20 results about "Real time forecasting" patented technology

The invention discloses a deep well rock mass instability failure time accurate forecasting and early warning system, and relates to the technical field of deep well rock mass failure early warning, and the deep well rock mass instability failure time accurate forecasting and early warning system comprises the following steps: collecting a plurality of deep well rock samples to carry out a uniaxial compression experiment, and collecting acoustic emission signals in real time based on an acoustic emission sensor to obtain original acoustic emission signal data; carrying out self-adaptive preprocessing, and carrying out data classification and integration to obtain rock sample acoustic emission parameter data; constructing an original damage forecasting model, and performing model training and verification optimization based on the rock sample acoustic emission parameter data to obtain a rock damage forecasting model; collecting an acoustic emission signal of the deep well rock mass, and predicting the instability failure time of the deep well rock mass in real time based on the rock failure forecasting model; the method is used for solving the problems that when an existing deep well rock mass damage early warning technology carries out early warning on rock mass instability damage through acoustic emission monitoring, single or few characteristic parameters are manually processed and analyzed, and the early warning reliability and timeliness are not high.

The invention relates to the technical field of meteorological disaster early warning, and provides an urban waterlogging early warning method and system based on grid meteorological prediction and an intelligent terminal, and the method comprises a data collection and local prediction step, a cloud fusion and regional prediction step, and a waterlogging evaluation and early warning response step. The lightweight AI model is deployed at an edge sensing terminal and an early warning terminal through a cloud edge-end collaborative architecture, local real-time prediction of meteorological data and dynamic fusion evaluation of waterlogging risks are realized, and the accuracy and efficiency of urban waterlogging early warning can be significantly improved.

The application provides a wind power short-term power prediction method, system and device affected by icing. First, weather data and icing data on the surface of a fan blade are acquired, the icing data is analyzed to obtain an icing type, then changes in the weather data are monitored and analyzed to obtain weather prediction data, and finally, the wind power short-term power is predicted in real time according to the icing type and the weather prediction data. The application can observe the icing structure characteristics from multiple directions, subdivide the icing structure, facilitate more detailed understanding of the type of the icing, better control the change trend of the icing, and make more reasonable prediction on the short-term power.

The invention provides a BMS (Battery Management System) control method and system based on active identification and reverse compensation, which are applied to a battery management system, and the method comprises the following steps: determining an SOC (State of Charge) initial value based on a standing voltage and an environment temperature; calculating an SOC correction coefficient according to the influence factors and dynamically updating the SOC correction coefficient; fusing an ampere-hour integral method and the correction factor to complete SOC estimation; carrying out real-time prediction and correction on the SOC by adopting an extended Kalman filtering algorithm and fusing voltage and current observation values; obtaining a current SOH value in combination with the internal resistance change rate and the capacity attenuation factor; and extracting a characteristic curve and comparing the characteristic curve with a reference sample to realize health state identification, then triggering reverse compensation of SOC estimation parameters according to an identification result, dynamically adjusting estimation parameters such as effective capacity and internal resistance, and improving SOC estimation precision under an aging working condition. The method is flexible in parameter updating, high in SOC estimation robustness and suitable for dynamic energy management and health management of the lithium iron phosphate battery pack in a complex working environment.

This invention relates to the field of meteorological disaster early warning technology, and provides a method and system for urban flooding early warning based on grid-based meteorological forecasting and intelligent terminals. The method includes: data acquisition and local forecasting steps, cloud fusion and regional forecasting steps, and flooding assessment and early warning response steps. Through a cloud-edge-device collaborative architecture, a lightweight AI model is deployed on edge sensing terminals and early warning terminals, enabling real-time local forecasting of meteorological data and dynamic fusion assessment of flooding risk, which can significantly improve the accuracy and efficiency of urban flooding early warning.

