298 results about "Decision function" patented technology

The invention discloses an underground construction decision-making method based on three-dimensional geological modeling and risk hot area identification, and relates to the field of fusion of artificial intelligence and geological engineering. The method comprises the following steps: firstly, acquiring drilling data, geological radar images and seismic reflecting layer information, constructing a three-dimensional geological voxel model with spatial topology constraints, and accurately describing a geological unit structure by adopting an irregular grid mode; and then, extracting a time sequence characteristic index under construction disturbance, forming a continuous time sequence characteristic vector, inputting the continuous time sequence characteristic vector into a convolutional recurrent neural network model with a space attention aggregation mechanism and a deep memory unit, and predicting a risk heat value of each space position. And on the basis, through heat gradient clustering and neighborhood consistency analysis, a dynamic high-risk hot area is identified, and a risk hot area map is constructed. And finally, in combination with the construction stage, the equipment plan and the sensor feedback information, constructing a multi-target auxiliary decision function, and generating a construction decision result including operation path reconstruction, rhythm adjustment and power limit and control suggestions.

The invention provides a food additive formula optimization method, medium and system, and belongs to the technical field of computational chemistry, the method comprises the following steps: firstly, establishing a food additive evaluation index system, collecting samples, measuring the content of each component to construct an initial formula component matrix, then carrying out feature extraction based on an improved convolutional neural network model, and calculating the formula of the food additive. Formula optimization is carried out in a mode of combining one-way optimization and multi-objective optimization, comprehensive evaluation is carried out by utilizing a multi-level judgment function, and finally, a final optimized formula is obtained through stability verification and fine adjustment. The whole optimization process is systematic and comprehensive, the complex interaction among the components is considered, the balance of a plurality of performance indexes is also considered, and the efficient optimization of the formula is realized. The technical problem that in the prior art, the optimization effect is not ideal due to the fact that complex nonlinear interaction among components is difficult to accurately characterize in the food additive formula optimization process is solved.

The invention provides a linkage early warning method based on security situation assessment and application thereof. The linkage early warning method comprises the following steps: data preprocessing and feature extraction; an abnormal evaluation model and a normal evaluation model are constructed, and training and parameter adjustment are carried out; evaluating the data acquired in real time by using a safety evaluation function, judging the current safety state of the hydraulic power plant, and outputting a safety parameter; constructing a decision function, and determining whether the current state is abnormal or not based on the safety parameters and environmental factors EnvFactors; dynamically selecting and determining to select an abnormal evaluation model and a normal evaluation model for real-time evaluation, and judging whether the current state needs early warning or not; when early warning is needed, an emergency command system and early warning information are linked to carry out early warning and emergency response, and possible safety risks of the hydraulic power plant are prompted. According to the invention, by constructing the security evaluation function and the decision function and combining the neural network model, the problem that the existing system lacks enough flexibility and adaptability and cannot effectively cope with variable network security threats is solved.

The invention provides an automatic driving behavior decision-making method, driving decision-making equipment and a readable storage medium, and relates to the technical field of automatic driving. According to the method, the driving behavior strategy network and the driving cost evaluation network obtained through training based on the adaptive dynamic planning technology are called, and the Monte Carlo tree search is carried out based on the real-time driving environment state of the target vehicle, so that the adaptive dynamic planning technology is integrated into each decision stage of the Monte Carlo tree search algorithm; the Monte Carlo tree search algorithm is guided to focus on effective search of high-value decisions, the workload of invalid search is reduced, the resource loss burden and the time loss are calculated, and the decision efficiency and the decision accuracy of the Monte Carlo tree search algorithm are improved. Therefore, the optimal driving action of the target vehicle in the complex driving environment at the current moment can be quickly and reliably decided, so that a driving behavior decision function which is quick, good in robustness and high in decision interpretation can be realized under the complex driving environment of other traffic participants.

