85 results about "Transition probability matrix" patented technology

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.

This invention relates to the fields of tunnel engineering and 3D geological modeling technology, specifically to an intelligent modeling method and system for 3D geological uncertainty in tunnels based on transition probability geostatistics and sparse boreholes. The method includes: S1, integrating multi-source data to construct a 3D geological conceptual model; S2, statistically characterizing a one-dimensional transition probability matrix, calculating and fitting a spatial continuity and 3D anisotropic variability function model; S3, calculating the prior spatial probabilities and transition adjustment factors for various lithologies, obtaining the posterior lithology distribution of nodes through Bayesian intelligent updating, and initially assigning lithology categories to nodes through random sampling; S4, assigning the most probable lithology category to each grid node, calculating the variance of lithology values ​​across all implementations, and measuring model uncertainty; S5, outputting the optimal 3D uncertainty model for the tunnel; and S6, verification and evaluation. This invention can effectively achieve 3D heterogeneous modeling and explicit quantification of uncertainty under strong geological constraints.

The application provides a vehicle suspension state parameter estimation method and device, and relates to the technical field of intelligent control of vehicle suspension, and the method comprises the steps of: constructing an interactive state observer based on at least two physical characteristic parameters of a vehicle suspension; determining prior state prediction parameters corresponding to each sub-observer based on a previous model probability and a transition probability matrix corresponding to each sub-observer; updating the prior state prediction parameters of each sub-observer based on current observation data corresponding to each sub-observer to obtain posterior state estimation parameters of each sub-observer; adaptively determining a current forgetting factor corresponding to each sub-observer based on the current observation data, an observation matrix and the prior state prediction parameters of each sub-observer; and fusing the current model probability, the posterior state estimation parameters and the current forgetting factor corresponding to all sub-observers to obtain a current overall state parameter estimation value corresponding to the interactive state observer. The application can improve the estimation accuracy of the suspension state parameters.

The invention discloses a method for rapidly detecting the impurity content in the preparation process of organic silicon emulsion, and relates to the technical field of impurity detection.The method comprises the steps that technological parameters of all current steps in the preparation process of the organic silicon emulsion are collected in real time; calculating the probability of various impurities introduced in the step; when the impurity introduction probability of the single step exceeds a first preset threshold value, generating a high-risk impurity list and triggering online directional detection; if the impurity introduction probabilities of all the steps do not exceed a first preset threshold value, constructing a transition probability matrix for describing the state change of the impurities in the whole process; calculating and predicting steady-state probability distribution of various impurities in the finished product; and performing finished product terminal detection on the impurities with the prediction probability exceeding a second preset threshold. The method has the advantages that multi-level early warning of impurity risks is achieved, source abnormity is positioned through the probability model, a transfer matrix tracks a full-process impurity migration path, accurate directional detection is combined with a terminal focusing strategy, and the impurity detection efficiency is improved.

This invention provides a rapid depression detection method based on a weighted degree transfer network, comprising: acquiring and preprocessing the EEG signals of a subject to obtain a one-dimensional EEG time series; mapping the one-dimensional EEG time series into a complex network using a weighted horizontal visualization algorithm; extracting degree and intensity sequences; using the deduplicated set of degree values ​​as nodes of the new network; tracing the degree transfer path between adjacent time points; calculating the product of the degree difference and intensity difference between adjacent time points as the edge weights of the degree transfer path, thereby constructing a weighted degree transfer network; normalizing the network to obtain a transition probability matrix; calculating its Shannon entropy and MPR statistical complexity as a joint feature vector; and using a machine learning classifier to identify the depression state. This invention does not require presetting parameters such as embedding dimension, and by fusing the dynamic difference features of degree and intensity, it can quickly and accurately capture abnormal nonlinear dynamic patterns in the EEG signals of depression.

The application relates to the technical field of automobile network security, in particular to a vehicle-mounted safety early warning method and device, a vehicle and a storage medium, the method comprising the following steps: mapping initial multi-modal data of a current vehicle to a unified space-time dimension to obtain calibrated multi-modal data, inputting the calibrated multi-modal data into a space-time Markov chain model, inferring the running state of the current vehicle by using a dynamic transition probability matrix, predicting the state evolution trend in future continuous time steps to obtain a prediction result, comparing the prediction result with a preset risk threshold, and determining a current risk level according to the prediction result in the case that the prediction result meets a preset early warning risk condition, and triggering a target alarm mechanism based on the current risk level. Therefore, the problems that a traditional VSOC depends on artificial rules, has a high false alarm rate and is difficult to identify complex attacks are solved, intelligent fusion of multi-modal data and dynamic risk prediction are realized, and the accuracy and real-time performance of threat identification are improved.

