58 results about "State evolution" patented technology

The present invention relates to an attack and defense differential game-based network security defense decision determination method and a device. The method comprises the steps of constructing a state evolution model NIRM for network nodes according to an SIR model during the network attack and defense process; according to a network node state migration path, acquiring a node state change differential equation set based on the state evolution model NIRM; according to the node state change differential equation set, constructing an attack and defense differential game model ADDG; according to an attack and defense return strategy and a corresponding execution cost, executing a revenue function of the attack party and the defense party during the differential game process; according to the revenue function and the attack and defense differential game model ADDG, solving out a saddle point strategy for the attack party and the defense party through the dynamic planning method, determining an optimal defense strategy and outputting the optimal defense strategy. According to the technical scheme of the invention, the problem that actual requirements cannot be met through the traditional dynamic game analysis method can be solved. Compared with the existing network security defense strategy, the attack and defense process in the continuous and real-time engagement condition is analyzed. The timeliness, the pertinence and the guiding significance of network defense decision results are better.

The invention provides a travel time predicting method based on a network convergence and taking account of a traffic state time-space relation. The method obtains high-precision data by subjecting floating car data to refined processing. In particular, the process for subjecting the floating car data to refined processing ingeniously uses a convolutional neural network and a recurrent neural network which are mainly used in the field of artificial intelligence currently, fuses the networks, fully takes account of the time-space relation of a traffic state, deeply excavates a traffic state evolution rule, and ultimately achieves precise travel time prediction. Compared with various conventional travel time prediction models, the method fuses the time-space relations of the traffic state, fully excavates historical data characteristics, overcomes a difficulty that other methods cannot cope with the high time-varying characteristics and non-linear change characteristics of the traffic flow, does not need priori knowledge and parameter identification, and has good fault tolerance and robustness, high precision, and good stability.

The invention discloses an intersection collision-avoiding method based on a dynamic Bayes network, and mainly solves the problems that an existing algorithm is not well adapted to the complex road layout of an intersection, a large amount of data processing is needed, and the calculation complexity is high and the time complexity is high. The method comprises the steps of: 1) determining vehicle states, road conditions and driver behavior information, and adopting the dynamic Bayes network to carry out modeling on vehicle state evolution; 2) determining the safe driving behavior of a target vehicle according to the current environment conditions; and 3) deriving the intention behavior of the driver at the intersection, carrying out risk estimation based on comparison between the safe driving behavior and the intention behavior, and when potential risks are detected, taking different measures according to the practical condition for avoiding a collision. According to the invention, the complex vehicle track prediction process is avoided, the calculation amount is reduced, the vehicle collision in other scenes can be flexibly avoided, and the method can be applied to an intelligent traffic system.

The invention provides a road network state predicting method based on a recurrent neural network. The road network state predicting method comprises the following steps of 1, establishing a sample set; 2, modeling the recurrent neural network; 3, predicting the road network state of the next moment. The road net state predicting method has the advantages that the road network state evolution rule is acknowledged in a macroscopic viewpoint; the time sequence rule of the road network state change is fully considered by a recurrent neural network algorithm, so as to reach better predicting effect.

The invention discloses a simulation tester for similar damage of tailings dam bursting, which is provided with a simulation reservoir, and the dam of the simulation reservoir is connected with a simulation cleugh. The simulation tester for similar damage of tailings dam bursting is characterized in that the bottom of the simulation reservoir at the tail of the reservoir is provided with a jacking pushrod; the jacking pushrod is arranged on a hydraulic cylinder; the simulation cleugh consists of at least two grooves connected end to end in sequence; the neighboring grooves are connected by an adjustable joint; the dam of the simulation reservoir consists of a fixed bracket and at least one movable baffle. The simulation tester for similar damage of tailings dam bursting has the obvious effects of strong compatibility, simple structure, low cost, easy maintenance, and convenient operation, can simulate tailings reservoirs with different heights and volumes, and can all obtain the data like fluid state evolution characteristics of slurry out of the burst dam, the stress field and flow field distribution rules during the moving process of slurry, and the relationship of the alluvial fan scope of slurry and sedimentary thickness with the moving distance of slurry when facing to the grooves of the tailings dam with different bursting widths, directions and slopes.