The invention relates to the field of hydrogen leakage and combustion detection, and discloses a hydrogen leakage and combustion identification method, which comprises the following steps: S1, collecting multiple types of detection signals from acoustic, electrochemical and infrared thermal image sensors; s2, preprocessing the collected signals to generate a multi-dimensional feature space; s3, constructing a topological structure in the multi-dimensional feature space, extracting topological features, and generating topological descriptors; s4, analyzing the time dynamic change of the topology descriptor based on a Markov chain model; s5, calculating the topological similarity between the real-time signal and the known mode, and carrying out mode matching; and S6, calculating a signal state through a comprehensive decision function, judging whether a hydrogen leakage or combustion event exists, and triggering a corresponding alarm. According to the invention, through multi-sensor cooperative monitoring, signal preprocessing, topological structure analysis and dynamic modeling, high-precision real-time monitoring and reliable alarm of hydrogen leakage and combustion are realized, and the system has the advantages of high sensitivity, low missing report rate and rapid response.

The invention discloses a broadband oscillation identification method and system based on a multi-band Nyquist criterion, and the method comprises the steps: dividing the frequency of broadband oscillation into a low frequency band, a middle frequency band and a high frequency band with overlapped frequency bands, and obtaining the oscillation signals of a monitoring node in a power grid in different frequency bands; respectively calculating the equivalent impedance of the non-overlapped frequency band of each frequency band and the equivalent impedance of the overlapped frequency band in the two adjacent frequency bands so as to construct the equivalent impedance of the low frequency band, the middle frequency band and the high frequency band with the overlapped frequency bands; establishing a Nyquist criterion of each frequency band based on a transfer function of dynamic characteristics of each frequency band in an open-loop state of the system; a generalized Nyquist criterion of the overlapped frequency bands is constructed by using the multi-frequency-band coupling matrix; constructing a comprehensive decision function according to the recognition result of the generalized Nyquist criterion on the system stability, the broadband oscillation comprehensive characteristic quantity and the change rate of the high-frequency-band impedance module value along with the frequency; and according to the value of the comprehensive decision function and a threshold value, a broadband oscillation identification result is determined, and the broadband oscillation identification capability is improved.

The invention provides a multi-sensor fusion and environment feature recognition method and system for digital twin equipment, and the method comprises the steps: constructing a digital twin model of the equipment in a visual platform, and setting a self-updating mechanism; deploying a multi-sensor array at a key installation part of the equipment physical entity, collecting multi-source sensing data and uploading the multi-source sensing data to a twin platform; carrying out noise reduction processing on the multi-source sensing data and completing data fusion to obtain fused data; constructing a diffusion model corresponding to a noise scheduling strategy, generating new sensing data and a corresponding label, and forming analysis data; a multi-channel deep learning model is constructed to extract feature parameters from analysis data, regression prediction is carried out, and an identification result is obtained; the system is integrated to a twin platform and is combined with a control decision function. According to the invention, real-time regression prediction of environment characteristic parameters based on sensing data is realized, deep fusion of environment perception and control decision of equipment is promoted, and perception capability and adaptive adjustment capability of a control decision system of the equipment are improved.

The invention discloses a multi-factor network security authentication method and system based on an SDN (Software Defined Network). The method comprises the following steps: constructing a multi-dimensional authentication space comprising an identity feature dimension, a behavior feature dimension and an environment feature dimension in an SDN controller; decomposing the eigenvalues into deterministic sequence components and random sequence components by adopting a dynamic sequence decomposition method, and calculating the correlation degree of the eigenvalues among dimensions; according to the method, the stability index is calculated based on the feature stability evaluation function, the fusion authentication decision function is constructed to carry out authentication decision, safe and reliable network authentication is achieved through the technical means of multi-dimensional feature fusion analysis, dynamic sequence decomposition, self-adaptive decision and the like, and the method has high practical value.

