18results about How to "Fully reflect" patented technology

The present application relates to the field of smart meter data acquisition, especially to a CPU occupancy-based meter data acquisition optimization method and system, the method comprising: obtaining the signal strength and working state of concentrator and meter nodes, establishing a data acquisition network model and calculating the load rate to reflect the network busy degree. Vector coding is used to generate initial particle swarm, each particle represents a data acquisition path, the transmission efficiency and communication quality of the path are evaluated, and the path score is obtained by weighted fusion based on the load rate. The particle with the highest score is selected as the global optimal solution to guide the particle swarm learning, the learning ratio is adjusted through the negative exponential function, the parent node vector is updated and iteratively optimized until the score no longer improves, the optimal path is output, and the data acquisition path optimization is completed. The present application dynamically adjusts the weight and optimization strategy, adapts to network changes, reduces the hop count, avoids weak signal links, and improves the transmission efficiency and stability of the data acquisition path.

This invention discloses an apparatus and method for detecting anomalies in a continuous crystallization process. These are corresponding solutions, in which: image analysis methods are used to extract the color information of each pixel in the crystallization image; based on the color attributes of each pixel in the image, principal component analysis is used to extract principal components; these principal components comprehensively consider the correlation between the color attributes of different pixels and can reflect the essential features of the image; based on the principal components, a multivariate statistical process control model can be constructed, thereby enabling real-time detection of the state of new continuous crystallization process images. Overall, the above-mentioned solutions provided by this invention can effectively support the automation and intelligent development of continuous crystallization process state detection.

This invention discloses a method for classifying township shrinkage characteristics based on multi-scale correlation: First, target townships are identified, and study townships are selected, with their spatial boundary data extracted. Second, the spatial distance between the target townships and the study townships is measured, constructing a spatial distance matrix. Third, the spatial distance matrix is ​​reclassified, generating first, second, and third-level spatial relationship matrices. Fourth, the population size change rate, socio-economic vitality change rate, and construction land size change rate for each target township at each spatial level during the study period are calculated. Fifth, a shrinkage characteristic vector set for the target townships is constructed, classifying the shrinkage characteristics. Finally, a shrinkage characteristic map of the target townships is drawn. This invention accurately identifies and classifies township shrinkage characteristics, assesses township development status, and effectively guides the allocation of construction land indicators, the configuration of public service facilities, and the formulation of infrastructure construction standards in territorial spatial planning.

The application discloses a detection method of Baizhu San standard decoction fingerprint combined with liquid chromatography-mass spectrometry, the fingerprint and application thereof, and belongs to the field of traditional Chinese medicine quality standard research and application. Test sample solution and single control sample solution are injected into a high performance liquid chromatograph to obtain respective chromatograms; mass spectrometry analysis is conducted on the test sample solution to obtain mass spectrometry results; common peaks existing in the chromatogram of the test sample solution are taken as common peaks, chemical components in the Baizhu San standard decoction are identified in combination with the chromatogram of the single control sample solution and the mass spectrometry results of the test sample solution, and the Baizhu San standard decoction fingerprint is obtained. The fingerprint is constructed through liquid chromatography-mass spectrometry, 10 intersecting components are identified in combination with the mass spectrometry information of the control sample, so that the quality control of the Baizhu San standard decoction is realized, the method is simple, easy to operate, high in precision, good in stability and reproducibility, and can effectively solve the technical problem that the quality of the Baizhu San cannot be effectively evaluated.

The application discloses a bridge influence surface identification method and device based on a new driving strategy of a mobile vehicle, a computer and a storage medium, and relates to the field of bridge structure safety detection.The application solves the problems of low calculation efficiency and low accuracy of existing identification technology without considering the efficiency and accuracy of loading on various bridges.The method comprises the following steps: setting a driving path of a vehicle, and obtaining a plurality of influence lines with a lateral distance of a vehicle width; collecting a bridge dynamic response; constructing an influence surface identification model according to the bridge dynamic response and the plurality of influence lines with the lateral distance of the vehicle width; obtaining a plurality of longitudinal influence lines of the bridge according to the influence surface identification model with singular value decomposition; and obtaining a fitted bridge influence surface by using a modified Hermite interpolation to process the plurality of longitudinal influence lines of the bridge.The application is applied to the field of bridge health monitoring.

This invention provides a method and system for intelligent handover of 2M unframed dual-path protection based on redundancy check, relating to the field of communication technology. The method includes acquiring and aligning dual 2M unframed bitstreams, generating check results using cyclic redundancy check, statistically analyzing error feature vectors within a sliding time window, constructing a link quality trajectory, extracting quality degradation warning signals, constructing a phased handover strategy based on the warning signals, and determining the handover timing sequence and executing link handover using a neural network evaluator. This achieves adaptive optimization of the handover strategy, improving the intelligence and reliability of link handover.

