4985results about How to "Improve resource utilization" patented technology

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

The invention provides an intelligent task alarm rule self-learning method and system based on support priority, and relates to the technical field of intellectualization, and the method comprises the steps: constructing a bidirectional feature extraction network to collect task operation data, and combining historical alarm data to generate a task exception association model; and carrying out migration mapping on the model and a business scene knowledge base by utilizing a migration learning algorithm, generating a task priority quantitative index, and constructing a self-adaptive priority scoring system. And training an alarm rule generator based on the model and a scoring system, generating a multi-level alarm strategy tree, and realizing differential alarm. And the incremental learning engine monitors a task state in real time, dynamically adjusts an alarm response strategy and feeds back data to optimize a rule. A federal learning mechanism is adopted to cooperatively train a model, and the generalization ability and adaptability of alarm rules are improved, so that more accurate and efficient task alarm is realized, the false alarm rate is reduced, and the operation and maintenance efficiency is improved.

The invention relates to the technical field of dispatching automation, and discloses a dispatching automation system application state management method which comprises the following steps: dynamic task state management: monitoring dynamic state indexes of various computing tasks in real time; according to different characteristics of task types, making differentiated priority management strategies; the task priority is dynamically adjusted, and it is ensured that key tasks are preferentially and efficiently executed; dynamic resource state management: continuously monitoring dynamic state indexes of various computing resources, wherein the dynamic state indexes comprise utilization rates and load conditions; dynamically adjusting a resource allocation scheme according to the resource state; dynamically optimizing resource allocation to ensure that the overall resource utilization rate of the system is continuously improved; and optimizing a self-adaptive scheduling strategy: introducing a self-adaptive scheduling algorithm based on reinforcement learning. According to the application state management method of the dispatching automation system, dynamic state management is adopted, a traditional static dispatching mode is abandoned, and the method can better adapt to a complex and changeable computing environment.

The invention relates to the field of vehicle data analysis, in particular to an automobile data analysis method and system based on an intelligent diagnostic instrument. The method comprises the following steps: acquiring vehicle multi-modal monitoring data based on an intelligent diagnostic instrument; operation power change calculation is conducted on the vehicle multi-mode monitoring data, time sequence state feature mining is conducted, and vehicle time sequence state features are obtained; carrying out multi-frequency noise reduction optimization on the vehicle multi-modal monitoring data, and carrying out normalized periodic evolution mining so as to generate normalized vibration periodic evolution characteristics; potential feature association deep mining is carried out on the vehicle time sequence state features and the normalized vibration period evolution features, then association graph modeling is carried out, and a vehicle multi-modal feature knowledge graph is constructed; and abnormal parameter deviation calculation and fault positioning traceability are carried out on the real-time operation parameters of the vehicle based on the vehicle multi-modal feature knowledge graph, so that a potential abnormal fault positioning point is obtained. According to the invention, efficient and accurate vehicle fault risk prediction analysis is realized.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention relates to the technical field of power systems, in particular to a substation main equipment self-checking system based on a multi-source heterogeneous data fusion technology. The self-inspection system comprises a data acquisition and preprocessing module, a self-adaptive data fusion algorithm module, an equipment fault mode dynamic identification and intelligent repair module, a multi-scale time sequence prediction and emergency plan generation module, an equipment health state evaluation and residual life prediction module, and a fault diagnosis and knowledge base updating module. A user interaction and remote monitoring management module, and an intelligent prediction and optimization scheduling module; according to the substation main equipment self-checking system based on the multi-source heterogeneous data fusion technology, comprehensive monitoring and intelligent management of substation equipment are achieved by integrating various data acquisition and processing technologies.

The invention discloses an industrial equipment real-time monitoring system based on edge computing, and the system comprises the following steps: collecting, processing and analyzing the operation data of industrial equipment through distributed edge computing nodes, constructing a node cooperation mechanism, and completing the processing of the data; the dynamic data scheduling module is used for dynamically optimizing a data transmission path and task allocation according to node performance; the intelligent model dynamic migration module is used for adjusting lightweight model deployment in combination with node resource conditions; the multi-modal fault sensing module is used for extracting and analyzing multi-modal features by using a cross-node collaborative algorithm; the edge node self-adaptive management module is used for adjusting node operation parameters in real time and repairing an abnormal state; and the equipment relevance reasoning module is used for constructing an equipment relevance model through a graph calculation method and generating a real-time relevance graph. According to the method, efficient monitoring and anomaly detection of industrial equipment are realized through resource optimization and intelligent analysis of collaborative edge computing nodes.

