80 results about "Future trend" patented technology

A method of balancing a user's input to an interactive computer program with the program's output is obtained by continually measuring the difference between the user's input and the program's output and adjusting one or more parameters of the program's output so that the difference from the user's performance is progressively reduced. The adjustment may be obtained dynamically through negative feedback dampening of the measured difference (delta) between user input and program output, and/or by selection of predetermined apposite values for program output corresponding to the measurement of user input. The adjustment results in dynamic generation and/or selection of premodeled segments of interactive output in closer balance with user input. The adjustment method can be applied to video games, educational games, productivity programs, training programs, biofeedback programs, entertainment programs, and other interactive programs. In video games, the adjustment method results in balancing user performance with game difficulty for a more engaging game experience. It can also enable embedded advertising to be triggered when the user is in an optimum state of engagement. The adjustment method may be performed by projecting future trends of user performance, selecting predetermined or dynamically determined levels of value, modifying user control of input devices, or even modifying the program's challenges to user capability over time.

The invention provides sensor, preferably biosensor devices and method of fabrication. The devices have significant advantages over the prior art methods having compatibility with future trends in clinical diagnostics and chemical detection. The underlying principle involves the integration of nanometer diameter, micron long metal or semiconductor rods onto a substrate to form a suspended nanomechanical cantilevers. The cantilever rods are rigidly attached to the substrate on one or both ends, and resonate at a characteristic frequency depending on the diameter, length, and stiffness of the rod. The metal or semiconductor rods are integrated onto the substrate using electrofluidic or fluidic assembly techniques. A receptor coating is placed on the metal or semiconductor rods prior to or following rod alignment using self-assembly chemistries. Sensing is accomplished when the target agent binds to the receptor substance, causing a change in the mass of the cantilever rod, and a corresponding change in the resonant frequency. This change in resonant frequency can be detected using an electrical readout. The sensing circuitry is integrated with CMOS or TFT technologies to form compact multi-analyte senor arrays on single crystal silicon, glass, or polymeric substrates. Circuits can also be included on the substrate to transmit the array data via wireless methods to a remote workstation for analysis. Devices may be integrated on chips with other analysis devices.

A method for performing a network handoff between different network communication providers for a wireless endpoint includes: calculating a first cost value for a first network detected by the wireless endpoint according to a first cost function calculation, calculating a second cost value for a second network detected by the wireless endpoint according to a second cost function calculation, utilizing a cost function based wavelet predictor to determine a network handoff time according to at least one of the first and second cost functions, and executing the network handoff between the first network and the second network at the network handoff time, wherein the cost function based wavelet predictor is for predicting future trends of various network characteristics.

The invention provides a public information intelligent analysis platform based on big data analysis and mining. The public information intelligent analysis platform comprises a big data preprocessing module, an intelligent analysis module and an information presentation module. The big data preprocessing module is used for storing and preprocessing enterprise information via a clouded mode. The intelligent analysis module is used for performing event cause and effect analysis, assessment of the overall situation of the company and future trend prediction and returning analysis results to an upper layer by comprehensively utilizing the preprocessed data according to data analysis tasks transmitted by the upper layer. The information presentation module is used for generating the data analysis tasks to the intelligent business analysis module and receiving the returned results, presenting the analysis results of the intelligent analysis module and providing an operation interface to a user. Intelligent analysis is performed on mass data in enterprise operation by adopting a clustered computing method via flexibly establishing and configuring analysis models so that problems of poor real-time performance, high effectiveness and interactivity of a conventional data processing method are solved, the user is assisted to perceive the enterprise situation in real time and thus enterprise management efficiency and business processing level are enhanced.

The invention provides a monitoring index abnormity detection method, a model training method, device and equipment. The method comprises: predicting the predicted value of the monitoring index at thetarget time through a pre-trained monitoring index prediction LSTM model, and comparing the actual value of the monitoring index at the target time with the predicted value; judging whether the deviation degree of the actual value and the predicted value exceeds a preset threshold value or not; and if yes, sending abnormal alarm information. Through a deep learning mode, the potential change ruleof the monitoring index is learned based on historical data, the future trend of the monitoring index is more reasonably predicted, and whether the monitoring index is abnormal is judged by comparingthe predicted value with the actual value, so that the probability of false alarm can be reduced.

