62results about How to "Enables real-time forecasting" patented technology

The invention discloses a traffic flow prediction method of a divergence convolution recurrent neural network based on a space-time diagram. The traffic flow prediction method is characterized in thata directed weighted graph of a road network is constructed based on spatial features of a traffic network, then a traffic flow prediction model of a graphic divergence convolution recurrent neural network is constructed by taking the directed weighted graph as a basic unit of prediction, deep learning is carried out by means of time-space characteristics of the traffic network, time-space prediction is carried out on the traffic flow of the traffic road network, a final traffic flow prediction model is constructed, and real-time prediction of the traffic flow is realized. The traffic flow prediction method has the advantages of precise prediction and high fitting degree.

The invention provides a method for constructing a photovoltaic power station generation capacity short-term prediction model based on multiple neural network combinational algorithms and belongs to the technical field of photovoltaic power generation, power grid connection technology and solar energy photovoltaic forecasting. The method overcomes the problem that a usually-used algorithm for constructing the photovoltaic power station generation capacity short-term prediction model is single and is likely to fall into local optimization, further resulting in big measurement error of the prediction model. The technical construction method of the invention is realized as follows: firstly using four different neural network algorithms to construct sub-models for neural network prediction; secondly screening and classifying weather information and analyzing the suitability of the various sub-models for neural network prediction; giving weighted parameter values of the sub-models in a combined model according to the suitability to further make the combined neural network model for prediction suitable for different weather conditions and then completing the construction of the photovoltaic power station generation capacity short-term prediction model. The method is mainly used for photovoltaic power station grid connection short-term prediction.

The invention relates to a sensor-based odor monitoring system of an organic waste treatment facility. The odor monitoring system comprises a cloud platform and a plurality of odor gas data acquisition devices; the odor gas data acquisition devices are responsible for collecting basic data in an environment and uploading to the cloud platform; the cloud platform is responsible for inputting the received basic data into calculation models in the cloud platform for operation and feeding back a result to an environmental monitoring department; the cloud platform comprises a server and an odor pollution forecast module; the odor pollution forecast module calculates an odor gas concentration value according to data transmitted by the odor gas data acquisition device, performs evaluation, forecast and early warning on an odor pollution situation and feeds back a result to the environmental monitoring department; a plurality of odor gas concentration calculation models are preset in the odor pollution forecast module; and a pollutant diffusion model is further preset in the cloud platform. The system can accurately monitor an environmental odor concentration and odor strength in real time, judges and forecasts odor pollution diffusion and performs the early warning on odor pollution.

The invention discloses a flight delay real-time probability prediction method based on a Bayesian network algorithm, and the method comprises the steps: formulating a flight delay judgment standard,analyzing a delay wave and the impact on flight delay, and determining the release fairness of departure flights; analyzing the delay characteristics, determining flight delay factors, and creating aflight delay dynamic prediction model based on a Bayesian network; adopting a dynamic prediction technology based on a time sequence to predict the present transverse wave and measurement index to obtain a final flight delay prediction value, and generating a prediction set; and carrying out probability prediction on prediction set data by utilizing the flight delay dynamic prediction model obtained by training, and obtaining a prediction value of each flight delay level by adopting a probability maximum principle. According to the invention, real-time probability prediction can be carried outon the departure delay level of a single flight of an airport every day, the flight delay prediction precision is improved, a delay early warning notice is issued to passengers in time, an operationstrategy is adjusted in time, and various adverse effects caused by flight delay are reduced.

The invention discloses a demand prediction method and system for shared equipment. The method comprises: acquiring positioning data of a target area within a predetermined time period; inputting the positioning data to a preprocessing layer of a pre-established neural network model, and processing the positioning data according to a preprocessing rule to obtain sample data corresponding to the target area; inputting the sample data to a hidden layer of the neural network model to carry out sample training; and after a prediction instruction is acquired, receiving current positioning data and inputting the current positioning data to the trained neural network model to obtain a prediction result. According to the method, the neural network model is established using a neural network algorithm, a large amount of sample data is trained by deep learning, and after the current positioning data is input, a prediction result of demand corresponding to the target area can be obtained, so that real-time prediction is realized. In addition, after the positioning data is acquired, the positioning data is preprocessed, so that the accuracy of the prediction result can be improved.

