27results about How to "Strong representative" patented technology

The invention discloses a human face sensitive data protection method based on an elastic anti-interference privacy protection mechanism, belongs to the field of information processing, and provides a dynamic self-adaptive sampling strategy based on information entropy in a data preprocessing stage, and the sampling rate is adjusted according to the distribution characteristics of a human face data set and the weight of sensitive information. And the model performance is maximized on the premise of ensuring user privacy. In order to obtain a better privacy protection effect, an entropy adjustment differential privacy mechanism is designed, and Gaussian noise of different degrees is adaptively added according to an information entropy value of face data after a face data set is dynamically and adaptively sampled, so that refined privacy protection of face sensitive data under differential privacy constraint is realized. In a model training stage, an elastic anti-interference training method is provided, diversified adversarial samples are dynamically generated in different stages of model training, and the over-fitting risk of the model is reduced while the robustness of the model to various types of adversarial attacks is enhanced.

The invention discloses a distributed photovoltaic meteorological resource real-time monitoring station group site selection method and system, and the method comprises the steps: 1, obtaining photovoltaic historical resource data of a target region, and constructing a historical data set; 2, based on the historical data set, analyzing space-time distribution characteristics of distributed photovoltaic radiation resources in a target area, and dividing clusters with consistent radiation resource changes; 3, respectively selecting an optimal center point of each cluster; and step 4, performing micro-region site selection based on each cluster center point to determine an optimal site establishment position. According to the method, the existing distributed layout condition is considered, the future distributed development planning is also considered, and the method has the characteristics of full coverage of regional monitoring information, high representativeness of monitoring stations, scientific and reasonable site selection and excellent economical efficiency of a monitoring layout scheme.

The invention provides a method for characterizing the heterogeneity of a millimeter-level shale oil and gas reservoir. The method mainly solves the problem that the reliability and accuracy of a reservoir evaluation result of an existing volume sampling method are low. The method is characterized by comprising the following steps that S1, shale millimeter-level mineral components are recognized and drilled; s2, based on the obtained powdery sample, oil-containing and non-oil-containing tests are carried out; determining occurrence pore size distribution to carry out reservoir characterization through comparison before and after pumping; s3, on the basis of the obtained powdery sample, carrying out oiliness characterization on different lamina / layered components; and S4, carrying out millimeter-level shale oil reservoir heterogeneity characterization on the basis of the reservoir property and oiliness results of the different strata / layered components. According to the method provided by the invention, the identification precision of the shale formation / stratified structure can be enhanced, millimeter-level fine representation of lithology of different formation / strata is realized, and favorable support is provided for shale oil enrichment mechanism, sweet spot prediction and the like.

The invention provides a tunnel multi-modal information processing and predicting method based on a Transform technology. The method comprises the following steps: S1, acquiring various sensor data of a tunnel and a vehicle; s2, constructing vehicle composite features according to the sensor data; s3, synthesizing the composite features into a corresponding one-dimensional data sequence, performing one-dimensional convolution processing, inputting processing results into a plurality of corresponding multi-layer perceptron to obtain composite feature weights, and obtaining weighted features according to the composite feature weights, adding position information to the convolution processing result of each one-dimensional data sequence, then carrying out cross-modal fusion, and carrying out projection transformation on the cross-modal fusion result to obtain a corresponding single-modal feature; and S4, weighting the single-mode features by using the weighted features, and inputting a weighted result into the constructed Transform model to obtain a tunnel risk prediction result. The method has the advantages of high prediction accuracy and high efficiency.

The invention relates to the technical field of mobile communication, in particular to an urban resident lifestyle identification method based on Wi-Fi handshake data. Wi-Fi handshake data of the mobile terminal are collected; based on the Wi-Fi handshake data, identifying visit events of an individual at different points of interest (POI), and forming a continuous visit sequence according to a time sequence; compressing the visited sequence, and extracting a long-term repeated high-frequency sequence mode; calculating the similarity between the users based on a high-frequency sequence mode, and obtaining a user group cluster with similar lifestyles through clustering; and performing feature analysis on each user group cluster, extracting a core visit sequence, and generating a lifestyle group portrait. Through the technical combination of Wi-Fi handshake data acquisition, sequence compression clustering and sample representativeness correction, comprehensive optimization of urban resident lifestyle identification on coverage integrity, result accuracy, implementation efficiency and cost control is realized, and high-quality data support is provided for urban related decisions.