The invention provides an emergency resource intelligent scheduling optimization method and system based on large model analysis, and relates to the technical field of emergency resource scheduling. The method comprises the steps of collecting emergency event data and environment data; extracting emergency event features and environment features, constructing a prediction model based on a long short-term memory network, and obtaining a required emergency resource list; constructing an emergency resource evaluation model based on an analytic hierarchy process, and obtaining priority scores of the required emergency resources; constructing a multi-objective optimization model, and solving the multi-objective optimization model by using a particle swarm algorithm to generate a scheduling scheme; and constructing a decision function, and solving by using a genetic algorithm to obtain a scheduling decision. According to the method, through accurate prediction, scientific evaluation, multi-objective optimization and decision in combination with priority, the accuracy and efficiency of emergency resource scheduling and the resource utilization efficiency can be remarkably improved, powerful support is provided for emergency management work, and the method has important practical significance and application value.

The invention discloses a multi-modal large-model multi-agent collaborative scheduling and distribution method, and relates to the technical field of agent deploying.The method comprises the steps that a complex task input by a user is received, the complex task is disassembled into atomic-scale subtasks, and a task decomposition graph is generated; establishing an intelligent agent capability evaluation model, and obtaining a comprehensive capability score of each intelligent agent according to the capability behavior of each intelligent agent in the candidate intelligent agent set; the intelligent agent set generation module is used for dynamically matching and distributing roles through a decision function according to subtask types and intelligent agent comprehensive capability scores, and generating a candidate intelligent agent set; and according to the task decomposition graph and the comprehensive capability score of each agent, constructing a task-agent ant colony allocation algorithm, and solving an allocation scheme. The problems that an existing multi-agent system lacks a dynamic role adjustment mechanism, flexible cooperation cannot be achieved according to task progress and agent states, and the efficient and flexible task processing requirement of a multi-mode large model application development platform is difficult to meet are solved.

The invention provides a cell switching method and a related device applied to the field of communication. In the technical scheme provided by the invention, a decision-making device obtains at least one sensing result from at least one sensing device and a sensing type of each sensing result; and determining a cell switching strategy of the terminal equipment based on a cell switching decision function associated with at least one sensing type corresponding to the at least one sensing result one by one. According to the technical scheme provided by the invention, the decision-making equipment can obtain the cell switching strategy based on the sensing result by utilizing the cell switching decision-making function as long as the sensing result of which the sensing type is associated with the cell switching decision-making function is obtained, so that the efficiency of obtaining the cell switching strategy can be improved, the time delay of obtaining the cell switching strategy is reduced, and the user experience is improved. And thus, the cell switching delay of the UE is reduced.

Provided are a method and system for auxiliary emergency braking after a braking failure, and an electronic device, a computer-readable storage medium, a computer program product and a computer program. The method comprises: obtaining a braking system failure factor parameter numerical value on the basis of a braking system health state observer or a fault diagnosis module of a current vehicle; constructing a braking risk decision function on the basis of a braking system failure factor and related data of the current vehicle to calculate a current braking risk value, and obtaining a desired lateral displacement of the current vehicle on the basis of the current braking risk value and steering system data; constructing an integral heuristic barrier Lyapunov function on the basis of the desired lateral displacement of the vehicle and vehicle state information after decision making, and obtaining a desired vehicle traveling yaw angle on the basis of a calculation result of the function; and converting the desired vehicle traveling yaw angle into an input front wheel steering angle value of the current vehicle, and converting the input front wheel steering angle value of the current vehicle into steering-assist electric motor output data, so that a steering system intervenes in and controls safe braking after a braking process fails.

