5471 results about "Iterative refinement" patented technology

The invention relates to the technical field of communication scheduling, discloses a communication scheduling network management intelligent optimization system and method based on AI dynamic decision, and solves the problems of insufficient scheduling dynamics, closed loop deficiency and poor edge adaptation in the prior art. Comprising a multi-dimensional data fusion acquisition module, a dynamic AI decision engine module, a cross-domain collaborative scheduling module and an intelligent closed-loop feedback optimization module. The dynamic AI decision engine module evaluates business value and resource pressure based on an edge-center collaborative architecture, predicts transmission quality and quantifies strategy income, the cross-domain collaborative scheduling module realizes intra-domain resource slicing and inter-domain strategy negotiation and path optimization, the intelligent closed-loop feedback optimization module constructs a data closed loop to iteratively optimize model parameters, and the dynamic AI decision engine module performs multi-domain collaborative scheduling on the basis of the edge-center collaborative architecture. Intelligent scheduling and autonomous optimization of network resources are realized, and the real-time performance, the reliability and the resource utilization rate of a communication network are improved.

A composite AI system and method for advanced reasoning and automation that integrates symbolic knowledge graphs and algorithms with non-symbolic, or connectionist, models such as neural embeddings. A hierarchical architecture enables dynamically distributed, cooperative reasoning through layperson and expert-led challenge-based verification, model blending, model fitness and retraining and selection, comprehensive feedback loops at individual model or model blend or process flow with or without supervision, and specialized routing of processing to account for various operational risk, regulatory, legal, privacy, or economic considerations. Models, datasets, knowledge bases, simulations and simulation components, and embeddings are iteratively refined using knowledge graph elements and model, process, simulation or flow / process optimal hyperparameters which are recorded and tracked. Extraction of symbolic representations from connectionist models links them to curated ontologies of facts and principles.

The invention discloses a power load prediction method based on a two-channel cross attention network, and belongs to the technical field of time sequence prediction in deep learning. The method comprises the following steps of obtaining a power load data set, performing preprocessing on the power load data set, decomposing power load data into a plurality of local components by applying VMD, iteratively optimizing VMD parameters through an APSO algorithm, and screening a subsequence with the lowest complexity to be combined with original features to serve as model input; and then constructing a dual-channel cross attention network model, processing time sequence data by using a parallel mechanism of a BiLSTM channel and a Transform channel, enhancing the feature expression ability through an improved cross attention layer, and performing power load prediction by using an MLP fused KAN network. According to the method, the time sequence prediction of the power load is carried out by constructing the two-channel cross attention network, the prediction precision and robustness are high, and the accuracy of load time sequence prediction is remarkably improved.

The invention provides a network topology intelligent generation method and system based on deep learning and topology analysis, and relates to the technical network intelligence field, and the method comprises the steps: obtaining historical topology data, carrying out the feature extraction based on time sequence division, constructing a graph neural network model, training, and evaluating the evolution trend and stability of a network topology structure. And constructing a deep reinforcement learning model to generate an optimization strategy, and carrying out iterative optimization until a network topology structure meeting requirements is generated. The network structure can be adaptively optimized, the network performance is improved, the operation and maintenance cost is reduced, and the network stability is enhanced.

The invention discloses a large-model-assisted self-lifting multi-modal industrial equipment knowledge graph construction method. The method comprises the following steps: collecting and preprocessing multi-modal data; obtaining structured knowledge data in a triple form based on a few-sample prompt; gradually self-optimizing the triple from three evaluation dimensions in combination with thinking chain skills to ensure normalization, integrity and reliability of knowledge expression; the problems of data redundancy and ambiguity in the knowledge graph are solved by adopting a triple-step entity alignment method and utilizing the self-lifting capability of a large model; based on a knowledge graph completion template and a retrieval enhancement mechanism, a relationship prediction and entity completion task is completed by applying a large model, so that the integrity and applicability of the knowledge graph are ensured. According to the method, a large amount of manually labeled data is not needed, the automation level and knowledge quality of knowledge graph construction are improved through the self-lifting thought of a large model and multi-time iterative optimization, and powerful support is provided for industrial equipment management, process optimization and intelligent diagnosis.

