269 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 relates to the technical field of intelligent customer service, and discloses an intelligent customer service dialogue generation method and system based on user intention recognition, and the method comprises the steps: obtaining an input first user text from an intelligent customer service system, and carrying out the feature decomposition and attention fusion of the first user text, and obtaining a fusion intention feature matrix; performing intention migration analysis and dynamic Bayesian analysis on the dialogue sequence data in the historical dialogue corpus to obtain an intention state transition probability matrix; an improved Transform network model is input for joint training, and a dialogue generation model is obtained; a newly input second user text in the intelligent customer service system is decoded and generated based on the dialogue generation model, meanwhile, the intention state transition probability matrix serves as a constraint condition, a reply text conforming to the dialogue intention is obtained, the accuracy and robustness of intention recognition are improved, the reply smoothness is guaranteed, and meanwhile the user experience is improved. And the generated content is ensured to conform to an expected intention transfer rule.

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 an intelligent building equipment control method and system, and relates to the technical field of intelligent building equipment control, and the method comprises the steps: fusing the multi-mode association topology of two groups of data through employing a Kruskal algorithm, constructing a minimum spanning tree, setting the reciprocal of an edge weight as a pheromone value, and selecting an optimal control path through employing a maximum flow algorithm-ant colony algorithm. The method comprises the following steps: converting phase space data by using a Takens embedding method, randomly selecting a pair of adjacent phase space data from a time sequence as initial points, analyzing the state of building equipment by using a Lyapunov index, constructing a hidden Markov model, and dynamically correcting a transition probability matrix and a transmission probability matrix by using the Lyapunov index to obtain the predicted state of the building equipment. The optimal control path is selected by constructing the minimum spanning tree and combining the maximum flow algorithm-ant colony algorithm, the resource utilization rate and the control precision are improved, and the comprehensiveness and adaptability of equipment control are enhanced based on the combination of the hidden Markov model and the Lyapunov index.

The invention provides a dynamic spreading factor anti-interference method based on annealing Q learning. The method comprises the following steps: determining an information transmission rate according to a spreading factor; determining system throughput and an optimization target according to the signal-to-noise ratio of the receiving end; determining a state space, an action space, a state transition probability matrix and rewards in the decision process; calculating a reward obtained by the selection action according to a broadband spectrum sensing result; the interference condition at the current moment is obtained through broadband spectrum sensing; deciding a spreading factor of the next time slot according to the Q value table; the user selects the action of the next time slot according to the decision information and updates the state; the award obtained by selecting the corresponding action is calculated according to the broadband spectrum sensing result; selecting the spreading factor of the next time slot according to the Q value table, and updating the Q value of the selection action in the state; and the user makes the most anti-interference strategy in a dynamic interference environment. According to the method, the system throughput is maximized, and time delay and overhead caused by channel switching are avoided.

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 three-dimensional geological modeling method, system and equipment based on a Markov chain and improved Monte Carlo, and belongs to the technical field of engineering rock geological modeling. On the basis that three-dimensional point cloud data of a rock mass is obtained through three-dimensional laser scanning, Markov chain transition probability matrix calculation is carried out through existing actual drilling and virtual drilling technologies, and then the stratum state of virtual drilling is confirmed based on a superimposed Markov chain. And finally, performing correlation simulation on the stratum thickness corresponding to the stratum state based on an improved Monte Carlo method. Compared with a traditional artificial three-dimensional geologic modeling method, the method can be more comprehensively suitable for engineering rock three-dimensional geologic modeling scenes in various complex scenes; meanwhile, based on the automatically constructed three-dimensional geologic model, the later engineering rock mass stability can be efficiently evaluated, and important data support is provided for rock mass engineering stability evaluation; the method is easy to implement, flexible to operate and remarkable in effect, and the application requirement is met.

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 relates to a digital human personalized emotion interaction method, which belongs to the technical field of emotion calculation, and comprises the following steps: S1, carrying out emotion quantification on interaction input of a user to obtain a pleasure-wake-dominate PAD emotion value in a continuous dimension space; s2, performing emotion state evaluation on the user interaction input emotion to obtain an interaction input emotion evaluation function; s3, calculating a digital human emotion transition probability matrix by using a Bayesian network; s4, calculating an emotional response calculation value of the digital human character corrected by the big five-factor model in the current emotional state; s5, the corrected emotion response passes through an emotion influence factor matrix, and a response with complex emotion features is output; and S6, performing PAD emotional space coordinate calibration on the emotional intensity of the digital person on the six basic emotions to obtain an emotional response of the digital person.

The invention relates to a vehicle trajectory prediction method fusing LSTM and GPR by using a Markov chain. The method comprises the following steps: firstly, carrying out data processing on an original data set; constructing an LSTM network model, and performing trajectory prediction according to the LSTM network model; constructing a Gaussian process regression model, and performing trajectory prediction based on the Gaussian process regression model; and finally, fusing the prediction results of the LSTM network model and the Gaussian process regression model by using a transition probability matrix trained by a Markov chain in each time step of predicting the trajectory in the future, so as to obtain a predicted trajectory of the target vehicle considering the influence of various factors. The method gives full play to the advantages of the LSTM neural network in the aspect of short-term prediction, and considers the influence of the vehicle condition information and the road condition information on the prediction of the future trajectory of the vehicle in combination with the GPR. The prediction results of the two are effectively fused through the Markov chain, and the accuracy and flexibility of trajectory prediction are further improved.

