87results about How to "Good prediction" patented technology

The invention discloses a wind power prediction method based on a modified particle swarm optimization BP neural network. The method includes the following steps: 1. encoding weight values and threshold values of a BP neural network as particles, and initializing the particles; 2. computing each particle fitness value with the difference between the result obtained from BP neural network training and an anticipated value as a fitness function; 3. comparing the fitness value of each particle and individual optimal particle to obtain a global optimal particle; 4. updating the speed and position of the particle; 5. determining whether the global particle meets termination conditions, if the global particle meets termination conditions, terminating the computing and outputting an optimal weight threshold value, and if the global particle does not meet termination conditions, back to step 2 and carrying out iterative operation; and 6. Using the optimal weight threshold value that is acquired by step 5 to connect an input layer, a hidden layer and an output layer of the BP neural network, and obtaining the result of wind power prediction on the basis of the result of the BP neural network. The method has fast convergence speed, high precision, and is not easily trapped to local extremum.

The invention discloses a short-term bus passenger flow prediction method based on depth learning and a passenger behavior mode, which comprises the following steps: 1, identifying and extracting characteristics of influencing factors of bus passenger flow; 2, reconstructing the data structure of bus passenger flow, reconstructing the input sample subdivision hour passenger flow xt into subdivision hour passenger flow matrix Xt, so that it can adapt to CNN and ConvLSTM models; 3. With historical passenger flow, the external and internal factors affecting bus passenger flow being used as inputdata, according to eight different dimensional data input schemes, namely, seven combined data input schemes considering internal influencing factors and one data input scheme without considering internal influencing factors, forecasting the bus passenger flow by using depth learning model, and obtaining the average relative error and absolute error of bus passenger flow forecasting through many experiments. The method simultaneously considers the external and internal factors of the bus passenger flow, and can not only predict the total bus passenger flow, but also predict the composition structure of the bus passenger flow.

The invention relates to an ultrashort combined predicting method for wind speed of a wind power plant. Firstly, wind speed time series of the wind power plant is acquired, and data pretreatment is carried out on the series to obtain input data of a prediction system; the input data is predicted respectively by a continuous prediction model, an ARMA (Auto Regressive Moving Average) prediction model and a wavelet-neural network prediction model, and three groups of prediction values are obtained through computation; and finally the three groups of prediction values are adopted to obtain the prediction value of the wind speed in the future 0-1 hour by a combined prediction method, and the last two groups of prediction values are adopted to obtain the prediction value of the wind speed in the future 1-4 hours by the combined prediction method. The combined prediction method is adopted, useful information of each single prediction method is fully utilized, the prediction precision of the wind speed in the future 4 hours of the wind power plant is improved, and a reference is provided for reasonably scheduling a power grid.

The invention discloses a real-time online individualized heat-exchanging intelligent temperature control system of mass concretes, and relates to the control on concrete water cooling in a high concrete dam casting procedure, wherein significance is provided for constructions of seamless dams. The system is characterized by comprising 1) installing a sensor in a newly casted mass concrete for measuring the temperature of the concrete in real time; 2) installing an interpolating digital temperature measuring device on water inlet/output pipes to measure the temperature of inlet and outlet water, and determining the average decreasing amplitude of the concrete temperature in real time according to a temperature difference of the inlet and outlet water; 3) determining a real-time flow according to the concrete temperature decreasing amplitude obtained in step 2); 4) installing an integrated flow and temperature control device integrated with temperature, flow and aperture controls on a water through pipe so as to realize real-time and online automatic collecting and feedback controls; 5) realizing the intelligent and individualized control on the mass concrete according to the temperature control information acquisition analysis of a temperature control acquisition instrument and the integrated flow and temperature control device and based on a temperature control gradient curve on time and space, so that an aim of casting the seamless dam is achieved.

The invention discloses a smart grid classification and fuzzy neural network based natural gas load prediction method and relates to the technical field of short-term load forecasting technology. The smart grid classification and fuzzy neural network based natural gas load prediction method comprises performing correlation analysis through Matlab to confirm an input variable; establishing a smart grid with a horizontal coordinate and a vertical coordinate to be the data and the average temperature through processed history data; selecting history data which is similar to a to-be-predicted date through the smart grid to train and predict a prediction model; performing de-noising processing through wavelets and training and predicting fuel gas loads through a fuzzy neural network with high adaptivity in combination with the complexity and multi-exterior-factor influences of the fuel gas load prediction process, wherein a process of modifying model parameters through errors is added to a training process and accordingly improvement of the final prediction accuracy is facilitated. The smart grid classification and fuzzy neural network based natural gas load prediction method can provide forceful reference for natural gas dispatching and confirms to material and technological foundation of a market development demand.

The invention discloses a multi-dimensional electric energy meter on-site state test method based on a decision-making tree mass. The method comprises the following steps of building a decision-makingtree by using collected data related to an electric energy meter and provided by an electricity information collection system; forming next-level branches through classifying each decision-making tree; generating the completely grown primary decision-making tree; pruning and optimizing on the primary decision-making tree based on a cost complexity pruning algorithm; using an independent test setto assess the accuracy of the primary decision-making tree after pruning and optimizing; selecting the optimal decision-making tree from each set of secondary decision-making trees, and forming a decision-making tree abnormity diagnostic model by all the optimal decision-making trees, wherein a judgment result wins in a voting manner; and transmitting an operational rule of the decision-making tree abnormity diagnostic model to an electricity information collection system Hadoop big data processing cluster, thus implementing abnormity detection on the abnormal running state of the running electric energy meter.