The invention discloses a power load prediction method based on multi-source heterogeneous data feature fusion, system equipment and a medium, and relates to the technical field of power system load prediction, and the method comprises the steps: obtaining a power load prediction data set, and carrying out the preprocessing; performing input feature processing on the static data, the graph data and the time series data based on a multi-layer perceptron, a graph convolutional network and a gating loop unit network, and fusing the processed features based on a feature splicing method to obtain comprehensive feature information; based on full-connection neural network construction, taking the comprehensive features as input, inputting training set data into a model, and performing iterative optimization according to a model effect to adjust model parameters and hyper-parameters; and based on the parameter optimization adjustment model, using the evaluated optimal model to predict the power load in the future time period. The method is superior to a traditional method in precision and stability, can better adapt to complex and changeable load prediction scenes, and has wide application prospects and remarkable actual value.

The invention discloses a deep learning-based small target defect recognition model training method, relates to the technical field of defect recognition model training, and aims at meeting small defect detection requirements, starting with high-resolution diversified data construction and accurate labeling, highlighting weak targets through multi-scale feature fusion and spatial attention, and realizing high-resolution target defect recognition. A hard case scene is processed in cooperation with layer-by-layer screening and secondary intensified training, real-time iterative optimization is achieved through multi-model fusion and online dynamic adjustment and optimization, finally, multi-mode and time sequence dimensions are expanded to capture deeper and dynamic defect information, the missing detection and false detection rate is greatly reduced, and the detection efficiency is improved. The detection efficiency and adaptability of micron-sized defects under a complex process background are improved; furthermore, by means of multi-source data such as infrared, X-ray or 3D morphology and a time sequence modeling means, multiple dimensions are fused, and hidden or early cracks are brought into a detection and prediction range, so that a high-reliability and evolvable intelligent recognition system for the tiny target defects is constructed.

The invention discloses a dynamic visual content collaborative generation method based on a cognitive logic chain, and belongs to the technical field of visual content generation, and the method comprises the following steps: S1, user intention analysis and data input; s2, dynamically constructing a cognitive logic chain; s3, intelligent scheduling of the multi-modal generation module; s4, cross-modal content collaborative generation is carried out; s5, collaborative editing and real-time feedback are carried out; s6, iterative optimization of logic chain driving; s7, multi-dimensional quality evaluation: constructing an evaluation matrix containing semantic consistency, visual attraction and user participation degree, predicting a content propagation effect in combination with a deep learning model, and generating a quantitative improvement suggestion report; and S8, updating the self-adaptive knowledge reversely marking the cognitive logic chain according to the finally adopted content version, extracting a new association rule, and injecting the new association rule into the rule base. Through deep semantic analysis and dynamic logic chain construction, the system accurately captures a core creation target of a user and converts the core creation target into an executable visual strategy.

The invention relates to a building design scheme multi-objective optimization comparison and selection method and device, equipment and a medium. The method comprises the steps of generating a multi-dimensional design parameter set by obtaining building information model data and parameterized design data; performing multi-dimensional target analysis and evaluation by using a multi-field joint simulation platform to generate a multi-dimensional evaluation index; a dynamic multi-objective optimization model is constructed through a dynamic weight adaptive algorithm in combination with project stage demands and user interaction data; carrying out iterative optimization by adopting an improved non-dominated sorting genetic algorithm to obtain an optimized design scheme gene sequence result, and introducing a spatial topology connectivity constraint to generate a Pareto optimal solution set; and according to the Pareto optimal solution set, generating an optimization scheme through user weight adjustment and scheme screening. According to the method, the optimal design scheme set meeting the project requirements can be quickly and efficiently generated and screened out, the project stage requirements and user preferences are met, and the design efficiency and the scheme quality are improved.

