2444 results about "Decision model" patented technology

Disclosed in the present invention are a fuzzy-logic-control-based coordination method and system for a power grid requirement response and an energy storage system, the method comprising: S1, collecting real-time power grid data and prediction data, and constructing a corresponding real-time power grid data set and a corresponding prediction data set; S2, using a fuzzy algorithm to convert the real-time power grid data set, the prediction data set and multi-dimensional renewable energy information into a fuzzy set; S3, customizing a power grid requirement response measure and an operation strategy of an energy storage system; S4, executing the strategy customized in step S3; S5, monitoring in real time the execution effect of the strategy and collecting operation data such as a power grid load matching degree, energy storage device response speed and efficiency, and a requirement response participation degree; and S6, periodically updating a decision model of a fuzzy logic controller. In the present invention, the fuzzy logic controller is used to process and analyze power grid data in real time, such that the uncertainty and ambiguity during power grid operation can be effectively handled, especially for the production capacity fluctuation of renewable energy and the rapid changes of power loads.

The present invention relates to an integrated dispatch decision-making system and method for a hub supporting multi-modal transportation coordination, and a device. The system comprises: a multi-modal hub passenger flow data fusion and analysis module, which is used for arranging and classifying collected multi-modal hub passenger flow data, performing preprocessing, performing conversion and mapping to obtain normalized passenger flow data, and finally extracting a data fusion feature, and using a normalized transport passenger flow model to perform multi-dimensional data fusion and analysis computation; a hub transportation coordination decision-making module, which is used for performing transport capacity and volume assessment on the basis of the fusion and analysis of the multi-modal passenger flow data, and using an optimal decision-making model to compute an optimal strategy result; and a hub coordination dispatch and command application module, which is used for providing an intelligent command assistance function to a rail transit dispatch and command personnel on the basis of information access of hub-line-network, and the normalized transport passenger flow model. Compared with the prior art, the present invention has the advantages of improving the connectivity and coordinated dispatch of multi-modal transportation in a complex hub scenario, etc.

The invention discloses a multi-modal large model interpretable diagnosis method and system for a wind turbine generator, and relates to the technical field of wind turbine generator fault diagnosis, comprising the step of combining multi-modal data (vibration, time sequence, image and text) and topological information to realize fault diagnosis through cross-modal contrast learning and topological modeling. The method comprises the steps of multi-modal feature extraction, standardization and alignment, and feature fusion through topology embedding optimization and a cross-modal attention mechanism. In the fault diagnosis process, dynamic correction and path reliability evaluation are introduced by using a regular Agent and a topology consistent Agent, weighted fusion is performed on each modal feature and a topology structure, and finally an accurate fault type and a component positioning result are output. Through combination of knowledge retrieval and a multi-Agent decision model, the adaptability and precision of fault diagnosis are improved, especially in a complex environment, the fault mode of the wind turbine generator can be effectively identified, and the system reliability is improved.

The invention relates to a server cluster monitoring system based on multi-node collaboration and an implementation method thereof, a dynamic topology network module is configured to reconstruct a connection topology among monitoring nodes in real time according to node performance and link quality, support mixed configuration of a star type, a ring type and a net structure, and realize multi-node collaboration. Multi-dimensional data capture from a physical layer to an application layer is realized through a cross-level index acquisition module based on an integrated hardware sensor interface and a virtualization layer probe, and each node is enabled to perform collaborative reasoning through parameter encryption sharing through a decision model based on federated learning. A monitoring task fragmentation strategy is dynamically adjusted through an adaptive elastic fragmentation unit according to network delay and load fluctuation, and an abnormal event association rule base is updated in real time through an incremental knowledge graph construction unit. High availability and elastic expansion are realized through a multi-node collaborative architecture, the monitoring efficiency is improved in combination with dynamic load balancing and hybrid detection, and an intelligent multi-level response mechanism is constructed to guarantee the service continuity.

