34results about How to "Strong randomness" patented technology

The invention discloses a power prediction method considering photovoltaic power clustering, which comprises the following steps of: firstly, acquiring historical data of a photovoltaic power station, decomposing a photovoltaic power time sequence by using an STL time sequence decomposition method, performing DBSCAN clustering on the decomposed photovoltaic power data, identifying a low-power cluster and a high-power cluster, and predicting the power of the photovoltaic power station according to the low-power cluster and the high-power cluster; establishing a photovoltaic power prediction model based on a time convolutional network and Times Net for low power and high power respectively; secondly, a badger algorithm is improved, Chebyshev chaotic mapping is adopted to improve initialization of a badger population, and a Gaussian mutation strategy and a lens imaging reverse learning strategy are introduced to obtain MHBA; and optimizing hyper-parameters of the TCN photovoltaic low-power prediction model and the TimesNet photovoltaic high-power prediction model by using an improved badger algorithm, and predicting photovoltaic power by using the optimized models to obtain a final prediction value. Compared with the prior art, the precision and effectiveness of the photovoltaic power prediction model can be improved.

The invention relates to the technical field of data processing, and discloses a CNN-BiLSTM-MATT time sequence prediction method, which comprises a plurality of feature value inputs and a feature value output. Carrying out data normalization processing; performing normalization processing on the input data, mapping a normalized data set between [-1, 1], and performing reverse normalization processing on the output data after prediction; the hyper-parameters in CNN-BiLSTM-MATT are optimized, the hyper-parameters in CNN-BiLSTM are initialized according to an IHO algorithm, the hyper-parameters comprise the learning rate, the L2 regularization coefficient and the hidden layer number, and the optimal hyper-parameter value is obtained in the iteration process; performing feature extraction: performing convolutional feature extraction by using a convolutional neural network CNN, and taking obtained data features as input of subsequent BiLSTM; and carrying out time sequence analysis. The method overcomes the defects of a current time sequence prediction method. The method can be widely applied to the parameter optimization problem, the convergence degree is improved, and the optimal solution is efficiently found.

The invention relates to a cold region wetland water quality risk factor screening method, and belongs to the technical field of water ecological water quality screening. Establishing a water quality monitoring data file; the water quality screening calculation comprises the steps of data file selection and identification, seasonal screening calculation, data file establishment, principal component scoring, main factor direct calculation score, non-rotation factor sample direct scoring, variance maximum orthogonal rotation, factor post-rotation scoring, Bartlett inspection, risk factor probability screening and conjecturing of over-standard elements in each month of the next year. The method has the advantages that the problem that risk factors cannot be quantitatively analyzed in a factor analysis method can be solved, and identification and analysis can be independently carried out in different seasons. According to a method for performing factor analysis and probability calculation according to seasons, the water quality risk factors are screened by combining factor analysis scores with probable probabilities.

The present invention relates to a vehicle sheet metal abnormal noise detection method and system, belonging to the field of vehicle detection technology. The method includes: obtaining an enhanced noise signal through a signal filtering model according to a vehicle noise signal, obtaining a smoothed noise signal through a Gaussian filter according to the enhanced noise signal, determining a signal parameter according to the smoothed noise signal, obtaining a parameter initial value by initializing the signal parameter, obtaining a vehicle abnormal noise signal through abnormal noise detection according to the smoothed noise signal combined with the parameter initial value, obtaining a vehicle abnormal noise signal through feature extraction of the vehicle abnormal noise signal, obtaining a vehicle abnormal noise signal feature through a feature selection model according to the vehicle abnormal noise signal feature, obtaining an abnormal noise signal feature value according to the abnormal noise signal feature value, obtaining a feature value set according to the abnormal noise signal feature value, obtaining an abnormal noise category according to the abnormal noise category through a normalized linear classification model, and uploading the abnormal noise category to a terminal, thereby realizing the category judgment of the vehicle sheet metal abnormal noise and improving the accuracy and efficiency of sheet metal abnormal noise detection.

