26 results about "Poisson point process" patented technology

The invention relates to the field of transaction anti-fraud, and discloses a real-time transaction anti-fraud system based on a multi-modal behavior atlas, and the system comprises an acquisition processing module which is used for obtaining time sequence data of equipment, transaction and geographic modals and carrying out the preprocessing of the time sequence data, and obtaining the processed data; the multi-mode construction module is used for calculating the time-varying fluctuation rate of a transaction mode, the local dispersion degree of an equipment mode and the trajectory bending degree of a geographic mode, and constructing the processing data into random manifold features containing a Riemannian metric tensor; and the cross-modal interaction module is used for modeling a time-varying driving relation between random manifold characteristics based on a stochastic differential equation and a Poisson process. The method comprises the following steps: converting equipment, transaction and geographic data into random manifold features containing Riemannian metric tensor, reserving nonlinear time sequence association of multi-modal data, and describing a random drive of'equipment motion-geographic trajectory 'and a time-varying trigger relationship of'transaction-equipment activity' through a stochastic differential equation and a Poisson process.

A method of determining anomalous Internet traffic includes: defining a time window across which to apply a Poisson distribution; modeling expected Internet traffic including, at least, an average rate of requests per unit time, using Poisson distribution based on historical traffic data for one or more multiples of the time window; recording data related to real time Internet traffic for, at least, one multiple of time window; analyzing data related to the real time Internet traffic to include extracting lead digits from one or more parameters of data related to real time Internet traffic including: calculating a frequency distribution of the extracted lead digits; comparing the calculated frequency distribution of the extracted lead digits to a Benford's Curve distribution; comparing calculated frequency distribution of extracted lead digits to the modeled expected Internet traffic; and identifying deviations of compared frequency distribution to the Benford's Curve and the modeled expected Internet traffic.

The invention relates to the technical field of Internet of Vehicles, and particularly discloses an information age optimization method in the Internet of Vehicles under error-prone channel conditions, which comprises the following steps of: (1) modeling a vehicle data extraction and base station service process as a Poisson point process; (2) constructing a dual-state channel model containing an ideal channel and an error channel, wherein the transition probability of the channel state is dynamically adjusted by the Doppler effect caused by the vehicle speed; (3) modeling information Aol based on a queuing theory; (4) the optimal data extraction rate is determined through simulation, and the vehicle data extraction rate is dynamically adjusted to minimize AoI according to the real-time channel state and the number of vehicles; and (5) designing a dynamic optimization algorithm, and adjusting the data extraction rate of the vehicle on line according to the real-time channel state and the queue load so as to maintain the optimal system AoI.

The application discloses a macro dynamic traffic flow generation and evolution method and system for mixed traffic flow, and the method comprises the following steps: acquiring traffic flow basic data containing average speed, vehicle density, vehicle type ratio and automatic driving penetration rate; introducing a heterogeneous disturbance factor dynamically adjusted according to a heavy truck ratio in a continuity equation of a classical LWR model to obtain an improved traffic flow model to simulate fluctuation effects caused by mixed flow; calculating macro traffic flow by using the improved model; generating a virtual vehicle injection sequence based on a non-homogeneous Poisson process; detecting downstream density and speed changes in real time, dynamically correcting upstream injection intensity when approaching saturation, and suppressing reverse shock waves; deducing a start-end point matrix according to intersection flow by using a maximum entropy model; and finally outputting a virtual vehicle sequence with position, speed, vehicle type and target path, and injecting the virtual vehicle sequence into an automatic driving simulation platform. The application can accurately reflect congestion wave evolution characteristics under high heavy truck ratio mixed flow and improve test fidelity.

The invention provides a mobile edge computing network resource deployment and load balancing method, which comprises the following steps that edge servers and users are respectively modeled as independent uniform Poisson point processes, the users are associated with the nearest edge server based on a distance criterion, and the service area of each edge server is represented by a Voronoi unit; considering calculation rate attenuation caused by resource sharing when frequency band multiplexing and edge servers execute tasks in parallel, and establishing a service time delay model; an effective workload index is provided, the service quality and the resource multiplexing gain are considered in a compromise, and the resource utilization rate of the whole network is quantified. Edge server resource deployment and a multi-edge server load balancing strategy are jointly designed, and the effective workload level of the whole network is improved.

