17results about How to "Accurate discrimination" patented technology

This invention discloses a graphic calibration system and method for CNC mold machining. The system includes: a data acquisition module configured to receive CNC machining tasks and acquire graphic data and measurement data of the mold to be machined based on the machining tasks; a data calibration module configured to perform geometric feature calibration on the graphic data based on preset calibration data, generate calibrated calibrated graphic data, and spatially register the measurement data and calibrated graphic data to obtain deviation data; a data processing module configured to map the deviation data to a machining parameter library in the machining task, calculate the correction values ​​of each machining parameter to obtain calibration parameters; and a data feedback module configured to feed back the calibrated graphic data and / or calibration parameters to the system, and perform the remaining graphic data input and machining parameter input for the machining task. This invention significantly improves mold machining accuracy and production efficiency, providing a high-confidence graphic input benchmark for CNC mold machining.

The application provides an image recognition-based personalized gait intention recognition method and system, and relates to the technical field of gait intention recognition.The application collects an infrared image pair and a distorted infrared image through a binocular infrared camera integrated with an equipment, respectively calculates and generates two depth maps, and performs weighted fusion to obtain a three-dimensional depth map of an environment under the feet of a user; multi-scale geometric analysis is carried out on the three-dimensional depth map, geometric features are extracted, and a multi-scale environment geometric feature tensor is generated; the tensor is input into a multi-task processing link, and user landing area semantic segmentation is executed in parallel, foot trajectory prediction is combined with real-time posture and historical gait of the user; according to the matching relationship between the semantic segmentation graph and the trajectory prediction sequence, the exploratory or confirmatory landing intention of the user is judged, corresponding intention instructions are generated, the personalized gait landing intention of an exoskeleton wearer can be accurately and robustly recognized, and reliable instruction basis is provided for intelligent control of the exoskeleton.

The application discloses a rock mass characteristic excavability prediction method, and belongs to the technical field of TBM and shield construction prediction. In view of the problems of low accuracy, difficulty in quantifying rock mass structure effect and inability to effectively support full-cycle decision of an existing prediction method, the method comprises the following steps: geological survey is performed on a tunneling section to obtain uniaxial compressive strength, rock mass brittleness index and volume joint number; a rock mass characteristic excavability index is calculated based on the above parameters; the tunneling section is divided into groups according to the index, and tunneling parameters are preset, and equipment selection is performed based on the thrust demand range predicted by the index; during the tunneling process, the actual thrust is compared with the predicted thrust converted based on the index to verify the accuracy, and the coupled function is corrected according to the actual data to realize dynamic optimization. The application is mainly used for equipment selection, tunneling parameter pre-planning and dynamic optimization in the construction process before tunneling, and can significantly improve the prediction accuracy and engineering guidance value.

The invention relates to the technical field of electric power system management, and provides an electric power system inertia safety domain evaluation method, device, equipment and medium, and the method comprises the steps: obtaining each generator speed regulator model parameter of an electric power system, and constructing an electric power system frequency dynamic response model based on each generator speed regulator model parameter; acquiring an active power disturbance quantity after active power disturbance occurs in the power system; on the basis of the active power disturbance quantity and a power system frequency dynamic response model, calculating critical inertia, including a frequency safety critical inertia value and a regulation economy critical inertia value; dividing an inertia operation domain of the power system into a plurality of inertia operation intervals according to the critical inertia; and the current inertia level of the power system is evaluated based on the system real-time inertia and the plurality of inertia operation intervals of the power system. According to the embodiment of the invention, objective quantification and multi-dimensional analysis of the inertia level of the system are realized by constructing a dual evaluation system fusing the frequency safety and the regulation economy.

The invention relates to the technical field of motor control, and discloses an electrical transmission system parameter self-tuning system based on a neural network, and the system comprises a state monitoring and gating module which is used for activating the system when the high-frequency energy characteristic of a torque instruction exceeds a threshold value; the homomorphic mapping and reference reconstruction module is used for constructing a homomorphic digital filter to convert a torque instruction into a homomorphic instruction aligned with a feedback speed time sequence; the momentum residual calculation module is used for calculating the difference value between the theoretical impulse and the actual momentum increment based on the momentum theorem; the morphological decoupling and reasoning module is used for calculating morphological similarity between the residual vector and the reference vector by using the neuron structure so as to output confidence; and the parameter updating module is used for adjusting the adaptive updating gain according to the confidence coefficient and iteratively correcting the inertia parameter. According to the method, through signal homomorphic reconstruction and waveform form reasoning, the influence of loop delay and load disturbance is effectively inhibited, and the accuracy and robustness of parameter setting are improved.