The invention belongs to the field of traffic control, specifically a queuing length prediction method in a multi-source traffic information environment. The method comprises the steps: enabling parking point data of a floating vehicle to be matched with an extracted normal queuing parking point on an intersection segment at first, and judging a lane group where the parking point belongs through a driving path of the floating vehicle; calculating the maximum distance between the queuing parking point of the floating vehicle and an intersection, calculating a moment when the floating vehicle passes by an exit detector at an upstream intersection, determining a parking wave velocity again, calculating a queuing state evolution point, calculating the position of a start wave and finally judging the maximum queuing length. The method provided by the invention achieves the accurate prediction of the queuing length at the intersection, provides strong support for the reasonable setting of signal control parameters, and is of great significance to the traffic congestion in an active mode of taking preventive measures.

The invention discloses a device for testing electric conductivity and soil-water characteristics of loess. The device comprises a base, wherein a sleeve is arranged on the base; a pull rod is arranged in the sleeve; the sleeve is divided into a plurality of layers from top to bottom; each layer of the sleeve is connected with a resistivity measuring system, a suction measuring system and a moisture measuring system; a porous plate is arranged at each of two ends of the sleeve; an upper cover plate is arranged on the porous plate at one end of the sleeve; and a pressure rod is arranged on the upper cover plate. The device for testing electric conductivity and soil-water characteristics of loess realizes the purpose of simultaneously obtaining the resistivity and soil-water characteristic curve of the loess on the same equipment, realizes automatic measurement, enables data acquisition to be convenient and reliable, records the resistivity, moisture volume percentage and suction evolution condition of the loess at any time, can reflect the microscopic evolution rule of moisture migration in the loess, and can realize loading-unloading and simulation of long-term deformation characteristics of remolded loess and the moisture migration and pore state evolution rule in the loess.

The invention discloses a repeating and simulating method for automatic generation control of a power grid. The method comprises the following steps of: storing historical editions of a power generation models of the power grid, and storing a new power generation model document of the power grid when the power generation model of the power grid changes; holographically storing AGC (Automatic Gain Control) measuring data, AGC operating parameters and AGC operation supporting data in the AGC operating process by a time sequence database in a second level, and storing associated data in a form of table structure model by a commercial database; and realizing overall repeating and simulation of AGC historical operating conditions. The method can be used for truly repeating the AGC control process in a power grid state evolution process and immediately finding problems of the AGC in the power grid, so that the AGC control policies are improved. Meanwhile, a simulation function based on the AGC historical control section is provided for dispatchers to train simulation of a historical AGC control process section, so that a simulating operation of AGC control is realized.

The invention discloses an assessment method for the coupling state of a process industrial electromechanical system based on network structure entropy. According to the method, quasi periodicity of asequence is solved through an FFT method first to determine time window width for coupling analysis, a DCCA algorithm is utilized to calculate the relevancy between every two of multiple variables, aweighting network model reflecting the coupling relation among the multiple variables is constructed, therefore, changes of the coupling relation among process monitoring variables can be acquired intime, quick and precise scheduling on upstream and downstream parts of the system is realized, and fine control of the system is realized; an NSEn method is used to calculate an entropy value of a monitoring variable coupling relation network model in each time window, and the dynamic coupling process of different parts of the system can be visually reflected through dynamic changes of a networktopological structure according to the coupling incidence relation of monitoring data; and the network structure entropy is used for quantitatively representing the state evolution process of the system to provide comprehensive scheduling and maintenance decision information for system management personnel, and the scientific and intelligent level of a decision for secure and reliable operation ofthe process industrial complicated electromechanical system under complicated conditions is raised.