The invention discloses a software reliability test failure data simulation regeneration method, and relates to the technical field of software testing, the method comprises the following steps: extracting an operation profile from test cases of system testing and confirmation testing, and determining a main path and the occurrence probability of each operation; performing cleaning and time and quantity conversion on the failure data, and generating continuous base data by adopting polynomial fitting; simulation regeneration failure data meeting Poisson process characteristic requirements are generated through a standardized sampling method; according to the method, the existing test data is fully utilized, the problems of insufficient data and low data quality due to limited test time are solved, and the accuracy and efficiency of software reliability evaluation are improved.

The invention discloses a transmission optimization method of an NOMA-assisted LEO satellite communication system, and relates to the technical field of satellite communication and wireless networks. Comprising the following steps: (1) constructing a satellite-ground integrated network transmission model, modeling distribution of ground users and low earth orbit satellites into a spherical Poisson point process, and realizing efficient access of nearest users and farthest users based on an NOMA technology; (2) proposing a three-dimensional to two-dimensional topological conversion method, defining a two-dimensional annular region through a density equivalent condition, and simplifying the system analysis complexity; (3) deriving a closed expression of a user association probability and a moment of a conditional success probability, and quantifying link reliability through Beta distribution approximation signal to interference plus noise ratio element distribution; and (4) the satellite orbit height, the satellite density and the power distribution factor are jointly optimized, and the user coverage probability is maximized. According to the method, the analysis complexity of a large-scale LEO system is remarkably reduced, the spectrum efficiency is improved through the NOMA technology, and the method is suitable for performance optimization of a 6G global satellite communication network.

The invention discloses an extreme wind speed prediction method based on short-term measured data, and relates to the technical field of wind load calculation and structural design. The method comprises the following steps: firstly, acquiring hourly wind speed data of a target area; a mixed Weibull distribution model is adopted to fit a probability density function of wind speed parent distribution, and non-static wind probability parameters are introduced, so that the fitting precision of static wind and a high-wind-speed tail part is effectively improved; then, based on the Rice formula, the wind speed crossing rate is calculated through discrete expectation, and the problem of subjective hypothesis of a probability density function in a traditional method is avoided; and finally, establishing a relationship between the crossing rate and the return period based on Poisson process hypothesis, introducing dimensionless parameters and adopting a tangent approximation method to efficiently and steadily solve an extreme wind speed in a specific return period. The method overcomes the serious dependence of a traditional annual maximum value method on long-term historical data, achieves accurate and rapid extreme wind speed prediction based on short-term data, is especially suitable for data shortage scenes such as new projects and post-disaster emergency evaluation, and provides a reliable basis for structural wind resistance design.

The invention belongs to the technical field of power system scheduling and protection, and discloses a non-homogeneous Poisson model-based power distribution system recloser event probability evaluation and rapid standby scheduling method, which comprises the following steps of: constructing a recloser event structure analysis model, and realizing event probability evaluation of a disturbance event through non-homogeneous Poisson process modeling; in combination with system power imbalance and reserve capacity analysis, a coupling relation between disturbance influence and reserve capacity is established; and introducing a conditional risk value mechanism to construct positive and negative symmetric risk constraints, integrating the positive and negative symmetric risk constraints into a joint scheduling optimization model, and performing hourly rolling optimization in combination with income maximization to form a risk-aware scheduling decision. The method can be widely applied to a virtual power plant and other power distribution systems containing distributed resources, effectively improves the risk identification, probability evaluation and standby resource scheduling capabilities of the power distribution system in response to short-time disturbance, and improves the economy, toughness and risk control capability of the system in a multi-disturbance uncertainty scene.

