208 results about "Transition probability matrix" patented technology

The invention relates to the technical field of network security, in particular to a network security big data state evaluation method based on pattern recognition, which comprises the following steps of: extracting multi-modal features from a network flow log, a system event log, a host behavior log and threat intelligence data, generating a feature matrix, performing feature dimensionality reduction by adopting an auto-encoding network, and obtaining a network security big data state evaluation result; carrying out attack behavior classification and abnormal mode identification in combination with unsupervised clustering and a graph neural network; constructing an attack transition probability matrix based on a Markov model; forming a time sequence attack chain; predicting an attack development trend; and a dynamic protection instruction is issued to the safety equipment. According to the method, the unknown attack detection capability can be improved, the time sequence attack traceability is enhanced, the security situation assessment is optimized, and the method is suitable for security situation awareness in cloud computing, industrial internet and large-scale network environments.

The invention provides a cloud native system fault root cause positioning method and device, and the method comprises the steps: determining the abnormal performance index data of each instance and a server based on the micro-service instance of each target micro-service and the performance index abnormal score of the server, so as to construct the nodes corresponding to each micro-service instance and the server, according to a fault propagation direction between the abnormal performance index data, constructing an edge between the nodes to obtain an index-level cause and effect graph; normalizing the performance index anomaly score to obtain a target anomaly score of each node, and constructing a transition probability matrix; and obtaining the access frequency of each node in the causal graph by using a random walk algorithm so as to obtain fault root cause positioning result data of the cloud native system. According to the invention, the automation degree, efficiency and accuracy of fault root cause positioning of the cloud native system can be effectively improved, the efficiency and reliability of fault early warning and recovery of the cloud native system can be effectively improved, and the operation stability of the cloud native system can be improved.

The invention discloses a seismic geological disaster monitoring and early warning device, and the device comprises an intelligent sensing system which carries out the real-time collection of geological parameters, facility states and environmental factors through a multi-mode sensor array, and comprises the steps: capturing the strain of a pipeline through a distributed optical fiber sensor, monitoring the vibration through an MEMS accelerometer, and providing deformation data through an InSAR satellite; the analysis system constructs a complex network modeling engine, fuses the historical transition probability matrix and the real-time deformation rate parameter by using a directed weighted network model, and combines a two-channel causal inference engine which comprises a physical causal channel and a data causal channel; the physical causal channel is embedded into a coulomb fracture criterion to calculate a fault stress accumulation rate, the Darcy law simulates pore pressure propagation, and the data causal channel generates an anti-fact sample through a CaualGAN and extracts a real causal chain in combination with a time causal convolutional network; and the execution and feedback system is used for triggering graded early warning based on an analysis result of the analysis system.

The invention discloses a medical data center network risk assessment method based on a random walk model. The method comprises the following steps of 1, obtaining topological structure information and a node set of a medical data center network; 2, constructing a weighted undirected graph according to the topological structure and the data traffic information between the nodes; 3, in the weighted undirected graph, performing simulation based on a random walk model to obtain a transition probability matrix between nodes; 4, calculating a node risk score of each node according to the transition probability matrix; 5, performing aggregation processing on the node risk scores of the nodes to obtain an overall network risk score; and step 6, generating an assessment report according to the overall network risk score. According to the method, the risk conduction probability between the nodes is quantified by constructing the combination of the weighted undirected graph and the random walk model, the limitation of a traditional static assessment method is broken through, and a cross-node risk propagation path caused by data flow can be identified.

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 belongs to the technical field of network security, and particularly relates to a multi-source network risk information fusion and risk assessment method, which comprises the following steps of: extracting nodes and edges of a real-time event through an entity extraction tool, and constructing a multi-source heterogeneous threat knowledge graph based on the nodes and the edges; calculating a random walk transition probability matrix of the edge, and calculating a time-sensitive personalized random walk value based on the random walk transition probability matrix; a graph neural network model is adopted for training, graph neural network embedding and prediction are carried out in combination with the features of the nodes, and the classification probability is output; and carrying out three-dimensional sub-graph segmentation on the predicted multi-source heterogeneous threat knowledge graph, constructing a mapping index, carrying out aggregation calculation to obtain a propagation path and path popularity of a high-risk node, analyzing a sub-graph evolution trend and carrying out early warning. The method is suitable for carrying out dynamic analysis and prediction on multi-dimensional attack risks, and trend attack analysis of the industry changing along with time is achieved.

