333 results about "Dynamic prediction" patented technology

The invention discloses a method for predicting an hourly cooling load of a central air-conditioner in an office building on line based on indoor temperature and humidity parameters. The method for predicting the cooling load comprises the following steps of: performing time sequence prediction on outdoor meteorological parameters and air-conditioner operation input parameters, establishing an Online support vector regression (SVR) dynamic prediction model of the air-conditioner cooling load by using the data, predicting 24-hour air-conditioner cooling load in the current day in advance, and performing compensation by using a residual sequence of the actual value and the predication value of the 24-hour air-conditioner load in the previous day. The predication data of the air-conditioner cooling load prediction model established by the method is high in reliability; and the method can be applied to occasions for prediction of the hourly cooling load of the central air-conditioner in the office building in a single building or a large range, energy-saving control of a central air-conditioner system, energy consumption prediction of the air-conditioner, power peak clipping in areas and the like.

Provided are methodologies to properly assess and manage operational risks at operations sites, e.g., a manufacturing, production or processing facility, such as a refinery, chemical plant, fluid-catalytic-cracking units, or nuclear energy plant, or a biological or waste management facility, airport or even financial institutions, or at any facility in which operations are often accompanied by risk associated with many high-probability, low-consequence events, often resulting in near-misses. In some operations, processes are monitored by alarms, but the invention operates on either process data or alarm data. The methods are based upon measurement of one or more variables, and/or utilization and management of the concept of “hidden process near-miss(es)” to identify a change or escalation, if any, in probability of occurrence of an adverse incident. The methodologies combine a plurality of subsets (also useful independently) of dynamically calculated leading risk indicators for dynamic risk management.

The invention discloses a four-wheel-drive electric car speed optimization control method. The method considers road grade and curvature information in a travelling path range overall, can minimize energy consumption of the car in a travelling process in a given path on the premise of ensuring the safety of the car. The method includes the following steps: acquiring road grade and curvature information through information systems such as a GPS ( Global Positioning System) and a GIS ( Geographic Information System); considering the road grade information and the car state, establishing a longitudinal dynamics prediction model, and establishing an energy consumption target function; considering the road curvature information, designing a constraint condition of the safety, and describing a model prediction control problem; and using the dynamic programming algorithm to solve the model prediction control problem, and acquiring a car speed optimization track. The four-wheel-drive electric car speed optimization control method can acquire the optimized target electric car speed and torque according to road working condition information, can be used for dynamics control of four-wheel-drive electric cars, and can improve the travelling economic and safe performance.

The invention relates to a steering early warning auxiliary device for a heavy truck, which includes an in-vehicle early warning component and an external early warning component. The device and the obstacle dynamic prediction module installed on the inner bracket of the instrument panel; the early warning components outside the vehicle include radar detection components and high-definition camera components, and the radar detection components and high-definition camera components are arranged in pairs on the vehicle body corresponding to the blind spot of the truck on the corresponding position. Due to the existence of the obstacle dynamic prediction module and the alarm indicating device, the device can firstly predict the trajectory of the truck and possible dangers, thereby reminding the driver. During the operation of the driver, the device can make accurate and fast judgments again. , In an emergency, the vehicle can be stopped quickly to avoid dangerous situations. The device can be used as a steering warning auxiliary device for heavy trucks, and its use is safe and reliable.

The invention discloses a method for measuring a gas and water relative permeability curve through coal-bed gas well production data. Average reservoir pressure and average water saturation are obtained with a material balance method, and therefore changes between absolute permeability and relative permeability can be obtained; changes of irreducible water saturation are predicted through the relation between irreducible water saturation and porosity; the absolute permeability and the relative permeability generated after end points are calibrated are coupled in the same state, a domain average effective permeability dynamic prediction model can be obtained, and an effective permeability curve can be obtained. The method solves the problem that in a laboratory, the relative permeability curve can not be accurately measured easily. Compared with the prior art, the positive and negative effect of a coal reservoir can be well reflected, the gas phase effective permeability can be effectively improved, and a powerful basis is provided for accurately predicting a gas and water output law of a coal-bed gas well and predicting future yield of a producing well.

The invention provides a cloud calculation resource disposition method based on dynamic prediction. Firstly a network jam problem brought by multiple mirror images sharing is changed through increasing quantity of mirror image templates, secondly quantity of mirror image versions in real time is adjusted through dynamic prediction of mirror image demand quantity of a could calculation at different times, further resource waste caused by time delay or excessive mirror images brought by insufficient mirror images is reduced.

The invention discloses a health prediction method and system for a new energy vehicle battery. The method comprises the following steps that: carrying out data analysis processing on vehicle data obtained in real time to obtain vehicle working condition data; independently executing data cleaning, data conversion and data reduction processing on the vehicle working condition data; on the basis of the vehicle working condition data subjected to data preprocessing, adopting a factor analysis method to extract data which influences a battery health degree, adopting a supervised learning method to mine a potential relationship between the data which influences the battery health degree and the vehicle working condition data, and constructing an initial battery health prediction model; carrying out model evaluation and algorithm optimization on the initial battery health prediction model to obtain an optimal battery monitoring prediction model, and finishing battery health prediction under a practical working condition. By use of the method, the dynamic prediction of the health state of the new energy vehicle battery is realized, the dynamic property and the economy of the vehicle can be improved, and the method has the advantages of being simple in operation and easy in implementation.

