7results about How to "Avoid complex calculations" patented technology

The invention relates to the technical field of information, in particular to an artificial intelligence decision support method and system for high-frequency transactions. The method comprises the steps of collecting order book data, and obtaining a fluctuation rate and a trading price difference based on the order book data; the window at the current moment is adjusted based on the volatility to obtain a window at the next moment, and window detection is completed through the hash value; counting the order quantity difference of the key prices at the current moment and the adjacent moment and the difference between the buying and selling price difference at the current moment and the maximum buying and selling price difference for the window with the data change, and obtaining a dynamic compression ratio based on the two differences; the DPCM algorithm and the dictionary compression algorithm are adjusted based on the dynamic compression rate, and then compression is carried out; and generating a decision signal based on the compressed data to complete decision support. The compression speed is increased, and the accuracy and integrity of the data are improved.

The application discloses a kind of quick cross path scheduling methods of inter-city area customized public transport system, the shortest distance method between regions is used to build start point area end point set and end point area start point set, to generate start point area travel sequence Yo by insertion method, to generate end point area travel sequence Yd by insertion method, then the Yo and Yd are optimized by quick exchange method, finally the minimum value of start point × Yo × Yd is solved, which is the final inter-city area customized public transport system target path, with the shortest bus travel distance as the target;The method does not use probability distribution, effectively avoids the fluctuation of the final path result, according to the characteristics that the distance between nodes in different regions is much larger than the distance between nodes in the same region, the inter-regional traffic node set is established, and it is used as the end point of the start point region and the start point of the end point region, respectively, to maximize the range of optimal solution, effectively reducing the overall calculation scale.

The low-power pulse wave signal arrhythmia classification method and system of the application comprises the following steps: using a large convolution kernel to extract the bottom local features of the original signal, and reducing the sequence length by half through pooling; adopting a leaky integral-discharge neuron activation to make the data pulsed; using a large convolution kernel and a hollow convolution to expand the receptive field and capture longer-range local timing features; dimensionally reducing the output features, cooperating with batch normalization, leaky integral-discharge neurons and maximum pooling, and aggregating the local features; calculating self-attention in each subsequence, combining a linear mapping layer, focusing the model on the feature correlation in the local subsequence through the aggregation of the sub-attention map, and extracting the waveform dependence in the short time window of the pulse wave signal; calculating QKV interaction on the whole sequence, combining a linear layer and a pulse neuron, capturing long-distance global feature correlation, and mining the waveform dependence in different time periods of the pulse wave signal. The application can achieve a classification performance equivalent to that of a deep artificial neural network.

This application discloses a method for determining a display mode and a vehicle, relating to the field of vehicle control technology. The method includes: determining the first importance of a vehicle function to be displayed to the occupants based on their attribute information; determining the second importance of the vehicle function to the current driving process based on the vehicle's current driving scenario; determining the vehicle's usage preference for the vehicle function based on its historical usage information; and fusing the first importance, second importance, and usage preference levels to determine the target display mode corresponding to the vehicle function based on the fusion processing result. The technical solution of this application embodiment can make the target display mode more closely match the actual driving conditions of the vehicle and the actual needs of the occupants, thereby improving the convenience and experience of occupants using vehicle functions.

This invention discloses a machine learning-based method and system for predicting the production capacity of gas-water two-phase gas in tight sandstone. The method includes: constructing production capacity prediction models for different well types using an artificial neural network; inputting data such as critical flow saturation, water saturation, water phase relative permeability curve index, starting pressure gradient, permeability, reservoir thickness, fracture conductivity, fracture length, cluster number, and stress sensitivity coefficient as input variables into the production capacity prediction model, and using cumulative gas and water production as output variables. The input variables are then imported into the corresponding production capacity prediction model for different well types to obtain the cumulative gas and water production for each well type. This method uses an artificial neural network to construct a production capacity prediction model to predict the cumulative gas and water production in tight sandstone, achieving rapid and accurate prediction of the production capacity of the gas-water two-phase gas in tight sandstone, avoiding complex calculation processes, and reducing computation time and production costs.

The present application relates to a kind of geothermal wellbore scale intelligent scale removal system, including wellhead monitoring module, downhole monitoring module, main control system, injection module;Main control system includes the analysis module, dosing scheme generation module, control module connected in sequence;Injection module includes the lifting unit connected, injection unit;Analysis module, for according to the wellhead geothermal fluid data, downhole geothermal fluid data and multiple-field multiphase multi-component non-isothermal phase transition flow model obtained by real-time monitoring, inversion obtains the real-time flashing face depth of geothermal fluid in wellbore, and the real-time scale rate of different depth positions;Dosing scheme generation module, according to the real-time flashing face depth, the real-time scale rate of different depth positions, real-time generates corresponding scale inhibitor dosing scheme, scale inhibitor dosing scheme includes scale inhibitor injection rate, scale inhibitor injection depth;Control module, for according to real-time scale inhibitor dosing scheme to lifting unit, injection unit is controlled.The present application can realize intelligent scale removal.