The invention discloses a polynomial filtering fault detecting method for a nonlinear system. The polynomial filtering fault detecting method comprises the steps of approximating by a polynomial, i.e., expressing m-order kronecker powers of a system state variable and a measurement output variable of the nonlinear system respectively by a polynomial to obtain a polynomial approximation model composed of a mu-order polynomial and a (mu+1)-order polynomial remainder term; expressing the (mu+1)-order polynomial remainder term in the polynomial approximation model as a low-order polynomial under the condition that no faults exist, wherein the coefficient of the low-order polynomial is equal to the product of a proportion matrix and an indefinite matrix; obtaining an augmented state evolution equation based on the proportion matrix and the indefinite matrix; designing a filter, i.e., establishing a filter function according to the augmented state evolution equation to obtain an estimated value of the system state variable at the (k+1)th moment; determining a filter gain coefficient to ensure that the upper bound of a square deviation of an augmented state evaluation error is minimum at the (k+1)th moment; detecting a fault, i.e., determining a detection threshold at the kth moment, and determining a fault detection policy according to the detection threshold.

The invention discloses a hybrid clustering algorithm based on adaptive cellular inheritance and optimal fuzzy C-means. The algorithm comprises the following steps that: utilizing Arnold Cat mapping to generate an initial population, and constructing a fitness function on the basis of the clustering criteria of the C-means; decoding individuals in the population to obtain a corresponding clustering center, distributing a degree of membership, and calculating a fitness value and the entropy of the population; carrying out state evolution on each individual, carrying out selection, dynamic intersection and a combined variation operation based on the entropy; automatically determining the fusion opportunity of the fuzzy C-means, and utilizing an implementation criteria to carry out a fuzzy C-means iterative operation; and judging whether an end condition is achieved or not, and outputting a final clustering result if the end condition is met. By use of the algorithm, the characteristics of the high global search capability of an adaptive cellular genetic algorithm and the high local search capability of a fuzzy C-means algorithm are further utilized. Compared with the prior art, the algorithm is characterized in that higher clustering efficiency and accuracy can be obtained.

The invention belongs to the technical field of network security, and particularly relates to a network security threat early warning method and device based on a qualitative differential game and anevolutionary game, and the method comprises the steps: constructing a multi-dimensional network security state space by combining a network system function and a topological structure of the network system function; introducing a qualitative differential game, constructing an attack and defense qualitative differential game model, and obtaining an attack and defense boundary grid according to theattack and defense qualitative differential game model; introducing an evolutionary game, constructing an attack and defense evolutionary game model, and obtaining a network security state evolution track according to the attack and defense evolutionary game model; and obtaining threat degrees of different security states according to a multi-dimensional space Euclidean distance between the network security state evolution track and the attack and defense boundary grid. The problems of time discontinuity and complete rationality of the threat analysis method based on the traditional game theory is solved, network security analysis more closely to attack and defense reality is carried out, the early warning timeliness, objectivity and accuracy are improved, and the method has important guiding significance for network security technology development.

The invention discloses a method for obtaining a state of an underground gasification burning face, and belongs to the technical field of underground gasification burning monitoring. The method includes the steps of arranging at least one microseism monitoring well; installing a microseism monitoring system, wherein the microseism monitoring system comprises a three-component detector and a mainframe box connected with the three-component detector through a cable; carrying out signal extraction, recognition and positioning of a microseism event; determining and analyzing a valid seismic source point; obtaining the state of the three-dimension surface of the underground gasification burning face. Compared with other monitoring means for the state of the underground gasification burning face, the method is not affected by environmental temperature, and the high-precision working state of the three-dimension surface of the underground gasification burning face and the state evolution rule can be achieved.

The invention relates to the field of electric power system simulation, in particular to a visual modeling method for the simulation data of a power distribution network cyber-physical system. The method comprises the following steps that: (1) determining an analysis target; (2) selecting an analysis angle; (3) constructing a data analysis and calculation model; (4) configuring an analysis resultdatabase; (5) constructing and configuring an analysis topic diagram; and (6) carrying out the visual displaying of analysis result data. By use of the method, the analysis modeling problem of a greatquantity of historical simulation data of the power distribution network cyber-physical system and the visual displaying problem of the analysis result are solved, the simulation data analysis of complex research contents, including the state evolution properties of the power distribution network cyber-physical system, the dynamic interaction influence of an information system and a physical power grid and the like under the function of multiple correlation events and the information system under a situation that the cascading failure of the power distribution network cyber-physical system isin the presence is realized, and therefore, complex and diversified simulation data analysis requirements under a power distribution network cyber-physical system environment can be met.