The invention relates to a method for water inrush prediction and seepage control for underwater-tunnel broken surrounding rocks. The method includes the steps of S1), exploring by adopting a geophysical exploration and advanced-level geological drilling method and performing tests; S2), establishing a saltation prediction model of analytic hierarchy grey correlation of water inrush of the surrounding rocks by adopting an analytic hierarchy grey correlation method; S3), establishing a three-dimensional porous continuous medium fluid-structure coupled finite element model of the underwater-tunnel broken surrounding rocks by adopting an orthogonal back-analysis method; S4), performing dynamic prediction and seepage control on water inflow of the broken surrounding rocks and performing intelligent fuzzy logic control and instability early-warning forecast on high-pressure water inrush of the broken surrounding rocks; S5), adopting comprehensive prevention and control measures. Compared with the prior art, the method has the advantages that instability of water inrush of the underwater-tunnel broken surrounding rocks under high water pressure can be predicted and subjected to economical, reasonable, safe and reliable comprehensive seepage control.

The invention relates to a robust random-weight neural network-based molten-iron quality multi-dimensional soft measurement method which belongs to the blast-furnace smelting automatic control field, in particular to a Cauchy distribution weighted M-estimation random-weight neural network (M-RVFLNs) based method for multi-dimensional parameter-dynamic soft measurement of the molten-iron quality in the blast-furnace smelting process. According to the method of the invention, the principal component analysis (PCA) method is adopted to chose main parameters which affect the blast-furnace molten iron quality as model input variables, a molten-iron quality multi-dimensional dynamic prediction model which has an output self-feedback structure and takes into account input-output data at different moments is constructed, and it is possible to carry out multi-dimensional dynamic soft measurement of the main parameters Si content, P content, S content and molten iron temperature which represent the blast-furnace molten iron quality. The method of the invention comprises the following steps of (1) choosing auxiliary variables and determining model input variables and (2) training and using the M-RVFLNs soft measurement model.

The invention discloses a dynamic migrating method of a virtual machine based on performance prediction. The method comprises the following steps of obtaining a characteristic vector by using a singular value decomposing principle by extracting system performance data and analyzing the similarity of the singular value to obtain; then, obtaining the performance prediction value at the next moment by a reverse SVD (Singular Value Decomposition) algorithm so as to start a dynamic migrating mechanism of the virtual machine according to future performance prediction values. According to the method, the performance prediction mechanism can make accurate prediction value and the dynamic prediction mechanism can balance the system load. The system can effectively cope with sudden load by effectively analyzing current information of a physical machine and effectively predicting the future load, so that the performance loss caused by unnecessary migration is reduced.

The invention belongs to the technical field of video coding and decoding, in particular to an H.264 based flexible video coding method which realizes that coded video can be output or transmitted in various code rates through realizing that the resolution ratio, the frame sequence length and the quantitative parameter are adjustable in real time in a process of coding the video based on the H.264 standard. The method also realizes that the relevant parameters of loop filter and dynamic prediction are adjustable in a coding process and the coding complexity in the coding process is controlled, thereby reasonably utilizing system resources. The coding method has the characteristics of high efficiency and easiness of implementation, and can be applied to multiple network video transmission and multiple processor systems.

The invention discloses a dynamic prediction method for wellbore flow in a coal-bed gas well. The dynamic prediction method comprises following steps: sampling liquid in a wellbore oil pipe in order to acquire real solid content of a wellbore; judging the flow pattern of gas-liquid-solid three phase flow in the wellbore and determining modeling parameters for the gas-liquid-solid three phase flow; establishing a gas-liquid-solid three phase flow pressure model in the wellbore based on the real solid content and the modeling parameters in combination with the position of the working fluid level and data of well body configuration; establishing a temperature distribution model based on the gas-liquid-solid three phase flow pressure model, the real solid content and the modeling parameters by combining with heat transfer parameters of a well body; and obtaining distribution results of pressure and temperature of gas-liquid-solid three phase flow pressure in the wellbore along the depth of the wellbore based on the pressure model and the temperature distribution model. The dynamic prediction method for wellbore flow in the coal-bed gas well has the capability of predicting flow state, flow rate, pressure and temperature distribution and other physical property parameters at any position in the wellbore based on liquid production capacity and gas production capacity of a wellhead, casing pressure and other physical property parameters.

The invention discloses a learning behavior dynamic prediction method and device, equipment and a storage medium. The prediction method comprises the following steps: based on the learning behavior data of a user in a historical period, adopting a long-short-time memory cycle neural network as a prediction model for training to obtain an optimal model; and acquiring learning behavior data of the user in the first time period, and outputting a prediction result based on the optimal model. The invention adopts LSTM-RNN as a prediction model to predict the output results, can improve the accuracyof the prediction, and uses an interactive visual analysis mode to display user information data, facilitates viewing and analysis of user data information at the background of the system, so as to help teachers to timely and intuitively know the results of data analysis, and then achieve the ''closed loop'' of the teaching process. The invention can be widely applied to various learning behaviorprediction systems.