The invention discloses an adaptive task division and decision joint optimization-based cloud edge computing resource collaborative scheduling method for a complex manufacturing system. The method comprises the following steps of 1, constructing a hierarchical cloud-edge collaborative computing network model; constructing a multi-objective optimization model, and defining an objective function and constraint conditions; 2, dynamically predicting and calculating a resource state through a resource sensing module based on an LSTM neural network, and generating a node resource prediction matrix; 3, dividing a calculation task generated by the manufacturing system into fine-granularity, medium-granularity and coarse-granularity subtask sets by adopting a multi-granularity subtask division algorithm (MSPA), and mapping the subtasks to corresponding calculation nodes; 4, constructing a task unloading decision model based on the D3QN, and dynamically selecting unloading nodes and an execution sequence of the subtasks in combination with a multi-objective optimization reward function; and 5, iteratively optimizing parameters of the D3QN model through a target network updating mechanism and a self-adaptive exploration strategy to realize real-time dynamic adjustment of a task scheduling decision.

The invention discloses a big data dynamic allocation and optimal scheduling method based on reinforcement learning. The method comprises the following steps: S1, constructing a real-time operation data set; s2, preprocessing the real-time operation data set, and constructing a reinforcement learning environment model; s3, initializing a hierarchical reinforcement learning model; s4, generating an optimal data distribution and scheduling strategy based on the hierarchical reinforcement learning model; s5, adjusting data distribution and task scheduling of each computing node in real time to balance the system load and maximize the resource utilization rate; s6, the hierarchical reinforcement learning model continuously monitors the system state and dynamically updates the strategy, and self-adaptive adjustment is carried out on resource allocation and scheduling in time when the system load or environment changes; and S7, through multi-round iterative optimization, gradually converging the hierarchical reinforcement learning model to an optimal strategy. Through application of reinforcement learning, automatic optimization of resource allocation and scheduling strategies is realized, and the resource utilization rate, the scheduling efficiency and the adaptability of the system are remarkably improved.

The invention discloses a compressed air energy storage system operation state evaluation method based on principal component clustering analysis. The method comprises the steps that system operation data are collected and preprocessed; performing multi-time scale decomposition and feature extraction on the preprocessed data set; performing nonlinear time-varying adaptive principal component analysis on the multi-time scale feature set, and extracting main features of a system operation state; carrying out clustering analysis on the dimension reduction feature space by adopting a PCA-clustering iterative optimization mechanism; establishing an evaluation model of each functional area of the system based on thermodynamic constraints, and calculating performance index values of the functional areas in combination with the principal component transformation matrix and an operation mode clustering result; dynamically adjusting the weight of each evaluation index; and generating a CAES comprehensive operation state evaluation result. According to the method, the problems of poor PCA adaptability, difficulty in multi-time scale data fusion, low abnormal mode detection sensitivity and the like under the nonlinear time-varying characteristic of the CAES system are solved.

The invention discloses an LCD display screen display fault judgment method, and relates to the related technical field of LCD display screen detection. Comprising the steps of synchronous acquisition and preprocessing of multi-source signals, feature extraction, establishment of a fault judgment model, generation of a dynamic test mode, dynamic generation of a targeted test image according to a preliminary detection result to excite a potential fault, iterative optimization of a test sequence by a Q-learning algorithm, and fault diagnosis. The invention also discloses a system for judging the display fault of the LCD screen. The system comprises a multi-source data acquisition module, a central processing unit, a memory and a user interface. Various data including display data, driving voltage / current monitoring data, temperature distribution, environment parameters and the like are synchronously collected through the optical sensor, the electric signal probe, the thermal imaging module and the environment sensor, all-directional information of the LCD display screen in the operation process can be obtained, and limitation and misjudgment possibly caused by a single data source are avoided.

The invention relates to the technical field of artificial intelligence assisted medical rehabilitation, in particular to a peripheral nerve injury personalized rehabilitation system and method based on a multi-modal large model, and the method comprises the steps: a feature fusion module employs space-time attention to fuse multi-modal time series data, and constructs an evaluation map; the personalized generation module is combined with historical data and reinforcement learning to generate a scheme containing virtual scene parameters; the interaction feedback module collects data through mixed reality and calculates action deviation; and the adaptive adjustment module adopts a meta-learning optimization model and a distributed iterative output scheme. According to the method, the cross-modal association precision of the motion features and the mechanical parameters is improved, dynamic matching of the training scene and the motion ability of the user is achieved, the virtual environment and the mechanical feedback threshold are optimized by dynamically adjusting the rehabilitation scheme parameters, the scheme optimization period is shortened based on an online iterative optimization mechanism, and the training efficiency is improved. The core defects of personalized adaptation lagging and low utilization efficiency of multi-modal data are overcome.