The invention relates to the technical field of air tightness detection, in particular to a diaphragm type energy accumulator air tightness detection method which is used for solving the problems that in the prior art, equipment operation characteristics cannot be accurately described, typical defect modes cannot be recognized in combination with spectral clustering, and a quantitative basis cannot be provided for equipment fault trend analysis and intelligent maintenance. The method comprises the following steps: constructing a low-dimensional state map to describe equipment operation characteristics, identifying typical defect modes in combination with spectral clustering, establishing a nonlinear correlation model to reveal a defect evolution relationship, optimizing classification model parameters by adopting an evolutionary algorithm, improving the identification accuracy, mining a most probable defect evolution path based on a transition probability matrix, and improving the identification efficiency. And a quantitative basis is provided for equipment fault trend analysis and intelligent maintenance.

The invention relates to the technical field of furniture production management, and discloses an intelligent management method and system for furniture production. The method comprises the following steps: collecting production data such as material consumption, equipment operation parameters and process completion time of each process node on a production line in real time; performing multi-dimensional feature extraction on the data to generate a comprehensive feature set containing time sequence, statistical and associated features; dividing continuous production stages according to a characteristic dynamic change rule and distributing identifiers; based on the identifier recombination data, calculating a transition probability matrix between adjacent stages; identifying a potential abnormal stage and generating a mark sequence by analyzing a state jump abnormal mode in the matrix; constructing a quality prediction model in combination with the abnormal mark and the real-time data, and outputting a quality prediction score of each process node; dynamically adjusting a procedure production parameter configuration scheme according to the deviation degree of the score and a preset threshold value; and performing similarity matching on the adjusted scheme and historical optimal configuration, and screening a to-be-verified configuration set to perform a simulation test.

The invention relates to the field of multi-energy coupling complementary scheduling, and discloses a successive approximation random dynamic planning method for a watershed water-wind-light integrated system, and the method comprises the steps: 1, inputting the description of uncertainty; a plurality of characteristic discrete values are sampled from theoretical probability distribution functions of runoff, photovoltaic output and wind power output to represent known continuous probability distribution, and uncertainty of input of the watershed water-wind-light integrated system is described; 2, solving a transition probability matrix; describing the continuity of an input variable time sequence by adopting a simple discrete time Markov chain; 3, considering an objective function and constraint conditions of the watershed water-wind-light integrated system, and constructing a complementary optimization scheduling model; 4, solving by adopting a successive approximation-random dynamic programming method (SDP-SA), and deducing a scheduling rule, namely a scheduling decision table, of the watershed water-wind-light integrated system by taking the time period initial storage capacity, runoff, photovoltaic output and wind power output scenes of the adjustable reservoir in each stage of sub-complementary system as multi-dimensional state variables and the time period final storage capacity as a decision variable; and 5, when an actual inflow condition occurs, looking up the scheduling decision table to perform interpolation calculation according to the reservoir capacity and the inflow condition of the current sub-complementary system and the wind power output and the photovoltaic output which are obtained by forecasting in the period of time, obtaining the reservoir capacity at the end of the period of time, and completing an optimal scheduling decision.

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 discloses a data query method and device for a storage system, equipment and a medium. The method comprises the steps of obtaining an address interval of to-be-queried data; determining at least one target partition corresponding to the address interval according to pre-stored data partition information of the storage system; when data query is performed on a first partition in the at least one target partition, determining a next to-be-queried second partition corresponding to the first partition according to a pre-stored transition probability matrix, and performing data pre-reading on the second partition to obtain at least one query result, elements in the transition probability matrix are used for representing pre-reading probabilities among data in the storage system; and summarizing the at least one query result to obtain a target query result corresponding to the to-be-queried data. The range query performance is obviously optimized and the random query delay is reduced by indexing at least one partition, and in addition, the data pre-reading is realized through the transition probability matrix, so that the query efficiency can be effectively improved and the access waiting time is reduced.

