step 2, constructing a Louvain bipartite graph; step 3, obtaining an outgoing transfer matrix and an incoming transfer matrix; step 4, obtaining an extended transfer matrix according to the outgoing transfer matrix and the incoming transfer matrix; 5, expanding the transfer matrix to obtain an enhanced transfer matrix; step 6, calculating the importance score of each node; 7, arranging the nodes in the network in a descending order according to the importance score of each node; and step 8, identifying important nodes of the network through a network disintegration algorithm. According to the method, based on group interaction, the interaction between nodes which are not directly connected is considered, the random walk is expanded to the Louvain bipartite graph, and the concepts of outgoing walk and incoming walk are added; and in combination with a network disintegration algorithm, the key nodes in the network are efficiently and accurately identified, and the robustness and the stability are high.

The invention relates to the technical field of underwater pipeline measurement, in particular to a manifold connection pose measuring device suitable for an underwater limited space and a solving algorithm. The device comprises an angle measuring mechanism and a rope length detecting mechanism, and is fixed on a flange end face of a detected pipeline through a centering mechanism; the angle measuring mechanism is composed of an orthogonal shaft system and a magnetic coupling encoder, and the linear displacement is mapped into an angle signal by combining a sliding rail and a measuring rope extension arm; the rope length detection mechanism winds a stainless steel measuring rope through a friction hub in an anti-corrosion shell, and the number of rotation turns is detected through an encoder so as to calculate the rope length. The method comprises the steps of establishing a measuring rope infinitesimal stress model, calculating a suspension curve based on a parabola theory, constructing an absolute coordinate system and a reference coordinate system, and resolving the relative pose of the two pipelines through pose parameters and a transition matrix. The working load of a diver is reduced through lightweight design and a low-friction shaft system, and high-precision pose measurement is achieved by combining the parabola theory and dynamic coordinate transformation.

The invention discloses a self-adaptive integrated navigation method based on geometric accuracy factors, which belongs to the technical field of underwater navigation and positioning, is used for underwater navigation and positioning, and comprises the following steps: initializing an inertial navigation system and an integrated navigation filter, and setting a state vector and a noise covariance matrix; predicted navigation parameters of the recursive carrier are mechanically arranged through inertial navigation, and the state transition matrix is used for state prediction; and dynamically adjusting an observation noise covariance matrix according to the geometric precision factor value, further calculating a Kalman gain, performing optimal estimation and correction on a prediction state by fusing acoustic observation information, and finally outputting a high-precision carrier position, speed and attitude. According to the method, the statistical characteristics of observation noise are dynamically remodeled by calculating and feeding back geometric precision factor values in real time, so that the Kalman filter has the capabilities of knowing the own geometric situation and adjusting the trust degree of each information source, and the global optimal navigation precision and reliability are realized under any motion track.

A label noise estimation method based on a manifold regularization transfer matrix provided by the present invention pre-trains a first network in a second network, and after distilling a data set, inputs the obtained sub-data set into the second network to obtain the probability of the class to which the data instances in the sub-data set belong and obtain a transfer matrix related to the data instances; further calculates the cross-entropy loss of the second network according to the data instance labels, and combines an association matrix expressing the consistency of the data instances belonging to the same manifold and a penalty matrix of the data instances belonging to different manifolds to calculate the loss function of the second network; adjusts the loss function to reduce the training of the second network to obtain a trained second network, thereby completing the estimation of the class to which the data instances belong. The present invention can reduce the estimation error without affecting the approximation error of the transfer matrix, and experiments prove that the present invention can achieve excellent performance in label noise learning.

The invention discloses a traffic state estimation method, system and device, and relates to the technical field of traffic state estimation, and the method comprises the steps: obtaining a historical traffic flow data set of a current traffic road section; fuzzy logic is introduced into a Poisson process model, and the arrival rates of vehicles at different road sections are modeled; modeling a transition probability of a traffic flow state by using a Markov chain, introducing a dynamic transition matrix to capture changes of the traffic flow state in different time periods, and further constructing a fuzzy state transition model based on the Markov chain; a fuzzy joint probability density function is obtained by combining a fuzzy Poisson distribution model and a fuzzy state transition model based on a Markov chain, and the traffic flow state is estimated; according to the method, the vehicle track and the traffic state can be estimated in various complex traffic scenes by combining the two models.