The invention discloses a mineral flotation froth image texture analysis and working condition identification method based on the Minkowski distance. The method includes the steps that firstly, froth image samples of mineral froth flotation under different working conditions are acquired and preprocessed; then, the preprocessed froth image samples are segmented through a watershed segmentation algorithm, each froth size is counted and acquired so that parameters in a Minkowski distance formula can be determined, a complex network model of froth images is established, and the energy and the entropy of the model are calculated and serve as froth image texture description indexes; finally, the acquired froth image texture description indexes serve as feature vectors of the image samples and a linear discriminant classifier is trained, so that the tested image samples are classified, and the working condition in the rear-time flotation process is identified. According to the method, calculation is simple, classification is high in accuracy, and the method can be used for real-time monitoring of the texture feature extraction, the classification identification and the flotation process working condition of the mineral flotation froth images.

The invention discloses an intranet attack early warning method. The method at least comprises the following steps: obtaining intranet attack event data regularly according to a first preset time interval; preprocessing the intranet attack event data to obtain a transaction data set, and storing the transaction data set in a database; generating an association rule from the transaction data according to an Apriori algorithm, storing the association rule in a database, and automatically updating the association rule; acquiring real-time attack event data regularly according to a second preset time interval, and preprocessing the real-time attack event data to obtain preprocessed data; matching the preprocessed data with the antecedent of the association rule through an exception analyzer toobtain a predicted unknown attack; and displaying the unknown attack and the association rule on a Web interface. According to the method, real-time prediction of the intranet attack event can be accurately realized, so that decision support can be provided for network information management personnel, and attack behaviors can be effectively prevented.

The invention relates to a method for quickly detecting an extracting solution of Ganmaoling granules by utilizing near-infrared spectrometry. The method comprises the steps of linarin content determination and/or chlorogenic acid content determination and/or solid content determination. According to the method provided by the invention, linarin content, chlorogenic acid content and solid content near-infrared online analytical methods are established in an extraction process of the Ganmaoling granules, and are applied to online detection, so that automatic detection and real-time prediction of various index contents of the extraction process of the Ganmaoling granules are realized; an established model is high in prediction precision, and high in accuracy, and meets the requirement of quantitative analysis in actual production; by the method, quick and efficient real-time monitoring and control can be carried out on quality of the extraction process, so that the safety, stability and effectiveness of the final product quality are favorably ensured.

The invention discloses a method for monitoring the gas flow development process in the blast furnace material distribution period and predicting the gas utilization rate. The method comprises the first step of data collecting and processing, the second step of infrared image processing, the third step of image feature extraction, the fourth step of material distribution period gas flow distribution dynamic changing model construction, the fifth step of gas flow center feature extraction, the sixth step of image and material surface position calibration, the seventh step of material distribution period gas flow center dynamic change model construction and the eighth step of gas utilization rate prediction model construction. According to the method, collected furnace roof infrared images are treated, the clustering algorithm, the statistics method, the feature recognition technology and the mode recognition technology are utilized for achieving dynamic tracking of the material distribution period gas flow distribution state and falling point distribution of the gas flow center on the material surface, the neural network algorithm is utilized for finding the relation between the material distribution period gas flow development process and the gas utilization rate, real-time prediction of the gas flow utilization rate is achieved, and assistance is provided for intelligent production.

The invention discloses a health level evaluation method of a thermal power generating unit based on data analysis, belongs to the field of thermal power generation, The aim of invention is to providea health level evaluation method of a thermal power generating unit based on data analysis to evaluate the operation status. The main points of the technical scheme are as follows: S1, acquiring historical operation data for a period of time from the real-time information monitoring and management system of thermal power generating units, screening the historical operation data, and selecting thehealthy operation data; S2, determining the parameters to be measured, selecting the independent variables related to the parameters, and carrying out curve fitting on the parameters and the independent variables related to the parameters to establish a mathematical model of each parameter; S3: establishing the scoring rules based on the mathematical models of each parameter. The method can be used to realize the real-time prediction of parameters and to judge whether the predicted value is normal or not.