The invention belongs to the technical field of medical data processing, and particularly relates to an electronic medical record intelligent quality control method and system based on a medical knowledge graph, and the method comprises the steps: obtaining target medical record data, and constructing an initial logic drive vector; calculating the dynamic propagation impedance of each edge in the general medical knowledge graph according to the deviation degree between the measured value of the physiological index in the target medical record data and the physiological constraint condition of the edge attribute in the general medical knowledge graph; constructing a transition probability matrix by using the dynamic propagation impedance, and obtaining a steady-state correlation distribution vector of each node through iterative calculation; and calculating a dynamic judgment threshold according to the steady-state correlation distribution vector, and performing exception verification on an actual disposal instruction in the target medical record data. According to the method, the real-time physiological status of the individual patient can be dynamically mapped into the constraint condition of map reasoning, potential taboo caused by abnormal physiological indexes is effectively identified, and personalized quality control of the electronic medical record is realized.

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 discloses a reservoir drought limit water level staging ecological flow optimization method. The method comprises the following steps: S1, generating an ecological window set; s2, constructing a hydrological state set, and calculating a corresponding hydrological state transition probability matrix; s3, setting physical relationship constraints; s4, constructing an improved particle swarm optimization model; s5, finally constructing a complete improved particle swarm optimization model; s6, performing iterative search on the improved particle swarm optimization model, and outputting an optimal particle solution composed of a reservoir water level control curve corresponding to the ecological window set and an ecological flow discharge sequence; and S7, converting the optimal particle solution into an executable control signal of a gate opening sequence, a flow guide unit adjusting instruction and a real-time reservoir water level set value which can be identified by a reservoir monitoring data acquisition and monitoring system, and executing the control signal through the monitoring data acquisition and monitoring system to implement stage-by-stage reservoir scheduling. According to the invention, organic unification of intelligentization of reservoir multi-target stage scheduling and engineering applicability is realized.

The invention relates to the technical field of wireless communication optimization, in particular to an optimization method and system for accessing a RedCap terminal to a 5G network, and the method comprises the steps: collecting data from the RedCap terminal, extracting frequency spectrum state information in the process of accessing the terminal to the 5G network, dividing channel states into a plurality of channel state categories, calculating the transition probability between the channel states, and generating a dynamic transition probability matrix. According to the method, data are collected from a RedCap terminal, frequency spectrum state information is extracted, channel states are classified innovatively, and a dynamic transition probability matrix construction method is combined, so that the change rule of the channel states is effectively described. Through frequency statistics and co-occurrence relation analysis of channel state categories, channel allocation is more accurate, and a channel allocation strategy can be adjusted according to dynamic changes, so that flexibility and high efficiency of resource scheduling are enhanced.

The invention provides a method, device and equipment for promoting retention through interaction scene prediction and a storage medium, and the method comprises the steps: obtaining a current multi-modal interaction data stream between users in an interaction scene, carrying out the preprocessing of noise filtering, framing processing, size normalization, time aggregation and the like, extracting emotion features, and generating a real-time multi-dimensional observation vector sequence. The sequence captures complementarity and time sequence dependence of multi-modal information, and overcomes limitation of single-modal static analysis. The observation vector sequence is input into a pre-trained hidden Markov model, and the model is trained based on historical sequences and defines a hidden state set, an initial probability, a transfer matrix and an emission probability; the optimal emotional state path at the current moment is obtained through Viterbi algorithm reasoning, and probability modeling and dynamic prediction of emotion transfer uncertainty are achieved. The current emotion state is extracted from the path, the mimicry representation of the virtual pet is driven to be displayed on the interactive interface, visual feedback is formed, emotion connection is enhanced, and the user retention rate is increased.