The invention discloses an intelligent factory data intelligent analysis and management system, and relates to the technical field of data analysis. Multi-source data are uniformly accessed through an industrial gateway, and a knowledge graph is constructed after cleaning and standardization; an edge node deploys a lightweight AI model to realize real-time analysis such as equipment anomaly detection; intelligent distribution of cloud side tasks is realized based on a decision model; optimizing production scheduling and quality control by using an algorithm; a zero-trust architecture is adopted to guarantee safety, and efficient energy management is realized in combination with reinforcement learning; all the modules work cooperatively, and the intelligent level of a factory is improved. The operation efficiency and quality of the intelligent factory are effectively improved. Efficient data fusion processing is realized, and equipment anomaly detection is more accurate; the order delivery period is shortened, and the product reject ratio is reduced; network security protection is enhanced, and energy waste is reduced; the decision response speed is accelerated, the decision accuracy is improved, cost reduction and efficiency improvement of enterprises are comprehensively assisted, and the competitiveness is enhanced.

The invention relates to the technical field of farmland fertilization management, and discloses an intelligent fertilization management method and system based on machine learning. The method comprises the steps that a soil parameter set and an environment parameter set of a target farmland are collected, soil parameters comprise soil humidity, nitrogen phosphorus and potassium content and pH value, and environment parameters comprise illumination intensity, temperature and rainfall; constructing a soil nutrient dynamic change model according to historical data, and predicting a soil nutrient consumption trend in a future preset period; generating an initial fertilization scheme based on the nutrient consumption trend and the crop growth stage characteristics; monitoring the growth state of crops in real time by using a multi-mode sensor, and obtaining a leaf surface color index, a stem height and a fruit development progress to form a growth state data set; and inputting the growth state data set and the initial fertilization scheme into a fertilization decision model, and comparing growth state deviation to adjust the nutrient distribution ratio to generate an optimized fertilization scheme.

The invention provides a multi-modal information fusion feeding decision-making system and method for chicken breeding behavior recognition, and the method comprises the steps: collecting a multi-source heterogeneous data set, and extracting a primary fusion feature vector; constructing a triple knowledge graph; mining implicit association rules of the ingestion frequency and the body temperature; performing secondary fusion on the primary fusion feature vector and an implicit association rule to generate an intermediate decision feature; and generating a feeding decision instruction through the pre-training decision model and the expert rule base. According to the method, the knowledge graph is constructed, GNN reasoning is utilized, and a manual preset rule static mode is replaced; performing secondary feature fusion, generating intermediate decision features by combining primary fusion features and implicit rules, and then combining a pre-training model and an expert rule base, ensuring decision real-time performance, integrating domain knowledge, outputting accurate adjustment parameters, realizing full-link intelligence, improving the accuracy and adaptability of breeding chicken feeding decisions, and improving the accuracy and adaptability of chicken feeding decisions. Therefore, dynamic requirements of complex breeding scenes are met, and chicken flock health and breeding efficiency improvement are promoted.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention discloses an engineering machinery fault prediction and intelligent maintenance method based on deep learning, and belongs to the technical field of intelligent operation and maintenance of engineering machinery. The method comprises the following steps: firstly, performing timestamp synchronization and feature enhancement on multi-source sensor data to generate a space-time alignment tensor; fusing the image and time sequence features through a multi-modal feature distillation network, and constructing a cross-modal unified feature vector; thirdly, calculating a fault probability and residual life distribution, and constructing a Markov decision model in combination with a resource state; and finally, dynamically optimizing the maintenance instruction by using deep reinforcement learning, and continuously updating the model through closed-loop feedback. According to the method, early-stage accurate prediction of the fault and dynamic optimization of the maintenance strategy are realized, the problems of inaccurate prediction, decision lag, resource waste and the like in a traditional method are effectively solved, and the availability rate and the maintenance economy of equipment are remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle positioning, in particular to an unmanned aerial vehicle positioning method and system combining environment characteristics. According to the technical scheme, the method comprises the steps that multi-modal data of the environment where the unmanned aerial vehicle is located are collected through a multi-source heterogeneous sensor, and the multi-modal data comprise GNSS signal data, visual image data, laser radar point cloud data, inertia measurement data and electromagnetic interference monitoring data; and carrying out cooperative processing on the multi-modal data by utilizing the fusion deep learning model. A comprehensive basis is provided for subsequent processing through multi-source data collection, data quality and feature matching reliability are improved through fusion processing, timely and accurate positioning is guaranteed through real-time calculation, safe and efficient flight is achieved through a decision-making model based on accurate positioning, and the flight speed is high. The method not only solves the traditional technical problems of signal shielding, interference, dynamic interferent influence and contradiction between real-time performance and precision in a complex environment, but also shows excellent positioning stability and adaptability in various complex scenes.