The invention provides a muscle fatigue dynamic prediction method based on multi-channel sEMG. An sEMG signal is susceptible to external interference, so an average value of all channels is selected to be a reference value; a difference is made between the new reference value and the original multi-channel sEMG, so external interference can be effectively reduced; a time-scale least square method is translated according to time reference to achieve dynamic update and predict muscle fatigue time; MPF and MF parameters are important parameters reflecting muscle fatigue, so reliability is provided for muscle fatigue prediction; the MPF and the MF two parameters are employed for prediction of muscle fatigue time, so instability of single parameter prediction can be avoided and prediction robustness can be enhanced; and the muscle fatigue dynamic prediction method based on multi-channel sEMG has high accuracy, fast calculation speed, simplicity and important application values.

The invention is applied in the field of electric power systems, and in particular relates to a method for dynamic forecasting of demand for classified electric vehicles based on gray forecasting and support vector machines. Including: firstly, use the high-precision improved gray model to predict the number of different types of vehicles; then, based on the proportion of different types of electric vehicles and the nonlinear characteristics of the influencing factors, use the support vector machine regression method to obtain the classification by using the prediction samples Electric vehicles replace the proportional forecast results, and use the iterative method to continuously revise the forecast results; finally, match the first two forecast results according to vehicle types, establish a demand growth forecast model for electric vehicles by type, and combine the research on user travel patterns to determine the demand for electric vehicles by type. Accurate dynamic spatiotemporal forecasting is achieved. Therefore, the present invention has the following advantages: fully considering the characteristics of insufficient historical data and the influence of different factors on the development of electric vehicles, combined with the research on user travel rules, to achieve more accurate dynamic prediction.

The invention discloses a travel spot passenger flow dynamic prediction method; the travel spot passenger flow dynamic prediction method combines a statistics method with a nerve network, considers various factor influences on the traveling, and combines a trend extrapolation method, a monthly index method and a fluctuation coefficient method in the statistics with a BPNN; the travel spot passenger flow dynamic prediction method considers various factors affecting the travelling, uses the monthly index to divide history visitors into small scale data, uses the BP to fully study, and finally uses the fluctuation coefficient to dynamically predict the travel spot passenger flow according to the holiday real conditions, thus providing large conveniences for a travel spot manager, providing guidance effect for travelers, and dynamically predicting the holiday passenger flows according to the future holiday real conditions.

The invention discloses a gray model (GM) (1,1) prediction method of orthogonal interpolation based on Markov chain. According to the grey orthogonal method and the principle of the Markov chain, the Gauss-Chebyshev orthogonal thought is used to predict overall trend of time-series data. Accuracy of the prediction is time-varying, the principle of the Markov chain has good advantages during the process of processing a time-varying system, and choosing the GM (1,1) prediction method of the orthogonal interpolation based on the Markov chain can solve the instability of the prediction result better, and therefore the grey Markov orthogonal model is put forward for the prediction of data of electricity consumption, and is suitable for a dynamic prediction process which is of a short-middle term, low in demanded quantity of data and large in data amplitude. The GM (1,1) prediction method of the orthogonal interpolation based on the Markov chain is scientific in inventive concept, simple to calculate, small in work load, and high in prediction accuracy, and good in usage value and wide in application range in the technical field of prediction.

The invention discloses an urban gas pipeline system danger forecast and evaluation method. The method comprises the following steps: 1) establishing a system danger evaluation index system; 2) carrying out index weight assignment through a rough set theory; 3) carrying out system danger grading; and 4) carrying out system dynamic prediction and analysis. The method can solve the problem that, for the previous gas pipeline system danger evaluation methods, evaluation index weight assignment depends on subjective grading, and since the subjective grading has too many subjective views of an evaluator, the assignment result is not objective; the method can also solve the problem of the static evaluation method, and thus dynamic evaluation of the system is realized, and dynamic prediction of the system can be carried out; and the method is of great significance to safety management of gas pipelines, and meanwhile, provides important theoretical reference basis for the public safety management of a city.

The invention relates to a method for predicting mine inflow and belongs to the field of mine mining, in particular to a method for predicting mine inflow based on a dynamic change of a permeability coefficient along with layered mining. The space of the mine excavation engineering is organically combined with the time distribution characteristics according to layered mining conditions, the influence of overburden failures on the permeability coefficient of an overlying aquifer caused by the mining is taken into consideration, the dynamic adjustment of the permeability coefficient matching with actual excavation activities in the research area is realized; a relationship between the permeability coefficient and the groundwater numerical model is built, the adjusted parameters can be imported into the simulation model in real time, the dynamic prediction of the water inflow along with coal mining is realized, and the prediction precision of the water inflow is improved.