The invention discloses a TCN-SVM rolling bearing fault diagnosis method fusing an SE attention mechanism, and belongs to the technical field of mechanical fault intelligent diagnosis. Aiming at the problems of feature redundancy, noise sensitivity, insufficient Softmax classifier generalization and the like existing in a traditional time sequence convolutional network (TCN), the invention provides a solution for collaborative optimization of a deep network and a support vector machine. The method comprises the following steps: acquiring a vibration signal of the rolling bearing; constructing a multi-fault sample set, and processing an original signal; constructing an SE-TCN feature extraction network, capturing multi-scale time sequence features by adopting expansion causal convolution, and embedding an SE module into a residual module to realize channel adaptive weighting; and a support vector machine (SVM) classifier decision function is constructed, and fault classification is completed through the RBF kernel SVM. Experiments show that the method has high fault recognition accuracy and robustness, and the problem of confusion of composite fault features of the rolling bearing is effectively solved.

The invention discloses a steel wire rope diameter measurement method and system based on machine vision, a medium and a terminal, and the method comprises the following steps: collecting an image of a steel wire rope through an area-array camera, and carrying out the preprocessing of the collected image; automatically adjusting a threshold value according to image characteristics, performing adaptive binarization processing on the preprocessed image, and extracting an effective area of the steel wire rope; extracting the edge information of the steel wire rope in the image of the effective area by adopting a Canny operator, and detecting the central axis of the steel wire rope in the image by using Hough transformation; the edge information and the central axis position serve as new image feature vectors to be input into a trained support vector machine SVM model, and a decision function is used for calculating a predicted diameter value; according to the method, the threshold value is automatically adjusted according to the image characteristics through self-adaptive binaryzation, the accuracy of edge detection is improved, the measurement of the diameter of the steel wire rope is optimized through the SVM algorithm, and the precision, efficiency and repeatability of measurement and the generalization ability of the model are improved.

The invention belongs to the technical field of spectral analysis, and discloses a Raman spectrum characteristic peak segmentation method based on distance vector and probability vector output. Spectral detail features are extracted through multi-scale wavelet decomposition, and a spectral multi-layer structure representation matrix is constructed; identifying a potential peak site and a hierarchical affiliation relationship thereof based on the local curvature change rate; generating an adaptive distance calculation kernel function in combination with the asymmetry index and the peak shape complexity coefficient; and calculating a distance vector of hierarchical perception and a probability vector extracted by deep learning, and constructing a joint segmentation decision function of a multi-layer peak structure. According to the method, the hierarchical relationship among the main peak, the shoulder peak and the sub-peak can be accurately distinguished, the complex conditions of peak overlapping, asymmetric peak shapes, low signal-to-noise ratio and the like are effectively processed, and the accuracy and the reliability of Raman spectrum analysis are improved.

The invention relates to the technical field of electronic noses, in particular to a double-center intuitionistic fuzzy large-interval classification method with unified marble loss, which comprises the following steps: defining and optimizing a large-interval distributor model, and introducing a marginal mean value and a marginal variance to describe marginal distribution characteristics; replacing a hinge loss function with a marble loss function, and defining a hyper-parameter of a control quantile; calculating a membership degree and a non-membership degree, verifying the rationality of the new intuitionistic fuzzy strategy through a theorem, and calculating the confidence coefficient of the sample; constructing a double-center intuitionistic fuzzy large-interval classifier model of unified marble loss, deducing a dual problem, solving an optimization problem to obtain model parameters, and calculating a decision function to classify new examples; a classifier model is used, the performance of the model is evaluated through a verification set, the classifier model is deployed in practical application, and a data classification task is carried out, so that the anti-drift performance of the electronic nose system is effectively improved, the mean value and the variance are taken as optimization targets, and the relatively strong anti-interference capability is realized.

The invention relates to the technical field of active safety of forklifts, and discloses a driving assistance method for stability adjustment of a forklift, which comprises the following steps: acquiring input parameters of the forklift for driving assistance; acquiring a fuzzy set corresponding to the input parameter; establishing a fuzzy rule base for logical reasoning based on the fuzzy set of the input parameters; constructing a decision function through a weighted average method to calculate a driving mode control value, and optimizing a weight through minimizing a decision error based on supervised learning; outputting a corresponding driving mode according to the driving mode control value; the invention further discloses a driving assistance system for forklift stability adjustment. The driving assistance system comprises a road surface recognition module, a forklift state acquisition module, a parameter fuzzification module, a fuzzy rule base storage module, a driving mode decision module and a driving mode execution module. By monitoring a plurality of input parameters, the overall performance and the adaptive capacity of the forklift system are improved, and efficient operation and safe operation of the forklift system in a dynamic complex environment are ensured.