This invention provides an intelligent evaluation method for rockburst hazard at working faces based on principal component analysis-probabilistic neural networks, belonging to the field of rockburst hazard evaluation technology. An evaluation index system is established and data is collected by combining rockburst hazard influencing factors, drill cuttings monitoring, critical stress index monitoring, and actual field conditions. Principal component analysis (PCA) is used to simplify the original evaluation index data, resulting in comprehensive evaluation index data containing information from the original evaluation indicators. The comprehensive evaluation index data is divided into a training set and a test set. A probabilistic neural network (PNN) is used to train the evaluation model on the data in the training set, and then the performance of the evaluation model is tested using data from the test set, and its accuracy is calculated. If the accuracy is greater than or equal to 90%, the evaluation model is considered acceptable; if the accuracy is less than 90%, it is considered unacceptable, and the PNN's smoothing factor needs to be modified and retrained until the accuracy of the evaluation result meets the set value.

The invention discloses an annular sampling method for titanium ingot GDMS detection, and belongs to the technical field of titanium ingot GDMS detection sampling. An annular sampling method for titanium ingot GDMS detection comprises the following steps that firstly, a detected titanium ingot is fixed, and surface treatment is conducted on the outer portion of the detected titanium ingot; step 2, determining a turning area: in the turning area 1, feeding leftwards from a position 1 at the speed of 3mm / min, meanwhile, feeding inwards at the speed of 0.025 mm / r, moving to a position 2 after feeding inwards by 2mm, stopping feeding inwards, and horizontally turning leftwards; the titanium ingot can be sampled in an annular sampling mode, the titanium ingot can be sampled in four positions, annular turning is carried out at the four sampling positions, the damage influence of the sampling on the titanium ingot is small, the sampling duration is 15 hours (including titanium ingot clamping), the sampling loss is 10 kg, sampling can be carried out at the 1 / 5 position, the 2 / 5 position, the 3 / 5 position and the 4 / 5 position of the titanium ingot, the representativeness is high, and the sampling accuracy is high. Therefore, the sampling loss of the titanium ingot can be reduced, the sampling time is shortened, and a representative sampling position can be selected.

The present application provides a method for determining a seed-rapid germination type karst plant, and relates to the technical field of vegetation restoration. The method for determining a seed-rapid germination type karst plant comprises: analyzing the configuration category of the embryo and the morphological characteristics of the endosperm of the seeds of the karst plant to be determined. If the configuration category of the embryo of the seed meets the target configuration subtype I and the endosperm is missing or degenerated, the karst plant to be determined is determined to be a rapid germination type; otherwise, the karst plant to be determined is determined to be a non-rapid germination type; the target configuration subtype I includes any one of a spoon type, a straight type, a lateral base type, a stacked type, a peripheral type or a filled type. The method for determining a seed-rapid germination type karst plant provided in the present application adopts a simple and easy biological analysis method, and can accurately and universally determine whether it is a rapid germination type plant by analyzing and determining the configuration category of the plant seed embryo and the morphological characteristics of the endosperm.

The invention relates to the field of health data analysis, in particular to a human body health data comprehensive analysis system which comprises a video frame type determination module, a frame extraction mode determination module, a data processing module and a data processing module, and is used for determining a video frame type according to an action domain variation degree and a same-frame reference value and determining a frame extraction mode according to frame extraction conditions, the feature acquisition unit is used for acquiring data features of the monitoring data acquired by the changed limb points in the extracted video frames; the mode judging unit is used for determining a monitoring motion state according to the same feature difference reference value and the feature dimension reference value and determining a feature processing mode according to the monitoring motion state; the first processing unit is used for determining a first feature selection mode according to a set condition; the second processing unit is used for determining a feature category according to the individual influence value and the behavior correlation value and determining a second feature selection mode according to a judgment condition; a motion recognition unit; according to the invention, effective features can be screened to improve the behavior recognition precision, so that the motion risk is reduced.