A residual life prediction method based on nonlinear wear and random jump comprises the following steps: firstly, constructing a health index of multi-source information fusion, and establishing a composite degradation model integrating a power law time scale function and a non-homogeneous Poisson process so as to simultaneously represent progressive accelerated wear and time-varying burst impact characteristics of equipment; then, designing an improved two-stage parameter estimation strategy, utilizing an expected condition maximization algorithm to process impact frequency hidden variables, identifying time-varying intensity parameters in combination with maximum likelihood estimation, and realizing robust identification of complex model parameters; deriving an approximate probability density function of the residual life based on a first passing time frame and a Gaussian approximation theory, and realizing probabilistic prediction through numerical integration; finally, a sliding window updating mechanism is established, and online self-adaptive adjustment and dynamic prediction of model parameters are achieved. The method is high in nonlinear fitting capability, sensitive in impact capture, high in prediction precision and accurate in uncertainty quantification under complex working conditions and limited observation data conditions.

The invention discloses a low-illumination target detection method and system based on a pulse neural network, and the method comprises the steps: taking a pre-trained YOLO model as a backbone network, carrying out the feature extraction of an input image through a feature coding adaptive method, converting a continuous feature graph into a space-time pulse sequence, and carrying out the coding optimization of the space-time pulse sequence. A space-time pulse sequence meeting the pulse neural network processing requirement is generated, space-time characteristics of the pulse sequence are optimized through characteristic value normalization, Poisson process pulse generation and time dynamic coding, and the pulse neural network is used for carrying out target positioning and classification on the space-time pulse sequence meeting the pulse neural network processing requirement. A target bounding box and a target category label of a target are generated, the detection performance in a low-illumination environment is improved through an end-to-end training framework, a YOLO backbone network and an SNN detection head are jointly optimized through a substitution gradient technology, a total loss function is minimized, the problem that an SNN pulse mechanism cannot be differentiated is solved, and high-precision detection is achieved under the condition of low power consumption.

The application discloses a RIS-assisted RSMA super-dense network and a coverage performance analysis method. The RIS-assisted RSMA super-dense network comprises: a plurality of micro base stations arranged in a hotspot area with dense users; the spatial positions of the micro base stations are modeled by using a Poisson cluster process, and the micro base stations are independently and identically distributed around a cluster center; a plurality of macro base stations, the spatial positions of which are modeled by using a Poisson point process, and the spatial positions of the macro base stations are independent and uniformly distributed; wherein each micro base station and macro base station is respectively equipped with RIS. The RIS-assisted RSMA super-dense network provided by the application can improve the coverage rate of the system and the regional spectrum efficiency of the system.

The present application provides a network optimization method, device and equipment based on the fusion of geometry and deep learning, belonging to the field of network resource allocation. The method includes: generating a random distribution of macro base stations, roadside units and vehicles by using a Poisson point process to construct the topological structure of a communication network; constructing a channel analysis function of the communication network according to the topological structure; deriving the analysis function to obtain the performance analysis result of the communication network; constructing a deep deterministic policy network on the premise of the analysis result; combining the deep deterministic policy network and the proximal policy optimization algorithm to jointly optimize the resource allocation and power control of the communication network to obtain a preliminary communication network; and locally optimizing the preliminary communication network according to an optimization constraint function to obtain a target communication network. The present application effectively improves the performance of a wireless network, reduces the link interruption probability and improves the resource utilization efficiency by combining random geometry, deep learning algorithms and local optimization.

The application discloses a simulation regeneration method for software reliability test failure data, and relates to the technical field of software testing. The method comprises the following steps: extracting an operation profile from test cases of system testing and validation testing, determining a main path and occurrence probability of each operation; cleaning, time and quantity converting the failure data, and generating continuous base data by polynomial fitting; and generating simulation regeneration failure data meeting the characteristic requirements of a Poisson process by a normalized sampling method. The application makes full use of existing test data, solves the problems of insufficient data and low data quality caused by limited test time, and improves the accuracy and efficiency of software reliability evaluation.

The invention discloses a caching method for jointly storing a push file and a request file, which relates to the technical field of D2D caching, and is characterized in that when a network load is in a non-peak state, hot contents are pushed to a user terminal with caching capability in a broadcast mode so as to meet subsequent frequent content access requirements; for a terminal direct transmission cache network in which all mobile users have cache capabilities, position distribution of the mobile users is modeled as a uniform Poisson point process, on the basis, randomness of content cache and content requests is combined, accurate analysis is performed on network interference, and approximate analysis in a specific scene is completed at the same time. And deducing a closed expression of the successful unloading probability of the D2D cache network by using a random geometry theory. Then, a Geo / G / 1 queuing model is established based on the access protocols, the model considers file transmission processes involved under different access protocols, and the queue state of a to-be-requested file in a user cache is analyzed;

