67 results about "State distribution" patented technology

The invention relates to the technical field of underground communication, in particular to an underground deterministic network optimization scheduling method. According to the method, underground roadway humidity, electromagnetic interference and channel blocking data are obtained in real time, a blocking state distribution diagram is generated, a reconfiguration demand threshold value is determined, and then a data packet dynamic reconfiguration sequence is formulated; dynamically adjusting time slot division granularity and antenna gain parameters by comprehensively evaluating multi-path reflection interference, dust influence and an equipment moving track, and generating an optimized time slot allocation scheme; and finally, adaptively adjusting time slot configuration according to a comparison result of the signal attenuation dynamic distribution and a preset threshold value. According to the invention, the dynamic influence of the underground complex environment on the communication quality is effectively overcome, the reliability, real-time performance and network resource utilization efficiency of data transmission are remarkably improved, and reliable communication guarantee is provided for mine safety production.

The invention discloses an unmanned aerial vehicle path planning method, which comprises the following steps: carrying out environment data acquisition and state modeling, and generating an initial task data set containing starting point distribution, end point distribution, a reference random process and original and real-time environment data; modeling and inferring by using a Gaussian process potential force model, and outputting a disturbance force prediction result containing a mean value and uncertainty; on the basis of the starting point, the terminal point, the reference random process and the disturbance force, constructing SRoudinger bridge distribution evolution and multi-objective optimization to obtain an optimal state distribution track; according to the uncertainty and the battery capacity, self-adaptively adjusting the regularization coefficient of the SRosine-Bridge entropy, and updating and optimizing the weight; and mapping the optimal state distribution track into a control input sequence, and triggering re-optimization when the environment suddenly changes. According to the method, Gaussian process potential force modeling and SRoudinger bridge optimization are fused to improve path precision, energy efficiency and environmental adaptability.

The invention relates to the field of unmanned aerial vehicle navigation and control, and particularly discloses an unmanned aerial vehicle three-dimensional track prediction method based on a pre-distribution state estimation model and particle swarm optimization, which comprises the following steps: initializing a particle set, and calculating an initial state mean value and a covariance matrix by adopting unscented Kalman filter (UKF); predicting the state of the particles by using unscented Kalman filter (UKF); sampling the particles based on the state distribution predicted by the UKF; particle weights are calculated and normalized; carrying out resampling on the particles; and calculating a three-dimensional track estimation value of the unmanned aerial vehicle and predicting a future track. According to the method for predicting the three-dimensional flight path of the unmanned aerial vehicle, high-precision state estimation of the UKF is introduced on the basis of particle filtering, so that the algorithm can effectively solve the problems of particle degradation and sample depletion encountered by a traditional PF algorithm in the three-dimensional flight path prediction of the unmanned aerial vehicle, and the prediction precision and stability are improved; and the future trajectory of the unmanned aerial vehicle can be accurately predicted in a complex three-dimensional environment.

The invention relates to a deep learning compensation method and system for mechanical assembly errors, and relates to the technical field of mechanical assembly optimization, and the method comprises the steps: synchronously collecting dispensing images and dispensing point cloud data according to a preset monitoring time interval, and recognizing and outputting a dispensing state distribution sequence; key state recognition and feature extraction are carried out on the dispensing state distribution sequence, window fluctuation analysis of assembly errors is carried out, and a compensation time window sequence is output; a predicted dispensing state distribution sequence is obtained through prediction, optimal optical performance is taken as a target, lamination control parameter optimization is carried out, and an optimal lamination parameter sequence is output; and according to the compensation time window sequence and the optimal pressing parameter sequence, the fitting assembly process in the preset time zone is optimized and controlled. According to the invention, the problems of insufficient fitting precision, low production yield and uncontrollable camera optical performance of the 3C camera module caused by the fact that the traditional mechanical assembly only depends on a global fixed pressing strategy are solved.

