962results about How to "Achieve forecast" patented technology

The invention relates to the technical field of digital earth, in particular to a digital earth prototype system comprising a data receiving and fast processing sub-system, a metadata service sub-system, a model base sub-system, a space information database sub-system, a mesh calculation sub-system, a map application service sub-system and a virtual reality sub-system. With the adoption of the invention, it is able to construct a data resource sharing platform, a knowledge sharing platform, a computation resource sharing platform and a coordinated operation platform of the digital earth prototype system. Besides, with the establishment of the prototype system, the invention resolves relevant key technologies for the construction of a technology service platform for the application of space data and forms a service system for the application of space information in earth technology.

The invention provides a finite element prediction system for welding and solidifying a crack in virtual environment, which relates to the technical field of finite element prediction software for welding and solidifying a crack, and the complex simulation and the prediction processes of welding and solidifying a crack are simplified into the simple operation and settings of an interface. A preliminary processing subsystem comprises an interface operating module, an MSC.Marc interface document module, a subprogram.f file module and an invocation MSC.Marc software executing and computing module, and a post processing subsystem comprises a MSC.Marc software computed result processing module, a MatrixVB function module, a Matlab software module and a graphic display module; a solidified crack predicting subsystem comprises a resistivity curve loading module, a judgment module and a result output module. The application of the invention enables a common welding engineering technician to carry out the numerical simulation and prediction for welding and solidifying a crack; the computed result is processed, and the storage space of a result file is decreased; the result file is converted into a file format convenient for graphic display, and the computed results of a temperature field and a stress strain field are visually displayed in a graphic form.

The invention discloses a federation learning method, a system and a readable storage medium. The federated learning method includes the following steps: the data terminal performs federation trainingon the multi-party training samples based on the gradient descent tree GBDT algorithm, to construct a gradient tree model, wherein the data terminal is a plurality of, the gradient tree model comprises a plurality of regression trees, the regression trees comprise a plurality of partition points, and the training sample comprises a plurality of features, the features correspond to the partition points one by one; the data terminal performs joint prediction on a sample to be predicted based on the gradient tree model to determine a prediction value of the sample to be predicted. The inventioncarries out federation training on multi-party training samples through GBDT algorithm, realizes the establishment of gradient tree model, and is suitable for scenes with large data volume and can well meet the needs of realistic production environment through the gradient tree model. Forecast the sample to be forecasted jointly, and realize the forecast of the sample to be forecasted.

The invention discloses a device and a method for evaluating oil paper insulation aging and predicting the service life of a transformer, and belongs to the technical field of transformers of an electrical power system. The device comprises a long-term aging device, a polymerization degree test device, a tearing strength test device and the like. An accelerated aging test is performed in the long-term aging device at the temperature of 130 DEG C with an oil paper mass ratio of 20:1; furfuraldehyde content in oil and polymerization degree of a piece of insulation paper are measured; the insulation paper is subjected to a mechanical strength test; the concentration change of furfuraldehyde in oil during a process of insulation paper aging is analyzed; the paper and water content during a paperboard aging process are tested; and gas content in oil is tested. Most of transformers are subjected to insulation aging and quit operation primarily due to the fact that the mechanical strength of the insulation paper is lowered and cannot support sufficient insulation. The device and the method which are provided by the invention can evaluate the insulation state of the transformer and predict the residual operation life of the power transformer. Therefore, the examination and repair can be reasonably arranged according to the equipment aging condition, the optimization of equipment use ratio is facilitated; and the management level of power enterprises is improved.

The invention relates to an off-grid grid-connected hybrid photovoltaic power generation control system and an economical operation optimization method of the off-grid grid-connected hybrid photovoltaic power generation control system. The control system is composed of a photovoltaic array, a grid-connected inverter, a storage battery pack, a bi-directional inverter, a load, a power meter, a public grid, a demand response control system and a switch set. The photovoltaic array is connected into an alternating-current side through the grid-connected inverter, and the storage battery pack is connected into the alternating-current side through the bi-directional inverter. The grid-connected inverter is connected with the bi-directional inverter through a switch, and the public grid and the load are connected through the switch set (S1-S4). The whole off-grid grid-connected hybrid photovoltaic power generation control system is uniformly managed and controlled by the demand response control system. Through the turn-on and turn-off combination of the switch set, the system supports eight different operation modes to achieve the optimization of economic benefits and peak shifting of the grid, and the service life of the system is prolonged through the method of limiting the discharging depth and charging and discharging power of storage batteries.