The invention provides a large model cue word design method, system and device in an industrial scene and a medium, and belongs to the technical field of artificial intelligence. The method comprises the following steps: constructing a private cue word template library, and forming a domain special knowledge unit set through multi-source industrial data collection, intelligent semantic analysis and classified storage; customizing a four-layer structured template, sequentially establishing a task overview layer, a step disassembly layer, an instruction refining layer and an output specification layer, and constructing a layered mapping model from task definition to output execution; template library management and iterative optimization are carried out, and dynamic updating and performance improvement of a cue word system are realized through centralized system management, multi-source feedback acquisition and data-driven optimization. According to the method, a cue word design and optimization system based on a hierarchical structure is constructed for complex task requirements in an industrial scene, and the task processing performance of a large model in scenes of industrial production, quality detection, equipment operation and maintenance and the like is improved.

The invention discloses a path planning heuristic function generation platform and method based on collaborative optimization of a large-scale language model and evolutionary computation. According to the technology, the large-scale language model (LLM) and evolutionary computation (EC) work cooperatively. The platform generates or mutates a heuristic function expressed as an executable code through LLM based on a structured prompt containing an environment context and performance feedback; and an EC framework (such as genetic programming) is combined with performance evaluation feedback to perform selection and iterative optimization on a heuristic code population, and population diversity is maintained. The method aims at overcoming the limitation that a traditional heuristic design is difficult and poor in adaptability, a high-quality heuristic function adapting to a complex and dynamic environment is automatically generated, and therefore the efficiency of a path planning algorithm and path quality are remarkably improved.

The embodiment of the invention provides a document generation method and device based on artificial intelligence, and the method comprises the steps: extracting multi-modal data features of a historical multi-modal file through natural language processing and a computer vision algorithm, and constructing a corresponding knowledge graph according to the multi-modal data features, converting the multi-modal data subjected to feature extraction into high-dimensional vectors by using a vectorization algorithm, and storing the high-dimensional vectors to form a vector library; splitting a current document generation task into a plurality of sub-tasks by adopting a task planning algorithm, and executing the sub-tasks at the same time to obtain a first document; performing iterative optimization of document content according to the document first draft, the cue word and the language model to obtain an optimized first draft, acquiring related information from the vector library by using an attention mechanism-based retrieval algorithm, and fusing the related information and the optimized first draft through a text generation algorithm to obtain a final document; according to the invention, the accuracy and intelligence in the document generation process can be effectively improved.

The invention relates to the field of multi-modal large models, and discloses an optimization method and system for a self-adaptive multi-stage fine-tuning multi-modal large model. The method comprises the following steps: constructing a multi-modal data set; coding modal data in a single group of samples based on a pre-trained multi-modal basic large model to generate feature representation; calculating a task correlation score, a feature information amount score and a gradient scale score of each modal data feature to generate a single-modal score; calculating a modal alignment degree score, an information complementation degree score and a collaborative gain degree score among different modals to generate a cross-modal interaction score; constructing a depth adjustment demand index and an interaction strength demand index according to the two scores, selecting a combination strategy of depth fine adjustment / shallow fine adjustment and strong interaction / weak interaction according to index values, and performing adaptive optimization on the multi-modal basic large model; and verifying whether the model performance meets the requirements or not so as to iteratively optimize to meet the requirements. According to the method, computing resources are saved, and the representation and generalization ability of the model is improved.

The invention relates to a multi-modal large model dynamic compression and reasoning optimization method based on a MoE architecture. The method comprises the following steps: establishing an edge computing system conforming to medical equipment specifications, constructing a medical image analysis network based on an improved hybrid expert MoE architecture, and adopting a three-layer cascade structure of a feature coding layer, a dynamic routing layer and an expert execution layer; executing expert module dynamic loading and video memory optimization; executing knowledge graph compensation and domain knowledge injection; executing hardware instruction level optimization and calculation acceleration; executing multi-expert feature fusion and decision weighting; performing diagnosis result generation and confidence evaluation; performing real-time data return and model iterative optimization; executing multi-device cooperation and load balancing; executing system security monitoring and exception handling; and generating a structured diagnostic report. The problem that the precision loss of a multi-modal large model is difficult to meet actual requirements is solved, and medical feature adaptive dynamic compression, medical hardware collaborative energy efficiency optimization and cross-modal compensation of medical knowledge enhancement are realized.