The embodiment of the invention discloses a soil and rock stratum hidden danger information evaluation system based on same-hole measurement. The soil and rock stratum hidden danger information evaluation system comprises a multi-source information statistical analysis and storage unit, a same-hole monitoring device and a real-time prediction and early warning platform. Soil and rock stratum indexes obtained by the same-hole monitoring device are transmitted to the multi-source information statistical analysis and storage unit to be analyzed and processed, hidden danger information of the soiland rock stratum is evaluated through the real-time prediction and early warning platform, and collapse risk prediction and early warning are achieved. The method comprises the steps that a stratum core sample is drilled, a monitoring hole and an auxiliary detection hole radiating outwards with the monitoring hole as the center are formed, quantitative indexes of the physical property and the mechanical property of the soil body core sample are obtained, and a soil body soft stratum distribution model is constructed in combination with adjacent well geological survey data. The same-hole monitoring of geological, hydrological and deformation multi-source information is realized, the space-time four-dimensional dynamic information of the underground water level, the in-hole osmotic pressure, the deep displacement and the hole wall inclination angle can be acquired in real time for a long time in an unattended operation environment, the hidden danger monitoring cost of a soil layer and astratum is saved, the monitoring accuracy is improved, and the monitoring efficiency is improved.

The invention discloses an intelligent detection method for the water permeation rate of a membrane bioreactor MBR, and belongs to the field of online detection of water quality parameters of sewage treatment. Five process variables with high relativity to the water permeation rate are provided by using a feature analysis method based on biochemical reaction features in an MBR membrane sewage treatment process, a water permeation rate soft measurement technology is designed, and the water permeation rate soft measurement technology is inserted into an intelligent detection system, thus developing operation-facilitating human-machine interaction software; meanwhile, based on a functional requirement, the intelligent detection system for the MBR membrane sewage treatment process is designed; an intelligent water permeation detection system hardware platform, operation software and the water permeation rate soft measurement technology are integrated so as to form a complete intelligent water permeation detection system. The intelligent detection method can quickly and accurately forecast the size of the water permeation rate in the membrane sewage treatment process, and fills the blank of real-time detection of the water permeation rate in the MBR membrane sewage treatment process home and abroad.

The invention belongs to the field of control science and engineering, belongs to the field of environmental science and engineering, and relates to a soft sensing method of out-of-water TP (total phosphorus) for sewage treatment based on a reservoir network. Out-of-water TP concentration is an important monitoring index for urban sewage treatment plants and important index for water quality evaluation. In order to solve the problems, for example, out-of-water TP measurement process in current sewage treatment is complex, instrumentation and equipment are high in manufacture cost and high in maintenance cost and measurement results accuracy is low, the method of the invention employs principal component analysis to determine input variables of a soft sensing model; a reservoir structure optimizing algorithm based on contribution rate is designed, network structure is optimized, and network performance is improved; a soft sensing model of out-of-water TP is established based on a modified reservoir network to provide quick, effective and accurate measurement of out-of-water TP in sewage treatment, the level of real-time water quality monitoring is increased for urban sewage treatment plants, and normal treatment of urban sewage is guaranteed.

The present invention relates to a rib spalling real-time prediction method. A data processing main station, an electromagnetic wave receiving and transmitting device, a signal collector and a three-dimensional scanner are provided. The data processing main station is electrically connected with the three-dimensional scanner and the signal collector; the electromagnetic wave receiving and transmitting device is electrically connected with the signal collector; the three-dimensional scanner is configured to perform surface crack scanning and identification of the rib spalling and transmit the surface crack scanning and identification information to the data processing main station, and the data processing main station obtains the surface crack scanning and identification. The rib spalling real-time prediction method is reasonable in design, compact in structure and convenient to use.

