420results about How to "Optimize scheduling" patented technology

The invention discloses a logistics park vehicle path planning method and system, and the method comprises the steps: obtaining the real-time position and task state information of a vehicle in a park, dynamically constructing a vehicle path planning network model, dividing a road into nodes and edges, and forming a directed weighted graph; according to the directed weighted graph, an improved multi-lane algorithm is adopted, the path length, the congestion degree and the vehicle characteristic factors are comprehensively considered, and the optimal path of each vehicle is calculated; for the optimal path, introducing a virtual lane and a time dimension, pre-judging occupation conditions of multiple vehicles on the virtual lane for each time slice, and constructing a conflict constraint matrix; obtaining park high-precision map data, extracting road network topology, lane lines and traffic sign marking elements, performing lane-level refinement on the optimal path, and generating a driving track; and through a multi-sensor fusion positioning technology, obtaining high-precision position and attitude information of the vehicle, matching the high-precision position and attitude information with the driving track, and calculating a track tracking error.

The invention provides a first-aid platform work order management method based on digital twinning and artificial intelligence, and belongs to the technical field of information technology application innovation, and the method comprises the steps: collecting historical first-aid data and real-time data of a first-aid scene, and constructing a task scheduling system based on a knowledge graph; decomposing the task into a plurality of sub-tasks according to the work order data, and allocating an optimal first-aid resource combination for the sub-tasks; an optimization strategy is set to carry out collaborative allocation control on each resource, and task allocation is optimized; the optimized task allocation is monitored in real time, and meanwhile, in combination with real-time data flow monitored in real time, it is ensured that the change of the dynamic environment is responded at any time in the task execution process; and re-inputting the optimized task allocation and resource scheduling strategy into the knowledge graph, and carrying out closed-loop optimization. According to the invention, the problems of inaccurate task scheduling, weak real-time response capability, low resource utilization rate and the like in the existing work order management system of the emergency platform are effectively overcome, and the overall efficiency and the automation level of the emergency platform are greatly improved.

The invention relates to an information transmission method and device and a storage medium, and the method comprises the steps: S1, obtaining a predicted value of information transmission efficiency based on real-time network bandwidth data; s2, based on the predicted value of the information transmission efficiency, in combination with a real-time network load, carrying out capacity segmentation adjustment and sending priority configuration of the data packet, and generating data packet transmission configuration; s3, calculating data packet transmission time based on data packet transmission configuration in combination with a real-time network bandwidth, and determining a compression level of the data packet; s4, generating a hierarchical compression data packet based on a compression hierarchical calculation result; and S5, based on the hierarchical compressed data packets, performing error detection and correction in the transmission process, and performing data integrity verification on the transmitted data packets to generate an information transmission verification result. Compared with the prior art, the method has the advantages of reducing data transmission time, improving network utilization rate and the like.

The invention belongs to the technical field of power prediction, and discloses a new energy power prediction method and system based on multi-source meteorological data fusion, and the method comprises the steps: obtaining meteorological data from different meteorological data sources in real time or at regular time through a data obtaining module; carrying out preliminary screening and preprocessing on the obtained data by utilizing a data processing module; the data fusion module is used for integrating meteorological data from different sources by adopting an advanced data fusion algorithm to eliminate data redundancy; a mathematical model or a machine learning model suitable for new energy power prediction is constructed by using the prediction model module based on meteorological data of multi-source fusion; and the result output and evaluation module is used for outputting the prediction result to a user in a visual form, and carrying out real-time or regular evaluation on the prediction result. The method has the characteristics of diversified data sources, advanced data fusion technology, flexible prediction model construction, high real-time performance and accuracy, wide application scenarios and the like.

The invention relates to the technical field of dynamic scheduling, in particular to a dynamic optimization scheduling algorithm for a photovoltaic energy storage system, which is characterized in that based on a working environment of the photovoltaic energy storage system, environment illumination intensity data is acquired in real time, average power generation under the same illumination intensity is extracted, and weather conditions in a future time period of a current region are received through weather forecast data; and predicting the generating capacity in a future period to obtain generating capacity evaluation information. According to the invention, by capturing the environment illumination condition in real time and combining the historical power generation data, the future power generation condition of the photovoltaic system is effectively predicted, the load change of the power grid is monitored in real time, and the influence brought by specific activities is considered, so that timely response can be made when the demand of the power grid fluctuates, and the supply and scheduling of electric energy are optimized; according to the invention, continuous monitoring and abnormity processing are carried out on the power generation equipment, and the abnormal condition of the equipment is handled by correcting the discharge capacity in real time, so that the risk in operation of the energy storage system is reduced, and solid technical support is provided for stable operation of a power grid.