The invention discloses an Internet of Vehicles task unloading method for coping with random distribution of eavesdroppers, and belongs to the technical field of communication. The invention provides an Internet of Vehicles task unloading method for coping with random distribution of eavesdroppers, aiming at the problem that in a physical layer security technology, the leakage risk of sensitive information of a task vehicle is caused by the increase of the confidentiality interruption probability due to the loss of channel state information (CSI) of potential eavesdroppers. The method adopts an artificial noise assisted adaptive eavesdropping coding technology, establishes a potential eavesdropper random distribution model through a Poisson point process, and dynamically adjusts the base station artificial noise transmitting power, the task vehicle transmitting power and the secrecy rate according to the real-time position of a task vehicle, the potential eavesdropper distribution and the task unloading time delay constraint. By worsening the signal-to-noise ratio of a potential eavesdropper channel, reducing the eavesdropping channel capacity, improving the legal channel capacity and reducing the confidential interruption probability, the system energy consumption required by task unloading is minimized on the basis of ensuring safe task unloading.

The invention relates to the field of financial derivative pricing, in particular to a mixed fractional Brownian motion and jump diffusion model-based European option pricing method, which comprises the following steps of: introducing a jump diffusion process into mixed fractional Brownian motion to describe an asset price process; solving a mixed fractional jump diffusion process # imgabs0 # formula according to the asset price process; based on the self-financing investment strategy, calculating a partial differential equation of related derivative prices under the condition of mixed score jump diffusion; and deducing European style expansion option pricing in a mixed-order jump diffusion environment in an incomplete market based on the related derivative prices. According to the method, it is assumed that each target asset is influenced by geometric Brownian motion, second-fraction Brownian motion and a Poisson process together, and a pricing formula of European style expansion options is deduced. The invention aims to solve the problems of capturing long memorability and self-similarity of assets and reflecting sudden jump of asset prices.

A Poisson process-based random simulation method and system for daily ground snow load series belongs to the field of civil engineering technology. It solves the problem that the duration of each snowfall cannot be obtained from the measurement results, and therefore the duration of the event cannot be modeled in the simulation. Based on the actual evolution of the snow load, this method abandons the concept of event duration on the basis of the above-mentioned Poisson process model, introduces snow attenuation, and constructs the time series of the ground snow load based on the occurrence time of the snowfall event, the snowfall intensity and the snow attenuation, thereby completing the simulation of the daily ground snow load time series. The present invention is suitable for application scenarios such as building risk assessment in snowy areas and weather station data analysis.

The invention discloses a multi-band collaborative content cache deployment method of a millimeter wave self-organizing network, which relates to the technical field of wireless communication, and adopts self-organizing network modeling based on Poisson point process (PPP) to describe random distribution characteristics of small base stations and user equipment, and analyzes signal transmission characteristics in combination with a directional antenna gain model. The probability caching strategy is adopted, the cache placement probability is optimized, the cache hit rate is increased, and meanwhile content diversity is considered. Based on a millimeter wave LOS / NLOS propagation model, a mathematical expression of a content successful delivery probability (SCDP) is established to quantify the influence of a caching strategy on the network performance. According to the method, the network delay can be effectively reduced, the bandwidth consumption is reduced, the resource allocation is optimized, and the network adaptability and reliability are enhanced.

The invention discloses an energy harvesting D2D communication system long-term energy efficiency optimization method and system based on reinforcement learning and Lyapunov optimization, and relates to the technical field of power control and energy efficiency optimization, and the method comprises the steps: firstly carrying out the modeling of energy arrival and task arrival according to a Poisson process; and calculating the time-varying channel gain according to the path loss and the Rayleigh fading characteristic. And constructing a Lyapunov function to derive a Lyapunov drift term, and embedding the Lyapunov drift term into a reward function of reinforcement learning. In the reinforcement learning part, a self-adaptive double-depth Q network structure based on a Dropout mechanism is adopted, a state vector is used as input, and self-adaptive balance of exploration and utilization is realized through randomization of Q value distribution. And the intelligent agent dynamically adjusts the transmission power of the EH-D2D system according to the optimal action output by the training network, realizes the stability of parameter updating through the target network, and realizes long-term energy efficiency optimization and queue stability in an energy collection environment.