The invention is applicable to the technical field of wear identification of high speed train wheels, and provides a method and apparatus for identifying the polygonization of high speed train wheels.The method comprises the following steps: using a longitudinal strain sensor at the bottom of a rail to obtain a steel rail response signal under wheel excitation by monitoring the response generatedby the wheel excitation on a travelling section to the steel rail; preprocessing the steel rail response signal under wheel excitation; extracting signal features on the preprocessing basis of the steel rail response signal to construct a wheel polygonization index; and identifying a wheel polygonization fault by using the constructed wheel polygonization index. The method and apparatus providedby the invention have two advantages: on one hand, online identification of the polygonization state of the high speed train wheels is achieved, and wheel fault early warning is provided, and on the other hand, the state evolution of the high speed train wheels is tracked to guide the wheel repair work. On the basis of the high-efficiency identification algorithm software developed in the invention, a timely and effective detection and repair measure can be taken on the wheel having the polygonization fault.

The invention discloses an embankment service state evolution characteristic observing device under hydraulic driving. The device comprises a device trough. A soil embankment is arranged in the devicetrough. Tempered glass is arranged on one side of the device trough. A water stop is arranged between the tempered glass and the soil embankment. A pressure measuring tube, an inlet valve, an overflow hole and a water level gauge are arranged on the other side of the device trough. The tempered glass is provided with a drain valve and a sump. A rain spray is arranged at the top of the soil embankment. An image forming real-time tracking device is arranged outside the device trough. The image forming real-time tracking device is connected with a computer. According to the invention, various types of hydraulic environments which may be encountered in an actual embankment project are considered; rapid rise and fall of the water level, continuous high water level, different rainfall patterns,the water flow velocity and other conditions can be simulated; the changing process of seepage and stability of the embankment under the conditions can be observed and analyzed; and the seepage and stability observation and data acquisition of the embankment under different working conditions and hydraulic conditions are realized.

The invention relates to a fleet air defense resource sub-scheduling control system and method based on task set control and system operational state evolution analysis. A task set is established fora sub-scheduling plan of fleet air defense resources; under the control of the task set, the evolution analysis of the state of the system is carried out; a finite state machine (FSM) model representing the state evolution of the system is established; on the basis of the finite state machine (FSM) model of state evolution, the control variables of system state evolution are solved; and planning and air defense resource and facility control in the air defense resource sub-scheduling process are carried out by using the control variables.

The invention discloses a three-dimensional joint positioning and tracking method in a mobile ad hoc sensor network, and belongs to the technical field of sensor network communication. The method comprises the following steps: adopting a variation filtering algorithm for simultaneous positioning and tracking; separately establishing a hierarchical state evolution model in view of the randomness of the sensor and the unpredictability of target motion, and performing description in a three-dimensional space by using expanded Gaussian distribution, wherein the mean value and the variance are random variable and separately abide by independent distribution; performing target positioning and sensor tracking in the three-dimensional space at the same time by using the observation information between the sensors and between the sensor and the target; and it is proved that the variation filtering algorithm can be converged to Gaussian state estimation having the minimum actual distribution error from the theory. Meanwhile, by means of optimization state estimation and model parameters, the estimation accuracy is greatly improved, and the complexity of state description and unnecessary communication are avoided; and the joint positioning and tracking problem is solved.

The invention discloses an online monitoring system and method of a full life circle of a railway side slope, and belongs to the technical field of monitoring and early warning of railway side slopes.The monitoring system comprises a data collection and remote control terminal module, a data interface module, a big data center module, an online state monitoring module of full life circle of sideslope, an intelligent diagnosis prediction and early warning module of side slope state and a terminal display module of side slope state, online automatic monitoring is performed on the full life circle of the railway side slope, an environmental monitoring method, a side slope deformation monitoring method and a retaining wall monitoring method are organically combined in the full life circle ofthe side slope to dynamically monitor and intelligently diagnose the state evolution relationship and instability process of the railway side slope in real time from different climates, time periodsand angles, and by adoption of the online monitoring system and method disclosed by the invention, the change rule of the railway side slope can be monitored more comprehensively, accurately and efficiently, and can be intelligently diagnosed and predicted.