The invention provides a prediction method of a road traffic flow based on a graph convolution network. The method comprises the following steps of collecting vehicle GPS data in the past period of time; integrating the vehicle GPS data with actual road network information data to obtain a road traffic flow characteristic matrix, and converting a road and a road intersection point in the actual road network through line graph conversion to generate a road adjacency matrix; and based on the road adjacency matrix and the road traffic flow characteristic matrix, acquiring a predicted value of traffic flow data of the road in the next time period by using a spatial characteristic and a time characteristic of traffic flow data of the integrated road of a GCN network and an LSTM network. Through integrally using the spatial characteristic of the road traffic flow data extracted by GCN and the time characteristic of the road traffic flow data extracted by LSTM, and combining a time period characteristic and a weather characteristic, the road traffic flow data is predicted, and a prediction effect is better than the prediction effect acquired through using only time characteristic or the spatial characteristic.

The invention discloses a graph neural network traffic flow prediction method and system based on an attention mechanism. The method comprises the steps of obtaining to-be-predicted urban traffic flowdata; constructing a road network map according to the road connection relationship; preprocessing the urban traffic flow data to be predicted; and inputting the road network map and the preprocessedresult into a pre-trained neural network based on an attention mechanism, and finally outputting a prediction result of the urban traffic flow. Roads and checkpoints are encoded according to the roadnetwork information, a road network graph structure is established according to the upstream and downstream relationship of the roads, vehicle passing data of the checkpoints is counted under different time dimensions nd summarized to form a road network traffic flow data table; a graph neural network formed by stacking multiple layers of attention modules is constructed, a time sequence attention mechanism and the graph attention network are used for modeling the traffic flow in the whole road network, and the future traffic flow condition of a specified checkpoint is predicted.

The invention discloses a network security situation awareness model and method based on an attack graph. The model comprises a data preprocessing module, an attack feature library construction module, an attack state identification module, a security situation evaluation module and a security situation prediction module. The method comprises: firstly, collecting data sets of different sources, and obtaining asset vulnerability threat data and asset attack threat data after preprocessing; further analyzing and training by utilizing the asset attack threat data and the principal component dataof the attack information, and establishing an attack feature library; matching the attack portrait, and determining the attack state of the current equipment; then, calculating a security situation value of the whole network by utilizing the asset attack threat data; and finally, predicting the next attack state through the attack and defense game matrix of the attack state diagram so as to predict the network security situation. A good situation awareness effect and a network security situation evaluation and prediction function are realized, and the model and the method have relatively goodpracticability, high efficiency and expansibility.

The invention relates to the network safety, and provides an enterprise network safety event management system comprising safety devices, a safety event data acquisition module, a safety event data standardization module, a safety event situation assessment and prediction module, a safety device management linkage defense strategy generation module, a state protection module and a system management module. The method comprises the following steps: the system automatically collects safety event information generated by the safety devices in the enterprise network; the system carries out standardization format transition for the collected safety event information; the system analyzes the safety event information after standardization format transition so as to obtain network information safety situation, and predicts network safety trends; the system carries out association analysis for safety devices disposition safety application strategy according to the network information safety situation and the predicted network safety trends, thus generating the linkage defense strategy and sending the strategy to the safety devices. The enterprise network safety event management system and method are suitable for enterprise network safety event management.

The invention provides a transfusion monitor. The transfusion monitor comprises a shell, a fluid amount collecting and controlling device, a WIFI (Wireless Fidelity) communication module and a power supply management module, wherein the fluid amount collecting and controlling device, the WIFI communication module and the power supply management module are respectively arranged on the shell. The fluid amount collecting and controlling device is used for collecting relevant parameters in a transfusion device, and obtaining the dripping speed and residual liquid measure by the relevant parameters so as to obtain the residual time of the transfusion, realize the prediction on the transfusion completing time and achieve an early warning aim. The invention further provides a transfusion monitoring system based on a cloud service which comprises a transfusion monitor, a wireless receiving and dispatching module, a nurse station computer group, a cloud server and a patient terminal, wherein the transfusion monitor is used for transmitting monitoring information collected by the transfusion motor to the nurse station computer group through the wireless receiving and dispatching module, the nurse station computer group is used for transmitting the monitoring information to the cloud server, and the patient terminal and the nurse station computer group can visit the cloud server to obtain the monitoring information.