This invention provides a method for predicting water levels at multiple tide gauge stations during typhoons based on a dynamic attention graph cyclic network, comprising the following steps: acquiring water level data and meteorological data from multiple tide gauge stations during a typhoon; performing time-series feature processing on the acquired water level and meteorological data and creating a topology; defining a dynamic graph attention hierarchical aggregation network model structure and constructing a dynamic attention graph cyclic network model; training and optimizing the dynamic attention graph cyclic network model using the processed data and graph topology; and using the optimized dynamic attention graph cyclic network model to predict the water levels at multiple tide gauge stations along the coast in real time for the next A hours during a typhoon. This method effectively balances prediction accuracy and computational efficiency, solving the problems of insufficient prediction accuracy and wasted computing power.

The invention relates to a port coal moisture content prediction system and method, and belongs to the technical field of bulk material storage yard dust prevention and intelligent control. The method comprises the following steps: determining water content change data of different coal types under different meteorological conditions through experiments, and constructing a water-steam-heat coupling migration model; the method comprises the following steps: respectively establishing an evaporation rate prediction model based on multivariate nonlinear fitting and a moisture content direct prediction model based on a neural network according to coal type dusting characteristic classification, and determining an optimal prediction model through comparative analysis; the system calls the optimal prediction model according to meteorological data collected in real time and the current moisture content, dynamically predicts the moisture content at the next moment, compares the moisture content with the non-dusting critical moisture content, and generates an optimized water spraying dust suppression control instruction. Accurate and real-time prediction of the water content of the stacking surface and intelligent dynamic adjustment of the dust suppression strategy are achieved, the dust suppression efficiency and the water resource utilization rate are remarkably improved, and key technical support is provided for environment-friendly intelligent operation and maintenance of the open storage yard.

The invention relates to the technical field of deep learning time series prediction, and particularly provides an electrical load prediction method and device in typhoon extreme weather, and the method comprises the steps: obtaining typhoon feature data; taking the typhoon characteristic data as input of an electrical load prediction model corresponding to the target area, and obtaining electrical load prediction data, output by the electrical load prediction model, of the target area; wherein the wind data feature data comprises at least one of typhoon intensity information, typhoon proximity information and data time information. According to the technical scheme provided by the invention, the problem of power load prediction during a typhoon influence period is effectively solved, the light weight of the overall structure of the model is realized, the real-time prediction demand is supported, and the time requirement of a power system scheduling decision is met.

The invention relates to the technical field of electric power system dispatching, and provides a dispatching method and system for a wind and light energy storage system, and the method comprises the steps: obtaining the next-day theoretical power generation of wind power and photovoltaic power and the next-day power grid dispatching plan power; taking the maximization of the wind and light absorption rate and the minimization of the energy storage operation cost as targets, solving the combination of wind power, photovoltaic absorption power and energy storage charge and discharge power of the next day by using a particle swarm optimization algorithm, and taking the combination as a power generation plan of the next day and scheduling the system; when the system runs, wind power and photovoltaic real-time prediction and actual measurement power errors are collected regularly, and the larger the errors are, the smaller the scheduling step length is; and comparing the current plan power with the real-time required power of the power grid, storing energy for discharging if the power is insufficient, sharing wind power and photovoltaic power according to an expandable margin if the power is still insufficient, storing energy for charging if the power is excessive, and reducing the output according to the output down-regulation cost proportion if the power is still excessive. The method can solve the problem that a traditional scheduling plan is not flexible.

The invention provides an accumulated snow indication prediction method, system and equipment for earth surface solar radiation in an alpine region. The method comprises the following steps: acquiring multi-source environment parameter data of a target alpine region; performing identification based on the multi-source environment parameter data to obtain a key precursor signal state value in the snow accumulation process; performing feature fusion based on the state value of the key precursor signal in the snow accumulation process to obtain a multi-dimensional fusion feature vector of the state value of the key precursor signal in the snow accumulation process; constructing an earth surface solar radiation prediction model based on the multi-dimensional fusion feature vector; and performing real-time prediction according to the earth surface solar radiation prediction model to obtain an earth surface solar radiation prediction value in a future target time period so as to realize accurate prediction of earth surface solar radiation. According to the method, key physical parameters in the snow evolution process are identified and utilized as precursor signals, and multi-source data fusion and an advanced prediction model are combined, so that the prediction precision of the surface solar radiation of the high and cold region is remarkably improved.