The invention provides a highway service area electric power dynamic capacity expansion method considering an electric vehicle charging load. The method is divided into a long-term scheduling optimization stage and a short-term scheduling optimization stage. In the long-term scheduling optimization stage, on the basis of long-term prediction results of wind-light power generation, daily load and electric vehicle charging load power in a micro-grid in a service area, a target function with minimum grid-connected fluctuation is added on the target basis of economical efficiency and low-carbon performance, and a long-term scheduling optimization plan is preliminarily formulated; and obtaining a power change condition between the service area and the power grid. In the short-term scheduling optimization stage, a short-term scheduling optimization plan is carried out on the basis of a long-term result grid-connected power change and a short-term prediction result of each unit in the system by taking optimal operation economy and minimum grid-connected stability as targets.

The present invention discloses a multifunctional quantum random number platform architecture system and a method of use, which relates to the field of quantum information. The platform architecture system includes a random number generator cluster, a random number storage module, a functional module and a data service management module connected in sequence. The method of use of the platform architecture system includes step 1: user terminal registration; step 2: user terminal login and sending a request; step 3: the data service management module sends the request to the functional module, and the functional module pulls the random number; step 4: the functional module processes the random number and sends the processing result to the user terminal. The present invention solves the problem of the difficulty in obtaining current quantum random numbers and irreducible polynomials developed from quantum random numbers. The quantum random number platform architecture system of the present invention is multifunctional and integrates four functional services of random numbers, irreducible polynomials, keys and model simulation.

The invention provides a symmetric key generation method, apparatus and device, a medium and a product, a network device sends a first signal to a wireless sensor according to a target key generation sequence, and the wireless sensor sends a second signal to the network device according to the target key generation sequence, enabling the network equipment to generate a first symmetric key according to the first signal, and enabling the wireless sensor to generate a second symmetric key according to the second signal. The target key generation sequence is a random number sequence and has relatively high randomness, so that the channel change between the wireless sensor and the network equipment is further enhanced, and the randomness of the symmetric key generated by the wireless sensor and the network equipment based on the channel characteristic value is relatively high; therefore, the security performance of data transmitted between the wireless sensor and the network equipment is enhanced, and the security and stability of the whole wireless sensor network are ensured.

The present application relates to the technical field of image generation, and particularly relates to an image generation training method for an AIGC large model, comprising: obtaining standard images by preprocessing collected labeled images, injecting the standard images into an initial model for training to obtain a pre-training diffusion model, inputting random noise into the pre-training diffusion model, and generating initial images by reverse diffusion; optimizing the pre-training diffusion model according to the initial images to obtain an AIGC large model with image generation capability, processing image generation requirements by using the AIGC large model to generate target images; determining whether the number of reverse diffusion sampling steps needs to be increased; determining whether the sampling temperature of the image generation process needs to be increased; and determining the residual connection weight of deep features based on the attack iteration decay rate in the optimization process. The present application improves the generation stability of the target images.

The invention relates to the technical field of wireless communication, and provides a spread spectrum sequence generation method and device, equipment, a medium and a product, and the method comprises the steps: generating a basic sequence, and generating a password sequence or a chaos sequence; performing field extension mapping on the basic sequence to form a base sequence, and performing field extension mapping on the password sequence or the chaos sequence to form a field extension sequence; performing extension field addition operation on the base sequence and the extension field sequence to obtain an extension field addition sequence; and carrying out extension domain transformation on the extension domain addition sequence to obtain a spread spectrum sequence. According to the method, the spread spectrum sequence meeting the requirement can be generated, and the method has good correlation, long periodicity, good randomness and high safety.

A method for planning stair-climbing torque based on multi-pose information fusion first acquires the pose information of the lower limbs when climbing stairs through a pose sensor. Then, based on the pose information of the lower limbs, the motion information of the human lower limbs when climbing stairs is obtained. Next, kinematic calculations are performed on the entire lower limb pose data to obtain the motion information of the human lower limbs, and then the human motion trajectory is obtained. The vertical velocity of the ankle joint is used as the discrimination criterion. When the velocity is greater than a predetermined threshold, an assist phase torque is output and the device outputs torque to follow the human body according to the interactive force data. When the velocity is less than the predetermined threshold, a follow-up phase torque is used and no assist phase torque is output. Finally, the motion controller MCU outputs torque and the knee joint motor executes it. This invention patent utilizes the ankle joint, which has good periodicity and strong noise resistance, to recognize the motion state and improve the stability of state identification. By filtering the human-computer interaction force and the output torque, the accuracy of torque is improved while optimizing the user comfort of the exoskeleton wearer.

