45results about How to "Forecast in time" patented technology

The invention belongs to the field of intelligent transportation, and in particular relates to a method for predicting urban traffic flow based on time-space correlation. The method includes the following steps: 1) forecasting model training: generate corresponding forecasting models according to different time forecasting granularities; 2) real-time traffic forecasting: consistent with the model training process, the latest collected traffic will be used for traffic flow forecasting The data is added to the input end of the forecasting model, and then processed by the forecasting model to output the forecasted traffic for the next period. The invention accurately predicts urban road traffic flow, can realize intelligent traffic control and management, traffic information service, and provides real-time data for alleviating urban traffic congestion, and has significant social and economic benefits.

A regeneration water factory effluent residual chorine risk prediction method comprises: data monitoring and collection are performed so that an index monitored and collected is obtained; the data is transmitted to a server through a communication system; effluent residual chorine risk prediction, analysis and decision are performed by the server according to the collected data: the corresponding monitoring data stored in a data base of the server is read, the monitoring data is input in a water quality model and the effluent residual chorine risk prediction of technology is performed; the index influencing effluent residual chorine risk probability is judged according to a potential of hydrogen (pH) value, the water temperature, ammonia concentration and chemical oxygen demand distribution situation monitored under different risk probabilities; and solutions under different effluent residual chorine risk probabilities are made. The regeneration water factory effluent residual chorine risk prediction method is good in model prediction accuracy (the accuracy is higher than 95%) so that when facing water quality change, a researcher can predict effluent residual chlorine risk accurately in time. The regeneration water factory effluent residual chorine risk prediction method offers a certain reference to operation and management of an actual water factory.

The invention relates to a method for predicting the crystallization state of the sugarcane sugar boiling process. The method comprises the following steps of: establishing an original decision table by taking six factors comprising massecuite brix, massecuite temperature, vacuum degree, feeding flow, steam temperature and steam pressure as condition attributes and taking crystallization state level as a decision attribute, and performing discretization treatment on the original decision table; extracting a prediction rule of the crystallization state according to a rough set theory and storing the prediction rule of the crystallization state into an expert system knowledge base; performing rule matching by using an inference machine of an expert system according to the real-time value of the condition attributes and the existing rule of the knowledge base, and outputting the predicting result of the crystallization state; establishing an on-line crystallization state prediction learning model based on a support vector machine, and adding the predicting result of the crystallization state into the expert system knowledge base to realize on-line updating of the expert system knowledge base. The invention also provides a system for realizing the method. The system can accurately predict the crystallization state of the sugarcane sugar boiling process so as to effectively improve the automation level of the sugarcane sugar boiling process.

The invention discloses a thermal power generating unit variable-load speed predicting method based on a wavelet neural network. By establishing the wavelet neural network, timely, effective and active prediction for thermal power generating unit variable-load speed is achieved, prediction method is highly intelligent and prediction precision is quite high.

The invention discloses a cement finished product specific surface area prediction method and system. The method comprises the following steps: obtaining to-be-tested cement finished product data, wherein the to-be-tested cement finished product data comprises granularity data of the to-be-tested cement finished product at the current moment and a specific surface area experimental value of the to-be-tested cement finished product at the previous moment; the granularity data comprises a plurality of granularity values; the particle size values belong to different particle size ranges; inputting the to-be-tested cement finished product data into the trained specific surface area prediction model to obtain a specific surface area prediction value of the to-be-tested cement finished product,wherein the trained specific surface area prediction model is determined through a convolutional neural network algorithm and a back propagation algorithm. The method solves the problem of time-varying delay between the variable data and the to-be-measured index in the cement finished product specific surface area prediction process, and is low in measurement cost.

The embodiment of the invention discloses an energy consumption prediction method and device. A specific embodiment of the method comprises the steps of obtaining historical energy consumption data, historical energy consumption environment data corresponding to the historical energy consumption data and to-be-predicted energy consumption environment data corresponding to the to-be-predicted energy consumption data; and inputting the obtained historical energy consumption data, the historical energy consumption environment data and the to-be-predicted energy consumption environment data into apre-trained energy consumption prediction model to obtain to-be-predicted energy consumption data. The embodiment can be applied to the field of cloud computing, enterprise production conditions areinformed in real time by automatically collecting enterprise energy consumption data, and enterprise production condition prediction is carried out in time.