The invention relates to the technical field of intelligent lighting control, and particularly discloses a public place lighting dynamic dimming method and system based on state opposition prediction and light resource collaborative redistribution. The method comprises the following steps: firstly, dividing an illumination area into a plurality of supply and demand grids, calculating a normal comfort probability and an emergency sensitivity factor of each grid based on historical data, and constructing a state opposite relationship between the grids; then, multi-dimensional environment data are collected in real time, and the emergency sensitivity triggering probability of each grid in the future time period is predicted; when the triggering probability of a certain grid exceeds a threshold value, the system automatically identifies an opposite grid set of the grid, the illumination of the high-probability grid is improved to emergency guarantee illumination, meanwhile, minimum fluctuation flexible load reduction is carried out on the collaborative grid, and dynamic redistribution of light resources in space and time is achieved. According to the invention, data-driven modeling, real-time prediction and smooth dimming control are fused, and energy conservation, comfort and emergency reliability are considered.

The invention relates to the technical field of unmanned aerial vehicle distribution, and particularly discloses an unmanned aerial vehicle coffee distribution cooperative scheduling method and system for low-altitude economy, and the method comprises the steps: determining a distribution node and a terminal node according to historical order data, and distributing a terminal unmanned aerial vehicle for the terminal node; dividing the delivery path according to the delivery address of the coffee order, combining the coffee delivery orders according to the classification result and the path division result, and generating a delivery packet; a distribution task and a distribution node transfer task are generated according to the distribution package, the distribution task and the distribution airspace height are issued to an unmanned aerial vehicle end, and the distribution node transfer task is broadcasted; a temperature change prediction model is constructed, a coffee delivery path is updated according to the delivery temperature predicted in real time, the delivery path is segmented in the delivery process, the delivery efficiency is improved, and the waste of the transport capacity of the unmanned aerial vehicle is reduced; the temperature change problem of coffee is fully considered, the final delivery temperature of the coffee is guaranteed, and the user experience is improved.

The invention discloses an automatic risk avoiding control method and system for urban inland inundation vehicles based on the Internet of Vehicles. The method comprises the following steps: detecting the fording depth in real time; if the fording depth is greater than a set value, acquiring waterlogging point information from an urban waterlogging disaster real-time prediction and early warning system; planning a driving path according to the inland inundation point information; and controlling the vehicle to run along the driving path to realize vehicle risk avoiding control. By detecting the fording depth in real time, acquiring the waterlogging point information and planning the driving path, the method dynamically adapts to the waterlogging environment change, prevents a driver from mistakenly entering a deep water area due to misjudgment, insufficient experience or poor sight, and greatly improves the safety of fording driving.

The invention discloses a method, a system, equipment and a medium for mass power equipment to access a cloud master station, and belongs to the technical field of power equipment technology, and the method comprises the following steps: after the power equipment is started, dynamic registration is carried out on a power communication gateway, the gateway distributes a local area network IP address, and data interaction between the power equipment and the cloud master station is carried out; a message header and message data complete information are formed, an HPLC frame is formed through link layer packaging and uploaded to a gateway, data conversion and address conversion are carried out, the gateway accesses a prepositive server of a cloud master station, a real service server is obtained and then is directly connected and sent to a fixed service cluster server, and the equipment and the master station negotiate and authenticate through a secret key, and the real service server is directly connected to the fixed service cluster server. The method comprises the following steps of: performing high-throughput equipment authentication and data transmission through message middleware, performing large-scale management, predicting a load in real time and dynamically expanding and shrinking the scale of a container, and has the beneficial effects that the system fault tolerance, expansibility and service continuity in a massive multi-service power equipment access scene are remarkably improved; and the transmission safety and the service continuity are ensured.