The invention discloses a Kalman filtering transformer fault prediction method based on a neural network. The Kalman filtering transformer fault prediction method comprises the following steps that monitoring data of a transformer are obtained; the monitoring data are subjected to segmentation processing according to time periods, and a data set of each time period is obtained; outlier points of the data sets are judged, the number of the outlier points is counted, the outlier points are converted into normal data values, and null points and ultra-range points in the data sets are removed to form new data sets; the new data sets are subjected to regression analysis, a model of performing regression analysis is established, and the monitoring data are predicted to obtain prediction trend results; and a fault diagnosis model is established, the prediction trend results are input to the fault diagnosis model to conduct fault diagnosis, and fault diagnosis prediction results are obtained.Possible future trends of the monitoring data can be predicted. Through establishment of the fault diagnosis model and a neural network model and through model prediction, diagnosis results of possible future faults of a high voltage transformer can be obtained.

The invention discloses a method and a system for extracting Internet user network behaviors. The method for extracting the Internet user network behaviors to generate an information operation report comprises that: an Internet behavior analysis server acquires Internet communication information transmitted or received by each Internet terminal from a telecommunication operator Internet access gateway; and the Internet behavior analysis server extracts, classifies and stores the Internet communication information, and generates an information operation report according to the time interval. Therefore, the method provides the all telecommunication operator Internet user-based Internet behavior report comprising website Internet access quantity ranking, access quantity ranking of web pages in classified information, access quantity ranking of multimedia resources or the like to reflect the behavior analysis report of the current activities or the future trends of the users of the whole Internet industry.

The embodiment of the invention provides an information prediction method based on a blockchain. The information prediction method is characterized in that the method includes steps: receiving historical information of a disease and storing the historical information of the disease in a blockchain network; predicting the future trend of the disease according to the historical information of the disease through a disease prediction model and obtaining prediction results by blockchain nodes, wherein the type of the disease prediction model corresponds to the type of the disease; determining thedoctor-seeing people number according to the prediction results of multiple blockchain nodes; and determining medical resources after a future period according to the doctor-seeing people number. According to the technical scheme of the method, the outbreak condition of the disease in the future period can be effectively and automatically predicted, the preparation condition of required drug materials and medical personnel for dealing with the outbreak condition can be reminded in time, and the problem of high manpower cost caused by manual prediction of the outbreak condition of the disease is solved.

The invention discloses a deep dehydration device and a dehydration method for sludge. The device comprises a concentration tank, a conditioning tank and a membrane press filter that are sequentially connected, wherein a medicine adding device is arranged above the conditioning tank, and the medicine adding device is connected with the conditioning tank. The dehydration method comprises the steps of: delivering the sludge to the concentration tank for pretreating, delivering to the conditioning tank to be conditioned with a conditioning agent, delivering to the membrane press filter for filter-pressing, and accomplishing a deep dehydration process of the sludge after the filter-pressing is accomplished. The device and the method have the advantages that the problem that the dehydrated sludge is higher in water content can be solved; the water content of the sludge dehydrated by the device reaches below 60%; requirements of the existing environmental protection policy and a future trend are met; the problems that a transportation load of the sludge is large and the sludge leaks in a transportation process are solved; a foundation is laid for the subsequent drying and resourceful treatment and disposal; the operation is simple; and as CaO is added in a sludge conditioning step, the water content of the sludge can be reduced, the sludge can be sterilized, and environmental pollution caused by the subsequent disposal can be reduced.

The invention relates to the technical field of a transmit-receive frequency conversion channel, and discloses a millimeter wave 16-channel transmit-receive frequency conversion channel assembly, which mainly adopts a mixed design of an MMIC bare chip and an LTCC circuit and realizes a microminiature design through one or more frequency conversion. The transmit-receive frequency conversion channel assembly is in a three-layer structure on the whole, wherein the upper layer comprises a power control portion, the middle layer is a frequency conversion circuit portion and the lower layer comprises 16 paths of waveguide interfaces. The frequency conversion circuit portion comprises 16 paths of receiving channels, one path of emission channel and a local oscillator processing circuit. The receiving channels and the emission channel share the 16 paths of waveguide interfaces; transmit-receive time-sharing power supply is realized; channel switching is carried out through a radio frequency switch; power supply and control signals between the layers are connected through a miniature low-frequency connector; radio frequency signals are connected through a radio frequency isolator; and the layers are internally connected through via holes. Therefore, the millimeter wave 16-channel transmit-receive frequency conversion channel assembly realizes high-density integration of millimeter wave multi-channel transmit-receive frequency conversion channels, is small in size, light in weight and high in environment adaption capability, improves working stability and reliability of the system in various environment conditions, and meets the future trend of transmit-receive frequency conversion channel miniaturization.