The invention provides a distributed computing resource smart evolution method and system based on digital twinning, and relates to the technical field of resource evolution.The distributed computing resource smart evolution method comprises the steps that a smart sensing probe is deployed at a computing node to collect operation data, a double-layer attention mechanism is used for extracting features, and a digital twinning model is constructed based on a graph neural network; a resource allocation strategy and a scheduling decision are optimized in combination with a hierarchical reinforcement learning framework, a prediction sequence is generated through an attention-enhanced sequence prediction network, and finally an optimal configuration scheme is solved by adopting a particle swarm algorithm. According to the method, the utilization rate of distributed computing resources can be improved, the energy consumption of the system is reduced, and intelligent scheduling and optimal configuration of the computing resources are realized.

The invention provides an automobile part spraying control method and system, and relates to the technical field of automobile manufacturing, and the method comprises the steps: collecting spraying environment parameters in real time, generating a comprehensive digital model containing the environment parameters and part characteristics, and carrying out the intelligent partitioning of the surface of an automobile part through a clustering partitioning algorithm, and dividing the surface into a plurality of sub-regions; on the basis of each sub-region, combining with the current environment parameters, generating an optimal spraying parameter for each sub-region by utilizing an intelligent agent trained by a reinforcement learning algorithm, and on the basis of the spatial relationship between the optimal spraying parameters and the sub-regions, optimizing a global spraying sequence by adopting an ant colony path planning algorithm, and generating an optimal spraying path; the spraying robot sprays, a real-time quality evaluation result of spraying is generated, a pre-constructed fuzzy logic controller is adopted, spraying parameters are adjusted, sprayed parts are obtained, a full-period comprehensive data set is integrated, and spraying control parameters are iteratively updated.

The invention provides a computing system and method for GPU computing power scheduling, and the method comprises the following steps: 1, obtaining computing task data, and carrying out the information collection of each task through receiving a to-be-processed computing task queue; according to the invention, through task information collection and deep analysis, and GPU resource omnibearing monitoring and workload prediction, deep understanding of tasks and GPU resources is realized, highly accurate computing power distribution can be carried out, resource waste is avoided, and the resource utilization rate is improved; advanced technologies such as a knowledge graph, mixed integer programming and deep reinforcement learning are fused through the intelligent scheduling model, the fusion mode is innovative in the GPU computing power scheduling field, the limitation of singleness of a traditional scheduling model is overcome, the method can better adapt to a complex and changeable computing environment, the task execution efficiency is optimized, and the task scheduling efficiency is improved. The adaptability and competitiveness of the whole system in a complex environment are remarkably improved, and an excellent solution is provided for the GPU computing power scheduling problem in the field of high-performance computing.

The invention provides a Python monitoring task resource use method and system, and relates to the technical field of intelligent monitoring and resource optimization, and the method comprises the steps: extracting process call stack information through a multilayer convolutional neural network, constructing a process dependency graph, calculating a process criticality score, and recognizing a core monitoring process. And obtaining resource use data of the core monitoring process through a resource monitoring coroutine, determining an optimal sampling period by adopting a self-adaptive feedback control algorithm, and writing the data into a time sequence database. And performing data aggregation analysis by using a sliding time window algorithm to obtain a performance index, and constructing an anomaly detection model. Dimensionality reduction is carried out based on a principal component analysis method to construct feature vectors, and load types are divided by adopting a clustering algorithm; and generating a differentiated process optimization strategy according to the feature vector and the load type. When performance fluctuation is detected, an optimization strategy is executed, the anomaly detection model and the deep reinforcement learning model are updated according to the performance improvement index, and finally an optimization diagnosis report is generated.