This utility model relates to an automatic sampling device for flotation cell slurry, solving the problems of traditional sampling methods that require repeated sampling, are cumbersome to operate, and have low sampling efficiency. It includes a sampling box, a sampling handle, and a suction pipe. The sampling box has a movable cover on top; the upper half of the sampling box is equipped with grids at certain intervals and angles to allow the slurry to enter the sampling spoon from the side along the gaps between the blades; the lower half of the sampling box has intact side walls without openings; the bottom of the sampling box is inclined at a certain angle to the horizontal plane, and a tubular discharge hole is provided at the lowest point of the sampling box. The lower end of the sampling handle is connected to the sampling box, and the sampling handle is engraved with scales to determine the sampling depth. The suction pipe is connected at one end to the discharge hole at the bottom of the sampling box, and the other end is equipped with a pipe switch. This utility model provides accurate and representative sampling; it has a high degree of automation and high sampling efficiency; the device has a simple structure and is easy to operate.

The utility model belongs to the field of mine exploration and production, and particularly relates to a device for quickly determining the grade of copper-nickel sulfide ore, which is based on rock powder generated in the rock drilling process according to the mine production process and the structural characteristics of the ore structure. The grade of 140-200-mesh ore powder is analyzed through the steps of rock drilling liquid collection, centrifugal classification, drying and cake making, grinding and screening, grade determination through a handheld spectrograph analyzer and the like, and the centrifugal classification device suitable for the content of the utility model is designed according to self requirements. The ore grade determination time is greatly shortened, the problem that errors are large when a handheld spectrograph is used for analyzing mineralized non-uniform ore and star-like-sponge meteorite-like ore is solved, and mine production can be effectively guided.

This invention discloses an improved near-infrared spectral wavelength selection and modeling method for artificial bee colony algorithms, comprising the following steps: data preparation, chaotic initialization, fitness evaluation, establishing a PLS model based on wavelength subsets, using the regularized fitness function of RMSECV as the fitness Fitnessxi of nectar source xi; searching for foraging bees; searching for observation bees; searching for scout bees; outputting the optimal wavelength subset Sbest and establishing the final PLS model. The beneficial effects of this invention are: the method effectively improves convergence speed and accuracy; chaotic initialization improves the quality of the initial population, avoiding the algorithm from prematurely falling into local extrema; the adaptive perturbation factor balances the algorithm's global search and local exploitation, improving the ability to find the optimal wavelength combination in high-dimensional discrete space, and the selected wavelength subset has stronger representativeness, resulting in a final PLS model with lower RMSEP (root mean square error of prediction) and better robustness.

The invention relates to the technical field of soil sampling, in particular to a portable forest soil multi-point sampling device and a sampling method thereof.The portable forest soil multi-point sampling device comprises a holding mechanism, a mounting mechanism, a sampling mechanism, a sampling pipe fitting and a sealing piece; the holding mechanism is provided with a handheld cross rod for people to hold and use, and a driving cylinder is mounted in the holding mechanism to control the sampling mechanism to work; the mounting mechanism is mounted below the holding mechanism, and a sampling opening is formed in the mounting mechanism and used for soil sampling; the sampling mechanism is mounted below the mounting mechanism; the sampling mechanism is controlled by the holding mechanism to realize digging and sampling work; the sampling pipe fitting is mounted on the mounting mechanism, and is used for collecting and storing the collected soil sample for detection; the sealing element is mounted on the sampling pipe fitting, the sampling pipe fitting is taken out from the mounting mechanism after being collected, and the sealing element is mounted on the sampling pipe fitting for sealing.