The invention relates to the technical field of operation state evaluation, in particular to a switching power supply health degree evaluation method based on data analysis, which comprises the following steps of: establishing operation load correlation characteristics through mean value and range calculation, extracting continuous increasing paragraphs in a ratio sequence by using a difference mode, and calculating the health degree of a switching power supply; when the length of the interval exceeds a threshold value, a degradation starting point is recorded, a data segment is input into a long short-term memory network for time sequence modeling, offset is accumulated point by point in the modeling process and is compared with the threshold value in sequence, an abnormal path range is output, a degradation trend judgment result is generated, and a heat dissipation unit temperature rise and magnetic element jitter sequence is divided into four types of states; adjacent state changes are counted one by one, a proportion matrix is generated, convergence distribution is obtained through a Markov model through power accumulation, serious degradation and failure state distribution values are compared with a threshold value point by point, a critical interval is marked when the threshold value is exceeded, and life prediction distribution is formed; and life prediction can be combined with multi-dimensional state changes instead of depending on a single index.

The invention discloses an acceleration sensor output value correction method, and relates to the technical field of data processing, and the method comprises the steps: S1, analyzing the data drift trend of historical operation big data, and carrying out the clustering division according to the drift fault type of a known acceleration sensor; s2, on the basis of fault development type state distribution of a plurality of known acceleration sensors, obtaining a fault development type pointed by real-time operation data of a target acceleration sensor, and marking support factors in the fault development type pointed by the real-time operation data of the target acceleration sensor, determining that the real-time operation data of the target acceleration sensor points to the fault development type feature data set; and S3, predicting the hardware drift state of each stage of the fault development type of a plurality of known acceleration sensors, generating the hardware drift state of the stage to which the real-time operation data of the target acceleration sensor points, marking a hardware drift interval, and generating an output value correction value of the acceleration sensor.

The invention provides an agent inference method and a related device. The method comprises the steps of obtaining information of a robot and an environment, and constructing a generation model of an intelligent agent according to the information of the robot and the environment; the intelligent agent interacts with the environment through the generative model, the generative model deduces the real state and outputs the prior state distribution, and the generative model is adjusted according to the prior state distribution to minimize the surprise degree; wherein the free energy is obtained according to the expected value of the logarithm of the KL divergence of the variation distribution and the prior state distribution and the quotient of the variation probability and the conditional probability, and the free energy is minimized to minimize the surprise degree; the intelligent agent has more efficient perception ability in an incomplete information environment, the model training efficiency is improved, environment interaction develops towards an interested state, and falling into a local optimal solution is avoided.

The application provides a network element state management method and device, electronic equipment and a storage medium, wherein the method comprises: decoupling a service area in a to-be-processed satellite network from a preset satellite, and defining the service area as a geographical location area, wherein the service area in the to-be-processed satellite network is coupled with the preset satellite; determining a mobile network location state of the to-be-processed satellite network based on the geographical location area; hosting other states of the to-be-processed satellite network to a local user equipment to obtain a distributed local state library; running a state management process of key signaling of the to-be-processed satellite network based on the distributed local state library; and obtaining a stateless space-ground integrated mobile network based on the mobile network location state, the distributed local state library and the state management process, wherein the stateless space-ground integrated mobile network is a network composed of satellites and ground networks without state migration. The application can avoid signaling storm.

The invention provides a flow state distribution uniformity monitoring system in a wet anaerobic digestion reactor, and belongs to the technical field of online monitoring of anaerobic digestion reactors, the system comprises a reactor main body, a monitoring device arranged on the outer side of an observation port on the side wall of the reactor main body, an online monitoring sensor arranged in the reactor, and an AI computer connected with the reactor main body and the online monitoring sensor. According to the method, internal fluid images or videos are collected through monitoring equipment, and physicochemical parameters are collected through a sensor; processing the image data by using an AI computer to generate a flow field vector diagram; fusing the flow field and physical and chemical parameter data, and calculating a flow state uniformity quantitative index based on the evaluation model; and generating a regulation and control instruction for the stirring system according to the evaluation result, and displaying and transmitting the regulation and control instruction. According to the invention, real-time, online and quantitative monitoring and accurate regulation and control of the internal flow state of the reactor are realized, the problems of lagging, non-intuition, experience-dependent regulation and control and the like of the traditional method are effectively solved, and the operation efficiency, stability and economical efficiency of the anaerobic digestion process are remarkably improved.

