36results about How to "Avoid forecast errors" patented technology

The invention discloses a preliminary assessment and prediction method for earthquake casualties, which specifically includes the following steps: 1) analyzing the main factors affecting the earthquake casualties; 2) determining the relationship between the influencing factors and the casualties; 3) according to the determined function Relationships are modeled. Compared with other models, the error rate of the model of the present invention is smaller, indicating that the model of the present invention has higher accuracy and practical reference.

The invention relates to a waiting time predicting method for a queuing machine. The waiting time predicting method comprises following steps of collecting historical arriving moments of customers, the line lengths at the arriving moments of the customers and waiting time in a plurality of historical service periods, generating a customer waiting time table, recording the arriving moments of the customers and the line lengths at the arriving moments of the customers when the customers arrive, acquiring information about the customers required to wait in line, counting the waiting time of the customers by virtue of the customer waiting time table, then acquiring information about one queued customer accepting the service, recording actual waiting time of the queued customer, recounting the waiting time of the rest queued customers, and carrying out cyclic operation until the last service period ends. The waiting time predicting method has the advantages that the accuracy is high, a prediction error is avoided, the customers can conveniently and reasonably arrange the time, and the time availability is improved; meanwhile, the waiting time can be corrected according to queuing information of the customers at any time, so that the customers cannot miss service chances, and the vacant number rate is decreased.

The invention discloses a method for predicting the plane distribution of thin sand bodies based on fractional-order Hilbert transform. According to the vertical division of multiple sand body development sections, the attribute plane map of each sand body development section is extracted, and the best order corresponding to the development of each sand body development section is selected based on the actual drilling conditions. Amplitude property plan for thin sand body plane distribution prediction. The invention makes full use of the advantages that fractional Hilbert transform can improve the vertical resolution and can realize the perfect correspondence between the event axis and the acoustic logging curve on the seismic section, effectively predict the plane distribution characteristics of the thin sand body, and improve the thin sand body. Accuracy of Sand Body Plane Distribution Prediction.

The invention discloses an MIMO (Multiple Input Multiple Output) mode switching method and device. An MIMO mode which is possibly used at present is judged according to RI (Rank Indication) and CQI (Channel Quality Indicator) information reported by UE (User Equipment) and an instantaneous BLER (Block Error Ratio) value recorded by an eNB (evolved Node B), thereby avoiding forecasting the throughput / system capacity of various MIMO modes, efficiently avoiding the forecasting error and reducing the realizing complexity; and simultaneously, the MIMO mode which is practically used at present is judged by increasing the quantity of to-be-applied records corresponding to the to-be-applied MIMO modes generated based on periodicity, thereby increasing the quantity of switching sample points for judging the MIMO mode, avoiding the influence of an instantaneous channel error on the mode switching property and fully demonstrating the advantages of self-adaption switching.

The present invention relates to the technical field of information processing, and in particular to a user behavior prediction method, a prediction device, a computer-readable storage medium, and a terminal device. The user behavior prediction method provided by the present invention includes: obtaining voice evaluation information of the user evaluation target service, and converting the voice evaluation information into corresponding text information; analyzing the text information, and determining the evaluation object and the evaluation object of the user evaluation target service Corresponding evaluation results; extract the voice segment corresponding to the evaluation result in the voice evaluation information; conduct phonetic analysis on the voice segment to obtain the emotion label and tone label corresponding to the evaluation result; according to the emotion label, tone label, evaluation object and evaluation result, Predict user behavior to comprehensively and accurately predict user behavior through all-round analysis of emotion, tone, evaluation object and evaluation content, avoid user behavior prediction errors, and improve the accuracy of user behavior prediction.