The invention discloses an intelligent decision analysis system and method based on multi-source data, and belongs to the technical field of artificial intelligence. According to the invention, multi-source data related to bus operation is acquired, real-time acquisition and uploading of bus operation data are realized, data preprocessing is carried out, and the multi-source data are integrated; feature engineering is carried out, and a passenger flow prediction model is established; based on a passenger flow prediction model, obtaining predicted passenger flows in different time periods of the day, and predicting a driver state; establishing a bus scheduling model by using a deep learning model, and inputting predicted passenger flow, predicted driver state, historical data of bus operation and urban traffic condition data to obtain a bus scheduling scheme on that day; when the dispatched buses in a certain period of time of the bus route are not enough, a route from a bus station closest to a starting station of the bus route to the starting station is obtained, and idle buses are dispatched; and if no idle bus exists in the bus station, the buses of the rescue bus route allowed to be dispatched are dispatched.

The invention relates to a fishing light system photovoltaic power prediction method considering environment change and photovoltaic panel aging, and the method comprises the steps: obtaining fishing light system data, carrying out the power index analysis, classifying weather types, and obtaining a data set of each weather type; analyzing the correlation between each meteorological factor influencing the photovoltaic power generation and the photovoltaic power generation power, and screening out main influence factors; based on a photovoltaic array mechanism model, calculating theoretical power values of parameters in the data set, and identifying and optimizing power abnormal points; the relative aging degree of the photovoltaic module is calculated, so that the photovoltaic output power in the data set is corrected; searching historical data of similar days from the data set according to measured data of a prediction day, and constructing a sample set to perform iterative training on the neural network; and inputting the actually measured data of the prediction day into the trained neural network to obtain a photovoltaic power prediction result. Compared with the prior art, the method effectively improves the accuracy of power prediction of the fishing light system and the effectiveness of power supply.

The invention relates to the technical field of aerospace, in particular to a vertical take-off and landing airport scheduling method and system, electronic equipment and a storage medium, and the method comprises the steps: obtaining the state information of each aircraft associated with a vertical take-off and landing airport within a preset time period; and determining at least one scheduling strategy of the vertical take-off and landing airport based on the overall structure of the vertical take-off and landing airport and the state information of each associated aircraft, and performing scheduling according to the optimal scheduling strategy. According to the method, the scheduling process can be more comprehensively optimized, the urban air traffic congestion is effectively relieved, the flight delay cost is reduced, and the overall operation efficiency of the vertical take-off and landing airport is improved.

The invention discloses a tobacco enterprise composite scheduling method based on multi-agent collaborative optimization, and the method comprises the steps: obtaining the system data of a tobacco enterprise, and obtaining a plurality of corresponding decision-making schemes based on a sales prediction agent, a production plan agent, a purchase stock agent, and an inventory management agent; performing iterative optimization on the decision scheme based on a Nash equilibrium game and a Lupinstein bargaining model, and calculating a system utility value to obtain an optimal decision scheme; according to the optimal decision-making scheme, based on an adaptive mechanism, adaptive optimization is carried out, and an adjusted decision-making scheme of each agent is obtained, so that tobacco enterprise composite scheduling of multi-agent collaborative optimization is realized; the sales prediction agent, the production plan agent, the purchase stock agent and the inventory management agent perform information interaction through an attention mechanism. Interactive cooperation among multiple agents is realized by introducing an attention communication mechanism and a game theory cooperation mechanism, and overall optimization of tobacco enterprise scheduling is promoted.

The invention discloses a new energy power prediction monitoring platform and method for power grid dispatching optimization, and relates to the technical field of power systems, and the method comprises the steps: determining a distribution diagram of a target new energy power station, collecting the historical data and weather forecast data of the power station, and obtaining a plurality of weather forecasts through parallel calculation. And based on weather forecast and historical data, establishing a weather power prediction model. And generating a plurality of power prediction results through the prediction model according to user requirements, performing rolling prediction according to the new energy type, and continuously optimizing the model based on the prediction results. And adjusting a scheduling plan and a power transaction strategy of the power grid through a plurality of power prediction results so as to optimize the utilization of new energy and the scheduling of the power grid. The technical problems of inaccurate new energy power prediction and untimely response in the prior art are solved, and the technical effect of improving the new energy power prediction precision and real-time performance is achieved.