The invention discloses a service clue whole-process automatic processing method and system based on SPM, and belongs to the technical field of information processing.The method comprises the steps that by collecting multi-source clue trajectory data, a cross-system federated feature pool is constructed, and basic features are extracted and combined to form an original clue unit; performing feature enhancement on the original clue units to obtain clue feature vectors; acquiring a system endpoint load matrix, generating an initial endpoint mapping strategy by using a reinforcement learning algorithm in combination with the clue feature vector, and outputting an optimal endpoint mapping strategy through game optimization of a risk balance algorithm; deploying a situation responder according to the optimal strategy, generating a customized dynamic interaction content stream, pushing the customized dynamic interaction content stream, and collecting interaction behavior feedback data at the same time; establishing a clue life cycle tracking chain, and dynamically adjusting a transition probability matrix and a termination state probability in a pre-established clue state probability model based on the collected feedback data; according to the invention, accurate processing and effective pushing of clue data are realized.

The invention provides a pension-oriented AI multi-technology fusion intelligent dialogue model construction method and system, and relates to the technical field of AI technology-based model construction, and the method comprises the steps: receiving a session data stream inputted by a user through a remote session terminal, and extracting a dynamic semantic feature sequence in the session data stream; the method comprises the following steps: collecting a sound wave signal and a mouth action video signal of a user, extracting a sound wave feature vector in the sound wave signal, extracting a mouth action feature vector in the mouth action video signal, and splicing the sound wave feature vector and the mouth action feature vector according to a timestamp to generate a cross-modal vector sequence; constructing an AI intention recognition model, inputting the dynamic semantic feature sequence and the cross-modal vector sequence into the AI intention recognition model, and outputting a user intention category label; and outputting a large language model by using the state transition probability matrix based on the user intention category label.

The invention relates to the technical field of system performance monitoring, and discloses an intelligent software system performance monitoring and automatic tuning method and system. The method comprises the steps of collecting performance data during operation, marking and preprocessing the performance data, and dividing the performance data into a training set and a real-time monitoring set; constructing a multi-dimensional state sequence by using the training set data, generating a dynamic mode fragment, and constructing a system behavior map and a mode transition probability matrix based on mode entropy clustering analysis; inputting the real-time monitoring set fragments into the map to calculate the matching degree, and outputting a real-time resource allocation scheme through a Markov decision process; system parameters are adjusted according to the scheme, and iterative optimization is carried out if the difference exceeds a threshold value. The system comprises a data acquisition and preprocessing module, a dynamic mode construction module, a mode optimization module, a real-time tuning decision module and an iterative optimization module. The system performance can be comprehensively monitored, intelligent automatic tuning is realized, the stability and performance of the system are improved, and the method is particularly suitable for a distributed system.

The invention discloses a battery pack equalization management control system of an energy storage lithium battery module and a method thereof, and relates to the technical field of battery pack equalization management. The method comprises the following steps: calculating a consistency evaluation value of a battery pack at a t moment in an observation period T, dividing the consistency evaluation value into lambda states by utilizing a classification threshold value, describing a consistency state transformation process of the battery pack by utilizing a Markov chain model, and defining an influence factor state; obtaining a consistency evaluation value time sequence lambda u, v, w and r of the battery pack when the environment temperature is in the u state, the charging and discharging current is in the v state, the power grid voltage variation range is in the w state and the power grid frequency deviation is in the r state, and calculating the transition probability of the consistency of the battery pack from the state i to the state j through one step, and constructing a transition probability matrix to predict the current consistency state of the battery pack at the next moment, and when the predicted consistency state is abnormal, adjusting the current influence factors until the predicted consistency state is normal.

The invention discloses a network security state prediction method and device of a border gateway protocol, equipment and a readable storage medium, and relates to the technical field of Internet. Comprising the following steps: acquiring relevant parameters of a border gateway protocol; the related parameters comprise the attack prefix injection rate, the network defense capability and the attacked prefix importance of the border gateway protocol; determining the attack risk of the border gateway protocol based on the related parameters of the border gateway protocol; and calculating a network security state prediction result of the border gateway protocol based on the attack risk and the initial transition probability matrix of the border gateway protocol. According to the method, the accuracy of network security state prediction is improved.

