40results about How to "Quick and accurate identification" patented technology

The invention discloses a method and a system for improving Mura defect classification precision, electronic equipment and a storage medium, which are used for realizing accurate classification of Mura defects of low-gray-scale images. The method for improving the Mura defect classification precision comprises the steps of obtaining an image on a display screen to be detected; performing texture filtering processing on the image to obtain a filtered image; performing graying processing and defect segmentation processing on the filtered image to obtain a to-be-detected image; inputting the to-be-detected image into the Mura classification model to obtain a classification result; wherein the Mura classification model is obtained through the following training method: obtaining first data collected by a plurality of sensors; preprocessing the first data to obtain second data; extracting feature data in the second data; fusing the feature data according to an orthogonal sequence fusion algorithm to obtain a fusion result; and training according to a fusion result to obtain a Mura classification model.

The invention discloses an unmanned aerial vehicle visual guidance obstacle avoidance system and method based on polarization information, and relates to the technical field of unmanned aerial vehicle navigation and obstacle avoidance. Polarization image information in a flight scene is collected in real time through a polarization sensor, and the perception capability of a complex environment is enhanced. Polarization parameters are extracted through a compression observation method, the efficiency and accuracy of feature extraction are improved, a visual attention mechanism is combined, an obstacle background area is accurately recognized, a self-adaptive decision fusion model is constructed, the feature weight is optimized, and the reliability of target contour features is enhanced. Preliminary path search is carried out based on target contour features, and a centralized optimization sampling method is adopted, so that the probability of generating an optimal flight path in a complex environment is effectively improved. And the obstacle avoidance flight module is used for smoothing the flight path, so that the smoothness and controllability of the flight path are ensured, and the navigation and obstacle avoidance capabilities of the unmanned aerial vehicle in a complex environment are effectively improved.

The invention provides a PD-L1 immunohistochemical section tumor identification detection method and system, and the method comprises the steps: collecting various organ PD-L1 sections of a clinical pathological experiment, carrying out high-resolution scanning, pre-identification screening and cloud data storage, and obtaining a PD-L1 section data set of various organs; automatically labeling each tumor cell labeling pixel-level mask, automatically generating a labeling bounding box, and obtaining a tumor cell region labeling data set; according to the tumor cell region labeling data set, preprocessing all positions labeled as effective regions, creating a YOLO v8 deep learning model, training to gradually adjust network weight, and outputting a prediction result of the YOLO v8 deep learning model; and analyzing and calculating whether the prediction evaluation index information of the YOLO v8 deep learning model accords with a training target evaluation index, completing model training, and carrying out accurate positioning and pixel-level region identification detection on PD-L1 immunohistochemical pathological section tumor cells.

The invention belongs to the technical field of fish genetic identification, discloses a multi-site large yellow croaker wild domestication population identification system based on an agarose gel electrophoresis technology, and particularly discloses a group of InDel molecular markers for large yellow croaker wild and domestication population identification, and the InDel molecular markers comprise InDel11-5053756, InDel20-14004876, InDel3-28345321 and InDel21-17327392. The InDel molecular marker detection primer is used for amplification to obtain a PCR product, agarose gel can be used for detecting strip polymorphism, extra restriction enzyme digestion or complicated polyacrylamide gel electrophoresis operation is not needed, and identification of large yellow croaker wild and domesticated populations can be rapidly and accurately achieved.

The invention relates to the technical field of pulse state recognition, in particular to an electric spark discharge pulse state recognition and control system and method.The electric spark discharge pulse state recognition and control method comprises the following steps that gap voltage is obtained, and whether the gap voltage is rising edge voltage or not is judged; comparing the gap voltage which is judged to be the rising edge voltage with a preset threshold voltage; when the gap voltage is the same as any one of the preset threshold voltages, a trigger state identifier is set to be 1, and counting is started from a preset value A1; when any one of counting stopping conditions is met, counting is stopped, the state identifier is reset, and the counting value is returned to the preset value A1; calculating delay time between two time points of counting starting and counting stopping; judging the delay time and outputting a judgment result; and performing control according to the judgment result. According to the invention, based on the pulse voltage threshold and the time characteristic, the short-circuit arc pulse and the arc discharge pulse are rapidly and accurately identified.