The invention discloses an automobile door cover high-precision self-adaptive assembling and adjusting system based on physical field digital twinning, and the system comprises a multi-mode flexible sensing system which is used for collecting the geometric and mechanical data of a door cover and a door frame in real time; the digital twinning and real-time simulation prediction module is used for constructing and synchronizing a high-fidelity virtual model and predicting a gap and a surface difference after assembly based on a physical information neural network; the reinforcement learning intelligent decision model is used for outputting an optimal adjustment instruction according to the prediction deviation; and the automatic guiding and executing module is used for executing the adjusting instruction and completing assembling and tightening of the door cover. According to the automobile door cover high-precision self-adaptive assembling and adjusting system based on physical field digital twinning, high automation of door cover assembling is achieved through intelligent sensing, real-time optimization and self-adaptive control.

The present invention relates to the technical field of power distribution networks. Disclosed are a multi-energy storage power distribution network voltage regulation method and apparatus, and a device. The method comprises: establishing a voltage optimization model for a power distribution network, converting an energy storage constraint in the voltage optimization model of the power distribution network on the basis of physical information of an energy storage system, establishing a security model of the energy storage system, forming a training guide model of the energy storage system on the basis of a Markov decision model, training each energy storage system by using a TD3 algorithm, sharing network parameters obtained through training between different energy storage systems, and adjusting the voltage of the power distribution network. By means of optimizing an operation strategy of an energy storage system, the present invention realizes efficient and sustainable operation of the energy storage system. A charging and discharging strategy of the energy storage system is optimized by means of a DRL algorithm having shared parameters, improving voltage stability of the power distribution network, reducing energy loss, improving the overall efficiency of the system, enhancing the collaboration performance of multiple energy storage systems, and optimizing control of the energy storage system in an active power distribution network.

The invention provides a picking mechanical arm operation method and system based on visual feedback. The picking mechanical arm operation method comprises the steps that firstly, a multi-dimensional visual perception set including the shape contour, surface physical characteristic visual representation, connection part space distribution, growth environment dynamic interference and the like of a picked object is obtained; a mechanical arm collaborative decision-making model is constructed based on the multi-dimensional visual perception set, stress distribution characteristics of connection parts are analyzed, and optimal contact area parameters and mechanical arm posture collaborative parameters are determined; according to the parameters, generating a sub-time sequence action instruction set containing action information in the contact stage, the clamping stage and the separation stage; when the mechanical arm is controlled to execute the instruction set, a dynamic visual feedback set of the picking scene is obtained in real time; and finally, input parameters of the collaborative decision model are updated based on feedback, and instruction set parameters are adjusted, so that the mechanical arm completes picking operation on the premise of keeping the integrity of the picked object, and the intelligent level and the picking operation quality of the picking mechanical arm are improved.

The invention provides a high-altitude operation risk early warning method and system based on camera image recognition, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a video image sequence of a high-altitude operation scene, and generating a fusion feature map containing environment and operation main body features through multi-level feature extraction; performing spatial dimension segmentation and regional feature comparative analysis on the fusion feature map to obtain a spatial risk distribution map containing risk region identification information, processing the spatial risk distribution map of continuous frames based on a time sequence feature fusion rule to generate a dynamic risk evolution map, and calling a risk decision model to perform mode recognition to obtain a dynamic risk evolution map; and generating a risk level classification result and a risk position coordinate set according to the risk level classification result and the risk position coordinate set, and finally generating a risk early warning signal and sending the risk early warning signal to the monitoring terminal, thereby comprehensively, accurately and dynamically monitoring the high-altitude operation risk, and improving the accuracy and timeliness of risk early warning.