The embodiment of the invention discloses an explosion source flame detection method and system, and the method comprises the steps: collecting a multi-mode signal of an explosion source flame, and the multi-mode signal comprises an acoustic vibration type signal and an optical type signal; preprocessing the acoustic vibration type signal and the optical type signal to generate an acoustic vibration feature set and an optical feature set; performing topology analysis on the acoustic vibration feature set by using a persistent coherence algorithm to identify a dynamic behavior mode of the explosion source flame, and obtaining a dynamic behavior mode identification result of the explosion source flame; the dynamic behavior pattern recognition result and the optical feature set are converted into a symbol sequence through a symbolic dynamics algorithm, a cross-modal symbol pattern is analyzed through a transition probability matrix, and comprehensive feature representation of the explosion flame is generated; and based on the comprehensive feature representation, judging whether an explosion source flame exists through a classification algorithm, and outputting a detection result. According to the invention, high-precision explosion source flame detection suitable for a complex environment is realized.

The present invention proposes a method and system for constructing an AI multi-technology fusion intelligent dialogue model for elderly care, which relates to the technical field of model construction based on AI technology. It receives a conversation data stream input by a user through a remote conversation terminal, and extracts a dynamic semantic feature sequence in the conversation data stream; collects the user's voice signal and mouth movement video signal, extracts the voice feature vector in the voice signal, extracts the mouth movement feature vector in the mouth movement video signal, and splices the voice feature vector and the mouth movement feature vector according to the timestamp to generate a cross-modal vector sequence; constructs an AI intent recognition model, inputs the dynamic semantic feature sequence and the cross-modal vector sequence into the AI ​​intent recognition model, and outputs a user intent category label; based on the user intent category label, uses the state transition probability matrix to output a large language model.

The present application relates to a seamless positioning method, device and storage medium for an indoor and outdoor mobile robot, the method comprising: S1, constructing a GNSS / IMU-based combined positioning sub-model outdoors and a vision / IMU-based combined positioning sub-model indoors; S2, inputting interaction; S3, model filtering; S4, transition probability correction: correcting a transition probability matrix using a new information vector; S5, model probability updating: calculating a likelihood function of the combined positioning sub-model and updating model probabilities of the combined positioning sub-models; S6, outputting interaction: fusing current time state estimation values and covariances output by the combined positioning sub-models, weighting the model probabilities to obtain system final state estimation values and covariances, and finally inputting the updated model probabilities to step S2 for next iteration. Compared with the prior art, the present application realizes seamless positioning service in indoor and outdoor navigation.

The invention discloses an electric power system key line identification method based on a hypergraph model, and the method comprises the steps: 1, defining each power transmission path as a hyperedge through power flow tracking, defining each line as a node, and constructing a hypergraph model of an electric power system; 2, performing K-Shell decomposition on the hypergraph model, and calculating the structural importance of each power transmission line; 3, calculating the magnitude of the load flow of the hyperedge, and calculating the magnitude of the weight of the hyperedge by fusing the importance of the branch structure and the magnitude of the load flow of the hyperedge through an entropy weight method; 4, descending sorting is carried out according to hyperedge weights, and a key line sequence is output; step 5, constructing a node transition probability matrix based on hyperedge weight; step 6, screening unconnected high transition probability node pairs to generate a candidate branch set; and step 7, with maximization of the evaluation index load balancing entropy LBE as a target, adding branches in an accumulated manner until the LBE reaches a peak value, and completing structure optimization. According to the invention, the recognition precision of the key line can be improved, and balanced power flow distribution can be realized.