This invention discloses a satellite fire detection method based on dynamic exponential features and deep learning. The method includes: acquiring multi-band spatiotemporal observation data from geostationary meteorological satellites; filtering candidate pixels from the full-disk satellite data using multiple threshold conditions; extracting spatiotemporal input features and channel input features of the candidate pixels based on the multi-band spatiotemporal observation data from the geostationary meteorological satellites; and inputting the spatiotemporal input features and channel input features of the candidate pixels into a pre-trained fire spatiotemporal network model to obtain the fire detection result. This invention effectively solves the problem of high false alarms and false negatives caused by cloud cover, high-temperature ground surfaces, and vegetation interference in traditional methods, overcomes the difficulties of weak fire signal extraction and spatial positioning in mixed pixel backgrounds, and significantly improves detection accuracy, stability, and computational efficiency. It can provide key technical support for forest fire monitoring and emergency decision-making.

The invention relates to the technical field of optical detection, and discloses a method for detecting the collagen content of pigskin. The method establishes an initial optical profile of a pigskin sample before contacting a detection medium. And generating a special detection path aiming at the sample according to the contour, and specifying a moving track and a staying node sampled by the probe. Injecting a medium at a path starting point, and starting a synchronization mechanism to synchronize probe movement with a medium diffusion front edge; when the probe arrives at the termination node, the spectrum sequence of each node on the path is completely recorded, and the sequence comprises the dynamic characteristic absorption information of the collagen. And carrying out cross-node correlation fusion on the spectrum sequence to generate a fusion spectrum characteristic spectrum of the sample. And comparing the map with a pre-stored feature-content mapping model, and outputting a collagen content value. According to the invention, adaptive sampling and dynamic process tracking are realized, and the accuracy and reliability of detection are improved.

The invention relates to a network teaching video knowledge point classification extraction method and device, equipment and a medium, and relates to the field of video processing, the method comprises the following steps: using a preset character recognition model to extract text data of each video key frame in a video key frame sequence to generate a text sequence corresponding to each video key frame; inputting a text sequence corresponding to the video key frame into a knowledge point classification model trained to a convergence state, and generating a corresponding knowledge point label for each video key frame; and based on the timestamps corresponding to the video key frames and the knowledge point tags, cutting the network teaching video into a plurality of video clips, and merging the video clips corresponding to the same knowledge point tag to determine the network teaching video corresponding to each knowledge point tag. According to the invention, the network teaching video corresponding to the required learning and teaching content can be quickly and accurately found, and the learning and teaching efficiency is improved.

The invention relates to a set of primer combination for cherry variety identification and application thereof. The application of 26 SNP sites in a cherry genome in any one of the following steps: (1) preparing a cherry SNP site detection kit; (2) performing genetic typing on cherry germplasm resources or genetic populations; (3) constructing a DNA fingerprint spectrum of the cherry germplasm resource or variety; (4) identifying the variety of the cherry to be detected; the number of the 26 SNP sites is YTSNP01 to YTSNP26, and the number of the 26 SNP sites is 2. The cherry primer combination developed by the invention can be used for identifying the cherry variety to be detected, so that the operation of distinguishing the authenticity of the variety becomes accurate and convenient, and the primer combination spans the whole genome of the cherry, has a unique amplification stable point and is relatively strong in representativeness; the detection result of the method is stable and reliable, the operation is simple and rapid, and large-scale and standardized detection can be realized.

The invention relates to a manual sampling method for metallurgical automobile-transported coke, which comprises the following steps of: in the whole unloading process, respectively sampling at three nodes of unloading start, unloading and unloading completion, screening and calculating the coke breeze mass percentage of a sample taken at each node, and recording the size fraction mass; summarizing the three-node data to obtain the overall coke breeze mass percentage; repeating the process to obtain multiple groups of data; deducing the sampling mass percentage of the three nodes by taking the actual content as the reference; and sampling as required according to the mass percent. Through scientific tests and data accumulation, the problem of inaccurate coke breeze content sampling is solved; according to the method, all steps of automobile transportation and unloading are covered, three key nodes are selected for sampling, and the problem that the coke breeze content is low in a traditional sampling mode is effectively avoided; and the mass percent of the sample required by each part is inversely calculated through a calculation formula, so that the representativeness and the accuracy of sampling data are ensured.