The application relates to the field of artificial intelligence and information retrieval technology, and discloses a temperature control acupuncture field knowledge retrieval method based on deep learning. In the field of temperature control acupuncture, acupoints, temperature and literature are abstracted as nodes and numbered according to a unified rule, a graph space is constructed, an adjacency matrix is filled according to the relationship among the three types of nodes, the node degree is calculated and normalized into a transition probability matrix; the acupoint number and temperature interval input by a user are received, a query node is obtained and an initial access vector is constructed, random walk is iterated on the graph according to the transition probability within a preset number of rounds, a converged access vector is obtained, and a literature node component is extracted therefrom as a retrieval score, the literature is sorted, and index or metadata information of the literature with a high score is output. Through the above steps, the acupoint and temperature conditions and the literature structure are uniformly modeled, the problems of dispersed constraints and low efficiency are alleviated, and the matching degree of the result and the temperature control context is improved.

This invention belongs to the technical field of logistics management, specifically relating to a method and system for predicting the arrival time of goods in transit based on real-time environmental data. It addresses the technical problems of existing methods, such as difficulty in quantifying uncertainty and neglecting real-time data quality and vehicle status. The prediction method includes: S1, calculating Shannon entropy based on the prior probability distribution of arrival time at the current moment; S2, adjusting the basic transition probability matrix to obtain a spatiotemporally correlated transition probability matrix; S3, generating evidence update weights by comprehensively considering the quantification index of prediction uncertainty and the credibility of observational evidence; and S4, obtaining the posterior probability distribution of arrival time at the current moment through Bayesian updating. This invention can improve the accuracy of arrival time prediction in complex traffic environments.

The invention provides a robot scene understanding method and system based on visual deep learning, and relates to the technical field of robot visual identification, and the method comprises the steps: building a target relation matrix through multi-layer pyramid feature decomposition and bidirectional feature transmission between local regions; and calculating a target transition probability matrix by using historical scene data to optimize the target attribute of the current scene. According to the method, dynamic modeling of the relationship between targets in the scene and historical scene knowledge migration are realized, and the accuracy and robustness of robot scene understanding in a complex environment are improved.

The invention discloses a probability model-driven smelting furnace life prediction method and system. The method comprises the following steps of collecting multi-source alarm and process data of smelting key components; feature engineering is carried out, degradation features are extracted, and discrete health states are divided; the current health state of the component is accurately recognized under the small sample condition by using the gray correlation degree theory; constructing a state transition probability matrix in combination with a Markov chain, and quantifying the randomness of the degradation process; and predicting the remaining service life of the component by solving an equation set taking failure as an absorption state. According to the method, the problem of life prediction caused by lack of historical failure data is effectively solved, dynamic, quantitative and interpretable evaluation of the residual life of key components such as an oxygen lance, a furnace lining and a flue is realized, and reliable technical support is provided for predictive maintenance decision-making of the smelting furnace.

The application discloses a high-temperature superconducting cable life prediction method and system combined with working condition behavior analysis, relates to the technical field of life prediction and reliability analysis of high-temperature superconducting cables, and comprises the following steps: collecting working condition data of a high-temperature superconducting cable in a running process; performing mode division on the running working condition based on the working condition data through a clustering method, and establishing a working condition mode transition probability matrix; performing equivalent processing on the running data in the working condition mode, and establishing a bidirectional coupling degradation model according to the equivalent processing result; and outputting a reliability function, a residual life of the cable system and a life contribution rate in each working condition mode according to the working condition mode transition probability matrix and the bidirectional coupling degradation model. Through working condition analysis and bidirectional coupling modeling, the application solves the problem of inaccurate cable life prediction.

The invention relates to the technical field of mineral processing and mineral preparation equipment optimization, and discloses a Markov chain-based ball mill ball supplement parameter determination method, which comprises the following steps: determining the rock abrasion degree: acquiring the abrasion degree parameter of to-be-ground ore, the parameter being used for representing the abrasion capability of the ore to a steel ball, and determining the abrasion degree of the to-be-ground ore to a steel ball; a basic basis is provided for subsequent steel ball abrasion rule analysis; and performing numerical simulation on a steel ball abrasion evolution rule: combining physical and mechanical parameters of the ore and the steel ball based on the ore abrasion degree parameter obtained in the step a. The method comprises the following steps: establishing association between ore characteristics and steel ball abrasion through a rock abrasion degree test, constructing a steel ball abrasion evolution model by using a discrete element method, converting the size fraction of a steel ball into a Markov chain state, calculating a transition probability matrix in a ball supplementing period, and finally determining ball supplementing parameters by combining ideal and actual ball load gradation difference and a ball supplementing principle. The steel ball abrasion transfer trend can be dynamically predicted, and the over-compensation or under-compensation phenomenon is avoided.