The invention discloses a scheduling method for real-time frequency regulation and control and distributed unit collaborative optimization of a power grid, and the method comprises the steps: calculating a power distribution proportion through employing a dynamic programming algorithm according to the charge state distribution of unit clusters, and determining a priority response sequence of each cluster; transmitting the preliminary allocation instruction to a corresponding cluster through a communication network, and correcting a transmission delay influence by adopting a feedback mechanism to obtain an adjusted power output scheme; according to the adjusted power output scheme, if it is judged that a local fault signal exists, switching to power distribution of a standby cluster is carried out, and a final batch coordination instruction is determined; extracting an execution parameter from the final batch coordination instruction, and testing a response effect by adopting a simulation verification module to obtain an optimized scheduling strategy; and generating a real-time monitoring report through the balanced resource use data, and determining whether the system stability index meets the requirement or not.

The invention relates to the technical field of remote scheduling, in particular to a remote scheduling method and system for roadbed and pavement construction equipment, and the method comprises the following steps: carrying out the division based on task units, recognizing an equipment track inflection point and an operation state conversion sudden change interval, analyzing the equipment operation direction consistency and operation state distribution, and evaluating the linkage task scheduling efficiency. And analyzing a stress distribution trend of the construction area, calibrating an abnormal level, identifying a corresponding number, and outputting an intelligent collaborative scheduling instruction set of the construction equipment. According to the method, by accurately extracting and analyzing the running track and the operation state of the equipment, the abnormal interval and the deviation area can be effectively recognized, resource allocation can be balanced, operation consistency can be improved, resource waste and low efficiency caused by improper task allocation can be avoided, risk level calibration of task units can be achieved, and the method is suitable for popularization and application. Potential problems are found in advance, effective countermeasures are taken, scheduling decision precision and flexibility are improved, and efficient and smooth execution of construction tasks is ensured.

The invention discloses a self-adaptive energy consumption optimization storage chip control method and device and a medium, and relates to the technical field of computer structures.The method comprises the steps that feasibility judgment is conducted on operation parameters in a candidate parameter set, reliability behavior prediction is conducted through a state evolution mapping relation, and an extension feasible set is formed; operation cost and maintenance cost comprehensive evaluation is carried out on the extended feasible set, multi-target optimization sorting is carried out, and a target operation parameter combination is output; performing prediction deviation analysis on the execution result packet, updating a prediction mapping relation and a historical parameter library, and outputting a self-adaptive control basis; according to the self-adaptive storage chip control method, the historical behavior data, the current junction temperature information and the current state distribution of the target storage partition are collected, so that the self-adaptive storage chip control which controls the model to continuously self-evolve along with the aging process of the chip and gives consideration to energy consumption optimization and service life reliability is realized.

A process includes establishing a peer-to-peer connection between a first client computing device and a second client computing device. The first client computing device is associated with a first participant in a coordinate grid environment provided by an application and the second client computing device is associated with a second participant in the coordinate grid environment. The process includes determining that a coordinate grid state of the first participant has experienced a state change, and in response, providing at least a portion of first participant state information associated with the state change from the first client computing device to the second client computing device via the peer-to-peer connection such that the at least the portion of the first participant state information updates previously stored first participant state information of the first participant maintained by the second participant associated with the second client computing device.

The application discloses a unified probability propagation prediction method and system suitable for WUI fire, and the method comprises the following steps: constructing a unified state space and discretizing; constructing an external driving model of building layer fire propagation; constructing a building layer dynamic Bayesian network with each building as a node, parameterizing a conditional probability table of the building layer dynamic Bayesian network based on a physical driving mechanism; establishing a mapping relationship between an internal state of the building layer and an external observation category; realizing state correction and rolling prediction based on Bayesian updating; when external observation is obtained, the system state is updated based on Bayesian updating in combination with observation information, a posterior state distribution is obtained, the posterior state distribution is taken as an initial state of the next time step prediction, a rolling assimilation closed loop is formed, and finally a multi-state probability risk field of building scale evolution over time is output. The application can realize unified coupling of a landscape, a building and a flying fire, obtain state probability output over time, and can be assimilated and corrected.