The present invention discloses a method and system for generating a dual random processing path with adjustable surface shape threshold, which belongs to the field of processing technology. First, a mapping relationship between surface shape data height and grayscale value is set, so that the surface shape height information of the workpiece is mapped to the grayscale value of the grayscale image; then, by autonomously setting the expected processing surface shape threshold, the area to be modified is delineated within the entire workpiece surface shape range, and the area to be modified delineated based on the target surface shape threshold is extracted to generate a random resident point cloud; finally, through a heuristic algorithm, a random processing path is generated in the area to be modified that passes through all the resident points, has a short distance, is continuous and does not cross. The present invention can delineate the area to be modified by setting the surface shape threshold, and efficiently generate a dual random processing path with random point cloud and random trajectory, has a strong ability to suppress intermediate frequency errors, avoids further deterioration of local surface shape low points, significantly improves the modification accuracy and efficiency, and helps to improve the convergence efficiency of surface shape errors of large-aperture optical elements.

The embodiment of the invention provides a random number generation method and a true random number generator, the method is applied to the true random number generator, and the true random number generator comprises an entropy source module, an entropy extraction sampling module and a post-processing module; the method comprises the following steps: generating logic high and low levels by using a first ring oscillator in the entropy source module; respectively inputting the logic high and low levels generated by each order of ring oscillator into a first input end of an exclusive-OR gate of a second ring oscillator in the entropy source module for signal processing to obtain an entropy source signal; sampling the entropy source signal by using the entropy extraction sampling module to obtain an initial random sequence; and eliminating the bias and correlation of the initial random sequence by using the post-processing module to obtain a target random sequence. According to the embodiment of the invention, the randomness of the random number is improved, the structure is simple, excessive resources do not need to be occupied, the frequency of the oscillator does not need to be changed by a programmable delay line, and the universality is high.

The application provides a Doppler positioning method based on frequency hopping spread spectrum, and belongs to the technical field of radio positioning, to solve the problem that the fixed carrier frequency mode adopted by the traditional Doppler positioning technology makes the signal extremely easy to be intercepted, oriented and interfered. The method comprises the following steps: step 1: describing an application scenario; step 2: in the application scenario, a frequency hopping measurement signal is sent by a transmitting end; step 3: in the application scenario, a receiving end performs frequency hopping synchronization and measures a Doppler shift; step 4: a Doppler shift is preprocessed; and step 5: the preprocessed Doppler shift is used to calculate the position of a terminal to be positioned in combination with the relative motion of the transmitting end and the receiving end. The method can obtain the low interception rate and anti-interference ability given by the rapid change of the carrier frequency, and can also use the frequency diversity characteristics of frequency hopping to reduce the positioning performance deterioration caused by the multipath effect, and is suitable for application scenarios with high requirements for robustness, positioning accuracy and other performances.

The invention relates to the technical field of power grid risk protection, and particularly discloses an intelligent control method and system for bird repelling equipment for a power grid, and the method comprises the steps: obtaining a power grid region, randomly selecting an equipment placement point according to the power grid region, and determining an application region of the equipment placement point; creating an equipment matrix according to the equipment placement points, randomly selecting different numbers of row and column positions in the equipment matrix, and obtaining a test matrix set taking the number as a label; acquiring bird repelling utility values corresponding to different test matrixes according to preset acquisition equipment, and calculating different numbers of average bird repelling utility values; determining selection probabilities of different numbers according to the average bird repelling utility value, and when a bird repelling instruction generated by the acquisition equipment is received, controlling the bird repelling equipment based on the selection probabilities; according to the invention, the activity condition of the birds is acquired through the acquisition device, then the selection condition of different bird repelling devices is determined, the randomness is very strong, the birds almost cannot predict which bird repelling devices work, and the bird repelling effect is very good.