The invention discloses a meter box topology identification method based on a high-speed broadband power line carrier technology. According to the method, a master station issues a ranging task to a concentrator, a carrier communication link is established with a plurality of terminal nodes through the concentrator, ranging data of the terminal nodes are collected, and signal strength data and signal-to-noise ratio data are received. According to background noise, normal noise, periodic impulse noise and sudden impulse noise existing in the low-voltage power line, noise analysis, frequency domain processing and filtering suppression are carried out on collected signals, so that effective data capable of reflecting communication link characteristics can be obtained. And after abnormal value detection and standardization processing are carried out on multiple rounds of measurement results, weights of different characteristics are determined based on correlation between communication characteristics and an actual topological relation, and a weighted linear regression model is constructed and used for predicting meter box affiliation of a terminal node or a relative position of the terminal node in a topology. And judging a topological structure of the terminal node according to a model output result, and verifying an identification effect through a model evaluation index.

The utility model discloses a cement detection sampling device which comprises a tripod, a probe rod group and a drill bit assembly, and realizes sampling of cement at different depths through a sliding sleeve structure and a deflection mechanism. The feeler lever group controls opening and closing of the sampling groove through deflection of the outer sleeve, the drill bit assembly is of a high-frequency vibration and cone structure and can effectively penetrate through a cement layer, friction among particles is reduced, and sampling precision and efficiency are guaranteed. In addition, the cable group is used for controlling starting and stopping of the polarization motor, and the stability and reliability of the sampling process are further improved. By means of the device, multi-depth cement sampling can be achieved, comprehensiveness and representativeness of sampling data are guaranteed, and the device is suitable for quality detection of the cement storage tank.

The invention discloses an e-commerce seckilling application-oriented benchmark test method, which is characterized in that a benchmark test system is deployed in a computer system in which a to-be-evaluated e-commerce seckilling application is operated, and the benchmark test system comprises a data acquisition and processing system, a Transform performance evaluation model and a report generation subsystem; when the to-be-evaluated e-commerce seckilling application has no interference flow, starting the benchmark test system, initiating a pressure test, obtaining performance data of the computer system through the data acquisition and processing system, and ending the pressure test; inputting the obtained performance data into a Transform performance evaluation model to obtain a performance score; inputting the performance data and the performance score into a report generation subsystem, and generating and outputting a user report; the method has the advantages that the evaluation result is more accurate and objective, the special requirements of e-commerce seckilling application are fully considered, and the method has higher representativeness and applicability in the field of e-commerce application.

The application relates to the technical field of optical detection, and discloses a pigskin collagen content detection method. Before contacting a detection medium, the initial optical profile of a pigskin sample is established. According to the profile, a special detection path for the sample is generated, and the moving track and the stay node of the probe sampling are specified. The medium is injected at the starting point of the path, and a synchronization mechanism is started to synchronize the probe movement with the medium diffusion front. When the probe reaches the terminal node, the spectral sequence of each node on the path is recorded completely, and the sequence contains the dynamic characteristic absorption information of collagen. The spectral sequence is subjected to cross-node correlation fusion to generate the fusion spectral feature map of the sample. The map is compared with a pre-stored feature-content mapping model, and the collagen content value is output. The application realizes adaptive sampling and dynamic process tracking, and improves the accuracy and reliability of detection.