The application discloses a content recommendation method and related products, which comprises the following steps: obtaining a current intention state distribution of a first object; predicting a predicted intention state distribution corresponding to the first object according to a historical intention state sequence of the first object and a current interaction environment feature; performing multi-dimensional confidence evaluation on the first object according to the current intention state distribution, the current interaction environment feature and the predicted intention state distribution to obtain a comprehensive confidence; obtaining a current intention state recommendation score and a future intention state recommendation score corresponding to a target candidate content; performing weighted fusion processing on the current intention state recommendation score and the future intention state recommendation score according to the comprehensive confidence to obtain a final recommendation score corresponding to the target candidate content; and using the final recommendation score to determine a ranking position of the target candidate content in a content recommendation set.

The invention discloses an aeration dynamic equilibrium digital control method and system coping with flow state distortion, and relates to the technical field of sewage treatment.The method comprises the following steps that a sewage treatment aerobic pool multi-point dissolved oxygen and flow state synchronous monitoring system is constructed, spatial distribution data of dissolved oxygen, water flow velocity and density in a pool are continuously collected, and the spatial distribution data are calculated; generating a flow state distribution map reflecting flow state distribution characteristics in the pool; and carrying out cooperative feature extraction on the collected water flow velocity gradient and dissolved oxygen difference based on the flow state distribution map, and identifying an abnormal region with flow state distortion. Through multi-point monitoring and dynamic weighting control, real-time identification of the flow state of the aerobic tank and accurate adjustment of the air volume are realized, the distribution balance of dissolved oxygen is maintained, and the oxygen transmission efficiency and the operation stability are improved; by introducing a fluid correction factor and a time sequence prediction model, a control closed loop with self-learning and energy-saving optimization capabilities is established, and the intelligence and economy of the aeration process are improved.

An interoperation behavior routing optimization method based on a soft bus comprises the following steps that S1, a main device in an Internet of Things subnet initiates a networking broadcast request, and an interoperation behavior neighbor table of all devices in the subnet and available resources of the devices is generated according to the delay condition of feedback of an application layer of the devices in the subnet and in combination with a communication overhead value in the subnet; s2, adjacent Internet of Things devices synchronize interoperability behavior neighbor tables through a soft bus, the interoperability behavior neighbor tables are stored in an Internet of Things terminal device shared interoperability state distributed database and undergo an exchange state, a loading state and a complete adjacent state, and finally it is achieved that the interoperability behavior routing state tables stored in all terminal devices are the same and the interoperability behavior routing state tables stored in all the terminal devices are not the same. Forming an interoperation behavior routing state table; and S3, based on the interoperation behavior routing state table, each terminal device uses a shortest path priority algorithm to calculate a ring-free tree which takes the terminal device as a root and has a shortest path so as to form an interoperation behavior routing table.

The present application relates to the technical field of remote scheduling, in particular to a kind of roadbed pavement construction equipment remote scheduling method and system, comprising the following steps: based on task unit division, identify device trajectory inflection point and operation state conversion mutation interval, analyze device running direction consistency and operation state distribution, assess linkage task scheduling efficiency, analyze construction area stress distribution trend, calibrate abnormal grade, identify corresponding number, output construction equipment intelligent collaborative scheduling instruction set.In the present application, by accurately extracting and analyzing the running track and operation state of the equipment, the abnormal interval and deviation area can be effectively identified, which helps to balance resource allocation, improve operation consistency, avoid resource waste and low efficiency caused by improper task allocation, and realize risk level calibration of task unit, so as to find potential problems in advance and take effective measures, improve scheduling decision accuracy and flexibility, and ensure efficient and smooth execution of construction tasks.