The invention discloses a visual network freight platform transportation transaction management system. The system comprises an order management module, a vehicle scheduling module, a transportation monitoring module, a transaction settlement module and a visual display module. Each module has innovative functions, such as order intelligent classification, vehicle optimization scheduling algorithm to minimize comprehensive cost, real-time monitoring and accurate settlement. The corresponding management method adopts advanced technologies to ensure efficient operation of each link, including block chain recording, intelligent contract settlement and the like. The whole system realizes efficient management, real-time monitoring and convenient visualization of transportation transaction of a freight platform, and improves the efficiency and quality of logistics transportation; according to the visual network freight platform management system, efficient and accurate order management is realized, vehicle scheduling is optimized, the cost is reduced, comprehensive and real-time transportation monitoring is guaranteed to be safe, safe and accurate transaction settlement is realized, convenient and visual display is convenient to make decisions, and the logistics transportation efficiency, quality and user experience are improved.

The invention discloses an electric power-traffic collaborative balance scheduling method and system for electric vehicle charging, and belongs to the technical field of electric power systems, and the method comprises the steps: obtaining the travel charging demand information, charging station information and traffic network information of each electric vehicle in real time; constructing a power-traffic coupling operation network model which comprises a power network model, a traffic network model and a coupling network model; using a multi-agent multi-layer graph convolutional learning algorithm to perform power-traffic collaborative balance scheduling, using a preposed graph convolutional network to perform coupling feature representation and environmental state perception on the coupling network model to obtain the coupling feature of each node, and using a charging station as an agent to perform power-traffic collaborative balance scheduling; and inputting the coupling characteristics of the node where the charging station is located into the state space as a part of observed quantity of the intelligent agent, and outputting a charging scheduling decision of each electric vehicle through a target strategy network and a target value network in the intelligent agent. The electric vehicle charging routing behavior can be optimized, and real-time balanced scheduling of the electric power-traffic network is realized.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention discloses an intelligent centralized control chain distributed multi-energy optimization scheduling device and method, and relates to the technical field of energy management. The method comprises the steps that energy samples are collected, energy characteristic indexes are established, and energy grading is conducted according to the indexes; historical power load data and load influence factor data are collected, and a power load prediction sequence is obtained by using the multi-time scale power load prediction model; collecting power supply and power grid data of the power system, calculating the power supply capability, and obtaining the power supply and demand balance degree according to the power load prediction sequence; and finally, forming a multi-energy optimization scheduling strategy according to the supply and demand balance degree and the energy classification. The device comprises an energy grading module, a load prediction module, a power supply and demand balance degree calculation module, an optimization scheduling module and a real-time monitoring and dynamic adjustment module, and is used for realizing the method. Through grading and optimal scheduling of energy, the energy utilization efficiency is improved, the operation cost is reduced, and the environmental pollution is reduced.

The invention relates to the technical field of equipment management, in particular to a medical beauty equipment cloud management system and management method. The method comprises the following steps: acquiring a medical beauty equipment number and medical beauty equipment parameters; carrying out cloud platform data uploading on the medical beauty equipment parameters based on the medical beauty equipment number, and generating medical beauty equipment monitoring data; consumable information digital registration is carried out on the medical beauty equipment monitoring data, and a beauty equipment consumable digital registry is generated; the method comprises the following steps: acquiring beauty equipment consumable data through a beauty equipment consumable digital registry to obtain beauty equipment consumable sensing data; consumable loss influence analysis is carried out on the beauty equipment consumable sensing data, and a consumable state monitoring data set is generated; and performing consumable service life prediction on the consumable state monitoring data set, thereby generating consumable service life early warning data. According to the invention, through real-time monitoring, intelligent analysis and dynamic adjustment of the cloud platform, the accuracy and comprehensiveness of cloud management of the medical beauty equipment are improved.