The application provides a website access request detection method and device and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: obtaining a target access sequence of a target user, the target access sequence comprising a plurality of target URLs corresponding to a target website and sorted according to access time; obtaining a target weight corresponding to each of the plurality of target URLs; obtaining a plurality of target transition probabilities corresponding to the target access sequence according to a stored corresponding relationship between a website and a page state transition probability matrix, the page state transition probability matrix comprising transition probabilities between pages of a website when the pages of the website are normally accessed; and determining whether an access request corresponding to the target access sequence is an abnormal access request according to the plurality of target weights and the plurality of target transition probabilities. In this way, abnormal access can be identified according to a page state transition probability matrix corresponding to normal user access and a target access sequence of a target user, thereby reducing the situation of missing abnormal access.

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.

A method of training, verification, and advanced notice for an autonomous system, comprising: by a machine learning classifier, wherein an adversary vehicle, and a primary vehicle are on the same path; creating a path position probability transition matrix, wherein the path position probability transition matrix comprises an acceleration parameters array of one or more acceleration parameters for a potential position, sorting a sample path, and using Ne elite path; creating a samples array, wherein the sample array is one or more paths; evaluating a sample path; sorting the sample path, based at least in part a custom score of a custom score function and selecting a subset of the sample paths as Ne elite paths; using the Ne elite paths to update the path probability transition matrix and the acceleration parameters array; and repeating until the Ne elite paths stabilizes for a predetermined number of iterations.

The invention provides an intelligent feature evolution analysis method and system for a time sequence path, and relates to the technical field of feature evolution, and the method comprises the steps: obtaining time sequence path data, and carrying out the preprocessing to generate a standardized feature sequence; performing Monte Carlo sampling and transition probability matrix calculation to obtain a characteristic evolution strength index; setting an evaluation time window by adopting a beam search algorithm, calculating a stability score, and selecting an optimal path branch; and finally, determining an optimal characteristic evolution path based on a weighted combination of the evolution strength index and the stability score, and generating an analysis report. According to the method, the evolution law of key features in the time series data can be effectively identified, and the analysis accuracy and efficiency are improved.

The invention discloses a full-coverage path planning method. The method comprises the following steps: determining a grid map of an area to be subjected to path planning, and determining a transition probability matrix corresponding to the grid map; an initial population corresponding to the grid map is determined according to the transition probability matrix and preset selection strategies, and the preset selection strategies comprise a greedy selection strategy and a fuzzy selection strategy; determining a local distance contribution value of a target path in the initial population, executing a multi-modal disturbance operation on the target path according to the local distance contribution value, and generating a filial generation population corresponding to the initial population; and performing iterative optimization according to the initial population and the offspring population until an optimal path corresponding to the grid map is determined. According to the invention, technical problems of path redundancy, slow convergence speed and poor adaptability to a complex environment existing in a path planning algorithm in a complex obstacle scene in the prior art are solved.

The embodiment of the application provides a photovoltaic cleaning strategy optimization method and system based on a Markov chain, and an electronic device, the method comprising: obtaining historical meteorological data and photovoltaic operation data, performing data training after preprocessing, determining a prediction model of photovoltaic theoretical power and photovoltaic dust loss; discretizing the dust level in the photovoltaic dust loss prediction model, determining the corresponding dust state, and based on the MDP framework, combining the random event probability, constructing the transition probability matrix of the dust, and further determining the revenue function; integrating the prediction data of the photovoltaic theoretical power and the random event, rolling solving the MDP model through an iterative algorithm, and adaptively adjusting and optimizing the time domain, taking the maximization of the revenue function as the iteration target, and generating the photovoltaic cleaning strategy of the current time domain; simulating the operation of the photovoltaic cleaning strategy and the remaining benchmark strategy, and calculating the multi-dimensional evaluation index in the simulation operation process.

The invention provides a time series data synthesis method and device and a storage medium, relates to the technical field of data processing, and can improve the accuracy of a time series data synthesis result. The method comprises the steps of determining a plurality of patch sequences based on a preset window and input data; the input data comprises a plurality of data sequences and a category label of each data sequence in the plurality of data sequences, and the plurality of patch sequences are obtained by intercepting each data sequence through a preset window; based on each patch sequence and a class condition semi-Markov rule, state parameters of each data sequence under each class label are determined, and the state parameters comprise at least one of initial state distribution, a transition probability matrix and state duration time distribution; based on the state parameters and the input data, a synthetic data sequence of the input data is determined.

