227 results about "State-transition matrix" patented technology

The invention provides a breakwater monitoring data preprocessing method and system based on Kalman filtering, and relates to the technical field of breakwater structure safety monitoring. The method comprises the following steps: acquiring original motion data of acceleration, inclination and displacement through a motion attitude sensor to obtain an original data sequence; initializing a state vector and an error covariance matrix; dynamically correcting the state transition matrix and calculating a prediction state vector and a prediction error covariance matrix; a Kalman gain is generated; updating a state vector and an error covariance matrix; and extracting the filtered motion data as a preprocessing result. According to the method, the state transition matrix is dynamically corrected by introducing the wave force feedback, so that the Kalman filtering algorithm can adapt to the wave impact environment, noise interference in monitoring data is effectively inhibited, and the accuracy and reliability of key motion parameter data of the breakwater are remarkably improved.

The invention provides a task alarm processing method and system based on intelligent grading, and relates to the technical field of task scheduling alarm, and the method comprises the steps: collecting execution behavior data of each task in a task scheduling system in a plurality of time windows, carrying out the time sequence coding, and converting the execution behavior data into a state transition matrix, identifying an abnormal task and quantifying an alarm intensity value by analyzing non-stationary features; constructing a data flow direction and resource competition relation heterogeneous graph between the tasks, and mapping the abnormal tasks to corresponding nodes; generating a context sensing vector through multi-hop neighborhood aggregation, forming an alarm cluster based on semantic distance clustering, and determining a core node; and determining a control instruction according to the alarm intensity of the core node, performing conflict detection in combination with a resource competition relationship, adjusting a task execution time sequence, and predicting a failure probability output risk type based on a state transition path. According to the invention, intelligent grading and accurate processing of alarms can be realized, and the reliability and resource utilization efficiency of a task scheduling system are improved.

A vehicle-mounted GNSS positioning method based on multi-motion model interaction includes: establishing a position-constant velocity (PCV) model and a position-constant steering angular velocity (PCSAV) model for two attitudes of a carrier (i.e., linear motion and turning motion) respectively to obtain a state estimation vector and a state transition matrix of the carrier of the PCV model and the PCSAV model at a previous moment, introducing an interacting multiple model (INM), establishing a heuristic position-velocity filtering (HPV)-IMM model based on the IMM model to achieve an information filtering interaction between the PCV model and the PCSAV model, and obtaining a state estimation vector and an error covariance matrix of the carrier at a current moment, so as to obtain a position and velocity of the carrier at the current moment. The present disclosure solves the problem of low accuracy of a traditional single kinematic model in multi-motion attitude vehicle positioning.

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

The invention relates to the technical field of electric heat tracing intelligent temperature control, and discloses a control box electric heat tracing band intelligent temperature control system and method. The method comprises the steps of collecting data of a plurality of temperature sensors in a control box, constructing a temperature sequential sequence, and forming a sequential diagram structure reflecting correlation strength among the sensors by calculating spatial proximity and time correlation. And inputting the sequential graph into a state space model, and dynamically adjusting a state transition matrix of the model according to an edge weight of the graph so as to accurately simulate a heat transfer dynamic process in the box. A state output by the model is separated into a periodic component and a trend component, the periodic component is matched with a reference pattern library to identify an abnormal fluctuation phase, and multi-scale decomposition and slope curvature analysis are performed on the trend component to quantify a change trend. According to the method, dynamic and accurate modeling of the complex thermal environment is realized, the temperature can be controlled more accurately, and abnormity can be warned in advance.

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

The invention discloses a real-time SLMP fuel cell hybrid electric vehicle energy management method. The method comprises the following steps: firstly, collecting vehicle operation data in real time, constructing an operation state vector containing kinetic parameters, and initializing a state transition matrix as agent action input; on the basis, the speed and the acceleration are predicted through a self-learning Markov predictor, and dynamic self-adaptive power distribution of the fuel cell and the power cell is achieved in combination with a multi-objective optimization function and power distribution constraints. The method passes hardware-in-the-loop (HIL) verification, aims at reducing hydrogen consumption and improving efficiency, and exits if two times of circulation are not passed. Compared with a traditional method based on rules or static optimization, the method innovatively embeds a self-learning mechanism, has the advantages of being high in adaptability, high in prediction precision and good in generalization performance, and is suitable for energy management optimization under complex working conditions.