The application belongs to the technical field of extreme scenario recognition, and particularly relates to a power system extreme scenario recognition method and system. The method comprises the following steps: obtaining net load data of a power system based on time series data of wind power, photovoltaic and load of the power system, and forming daily net load samples by daily slicing; adopting a dynamic time warping (DTW) algorithm to construct a difference matrix between the daily net load samples; performing principal component analysis on the difference matrix after standardization processing, obtaining a low-dimensional feature vector, and weighting the low-dimensional feature vector according to the inverse square root of the corresponding eigenvalue of the principal component in the principal component dimension to obtain a weighted low-dimensional feature vector; calculating the Euclidean distance from the weighted low-dimensional feature vector corresponding to each day to the space mean point in the feature space formed by the weighted low-dimensional feature vector, and constructing a threshold value by using a standard deviation method according to the mean and standard deviation of the Euclidean distance, and identifying the extreme scenario of the power system based on the threshold value.

The invention discloses an entry petroleum risk assessment and prediction method and system, and the method comprises the steps: firstly selecting a sample country according to the availability of data and the proportion of petroleum import, and guaranteeing the representativeness of a sample and the reliability of the data; by collecting various index data, the risk assessment is more comprehensive and accurate, an authoritative data source is used, and the accuracy and reliability of the data are ensured; furthermore, on the basis of the collected three-level index data, an entry and exit petroleum risk index is obtained through comprehensive analysis, the petroleum import risk of each country is quantified, and a complex risk factor is converted into a specific index; the time sequence model is used for predicting the entry petroleum risk index, the future risk trend is predicted, response measures can be taken in advance, and the future possible risk influence is reduced.

This invention discloses a dynamic temperature measurement and compensation method for microwave heating based on multi-source data fusion, belonging to the field of dynamic temperature field monitoring and compensation technology. The method includes the following steps: 1. Using different types of temperature sensors, measuring the dynamic temperature data of materials during the process of multiple parameter changes under microwave heating, and then calculating the first-order difference between adjacent time points; 2. Calculating the proportion of volatility and negative change; 3. Calculating the stability score of temperature measurement data from different sensors, and then calculating the normalized weight of each sensor to ensure that the sum of the weights of multiple sensors is 1; 4. Calculating the weighted fusion temperature as the final temperature; 5. Regression analysis to construct a temperature measurement compensation model corresponding to each temperature sensor. This invention proposes an adaptive weighted fusion algorithm for multi-source temperature sensors based on dynamic stability assessment, solving the problem of limited accuracy of single sensors in existing temperature measurement scenarios, and improving the accuracy and stability of temperature measurement.

The invention discloses a preparation method for cutting a test piece for testing mechanical performance on a mining impactor piston, and belongs to the technical field of mechanical cutting machining, and the preparation method comprises the following three steps: separating the piston, dividing the piston into a small-diameter pipe joint and a large-diameter pipe joint, and cutting a disc A and a disc B; forming and processing the test piece, cutting the test piece from the end surfaces of the disc A and the disc B at equal thickness, reserving the outer wall of the pipe joint for hardness detection, and testing the rest part for other performances; the surface roughness of the test piece is precisely polished and deburred to ensure that the maximum roughness Ra of the to-be-detected surface and the maximum roughness Ra of the non-detected surface are 1.6 microns and 3.2 microns. A test piece is directly cut out from a finished piston, so that the mechanical performance of each part of the piston, especially the performance difference between a small-diameter pipe joint and a large-diameter pipe joint, is accurately reflected. Compared with the prior art, representative test piece data is provided, the manufacturing quality of the piston can be comprehensively evaluated, and the reliability and stability of the mining impactor in a high-load and high-frequency vibration working environment are ensured.

The present application provides a kind of multi-process driven horizontal broken stratum excavation reserved deformation determination method, the present application determines underground engineering excavation reserved deformation from geology-geophysical exploration-artificial intelligence multi-angle driving, solves how to determine the difficult problem of underground engineering excavation reserved deformation, overcomes the previous simple based on the shortcoming of surrounding rock grade, based on geology analysis, surface and hole interior geophysical exploration, numerical simulation and artificial intelligence algorithm multi-process driving determines underground engineering excavation reserved deformation, based on Delphi theory invites expert team to analyze the above result, finally comprehensively obtains underground engineering excavation reserved deformation, evaluation information is rich, and the result is high in accuracy.