The invention discloses a multi-task collaborative robot scheduling method and system oriented to an intelligent factory, and the method comprises the steps: receiving the input data of a plurality of tasks to be executed by the intelligent factory, the input data comprising the task type, the dependency relationship between the tasks, the task priority, the resource demand of each task, and the expected completion time of each task; determining an initial task execution sequence of the tasks according to the task types, the dependency relationships and the task priorities; obtaining current resource state data of all available robots in the intelligent factory; according to the resource demand of each task, the expected completion time of each task, the initial task execution sequence and the current resource state data of all available robots, a scheduling algorithm is adopted to generate a multi-task cooperative scheduling rule; and controlling the plurality of target robots to execute the plurality of tasks according to the multi-task cooperative scheduling rule. According to the method, the resource utilization rate of the robot in the intelligent factory can be effectively improved, and the task execution efficiency is optimized.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses an AGV task allocation method and system, and relates to the technical field of automatic navigation trucks, and the method comprises the steps: carrying out the initialization of AGV state information; acquiring environment information in real time through ground sensor equipment; performing priority evaluation on the task instructions, and adjusting task priorities in real time according to emergencies; task merging and splitting are carried out; task allocation is carried out by using an ant colony algorithm, and a task allocation result is output to the corresponding AGV to guide the AGV to execute operation according to the allocated task; an optimal path is planned for each AGV, and path planning is dynamically adjusted in the task execution process of the AGVs; and optimizing a task allocation algorithm by adjusting a pheromone volatilization coefficient. Limited AGV resources are reasonably scheduled by arranging a task instruction obtaining module, a task information processing module and a task planning and distributing module, high environment perception and adaptability are achieved, and task priorities and path planning can be rapidly adjusted.

The invention provides a visual project management method and system based on task tracking, and relates to the technical field of project management, and the method comprises the steps: collecting project risk information through employing a context awareness technology, generating a risk propagation network, and calculating a risk diffusion coefficient; performing hierarchical analysis on project requirements based on the risk diffusion coefficient to obtain a task decomposition scheme, constructing a task transmission chain based on the task decomposition scheme and generating a visual task relation graph, and generating a task state transition rule and an alternative task path based on an early warning state of a task node, meanwhile, constructing a task elastic scheduling mechanism and storing the task elastic scheduling mechanism into a rule database; and identifying a preorder task node and a post-order task node on the task transmission chain, sending the task reconstruction instruction to the preorder task node and the post-order task node to realize dynamic task adjustment, updating the risk propagation network according to an adjustment result, and optimizing the risk diffusion coefficient.

The invention provides an intelligent logistics optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion processing of the Internet of Things sensor data and historical business data of a multimodal transportation reloading node in a logistics process, and obtaining a spatial-temporal feature vector; performing operation adaptability evaluation on the reloading nodes by using a P median model according to the spatial-temporal feature vectors to obtain a comprehensive score reflecting the node operation condition; a Tabu search algorithm is adopted to carry out combinational optimization on operation parameters of the reloading nodes based on the spatial-temporal feature vectors and the comprehensive scores, and an initial reloading scheme is obtained; and according to the initial reloading scheme, performing multi-agent cooperative scheduling on the reloading nodes on the digital twin platform to obtain a logistics resource optimal allocation scheme. Through combination of multi-source fusion and an efficient optimization algorithm, dynamic operation analysis and collaborative scheduling of multimodal transport reloading nodes are realized, logistics timeliness and resource utilization rate are improved, and the method is suitable for a complex logistics network and a changeable market environment.

The invention discloses a Transform large model training method based on cloud edge collaboration. The method comprises the following steps: S1, constructing a data set; s2, preprocessing the data set; s3, segmenting the preprocessed data set by using a data parallelism strategy, and performing distributed training by combining data parallelism and model parallelism technologies; s4, monitoring computing resources and network conditions of the edge equipment and the cloud server in real time, dynamically adjusting a data distribution strategy, and optimizing computing load distribution; s5, a multi-agent dominant actor-commentator algorithm is combined with a multi-arm tiger machine strategy, and the layered structure of the Transform model is adjusted in real time; s6, performing partial model reasoning tasks on the edge equipment in combination with the edge reasoning capability; s7, protecting data privacy by adopting a federal learning strategy and utilizing a differential privacy technology; and S8, performing hardware acceleration by utilizing heterogeneous computing resources. According to the method, a cloud edge cooperative computing and intelligent scheduling method is adopted, and large model training and reasoning with efficient privacy protection are realized.

The invention discloses a hydropower station equipment state monitoring method and system, and relates to the technical field of hydropower station equipment monitoring, and the method comprises the steps: collecting the hydropower station equipment state through multi-layer data, and dynamically adjusting the collection frequency according to the change of the equipment state; performing signal quality evaluation on the collected data to generate fusion data; inputting the fused data into a trend analysis module, and generating a trend model of the equipment state in combination with historical data and real-time data; according to an analysis result of the trend model, combining the plurality of abnormal states into an event mode, and comparing the event mode with a known fault mode to determine a risk level; and generating a corresponding early warning signal based on the risk level, and performing corresponding response processing on early warning events of different levels. According to the hydropower station equipment state monitoring method provided by the invention, through multi-level data acquisition and dynamic frequency adjustment, the accuracy of data acquisition and the resource utilization rate are effectively improved, so that the system can adapt to different equipment states.