A computer-implemented method and a computer system for automating market research is disclosed. The computer-implemented method includes receiving a query from a user within a Market Research System (MRS). Collecting context relevant to the query based on availability of internal information retrieval by querying the user with one or more clarification questions using one or more Artificial Intelligence (AI) agents. Forming a strategy to retrieve data from available data sources based on the context. Retrieving data from the available data sources based on the strategy. Analysing the data to determine if the retrieved data is sufficient to complete the research objective. Requesting modification or additional information in the data, responsive to the data not being determined as sufficient to complete the research objective. Determining market research parameters based on the analysed data. Iteratively optimizing market research parameters by activating the one or more AI agents.

The embodiment of the invention relates to the technical field of data processing, in particular to an intelligent monitor temperature drift correction method and system based on a temperature compensation algorithm, and the method comprises the steps: collecting the environment temperature data of an environment where an intelligent monitor is located in real time, and obtaining a preset sensor aging factor in the intelligent monitor; based on the environment temperature data and a preset temperature-drift characteristic model, the temperature drift characteristic of the intelligent monitor is extracted, and a nonlinear compensation curve matched with the temperature drift characteristic is dynamically generated; performing iterative optimization processing on compensation parameters of the nonlinear compensation curve by adopting a self-adaptive compensation parameter optimization algorithm, and performing dynamic correction on the compensation parameters by fusing sensor aging factors in the iterative optimization processing process to obtain an optimized compensation parameter set; and performing real-time correction processing on original measurement data of the intelligent monitor according to the optimized compensation parameter set, and outputting a target measurement result after temperature drift noise suppression.

The invention relates to the technical field of archive information management, and provides an archive information extraction management method and system based on multi-modal learning, and the method comprises the steps: collecting multi-modal archive data, carrying out the preprocessing of the multi-modal archive data, and obtaining the processed multi-modal archive data; performing feature extraction on the multi-modal archive data, and obtaining an archive comprehensive feature vector through weight distribution and feature alignment; based on a preset archive knowledge graph, performing multi-modal entity recognition on the archive comprehensive feature vector, constructing an archive relation graph, and performing iterative calculation through a graph neural network to obtain an archive association rule; updating a preset archive knowledge graph according to the archive association rule to obtain an archive enhanced knowledge graph, and performing iterative optimization on the archive association rule to form and store an archive management strategy network; and constructing an archive information index system based on the archive enhanced knowledge graph and the archive management strategy network. According to the invention, efficient management and accurate retrieval of archive information are realized.

The invention relates to the technical field of data processing, can be applied to business scenes such as financial science and technology and medical health, and discloses a self-adaptive data processing optimization method, device and equipment and a medium, and the method comprises the steps: collecting operation data, host performance data, network state data and historical task data of a target data source, and constructing an analysis model in combination with recovery parameters and strategy preference, predicting a task load state, resource consumption and execution duration, generating a task execution strategy, completing task scheduling and execution monitoring, and collecting execution feedback data for iterative optimization of the analysis model. According to the method, an analysis model is constructed by fusing multi-source system data and historical task information, a task execution strategy is generated in combination with a dynamic prediction result and a strategy weight, intelligent task scheduling and process monitoring are realized, and feedback data is used for model iterative optimization. The task execution efficiency is improved, the resource use rationalization is realized, and the model adaptive capability is enhanced.