The invention provides a method for monitoring voltage and capable of improving power grid voltage quality. The method for monitoring the voltage and capable of improving the power grid voltage quality includes: obtaining real time voltage data and voltage statistic data of a plurality of monitoring points; analyzing history status of voltage quality indexes according to the collected voltage statistic data of all the monitoring points; performing online prediction on future trends of the voltage quality indexes according to the current collected real time voltage data of all the monitoring points. A system for monitoring the voltage and capable of improving the power grid voltage quality, which is used to achieve the method, comprises a data acquisition unit, a history time period voltage quality index analysis unit, a future time period voltage quality index prediction unit and a data storage unit. The method and the system for monitoring the voltage and capable of improving the power grid voltage quality achieve deep analysis for the voltage quality and timely judgment for the future trends of the power grid voltage quality, and improve the effective degree of power grid voltage quality monitoring.

The invention discloses an anti-collision alarming radar system for ships, comprising a marine radar, an anti-collision alarming device and a three-port adaptor that connects a transmitter/a receiverof the marine radar with a display terminal and the anti-collision alarming device. The anti-collision alarming device comprises a display, an alarming apparatus, a data collecting board and a computer board, wherein the data collecting board is used for collecting detection signals, echo signals and bow signals from the radar to calculate the target distance and the angle and sending frame data formed by radar scan to a computer board; and the computer board is used for extracting the target point track data from the frame data to predict the future trend of target motion by associating and smoothing process via a track association algorithm and to evaluate and judge the risk level of the target in real time and then controlling the display and the alarming apparatus to display information and send the alarm signal. The invention further discloses an anti-collision alarming radar device for ships. The invention can be realized with low cost and is of great importance to ensure safetyof navigation of the ships.

The invention relates to an omnipotent single-hand moved chord keypad, a mark and a structure thereof. The omnipotent single-hand moved chord keypad is provided with auxiliary functional keys, such as Shift, Fn, Sym, and the like, under a 12-key type 3X4 numeric phone keypad. All the characters and functions of a QWERT 102 keypad are completely replaced by a chord (combination) key of the omnipotent single-hand moved chord keypad. The omnipotent single-hand moved chord keypad can be hinged with a phone and a handheld computer, for quickly inputting Chinese characters and English letters anytime and anywhere, and can be made into input devices, such as a mouse keypad, a touch plate keypad controlling computer or remote controller, a password input unit, and the like. The omnipotent single-hand moved chord keypad can be used for the vehicle-mounted and moveable equipment or the keypads of electronic devices of various instruments and large-size equipment in various industries. The omnipotent single-hand moved chord keypad has the characteristics of small volume, complete function, high inputting speed, complete mark, comfortableness in operation, benefit to health, multifunctional key, convenience in opening and closing, suitability for touch typing, accordance with a present habit of a user for operating a computer keypad, suitability for the inputting of various characters in the world, suitability for the quick inputting of the user while walking, and suitability for a future trend of the miniaturized and movable equipment.

The invention provides a trend load flow calculating method based on online status estimation and real-time scheduling plans. The method comprises the following steps of determining future trend operating mode data of a power grid; performing rationality identification and automatic adjustment on real-time scheduling plans; establishing a multi-section active power control model to perform automatic fine-tuning generation active power control; performing reactive power voltage local control, and distributing reactive power unbalance of hub nodes; performing load flow calculation to generate load flow calculation data used for power grid future trend dynamic safety assessment. The trend load flow calculating method based on online status estimation and real-time scheduling plans solves the problem that traditional online dynamic safety assessment algorithms cannot effectively analysis future power grid short safety and stability within a short time, and by analyzing further power grid safety change trend in advance, assists scheduling personnel to arrange and adjust current operating manners.