The invention belongs to the technical field of aero-engine state monitoring and intelligent fault diagnosis, and discloses a multi-modal information fusion bearing fault diagnosis method based on self-supervised learning. The method comprises the following steps: firstly, through mask reconstruction self-supervision pre-training, extracting stable feature representation insensitive to mask disturbance from an unlabeled multi-modal signal, and dynamically updating each modal feature reference point by using an index moving average algorithm; in a downstream fault diagnosis task, a multi-modal joint decision model comprising a pre-training encoder, a single-modal classifier and a fusion classifier is constructed, and adaptive weighted fusion of multi-modal decision is realized through contribution degree calculation based on a cooperative game Shapley value in combination with a deviation degree of modal features and a reference point. According to the method, the dependence of the deep neural network on fault labeling data is effectively reduced, the accuracy and robustness of the diagnosis system in a multi-modal signal diagnosis scene are improved through a dynamic fusion mechanism, and the method is suitable for industrial scenes with limited sample label resources.

The invention provides a knowledge base and business system collaboration method under an AI platform, and belongs to the technical field of AI platform digital data processing.The method includes the steps that triple semantic analysis is conducted on query by constructing a multi-level semantic vector representation module, a parallel data retrieval engine is started, vector retrieval, graph reasoning and real-time data pulling are executed at the same time, and the query efficiency is improved; establishing a dynamic confidence evaluation mechanism to evaluate the quality of the data source, executing a weight distribution algorithm based on reinforcement learning, dynamically calculating the weight of the data source according to a query type by adopting a graph convolutional network intelligent routing decision model, and implementing multi-source data fusion and consistency verification to solve data conflicts through a weighted voting mechanism. An intelligent result sorting and filtering system is established, a multi-dimensional evaluation strategy is adopted to output high-quality answers, a continuous learning and feedback optimization loop is constructed, system performance is continuously optimized through active learning and a graph shortest path algorithm, and the technical problem that knowledge base data and a service system cannot effectively and uniformly make decisions is solved.

The grouting drilling orifice reinforcement construction method based on intelligent control comprises the five steps that S1, drilling machine parameter signals are collected through operation of a drilling machine, and meanwhile geological data signals are collected through in-hole detection equipment; s2, the drilling machine parameter signals and the geological data signals are input into an intelligent decision-making center for fusion processing, and geological sensing signals are generated; s3, based on the geological sensing signal, calling a preset decision-making model through the intelligent decision-making center for calculation, and generating a grouting control instruction signal; s4, grouting operation is executed according to the grouting control instruction signal, and meanwhile a grouting process monitoring signal is collected and fed back to the intelligent decision center in real time; and S5, after grouting is completed, hole opening stable monitoring signals are continuously collected, and when the effect evaluation signals do not reach the standard, reinforcing grouting signals are automatically triggered, and grouting operation is executed again. According to the intelligent die adjusting method for the die-casting machine, the problems that drill holes are prone to collapse, reinforcement depends on experience, and quality is difficult to control can be solved.

The embodiment of the invention provides a dynamic flexible workshop scheduling method and related equipment, and belongs to the technical field of industrial intelligent manufacturing and production scheduling. The method comprises the steps of obtaining current state information of a workshop in response to a scheduling event; inputting the state information into a pre-trained scheduling decision model for processing; the model outputs state feature embedding through a two-stage feature extraction network: in the first stage, feature extraction is performed on a heterogeneous disjunction graph by using a graph attention network, and in the second stage, expert output is dynamically fused through a hybrid expert model; and finally, outputting and executing a process-machine pairing decision by the actor network. Wherein the model is trained by adopting a near-end strategy optimization algorithm based on multiple commentators; and a meta-learning framework is integrated during training, so that the model obtains strong generalization ability. According to the method, the defects of an existing scheduling method in the aspects of state characterization, multi-target tradeoff and environmental adaptability are effectively overcome, and the efficiency, quality and robustness of dynamic flexible workshop scheduling are remarkably improved.