The application discloses a kind of chip defect detection method, device and terminal equipment, comprising: based on the state distribution information of chip acquisition chip based on the running data of chip, and based on the state distribution information obtains the abnormal running state of chip, abnormal running program and associated abnormal structure;Based on the three-dimensional temperature distribution map of chip is constructed based on the temperature data of chip, determines temperature abnormal structure based on the three-dimensional temperature distribution map;Based on the associated abnormal structure and temperature abnormal structure predict the target abnormal structure of chip and abnormal information, generate defect detection result.Through running information, temperature data and so on multiple angle analysis each chip target abnormal structure, and the abnormal information of target abnormal structure, the comprehensiveness of each abnormal structure of chip is identified to improve the abnormal information of each abnormal structure, to comprehensively improve the efficiency of chip defect detection.

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

The invention discloses a convergence control method for a job event flow path. The method comprises the following steps: serializing each process node and forming potential energy distribution mapping; constructing a path force field of the reference path; mapping the multi-dimensional state parameters of the to-be-processed job event to a path force field, and generating a drift metric; calculating a potential energy change function, and dividing the to-be-processed event into at least a steady-state region, a metastable-state drift region and an unsteady-state distribution region; determining disturbance sources influencing the potential energy track of the to-be-processed event, and determining a coupling strength parameter and a disturbance projection direction vector of each disturbance source; superposing at least one compensation sub-force field on the basis of the path force field to generate a corrected potential energy field; calculating a potential energy gradient function and a minimum potential energy value of the event to be processed, and generating an event convergence degree function; and performing mapping comparison on the event convergence degree function and a preset threshold value set, and generating a control instruction set according to a comparison result.

This invention relates to the field of space debris environment modeling technology, specifically to a short-term evolution method for continuum disintegration debris based on numerical methods. The method includes: Step 1, establishing an initial distribution model of disintegration debris, including a disintegration debris velocity increment model and a numerical model of the initial distribution of disintegration debris; Step 2, sampling feature points of the continuum using a gradient-based sampling method, selecting the top N samples with the highest L2 norm scores of the gradient vectors as feature points of the continuum to be evolved; Step 3, processing the continuum equations using numerical solutions, considering the six orbital roots and four perturbation factors to perform short-term evolution of feature points; Step 4, reconstructing the final state distribution of disintegration debris using a Student's t-mix model. This invention fills the gap in previous research in this field, which only focused on ultra-short-term and long-term evolution, and has the beneficial technical effects of requiring fewer samples, having lower computational costs, shorter computation time, higher result accuracy, and stronger robustness.

The invention provides a micro-channel fluid simulation method and system, and the method comprises the steps: constructing a D2Q9 model through a lattice Boltzmann method according to a to-be-simulated micro-channel geometric structure, setting basic parameters of the D2Q9 model, and initializing distribution functions of a flow field, a temperature field and a phase field and a corresponding equilibrium state distribution function; respectively executing collision and propagation operations on distribution functions of a flow field, a temperature field and a phase field based on a single relaxation lattice Boltzmann method, and simulating movement among fluid particles, energy transmission and evolution of a two-phase interface; setting boundary conditions of the D2Q9 model, and calculating a macroscopic physical quantity representation simulation result of the fluid at each moment according to an evolution result of each distribution function after collision propagation; and when the D2Q9 model meets a preset simulation termination condition, outputting a D2Q9 model simulation result in a standard data format. According to the scheme, the accuracy of micro-channel internal flow simulation can be improved, and the simulation calculation efficiency is guaranteed.

The invention belongs to the field of machine learning, and provides an imitation learning method and system based on potential spatial diffusion, and a storage medium. According to the method, a potential spatial diffusion mechanism is introduced into diffusion modeling, so that the calculation burden caused by operation of the diffusion model in an original high-dimensional state space is effectively reduced, the training time and video memory consumption are remarkably reduced, and the training stability is improved. Meanwhile, a comparative learning network based on a momentum queue mechanism makes up for the defect that a traditional diffusion model can only capture distribution level information and is difficult to establish a semantic corresponding relation. The method achieves the comparative learning of semantic alignment in the time dimension, retains the feature stability, and effectively inhibits the problem of learning instability caused by strategy drift. On the other hand, state distribution and behavior semantic features are modeled through a diffusion path and a comparison path in the strategy training process, and the multi-channel and multi-scale learning mechanism can show higher consistency and stability.