The invention discloses a sample prediction method based on federation training, comprises the following steps: federating two aligned training samples by using an XGboost algorithm to construct a gradient lifting tree model, wherein the gradient lifting tree model comprises a plurality of regression trees, and a split node of the regression tree corresponds to a feature of the training sample; based on the gradient lifting tree model, jointly predicting the sample to be predicted to determine a sample class of the sample to be predicted or to obtain a prediction score of the sample to be predicted. The invention also discloses a sample prediction device based on federation training and a computer-readable storage medium. The invention realizes federated training modeling by using trainingsamples of different data parties, and further realizes sample prediction based on the established model.

The invention provides a near-bit geology guide detecting system, comprising an electricalresistivity detecting unit, a gamma detecting unit, a position sensing unit and an insulating dipole transmitting unit, wherein the electricresistivity detecting unit is used for measuring the electricresistivity a near-bit stratum to generate electricresistivity data; the gamma detecting unit is used for measuring near-bit gamma rays to generate gamma ray data; the position sensing unit is used for measuring near-bit inclination and an implemental surface to generate position data; and the insulating dipole transmitting unit is used for transmitting the electricresistivity data, the gamma ray data and the position data to the ground through a wireless electromagnetic signal channel. A sensor is close to the near bit as much as possible, so when a drill drills into the sensor, the sensor makes an early prediction to information of the front part of the drill and information of the periphery stratum of the drill, thereby the aim of geology guide is achieved during drilling process.

The invention relates to a laminated and partitioned automatic current control method applicable to a multi-target power grid, and belongs to the technical field of automation of power system dispatching. The method comprises multi-region control, multi-target control and laminated and partitioned coordinated control of section current; a laminated and partitioned power grid current coordinated control system and an adaptive control strategy; a sorting and allocating strategy which is used for adjusting the output of machine sets in real time, comprehensively takes voltage class, power grid region and energy-saving dispatching principle into account, and comprises a sorting and allocating strategy of generator unit clusters of different voltage classes in different power grid regions and a sorting and allocating strategy of generator units of same voltage class in the same power grid region; and partitioned independent super-short-term load forecasting control carried out in each control region of the power grid. The laminated and partitioned automatic current control method disclosed by the invention can be flexibly applicable to various power grid operating modes, and ensures that the power grid runs stably in an important power supply section circuit overhauling mode; and a hydro-power generating unit and a thermal power generating unit are controlled to generate power more consistently, and the method provides a powerful technical support for energy-saving power dispatching.

The invention provides a dynamic people flow early warning method based on mobile network signaling data. The method includes the steps of: (1) separating a people flow early warning target region and people flow early warning observation regions; (2) counting and obtaining the people flow transition probability of each observation region distributed along with the travel time; (3) simultaneously counting a people flow leaving ratio in each period at the target region; and (4) predicting the total customer number from each observation region into the target region at the statistics time according to the real-time people flow in the observation regions and the people flow transition probability distributed along with the travel time, and predicting the customer number left from the target region before the statistics time according to the real-time people flow in the target region and the people flow leaving probability in the period at the region, thereby predicting the people flow size at the statistics time, and judging early warning grades. According to the invention, the current mobile network information is fully used, the target region and the observation regions are introduced, the statistics of the people flow at the target region is realized according to the people flow conditions of the target region and the observation regions, and the people flow early warning at the target region is finished according to a designate standard of the early warning grades.

The invention discloses a method and a device for predicting cell interference. The method comprises the following steps: dividing a coverage area of a cell to be predicted into grids, and determining latitude and longitude information corresponding to each grid; acquiring latitude and longitude information of a base station in the cell to be predicted, and acquiring latitude and longitude information and signal transmission power of a base station in a cell adjacent to the cell to be predicted; calculating a distance from each grid to each base station according to the latitude and longitude information of each grid and the acquired latitude and longitude information of the base stations; and determining interference values of the cell adjacent to the cell to be predicted for each grid according to a propagation model, the distance from each grid to each base station and the signal transmission power of the base station. The method and the device can realize the prediction for the cell interference.

A method for monitoring fatigue strength of driver includes shining driver eye with infrared ray to obtain multiimage of different retina image at the same time, carrying out difference processing for collected images to obtain pupil images, using kalmen filter to trace pupil in real - time to obtain pupil characteristic parameter, processing the parameter to obtain maximum value of pupil size and real - time coroclisis percentage of pupil, calculating out PERCLOS value f and using BP network sorter to judge fatigue strength of driver based on obtained value f. The monitoring device is also disclosed.