The invention relates to the technical field of applying an online safety analysis result to load prediction, and particularly relates to a power dispatching online trend early warning system based on ultra short term load prediction. Particularly, through statistics, analysis and prediction on load data, a power grid future trend is calculated, and a power grid short term trend early warning and accident post-treatment plan is formed. The system of the invention comprises the following operation steps of short term load prediction based on electricity consumption large data analysis, trend tendency analysis through ultra short term load obtained based on an ARIMA algorithm, power grid risk assessment and warning, equipment maintenance schedule management, data access panoramic surveillance, treatment in the accident and power supply restoration after the accident. Linkage between the online trend early warning and dispatching plan change information can be realized, and a trend analysis early warning and assistant decision-making system with integration of a province and regions can be formed.

The invention relates to a storage capacity predication method which comprises the following steps that S1, a formulary to be selected is acquired; S2, an original data sample is acquired; S3, a data sample is acquired; S4, the data sample is divided; S5, a training set is divided; S6, a predictor formula is selected; S7, parameter solving is carried out on the predictor formula; S8, predication is carried out through the predictor formula. The invention further discloses a storage capacity predication system which comprises a system management module, a storage resource real-time monitoring module, a storage data processing module, a predication model study module and a future trend predication module. According to the technical scheme, the storage capacity of IT resources can be accurately predicated, and therefore the resource utilization rate is improved, the failure rate is reduced, and operation and maintenance quality is improved.

Embodiments of the invention are directed to a system, method, or computer program product for a predictive analytics platform. First, the platform may be populated by individuals or entities with data. The data may then be categorized into a searchable format. The data may be analyzed to create feed category data and patterns that may provide indications of future trends. The future trends may be in the financial, risk, or product development fields. In this way, the invention may combine data from multiple sources into a single platform for predictive analytics, thus using current data to attempt to predict future trends. Individuals and entities may have access to the platform to analyze the data on the platform or receive patterns previously provided by the system, in order to further predict future trends.

The invention discloses a management system and management method of a virtual machine. The management system of the virtual machine comprises a resource management module, a resource monitoring module and a resource scheduling module. The resource management module is used for providing a unified management interface, receiving an external request and calling the resource monitoring module. The resource monitoring module is used for acquiring historical operating data of different host machines and predicting the using trends of the resource usage rates of different host machines according to the historical operating data. The resource scheduling module is used for responding to the external request and managing the virtual machine according to the using trends of the resource usage rates of different host machines. A usage rate future trend model is established by monitoring the actual resource utilization rates of the host machines based on the historical operating data, an allocation and migration means of the dynamic virtual machine is adopted, and finally resource utilization rate maximization is achieved, so that the virtual machine is stable and safe in operation.

The invention discloses an evaluation method of a slope monitoring and early warning system based on the Big Dipper, and relates to the technical field of engineering monitoring. The evaluation method includes the steps that 1, monitoring data including displacement in a continuous time period, of a slope and the like is obtained through a Big Dipper measuring module and preprocessed, so that the monitoring data meets the requirement of system construction; 2, a data management module is used for graphical display, parameter setting, text output and data updating, storage and modification; 3, according to the Big Dipper displacement monitoring data obtained through continuous measurement, a gray differential prediction model is set up; 4, slope stability evaluation and early warning are conducted. The slope monitoring can be evaluated and predicted, a basis and reference are provided for judging the future trend of the slope, limitation of a traditional prediction model is overcome to a certain degree, which is beneficial for reducing randomness of a time sequence and improving prediction accuracy, the gray prediction model needs few samples in monitoring and prediction, and the requirement for the samples is not strict.

The invention discloses a wavelet transform-based fine-granularity self-learning integration prediction method which comprises the following steps: by adopting time sequence decomposition based on wavelet transform, predicting time sequences of different variable coefficients with different granularities, so as to relatively precisely reveal the variation rules of the time sequences; with the combination of the time sequence decomposition, extracting characteristics of a plurality of related factors, sufficiently capturing main influence factors, and predicting future trend through rule statistics, thereby being rapid, convenient and simple; applying a model-based aggregation algorithm frame to the regression process, thereby enabling the model to have robustness which is relatively good when being compared with that of a single based learner; predicting with the combination of composite models based on wavelet transform, SVR and Ensemble, thereby obtaining prediction performance which is relatively precise when being compared with that of a conventional single model. The wavelet transform-based fine-granularity self-learning integration prediction method can be widely applied to the technical field of big-data mining and machine learning.