16results about How to "Solve randomness" patented technology

The invention relates to the technical field of load monitoring, and discloses a load monitoring method of a low-voltage transformer area and a line electric leakage detection method based on a low-voltage transformer area load detector device. Comprising the steps of current signal acquisition and processing, current-voltage conversion, signal processing, Fourier transform FFT processing, real-time current value calculation and output, data transmission, remote monitoring and the like. According to the load monitoring method and the line leakage detection method for the low-voltage transformer area, a real-time current signal is acquired through an open current transformer, and is processed by means of a Fourier transform FFT algorithm, so that the load distribution condition of the low-voltage transformer area can be accurately monitored; the problem of randomness of a traditional measurement method is solved, it is ensured that the collected load data accurately reflects the actual power consumption condition of each household, and reliable support is provided for follow-up three-phase load adjustment. The three-phase load condition of each branch line in the multiple branches of the low-voltage line can be accurately monitored by simultaneously monitoring the branch lines in the multiple branches of the low-voltage line.

The invention discloses a smart city system, a data transmission method and a program product, the smart city system at least comprises a smart lamp post, an edge server and a cloud server, the smart lamp post is provided with a binocular camera and a data transmission device, the binocular camera is configured to acquire an image in a field of view, calculate a disparity map based on the image, and transmit the disparity map to the cloud server; the data transmission device is configured to send the image and the disparity map to the edge server; the edge server is configured to cut the image based on a parallax value in the parallax image to obtain an image block, generate an encryption key based on the parallax value corresponding to the image block, encrypt the image block by using the encryption key, generate encrypted data, and send the encrypted data to the cloud server; and the cloud server is configured to decrypt the encrypted data and construct a smart city. According to the technical scheme of the invention, the data transmission amount in the construction process of the smart city can be reduced, and the safety of data transmission can be improved.

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.

The present invention discloses a method for calculating the amount of earth pressure balance shield slag improver, comprising the following steps: based on bentonite particles, the improved soil particles are layered and wrapped, and the number of wrapped layers is obtained by a slump test; based on the number of wrapped layers, the number of bentonite particles required to wrap a single improved soil particle is obtained; the number of improved soil particles in a sandy and gravel soil body is obtained; based on the functional relationship between the number of bentonite particles required to wrap a single improved soil particle and the number of improved soil particles, the number of bentonite particles required to improve the sandy and gravel soil body is obtained, and then the required bentonite mass is obtained. The present invention utilizes the microstructure of the improved soil particles and bentonite particles in the sandy and gravel soil body combined by various inter-particle forces, and derives a formula for calculating the injection mass of the bentonite improved sandy and gravel soil body, thereby solving the problem of selecting the amount and proportion of the bentonite improved sandy and gravel soil body in principle, and the amount and proportion obtained are more accurate.

The present invention relates to a method and system for measuring the broadband high-voltage dielectric response of electrical equipment insulation, and belongs to the field of electric power technology. The method adopts the method of frequency segmentation and power supply complementation, and specifically includes: S1: determining measurement parameters; S2: determining the frequency segmentation point; S3: dividing the measured frequency range into two parts according to the frequency segmentation point, using a signal generator and a high-voltage amplifier as power supplies for the frequency points at and below the segmentation point, and using a capacitance variable frequency power supply and a high-frequency transformer as power supplies for the frequency band above the segmentation point; S4: measuring from the highest frequency point to the frequency segmentation point, and then replacing the power supply, measuring from the frequency segmentation point to the lowest frequency point; and using an oscilloscope to obtain the test voltage waveform and response current waveform of each frequency point; S5: based on the discrete time domain test voltage waveform and response current waveform, determine the dielectric characteristics of the insulation of the electrical equipment. The present invention can realize broadband measurement of the dielectric response characteristics of high-voltage and large-capacity electrical equipment.

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 a series filter voltage instantaneous drop detection method. The method specifically comprises the following steps: (1) obtaining a distortion voltage value of a three-phase voltage based on a voltage compensation instruction of an instantaneous reactive power dq0 method; and (2) taking the group of distorted voltage values as input values to enter a PI controller, performing PI parameter optimization by the PI controller based on an improved immune genetic algorithm, and performing detection and voltage compensation by driving an inverter bridge after a PWM signal is generated. The method has the advantages that decoupling control is conducted on the cross coupling amount between the dq axis of the series filter, the control performance is improved, the improved immune genetic algorithm is adopted to optimize the controller parameters, the problems of blindness and randomness of searching of a traditional genetic algorithm are solved, meanwhile, the PI controller parameter optimization speed is high in response, robustness is high, and the method is suitable for large-scale popularization and application. The problem that controller parameters are difficult to adapt to changing environments is solved, and the voltage detection precision and compensation performance of the series filter are improved.