The invention relates to the technical field of intelligent decision making, can be applied to business system platforms of financial science and technology, medical health and the like, and discloses a speech synthesis method, device, equipment and medium based on an optimization strategy algorithm, comprising: extracting a time sequence processing network unit and a data sampling scheduling unit in a speech synthesis model; mapping a denoising function in the time sequence processing network unit into a multi-step Markov decision function, and converting an ordinary differential equation in the data sampling scheduling unit into a multi-source stochastic differential equation; sampling multiple groups of independent audio tracks corresponding to the input text based on a multi-source stochastic differential equation; calculating a strategy gradient modulation factor by using an optimization strategy algorithm and a multi-step Markov decision function; optimizing strategy parameters of the speech synthesis model according to the strategy gradient modulation factor to obtain an optimized speech synthesis model; and obtaining a to-be-converted text, and synthesizing voice corresponding to the to-be-converted text by using the optimized voice synthesis model. And the speech synthesis accuracy is improved.

The invention relates to the field of printed matter quality detection, in particular to a printed matter quality detection method based on artificial intelligence. Comprising the steps of collecting image data, constructing and enhancing a data set, correcting an image by adopting a feature point detection and matching algorithm, extracting an image feature vector through a pre-trained deep learning model VGG16, inputting the feature vector into a support vector machine classifier for quality classification, and giving a quality problem description detection report for unqualified printed matters. Wherein the image enhancement comprises zooming, contrast adjustment and color conversion; the feature point detection and matching algorithm can eliminate image distortion; the VGG16 model calculates feature mapping and extracts feature vectors through a convolution layer and a pooling layer, and finally standardization processing is carried out. And the support vector machine classifier outputs a classification result according to the decision function and performs quality feedback. The method improves the quality detection speed and efficiency of the printed matter.

The invention discloses a rope net ladder data production scheduling and traceability system based on big data storage, and particularly relates to the field of data scheduling and traceability, comprising the following steps: the system obtains total factor data of equipment, process, quality and the like through a data perception and digital acquisition module and standardizes the total factor data; the integrated storage and quantitative analysis module constructs functions such as efficiency evaluation and quality risk to quantify production indexes; the adaptive decision optimization and scheduling module generates an optimal scheduling scheme and a dynamic rescheduling instruction based on a multi-target comprehensive decision function; the full-link tracing and self-feedback optimization module realizes full-link bidirectional tracing and drives process parameter and model self-feedback optimization; the system realizes the intelligent control of the whole production process, balances the efficiency, quality, cost and delivery target, improves the resource utilization rate and production stability, and is suitable for the precise production scene of structural members such as rope net ladders.

The invention relates to the technical field of artificial intelligence, and discloses a voice noise reduction method and device based on a vibration sensor, equipment and a medium, and the method comprises the steps: synchronously collecting a mixed signal and a reference signal of the vibration sensor, generating linear echo estimation, and calculating an initial residual error, extracting time-frequency characteristics of the initial residual error and the sensor reference signal to obtain nonlinear echo estimation; adjusting the initial residual error according to a double-talk state decision function and the nonlinear echo estimation to obtain a target residual error; and processing the target residual error through a rear Kalman filtering noise reduction module, and outputting pure near-end voice. The method has the advantages that noise in voice signals is effectively reduced, the voice recognition precision and definition are improved, the advantages of adaptive filtering and deep learning are combined, the method has good adaptability to various environment characteristics, and the response capacity and robustness to dynamic environment changes are improved.