The invention relates to the technical field of petrochemical engineering, and provides a molecular-level refining process reaction network simplification method, device and equipment, and the method comprises the steps: determining a reaction network topological graph corresponding to a target refining process; generating a transition probability matrix among nodes in the reaction network topological graph, and determining node importance in the reaction network topological graph according to the transition probability matrix; according to the node importance of each node in the reaction network topological graph, executing an iteration process of node deletion, and stopping iteration until a first iteration stop condition is met; and outputting the corresponding reaction network topological graph when the first iteration stop condition is reached as a target reaction network. According to the embodiment of the invention, the calculation amount of refining process reaction process simulation and the hardware resource requirement on computer equipment can be reduced.

The invention provides a preschool education visual interaction method and system, and the method comprises the steps: obtaining the initial interaction data of a preschool child, analyzing the operation complexity and structural features, constructing an initial multi-dimensional cognitive state vector, analyzing the continuous trajectory of the child in the interaction process, decomposing the continuous trajectory into behavior sequences of exploration, delay, correction and confirmation elements, and carrying out the recognition of the initial multi-dimensional cognitive state vector. Calculating a behavior primitive transition probability matrix, updating a cognitive state vector according to the matrix and a calculation gain value, when the cyclic probability formed by the delayed primitive and the correction primitive exceeds a preset threshold value, reconstructing a task visual element in a non-prompt manner, and based on the updated cognitive state vector, calculating the cognitive state vector. And calculating a cognitive coupling degree between the state vector and a task preset cognitive load vector in a teaching task library, and selecting a task of which the coupling degree is in a target interval as a next round of interaction task, thereby realizing personalized teaching recommendation.

The invention discloses a micro-service abnormity positioning method based on spectral analysis and error reconstruction, and the method comprises the steps: 1, injecting an agent into each micro-service instance, and achieving the collection of the index information and link information of the micro-service instance; the agent comprises at least two modules, namely a link information acquisition module and an index information acquisition module, and can be realized by integrating existing open source components; step 2, performing aggregation calculation by using the collected link data, and constructing a service calling link; step 3, data preprocessing is carried out on the collected index data, and then training of an auto-encoder is carried out; in the anomaly detection stage, coding reconstruction is carried out on the current index data of the system through a variational auto-encoder, the coding reconstruction is compared with a set threshold value, and whether the micro-service instance is abnormal or not is detected; step 4, based on the obtained calling link and the reconstruction error, calculating scores of abnormal nodes from two perspectives of index abnormal scores and calling link abnormal scores by using spectral analysis and a Pearson's correlation function, and explaining weights of the scores by using a proper weight; and step 5, constructing a transition probability matrix based on the abnormal score of the node, and sorting abnormal examples or middleware by using a random walk algorithm for many times.

This disclosure relates to the field of logistics and transportation technology, and proposes a freight volume prediction method, device, electronic device, and readable storage medium. The method includes: acquiring historical waybill data, and generating a multi-dimensional initial probability transition matrix based on the historical waybill data, the initial probability transition matrix including historical route resource information and route delivery probability information; acquiring real-time waybill barcode scanner operation data, and determining the planned route resource information, real-time vehicle status, and real-time freight volume summary data for the current day based on the real-time waybill barcode scanner operation data; adjusting the route delivery probability information of the initial probability transition matrix based on the planned route resource information for the current day, historical route resource information, and real-time vehicle status to obtain a target probability transition matrix; and determining the expected freight volume allocated to each target route based on the real-time freight volume summary data and the target probability transition matrix. The technical solutions provided by one or more embodiments of this disclosure can accurately predict the allocated freight volume of each route.

This disclosure relates to the field of text processing technology, and particularly to a keyword extraction method, apparatus, device, storage medium, and computer program. The method includes: inputting text data to be processed into a pre-trained word vector model for vector mapping processing to obtain word vectors; inputting the word vectors into a bidirectional long short-term memory network for global feature extraction to obtain global features, wherein the global features represent the global features corresponding to the sentence; inputting the word vectors into a text convolutional neural network for local feature extraction to obtain local features, wherein the local features represent the local features corresponding to the word; constructing a probability transition matrix based on the global features and the local features, wherein the probability transition matrix is ​​used to calculate the importance score corresponding to the word; and selecting keywords from the words based on the importance score. This method can efficiently and accurately extract keywords from text data.