The invention discloses a focusing method and device and a storage medium, and relates to the technical field of video image focusing, and the method comprises the steps: determining a to-be-focused target block in a current frame of video data and a plurality of target sub-blocks in the target block, and obtaining a to-be-focused region set, each target sub-block being obtained through the division of the target block; an image original pixel of the target block is acquired, a phase difference value of each to-be-focused area in the to-be-focused area set is determined based on the image original pixel, and each to-be-focused area comprises the target block and each target sub-block; and determining a target focusing area according to each phase difference value, and focusing the target focusing area. According to the method, the target block needing to be focused is further subjected to region division, and the phase difference values of the divided regions are evaluated, so that a better region of interest of the target block can be quickly focused in a complex scene, and the focusing efficiency and the imaging quality of the automatic focusing system are improved.

The invention provides a host load anomaly detection method and system, electronic equipment and a storage medium, and relates to the technical field of network engineering. The method comprises the following steps: acquiring monitoring index data of a target host; performing type classification on the monitoring index data according to a fixed index, a non-black and non-white index and a dynamic change index, and constructing a data set under the corresponding classification; and inputting the monitoring index data classified into the dynamic change indexes into an anomaly detection model for detection so as to understand position information in a corresponding data sequence through position coding, learning representation of an input sequence by using a multi-head self-attention mechanism of a Transform encoder and a full-connection feedforward network, and outputting an anomaly judgment result through a linear layer and a softmax layer. According to the technical scheme, the host load abnormity can be quickly and accurately detected.

The invention relates to the technical field of building construction supervision, in particular to a safety early warning method and system for a concrete construction process, and the method comprises the steps: firstly obtaining data of a monitoring point of a support frame and an adjacent monitoring point, and constructing a deformation feature vector of the support frame; the support frame deformation feature vector comprises a distance feature, an angle feature, a deformation feature and a vibration frequency feature; then determining the number of clustering clusters according to a BIM model of a building, carrying out clustering analysis on the deformation feature vector of the support frame, and judging whether an abnormal cluster exists or not; support frame monitoring points in the abnormal cluster are obtained, and an abnormal support frame monitoring point set is obtained; and obtaining the distance feature, the angle feature, the pouring feature component and the vibration feature component of the abnormal support frame monitoring point to construct an abnormal evaluation index, and if the abnormal evaluation index is greater than a preset abnormal threshold value, triggering safety early warning. By means of the method, real-time monitoring of the formwork supporting frame in the concrete construction process is achieved, and construction safety is improved.

The present application relates to a system for profiling a tray, the system comprising: a turntable configured to rotate the tray; a light source positioned adjacent to the turntable, the light source configured to illuminate the tray when the tray is rotated on the turntable; a camera positioned relative to the light source, the camera configured to capture an image of the tray when the tray is rotated on the turntable; and a photographing compartment enclosing an area containing the turntable, the light source, and the camera, where the photographing compartment includes sides and a top to reduce an amount of ambient light entering the enclosed area.

The invention discloses a human body behavior recognition device and method based on an infrared image, and the method comprises the following steps: S1, collection of the infrared image: employing a single-chip microcomputer to drive an infrared sensor, enabling detected temperature data to form an 80 * 64 * n digital matrix, transmitting the 80 * 64 * n digital matrix to the single-chip microcomputer through an SPI, and storing the 80 * 64 * n digital matrix, then the temperature digital matrix is transmitted to a PC end in a UART communication mode; s2, preprocessing the infrared image; s3, making a human body behavior data set: designing and collecting infrared images of seven behavior actions of bending down, falling down, lying flat, standing, sitting still, standing-to-sitting and walking; s4, designing a behavior recognition algorithm; s5, behavior recognition algorithm training; and S6, identifying seven behavior actions. According to the method, the accuracy is higher, the recognition speed is higher, terminal deployment with limited computing resources is facilitated, and the actual application requirement is met.

The invention, which relates to the technical field of orchard picking management, discloses an orchard picking adaptive management system comprising an environment monitoring module, a data analysis and prediction module, an automatic control module, an intelligent picking module, a pest and disease monitoring module and a pest and disease control module. The environment monitoring module is used for collecting orchard environment data and transmitting the orchard environment data to the data analysis and prediction module, and the data analysis and prediction module is used for preparing and preprocessing the data, determining a model order and establishing, checking and optimizing an ARIMA model for yield prediction. According to the invention, through the integrated ARIMA model, the orchard picking adaptive management system can carry out accurate yield prediction based on the historical time sequence data of the orchard; the problems of inaccurate orchard yield prediction, complex data processing and model establishment, difficult model optimization and selection, lack of basis for picking plan making and low orchard management efficiency can be solved.