The invention discloses a neural network-based method for analyzing surface subsidence caused by metro excavation. The method is characterized in that a relationship between a horizontal distance from an excavation face to a ground monitoring point and surface subsidence at the monitoring point is studied; training is carried out by taking rock and soil mechanics parameters of each existing metro as input values and taking surface subsidence quantities at detection points in different positions of the horizontal distance between the excavation face and the monitoring point as output values through applying a neural network; surface subsidence situations above other metros that are about to be constructed are analyzed by using the network after the training; and the method mainly comprises related data preparation as well as simulation process prediction result and accuracy check. According to the method, the surface subsidence quantities at the detection points can be effectively predicted according to the rock and soil mechanics parameters of each existing metro. The method can be widely used in a metro excavation process and provides measurement bases for ensuring safety of ground buildings and preventing non-normal subsidence.

The invention discloses a gear box fault prediction, monitoring and diagnosis method and a corresponding device. The method comprises the steps: collecting working data of a gear box and train operation data; and carrying out intelligent analysis processing on the working data of the gear box and the train operation data, and predicting and obtaining fault data of the gear box. According to the invention, the technical problem that the fault of the train gearbox cannot be accurately predicted in time is solved.

The invention discloses a traffic control method and device and electronic equipment. The method comprises the steps of obtaining travel trajectories generated when a plurality of traffic objects movefrom a target departure area to a target arrival area; determining a target track meeting a first preset condition according to the travel track; determining a traffic mode corresponding to the target track as a target traffic mode; and carrying out traffic control on a target path corresponding to the target track according to the target traffic mode. The traffic experience of the traffic objectin the process of moving from the target departure area to the target arrival area can be improved.

The invention relates to an enterprise risk conduction prediction method and device based on graph features and a storage medium, and the method comprises the steps: 1, building enterprise associationgraph data based on bank data, and storing the enterprise association graph data in a graph database; 2, extracting enterprise-enterprise node pairs used as enterprise risk conduction prediction object samples from the graph database; 3, performing column division on the graph mode characteristics of the corresponding data for enterprise-enterprise nodes, and obtaining a risk conduction edge weight by utilizing logistic regression; 4, constructing a characteristic variable of a LightGBM algorithm model for the corresponding topological structure based on the enterprise-enterprise node pairs,and outputting to obtain each unilateral conduction probability result after the model is trained; 5, performing multilateral probability fusion on each unilateral conduction probability result to obtain a risk conduction integration probability result; and 6, performing logistic regression on the risk conduction integration probability result to obtain a final enterprise risk conduction prediction result. The method has the advantages of accurately predicting enterprise risks and the like.

The invention relates to the technical field of computers, and provides an emergency prediction method and a device, a storage medium and a server. The prediction method comprises the following steps:acquiring a monitoring video of a public place; detecting portraits contained in each frame of image in the monitoring video and positions of the portraits in each frame of image; performing posturerecognition on each portrait contained in each detected frame of image to obtain the posture of each portrait; according to the positions of the portraits in the continuous multi-frame images of the monitoring video, respectively drawing advancing routes of the portraits; and inputting the position of each portrait in each frame of image, the posture of each portrait and the advancing route of each portrait into a pre-constructed emergency prediction model to obtain the probability of generating various emergencies. According to the method, emergencies can be predicted in time, and the life and property safety of personnel can be protected.

The invention is suitable for the technical field of data processing, and provides a household appliance maintenance order prediction method, a device and terminal equipment. The method comprises the steps: obtaining and processing historical household appliance maintenance order data; carrying out eMD decomposition on the processed historical household appliance maintenance order data to obtain a first component and a residual component, and VMD decomposition is carried out on the first component; obtaining final data according to the residual component and the decomposed first component; dividing the final data into a feature set and a label set, dividing the feature set into a feature training set and a feature test set, and dividing the label set into a label training set and a label test set; constructing and training a to-be-trained order prediction model to obtain a household appliance maintenance order prediction model; and using the home appliance maintenance order prediction model to predict the home appliance maintenance order quantity in a preset time period. According to the invention, accurate data can be provided for the household appliance maintenance manufacturer, and the household appliance maintenance manufacturer schedules the maintenance workers according to the predicted household appliance maintenance order pre-quantity so as to complete the order in time.