The invention provides a patient cardiovascular and cerebrovascular disease data processing method and system, and the method comprises the steps: extracting an electroencephalogram signal data set of a corresponding frequency band of a patient according to the frequency bands of real electroencephalogram signals EEG corresponding to a plurality of diseases, the electroencephalogram signal data set comprising electroencephalogram signals EEG in a plurality of time periods; performing denoising processing on the EEG in the EEG data set to obtain a denoised EEG; performing dispersion evaluation processing on the de-noised EEG to obtain dispersion evaluation of each segment of de-noised EEG; extracting the de-noised EEG of which the dispersion evaluation is smaller than a preset threshold as an EEG for training; according to the method, the EEG for training is subjected to feature extraction and compared with the real EEG of the corresponding disease so as to verify the EEG in the EEG data set, the authenticity of the data is verified on the aspect of an algorithm, and time waste caused by the fact that a large amount of time is adopted to verify the reliability of the data is avoided.

The application belongs to the technical field of information processing, and particularly relates to a document-level relation extraction method based on a generative model, which comprises the following steps: firstly, in the training stage, a generative model is used to compress and refine the document, and the relation summary of the subject and object in the document is extracted, so that information closely related to the target is screened out; secondly, in the testing stage, a lightweight discriminant module is introduced, and the relation summary output by the generative model is mapped to a predefined relation category; and the generative model has self-adaptive learning ability and can dynamically and autonomously learn human carefully selected examples to generate output closer to human intention.

The invention relates to an alternating-current variable-frequency random pulse width modulation method, system and device and a medium. The method comprises the following steps: generating a first pseudo-random binary sequence and a second pseudo-random binary sequence by using two linear feedback shift registers; a digital module is designed, the digital module is a derived clock generator, and the derived clock generator comprises a triangular carrier generator, a sine carrier generator and an arcsine carrier generator; according to the first basic frequency f1 = 2kHz, the second basic frequency f2 = 4kHz, the third basic frequency f3 = 3kHz, the first pseudo-random binary sequence and the second pseudo-random binary sequence, calculating a random frequency by using a preset carrier frequency formula; according to the random frequency and a digital module, clock signals with the frequencies of 400 kHz, 300 kHz and 200 kHz are obtained; generating a carrier signal with a random frequency by combining a triangular carrier generator, a sine carrier generator and an arcsine carrier generator according to the clock signal; a carrier signal with a random frequency is applied to a generation process of alternating-current variable-frequency random pulse width modulation, and random pulse width modulation control is realized.

The present invention discloses a method for securely distributing quantum keys on the edge side of the Internet of Things. The communicating parties perform bidirectional detection and sampling of channel characteristics through pilot signal transmission, and then perform signal preprocessing; then, an improved differential value-based quantization method is used to convert the obtained channel measurement value into a binary bit sequence, thereby becoming a preliminary key material; then, based on the Cascade protocol, inconsistent bits in the preliminary key material between a pair of legally participating communicating parties are detected and corrected; a privacy amplification process is performed using a Hash function, and privacy amplification allows the communicating parties to extract a shorter but confidential key from the preliminary key; finally, the generated key is used to encrypt and transmit the quantum key, and the quantum key is securely distributed from the edge gateway to the Internet of Things terminal device. This method utilizes the reciprocity characteristics of electromagnetic wave propagation in the wireless channel, and uses the wireless channel as a natural random source for generating symmetric keys, thereby avoiding a complex key distribution process.

The present invention relates to the field of in-vehicle network communication technology, and discloses a dynamic stream encryption authentication communication method and system capable of detecting CAN replay attacks. The method comprises: an in-vehicle gateway assigns quantum random numbers to two ECUs that are ready to communicate; each ECU generates associated data based on context information generated by historical CAN data frames on the bus, and updates the encryption state value using the associated data and the key value; the sender ECU encrypts the CAN message plaintext using the updated encryption state value, calculates the corresponding first authentication tag, and then sends the encrypted data and the first authentication tag to the receiver ECU; the receiver ECU decrypts the encrypted data using the updated encryption state value, calculates the second authentication tag and compares it with the first authentication tag, and determines whether to accept the CAN message plaintext based on the comparison result. The present invention can effectively detect CAN replay attacks and improve the security of CAN data encryption.