The invention discloses a method for safety evaluation of monitoring information of an in-service bridge. The method comprises the following steps: analyzing the internal linkage between a structural response signal and state evolution of a bridge structure, excavating essential connotation of the monitoring information of the bridge, and constructing the time sequence of the monitoring information of the bridge; establishing an echo state network bridge monitoring information characteristic model as a bridge structure state characteristic system; constructing damage sensitive indexes of the bridge structure and obtaining critical parameters of damage evolution; constructing a bridge state evolution mode; performing safety evaluation on the bridge. According to the method disclosed by the invention, by integrating information science, data mining, mode identification and other disciplinary knowledge, intersection and integration of multiple disciplines are realized, beneficial explosion and technical support are provided for further improving the application quality of a bridge health monitoring system and widening the research direction of state evaluation of the bridge.

A cellular automata-based single particle soft error system level propagation analysis method comprises the steps of (1) dividing function modules: analyzing the hardware structure, interface characteristic and function implementation logic of a system to divide the function modules of the system, wherein each function module is a sub circuit system capable of completing tasks independently; (2) establishing a cellular space, and building a cellular automata: taking the whole system as one cellular space, and each function module as one of the cells, and selecting an appropriate neighbor relation based on the connection relation of the cells; determining the coupling degree and transmission rule among the cells; determining the state evolution process of the cells; (3) performing propagation analysis: assuming that a single particle soft error occurs on a certain function module, which means that the corresponding cells join the fault disturbance, on the basis of the propagation rule, analyzing the propagation process and influence of the single particle soft error in the system. The cellular automata-based single particle soft error system level propagation analysis method has the advantages of simple principle, easy operation, low complexity, capability of improving the analysis method precision and the like.

The invention relates to a fleet air defense decision making and automatic scheduling method and system using a rolling time-domain framework. A finite state machine (FSM) model based on state evolution is established, and the model is solved to obtain an air defense resource control task set. A guidance resource and a launching resource execute air defense tasks under control according to the task set. Meanwhile, the type and intensity of disturbance in the process of task execution are monitored and evaluated in real time, and task rescheduling is carried out on the basis of the original scheduling plan according to the type and intensity of disturbance. An optimized task set can be searched by reasonably designing the values of control variables and accurately adjusting the process of system state evolution, and therefore, fleet air defense decision making and automatic scheduling based on a rolling time-domain framework is realized.

The invention provides a method for analyzing the dynamic characteristic and internal flow characteristic of a pump turbine in the runway transient process, and belongs to the technical field of pumpturbine characteristic analyzing. According to the method, a CFD numerical simulation method is utilized for simulating the instantaneous transient process, and the change law of unit parameters and the external characteristic is determined. Through transient calculation in the runway transient process, the change law of flow-field pressure fluctuation at all components of the pump turbine with time is acquired, and the frequency components and source of the flow-field pressure fluctuation are concretely analyzed through the fast Fourier transform and the short-time Fourier transform. The pressure of a drainage basin and the flow-state evolution process in the runway transient process within a period are analyzed, and the forming reason of special frequency components is explained from thevorticity angle.

The invention aims to disclose a multi-type unmanned aerial vehicle mobile base station deployment method based on an average field pattern game. For a ground communication system with a multi-performance unmanned aerial vehicle as an air mobile base station, the influence of deployment decisions of different types of unmanned aerial vehicle mobile base stations on state evolution of the whole system is fully considered, an average field type game system model is established, and an optimal deployment scheme of the multi-unmanned aerial vehicle mobile base station is obtained through a numerical solution, wherein the performance difference of a plurality of unmanned aerial vehicle aerial mobile base stations is embodied in two aspects of flight speed and service radius, and the different parameters are introduced into an average field item to describe a state equation and a cost function of interaction with other unmanned aerial vehicle mobile base stations. The problem of high deployment energy consumption under the ground communication network of the multi-type unmanned aerial vehicle mobile base station in practical engineering application is solved.