The invention discloses an SM4 encryption method based on an FPGA, and relates to the technical field of data encryption, and the method comprises the steps: obtaining to-be-encrypted plaintext data; a plurality of SM4 encryption cores are configured on the FPGA chip to perform parallel encryption operation on plaintext data to be encrypted; wherein each SM4 encryption core adopts a pipeline architecture, and the pipeline architecture comprises an access device, a round function arithmetic unit and a key adder; an extraction circuit based on physical noise and integrated on the FPGA chip is utilized to generate N-bit random numbers as encryption keys of each SM4 encryption core; handshake signals are collected to calculate the operation efficiency of each SM4 encryption core; adjusting FPGA resource allocation among the SM4 encryption cores by adopting a priority scheduling algorithm according to the operation efficiency; in the encryption operation process, power consumption equalization is carried out by adopting polynomial and logical operation hybrid calculation, and time sequence randomization is carried out by adopting a random waiting period. Aiming at the problem of weak anti-difference sensitivity in the prior art, the side channel attack resistance is improved.

The invention relates to the technical field of storage equipment management, in particular to an intelligent control method and system for storage equipment, and aims to solve the problems of task allocation, path optimization and equipment collaboration in a complex storage scene. According to the method, a storage equipment function model comprising an upper-layer frame model and a lower-layer equipment model is constructed, task allocation and path planning are optimized by using a neural network model in combination with task requirements, and a storage equipment control instruction is generated. In the task execution process, task priorities and path planning are adjusted in real time to adapt to dynamic environment changes, and it is ensured that tasks are completed efficiently. According to the method, a task distribution diagram and a path planning scheme are further optimized through a dynamic task diagram and a dynamic energy field, the problems of equipment congestion, too high energy consumption and task delay are effectively solved, the collaboration efficiency and the resource utilization rate of storage equipment are improved, and the method is suitable for various complex storage scenes.

The invention provides a private domain traffic peak identification and route scheduling method and system based on time sequence prediction, and relates to the technical field of private domain traffic management, and the method comprises the steps: predicting traffic data and confidence through a multi-dimensional heterogeneous graph network and a causal reasoning mechanism, and generating a dynamic threshold based on empirical mode decomposition and a generalized extreme value distribution model, and predicting a peak value by combining a fuzzy inference device. And then, constructing a distributed collaborative early warning mechanism, and generating graded early warning signals and early warning grades according to the multi-dimensional early warning indexes and node consensus. And finally, constructing a multi-target routing optimization model, performing routing resource grouping based on the link reliability score, searching candidate paths by using multi-agent reinforcement learning, determining a distribution proportion according to early warning information and a peak prediction result, optimizing a path selection strategy in a peak time period, and scheduling private domain traffic to a selected path. According to the method, the private domain flow peak value can be effectively identified, the service quality is guaranteed through dynamic routing scheduling, and the user experience is improved.

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 game theory improved model-based cloud computing node storage calculation training resource competition scheduling method. The method comprises the following steps of S1, constructing a global resource scheduling system; s2, carrying out initial division on the cloud computing nodes in each logic partition; s3, executing a local resource competition game model; s4, the global resource scheduling system collects a local resource competition result of each logic partition and residual available resources in each logic partition; s5, executing the global collaborative game model; s6, dynamically updating the resource state information of the cloud computing node; s7, the global resource scheduling system performs adaptive adjustment on resource demand strategies in the local game model and the global cooperative game model; and S8, the global resource scheduling system stores and analyzes past resource competition results and task execution conditions, and each cloud computing node updates a resource demand strategy according to the data. According to the invention, the problems of resource bottleneck and overload in the prior art are solved, and the resource utilization efficiency and the scheduling flexibility are greatly improved.

The invention discloses a large model service platform for high-efficiency concurrent computing, particularly relates to the technical field of data analysis, and realizes optimal management of the large model service platform for high-efficiency concurrent computing through a task allocation module, a node health assessment module, a node division module, a dynamic adjustment module and a fault identification module. The task distribution module reasonably distributes computing resources according to task priorities and real-time monitoring data; the node health assessment module accurately assesses node health conditions through preprocessing and feature extraction; the node division module classifies the nodes according to the health assessment result; the dynamic adjustment module dynamically adjusts a resource allocation strategy by using fuzzy logic; the fault recognition module recognizes and repairs fault nodes in time by further analyzing the health conditions of the high-load nodes; the overall performance and the resource utilization rate of the system are improved, efficient and reliable execution of intelligent financial services is ensured, and the stability and the reliability of the system are enhanced.