The present invention discloses a training-free non-invasive variable frequency air conditioner load monitoring method. According to the variable frequency air conditioner power waveform, the load events of the variable frequency air conditioner are divided into three categories: gentle slope type, step type and peak type; the step type and peak type load events are detected and marked by the difference method, and the gentle slope type load events are detected and marked by the curve segmentation linearization method; by serially reorganizing the detected load events, the variable frequency air conditioner gentle slope type load events that are as complete as possible are obtained; according to the specificity of the air conditioner startup power waveform, the variable frequency air conditioner startup event is screened out from the obtained complete gentle slope type load event, and other events matching the variable frequency air conditioner startup event are further searched, and a legal variable frequency air conditioner load event sequence is formed. The method of the present invention is more adaptable to the randomness of the variable frequency air conditioner operating state change, and the overall detection effect is better. At the same time, it takes into account economic practicality and can effectively promote the air conditioning load to actively participate in the power system demand response.

The present invention relates to the field of chaotic system application and digital image encryption technology, and in particular to a digital image encryption method based on a combined chaotic pseudo-random number generator and the same. The encryption method includes: obtaining an image matrix composed of the size, number of channels and pixel values ​​of each sampling point of the original image; determining a grayscale image matrix according to the size, number of channels, image matrix and a preset first transformation algorithm of the original image; determining a diffusion image matrix according to the grayscale image matrix and a preset first diffusion algorithm. The beneficial technical effect of the present invention is that the combined chaotic pseudo-random number generator is not easy to degenerate on a limited precision platform such as a computer, has high sensitivity to changes in system control parameters and initial values, and generates a pseudo-random sequence with a long period and strong randomness. The encryption method has a large key space, high sensitivity to small changes in the plaintext image, strong resistance to various attack methods such as brute-force attacks and differential attacks, and high security.

The invention belongs to the technical field of passwords, and particularly relates to an improved RSA information encryption method, which comprises the following steps of: S1, generating a modulus, a public key index and a private key based on an RSA method; s2, splitting the information M into sub-information; s3, encrypting the sub-information through a modulus and a public key index; and S4, decrypting the encrypted sub-information through a private key, and combining the decrypted sub-information into source information M. By adopting the method, the encryption security of the traditional RSA algorithm can be improved.

This invention relates to the field of chaotic system theory and discloses a quantum image encryption algorithm based on chaotic systems and DNA dynamic coding. The algorithm includes a quantum image coding module, a chaotic sequence generation module, a DNA dynamic coding and operation module, and a central control module. The algorithm also integrates a key management unit. By using an enhanced quantum phase representation method to convert classical images into quantum images, and utilizing quantum gate operations controlled by chaotic sequences to achieve quantum scrambling of pixel positions and preliminary diffusion of pixel values, the system can efficiently perform preliminary confusion and perturbation of image information directly at the quantum level, laying a secure preprocessing foundation for subsequent encryption. By using a parallel multi-chaotic system to generate a dynamic key stream and introducing a dynamic perturbation mechanism based on image hash values, the generated key stream has stronger randomness and is dynamically related to the plaintext image content, enhancing its ability to resist security threats.

The invention discloses a template calculation program block size prediction method and system based on LCE machine learning, and the method comprises the steps: generating training data through artificially synthesizing template calculation programs with different feature values for single-time-step template calculation; limiting and collecting the obtained training data in combination with the characteristics of actual application, hardware platform resources and GPU parallel characteristics; using the obtained training data as a training set of a template calculation program block size prediction TSSLCE model, using the template calculation program as a test set to pre-train the TSSLCE model, and selecting an optimal TSSLCE model parameter through a grid search method and five-fold cross validation; performing feature extraction of overall features and fine-grained features on the template calculation program to serve as input of a machine learning model; and the machine learning model uses the calculation rate of the template calculation program as the output of the TSSLCE model, and performs corresponding preprocessing operation on the output of the TSSLCE model to obtain the optimal block size. And the calculation efficiency of the template is effectively improved.

This invention relates to a horizontal sand mill and its sand milling process for paint production, belonging to the field of grinding equipment technology. It includes a rotating reference barrel, a rotating power system, a sand milling dispersion system, and several adaptive actuating devices. The sand milling dispersion system is installed inside the rotating reference barrel, and the rotating power system drives the sand milling dispersion system. The adaptive actuating devices include an actuating reference ball, an upper sealing plate of the reference ball, a lower sealing plate of the reference ball, an actuating rotating shaft, an actuating rack, and a return spring. The actuating reference ball is installed on the sand milling dispersion system. The actuating reference ball has an actuating space arranged perpendicular to the rotation axis of the sand milling dispersion system. The upper and lower sealing plates of the reference ball are installed at both ends of the actuating space. The two ends of the actuating rack are respectively installed on the upper and lower sealing plates of the reference ball via return springs. The actuating rotating shaft and the actuating rack mesh and drive each other. The end of the actuating rotating shaft is equipped with an actuating plate, achieving sufficient flow of the grinding media and good grinding effect.