The invention discloses an intelligent optimization method for a multi-type well seam joint control fine injection-production mode, and relates to the technical field of oil-gas field development. The method comprises the following steps: setting a well seam joint control fine injection-production mode, establishing an oil reservoir numerical simulation model in oil reservoir numerical simulation software, obtaining multiple groups of oil reservoir injection-production schemes based on a Latin hypercube sampling method, performing simulation according to each group of oil reservoir injection-production schemes by utilizing the oil reservoir numerical simulation model, generating multiple pieces of sample data, and establishing a sample database; a deep learning agent model is established, after the sample database is utilized to train and train the deep learning agent model, a particle swarm optimization algorithm is adopted to carry out single-target pre-search global optimization to obtain a preferred reference strategy, a reinforcement learning dynamic decision model is established, and a reinforcement learning agent is obtained through training based on a PPO near-end strategy optimization algorithm; and the optimal injection-production development scheme of the oil reservoir is obtained by utilizing the reinforcement learning agent, so that rapid optimization and decision support of the oil reservoir injection-production scheme in a new multi-type well seam joint control mode are realized.

The invention relates to the technical field of charging station management, and particularly discloses an intelligent cooperative charging station management system and method for an electric automobile, and the system comprises the steps: integrating real-time communication and honeycomb partition management through a cooperative decision module, constructing an efficient and state-aware charging network, and carrying out the state-aware management of the charging network; the decision model can accurately adapt to the actual operation state of the target charging station; the cooperative scheduling module optimizes resource allocation by using a state matrix and an aggregation space, so that the utilization efficiency of charging station resources is remarkably improved; and the intelligent charging scheduling decision module combines reinforcement learning rapid iteration and historical data driven prior knowledge and demand prediction, so that the generation of an optimal scheduling strategy is accelerated, the excessive dependence of decision on real-time data is reduced, and the response capability and operation stability of the system for responding to dynamic demands and complex scenes are greatly enhanced, and thus, the system is more intelligent. And finally, efficient, intelligent and networked collaborative management of the electric vehicle charging service is realized.

The invention discloses a multi-AUV adaptive path planning method for a complex underwater environment, and belongs to the technical field of cooperative control of autonomous underwater vehicles, and the method comprises the following steps: S1, carrying out the grid division of a sea area, and generating a local observation tensor; s2, constructing a Markov decision model according to the local observation tensor and the navigation state tensor; s3, inputting the local observation tensor and the navigation state tensor into a double-branch cross attention network to generate Q value estimation; s4, adjusting Q value estimation by using the dual Q network; s5, distributed collaborative learning is carried out; and S6, generating an initial path according to the adjusted Q value estimation, and correcting the initial path by using a distributed collaborative learning result to obtain a global optimal path. According to the method, the problem of insufficient path planning robustness caused by dynamic interference and static obstacle coupling in a complex underwater environment is solved, and an end-to-end optimization link from local observation to global decision is formed.

The invention relates to the technical field of artificial intelligence, and discloses a traditional Chinese medicine diabetes dry eye treatment difference evaluation system and method based on artificial intelligence, and the system comprises a four-diagnosis integrated module, a syndrome modeling module, an intelligent decision module, a curative effect feedback module, a knowledge base management module and a self-adaptive learning module. Multi-mode traditional Chinese medicine four-diagnosis data of tongue condition, pulse condition and eye diagnosis and modern physiological parameters of metabolic indexes are fused, a deep learning method is adopted for standardization processing, the problem that traditional Chinese medicine syndrome differentiation depends on subjective experience is solved, traditional Chinese medicine syndrome feature extraction of diabetes dry eye has objectivity and quantifiability, and the traditional Chinese medicine syndrome feature extraction efficiency is improved. The comprehensiveness and the accuracy of the diagnosis basis are improved; and the curative effect feedback module compares a prediction result with revival curative effect data in real time, and the knowledge base management module is linked to carry out case feature matching and strategy optimization, so that continuous optimization and long-term reliability of the treatment decision model are ensured.