The invention discloses an earth surface solar radiation short-term prediction method based on a residual network and channel attention. The earth surface solar radiation short-term prediction method comprises the following steps: S1, obtaining weather forecast product data and topographic data; s2, preprocessing the meteorological and topographic data; s3, constructing a sample set; s4, constructing a convolutional neural network prediction model comprising a residual network module and a channel attention module; s5, carrying out training optimization on the prediction model; and S6, obtaining and applying an earth surface solar radiation short-term prediction model. According to the technical scheme, numerical forecasting data, topographic data and site observation data are fused, the convolutional neural network model with space-time embedding, channel attention and a residual structure as main bodies is combined, and the regional solar irradiance is predicted in real time. Adaptive feature screening can be carried out by incorporating a prediction structure of a space-time relationship; and predicting a prediction result of the solar irradiance of 0-72 hours in the area in a rolling manner day by day. In the loss function, the prediction standard deviation is improved through Gaussian weighting of daytime peak values.

The embodiment of the invention provides a wind turbine yaw angle real-time adjustment method, which belongs to the technical field of power generation, and specifically comprises the following steps: step 1, collecting parameter information of a target wind power plant, and carrying out meteorological data simulation on the target wind power plant by utilizing a mesoscale meteorological mode according to the parameter information to obtain a simulation result; and 2, taking the simulation result as an optimization background, and optimizing the yaw angle of each wind turbine in the target wind power plant by utilizing an improved particle swarm search algorithm. According to the scheme disclosed by the invention, wind characteristics such as incoming flow wind speed and wind direction at the site of the wind power plant are predicted in real time through the mesoscale mode WRF, and the optimal yaw angle matrix of the wind turbine at the fixed point of the wind power plant is optimized based on the wind turbine wake flow model capable of considering yaw and the improved particle swarm optimization; and the adjustment precision, the real-time performance and the adaptability are improved.

The invention is suitable for the field of photovoltaic power generation, and discloses a haze weather photovoltaic output prediction method, which comprises the following steps: acquiring real-time multispectral sky image data, real-time meteorological data and real-time solar radiation data in haze weather; extracting a real-time haze feature vector from the real-time multispectral sky image data; on the basis of the real-time haze feature vector and the real-time solar radiation data, a solar radiation attenuation real-time prediction value is obtained through prediction of a pre-constructed double-branch network structure; processing the solar radiation attenuation real-time prediction value and the real-time meteorological data to obtain a multivariate time sequence to be predicted; and inputting the multivariate time sequence to be predicted into a photovoltaic output prediction model constructed based on a time sequence model to obtain a photovoltaic output prediction result, and the method can perform high-accuracy prediction on the photovoltaic output in the hazy weather.

This invention relates to the field of aviation equipment technology, specifically to a method for early warning of airport runway icing risk by fusing neural networks, comprising the following steps: S1: Establishing an airport runway icing risk warning model by fusing a temporal convolutional network and a bidirectional gated recurrent unit; S2: Determining the model input quantities and performing normalization processing to eliminate the influence of dimensions; S3: Adding an attention mechanism to the fused model; S4: Establishing a Golden Jackal optimization algorithm model; S5: Improving the Golden Jackal optimization algorithm and using it to optimize the parameters; S6: Predicting the airport runway risk level; This invention, through the analysis of historical meteorological data, enables the model to predict the icing risk level in real time, helping airport managers to take countermeasures in advance and reduce safety hazards caused by runway icing.

The application belongs to the technical field of wind power generation equipment safety, and particularly relates to a wind turbine blade partition ice melting system and method based on multi-modal perception, which comprises a multi-modal perception module, an intelligent control module and an execution and energy saving module; the multi-modal perception module comprises a plurality of infrared array sensors arranged in an equidistant array along a blade leading edge, and a vibration accelerometer installed at a blade root, which are used for monitoring blade surface temperature distribution and vibration characteristics in real time; the intelligent control module comprises an edge computing unit integrating an AI algorithm based on a long short-term memory neural network (LSTM), and a partition control strategy, which are used for realizing real-time prediction of ice layer thickness distribution and partition control according to data collected by the multi-modal perception module. The application can effectively solve the problems of high energy consumption and high misjudgment rate of traditional ice melting technology, has the advantages of high detection precision, fast response speed, long service life and the like, and is suitable for anti-icing protection of various large wind turbine generators in a high-humidity low-temperature environment.