5results about How to "Solve randomness" patented technology

The invention belongs to the field of business method processing, determines a target network community hotspot object through an artificial intelligence technology and a corresponding algorithm model, and constructs a business district with an AI Mall core shop and a traditional entity common node shop within the range based on digital assets, a block chain technology and the obtained target network community hotspot object. Business district construction and passenger flow attraction are performed through a core shop taking AI Mall as a center, so that flow conversion from a market public domain to a business district private domain is realized, and the problems of difficulty in financing, high drainage cost, capital burning and the like of traditional small, medium and micro entity enterprises are solved. The invention provides a business district construction method and a corresponding entity business district by taking extraction and display of hot objects, uplink sharing of information data and a bridge taking digital asset real world assets as entity economy WEB3 as basic features. According to the method, the randomness and inaccuracy of dependence on human experience in the hot object and IP extraction process of the traditional joint activity are solved, the traditional form of single fighting and the loose membership state of the shop are abandoned through the systematic business district construction process, and the construction efficiency is improved on the basis of reaching the consensus. And a business entity alliance with close dependency relationship and maximum sharing of passenger flow and revenue is formed.

This invention discloses a low-dispersion cross-linked polyethylene experimental sample and its preparation method, belonging to the technical field of polymer insulation material testing. The method includes: degassing and cooling an initial cross-linked polyethylene block at a preset degassing temperature to obtain a basic cross-linked polyethylene block; repeatedly performing a cyclic annealing operation consisting of heating annealing and interface cooling until a preset target number of cycles is reached to obtain a target annealed cross-linked polyethylene block; then cutting it according to the required dimensions and performing a heating stress-relieving treatment to obtain a low-dispersion cross-linked polyethylene experimental sample; wherein the annealing temperature is higher than the crystallization melting initiation temperature but lower than the main melting peak temperature. Therefore, by implementing this invention, the problem of high data dispersion and poor statistical consistency in electrical tree initiation voltage testing caused by the random distribution of internal micro-defects in existing cross-linked polyethylene experimental samples can be solved.

The invention discloses an intelligent voltage regulating transformer fault diagnosis method and system based on an improved CNN-BiLSTM model. The method comprises the following steps: firstly, collecting historical data of a voltage regulating transformer for preprocessing; then training is carried out based on historical data of the voltage regulating transformer to obtain a transformer fault diagnosis model, the transformer fault diagnosis model is an improved CNN-BiLSTM model, and an ECA-TAM attention mechanism is introduced into the CNN-BiLSTM model; and finally, inputting the current operation data of the voltage regulating transformer into the transformer fault diagnosis model to obtain a fault diagnosis result of the voltage regulating transformer. According to the method, an ECA-TAM attention mechanism is introduced into the CNN-BiLSTM network model for voltage regulating transformer fault diagnosis, purer and more representative feature input can be provided, interference of redundant information on time sequence modeling is reduced, the key dynamic learning ability is improved, efficient and accurate diagnosis of voltage regulating transformer fault types is achieved, and the fault diagnosis efficiency is improved. The accuracy and timeliness of transformer fault prediction are improved, and stable and safe operation of an electric power system is guaranteed.

The application discloses a kind of intelligent risk early warning method and system based on multi-source data fusion, belong to construction risk assessment and early warning technical field, the method is: obtaining multi-source information, obtains early warning control variable based on multi-source information and preset quantization processing mode;Early warning control variable and preset grading early warning standard are used to build initial early warning cloud picture;Based on initial early warning cloud picture, two-dimensional matrix distribution and preset two-dimensional normal cloud model obtain three-dimensional early warning cloud picture;Improved prediction model is obtained by using posterior probability support vector method to optimize and correct three-dimensional early warning cloud picture;Risk early warning result is obtained based on improved evidence fusion method and improved prediction model.The application provides a kind of intelligent risk early warning method and system based on multi-source data fusion, realizes to fuse multi-source data as the basis to combine historical data using posterior probability support vector method and improved evidence fusion method to build corrected three-dimensional early warning model, significantly improve the accuracy, effectiveness and applicability of risk early warning.

The application discloses a kind of on-chip light quantum bit feature enhancement extraction methods, suitable for portable field detection in biomedical detection field.The method is realized by combining various image preprocessing and feature extraction techniques, and the rapid and high sensitivity recognition of light quantum bit feature is realized.The method mainly includes the following steps:1) image preprocessing: automatically analyze the brightness characteristics in the original image, identify and classify the brightness extreme image;2) image enhancement: wavelet transform denoising and non-local mean denoising technology are used to improve the image quality, and the image contrast is optimized by histogram equalization;3) feature extraction and enhancement: difference analysis and superposition processing are performed on the processed image, and the target feature is enhanced while the background noise interference is reduced.The application guarantees the efficiency and portability, significantly improves the accuracy and repeatability of detection, and is suitable for rapid biomedical detection in resource-limited environment.