The invention discloses a regional mountain torrent risk prediction method and system. The method comprises the steps: S1, predicting the weather information of all hills in a region based on a meteorological model; S2, screening out hills needing risk prediction based on the weather information; S3, dividing hills needing risk prediction into different risk prediction levels according to the basic information of the hills; wherein different risk prediction levels correspond to different risk prediction periods; S4, preliminarily determining disaster-causing factors for mountain torrent evaluation; S5, screening the preliminarily determined disaster-causing factors to obtain main disaster-causing factors influencing the mountain torrent; S6, training and generating a plurality of mountaintorrent risk prediction models based on the main disaster-causing factors; S7, several risk prediction models with the best performance are selected to be combined to form a final risk prediction model; and S8, performing mountain torrent risk prediction on the hill to be predicted. According to the method, the risk prediction of the regional hill is realized, the realization cost is low, the coverage is wide, the processing efficiency is high, and the safety of the hill is improved.

The present invention relates to a rolling bearing fault diagnosis method and system based on a convolutional neural network. The method comprises the following steps: the step S1, constructing a database configured to reflect the work condition of a rolling bearing; and the step 2, identifying the work condition of the current rolling bearing through the database. The rolling bearing fault diagnosis method and system based on the convolutional neural network obtain the vibration signals of the rolling bearing through an accelerometer, and the fault diagnosis method of the rolling bearing employs the powerful image identification capability of the convolutional neural network model to identify faults of the rolling bearing through deep learning of big data so as to realize the monitoring and forecasting of the faults of the rolling bearing and substantially improve the accuracy and the timeliness of the rolling bearing fault diagnosis.

The invention provides a simulation method for residual stress prediction of micro-milling nickel-based superalloy, belongs to the field of finite element simulation cutting, and relates to a finite element simulation method for residual stress prediction analysis in the cutting processing process of difficult-to-process materials of the nickel-based superalloy. According to the simulation method, the finite element simulation method is used; the elastic-plastic constitutive relationship of materials is considered; and a Johnson-Cook fracture failure criterion is used for judging the failure, so that residual stress prediction values under different cutting parameters can be obtained. A micro-milling cutter for experiments is shot into a picture through a scanning electron microscope; the picture is drawn into a micro-milling cutter solid model through software; a three-dimensional processed workpiece model is built; and the model is subjected to lattice division. The method has the advantages that the surface residual stress can be measured, and the residual stress in the depth direction can also be measured, so that the problem of measurement difficulty of the residual stress in the depth direction in the measurement process is solved. The cost is reduced, and the labor is saved. In a prediction model, the cutting parameters are variable; and the prediction on the surface residual stress of the difficult-to-process materials is effectively realized.

The invention discloses a vision auxiliary device and method for automatic landing of an unmanned aerial vehicle, and relates to the field of unmanned aerial vehicles. The problems that according to an existing vision auxiliary device which acquires and recognizes runways and the horizon through an airborne photographing device so as to sense the landing posture of the unmanned aerial vehicle, due to the lack of a multi-scale analysis mechanism, the landing requirement cannot be easily met, the posture and position calibration effect is poor, and deep prediction of the movement behavior of the unmanned aerial vehicle lacks are solved. The vision auxiliary device comprises a two-shaft rotary table, a rotary table motor, a power module, a single-view fixed-focus camera, a vision auxiliary information processing module and an information storage module. The vision auxiliary information processing module comprises a landing track re-planning module, a data comparator, an information filter, a computer vision processor based on image control, a computer vision processor based on position control, a first feedback correction device, a second feedback correction device, an information fusion parameter extractor and an RS485 communication interface. The vision auxiliary device is simple in structure, low in power consumption, high in efficiency and good in compatibility.

The invention discloses a social network link prediction method based on multi-source heterogeneous data fusion. Link prediction is carried out by using a social network based on geographic position information, wherein the social network comprises two heterogeneous data sources, namely a user relationship topological graph and a user sign-in record. The invention provides a mixed frame. The association between two heterogeneous data sources, namely a user relationship topological graph and a user sign-in record, in a social network based on geographic position information is fully captured through a model AL. The problem that the prediction result is inaccurate when a single data source in a social network based on geographic position information is used for link prediction is solved, andthe link prediction effect is effectively improved. And meanwhile, local sensitive hashing is applied to improve the calculation speed of deep learning for training and reduce the storage overhead.