The invention relates to a cemented filling body damage evaluation method, which comprises the following steps of: filling body sample measurement stress-strain, acoustic emission ringing counting and accumulated ringing counting experimental data in a metal mine are obtained, stress-strain, time-ringing counting and time-accumulated ringing counting curves are drawn, the damage rate D and the damage rate D'of the filling body are defined according to the sound emission ringing count of the filling body, when the value of D 'is gradually increased from 0 to a stage 1, the stage is an internal fine crack rapid increasing stage, when D' = 1, the initial stage is an elastic-plastic stage, after D '= 1, D' is gradually reduced from 1 to 0, the value of D 'is reduced to be smaller than or equal to 0.6, and when D is larger than 0.8, it is indicated that the filling body is developed to the damage stage, and the interior of the filling body is seriously damaged. According to the method, the damage rate D'is defined through sound emission ringing counting to judge the damage speed in the filling body, the damage stage of the filling body is further judged, the damage speed in the filling body can be accurately and timely pre-judged to achieve safe production, and the method can also be used for filling body-surrounding rock early warning in actual engineering.

The invention discloses a fault positioning method suitable for electric power equipment, and the method comprises the steps of building a forward modeling calculation model based on a multi-physical field simulation strategy, and carrying out the forward modeling simulation calculation of each single physical field in the electric power equipment through the forward modeling calculation model; performing inversion calculation according to observable data of the transformer acquired by the monitoring system to obtain loss distribution in the transformer; constructing a power equipment inversion model according to the forward modeling calculation model and the loss distribution; inputting the surface temperature of the transformer into the power equipment inversion model, and performing multi-physical field multi-parameter inversion optimization on the multi-point temperature of each component in the transformer by adopting a genetic algorithm to obtain a temperature field parameter distribution condition in the transformer; performing fault positioning on the electric power equipment according to the temperature field parameter distribution condition in the transformer. According to the invention, the fault abnormity of the power equipment can be accurately predicted in time, and an important basis is provided for operation and maintenance personnel to deal with abnormal phenomena.

The invention discloses a vehicle window fogging prediction method and system, electronic equipment and a storage medium. The method comprises the steps: predicting a corresponding real-time fogging parameter according to an obtained feature parameter, and the feature parameter comprises at least one of a vehicle parameter, a personnel parameter, an environment parameter and a road condition parameter; predicting a future fogging parameter according to the real-time fogging parameter; and judging whether the vehicle window fogs in the future or not according to the future fogging parameters. According to the invention, no additional equipment is needed, the characteristic parameters can be directly obtained through the existing equipment in the vehicle, and the real-time fogging parameters capable of reflecting the temperature and humidity of the vehicle window can be predicted through the directly obtained characteristic data. The future fogging parameter reflecting the future fogging trend can be predicted through the real-time fogging parameter, so that the future fogging condition of the vehicle window can be predicted timely and accurately.

The embodiment of the invention provides a preparation method of an intelligent indication label and a residual shelf life prediction method and device. The residual shelf life prediction method comprises the steps that an intelligent indication label image is collected, and the storage temperature of a to-be-detected product is obtained; wherein the color of the intelligent indication label changes along with the storage of the to-be-detected product; a redness value of the intelligent indication label is obtained according to the image, and a residual shelf life prediction model of the corresponding to-be-detected product is obtained according to the storage temperature; and the remaining shelf life of the to-be-tested product is obtained according to the redness value and the remainingshelf life prediction model. The residual shelf life prediction method and device provided by the embodiment of the invention obtain the redness value of the intelligent indication label by collectingthe image of the intelligent indication label, the remaining shelf life is obtained according to the redness value and the preset remaining shelf life prediction model, the automation level of remaining shelf life prediction is improved, time and labor are saved, and rapid, timely, lossless and low-cost remaining shelf life prediction is achieved; and the preparation of the intelligent indicationlabel based on the anthocyanin is realized.