The invention discloses a machine learning-based dynamic regulation and control method and system for the utilization ratio of a sintering return mine cold-pressing block blast furnace, and relates to the technical field of blast furnace ironmaking, and the method comprises the following steps: collecting and preprocessing blast furnace multi-source process parameters in real time, calculating the deviation degree between the selected parameters and a forward motion reference value, and generating parameter state distribution feature vectors; fusing the feature vector and original process data, inputting the fused feature vector and original process data into a pre-trained machine learning model, outputting an optimal cold pressing block charging proportion, and converting a proportion instruction into a control signal of a feeding system through a controller to realize proportion dynamic adjustment; the system comprises a data acquisition and preprocessing module, a parameter state feature generation module, an intelligent decision module and an instruction execution regulation and control module. According to the method, the parameters reflecting the operation state of the blast furnace are calculated and classified, the input quality and decision accuracy of the model are remarkably improved, real-time and self-adaptive accurate regulation and control of the cold pressing block proportion are achieved, and reasonable utilization of resources is promoted while smooth operation of the blast furnace is guaranteed.

The application discloses a kind of based on look-up table's memristor conductance regulation method and its application. Among them, the conductance regulation method includes the following steps: when the conductance of memristor needs to be regulated to target conductance interval, the pulse sequence information matched with target conductance interval is found from the look-up table made in advance, and the look-up table stores the corresponding relationship between at least two conductance states and corresponding pulses;According to pulse sequence information, corresponding pulses are applied between the upper and lower electrodes of memristor, and the regulation of the conductance of memristor is completed.The application statistically analyzes the conductance state distribution of the device, manufactures the look-up table, programs and regulates the conductance of memristor using the look-up table method, which can effectively reduce the number of read operations, and different conductance states correspond to known pulse design and pulse sequence design, without repeated verification whether to reach target conductance interval, the programming process is simple, and since all conductance states are known, there is no situation that cannot be written into the conductance interval during the programming process.

The tracking device (1) for tracking a target (2) comprises: a prediction module (100) configured to predict a state distribution of the target (2) at a certain time k; a correction module (120) configured to define an accuracy of the state distribution predicted by the prediction module (100) by a likelihood function at at least one observation point of the target (2) observed at the certain time k, and correct the likelihood function according to a number M of the observation points; and an update module (140) configured to update the state distribution at the certain time k based on the likelihood function corrected by the correction module (120).

The invention discloses an unmanned aerial vehicle communication multi-priority queue modeling and performance analysis method, and the method comprises the steps: building an extensible state model through a WiMarkov chain and a hypercube transfer unit, and achieving the low-delay access of different priorities through a threshold and a window in combination with a weighted COS statistics and fixed backoff mechanism; semi-analytical expressions of the success rate and the time delay are obtained through steady-state distribution and generation function approximation, and parameters are learned through optimization constraints. Simulation shows that the low-load success rate is larger than or equal to 99%, the high-priority delay is still kept at the millisecond magnitude during high load, and the method is suitable for an unmanned aerial vehicle network communication system with the strict real-time requirement.

An interface state testing method and a reliability testing method belong to the technical field of power device testing, and the interface state testing method comprises the following steps: applying a plurality of cluster pulse voltages to a grid electrode of a device to be tested according to a time sequence, and obtaining a charge pump current corresponding to each cluster pulse; obtaining a charge pump curve of the to-be-tested device according to the charge pump current corresponding to each cluster of pulses; and according to the charge pump curve of the to-be-tested device, calculating the average value of the interface state distribution of the to-be-tested device. According to the invention, bias voltage does not need to be applied to a drain-source loop, and the interface trap information of a single vertical MOSFET device can be accurately extracted by controlling the gate pulse and measuring the drain current.

A memory operation method, including: applying a first programming signal to a memory cell, wherein a first pulse voltage of the first programming signal is less than a target voltage; applying a second programming signal to the memory cell, wherein a magnitude of the second programming signal is less than a magnitude of the first programming signal; and reading a programming state of the memory cell to determine whether the programming state matches a target state corresponding to the target voltage. Accordingly, the state distribution within the programmed memory cell can be more tighter and precise.