The invention discloses a vehicle lane changing decision and planning control method based on a dynamic risk field, and the method comprises the steps: obtaining vehicle state parameters of an automatic driving vehicle and an obstacle vehicle under a Frenet coordinate system through coordinate transformation, and building an automatic driving vehicle lane changing decision function according to a preset driving style, the optimal target lane and the expected lane changing speed are solved, a decision instruction is sent to the planning control module, and a trajectory planning and tracking control integrated motion control strategy of the automatic driving vehicle is established by combining the decision instruction and the dynamic risk field model of lane changing of the automatic driving vehicle; the optimal front wheel steering angle increment and the optimal speed increment in the lane changing process of the automatic driving vehicle are obtained through optimization solution and serve as control signals to be sent to a braking or driving controller to conduct motion control over the vehicle, and finally lane changing operation of the automatic driving vehicle is completed. According to the invention, the adaptability of the automatic driving vehicle to a complex road environment is enhanced, and the lane changing efficiency, comfort and safety of the vehicle are improved.

The invention discloses a demand-driven distributed collaborative scheduling method and system in an unreliable network, and the method is characterized in that the method comprises the following steps: constructing a multi-agent model composed of a plurality of individual independent generator sets, and solving the scheduling parameters of a consistency error system through the collaborative error convergence of the multi-agent; describing a data updating demand by using a decision function so as to obtain a data interaction driving mechanism among multiple agents containing scheduling parameters; and judging whether a transmission attempt exists in the attack interval, and implementing multi-agent distributed cooperative scheduling for the attack transmission attempt according to a data interaction driving mechanism and a secure transmission scheduling protocol.

The invention provides a watermark processing method and device, watermark processing equipment, a program product and a medium, and relates to the technical field of privacy computing. The method comprises the following steps: acquiring first data which is requested to be input into a target model by a user through an application programming interface (API); aiming at the first data, judging whether watermarking processing needs to be carried out or not; when it is determined that watermark processing needs to be carried out, based on a backdoor function corresponding to the target model, an API response corresponding to the API request is generated according to the first data, and the backdoor function is constructed according to a decision function of the target model; the API response is output to the user, the first data is added to a trigger set corresponding to the target model, and the trigger set is used for verifying an alternative model of the target model. According to the scheme of the invention, the problem that the existing digital watermarking technology is difficult to effectively defend model extraction attacks initiated through an API (Application Program Interface) is solved.

The invention discloses a non-signalized intersection traffic control method and system based on a multilayer game, and a computer storage medium, and belongs to the technical field of intelligent traffic and automatic driving, and the method comprises the following steps: 1, carrying out the grid division of an intersection; the method comprises the following steps: dividing a non-signalized intersection area into a plurality of equidistant grids to form a virtual logic grid, and establishing a logic coordinate system; 2, collecting state information; state information of each vehicle entering the intersection area is collected in real time; step 3, gaming in the grid; gaming optimization is carried out on the vehicles in each grid based on a revenue decision function, and the vehicles calculate an optimal strategy through the revenue decision function according to road conditions and states of surrounding vehicles; step 4, cross-grid gaming is carried out; the cross-grid game is realized through bidirectional information transmission between vehicles; 5, multi-layer game optimization and convergence are carried out; and the in-grid game and the cross-grid game are executed layer by layer, so that all vehicles reach global Nash equilibrium.

The invention relates to the technical field of ecological compensation, and discloses an ecological compensation and biodiversity monitoring method based on big data, comprising the following steps: S101, acquiring ecological data of a to-be-monitored area, regularly dividing the area into N grid units, and constructing corresponding ecological data structure units; s102, based on the ecological service characteristic parameters and the biodiversity characteristic parameters, calculating ecological service value characteristics and biodiversity pressure indexes; s103, carrying out coupling processing on the ecological service value characteristics and the biodiversity pressure index, and calculating an ecological coordination coefficient and an elastic regulation coefficient; s104, calculating an ecological compensation amount through a first preset decision function; s105, calculating a cost-benefit ratio according to the ecological service value characteristics and the ecological compensation amount, and performing automatic adjustment when the cost-benefit ratio does not reach a preset performance standard threshold value; according to the invention, accurate identification, dynamic adjustment and performance closed-loop optimization of ecological compensation are realized.

