84 results about "State distribution" patented technology

The invention relates to the technical field of power distribution cabinet detection, and discloses an intelligent power distribution cabinet state dynamic monitoring method and system, and the method comprises the following steps: collecting multi-source data generated in the operation process of a power distribution cabinet; preprocessing the multi-source data to generate a standardized data set; a random field model is constructed and used for describing the spatial relevance between units in the power distribution cabinet and the relation between the state of each unit and multi-source data, and model parameters are estimated through the maximum logarithmic posterior probability. An intelligent power distribution simulation model and a multi-source data acquisition technology are adopted, multi-dimensional parameters are acquired in real time through a high-precision sensor, monitoring efficiency and decision accuracy are remarkably improved, data analysis integrity and model sensitivity are ensured through dynamic time modeling and capturing of space and time relevance of the power distribution cabinet, and the power distribution cabinet can be monitored more accurately. And the state distribution visualization and maintenance priority strategy generation module optimizes the maintenance plan, reduces the operation and maintenance cost, and guarantees the stability of the power distribution system.

The embodiment of the invention discloses a remote state monitoring method and system based on an intelligent water meter, and the method comprises the steps: obtaining pipeline BIM model data of an intelligent water meter pipeline network of a target building, and synchronously collecting a real-time monitoring data set of a plurality of sensing nodes in the intelligent water meter pipeline network; performing abnormal state recognition processing on the real-time monitoring data set, and generating an abnormal state distribution map matched with the spatial position of the pipeline BIM model data; based on the abnormal state distribution map and a preset pipeline topology constraint condition, performing dynamic visual reconstruction processing on the pipeline BIM model data, and generating a visual output interface with an abnormal state mark; and performing association mapping on the visual output interface and the real-time monitoring data set to generate a state monitoring feedback strategy, and triggering maintenance response operation of the intelligent water meter pipeline network through a remote service port.

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 discloses a method for searching individuals in a distributed manner in a big data set, and aims to solve the technical problems of conflict avoidance and state uniqueness distribution in a large-scale node structure data set. The method comprises the following steps of undirected graph establishment, structure mapping, four-class state initialization, adjacency detection and syn-position adjustment until the states of all adjacent nodes are different. Compared with the prior art, the method is based on graph structure mapping, odd-even state classification and syn-position adjustment, is used for carrying out distributed search and accurate positioning on individuals in a big data set, constructs a globally unique and locally adaptive coding method system through four-color state division and local adjustment, and has the advantages of unique state traceability, accurate recheck, high efficiency and the like. The method is flexible in state compression and expansion, can be expanded into various states, is high in distributed deployment capability, is high in universality, and is suitable for multiple fields of plane maps, image modeling, graph neural networks and file systems.

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.

Proposed is a new posterior distribution estimation technique. This estimating device comprises at least one memory and at least one processor, wherein, when estimating a posterior distribution of a state at a second time point based on observed values up to the second time point, in a case of a non-linear state space model in which the state distribution at the second time point, evolved over time on the basis of the state at a first time point, is described by a linear Gaussian distribution, and the distribution of observed values at the second time point, based on the state at the second time point, is described by a non-linear Gaussian distribution, the at least one processor uses a model that estimates a linear Gaussian distribution and that has parameters that are optimized to maximize a lower bound on a log-likelihood, to calculate a posterior distribution of the state at the second time point based on the observed values up to the second time point.

This invention relates to a decoupled evolutionary modeling and service recommendation method for user interest state distribution. The method first collects user interaction data within a service system to construct a sequence of user-item-time triples. A graph convolution algorithm is used to perform initial static embedding learning of users and items, extracting collaborative signals from the user-item interaction graph. A decoupled state space model is constructed, representing user interest states as distributed variables composed of an orthogonal rotation matrix and a diagonal scaling matrix, respectively capturing the evolution of interest direction and the changing diversity of interest. A user interest precision matrix is ​​generated based on the rotation-scaling transformation, and the anisotropic Mahalanobis distance is used to measure the degree of match between users and items. A recommendation list is constructed based on the distance scores, with the top K best items selected as the recommended results. This method overcomes the static embedding limitations of user interest modeling, achieving invariant preservation and potential preference extrapolation in a dynamically evolving structure, thereby improving the expressiveness and generalization performance of the recommendation system.

The invention relates to the technical field of industrial Internet of Things, and discloses an industrial Internet of Things automatic storage intelligent scheduling system and method, and the method comprises the steps: determining a parameter range of storage equipment in a target scheduling scene, setting different scheduling parameters, and enabling the equipment to schedule a storage task according to the parameters; then acquiring position information of articles in the storage tasks under different parameters, generating a storage space state distribution diagram, acquiring equipment operation data, determining a response delay rate, and fitting to obtain a scheduling efficiency coefficient; and finally, determining the task matching degree based on the efficiency coefficient and the state distribution diagram, and further obtaining the optimal scheduling time. Through data-driven intelligent scheduling optimization, efficient utilization of the storage space and improvement of the equipment operation efficiency are realized, the optimal scheduling time can be accurately determined, the scheduling efficiency and the intelligent level of automatic storage in the industrial Internet of Things environment are improved, and the industrial application value is wide.

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.

The invention relates to a user interest state distribution-oriented decoupling evolution modeling and service recommendation method, which comprises the following steps of: firstly, collecting interaction data of a user in a service system, and constructing a user-article-time triple sequence; performing initial static embedding learning on the user and the article based on a graph convolution algorithm, and extracting a cooperative signal in the user-article interaction graph; constructing a decoupling state space model, representing a user interest state as a distribution variable composed of an orthogonal rotation matrix and a diagonal scaling matrix, and respectively capturing interest direction evolution and diversity change characteristics; generating a user interest precision matrix according to rotation-stretching transformation, and measuring the matching degree between a user and an article by adopting an anisotropic mahalanobis distance; and constructing a recommendation list according to the distance scores, and taking the first K optimal articles as recommendation results. According to the method, static embedding limitation of user interest modeling is broken through, invariant keeping and potential preference extrapolation in a dynamic evolution structure are achieved, and the expression ability and generalization performance of a recommendation system are improved.

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 invention discloses a memory-free ISAC capacity-distortion balance estimation method based on a data-driven neural network, and belongs to the technical field of machine learning and information theory crossing. In order to solve the problem of capacity-distortion relation calculation in a state dependent channel (SDMC), the method is realized through three parts of neural network modules: a conditional mutual information neural estimation module (CMINE) represents conditional mutual information between estimated input and output based on DV; a neural state estimation module (NSE / DNSE) estimates a channel state from the signal and minimizes distortion; an input distribution optimization module (NDT / PMFG) optimizes the input distribution under distortion constraints. The method comprises the following steps of: training CMINE and NSE / DNSE through an alternating optimization strategy training module: in a first stage, fixedly inputting a distribution optimization module; in the second stage, the first two are fixed, and the input distribution module is optimized. An explicit channel model or state distribution is not needed, the capacity-distortion curve is directly estimated from the data, the problem that a traditional method depends on a probability model is solved, and the method is suitable for continuous and discrete scenes.

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.