The invention belongs to the field of industrial process fault diagnosis, and discloses a fault diagnosis method based on an enhanced multi-mode switching dynamic system model, an LDS model is used as a penetration point, a transfer matrix is used to describe mode switching so as to establish an SLDS model, in addition, a hidden Markov chain is combined with the transfer matrix, and a fault diagnosis result is obtained. And an enhanced hidden Markov transfer matrix is constructed to further consider the high-order dynamic characteristics of multi-mode switching of process data, the establishment of an aSLDS model is completed, and the fault diagnosis performance is improved. The aSLDS model not only provides a new method for the fault diagnosis technology, improves the effectiveness and superiority of fault detection and classification, but also supplements process operators to grasp dynamic information among multi-modal data features and multiple groups of data modals, guarantees the safety and reliability in the industrial production process, and improves the fault diagnosis efficiency. And further development of fault diagnosis in an actual industrial process can be promoted.

The invention relates to a target track association method in a multi-target environment. The method comprises the following steps: firstly, preprocessing a new received trace point; secondly, predicting the next position of each track by using the existing track historical information and adopting a Kalman filtering algorithm, meanwhile, adjusting a state transition matrix and a process noise covariance matrix in real time by considering factors such as ocean current and wind direction in an offshore environment, and adjusting an observation noise covariance matrix according to meteorological information; then comprehensively considering distance, speed difference and motion direction difference to calculate a correlation degree magnitude between a new receiving trace point and each predicted track position; and finally, judging whether the newly received trace point belongs to the existing target track or forms a new target by adopting a threshold judgment method according to the correlation degree value, and updating the state of the track. The method can effectively cope with multi-target intersection, observation loss and clutter interference, improves the stability of a matching result, can realize continuous tracking of a marine target, can adjust parameters according to the environment and target characteristics, and has high adaptability.

The invention relates to the field of fire risk assessment, and discloses a Markov chain model-based fire risk assessment method, which comprises the following steps of: defining a fire state set S, constructing a fire state transition matrix P according to historical fire data, and calculating steady-state distribution # imgabs0 # of each fire state by using the fire state transition matrix P, according to the distribution # imgabs1 # of the current fire state, the fire state distribution # imgabs2 # of the next moment is predicted, the fire state changes of multiple moments in the future are obtained after multiple times of repeated prediction, the expected value # imgabs4 # of the fire risk is defined for each fire state # imgabs3 #, the total expected value R of the fire risk is calculated, the future fire occurrence situation is simulated for multiple times through the Monte Carlo method, and the fire state changes of multiple moments in the future are obtained. Evaluating a future risk scene; and obtaining probability distribution of states at different moments in the future according to a fire occurrence condition simulation result, and providing a basis for a fireproof plan. Real-time assessment of fire risks is realized through dynamic modeling, accuracy and scientificity of assessment are improved, and fire prevention and emergency management decisions are effectively supported.

Systems and methods for assigning a lane to an object in an environment of an autonomous vehicle are disclosed. The methods include assigning an instantaneous probability to each of a plurality of lanes in the environment based on a current state of the object, generating a transition matrix for each of the plurality of lanes, and identifying the lane in which the object is moving at the current time t based on the instantaneous probability and the transition matrix. The instantaneous probability is a measure of likelihood that the object is in that lane at a current time. The transition matrix encodes one or more probabilities that the object transitioned either into that lane or out of that lane at the current time.

The invention belongs to the technical field of intelligent vehicles, and discloses an autonomous mobile robot multi-target tracking method and system based on a dynamic adaptive motion model. Detecting object information in the environment, and obtaining detection frame data of each frame of laser radar and camera; laser and camera data are projected to the ground to calculate the Euclidean distance so as to realize multi-sensor fusion and different frame data association operation. A corresponding adaptive state transition matrix is set for a previous frame trajectory, and a prediction bounding box and a covariance matrix of the previous frame trajectory in a current frame are calculated by using a Kalman filtering technology. The long-term and short-term memory module stores historical information of a management target and dynamically adjusts a tracking strategy and parameters. According to the invention, the tracking effect of the multi-target tracking algorithm on the autonomous mobile robot is successfully improved, powerful technical support and guarantee are provided for the intelligent robot to realize accurate multi-target tracking and efficient operation in a complex environment, and further development of the intelligent robot technology in practical application is powerfully promoted.