The invention discloses an electric vehicle charging load scene clustering method and system, and belongs to the technical field of load prediction and data analysis, and the method comprises the steps: collecting the historical time series data of an electric vehicle charging load, and constructing a feature vector; training under multiple confidence levels to obtain a probability distribution model of the charging load; generating a plurality of charging load time sequence scenes; performing dynamic modeling on each scene by applying a Gaussian hidden Markov model, extracting a state transition matrix, a state mean value and an initial state probability parameter, and combining the state transition matrix, the state mean value and the initial state probability parameter into a feature vector representing dynamic features of the scene; clustering analysis is carried out on the feature vectors representing the dynamic features of the scenes, and the scene closest to the clustering center is selected from each cluster to serve as a typical scene to be output. By accurately describing the uncertainty of the charging load, a charging load time sequence scene conforming to statistical distribution is generated, and a reliable and efficient decision basis is provided for power grid dispatching, planning and risk assessment.

The invention relates to the field of power system state evaluation and equipment intelligent operation and maintenance, and provides an electric energy meter state evaluation method and system based on big data analysis. The big data analysis-based electric energy meter state evaluation method comprises the steps of obtaining electric energy meter operation error data, and extracting typical period trend and residual disturbance information; constructing a state transition matrix according to the typical period trend and the residual disturbance information; converting the state transition matrix into a dynamic structure tensor to quantify evolution strength and path activeness between error states; constructing a trend function based on the dynamic structure tensor and the error multi-scale trend function, and depicting the evolution strength of the error under the action of each structure path; and based on the typical period trend, the residual disturbance information, the trend function and the dynamic structure tensor, constructing a prediction function, and carrying out continuous prediction and state evaluation judgment on the future error change trend. According to the invention, continuous, batch and automatic analysis of the error state of the electric energy meter running in the network can be realized.

The invention relates to the technical field of syndrome evolution prediction, in particular to a syndrome evolution prediction method, system and device based on multi-modal data driving and a medium, the method comprises the steps that multi-modal syndrome data is acquired and preprocessed, and the multi-modal syndrome data comprises clinical symptom data, tongue condition feature data and pulse condition feature data; extracting bidirectional time sequence characteristics in the preprocessed multi-modal syndrome data through a bidirectional long-short-term memory network, and generating a hidden state sequence; weighting the hidden state sequence by using an attention mechanism to obtain context vectors of contribution weights of different time steps; and inputting the context vector into a hidden Markov model of which the state transition matrix is constrained and corrected by the traditional Chinese medicine theory, performing syndrome state reasoning and evolution trend prediction, and outputting a prediction result. The objective of the invention is to improve the prediction precision and interpretability of syndrome evolution.

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

The invention provides a learning enhanced Kalman filtering method for forest emergency positioning and a related device. The method comprises the following steps: acquiring time sequence measurement data of an unmanned aerial vehicle and a ground target node; inputting time sequence measurement data into an error compensation network, and outputting a ranging error compensation amount and an observation noise covariance matrix; performing distance measurement compensation on the time sequence distance measurement data, feeding back the distance measurement data subjected to error compensation and a state error of the ground target node to a state prediction network, and outputting a state transition matrix and a process noise covariance matrix; state prediction is carried out based on a state transition model and a process noise covariance matrix, Kalman filtering fusion is carried out on a prediction state, compensated distance measurement data and an observation noise covariance matrix, three-dimensional position estimation of a ground target node is determined, combined improvement of measurement correction and prior prediction precision is realized, and the accuracy of measurement correction and prior prediction is improved. On the basis of keeping the optimality of traditional filtering, modeling is carried out on time-varying noise and non-linear motion, and the positioning precision in a forest complex environment is improved.

The invention relates to the technical field of high-precision positioning, in particular to a high-precision six-degree-of-freedom magnetic positioning system and method, and the method comprises the steps: generating a three-dimensional low-frequency magnetic induction field with a preset frequency in a space through a three-axis orthogonal transmitting coil; detecting the three-dimensional low-frequency magnetic induction field through a three-axis orthogonal receiving coil to obtain an induced voltage signal; transmitting the digitized induced voltage signal to a control layer; in the control layer, the position and attitude of the receiver are estimated in real time by using a joint estimation algorithm fusing error state extended Kalman filtering and particle swarm optimization; the error state extended Kalman filtering comprises the steps of constructing a state transition matrix F of a target carrying a receiver, and constructing a process noise covariance matrix Qk and a parameterized measurement noise covariance matrix R of the system through translation acceleration noise sigma a and rotation noise sigma omega. According to the invention, the PSO module is utilized to dynamically and adaptively adjust the key parameters of the ESKF, and high-precision positioning and low-delay response are realized.