The invention belongs to the field of wind power operation and maintenance, relates to a data analysis technology, and is used for solving the problem of low fault processing efficiency caused by the fact that scheduling of operation and maintenance personnel cannot be optimized in combination with an equipment operation state and a risk degree in the prior art. The intelligent management system comprises a wind power operation and maintenance platform, and the wind power operation and maintenance platform is in communication connection with a data acquisition module, an equipment operation evaluation module, an equipment risk evaluation module, an operation and maintenance grading module and a task distribution module. The equipment operation assessment module and the equipment risk assessment module assess the operation state and the risk level of the operation and maintenance equipment, and the operation and maintenance grading module and the task allocation module are used for sorting the operation and maintenance priority of the operation and maintenance equipment and the operation and maintenance capability of operation and maintenance personnel and allocating operation and maintenance tasks to the corresponding operation and maintenance personnel; the operation and maintenance task sequence and the operation and maintenance personnel sequence can be combined, scheduling of the operation and maintenance personnel is optimized, and the fault processing efficiency is improved.

The invention provides an edge node, a scheduler and a dynamic scheduling method and system of an edge computing scene. The scheduler calculates network quality key indexes among the edge nodes according to round-trip time and loss conditions of the test data packets recorded by the edge nodes; the scheduler determines an execution sequence and a scheduling strategy of the tasks; and the scheduler determines a dynamic scheduling strategy of the tasks according to the network quality key indexes among the edge nodes, the execution sequence and scheduling strategy of the tasks and the load conditions of the edge nodes, distributes the tasks to the edge nodes, monitors the execution conditions of the tasks in real time, and adjusts and optimizes the tasks as required. According to the method, the task can be dynamically adjusted according to the network quality, and the service is scheduled to the edge node meeting the requirement, so that the capability deficiency of an edge computing task scheduler in scheduling based on the aspects of network quality and bandwidth is made up.

The invention relates to the technical field of illumination night scene control, in particular to an urban travel illumination night scene interconnection control method and system based on a photovoltaic technology. The method comprises the following steps: acquiring scenic spot people flow monitoring parameters and photovoltaic illumination night scene equipment state data; carrying out spatial position positioning calculation on all the photovoltaic illumination night scene equipment, and carrying out spatial distribution analysis to obtain a plurality of equipment area distribution diagrams; carrying out regional electricity load calculation on the plurality of equipment region distribution diagrams based on the state data of the photovoltaic illumination night scene equipment, and carrying out electricity load demand prediction so as to generate electricity load demand prediction data of each region; and carrying out people flow distribution calculation on the scenic area people flow monitoring parameters based on the plurality of equipment area distribution diagrams, and carrying out short-term people flow situation prediction to generate people flow situation prediction data of different areas. According to the invention, illumination night scene control with efficient response and low energy consumption is realized.

The invention discloses a self-adaptive resource scheduling method for improving cluster reconstruction efficiency, and relates to the field of resource scheduling distribution, and the method comprises the steps: monitoring the resource use data of each node in a cluster in real time through a monitoring agent, and obtaining the health state of the node through a heartbeat mechanism; collecting input task requests, predicting the resource demand quantity of each task, and performing preliminary queue processing on the tasks to obtain a task queue group; setting a resource competition constraint according to the task queue group and the resource use data and the health state of each node, obtaining dynamic resource scheduling corresponding to each task by using an optimization algorithm, and allocating the tasks to each node; a resource scheduling dynamic graph is constructed, a visual interface is set, and the resource scheduling dynamic graph is arranged on the visual interface and used for displaying resource scheduling, providing a user-friendly interface and realizing high-efficiency scheduling of adaptive resources in a cluster reconstruction process.

The invention relates to the technical field of solid waste sorting scheduling, in particular to a real-time monitoring optimization method and device for a solid waste collection, transportation and sorting ERP system. The method comprises the following steps: obtaining a real-time solid waste sorting log based on a solid waste collection, transportation and sorting ERP system; solid waste carrying vehicle tracking is carried out on the real-time solid waste sorting log, and a carrying track distribution visual view is constructed; acquiring an omnibearing monitoring image of a real-time sorting site; equipment sorting behavior visual identification is carried out on the real-time sorting site omnibearing monitoring image, dynamic sorting evolution is carried out, and a dynamic sorting twinborn model is constructed; carrying out multi-time-point solid waste sorting simulation on the dynamic sorting twinborn model, carrying out sorting load demand prediction, and generating a sorting load demand prediction value; and carrying out vehicle load calculation on the sorting load demand prediction value one by one, and then carrying out dynamic vehicle scheduling decision making to obtain a dynamic vehicle scheduling strategy. According to the invention, efficient and real-time solid waste sorting scheduling optimization is realized.