The invention relates to an AI-based automatic adaptation method for a work approval process, and the method comprises the steps: collecting multi-source approval data, and carrying out the preprocessing of the multi-source approval data, and forming standardized approval data; analyzing the system rule text by using the pre-trained AI large model, and extracting a structured approval rule comprising a trigger condition, an approval role and a process node sequence; matching a basic examination and approval template according to the form field and the user permission information, and dynamically generating an adaptive process comprising multiple stages of examination and approval nodes, aging parameters and an additional examination and approval link; and through resource scheduling optimization and time domain correlation analysis, establishing an optimized mapping relation based on flow execution characteristics such as approval timeliness deviation and node skipping frequency, and outputting a visual flow chart and execution parameters. A business logic writing mode is replaced by automatic analysis of an AI large model on an unstructured rule; the dynamic template matching and continuous iterative optimization mechanism can quickly respond to business changes, and resource scheduling optimization and automatic process generation shorten the implementation period and reduce the operation and maintenance cost.

The invention relates to the technical field of unmanned aerial vehicle inspection, and discloses a power transmission line unmanned aerial vehicle inspection task intelligent allocation collaborative planning method based on a genetic algorithm. The method comprises the following steps: firstly, collecting and analyzing basic information of a power transmission line, an unmanned aerial vehicle, a surrounding environment and weather, and establishing a space model of unmanned aerial vehicle inspection by using a grid method; based on the collected information, establishing a mathematical model to allocate inspection tasks of the unmanned aerial vehicle; a genetic algorithm is used for coding and optimizing a routing inspection path, meanwhile, a fitness function is designed to comprehensively consider multiple factors of a search effect, and quantization, weight distribution and optimization selection are carried out; and an optimal solution is found through iterative optimization, and intelligent distribution and collaborative planning of unmanned aerial vehicle inspection tasks are realized, so that the requirements of regional inspection are met. According to the technical scheme, the flight threat degree under different meteorological conditions can be quantitatively evaluated by considering the wind speed, rainfall and visibility, and the flight plan is adjusted according to the flight threat degree.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention relates to the technical field of fault analysis, and particularly provides a monitoring fault analysis method fused with a multi-modal knowledge base, which comprises the following steps: collecting original data of a monitoring fault log, and preprocessing and storing the original data; performing data cleaning and feature extraction on the obtained original data of the monitoring fault log; constructing a searchable knowledge base based on the cleaned data; when the system triggers an alarm, mixed retrieval is executed through a dynamic routing mechanism; aggregating the plurality of retrieval results to generate an executable repair scheme; iteratively optimizing the decision process through manual feedback; and continuously optimizing the knowledge base and the diagnosis model to form a closed loop iteration mechanism. According to the scheme, the accuracy and response efficiency of fault diagnosis are improved.

The invention discloses a multi-modal image automatic labeling system and method, and relates to the technical field of image data processing. According to the multi-modal image automatic labeling system and method, time sequence alignment is carried out on video streams and laser radar point cloud data through an asymmetric dynamic time warping algorithm, semantic and geometric features are extracted, and the elastic coefficient of the algorithm is dynamically adjusted. And combining a modal perception attention mechanism, dynamically allocating fusion weights of the video stream and the laser radar according to the features, generating a cross-modal joint feature vector, and outputting a preliminary labeling result. And generating an annotation robustness index by calculating the prediction entropy and the three-dimensional intersection-to-union ratio confidence of the target detection frame, and iteratively optimizing the annotation result. And mapping the cross-modal features and the labeling result into a space-time correlation map, and displaying the three-dimensional positioning, motion trail and modal contribution degree thermodynamic diagram of the target in real time. The problems of time alignment, feature fusion and labeling robustness are effectively solved, and a high-precision and interpretable automatic labeling solution is provided.

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

The invention provides a forest land tree height measurement and determination method and system based on laser radar point cloud data. Stress wave signals are collected based on a trunk base acoustic emission sensor array to generate an acoustic characteristic parameter set, the digital twin model is driven to complete forest stand structure topological optimization, and a three-dimensional growth vector model reflecting the internal mechanical state of a trunk is formed. And synchronously fusing high-precision slope point cloud data returned by the unmanned aerial vehicle laser radar in real time, correcting a terrain distortion error through a dynamic splicing algorithm in combination with stress distribution characteristics, and generating a crown segmentation boundary constrained by physical characteristics. And finally outputting a tree height parameter corrected by the abrupt slope topography through model iterative optimization and space vector analysis. According to the technical scheme, synchronous sensing of the three-dimensional shape and the mechanical state of the tree in the complex terrain environment is achieved, and the tree height measurement error is reduced.