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 relates to the technical field of biological computing, in particular to a single-cell Hi-C atlas interpolation method and device, and the method comprises the steps: obtaining single-cell sequencing library data and a reference genome, determining the position of the sequencing data in the genome, and carrying out the statistics of the interaction frequency to obtain a Hi-C contact matrix; performing interpolation on the Hi-C contact matrix of any chromatin of the single cell according to similarity of adjacent nodes to obtain an adjacent node interpolation map of the single chromatin of the single cell; calculating an interpolation map of each chromatin of each cell, performing representation learning by taking the interpolation map as an adjacent matrix, splicing vectors to obtain a cell pre-embedded vector, and performing dimensionality reduction to obtain a whole cell embedded vector matrix; a cellular level similarity matrix is calculated by using a single cell embedded vector matrix and converted into a Markov probability transfer matrix, and the Markov probability transfer matrix is used for correcting an adjacent node interpolation map to obtain a Hi-C interpolation map, so that the interpolation accuracy is improved.

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 belongs to the field of plant disease prevention and control, and discloses a plant leaf spot diffusion prediction method and system based on a Markov model and a long-short memory time network, and the method comprises the steps: continuously collecting a grape leaf spot diffusion process image series, extracting the related leaf spot features of each image, and carrying out the prediction of the leaf spot diffusion; constructing features extracted from the series of images into a time sequence; training an LSTM (Long Short Term Memory) prediction model, so that the LSTM prediction model can predict a subsequent spot feature region; the method comprises the following steps of: converting a test picture into gray scale maps according to leaf surface features, regarding the change of pixels of each leaf surface gray scale map as a discrete Markov process, calculating a probability transfer matrix of the next step through leaf surface pixel features predicted by an LSTM network, calculating a next leaf surface gray scale map by adopting a discrete Markov model, and calculating a probability transfer matrix of the next step; and a final diffusion prediction map is obtained through image adjustment. The method predicts and simulates the diffusion process of leaf scabs to a certain extent, and the model has generalization and is easy to popularize to spot diffusion research of other plant disease species.

The invention discloses a malicious code classification method and device and a storage medium. The classification method comprises the following steps: forming a byte stream by using a binary file of a malicious code according to 8 bits as a group, converting each byte into a decimal unsigned integer, arranging the decimal unsigned integer into at least one two-dimensional matrix, and generating a grayscale image by each two-dimensional matrix; the byte stream is segmented into at least one byte segment, each byte segment serves as a Markov chain to generate a Markov transition probability matrix, and a corresponding Markov image is generated through the matrix; the grayscale images and the Markov images are paired one by one and then serve as dual-channel images of the same malicious code snippet; and inputting the dual-channel image of each malicious code snippet into a trained neural network-based malicious code dual-channel image multi-classification model for classification prediction to obtain a malicious code type prediction result. According to the scheme, the malicious code classification accuracy and efficiency can be improved, and the recognition capability of unknown and variant malicious codes is enhanced.

The invention discloses an instrumentation fault detection system based on a big data technology, and relates to the technical field of intelligent management, the instrumentation fault detection system comprises an acquisition module, an analysis module and an early warning module, information of a sensing sub-device is subjected to first analysis to obtain a first fault, information of a transmission sub-device is subjected to second analysis to obtain a second fault, and the first fault and the second fault are acquired; and performing third analysis on the information of the power supply sub-equipment to obtain a third fault, performing fourth analysis on the information of the execution sub-equipment to obtain a fourth fault, constructing a transition probability matrix, and performing prediction and early warning on the fault of the instrument equipment. According to the invention, through the acquisition module, potential faults are found in time, various sub-devices are analyzed carefully, the accuracy of fault discrimination is ensured, a transition probability matrix is constructed, future faults are predicted by using historical data, and the reliability of the devices is improved; and the intelligence and automation level of equipment fault detection is obviously improved.

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 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 provides a user group classification method based on spatio-temporal trajectory features, and belongs to the field of mobile communication and spatio-temporal data mining, and the method comprises the steps: S1, obtaining the original signaling data of a user, constructing a trajectory sequence, and calculating and constructing a transition probability matrix; s2, constructing a feature extraction module to generate time feature initial embedding H and space feature initial embedding F; s3, inputting H into a time attention perception module, and generating a time feature embedding representation sequence # imgabs0 #; s4, inputting the base station sparse directed graph and the distance matrixes D and F into a spatial structure sensing module, and generating a spatial feature embedding representation sequence # imgabs1 #; s5, inputting the # imgabs2 # and the # imgabs3 # into a spatio-temporal information fusion module, and generating a spatio-temporal feature embedding representation sequence # imgabs4 # of the trajectory sequence; and S6, inputting the # imgabs5 # into a classification result prediction module, and outputting a user group category. According to the method, the time and space characteristics in the signaling trajectory data can be captured, and accurate classification of user groups is realized.

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 present invention discloses a method for predicting the collapse risk of transmission towers considering the cumulative damage during typhoon processes, including: modeling the time-varying risk assessment process of transmission towers as a Markov process, establishing a transfer probability matrix of transmission towers and determining the corresponding relationship between the vulnerability curve and each element in the transfer probability matrix; based on multi-state vulnerability, according to the established risk prediction model, using finite element numerical analysis, performing limit state definition, wind-induced response analysis and statistical analysis fitting to obtain a multi-state transmission tower vulnerability curve library; combining the time-varying wind field prediction information, obtaining the transfer probabilities between various states of the transmission tower from the vulnerability curve library, and filling the transfer probabilities between various states of the transmission tower into the transfer probability matrix to realize the time-varying prediction of the collapse probability of the transmission tower and the failure probability of the transmission line. The present invention considers the possible initial damage of the transmission tower during the prediction process and realizes a more reliable prediction of the collapse risk of the transmission tower.