The invention relates to the technical field of intelligent bid evaluation, in particular to a technical bid and dark bid electronic review method based on multi-dimensional dynamic dislocation sequencing, and the method comprises the steps: obtaining review data of a bid invitation project, the review data comprising scoring point information, bidding unit information and review expert information; for the first scoring point, randomly sorting all bidding units and all review experts; according to the sequence of the scoring points, performing random sorting operation of all the bidding units and all the review experts on each following scoring point until all the scoring points complete the random sorting operation; and reviewing each scoring point based on the randomly sorted bidding units and reviewing experts. The identity recognition possibility in the review process can be eliminated to the maximum extent, human factor interference is reduced, independent review of each judge is achieved, and therefore the fairness in the review process is guaranteed.

The present invention relates to the technical field of image processing, and in particular to a reproductive diagnostic image enhancement method based on computer vision assistance, comprising: obtaining a standard magnetic resonance image of the reproductive system and a to-be-processed magnetic resonance image of the reproductive system, and obtaining a corresponding local tissue image region; calculating a local grayscale signal error and a grayscale equal error probability, and calculating a local grayscale information entropy; obtaining a reproductive system magnetic resonance artifact correction region; obtaining singular values ​​of an image of a reproductive system magnetic resonance artifact correction region, obtaining a correction boundary interval, and calculating a local interval distribution probability of each different interval; obtaining an artifact correction singular value set and reconstructing the image. The present invention effectively improves the quality of the reproductive system magnetic resonance image.

The invention relates to a reclaimed water wetland water quality risk factor probability screening method, and belongs to the technical field of water ecological water quality screening. Establishing a screening model calculation file, carrying out Butterfly normal distribution variance homogeneity test, carrying out sequence calculation on parameters of an empirical normal distribution function, carrying out probability calculation by adopting a sequence for calculating the empirical normal distribution function, and screening the probability of the water quality risk factor. The method has the advantages that aiming at the characteristics of large information amount, large information repetition amount, strong randomness and fixed monitoring data position of water quality monitoring data, the occurrence probability and probability and possible occurrence time of various screened water quality indexes are clarified by classifying and analyzing the characteristics of the information and comparing common characteristics. The algorithm model can assist related departments in effectively utilizing monitoring data to master possible occurrence conditions of wetland water quality risk factors, determining possible probabilities and possible occurrence time of various screened water quality indexes, and evaluating possible water quality risks.

The invention belongs to the technical field of information processing, and particularly relates to a document-level relation extraction method based on a generative model, which comprises the following steps of: 1, a training stage: compressing and refining a document by adopting the generative model, and extracting a relation abstract of a subject and an object in the document so as to screen out information closely associated with a target; and 2, in a test stage, a lightweight discrimination module is introduced, a relation abstract output by the generation model is mapped to a predefined relation category, the method further comprises guide generation, the generation model has adaptive learning ability, examples carefully selected by human can be dynamically and autonomously learned, output closer to human intention is generated, and the method further comprises guide generation. According to the method, the relation abstract of the subject and the object in the document is accurately captured and extracted, the relation abstract is mapped to the predefined relation category, and double improvement of the accuracy and controllability of relation extraction is achieved.

The invention relates to the technical field of image processing, in particular to a reproductive department diagnostic image enhancement method based on computer vision assistance, which comprises the following steps: acquiring a reproductive system standard magnetic resonance image and a reproductive system magnetic resonance image to be processed, and acquiring a corresponding local tissue image area; calculating error probabilities such as local gray level signal errors and gray levels, and calculating local gray level information entropy; acquiring a magnetic resonance artifact correction area of the reproductive system; obtaining a singular value of the reproductive system magnetic resonance artifact correction area image, obtaining a correction boundary interval, and calculating a local interval distribution probability of each different interval; and obtaining an artifact correction singular value set and reconstructing the image. The method effectively improves the quality of the magnetic resonance image of the reproductive system.