The invention discloses an intelligent interpretable traffic signal adaptive control method. The method comprises the following steps: training an intelligent agent through reinforcement learning according to environment state information of an intersection; a timing decision of each phase of the intersection is generated by using the intelligent agent; guiding the first large language model to generate pre-training data by the timing decision and the cue word to perform LoRA fine tuning on the second large language model, and inputting the cue word into the fine-tuned second large language model to enable the second large language model to generate a plurality of reasoning tracks to generate positive samples and negative samples; performing all-parameter fine tuning on the second large language model to obtain a traffic control signal decision model; and inputting the constructed cue word into a traffic control signal decision model to obtain each phase timing scheme of the intersection. According to the method, the defects that an intelligent traffic signal control algorithm based on deep reinforcement learning lacks interpretability and the cross-scene generalization ability is poor are overcome, and the method has important significance in improving the decision credibility and the deployment efficiency.

The invention discloses a large language model illusion detection method and system based on an adaptive sampling strategy, and the method comprises the steps: carrying out the word segmentation and coding of a to-be-detected text and each sampling text, obtaining the respective token identifiers, carrying out the splicing, obtaining a plurality of text pair sequences, and carrying out the coding processing, and obtaining the coding results of a plurality of text pairs; the coding results of the text pairs are input into a consistency evaluation model to be processed, and consistency scores of the multiple text pairs are obtained; and obtaining an embedded vector of a to-be-detected text, calculating statistical information of consistency scores of the plurality of text pairs, inputting the embedded vector and the statistical information into the sampling decision model for decision making, determining whether to perform sampling again, if so, performing sampling again, and if not, obtaining a hallucination detection result based on the consistency scores. A sampling decision is innovatively designed after consistency scoring, and the sampling decision realizes adaptive sampling and scoring in the illusion detection process by means of a near-end strategy gradient optimization training sampling decision model in reinforcement learning.

The invention relates to the technical field of industrial unmanned aerial vehicle application and intelligent scheduling, in particular to an unmanned aerial vehicle road inspection task intelligent planning management system which comprises a task input module, an intelligent planning engine, a task execution management module, a data center and a feedback optimization module. The task input module receives, verifies and analyzes inspection task requirements and road basic information; the intelligent planning engine decomposes task priorities based on highway multi-dimensional attributes, dynamically allocates unmanned aerial vehicle resources and generates an optimal path meeting multiple constraints; the task execution management module issues an adaptation instruction, monitors the state of the unmanned aerial vehicle and processes inspection data; the data center uniformly stores whole-process data; the feedback optimization module optimizes the decision model based on historical data. The method improves the inspection planning precision and the resource utilization rate, guarantees the data quality, achieves the self-evolution, and guarantees the high efficiency and reliability of the road inspection.

The invention discloses a double-arm collaborative planning method, system and device based on reinforcement learning and a medium, and belongs to the technical field of mechanical arm control. According to the current state in the state space, a control action is generated, and three-dimensional displacement increment instructions of the left arm end effector and the right arm end effector are obtained; after the three-dimensional displacement increment instruction is responded to and double-arm cooperative control is executed, a mixed reward function is calculated; experience enhancement processing is carried out on the execution track, target resetting is carried out on the failure track, pseudo target experience is generated, and the original experience and the pseudo target experience are stored in a playback buffer; and updating parameters of the strategy network and the Q value network according to the empirical samples in the playback buffer, and completing optimization of the double-arm collaborative trajectory planning strategy. According to the method, the problems of sparse reward and local optimum in two-arm collaborative planning are effectively solved by fusing maximum entropy reinforcement learning and an experience playback mechanism, and the training efficiency and the strategy generalization ability are improved.