The utility model provides a classical guitar sound hole sound bucket belongs to guitar sound hole sound bucket field, including the resonance box, the neck and the head, the outside wall of fingerboard is provided with a plurality of silk, a plurality of strings are fixedly connected on the bridge, the side wall of resonance box is provided with the sound hole, the sound hole is roundly provided, the sound hole is located the top of bridge, the inner side wall of resonance box is fixedly connected with the main sound bucket, the inner side wall of main sound bucket is provided with the internal thread, the inner side of main sound bucket is provided with the vice sound bucket, main sound bucket and vice sound bucket all are circular tubularly provided, the position of sound hole corresponds with main sound bucket, the inner side wall of vice sound bucket is fixedly connected with the plucking platform, and the plucking platform is equidistantly arranged on the inner side wall of vice sound bucket, the utility model discloses, through setting up main sound bucket in the sound hole, main sound bucket makes the flowing mode of resonance box inside air through the improvement, makes the sound in the musical instrument more fully reflected and diffused to improve the whole resonance efficiency, makes the sound more full.

This invention discloses a data compliance dynamic detection method and system based on deep learning, belonging to the field of data security processing technology. It addresses the problem that existing methods, which focus primarily on text data processing when performing compliance detection based on extracted comprehensive semantic words, lack the ability to comprehensively process multimodal data and comprehensively evaluate complex compliance issues, leading to inaccurate compliance assessments. The method includes preprocessing the original data stream, extracting features from the preprocessed data stream based on data stream type, and using a compliance analysis model to identify and analyze multimodal feature sets based on a standard rule base. In this invention, preprocessing the original data stream filters and reduces noise in the multimodal data, ensuring the authenticity and validity of the data. Furthermore, the compliance analysis model's identification and analysis of multimodal feature sets ensures comprehensive processing capabilities for multimodal data and comprehensive evaluation capabilities for complex compliance issues.

The application provides a quantitative description method of three-dimensional test ice shape, an electronic device and a storage medium, which comprises the following steps: obtaining an image of a three-dimensional test ice shape of a model surface in an aircraft icing wind tunnel test, so as to obtain point cloud data of the three-dimensional test ice shape; projecting the three-dimensional point cloud data along the model height direction onto a two-dimensional plane to obtain two-dimensional point cloud data; clustering the two-dimensional point cloud data by using a BLSOM neural network to obtain a two-dimensional average ice shape; calculating a tolerance band of the two-dimensional average ice shape by using a probability statistical method; and quantitatively describing the three-dimensional test ice shape by using a combination of the two-dimensional average ice shape and the tolerance band. The application describes the characteristics of the three-dimensional ice shape by using the combination of the two-dimensional average ice shape and the tolerance band, and can more comprehensively reflect the characteristic information of the three-dimensional test ice shape.

The utility model relates to mechanical detection technical field especially relates to a kind of probe mounting bracket for shaft body vibration detection, it includes the rotating shaft body to be measured and the bearing bush surrounded in rotating shaft body, the side surface of bearing bush is fixed with two mounting brackets, each the mounting bracket is fixed with one vibration probe, the vibration probe is perpendicular to the circumferential surface of rotating shaft body and is close not contact, the axis of two the vibration probe is perpendicular to each other.The utility model is fixed with bearing bush and is not loose and moves under vibration as the mounting bracket of vibration probe installation base, the axis of two vibration probes after installation coincides with the radial of rotating shaft body, and two the vibration probe is perpendicular to each other, so that the two measuring points on the rotating shaft body checked are also perpendicular to each other, to improve vibration detection accuracy.

This invention provides a method and system for understanding lift operation scenarios by integrating a multimodal perception model, relating to the field of artificial intelligence technology. First, it collects scene-triggered perception information such as visual, mechanical, and positional data of the lift operation scenario to form a trigger perception information set. Then, it performs cross-modal interactive mapping processing on the trigger perception information set to obtain interactive mapping perception information. Based on this, it performs scene adaptation calibration processing to obtain a scene-adapted perception model. The interactive mapping perception information is input into the scene-adapted perception model to perform multi-dimensional situational deduction and generate a panoramic situational description. Finally, based on the panoramic situational description, it generates dynamically adapted lift operation control commands and transmits them to the execution control unit, achieving accurate understanding and intelligent control of the lift operation scenario.

The application discloses a performance test method, device and system, to solve the problems of low test efficiency, incomplete test results and low accuracy of the existing performance test method. The method comprises: determining a first parameter value dataset corresponding to a network parameter of a gateway device under the condition that a wireless signal is transmitted between the gateway device and a transceiver, the first parameter value dataset comprising a plurality of parameter value subsets, wherein the first parameter value dataset is a parameter value corresponding to the network parameter under a first test angle; determining whether the network parameter of the gateway device meets a preset adjustment condition according to a first test result corresponding to the first parameter value dataset and / or the first parameter value subset; and adjusting the network parameter according to a second parameter value dataset if the network parameter meets the preset adjustment condition. This technical solution improves the efficiency of network performance testing and ensures that the final test results can more comprehensively and accurately reflect the performance of the gateway device.