The invention discloses a non-linear scene multi-target tracking method and system and a storage medium, and the method comprises the steps: constructing a non-linear motion model and a measurement model, and specifically, representing a non-linear state process and a measurement process through a non-linear model; according to multi-target tracking in a two-dimensional plane, determining a motion equation of each target, setting the motion equation as a collaborative turning model, and determining a motion equation of the target; constructing a system state transition matrix, a process noise distribution matrix and a measurement equation; according to the nonlinear motion model and the measurement model, extraction of multi-target state estimation is carried out through an FAUKF-PHD filter, a state vector of each target is obtained, and tracking of each target is completed; and verifying the tracking result of each target based on the multiple pieces of preset scene information. The method is high in tracking precision and low in cost, can improve the effectiveness of the method, and can be widely applied to the technical field of computers.

The invention discloses a rock mass fracture parameter prediction method and a rock mass fracture model construction method. The prediction method comprises the following steps: determining the scale of a fracture unit by using a first-order Markov test theory, and dividing the fracture unit of a known parameter region according to the scale of the fracture unit; performing optimal transfer matrix calculation on the fracture density of the divided fracture units by using a first-order Markov chain theory; performing fracture density prediction on the unknown parameter region by using the optimal transfer matrix obtained by calculation, and completing height data distribution of a trace midpoint and a fracture surface in a uniform distribution form; using a Kriging interpolation estimation method to complete occurrence prediction of the fracture at the distributed position; and using a Kriging interpolation estimation method to complete trace length prediction of the fracture at the distributed position. According to the method, the Markov chain and Kriging interpolation estimation methods are fused to predict and estimate the density, occurrence, trace length and other parameters of rock mass fractures, and statistical errors are greatly reduced.

Method for recognizing the waveform of a signal received from a transmitter, comprising electromagnetic pulses, using a waveform model according to a hidden Markovian process, comprising a comb of values ​​µ representing process states corresponding to possible values ​​of a pulse parameter, a transition matrix P between consecutive values ​​of the comb, and the standard deviations σ of measurement of the states, the method comprising: - receiving N measurements of the pulse parameter, - determining a score, by traversing (606) a tree whose each node corresponds to one of the process states µk and providing a prediction of the measurement of the pulse parameter, - recognizing the waveform of the signal from the score, the score being updated (607) iteratively, for each node traversed of the tree associated with a process state µk according to a precondition indicating whether the prediction reaches a pulse.

METHOD FOR DETECTING A DRONE AND ITS REMOTE PILOT AND SYSTEM FOR IMPLEMENTING IT Method (500) for detecting a drone (21) and / or a remote pilot (22) in a monitored area (300), said method comprising an acquisition step (510) on at least one receiver (111) of a plurality of frames of a digital signal (yn, y'n) to be analyzed, said method comprising the following steps: (513) digital frequency filtering of the digital signal (y'n) in narrow frequency bands (wk) producing a number K of filtered digital signals (zn,k); (521) estimation of the energy levels (Ek) of the filtered digital signals (zn,k) obtained in the digital filtering step (513); (523) construction of a time-frequency state matrix (SM) of the energy levels (Ek) ;(524) calculation of a transition matrix (MT), based on a first-order derivative of the state matrix (ME) ;(525) calculation of an average rate of change (α) of the transition matrix (MT);and (526) detection of the presence of the drone (21) and / or the remote pilot (22) as a function of the value of the average rate of change (α). Figure of the abstract: Figure 1;

This invention relates to the field of data prediction technology, specifically to a collaborative control method for the opening degree of hydropower station gates based on BeiDou technology. This method constructs state parameters and analyzes the transition of these parameters over historical periods to obtain the transition probability for each state parameter transition. By combining the correlation between meteorological data and opening data, as well as the correlation between historical meteorological data and predicted meteorological data, the transition probabilities are continuously weighted to obtain a second weighted transition probability. The quantitative characteristics of the state transition of the flood discharge channel gate under its predicted state transition are statistically analyzed for the non-flood discharge channel gate, thereby obtaining the upper-level influence weight, obtaining the second state transition matrix of the flood discharge channel gate, and performing state prediction. This embodiment of the invention, by combining the influence of historical meteorological data and further combining the control influence between gates, obtains accurate gate state prediction results.

The invention relates to the field of milling, in particular to a method for predicting milling chatter stability, which comprises the following steps of: 1, establishing a milling process dynamic differential equation containing a regeneration effect; 2, converting a time-delay kinetic equation of the milling system into a state-space equation; 3, solving through a third-order Hermite interpolation polynomial approximation state item and a second-order Newton interpolation polynomial approximation time delay item; and 4, constructing a Floquet transition matrix according to the solution of the state-space equation, and judging the stability of the milling system. The invention provides a stable lobe graph drawing method based on an improved full discrete method. According to the method, the calculation precision is remarkably improved, the calculation efficiency is effectively improved, and the requirements in practical application can be better met.