The invention relates to the technical field of hydropower station optimization scheduling, in particular to a hydropower station multi-target scheduling decision-making method and system, and the method comprises the steps: obtaining the multi-source heterogeneous data of a target cascade hydropower station, and constructing and dynamically updating a scheduling knowledge graph fusing the cascade hydraulic coupling and collaborative operation association relationship; identifying a reference scheduling time period and a non-reference scheduling time period and establishing a differential output constraint; performing feature compression on the scheduling knowledge graph, extracting a key feature sub-graph influencing a scheduling decision, and predicting a state evolution path of related scheduling elements of the target cascade hydropower station in a future scheduling time domain; constructing and solving a multi-target dynamic decision model, and generating a candidate scheduling scheme set; and performing cross-scale conflict detection based on the candidate scheduling scheme set, performing hierarchical re-optimization on the candidate scheduling scheme set according to a detection result, and outputting and executing a scheduling decision result. The objective of the invention is to adapt to the dynamic demand of the power market for cascade hydropower station scheduling and realize rapid and accurate collaborative scheduling decision.

The two-arm robot cooperative control method comprises the steps that an environment model is initialized; according to the double-arm robot cooperative control strategy, a Markov decision model is constructed, the Markov decision model comprises state space and action space definition, an environment interaction mechanism and a mixed constraint hierarchical near-end strategy optimization algorithm, and the execution process of the model is formally described as state observation, strategy reasoning, action execution, reward calculation and strategy updating. The grabbing task of the double-arm robot is converted into a learnable control strategy optimization problem; the dynamic mixing constraint comprises the following steps: carrying out weighted mixing on a near-end cutting target and a divergence KL penalty target; according to the layered time scale optimization, strategy optimization is divided into bottom-layer optimization and high-layer optimization based on a dynamic mixed reduction target; and training a double-arm robot cooperative control strategy, deploying the strategy to a mechanical arm, and carrying out a mechanical arm grabbing task. The method has the characteristics of double-arm collaborative distribution, difficulty in collision and good self-adaptability.

The invention relates to an AI-based planned land site selection multi-objective optimization method and system. The method comprises the following steps: constructing a city data cube; constructing a cellular automaton model and generating a land utilization potential map; constructing a game decision model, and generating a preliminary site selection scheme set; inputting the preliminary site selection scheme set into a cellular automaton model to generate an evolution simulation data set; constructing a multi-target evaluation model, and calculating the relative closeness between each scheme in the preliminary site selection scheme set and an ideal solution; generating a site selection decision report according to the relative close degree; in conclusion, the urban data cube is constructed by integrating multi-source real-time and historical data, the dynamic land potential map is generated by applying the cellular automaton model, and the multi-target scientific balance is realized by introducing the multi-agent game decision. Automatic processing and consistency guarantee of planning rules are realized, a scientific quantitative tradeoff mechanism in a multi-target conflict scene is provided, and the method has the effect of improving data timeliness and dynamic response capability of site selection decision.

The invention discloses a multi-level detail automatic simplification method for an oblique photography live-action three-dimensional model, and relates to the technical field of three-dimensional model simplification and computer graphics, and the method comprises the steps: obtaining oblique photography original data and three-dimensional model basic information; preprocessing the model, performing adaptive Gaussian filtering denoising, improving RANSAC to remove outer points, compressing textures in a blocking manner, correcting mapping coordinates, and repairing a topological structure; extracting multi-scale features; constructing a simplified decision model, and determining a simplification rate and a priority by combining an observation distance, scene precision and hardware performance; performing hierarchical simplification, vertex hierarchical improved edge folding, patch hierarchical adaptive deletion and regional hierarchical grid reconstruction; performing multi-dimensional quality evaluation, and if the requirements are not met, performing backtracking adjustment; and outputting a simplified model stored according to the LOD hierarchy, wherein the simplified model comprises transition information and a simplified log. According to the method, the data quality is improved through refined preprocessing, the simplification pertinence is enhanced through multi-dimensional feature extraction and intelligent decision, and the application value of the model is improved.