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

The invention discloses an optimal distribution method for electric vehicle charging stations, and the method comprises the steps: constructing a state transition matrix based on the travel time of a driver of a to-be-charged electric vehicle, the charging cost, the number of available charging piles of the charging stations, the comprehensive position of the charging stations, and the attraction of the charging stations, and generating charging demand probability distribution in real time; calculating a reachable radius according to the residual electric quantity and the energy consumption rate of the to-be-charged electric vehicle, screening a candidate charging station set, fusing a driver preference weight, and establishing a multi-target distribution cost evaluation mechanism; constructing an optimal distribution model of the electric vehicle charging stations; an initial population is generated through greedy random adaptive search, a multi-parent cross strategy or a directional exchange strategy is dynamically executed according to the remaining time of a driver, and a corresponding optimal charging station distribution scheme is output. According to the method, the optimal charging station is allocated for the electric vehicle user, and the driving distance cost, the charging currency cost and the destination walking distance cost are reduced to the maximum extent.

The invention discloses a state-limited off-line reinforcement learning control method for an automatic driving scene. The method comprises the following steps: (1) constructing a vehicle driving data set based on a multi-modal fusion perception technology, and carrying out data preprocessing; and (2) training a forward dynamics model, an inverse dynamics model and a reward model by using the preprocessed data, constructing a strategy network and a value network of an actor-commentator framework at the same time, and calculating a reachable relationship between states in the training process for guiding strategy learning and value evaluation. (3) constructing a strategy network online reasoning module based on real-time vehicle state perception, generating a continuous control action by adopting a strategy gradient optimization algorithm, obtaining a state transition matrix after the action is executed, storing transition data into an experience playback buffer area, carrying out iterative optimization on network model parameters through a priority experience playback strategy at regular intervals, and obtaining a state transition matrix after the action is executed; and the evolution of the driving strategy is realized.

The application discloses a kind of complex environment under unmanned plane patrol path decision method, it is related to unmanned plane path planning technical field, method includes: with each to be patrolled position as node to construct undirected topological graph;According to the topological constraint of undirected topological graph and node importance generates the steady state distribution of each node patrolled by unmanned plane;Generate multiple candidate state transition matrix with steady state distribution;Dynamic environment evaluation is carried out to the obstacle of each candidate state transition matrix corresponding path, to determine the selectable speed and direction of unmanned plane on selected path;According to the weight of decision network dynamic adjustment according to dynamic environment evaluation result, and then generate execution motion command using the decision network after weight adjustment;According to motion command, unmanned plane is controlled and moves between each to be patrolled position.The application can realize the intelligent decision of unmanned plane under complex environment efficiently by dynamic environment evaluation, planning path, adjusting the weight of decision network, generating motion command.

The invention discloses a quadruped robot advancing path selection method and system based on Kalman filtering, and belongs to the technical field of robot movement control. The method is used for solving the technical problems that sudden change of foot end contact force of a quadruped robot or terrain slope change can cause model mismatch, and then filtering divergence or estimation deviation is caused. A kinematic model is corrected in real time through a slope angle, so that system state prediction is more fit with an actual terrain, noise covariance is dynamically enhanced when contact stiffness is suddenly changed, and the situation that small-probability large sudden change is excessively suppressed by filtering is avoided; the method can dynamically adapt to changes of terrain gradient and contact rigidity, according to the output filtering optimal state estimation and in combination with a preset foot end landing point stability evaluation function, an advancing landing point meeting force-pose constraint is generated, the robot is driven to execute, and the quadruped robot can autonomously select a stable landing point in an unknown complex terrain.

The invention relates to the technical field of temperature control, and discloses a BMS temperature control method and system, and the method comprises the steps: carrying out the multi-dimensional coupling of battery data, and obtaining a micro-region thermal state; predicting heat accumulation and generating a heat dissipation instruction based on load abrupt change superposition feedforward compensation; calculating heat distribution deviation evaluation stability according to the feedback signal; defining a lead compensation range by using a heat evolution model to generate parameter configuration; system response characteristics are obtained through calibration of the frequency spectrum and the state transition matrix; and fusing the temperature gradient distribution to generate a final advanced adjustment instruction. According to the method, accurate prediction and full-link advanced active intervention of micro-region thermal unbalance can be achieved, the problem of heat accumulation caused by load dramatic change is effectively solved through multi-dimensional feature coupling and a dynamic closed-loop calibration mechanism, and the system response speed and the temperature field balance are remarkably improved.