The invention provides a data private storage and access method, a medium and a system for public cloud services, and belongs to the technical field of electric digital data process.The method includes the steps that a pyramid structure neural network model is established, data access and frequency information modification are collected, and resource consumption vectors are calculated; the data classification layer and the feature extraction layer are used for carrying out feature analysis, a high and low frequency division matrix is obtained through the matrix decomposition layer, a data redundancy storage index is calculated in combination with the resource evaluation layer, a comprehensive judgment function is established for storage strategy optimization, and finally multi-level storage distribution and access control of data are achieved. According to the method, the data access characteristics can be identified more accurately, the storage resources can be allocated more reasonably, the data access process can be managed more efficiently, and the technical problem in the prior art that the public cloud service data cannot be dynamically and optimally allocated in a multi-level storage system according to the access characteristics and resource consumption is solved.

The invention relates to the technical field of intelligent driving, and particularly discloses a curve active comfortable braking method which comprises the following steps: acquiring own vehicle state information, curve information, rear vehicle state information and a road adhesion coefficient through a vehicle-mounted sensor; determining a comprehensive expected deceleration based on the above information to construct a comfortable braking decision function, wherein the comprehensive expected deceleration simultaneously meets forward curve safety and backward rear-end collision prevention safety; and according to the comprehensive expected deceleration, a vehicle executing mechanism is controlled to conduct smooth braking. According to the invention, through an innovative collaborative decision-making model, three targets of comfort, forward safety and backward safety, which are often conflicting with each other in curve braking, are successfully integrated, and a smooth, safe, intelligent and self-adaptive complete solution is provided; and the comprehensive performance of the intelligent driving system under the curve working condition is fundamentally improved.

The invention relates to power load prediction, in particular to a dynamic hierarchical granularity model power load prediction method considering time sequence factors. According to the method, the fitting capability of periodic features, trend changes and sudden influences in load fluctuation is remarkably enhanced, and the problem of feature loss caused by neglect of a time sequence dynamic weight of an existing model can be solved. Comprising the following steps: S1, performing time sequence characteristic analysis on historical power load data, and converting an original non-stationary time sequence into a stationary time sequence through difference and logarithm transformation; s2, obtaining a cleaned power load data set; s3, based on the data cleaned in S2, selecting a Gaussian radial basis function (RBF) as a kernel function, and analyzing temperature, holiday identification and linear change trend key influence factors at the same time; s4, performing dynamic multi-level granulation on the data set, and dynamically adjusting the particle level according to the data mixing degree and the particle density; and S5, fusing the time sequence kernel function and the influence factor through a decision function, and predicting the future power load.

The invention discloses a power grid emergency management system and method, and the system comprises an intelligent sensing layer which is used for collecting multi-source original data through a space-air-ground monitoring network when a disaster occurs in an environment where power grid equipment is located, carrying out the time-space calibration, extracting key features, and carrying out the risk initial judgment, generating disaster intensity field data and power grid equipment state data; the dynamic deduction layer is used for matching a target physical model from a physical behavior model library according to the disaster intensity field data and the power grid equipment state data, carrying out power flow prediction, and generating an equipment failure risk time sequence map and a power grid state prediction migration path; and the decision execution layer constructs and solves a target decision function, generates a resource scheduling scheme and outputs the resource scheduling scheme to a corresponding target terminal. A sensing-deduction-decision-evolution closed-loop system is constructed through the layers, so that a response mechanism is changed from lagging disposal to advanced pre-control, and power grid emergency resource scheduling is realized more efficiently and accurately.