The invention discloses a clutter suppression and sensing parameter estimation method in a sensing mobile network. The method comprises the following steps: step 1, remote wireless units of M antennas transmit 5GNR signals through a uniform linear array; 2, the remote wireless unit receives signals reflected back in the environment; step 3, clutter suppression is carried out on the received signal by using a multi-pulse cancellation algorithm; step 4, converting the signal obtained after clutter suppression into a composite model used for solving a sparse multi-measurement vector recovery problem; step 5, using a UAMP-SBL for MMV algorithm to solve the composite model, and identifying L rows of the matrix A corresponding to the L paths in the composite model; step 6, introducing all sensing parameters except time delay into the matrix A, and extracting non-zero lines in the matrix A to obtain a matrix A '; and step 7, performing sensing parameter estimation according to the matrix A '. The method not only minimizes clutter interference, but also reduces the number of required sensing parameters, and simplifies the subsequent sensing algorithm.

The invention discloses a pipeline facility reconstruction and identification method based on a three-dimensional laser point cloud, and the method comprises the steps: obtaining to-be-reconstructed three-dimensional point cloud data, carrying out the sampling preprocessing of the three-dimensional point cloud data, and obtaining sampling point cloud data and first mapping information; combining and partitioning the sampling point cloud data to obtain a plurality of point cloud sample blocks and second mapping information, the number of point clouds in each point cloud sample block being the same, and sample points in each point cloud sample block being uniformly distributed in a point cloud space of the sampling point cloud data; according to the second mapping information, performing point cloud reconstruction on the point cloud sample block through a point cloud classification model to obtain point cloud reconstruction data; and sampling and restoring the point cloud reconstruction data according to the first mapping information to obtain a target point cloud reconstruction result. According to the method, the complexity of point cloud data processing can be effectively reduced, computing resources required by point cloud reconstruction are reduced, and the point cloud reconstruction effect and precision are improved. The invention relates to the technical field of point cloud data processing.

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 invention discloses a liquid phase-mass spectrum co-control combination method, which comprises the following steps of: 1, importing a dependency library, and setting a liquid phase control script, a mass spectrum control script and a path of a method configuration file; step 2, loading previous sample test configuration information or initializing the sample test configuration information, and modifying and storing the sample test configuration information according to requirements; 3, converting the sample information and the test configuration to generate configuration files suitable for liquid phase equipment and mass spectrum equipment; 4, sequentially calling a liquid phase control script to load a liquid phase configuration file, calling a mass spectrum control script to load a mass spectrum configuration file, and starting operation of the liquid phase equipment and the mass spectrum equipment; and 5, respectively generating and storing analysis results of each sample by the liquid phase equipment and the mass spectrum equipment. The invention also discloses a system for realizing the liquid phase mass spectrum co-control combination, and the system has a wide application prospect.

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 embodiment of the invention provides a video super-resolution method, system and device based on a diffusion model and a medium, and the method comprises the steps: constructing a training sample set which comprises a low-resolution sample video and a high-resolution sample video corresponding to the low-resolution sample video; an initial diffusion model is constructed, and the initial diffusion model comprises an encoder module, a time embedding module, a Unet network module and a decoder module; performing multi-round iterative training on the initial diffusion model by using the training sample set to obtain a target diffusion model; and performing video super-resolution processing on a to-be-processed low-resolution video by using the target diffusion model to obtain a high-resolution video. A time embedding module is introduced into an initial diffusion model, so that the randomness and uncertainty of an image generation process in the diffusion model are suppressed, and the model can learn video frame features of different time steps.

The invention discloses an on-chip light quantum bit feature enhancement extraction method, which is suitable for portable field detection in the field of biomedical detection. According to the method, by combining multiple image preprocessing and feature extraction technologies, rapid and high-sensitivity recognition of the light quantum bit features is realized. The method mainly comprises the following steps: 1) image preprocessing: automatically analyzing brightness characteristics in an original image, and identifying and classifying a brightness extreme value image; 2) image enhancement: adopting wavelet transform denoising and non-local mean denoising technologies to improve the image quality, and optimizing the image contrast through histogram equalization; and 3) feature extraction and enhancement: performing difference analysis and superposition processing on the processed image, enhancing target features and reducing background noise interference at the same time. According to the invention, the accuracy and repeatability of detection are remarkably improved while high efficiency and portability are ensured, and the method is suitable for rapid biomedical detection in an environment with limited resources.

The present invention belongs to the field of power supply technology, and provides a method, device and medium for improving the ability of a substation to absorb distributed photovoltaic energy, including collecting the load node power in the substation, the external load of the substation, the energy storage charging and discharging power, the load obtained from the power grid and the photovoltaic power generation information in the substation through the local management module of each substation, and uploading them to the centralized control station; establishing a load prediction model and a photovoltaic output prediction model in the centralized control station; each substation collects m load power values ​​and weather data every day, and brings them into the load prediction model for calculation; the distributed photovoltaic power source in each substation collects m historical data every day, and brings them into the photovoltaic output prediction model of the substation for calculation. The application of this method can help solve the problems caused by the randomness and intermittency of new energy power generation, improve the ability of the distribution substation to absorb local distributed photovoltaic power, and contribute to the sustainable development of the power system.

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.