The application discloses a kind of owner actual residence big data detection method and system for smart city, it is related to data processing technical field.The method includes: collecting image to be detected;Obtain target building window area image;Significance detection is carried out, and it is judged whether it is bright window;If yes, then the target building window area image is marked;Otherwise, then obtain bright window template image and dark window template image;The similarity of target building window area image and bright window template image and dark window template image is calculated, and then it is judged whether it is bright window, if yes, then mark;Otherwise, then the corresponding target building window area image is handled using low-light image enhancement method, and then it is judged again.The application combines multiscale target detection method, multiple significance detection model and multiple region color histogram comparison method, and combines low-light image enhancement method, to realize the accurate discrimination of window area image.

The invention discloses a lesion discrimination method and system based on an internal image of a digestive tract, and relates to the technical field of medical image processing, and the method comprises the steps: collecting an endoscopic image containing intracavity pressure change, and carrying out the illumination standardization and polar coordinate system mapping of the image, so as to eliminate the illumination difference and the shooting angle influence. And through low-amplitude step inflation stimulation, obtaining a response frame of an inflation stage, and substituting the response frame into the health potential energy model to generate an anti-fact health image. Thirdly, deformation increment and non-conservation residual errors are calculated based on optimization of a joint energy function, topological difference analysis is conducted through multi-stage pressure trajectory analysis, and a focus area is judged according to structural connectivity changes. And in combination with persistence and abnormal intensity analysis, outputting a lesion judgment result. The problem that illumination change and image quality are inconsistent in a traditional method is solved, accurate judgment of the lesion area is achieved, accuracy and stability of lesion detection are improved, and the method has high clinical application value.

The invention discloses a virus inactivation evaluation method based on image recognition and data processing, and relates to the technical field of biotechnology and medical detection, and the method comprises the following steps: preprocessing a collected microscopic image, and adopting a gray conversion and contrast adjustment method to enhance the light and shade difference between virus particles and polysaccharide residues in the image, so as to obtain a virus inactivation evaluation result; obtaining first image data after primary processing; according to the virus inactivation evaluation method based on image recognition and data processing, through extraction and analysis of morphological characteristics such as boundary integrity and shape regularity of virus particles, objective judgment of a virus inactivation state is realized, so that uncertainty caused by manual subjective judgment is reduced, misjudgment and missed judgment are reduced, and the accuracy of virus inactivation evaluation is improved. The accuracy, the stability and the repeatability of a virus inactivation evaluation result are improved.

The invention provides a batch bar code data acquisition method, system and device and a readable storage medium. The method comprises the following steps: acquiring a to-be-acquired bar code set, a coding type of each bar code and a scanning time interval between adjacent bar codes; analyzing the logic continuity of each bar code; determining the bar code of which the logic continuity does not meet the preset requirement as an abnormal bar code; obtaining a first product batch code of the abnormal bar code; the prefix field of the first product batch code comprises a first production time field, a first production line number field and a first serial number field; according to the prefix field, checking the product batch to which each abnormal bar code belongs; marking barcodes which do not belong to the same product batch in the abnormal barcodes; and rejecting the marked bar codes so as to realize acquisition of batch bar code data. According to the invention, the validity of batch bar code data acquisition results can be improved, and the deviation of the bar code data on the overall level is reduced.

The invention belongs to the field of cables, and provides a high-voltage cable insulation eccentricity quality detection method and a deviation adjusting tool, and the method comprises the steps: S1, drawing an inner identification line at the outer side of a semi-conductive wrapping tape; s2, intercepting a detection sample section; s3, drawing a main outer identification line and an auxiliary outer identification line in the axis direction of the detection sample section; s4, cutting a plurality of test pieces, and insulating the thinnest point position and the thickest point position; and S5, observing the orientation distribution of the thinnest point and the thickest point of each test piece. Compared with the prior art, the method has the advantages that the inner marking line is applied before the conductor enters the three-layer co-extrusion machine head, the main / auxiliary outer marking line with the space direction definition is established on the surface of the take-up sample section, and multi-point slicing detection and space direction reduction are combined, so that the appearance quality of a certified cable is not influenced, and meanwhile, the production efficiency is improved. And reasons causing poor insulation eccentricity can be visually distinguished, so that an adaptive deviation adjusting mode can be selected according to the actual eccentricity condition, and accurate positioning of the real insulation eccentricity position and accurate judgment of causes are guaranteed.

The application discloses a steel-cored aluminum stranded conductor broken strand defect identification method and device based on eddy current detection, relates to the technical field of steel-cored aluminum stranded conductor broken strand defect identification, and comprises the following steps: installing an annular vector coil array probe composed of a plurality of excitation coils on a steel-cored aluminum stranded conductor to be measured, constructing a vectorized excitation magnetic field on the surface of the steel-cored aluminum stranded conductor by controlling the driving parameters of the excitation coils; based on the vectorized excitation magnetic field, a multi-layer selective penetration strategy is constructed, and a set of excitation modes for detecting surface aluminum strands, inner layer aluminum strands and the surrounding area of the steel core is sequentially generated; eddy current signals are collected under each excitation mode and layered feature extraction is performed to form corresponding hierarchical feature vectors; fusion discrimination is performed based on the hierarchical feature vectors to generate an identification result of the broken strand defect of the steel-cored aluminum stranded conductor; and the application solves the problems of excitation magnetic field distortion, signal aliasing and insufficient recognition sensitivity of deep defects in the traditional eddy current detection in a multi-layer stranded structure.