The invention provides a brain disease prediction system, which comprises a mobile terminal, wherein the mobile terminal comprises an electroencephalogram signal acquisition device and a disease prediction module; the electroencephalogram signal acquisition device acquires the characteristic data of the electroencephalogram signal of a patient and sends the characteristic data to the disease prediction module; the disease prediction module receives the parameters of a prediction model of brain diseases of a patient, establishes the prediction model of the brain diseases of the patient according to the parameters, inputs the characteristic data of the electroencephalogram signal into the prediction model of the brain diseases of the patient, and performs real-time prediction on the brain diseases of the patient. The complex training task of the disease prediction model is completed outside the mobile terminal, the mobile terminal only receives the parameters of the disease prediction model and establishes the prediction model of the brain diseases of the patient according to the parameters, the requirements of low complexity and portability of the mobile terminal can be met, beforethe disease condition of the patient occurs, the system can quickly respond and remind the patient so as to greatly reduce the probability of the disease condition of the patient.

The invention relates to a method for rapidly detecting a preparation process of Qizhi Weitong granules with NIRS (near infrared spectroscopy) and an application. An NIRS online analysis method for a water extraction process, a volatile oil extraction process and an extracting solution concentration process of the preparation process of the Qizhi Weitong granules is established and applied to online detection, so that automatic detection of the preparation process of the Qizhi Weitong granules and real-time prediction of content of each quality control index are realized; an established model is high in prediction precision and accuracy and meets the requirement for quantitative analysis in actual production; compared with a traditional pharmacopeia method, the method has the advantages that on the premise that the accuracy is guaranteed, the analysis efficiency is increased greatly, the quality of the extraction processes can be monitored and controlled in real time rapidly and efficiently, and accordingly, guarantee of safety, stability and effectiveness of the quality of final products is facilitated.

The invention provides a shield utilization rate prediction and operation parameter optimization method and system based on SVR and PSO. The method comprises the following steps of 1, screening load and operation parameters from machine operation data, and preprocessing the screened data; 2, constructing a data set by using the preprocessed data, the geological type and the utilization rate of each ring, and dividing the data set; 3, establishing a shield utilization rate prediction model by using the SVR and the data set; 4, constructing an optimization equation with the maximum construction progress as a target and operation parameter changes, load changes and geological types as constraint conditions; and 5, obtaining optimal operation parameters under a specific geological type by using an equation established by PSO optimization, so that the construction progress is maximum. According to the method, the utilization rate can be accurately predicted, shield operation can be optimized, safe and rapid tunneling can be achieved, and good engineering application value is achieved.

The invention discloses a method and a system for predicting a passenger rate benchmark, and the method comprises the following steps: determining a core original variable in basic data of a data source, wherein the basic data of the data source represents a basic data set affecting the change of the passenger flow rate; performing data preprocessing on the core original variable to obtain a target data variable; processing the target data variable by using a preset regression model to obtain a target prediction variable; performing parameter adjustment on the target prediction variable to obtain a fitting model, and analyzing the fitting model to determine a regression equation; and taking the actual passenger flow rate as an input value of a regression equation, and predicting the targetpassenger flow rate of the prediction time in real time to obtain a prediction value of the target passenger rate. According to the invention, the real-time prediction of the passenger rate change isrealized, and the accuracy of the passenger rate prediction is improved.

The invention discloses a large-span bridge vibration response prediction method under typhoon action based on data driving. The large-span bridge vibration response prediction method comprises the following steps: S1, calculating a plurality of typhoon characteristic parameters, and extracting bridge vibration response caused by typhoon from bridge vibration response monitoring data; S2, dividingthe typhoon characteristic parameters and the bridge vibration response caused by the typhoon into a training set and a test set; S3, inputting the training set into a quantile random forest (QRF), obtaining an optimal hyper-parameter of the QRF by adopting a Bayesian optimization algorithm, comparing the importance of each parameter of typhoon characteristics by combining the optimal QRF, and determining a final input characteristic; and S4, inputting the corresponding typhoon characteristic parameters in the test set into the QRF according to an input characteristic comparison result, and taking an output value as a typhoon-induced response probability prediction value. For the large-span bridge vibration response prediction method, the accuracy and efficiency of the prediction result are obviously higher than those of other parameter optimization methods or finite element model methods, and the uncertainty of the prediction process can be considered.