The present application provides a method, device and electronic device for determining the comprehensive benefits of a power distribution system. The method includes: obtaining multiple target power usage scenarios of the power distribution system after it is put into use, establishing an indicator set corresponding to each target power usage scenario of the power distribution system, and constructing a target evaluation vector corresponding to each target power usage scenario based on the indicator set; obtaining the initial scenario vector and the transition probability matrix of the power distribution system, calculating the product of the initial scenario vector and the transition probability matrix to obtain a probability vector; calculating the sum of the products of the probabilities of occurrence of multiple target power usage scenarios in the probability vector and the evaluation values ​​of the corresponding multiple target power usage scenarios in the target evaluation vector to obtain a comprehensive benefit score of the power distribution system. Through the present application, the problem that the evaluation index system of the power distribution system in a single scenario is difficult to fully reflect the comprehensive benefits of the system is solved, and the purpose of determining the comprehensive benefits of the power distribution system under multiple scenarios is achieved.

The invention provides a multi-target path planning method and a related device, and belongs to the technical field of path planning. The method comprises the following steps: dispersing a working space of a multi-target path planning model to obtain a real cell space; the multi-objective path planning model takes the minimum driving path and the maximum safety as objective functions; the constraint conditions of the multi-objective optimization model comprise kinematics constraint, boundary condition and obstacle constraint; generalized cell mapping is established on the real cell element space for each state variable and control variable of the constraint condition and the target function, and a cell mapping relation based on a transition probability matrix of all controllable cells in the real cell element space is obtained; the cell mapping relation is stored by adopting a hash table; based on the cell mapping relation, solving the multi-target path planning model to obtain an optimal solution set; and performing path planning on multiple targets according to the obtained optimal solution set to obtain a multi-target path planning result. According to the method, the problems of low accuracy and calculation efficiency of multi-target path planning are solved.

The invention relates to the technical field of logging evaluation in oil-gas exploration, and discloses a lithology identification method and device based on logging information and electronic equipment. The method comprises the following steps: determining sensitive logging information; preprocessing logging information of the source research area and the target research area; establishing a lithology prediction model Mo based on the convolutional neural network for the source research area; finely adjusting the lithology prediction model Mo based on a small number of samples in the target research area to obtain a lithology identification model Mt of the target research area; calculating a Markov prior probability and a transition probability matrix between different lithologies for the sample set of the target research area; predicting a lithology result of the test set sample of the target research area by adopting a lithology prediction model Mt; using Markov prior probability to correct the predicted lithology result to obtain a posterior probability; and determining a final lithology category. According to the technical scheme of the invention, effective lithology identification can be carried out on a complex lithology reservoir, and the lithology identification precision is high; petroleum geology research and dessert optimization are facilitated.

The application discloses a self-learning visual positioning method, comprising the following steps: S1, acquiring image data; S2, image enhancement; first, extracting brightness information from the picture, S3, dividing the acquired image information into grids based on the YOLOv5 algorithm, and extracting feature information and label information in the grid information, then adjusting the weight information of the feature information and the label information through the adaptive learning method of Bayes, then merging the feature information according to the adjusted weight information, and converting the merged image information into three-dimensional coordinate information; S4, adjusting the predicted speed value and the predicted three-dimensional coordinate information through the least square method, S5, predicting the posture data through the Markov data transition probability matrix, the predicted speed information, the predicted three-dimensional coordinate information and the target frame information of the current position in the image information in the S3 step, and then positioning and moving according to the predicted posture data, and the positioning data is good in reliability.

The application discloses a software abnormal behavior dynamic analysis method based on a non-uniform Markov chain, which comprises the following steps: S1, collecting and preprocessing software log data; S2, dynamically dividing states by using K-means clustering to generate a preliminary state set and a transition relationship; S3, constructing an improved non-uniform Markov chain model; S4, performing abnormal detection by the improved non-uniform Markov chain model and calculating an abnormal score; S5, constructing an objective function and optimizing by using a grey wolf optimization algorithm; S6, feeding back the optimized parameters to S3 to update a time-varying transition probability matrix and the abnormal score calculation; S7, judging the abnormality according to the updated abnormal score and a warning threshold, outputting a warning signal and feeding back. The application realizes real-time and accurate detection and self-adaptive optimization of software abnormal behaviors by constructing an improved non-uniform Markov chain and a software abnormal behavior dynamic analysis method based on a grey wolf optimization algorithm, thereby significantly improving system security and stability.