The invention relates to a multi-intervention subject prediction method based on event evolution knowledge, and belongs to the technical field of artificial intelligence and natural language processing. According to the method, event evolution information transfer matrixes, including an event predicate transfer matrix, an event subject-predicate incidence matrix and an event object-predicate incidence matrix, are obtained by tracing event cases with similar evolution trends by utilizing the capability of a large language model in the aspect of identifying key node structure information in a historical chain; and obtaining regularized event case evolution information as an event chain hierarchical support of a model generation prediction entity. According to the method, the historical entities highly related to the predicates of the event to be complemented are identified by obtaining the dependency relationship between the entities and the predicates in the data set, and the core function of the predicates in the event triad is played, so that the missing entities in the event chain are predicted more accurately.

The embodiment of the invention provides a dialogue data generation method, a dialogue model training method and electronic equipment, and relates to the technical field of natural languages. The method comprises the steps of obtaining a knowledge system of dialogue data needing to be generated and student types, obtaining a state probability transition matrix corresponding to the student types and an initial state in the state probability transition matrix, circularly updating a current state according to the state probability transition matrix from the initial state, and executing an action corresponding to the current state. And generating at least one dialogue text based on the knowledge system, sequentially adding the role corresponding to the current state and each dialogue text into a dialogue list until the current state is updated to a termination state in the state probability transition matrix, and taking the dialogue list as a group of dialogue data. Therefore, a large amount of dialogue data can be generated without generating the dialogue data through a large amount of real dialogue interaction between teachers and students, the dialogue model is provided for training, and high labor cost can be saved.

The invention discloses a bridge cluster multi-agent full-period maintenance decision-making method under deep reinforcement learning, and the method comprises the steps: firstly dividing a bridge into an upper structure, a lower structure and a bridge deck system, and constructing a simulation environment of bridge cluster maintenance; defining the condition and bridge age of each bridge structure in the bridge cluster; defining three intelligent agents, namely agentsup, agentsub and agentdeck, respectively corresponding to the three part structures, and carrying out training and execution of bridge cluster maintenance; defining a state space and an action space of the deep reinforcement learning method; designing a reward function and determining a state transition matrix; constructing a plurality of agent networks based on a deep Q network algorithm, and training an agent neural network; and finally, testing the capability of the intelligent agent in practical application, and displaying a test result to a user. According to the invention, the structure complexity is greatly simplified, and the method is an important basis for practical application of the maintenance framework.

The invention provides a gas concentration detection method, a gas concentration detection device and electronic equipment. The method comprises the following steps: acquiring a spectral signal obtained by detecting to-be-detected gas in a gas cell by a quantum cascade laser under preset sampling parameters; calculating search direction information and search distance information according to the spectral signal and the initial matrix; calculating a transition matrix according to the spectral signal, the search direction information and the search distance information to remove redundant information in the spectral signal; under the condition that a change value between the transition matrix and the initial matrix meets a preset threshold value, determining the transition matrix corresponding to the change value as a target matrix; performing calculation and filtering processing on the target matrix and a preset reference signal to obtain a direct current filtering signal related to the direct current signal; performing signal conversion processing on the direct current filtering signal to obtain a target harmonic signal; and inputting the target harmonic signal into a gas concentration prediction model, and outputting gas concentration information of the to-be-detected gas.

The invention relates to the technical field of attitude estimation, in particular to an attitude estimation method in a magnetic interference environment, which comprises the following steps of: obtaining a conversion relation between an Euler angle and a quaternion; calculating the quaternion at the next sampling moment; respectively solving a first state transition matrix, a first process noise matrix and a first prior covariance according to the first state equation; respectively solving a second state transition matrix, a second process noise matrix and a second prior covariance according to a second state equation; elements in the nb-series rotation matrix are used as observed quantities of state variables in the parameter-series rotation matrix, a corresponding Kalman gain is calculated, and a Jacobian matrix is designed by using the observed quantities; calculating an observation noise covariance matrix by using the Jacobian matrix; updating the Kalman gain by using the observation noise covariance matrix; and updating the state equation and the posterior covariance by using the updated Kalman gain and the observed quantity. The method solves the problem of how to prevent the calculation of the roll angle and the pitch angle from being influenced by magnetic interference when the magnetic interference exists.

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.