The present disclosure relates to an energy recovery method, device, storage medium and vehicle, which is applied to a pure electric vehicle, can improve the accuracy of slope estimation in the energy recovery strategy, thereby improving the energy utilization rate of the vehicle, and guaranteeing the braking safety and driving comfort of the vehicle. The energy recovery method comprises the following steps: obtaining a first slope value of a position where a vehicle is located from a gyroscope; obtaining a second slope value of the position where the vehicle is located and a third slope value of a road section in front of the vehicle from a high-precision map module; when the driving road condition of the vehicle is a slope road condition, determining a target slope value of the position where the vehicle is located according to the first slope value, the second slope value and the third slope value; and performing energy recovery on the vehicle according to the target slope value.

This invention discloses a fake image detection method based on artifact decomposition. The method includes acquiring the image to be detected and inputting it into a trained three-branch artifact-aware encoder (scene consistency branch, imaging realism branch, and signal naturalness branch) for feature extraction, resulting in three artifact reflection maps. A trained cross-dimensional gated collaborative fusion module is used to fuse the features of the three artifact reflection maps, generating a unified artifact embedding representation. This unified artifact embedding representation is then input into a trained classification head, and the label classification head of the classification head outputs the true / false prediction probability of the image to be detected. This fake image detection method solves the problems of existing technologies, such as inability to uniformly detect multi-source heterogeneous fake images, poor generalization ability, and susceptibility to overfitting.

The application discloses a kind of marker compounds and screening method for distinguishing black tea origin and a kind of black tea origin identification method, belong to tea distinguishing technical field, the application first filters out kaempferol 3-O-(galloyl)-glucoside as the marker for distinguishing black tea origin as marker.It is stable to exist and higher in content in Sri Lanka black tea, while in the representative black tea in China, the content is significantly lower or difficult to detect, and the difference has stability and significance.The identification method of the application comprises: preparing standard extract of the black tea sample to be tested, and using high performance liquid chromatography-mass spectrometry to qualitatively and quantitatively analyze the extract.The scientific and accurate discrimination of the geographical origin of black tea can be realized by detecting the presence or absence of the marker compound or the amount of the marker compound.

This invention belongs to the field of transmission line inspection technology, and particularly relates to a method and system for detecting the icing thickness of transmission lines. It includes the following steps: acquiring an image of an uniced conductor and a corresponding image of an iced conductor; performing image segmentation on the uniced and iced conductor images respectively to obtain a first segmented image and a second segmented image; using coordinate traversal, obtaining a first sequence vector containing the coordinate points of the uniced conductor in the first segmented image and a second sequence vector containing the coordinate points of the iced conductor in the second segmented image; calculating a fluctuation coefficient based on the coordinate point data in the second sequence vector, determining whether the conductor shape is uniformly iced or has ice ridges based on the fluctuation coefficient, and determining the position of the longest point of the ice ridge; and calculating the icing thickness information for all locations based on the mapping relationship between the first and second sequence vectors. This invention can efficiently, accurately, and comprehensively determine the icing thickness of transmission lines.

The invention discloses a parent material source correction and discrimination method, which comprises the following steps: collecting reservoir extracts of target shale, obtaining saturated hydrocarbon data of a target layer section, and determining a numerical value of a parent material source index of a target divided layer section according to the saturated hydrocarbon data; dividing correction coefficients according to parent material sources to correct index values corresponding to the target layer sections; and determining the parent material source of the target layer section according to the corrected index characteristic value. According to the method, the shale parent material sources are divided by correcting the abnormal data and utilizing the value combination of saturated hydrocarbons C27, C28 and C29, and technical support can be provided for shale exploration.

The invention discloses a hydrogen production mode identification method and system based on a tree classifier, and belongs to the technical field of hydrogen analysis, the hydrogen production mode identification method based on the tree classifier comprises the following steps: S1, extracting the original component content of a hydrogen sample; s2, constructing engineering features based on the original component content, wherein the engineering features comprise a component proportion, a composition entropy, logarithm transformation, a component interaction item and a principal component; and S3, hydrogen production mode identification: taking the engineering characteristics as input, synthesizing minority class oversampling for relieving class imbalance, and performing hydrogen production mode judgment through a tree classifier. By adopting the technical scheme, the hydrogen production mode can be quickly analyzed by utilizing the tree classifier based on the original component content and engineering characteristics of the hydrogen sample.