This invention discloses a leak detection device equipped with a leak detection block (750), comprising: a main fuel line connection hole (762a) penetrating the center of the body; a first annular spatial connection groove (782) formed at a predetermined depth from one side surface of the body, located radially spaced from the center of the main fuel line connection hole (762a); a second annular spatial connection groove (792) formed at a predetermined depth from the other side surface of the body, located radially spaced from the center of the main fuel line connection hole (762a); and a first separator insertion hole (810) drilled from the outer peripheral surface of the body, spanning the first annular spatial connection groove (782a). 2) The second annular space connecting groove (792) is connected to each other in a communicative manner, wherein when the separator (850) is inserted into the first separator insertion hole (810), the head (852) of the separator (850) closes the portion of the first separator insertion hole (810) between the first annular space connecting groove (782) and the second annular space connecting groove (792), thereby closing the communicative connection between the first annular space connecting groove (782) and the second annular space connecting groove (792), while communicatively connecting the sensor insertion hole (854) leading to the end surface of the head (852) to the first annular space connecting groove (782) or the second annular space connecting groove (792).

The invention belongs to the technical field of contract auditing, and discloses a contract comparison and recognition method based on deep learning, and the method comprises the specific steps: 1, importing a recognition system; step 2, training based on a deep learning OCR model; step 3, performing OCR operation on the picture; 4, identifying and uploading the content; 5, contract output or modification is judged; and step 6, risk prompting. By automatically comparing contract texts, the system can quickly identify key information, the complexity of manual operation is reduced, the system has good adaptability, contracts of different fields and types can be processed without manually adjusting parameters, the manual intervention requirement is greatly reduced due to the high automation degree, contract auditing is more efficient, and the contract auditing efficiency is improved. According to the method, multi-language texts can be easily processed, the automatic data extraction function provides great convenience for subsequent data analysis, and compared with manual text transcription, the OCR technology has better cost benefits in large-scale document processing.

The application provides a kind of intelligent construction site safety monitoring and abnormal behavior detection method, it is related to behavior identification technical field, including: based on video frame data, through target detection algorithm, personnel and cigarette in the construction site multi-modal data are positioned, to output first detection result;Based on the first detection result, video frame data and construction site multi-modal data in the preset time period, utilize 3D CNN to combine attention mechanism, to output second detection result;Based on the second detection result and construction area security level information, by isolated forest and self-encoder algorithm, get comprehensive abnormal score.

The present invention relates to a fiber for fabric authenticity identification and a fabric authenticity identification system using the same, the fiber for fabric authenticity identification comprising an infrared light-emitting body, the fiber being included in a yarn or a fabric, the fiber having a function of fabric authenticity identification by mixing a special pigment with the fiber, and the fabric authenticity identification system using the same. Therefore, the effect of authenticity identification or counterfeiting prevention can be achieved. For example, for a specific functional product, analysis after fabric preparation becomes complex and infeasible, but rapid and accurate identification can be achieved through the fiber for fabric authenticity identification of the present invention.

The invention discloses an official document automatic formatting method and system, which relates to the field of artificial intelligence technology, and comprises the following steps: inputting an official document for identification and typesetting; preprocessing the input official document data; identifying the elements of the input official document data; preprocessing the data in the training library and inputting it into the word segmentation model of HMM for word segmentation, and outputting the word segmentation label; converting the word segmentation label, character and word of each word segmentation in the training library into vectors and inputting them into a language big model, and iteratively training the vectors; inputting the BERT model output vectors into the language big model to obtain semantically complete word segmentation vectors, and segmenting official document paragraphs of different elements based on the semantically complete word segmentation vectors and appropriate segmentation rules; dividing the segmented official document data into lines according to the upper limit of the number of words in each line, and then obtaining the semantically complete official document typesetting, which is conducive to improving work efficiency and smooth government orders.

The invention belongs to the technical field of image recognition. The invention provides a target recognition system and method based on machine vision. The method comprises the following steps: receiving feature description information of a specific target object input by a user, and performing semantic analysis on the feature description information by using a semantic analysis plug-in to obtain key feature data; retrieving a plurality of first target objects in the monitoring video based on the first static features, tracking each first target object for a preset duration, and extracting a second dynamic feature of each first target object from the tracking video; the similarity between each second dynamic feature and the first dynamic feature is calculated, the first target object corresponding to the second dynamic feature with the similarity higher than a similarity threshold is determined as a second target object, and the second target object is the specific target object. The specific target object can be recognized according to the feature description information of the voice and text types of the specific target object input by the user, and the image of the specific target object does not need to be obtained in advance.

