74results about How to "Solve the low prediction accuracy" patented technology

The invention provides a coupling large-scale data flow width learning rapid prediction intelligent algorithm based on network community detection and GCN. The algorithm comprises the following steps:step 1, community detection; step 2, space-time feature extraction; step 3, width learning rapid prediction; and step 4, large-scale real-time prediction of the space-time coupling width learning neural network. Compared with the prior art, the algorithm has the beneficial effects that intelligent community detection and GCN feature extraction are adopted, width learning is combined, the problemof large-scale node prediction is solved, and the algorithm has the advantages of being high in calculation speed, high in prediction precision, high in adaptive capacity and the like.

The invention discloses a combined wind power prediction method based on wind speed fluctuation characteristic extraction. The combined wind power prediction method includes the following steps that wind speed data acquired by training samples are normalized; time windows are established for the normalized wind speeds, and multifractal spectrum analysis is performed in the time windows; the widths omega of singular index alpha value taking intervals of the time windows and symmetry parameters S of peak value differences Deltaf (alpha) and f (alpha) of a singular spectrum function f (alpha) are analyzed and compared. The wind speeds are classified according to the parameters [omega, Delta f (alpha), S], and the sizes of the time windows are further adjusted. Divided categories are sequentially trained by using an extreme learning machine, a support vector machine and an optimization regression power curve method, average monthly precision comparison is conducted on produced prediction results, one of the methods is selected to serve as an optimum single algorithm for the categories, and trained models are obtained. Same classification and modeling are conducted on test samples, corresponding optimum single algorithms are selected for different models for respective prediction, and finally final prediction results are obtained through combination.

The invention discloses a multi-model landslide displacement prediction method and system, and aims to solve the problems of low accuracy of conventional single-model prediction and difficulty in determining a weight of a combination prediction model. The method comprises the following steps of obtaining landslide displacement monitoring data, and performing equal-time-interval processing on the monitoring data to obtain a displacement time sequence of a landslide; selecting multiple displacement prediction models to predict landslide displacement in a time range corresponding to the displacement time sequence to obtain a predicted displacement time sequence; through the predicted displacement time sequence and the displacement time sequence, obtaining a prediction model accuracy matrix, and according to the prediction model accuracy matrix, determining a prediction model weight matrix; and obtaining a final predicted displacement result in combination with the prediction model weightmatrix according to displacement data predicted by the selected displacement prediction models.

The invention relates to the field of intelligent water affairs, and discloses a residential water consumption prediction method based on an MIC-XGBoost algorithm, and the method comprises the steps: obtaining influence factor values and water consumption of each month, constructing a water consumption-influence factor value corresponding table of water consumption influence factors, and employing a maximum information coefficient algorithm to obtain a water consumption-influence factor value corresponding table of the water consumption influence factors; calculating a target influence factor which has the maximum influence on the water consumption, training the original XGBoost algorithm model according to the water consumption and the influence factor numerical value of each month to obtain a target XGBoost algorithm model, and predicting the water consumption by utilizing the target XGBoost algorithm model according to the influence factor numerical value of the current month to obtain the predicted water consumption of the current month. The invention further provides a resident water consumption prediction device based on the MIC-XGBoost algorithm, electronic equipment and a computer readable storage medium. According to the method, the problem that the prediction accuracy is low when the water consumption of residents is predicted by only depending on the XGBoost algorithm can be solved.

The invention provides a terminal equipment replacement prediction method and device, a storage medium and electronic equipment. The method comprises the steps that target operation data of a target object is obtained, and the target operation data is operation data generated by multiple selection operations executed by the target object on resource information on a target page within target time; according to the target operation data, a target time sequence and target object features are obtained, the target time sequence is used for recording multiple selection operations according to the sequence of execution time, and the target object features are used for describing a target object; and according to the target device data, the target time sequence and the target object features, machine change prediction is performed to obtain a first prediction result, the target device data being device data of a target terminal device held by the target object, and the first prediction result being used for indicating whether the target object changes the target terminal device or not. Whether the target terminal equipment is replaced by the target object can be objectively reflected, and the accuracy of machine replacement prediction is improved.

The invention discloses a large-range landslide deformation prediction method based on InSAR inversion and multiple impact factors, and the method comprises the steps: carrying out the InSAR inversion processing of SAR image data, and obtaining time sequence deformation data; clustering the time sequence deformation data to obtain a plurality of categories of time sequence deformation data; decomposing the time sequence deformation data of each category into a periodic term deformation sequence and a trend term deformation sequence; determining influence factors significantly related to periodic term deformation; respectively establishing an LSTM (Long Short Term Memory) model of each type of time sequence deformation data to predict each type of deformation; and adding the periodic term deformation prediction value and the trend term deformation prediction value of each category to obtain a deformation quantity prediction result of each category, and merging the deformation quantity prediction results of each category to obtain a large-range landslide deformation prediction result. According to the method, effective prediction of large-range landslide deformation can be realized, and the defects of small prediction range, high cost and the like in the prior art are overcome.

The invention discloses a trust prediction method based on an exponential smoothing method and a grey model. The method comprises the steps of obtaining a historical trust value sequence of a target vehicle in a vehicle-mounted ad hoc network; carrying out smoothing processing on a historical trust value sequence according to an optimal value of a smoothing coefficient in a predetermined exponential smoothing method to obtain a smooth trust value sequence, and then inputting the smooth trust value sequence into a grey model constructed based on the optimal value of the smoothing coefficient toobtain a trust prediction result. Visibly, according to the method, the historical trust value sequence is processed by utilizing an exponential smoothing method, the interference of sequence randomfluctuation on a prediction result is reduced, and in addition, parameter optimization is performed on a smoothing coefficient of the exponential smoothing method and an objective function of a grey model, so that the accuracy of the prediction result is further improved. The invention also provides a trust prediction device and equipment based on the exponential smoothing method and the grey model, and a computer readable storage medium, and the effects of the trust prediction device and equipment are corresponding to those of the method.

The invention discloses a method for predicting the critical frequency of the ionospheric F2 layer based on the ELM: obtaining the measured data of the influence factor of the foF2 and the value of the foF2 hour, and dividing the data into three groups as the training data, the test data and the verification data of the ELM model respectively; determining the input and output variables of the ELMmodel; the training data and test data are imported into the ELM model, and the ELM model is trained. The prediction error RMSE of the ELM model was compared with the expected value of the precision of the ELM model until the prediction error RMSE was less than the expected value and the optimal ELM model was determined after the training. The validation data is imported into the trained optimal ELM model, and the accurate output prediction value is obtained. The invention can realize accurate and fast prediction of foF2.

The invention relates to a nonlinear partial least square optimizing model-based forest carbon sink remote sensing evaluation method which comprises the following main steps of: (1) mapping the original variable to a high-dimension space to obtain a new variable by adopting a kernel function and carrying out standardization treatment; (2) carrying out regression analysis on the extracted component by adopting a least square method and reducing a regression coefficient; (3) evaluating a model by adopting LOO cross effectiveness; (4) repeating the step (2) to step (3) and adding 1 to the component number every repetition till the extracted component number reaches the maximal value; (5) repeating the step (1) to the step (4) and adding 1 to the subsection number M in the step (1) every repetition till M is equal to the preset number; and (6) searching the model with maximal related coefficient of an estimating value and a practical value from all the models and modeling with the M and extracted component number at the moment for being used as a final estimation model. The invention uses the optimized nonlinear partial least square regression for establishing a forest carbon storage predicting model and improves the forest carbon storage predicting precision.