The present invention discloses an implicated crime principle and network topological structural feature based recognition method for drug-target interaction. The method comprises: firstly, according to human protein-protein interaction data and drug-target interaction data, constructing a drug-target interaction group network which comprises a protein-protein interaction sub-network, a drug-target interaction sub-network and a drug-drug relationship sub-network; according to information of a protein primary structure descriptor, a fingerprint feature of drug molecules and the reliability of interaction, weighting nodes and edges in the network; proposing a new network topological structural feature for characterizing a drug-target interaction pair based on an implicated crime principle and a graph theory; and finally, constructing a model by using a random forest algorithm and predicting a potential drug-target interaction effect in a proteome scale. The method does not require information of three-dimensional structures and the like of protein and drug molecules, is simpler, quicker and more accurate, and has high potential for application to the fields of new drug research and development, pathological study and the like.

The invention discloses a method for detecting the quality of Brassica campestris based on the near infrared spectrum, which comprises the steps of fitting collected near infrared spectrum data of a Brassica campestris sample and chemical values measured by a standard method, and optimally establishing a model by applying partial least squares (PLS); selecting an optimal spectrum pretreatment method, and measuring the quality of the model by comparing a determination coefficient (R2) and a root mean square error (RMSECV) so as to construct a high-quality quantitative analysis model of the nearinfrared spectrum of Brassica campestris. According to the method, the surface color, the mass loss rate, the hardness and the VC content of Brassica campestris can be rapidly and accurately predicted, the grade of Brassica campestris can be judged, thereby laying a foundation for rapid and nondestructive quality detection research of Brassica campestris, and having very high practicability and wide applicability.

The invention relates to an alternating current conductivity and dielectricity prediction method of a graphene-polymer porous nanocomposite material with consistent orientation based on an effective medium method. The prediction method comprises the following five steps of 1, testing the geometrical parameters and the electrical properties of a component material; 2, preparing a graphene-polymer porous nanocomposite material sample; 3, establishing an equivalent alternating current conductivity and dielectricity prediction model; 4, calculating and extracting the material parameters and obtaining a complete prediction model; and 5, obtaining a prediction curve and checking the prediction model. According to the method, the influence of the microstructures and/or the parameters such as theporosity, the graphene content, a slenderness ratio of graphene, a maximum included angle between the graphene, a percolation threshold and the like on the electrical properties of a product is mainlyconsidered, and the prediction model is re-established. After the model is checked, a prediction result is found to be closer to an experiment value.

The invention belongs to the field of signal processing, and particularly relates to an industrial alarm flooding prediction method based on an N-gram model. The industrial alarm flooding prediction method based on the N-gram model comprises the following steps that (1) a historical alarm flooding data set is acquired, alarm variables therein are counted, a discrimination degree of each alarm variable is calculated, and the alarm variables with 0 discrimination degree are eliminated; (2) sequences in the processed data set are compared with emerging sequences in similarity one by one, and thesequences are arranged from high to low according to similarity scores; (3) a time window is set to segment the reprocessed data set, the number of each data segment is counted, and the next possiblealarm variable and the corresponding probability are calculated by using a sample data set; (4) the probability of predicting the next alarm and a corresponding confidence interval are calculated through a Bayesian probability model; and (5) iterative operations are performed on the steps (3) and (4). The industrial alarm flooding prediction method based on the N-gram model solves the problem of inaccurate prediction of carrying out alarm flooding prediction at present.

The present invention relates to the technical field of laser welding, specifically to a prediction method for corrosion resistance of a laser welding plate, and a system thereof. The system comprises a database, a pre-processing module, a corrosion resistance prediction module and a post-processing module, wherein the pre-processing module reads base information and process parameters of welding parts and base materials required by a laser welding process from the database to provide initiate conditions for the follow-up process, the corresponding optimal PLS prediction model and corrosion resistance rate prediction values having a target relative error of less than or equal to 5% can be obtained with the corrosion resistance prediction module through simulation of different process states, and based on the corrosion resistance rate prediction values, reverse calculation is performed according to an inverse mapping principle to obtain the optimal welding process scheme corresponding to the minimum prediction value, wherein the optimal welding process scheme is the combination of the optimal process parameters.

The present invention discloses an oil-gas microorganism gene exploration method. The method comprises the following steps: respectively collecting samples of surface shallow layers above a known oilwell, a gas well and a dry well in an exploration area, performing high-throughput sequencing after extracting DNA, establishing a microbial community composition pattern diagram in the exploration area according to a sequencing result, respectively screening out characteristic microorganisms in surface soil above the oil/gas well in the exploration area according to the pattern diagram, and designing primers according to attribute characteristics and carrying out a fluorescent quantitative PCR detection on the samples in the whole exploration area to detect the number of the characteristic microorganisms. A contour line of the characteristic microorganisms obtained by the oil-gas microorganism gene exploration method has a high coincidence rate with the known well, has a good coincidencerate with a trap line, and can carefully describe an oil area.