The application relates to the technical field of emotion recognition, and provides an emotion type intelligent recognition method, device, equipment and storage medium, which can be used for recognizing the emotion type of a user when a customer service answers a question of the user in a financial field, wherein the method comprises the following steps: converting multi-modal data into N emotion signals, and matching a plurality of emotion types corresponding to each emotion signal; calculating the frequency of each emotion type in a database to obtain emotion frequency; calculating the weight of each emotion signal in the corresponding i emotion types to obtain i recognition degree weights; multiplying the i recognition degree weights by the corresponding emotion frequencies respectively to obtain i emotion scores; summing the emotion scores of the same emotion type in the i emotion scores to obtain M emotion type scores corresponding to M emotion types; and screening j target emotion types according to the M emotion type scores. The emotion frequency and the recognition degree weight can be adjusted to each other, and more accurate emotion scores are obtained.

Embodiments of the present application disclose a short-term traffic flow prediction method based on a space-time graph neural network. The method comprises: acquiring a corresponding traffic road network in a preset area, and constructing a network graph structure corresponding to the traffic road network; acquiring historical traffic flow data corresponding to the preset area, and taking the historical traffic flow data as node features of the network graph structure to obtain a road network graph with time sequence features; inputting the road network graph with time sequence features into a neural network comprising multiple space-time modules for feature extraction to obtain space-time joint features corresponding to the preset area; and performing full connection layer mapping on the space-time joint features to determine a traffic flow prediction value of each node based on a mapping result. The present application can accurately determine the traffic flow prediction value of each node in the preset area, which helps to take measures in advance to cope with traffic congestion, optimize traffic resource allocation, and improve the operation efficiency and safety of the entire traffic system.

This invention relates to the field of electric drive system design and development for new energy vehicles, specifically to a method for testing and analyzing the dynamic stiffness of electric drive assembly mounts and the accuracy of simulation. The method involves standardized frequency response testing of the electric drive assembly mounts, identification of minimum dynamic stiffness, and the construction of a dynamic simulation model consistent with the test boundaries to extract the minimum dynamic stiffness on the simulation side. Then, it sequentially performs frequency and amplitude error analysis of the minimum dynamic stiffness points in the tests and simulations, as well as average dynamic stiffness error analysis. Finally, it comprehensively calculates accuracy performance coefficients based on multi-dimensional error coefficients and establishes a scoring system to achieve a quantitative evaluation of the test and simulation accuracy. This method solves the technical problems in existing technologies where evaluating the dynamic stiffness of electric drive assembly mounts requires multiple physical impact tests, which are resource-intensive, time-consuming, and lack a systematic and quantitative analysis method for testing and quantifying the accuracy of mount dynamic stiffness, thus failing to utilize the preliminary analysis results to guide subsequent mount design and development.

This invention provides a method and system for predicting the risk of preoperative condylar bone resorption, relating to the field of oral medicine technology. The method includes the following steps: acquiring and preprocessing small-field CBCT image data of the temporomandibular joint region of the patient to obtain the corresponding region of interest (ROI) of the condylar cancellous bone region; segmenting and extracting features from the ROI of the condylar cancellous bone region to obtain corresponding radiomics features; constructing a condylar bone resorption risk prediction model and inputting the radiomics features into the model for prediction to obtain the corresponding risk prediction results; generating patient risk grading information based on the risk prediction results to assist clinicians in making patient management decisions. This invention, by analyzing and modeling the radiomics features of the patient's preoperative condylar cancellous bone region, achieves the prediction of the risk of postoperative condylar bone resorption without using any postoperative images as prediction input.

The application discloses a kind of near parallel active fault tunnel excavation support method, belong to tunnel construction technical field, can solve the problem that existing technology is difficult to effectively protect buried fault, the method comprises: S1, according to the geological data of the crossing region of target tunnel determines the first predicted distribution of buried fault in crossing region;S2, according to the first predicted distribution determines the predicted distribution region of buried fault in crossing region, according to the exploration data of predicted distribution region, correct the first predicted distribution, obtain current predicted distribution;S3, according to current predicted distribution determines the distance between each to be constructed section of target tunnel and buried fault, and the to be constructed section with distance less than preset threshold is determined as high-risk construction section;S4, according to current predicted distribution determines the support measure of high-risk construction section, according to support measure, carries out excavation construction to high-risk construction section, and according to construction effect, the support structure of high-risk construction section is modified.The application is used for tunnel construction.

The application discloses a brain electrical signal detection method, device and equipment and a storage medium, relates to the technical field of brain electrical detection, and comprises the following steps: acquiring a plurality of brain electrical signals to be detected of a target user; wherein the brain electrical signals to be detected are collected by a plurality of electrode groups, and the plurality of electrode groups are arranged in zones; taking an electrode group as a node, taking a phase lag index value between brain electrical signals to be detected corresponding to any two electrode groups as a connection edge between the two nodes, and constructing a to-be-detected region connected graph of all brain electrical signals to be detected; and identifying a brain electrical signal detection result according to the to-be-detected region connected graph. The application can comprehensively reflect the overall brain activity state of the target user, solves the technical problem that the existing brain electrical signal detection method is based on single time-frequency domain features or nonlinear features for brain electrical signal detection, and has low accuracy, and the accuracy of brain electrical signal detection is improved.