The invention discloses a method for deriving the operation situation of an energy storage device based on LSTM. The technical scheme adopted by the present invention includes: identifying model parameters according to historical operation data; establishing the Thevenin equivalent circuit model of the energy storage battery; estimating the SOC and SOH of the energy storage device according to the identification parameters and historical operation data; establishing a thermal model of the energy storage battery; Identify the model parameters and estimate the core temperature of the battery; according to the historical operating data and the operating state of the energy storage device, use LSTM to predict the changes of the voltage and current of the energy storage device, so as to construct the voltage consistency index of the energy storage battery pack; Based on the SOC, SOH and core temperature of the energy storage device, the SOC consistency index of the energy storage battery pack is constructed, so as to deduce the operation situation of the energy storage device. The invention deduces the operation state of the energy storage device through the operation data that is easy to measure by voltage, current and ambient temperature, and avoids the large prediction error when using LSTM to predict multiple operation states such as SOC and SOH at present.

The invention relates to a soil type merging and multiple regression-based soil manganese content prediction method. The divide-and-conquer control of different spatial variation characteristics showed by soil trace elements in different soil types is involved; the spatial heterogeneity of the soil trace elements can be detected by analyzing a discrete degree of spatial distribution of effective manganese content of soil; the multicollinearity problem in local regression analysis can be diagnosed through the local regression analysis; and especially in a spatial prediction process, a comprehensive prediction model is built by measuring spatial distribution characteristics of the effective manganese content of the soil under the sampling densities of different soil samples.

The invention discloses a single-soldier guided rocket dynamic target prediction and guidance method, which can effectively reduce the normal overload of a rocket in the flight process and ensure therelatively high hit accuracy. The single-soldier guided rocket dynamic target prediction and guidance method comprises the following steps of: measuring the motion state of the target in a ground inertial coordinate system by ground fire control equipment, transforming into the inertial coordinate system of the rocket and transmitting to a computer on the rocket; using the computer on the rocket to estimate the flight time required for the rocket to hit the target and calculating the point of intersection of a guided missile and the target according to the motion state of the target, the initial position of the rocket in the inertial coordinate system of the rocket and the set speed of the single-soldier guided rocket; in the current guidance period, controlling the rocket to fly to the target point according to the set guidance law by taking the point of intersection of the guided missile and the target as the target point; when the current guidance period ends, obtaining the positioninformation of the target in the inertial coordinate system of the rocket by solving with the computer on the rocket, solving the remaining flight time of the rocket hitting the target, and updatingthe point of intersection of the guided missile and the target; and entering the next guidance period, and repeatedly executing till the rocket hits the target.

The invention provides a photovoltaic output power prediction method and system and a storage medium. The method comprises the steps of obtaining photovoltaic output data and meteorological data in apreset historical time period; decomposing the photovoltaic output power sequence by adopting a wavelet packet decomposition algorithm to obtain 2n decomposition sequences, and respectively carryingout single-branch reconstruction on the 2n decomposition sequences to obtain 2n reconstruction sequences; combining the 2n reconstruction sequences with the corresponding meteorological data to obtain2n data sets; respectively inputting the 2n data sets into corresponding pre-trained long-short-term memory neural network units to obtain prediction results of the 2n long-short-term memory neural network units on photovoltaic output power in a preset future time period; and performing weighted summation on the prediction results output by the 2n long-short-term memory neural network units to obtain a comprehensive prediction result of the photovoltaic output power in the preset future time period. The prediction precision can be improved.

A centrifugal pump flow prediction method based on power and differential pressure measurement, characterized in that the method includes the steps: 1. Based on the 'grey box' construction theory, the centrifugal pump is divided into a motor module, a mechanical model and a hydraulic module; 2. Based on the system analysis, construct the mathematical model of the overall flow prediction of the centrifugal pump; 3. Based on the internal loss model of the motor, locally construct the mathematical model of the motor output power prediction; 4. Based on the mechanical loss model, locally construct the output power of the mechanical transmission components (or centrifugal pump input power) prediction mathematical model; 5. Based on the fluid flow loss model, locally build a model between the input power of the centrifugal pump, the flow rate, and the rotational speed, and the model of the pressure difference, flow rate, and rotational speed of the centrifugal pump; 6. Combining the mathematical model of the motor, the mechanical Mathematical model and hydraulic mathematical model, establish a centrifugal pump flow prediction model based on power (speed and torque) and pressure difference; 7. Use compensation algorithm to correct the centrifugal pump flow prediction model to improve the flow prediction accuracy.