The application provides a UAV-assisted MEC resource optimization method based on NOMA, relates to the fields of communication and reinforcement learning, and comprises the following steps: S1: obtaining the distribution of user computing tasks based on a Poisson point process; S2: pre-deploying a UAV according to the distribution of the user computing tasks; S3: constructing a UAV-assisted MEC system model based on NOMA; S4: obtaining an optimization problem based on the UAV-assisted MEC system model, wherein the optimization problem is to minimize the weighted sum of system energy consumption and task completion delay; and S5: solving the optimization problem by using a deep reinforcement learning algorithm to obtain an optimal resource allocation scheme. The application can be widely popularized in the fields of communication and reinforcement learning, can be applied to problem solving in a large-scale user massive data scene, and has a certain reference value for the research on future UAV-assisted MEC networks based on NOMA.

The invention discloses an RIS-assisted RSMA ultra-dense network and a coverage performance analysis method, and the RIS-assisted RSMA ultra-dense network comprises a plurality of micro base stations which are deployed in a hot spot area with dense users; the spatial positions of the micro base stations are modeled by adopting a Poisson cluster process, and the micro base stations are independently and identically distributed around a cluster center; the spatial positions of the macro base stations are modeled by adopting a Poisson point process, and the spatial positions of the macro base stations are mutually independent and are uniformly distributed; wherein each micro base station and each macro base station are respectively provided with an RIS. According to the RIS-assisted RSMA ultra-dense network provided by the invention, the coverage rate of the system and the regional spectrum efficiency of the system can be improved.

The invention provides a neural mimicry retina-imitating sensing module bionic pulse coding method and system, and relates to the technical field of retinas, and the method comprises the steps: carrying out the asynchronous event extraction of a light intensity change signal based on a time difference mechanism, and obtaining a discrete event stream containing time-space coordinates and polarity information; performing space-time correlation analysis based on a local receptive field on each event, constructing a space-time neighborhood window, extracting a time interval distribution feature and a space topological relation feature, and generating a space-time feature vector; mapping the vector to pulse distribution probability distribution to form a bionic pulse coding sequence conforming to the statistical characteristics of the Poisson process; and dynamically adjusting space-time neighborhood window parameters according to the time domain density distribution of the pulse sequence to realize adaptive processing. According to the method, an information coding mechanism of a biological retina can be simulated, the bionic property and the calculation efficiency of a neural mimicry system are improved, and the self-adaptive processing capability on a dynamic scene is enhanced.

The application provides a radar radiation power control method, device and equipment based on MPC. The method comprises the following steps: establishing a target model; the target model comprises a target radiation model and a target state model, wherein the process of the target radiation signal is subject to a Poisson process; under a radar networking architecture, a measurement model is constructed through the target state model; at a preset decision time, the measurement model, an MPC algorithm and a PCRLB are used to measure tracking accuracy, and it is judged whether the center station in the radar networking architecture needs to be tracked actively and a radar radiation control result is obtained. In the application, the tracking accuracy and the anti-interference capability are effectively improved by combining the target radiation model, the target state model and the measurement model, the MPC algorithm and the PCRLB are used to measure the tracking accuracy on this basis, the optimization of the active tracking decision process is realized, and the resource utilization rate and the tracking effect are improved.

The application discloses a traffic state estimation method, system and device, and relates to the technical field of traffic state estimation. The method comprises the following steps: acquiring historical traffic flow data sets of a current traffic section; introducing fuzzy logic into a Poisson process model to model the arrival rate of vehicles at different sections; using a Markov chain to model the transition probability of traffic flow states, and introducing a dynamic transition matrix to capture the changes of traffic flow states at different time periods, so as to build a fuzzy state transition model based on the Markov chain; combining the fuzzy Poisson distribution model and the fuzzy state transition model based on the Markov chain to obtain a fuzzy joint probability density function, and estimating the traffic flow state; and the method can estimate the vehicle trajectory and the traffic state in various complex traffic scenarios by combining the two models.