The invention provides a behavior prediction method and device based on sequence state evolution, and the method comprises the steps: obtaining a human body action sequence of a current period; Inputting the human body action sequence into a preset prediction network to obtain a plurality of confidence coefficients of a plurality of prediction actions, and screening out a plurality of candidate prediction actions from the plurality of prediction actions; Inputting the category label corresponding to each candidate prediction action in the plurality of candidate prediction actions and the humanbody action sequence into a generator network of a preset action prediction model to generate a plurality of first candidate action sequences; Splicing the first candidate action sequence corresponding to each candidate prediction action into a human action sequence to generate a plurality of second candidate action sequences; And judging the truth degrees of the plurality of second candidate action sequences according to a preset truth degree judgment model, and determining the target action sequence as a predicted human body action sequence in the next period. Therefore, the performance ofbehavior prediction is improved when the method is used for behavior prediction.

The invention discloses a flow state evaluation method for a power grid with a UPFC based on standardized Euclidean distance. The flow state evaluation method is characterized by comprising the following steps that step one, a power grid flow optimization model considering the UPFC is established; step two, standardization processing is performed on load rate data of each line according to the optimal flow calculation result; step three, standardization processing is performed on the line load rate data under the working condition in view of a certain operation working condition to be evaluated; and step four, Euclidean distance between the working condition to the evaluated and the ideal optimal working condition is calculated, and the calculation result acts as the flow state evolution indicator under the working condition. An optimization objective is formulated from the two angles of line load balancing degree and line loss, the flow state evolution indicator is defined as the standardized Euclidean distance between the actual flow and the ideal flow, and finally different operation modes are evaluated according to the size of the indicator value.

The invention relates to a soil slope collapse accident diagnosis method based on a Bayesian network, and belongs to the technical field of civil engineering. When the number of the samples reaches aset threshold value, establishing a Bayesian network structure prototype, refining through data learning, and obtaining the most correct structure from the prototype structure; dividing each node element of the Bayesian network into a plurality of states, establishing a probability relationship between nodes by using arc segments, and training the sample data by using an EI algorithm to obtain a conditional probability of node state evolution in the Bayesian network; and establishing a Bayesian network model of slope collapse accidents caused by multiple elements. According to the invention, the Bayesian network is used to construct the slope collapse accident diagnosis system, the function of intelligent diagnosis of slope collapse accidents with complex reasons is realized, the accuracyand flexibility of accident diagnosis are improved, and the system can be used for accident diagnosis and also can be used for risk management of collapse accidents.

The invention discloses a structured construction process safety knowledge acquisition, expression and process monitoring method which comprises extracting construction process safety knowledge; constructing a construction safety process model; constructing a construction safety state evolution model; designating a field monitoring plan; and building a finite state machine that maps construction process safety by sensing data based on the information acquired in the previous steps. The method is used for realizing the safety monitoring of a construction process, makes up for the deficiency that a traditional method can only monitor the state of a specific entity, and has high real-time performance, good systematicness and good visualized performance.

The invention discloses a method and device for reporting the alert message broadcasting state. Among them, the method includes: state evolution base station eNB receives from the mobile management entity MME request message, wherein, status request message for reporting alarm message broadcasting state; the eNB response status request message, alarm reporting news broadcast status to MME. According to the invention, the network side can be achieved according to the CBC state request report alarm message broadcasting state effect.

The invention belongs to the field of network control, and specifically relates to a complex network node importance evaluation method considering cascading failures, including step 1: inputting complex load network data, and calculating the degree of the corresponding node; step 2: according to the initial load model and the limit capacity Model, calculate the initial load and limit capacity of the nodes in the complex load network, and initialize the cascading failure level t=1; Step 3: Calculate the state of node i after the cascading failure level t, the node i is in the cascading failure level t The final state includes the residual load capacity of the node itself and the residual load capacity of adjacent nodes at this time, and counts the number nt of all failed nodes when the cascade failure level is t; Step 4: Calculate the cascade failure level t by a single The node load update caused by the failure node; step five: calculate the node load update caused by all failure nodes when cascading failure t level; step six: calculate the number of overload collapse nodes and failure nodes of the entire network in the entire cascade failure process Proportion.