The invention provides a collaborative two-stage multi-runway departure flight ordering method, which is specially designed for relieving the congestion problem of a multi-runway airport in a peak period, ensures that the departure process of the flight is more efficient, and accords with scientific planning. The method comprises the following steps: constructing a multi-runway dynamic push-out rate control model to determine an optimal queuing threshold value of a taxiway; constructing a flight ordering and assigning model with the goal of minimizing flight delay and operation cost; and proposing a continuous Markov chain-based interactive whale algorithm, and carrying out flight push-out sequence and runway resource allocation. According to the method, the departure flight taxiing amount of the airport scene can be accurately identified, taxiway waiting time caused by runway occupation after the flight is launched is reduced, the actual significance is achieved for improving the flight punctuality rate and improving the overall airport operation efficiency, and the current green aviation development concept is met.

The invention discloses a task scheduling method for an intelligent fire-fighting management service platform, and relates to the technical field of fire-fighting informationization, and the method comprises the steps: collecting environment data and position coordinates, constructing a support vector machine (SVM) model, carrying out the task grade division of a monitoring region, and forming a task priority list; extracting a historical inspection record, calculating a deviation value between task completion duration and standard duration in the historical inspection record as an efficiency coefficient, and performing grade grouping on firefighters according to the efficiency coefficient; the position coordinates are converted into task distance parameters, the task matching degree is calculated in combination with the efficiency coefficient, a two-way selection queue is established, and firefighters are matched for the monitoring area according to a task priority list; and recording a task execution track of the firefighter through a positioning module, recording execution data of the task to a historical inspection record, and updating an efficiency coefficient to form a task scheduling mechanism. Through multi-dimensional data analysis and a dynamic evaluation mechanism, the task scheduling efficiency of the fire-fighting management platform is improved.

The invention discloses a multi-mode traffic demand prediction method based on a graph neural network, which is characterized in that technologies such as multi-source fusion, multi-graph processing and meta-information embedding are introduced, and a complex space-time dependency relationship of multi-mode data of an urban traffic system is captured; and carrying out traffic demand prediction on the fused multi-mode data by adopting a space-time diagram convolutional network, a diffusion convolutional recurrent neural network and a Graph-WaveNet network model. Compared with the prior art, the method has the advantages of adjusting distribution of traffic resources, optimizing scheduling, reducing congestion, improving traffic efficiency and the like, modeling is carried out by collecting spatio-temporal data of different traffic modes and combining multiple graph structures, high-precision prediction of multi-mode traffic demands is achieved, and the method is suitable for popularization and application. The method solves the problems of mutual influence between complex traffic modes in urban traffic, spatial heterogeneity and multi-mode data fusion, can be widely applied to the fields of intelligent traffic, urban planning, public traffic scheduling and the like, and has a good application prospect.

The invention provides a large-scale variable-frequency air conditioner accurate aggregation method based on external approximation Minkowski sum, which comprises the following steps: building physical models of a variable-frequency air conditioner, including a thermodynamic model, an electrothermal conversion model and a user comfort model; establishing an equivalent virtual battery model; representing an operation feasible region of the equivalent virtual battery model in a convex polytope semi-plane form, and performing aggregation by adopting an external approximation Minkowski sum method to obtain a feasible region of a variable frequency air conditioner cluster; and externally approaching the problem of redundant constraints generated by Minkowski and method aggregation, searching an optimal tangent plane of a feasible region after aggregation based on an auxiliary optimization method of linear programming, identifying and deleting constraints which do not belong to the feasible region, and obtaining an elastic feasible region representation form of the variable frequency air conditioner cluster. According to the method, the complexity of large-scale high-heterogeneity variable frequency air conditioner elastic adjustment feasible region description can be effectively reduced, and the privacy protection problem of a final user is compatible, so that unified management and scheduling of a power grid are facilitated.

The invention provides a maritime traffic accident severity prediction method based on feature engineering, and the method comprises the steps: constructing a maritime accident risk influence factor data set according to risk influence factors and accident severity in historical data, and carrying out the preprocessing of the data; coupling the influence factors through a feature fusion algorithm, and mining association rules; carrying out data balance analysis by using an SVM-SMOTE oversampling method, and balancing the sample proportion of serious accidents and non-serious accidents; the original influence factors are trained by using multiple machine learning models, and key influence factor subsets are screened out by calculating the importance degrees of the influence factors; and S4, evaluating an optimal model in the machine learning model through a UAR evaluation index, and predicting the severity of the marine traffic accident by using the optimal model and combining the key influence factor subset in the S4. By reasonably matching the combination of the prediction model and the oversampling method, the model performance is remarkably improved, and a comprehensive and profound insight is provided for predicting the severity of the marine accident.