The invention provides a deep learning photoetching hot spot detection method and system combined with a physical model, and relates to the technical field of photoetching processes, and the deep learning photoetching hot spot detection method comprises the following steps: obtaining data of a design layout and a mask layout, calculating light intensity gradient and surface stress distribution by using a double-physical field model, and forming a physical sensitive characteristic pattern; inputting the feature map into a physical constraint neural network for reconstruction and mapping; and constructing a state transition matrix based on the compensation state sequence, calculating a transition cost, determining an optimal compensation path, and generating layout compensation data. According to the invention, high-precision hot spot detection and effective layout compensation are realized.

The invention relates to a river channel sand body random model generation method and device based on hidden Markov model data filling, a medium and equipment. The method comprises the steps that logging data of a target area are collected and arranged; dividing logging data according to a logging gas-bearing interpretation conclusion, and carrying out analytical statistics on the velocity and density of longitudinal and transverse waves in the logging data; initializing an initial state probability, a state transition matrix, and a mean value and a covariance of Gaussian distribution in the hidden Markov model; inputting a three-dimensional observation vector composed of the velocity and density of the longitudinal and transverse waves into the initialized hidden Markov model for iterative training; performing down-sampling on the lithology data, then constructing a state transition probability matrix, and further constructing a lithology sequence conforming to a geological law through simulation; according to the lithologic sequence, calling a hidden Markov model to generate longitudinal wave velocity, transverse wave velocity and density corresponding to the lithologic sequence; and calculating a reflection coefficient of the synthetic stratum, setting a Ricker wavelet dominant frequency, and obtaining synthetic seismic response data through convolution.

The embodiment of the invention provides an unknown cross-site script security detection method and device generated based on a test script, and intelligent construction of the test script is realized through innovatively constructing a script generation mechanism driven by a Markov chain and through atomic element sequence analysis and a random walk strategy. And designing a DOM tree difference-based filtering analysis mechanism, and establishing a filtering rule identification strategy in combination with multi-dimensional mark extraction and node positioning. And a matrix iterative optimization mechanism is introduced, and self-adaptive optimization of the test script is realized through dynamic updating and convergence calculation of a state transition matrix. According to the method, the defects of the traditional technology in the aspects of script generation, filtering analysis, optimization adjustment and the like are effectively overcome, and the unknown cross-site script detection effect is remarkably improved.

The invention provides an industrial control security spoofing detection method and system based on a container technology, and relates to the technical field of industrial control network security, and the method comprises the steps: guiding an abnormal access request to a simulation service instance in a container environment, extracting an interactive operation to construct a multilayer directed graph, calculating a branch entropy value and a semantic deviation degree to form a feature vector, and carrying out the spoofing detection of the abnormal access request; and determining an attack stage based on the state transition matrix and the attack behavior knowledge base, controlling response data and implementing network isolation. The method can actively induce the attacker to expose the intention, accurately recognize the attack stage, and effectively protect the industrial control system from network attack.

The invention belongs to the technical field of navigation, and particularly discloses a combined navigation method for improving an AEKF algorithm under a GNSS incomplete condition, which comprises the following steps of: executing the following operations in each filtering period of the AEKF algorithm: determining state priori estimation in the current filtering period based on a state transition matrix and a state vector in the previous filtering period, the state vector is constructed based on the position, the speed and the clock error of the receiver; based on the state noise variance matrix and the error covariance matrix in the previous filtering period, determining error covariance priori estimation in the current filtering period; correcting an observation matrix and a measurement noise variance matrix based on the dynamic weight coefficients of the GNSS and the Loran C; and based on the corrected observation matrix and the corrected measurement noise variance matrix, obtaining the Kalman gain in the current filtering period, and updating the prior estimation. According to the invention, the overall performance of the integrated navigation system under the condition that the GNSS is incomplete can be improved.

The invention provides a Kalman filtering parameter setting method, device and equipment, a medium and a product, and relates to the technical field of digital processing. The method comprises the following steps: acquiring an angular velocity measurement value of the MEMS gyroscope measured at each moment in the previous period; and calculating an autocorrelation coefficient between the angular velocity measurement values, and determining the autocorrelation coefficient as a state transition matrix corresponding to the MEMS gyroscope in the current period. According to the method, the dynamic updating of the state transition matrix can be realized under the working condition that the MEMS gyroscope operates for a long time, the influence caused by temperature drift is effectively counteracted, and finally the purpose of improving the filtering precision is realized.