This invention discloses an intelligent online course recommendation method and system based on big data analysis, comprising the following steps: S1, collecting multi-source data and preprocessing it; S2, extracting learning state features, constructing course nodes and course connection edges, and building a course knowledge graph based on the association strength; S3, performing vector representation learning in the course knowledge graph, constructing a course transition probability matrix, and determining the initial recommendation set corresponding to the current learning state; S4, performing node propagation calculation in the course knowledge graph starting from course nodes, and screening the candidate course set; S5, performing candidate path search and sorting the candidate paths to form a recommendation priority sequence; S6, compiling the course recommendation list and collecting feedback data to incrementally update the course knowledge graph. This invention enables collaborative analysis in the online course recommendation process, improving the matching accuracy of course recommendations.

The application discloses a Markov attack path prediction method based on CVSS, and specifically comprises the following steps: step 1, scanning network host vulnerability information to generate a configuration file of.nessus; step 2, generating an attack graph: importing the configuration file generated in the previous step into Mulval, associating information between various host vulnerabilities through Mulval, and generating an attack graph; step 3, constructing a state transition graph: obtaining a simplified state transition graph according to the attack graph generated in step 2; step 4, initializing a Markov probability transition matrix: obtaining a probability transition matrix according to the state transition graph; and step 5, predicting an attack path probability. The method adopts a mode of measuring attack benefits to accurately predict a path to a single vulnerability level, realizes multi-step and multi-time prediction, simplifies the prediction method, and solves the problems of path redundancy, rationality and effectiveness of prior probability setting in the prediction path of the Bayesian model.

The invention discloses a power distribution station planning method based on statistical multiplexing, and the method comprises the steps: obtaining load data and distributed photovoltaic data, and constructing a refined substitution transition probability matrix between load subtypes; generating a prediction sub-type quantity distribution vector of each evaluation time node by using Markov chain recursion; superposing the typical daily net power curves of each sub-type to generate a total net load curve of the prediction area; carrying out hierarchical classification on the elastic load and identifying a bidirectional peak time period; performing bidirectional optimization scheduling on the semi-elastic layer and the full-elastic layer, and extracting a bidirectional statistical multiplexing capacity demand value; a bidirectional structural transition event is identified and a staged capacity expansion scheme is solved using dynamic programming. According to the method, the influence of production and elimination integrated load succession on the bidirectional power flow form can be reflected, and the power distribution station staged planning facing the bidirectional capacity requirement is realized.

The application discloses a non-smooth system random response calculation method based on improved generalized cell mapping. The method firstly establishes a random differential equation of a target non-smooth system and defines a collision condition; then selects a region of interest on a collision constraint surface, discretizes the region of interest into a cell state space and establishes a mapping relationship with an original space; then constructs a collision-to-collision mapping on the collision constraint surface, and establishes a one-step transition probability matrix of the generalized cell mapping method; finally, based on Markov chain theory, transient and steady-state probability distributions on the cell state space are calculated, and are restored to the original continuous state space. The application can accurately calculate transient and steady-state responses of a non-smooth system, and is suitable for long-term response analysis.

The invention discloses a multi-user anti-interference decision-making method based on sparse strategy propagation. The method comprises the following steps: step 1, determining the number of divided sub-time slots of users according to an interference detection history; 2, determining the information transmission rate of the sub-time slot according to the spreading factor; 3, determining system throughput and an optimization target according to the signal-to-noise ratio of a receiving end; 4, determining a state space, an action space, a state transition probability matrix and rewards in the decision process; step 5, obtaining lower-layer main network parameters according to the empirical samples and the loss function; step 6, obtaining an upper layer main network parameter according to the experience sample and the loss function; and 7, obtaining an optimal anti-interference strategy according to iterative training. According to the method, the decision complexity is reduced, and dimension disasters are avoided.

The invention discloses a photovoltaic power generation power data generation method and system, and belongs to the field of photovoltaic power generation, and the method comprises the steps: extracting day degree feature vectors day by day from photovoltaic power generation time sequence data, carrying out the clustering analysis, and obtaining K scene clusters for representing different power generation types, training a Markov chain model according to the scene cluster label sequence of the power generation day, and constructing a state transition probability matrix of a daily power generation type; splitting the photovoltaic power generation time sequence data into K subsequences according to the scene clusters, and training SARIMAX models corresponding to the scene clusters respectively; and simulating and generating a daily power generation type sequence with a preset length by using the state transition probability matrix, calling a corresponding SARIMAX model according to the cluster label of each power generation day, generating daily power time sequence data, and splicing according to a time sequence to obtain photovoltaic power generation power data. The power time sequence data generated by the method is consistent with scene distribution and time sequence evolution in the real world, and the authenticity is high.