The invention provides an unstructured database federated learning collaboration method and system based on swarm intelligence, and relates to the technical field of structured data, and the method comprises the steps: training a query pattern recognition sub-model, a path planning sub-model and a resource allocation sub-model through federated learning, and constructing a swarm intelligence collaboration network. After a user query request is analyzed by the query mode recognition sub-model, candidate query paths are generated and optimized by fusing an ant colony algorithm and a particle swarm algorithm, and a query execution scheme is generated. And the resource allocation sub-model performs resource scheduling accordingly. And generating a performance optimization target for updating local model parameters of each node, and fusing the updated parameters through a security aggregation algorithm to guide the next query optimization. According to the method, the query efficiency of the unstructured database is improved, and the resource consumption is reduced.

The invention provides a crop accurate management method and system based on a crop growth model. The method comprises the following steps: collecting environment data of a crop growth period by integrating a remote sensing technology, a field intelligent sensor and an unmanned aerial vehicle, and carrying out space-time alignment to form a comprehensive environment data set; and dynamically adjusting parameters of the crop growth model by using a Bayesian rule, evaluating the influence of environmental uncertainty on crops in combination with Monte Carlo simulation, and providing yield prediction with a confidence interval. Based on the prediction, a particle swarm optimization algorithm is applied to formulate management schemes such as irrigation, fertilization, pest control and harvesting opportunities, and meanwhile, a multi-objective optimization strategy is adopted to balance economic benefits and environmental protection. After the scheme is implemented, the crop growth response is monitored in real time so as to reinforce learning and automatically adjust management measures, and precise agricultural management is realized. According to the technical scheme provided by the embodiment of the invention, the crop management precision and efficiency are improved.

The invention discloses a containerized resource dynamic allocation method for a power distribution network system and electronic equipment. The method comprises the following steps: judging that resource use data is greater than a preset resource threshold value by monitoring the resource use condition of each physical node in a power distribution network system; if yes, calculating a load balancing index of each physical node, and generating a container migration task list based on the load balancing index, the service priority and the service quality requirement information; packaging container data corresponding to the to-be-migrated container in the migration task list, and determining container state snapshot information and network connection information of the to-be-migrated container; establishing a secure channel between the source node and the target node based on the network connection information, and transmitting container data and container state snapshot information of the source node to the target node; reconstructing a container operation environment on the target node, and generating a target container; and suspending the to-be-migrated container on the source node, and activating the target container on the target node. And efficient migration of the container is realized.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention discloses a robot pose estimation method based on laser point cloud and visual SLAM, and relates to the technical field of robot sensing and positioning, and the method comprises the steps: in a data collection and integration link, installing a specific camera and a radar to collect and compress data; a data preprocessing and feature strengthening stage: processing the image and the point cloud to improve the quality and extract the features; a cross-modal network is constructed through feature fusion and accurate matching, and a special matching mode is adopted; space-time calibration and coordinate unification are realized by means of a high-precision circuit; multi-modal fusion and reinforcement learning are applied to loop detection and intelligent optimization; correcting filtering and visualizing uncertainty by combining pose smoothing and intelligent output with a prediction model; according to the method, the pose estimation precision, reliability and environmental adaptability of the robot are effectively improved, resource utilization is optimized, the pose uncertainty is visually evaluated, and the overall performance and the intelligent level of the robot are improved.

The invention discloses a coastal port pile foundation structure load calculation method based on a coupling mode, and the method comprises the steps: a hydrodynamic module which evaluates the load and mechanical properties of a port pile foundation structure under different environment conditions; the water power module at least comprises an atmospheric mode module, a sea wave mode module, an ocean mode module and a coupler module; wherein the coupler module exchanges and synchronizes data among the atmosphere mode module, the sea wave mode module and the ocean mode module; the data conversion module is used for converting the wind field data, the wave field data and the flow field data obtained by the hydrodynamic force module into aerodynamic force and hydrodynamic force input files of the pile foundation structure; the finite element analysis module is used for receiving the aerodynamic force and hydrodynamic force input files, establishing a port pile foundation structure model and calculating stress distribution and displacement response of a pile foundation structure, and simulation results output by the finite element analysis module include but not limited to a stress nephogram, displacement field distribution and a buckling phenomenon of a pile foundation.