The application discloses a kind of based on embodiment intelligence's self-adapting rocker arm type carrying robot control method and system, belong to robot control field, the method includes: by being carried on robot car body and rocker arm mechanism on multi-modal sensor array obtains first perception dataset and second state dataset, extracts historical configuration matrix and exports feedforward configuration instruction to realize feedforward pre-adjustment;During obstacle crossing and load period, generate and execute the non-symmetrical compensation instruction of driving two sides rocker arm to carry out asymmetric telescoping and lifting;In distress period, according to the attribute of physical root node, match the corresponding quasi-physical escape sequence from state transition matrix and execute escape action;In edge control node, running reinforcement learning network module to output original speed instruction vector.The application guarantees the control completeness when facing new terrain, improves the accuracy of compensation action.

The embodiment of the invention discloses an extended target detection method, belongs to the technical field of radar detection, and solves the problem that the detection performance is reduced due to the fact that excessive noise is introduced or target energy is omitted during energy accumulation in an existing extended target detection method. Comprising the following steps: constructing a dynamic planning accumulation detection model of a distance extension target based on an echo matrix; on the basis of the minimum energy loss criterion, balancing parameters are configured for the value function, a double-probability joint constraint optimization model about the target energy and the noise energy is established, and the balancing parameters are determined by solving the optimization model; updating a value function and a state transition matrix of the joint state variable among the plurality of pulses through recursive operation based on the trade-off parameter; and for the value function obtained after updating is completed, extracting a corresponding energy ridge line, constructing an approximate probability model of the variation amplitude of the energy ridge line, and obtaining a target extension length value by solving an optimal variation threshold of the approximate probability model under a preset false alarm probability.

The invention discloses a wind power typical time sequence scene generation method considering an extreme scene, and the method comprises the following generation steps: S1, collecting meteorological observation data and historical original data of wind power output, and carrying out the preprocessing of the original data; s2, decomposing an annual power sequence based on extreme large / large / medium / small / random fluctuation identification; S3, constructing a monthly characteristic index system and clustering the power sequence based on a monthly scale; s4, calculating a state transition matrix of five types of fluctuations by taking the monthly clustering set as a unit; s5, reconstructing the discretized wind power sequence to generate a random wind power sequence meeting conditions, and finally combining the random sequences of all months to generate a random sequence of the whole year; according to the method, the panorama of the system operation risk can be accurately captured, the defects of a traditional typical scene method are effectively overcome, and the accuracy and reliability of time sequence production simulation, system safety evaluation and medium-and-long-term development planning are improved.

The application discloses a kind of communication power supply operating state intelligent monitoring methods based on machine learning, comprising the following steps: S1, obtains multiple-source monitoring data and pre-processes;S2, sequence segmentation is carried out using sliding time window and statistical feature vector is extracted, forms state variable set;S3, each state variable is encoded to construct state node and establish weighted directed connection, and construct operating state evolution diagram;S4, state transition matrix is constructed, main transition feature is extracted and low rank is approximately reconstructed, sequence learning is carried out to construct state update function;S5, to state node, multiple-step recursion is carried out, and the Euclidean distance between recursive state and risk boundary is calculated, to determine state monitoring result;S6, error is calculated and state update function parameter is iteratively updated.The application can realize the multiple-step recursion prediction and risk trend identification of communication power supply operating state, improve the accuracy and stability of communication power supply operating state monitoring.

The invention relates to the technical field of power distribution network reliability evaluation, in particular to a power distribution network reliability online evaluation method and device. The method comprises the following steps: establishing a state transition matrix of each element in the power distribution network according to a physical topological structure of the power distribution network, and sampling to obtain a full life cycle state matrix of the elements of the power distribution network; according to the obtained state matrix, power distribution network reliability evaluation considering vehicle network integration grading and source load volatility is carried out, and a first reliability evaluation index is calculated; according to the obtained system state matrix and the first reliability evaluation index, simulating a source load fluctuation scene to construct a data set, and training a graph neural network model; according to the trained target graph neural network model, the load values of the nodes and the real-time state data of the power distribution network are input, and the predicted reliability value of each node is obtained. According to the method, the problems that nonlinearity and dynamic changes in a power distribution network system are difficult to accurately reflect in the prior art and the operation complexity and time are difficult to meet the requirements can be solved.