The invention belongs to the technical field of data processing, and discloses a multi-source data information deep mining and fusion processing method oriented to performance maintenance, which comprises the following steps of: acquiring multi-source heterogeneous data representing the running state and the performance state of terminal equipment by using different data acquisition equipment, and performing data fusion processing on the multi-source heterogeneous data, and carrying out deep mining of an internal relationship on the fused data, determining key elements influencing the performance state of the terminal equipment, carrying out comprehensive diagnosis on the running state of the terminal equipment, and judging the change trend of the running state of the terminal equipment. According to the technical scheme, reliable evaluation can be carried out on equipment performance maintenance, so that the efficiency and the accuracy of equipment management are improved.

The invention discloses an air conditioner energy-saving operation optimization scheduling method and system. The method comprises the following steps that position coordinate data of all air conditioner units in a factory workshop are obtained; according to the position coordinates and the relative positions of the air conditioner units, the air conditioner units are divided into a basic air conditioner set and adjacent air conditioner sets; according to the spatial distribution relation of the air conditioning units, an influence relation graph between the air conditioning units is constructed; based on the heat generated in the workshop, the thermal load condition of each area is determined; based on the heat analysis result, the operation parameters of each air conditioning unit are set in combination with the mutual influence relation between the air conditioning units; scheduling optimization is conducted on the operation state of the air conditioning unit based on the operation parameters of the air conditioning unit; according to the air conditioner energy-saving operation optimization scheduling method and system, the problems of low energy efficiency, energy waste, unreasonable scheduling, lack of flexibility and the like of an air conditioner system are solved. Through intelligent optimization scheduling, the operation efficiency of the air conditioning system can be effectively improved, energy consumption is reduced, and the purposes of energy conservation and emission reduction are achieved.

The invention relates to a task scheduling method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a submitted target task, and adding the target task to a task priority queue; sorting all the tasks in the task priority queue based on the priority and deadline demand of each task in the task priority queue; task scheduling is conducted on the basis of the sequence of all tasks in the task priority queue, for the current scheduling task, a target computing node is determined on the basis of the transmission duration of data on which the current scheduling task depends and the earliest idle moment of each computing node, and the target computing node is used for completing the current scheduling task. By adopting the method, the task completion efficiency can be improved.

The invention discloses a regional power grid wind power generation power prediction optimization method which comprises the following specific steps: firstly, collecting data such as wind speed and wind direction through meteorological sensors arranged in a wind power plant and around the wind power plant, and carrying out cleaning and de-noising preprocessing; secondly, decomposing the meteorological data into intrinsic mode function components with different frequencies by adopting variational mode decomposition, constructing a convolutional neural network to perform feature extraction on each component, and adaptively adjusting the size of a convolution kernel according to the periodic characteristics of the data; then, a bidirectional long-short-term memory network based on an attention mechanism is used for fusing features, time sequence information is captured, power prediction is achieved through a full-connection neural network, and the weight of the FCNN is initialized through a random forest regression model to improve the initial performance of the model; according to the method, the prediction precision is improved through multi-level characteristic decomposition and a self-adaptive mechanism, the real-time response capability is good, and the requirements of the regional power grid for the precision and timeliness of wind power generation power prediction are met.

The invention provides a big dipper navigation-based large sprinkling machine walking path positioning and planning system, which comprises a map construction module, a path planning module, a real-time adjustment module and a monitoring management module, and is characterized in that the map construction module is used for importing basic geographic information data of a sprinkling irrigation area; the path planning module is used for selecting a proper path planning algorithm and setting related parameters; the real-time adjustment module is used for determining the environment information according to which adjustment is carried out and an adjustment rule; the monitoring management module is used for setting communication parameters with a remote monitoring center. According to the big dipper navigation-based large-scale sprinkling machine walking path positioning and planning system provided by the invention, the irrigation precision is improved, the position and environment information of the sprinkling machine can be obtained in real time by utilizing a big dipper navigation system and a sensor technology, and accurate positioning and path planning are realized. The irrigation coverage range of the sprinkler in a farmland can be more uniform, the situation of irrigation leakage or repeated irrigation is avoided, and the irrigation precision and effect are improved.