The invention discloses a remote fault monitoring processing method of unmanned automatic car washing equipment and a storage medium, and relates to the technical field of fault monitoring, the processing method comprises the following steps: carrying out link division on data stored in any car washing record; randomly selecting one car washing record, and evaluating each link to obtain the comprehensive completion degree and the actual completion degree of the car washing record; performing difference analysis on evaluation results of all links through difference comparison of the completeness; analyzing the correlation degree between any two adjacent links according to the data corresponding to each link; performing abnormity judgment on each link in the car washing record based on the correlation degree between the links; setting a risk judgment condition of each link based on the generation condition of each risk link; and evaluating a real-time car washing record newly generated on the car washing data management platform, and judging whether risk early warning is performed on the real-time car washing record or not.

The invention relates to the field of circuit board manufacturing, and particularly discloses an interlayer alignment method for a five-layer LCP circuit board and the five-layer LCP circuit board. A first LCP base material, a second LCP base material, a third LCP base material and a fourth LCP base material are stacked in sequence; each second sight hole in the second piece of LCP base material and each alignment ring on the third piece of LCP base material are positioned in each first sight hole of the first piece of LCP base material, and when the hole edge of the second sight hole in each corner of the second piece of LCP base material is positioned in the corresponding alignment ring of the third piece of LCP base material from each first sight hole of the first piece of LCP base material, the hole edge of the second sight hole in each corner of the second piece of LCP base material is positioned in the corresponding alignment ring of the third piece of LCP base material; and judging that the alignment fitting precision of the second piece of LCP substrate and the third piece of LCP substrate meets the requirement, and through the method, the alignment precision of the circuits between the inner layers of the five-layer LCP circuit board can be visually and accurately identified, the material waste is reduced, and the production cost is reduced.

The invention provides a method, a device and a product for identifying the protection range of a distribution network current type protection switch, and relates to the technical field of power systems. The method comprises the following steps: acquiring line data of a line in the power distribution network, wherein the line data comprises switch data of a protection switch, wire data of a wire and line structure data; when any line fault is detected, the length of a front-end line and the length of a rear-end line related to a target protection switch related to the faulted line are determined based on the line data, the length of the front-end line is used for indicating the length of a line traversed from the outbound switch to the target protection switch, and the length of the rear-end line is used for indicating the length of a line traversed from the outbound switch to the target protection switch. The rear-end line length is used for indicating the line length traversed from the target protection switch to the specified line position; determining a protection range of a target protection switch based on the line data and the front-end line length; and when it is judged that the protection range is smaller than the length of the rear-end line, a corresponding alarm signal is sent out. The operation stability of the power system is improved.

The invention provides a storage battery charging and discharging management method of a power take-off power generation system and a computer system, relates to the technical field of storage battery management, and solves the problems of failure report failure and false report of a storage battery and inaccurate calculation of the number of charging and discharging cycles in the prior art. The method comprises the following steps: collecting state data of a single storage battery, and calculating corresponding extreme value data; according to the extreme value data, whether a single overvoltage fault, a single undervoltage fault and a battery temperature fault occur or not is judged in sequence, and then the charging and discharging state of the storage battery is limited; when the single storage battery is allowed to be in the charging state or the discharging state at the same time, the charging and discharging process of the single storage battery is executed, and the cycle index corresponding to the charging and discharging process is calculated. The method is suitable for the frequent charging-discharging or shallow charging and shallow discharging state of the storage battery in the power take-off power generation system, the working reliability and accuracy of the storage battery in the power take-off power generation system are improved, and the service life of the storage battery is prolonged.

The invention relates to the technical field of food processing, and particularly discloses an eel food pretreatment process, which adopts an image processing technology, collects image information in an eel processing process through a camera, divides viscera residue, blood residue and injury areas, and can calculate a viscera removal score according to an identification result so as to obtain a viscera removal result. Compared with the prior art, the method has the advantages that the machining efficiency can be remarkably improved, the labor cost can be reduced, the loss rate in the machining process can be reduced, the consistency of the product quality can be effectively improved, the competitiveness of the product in the market can be enhanced, and the market competitiveness of the product can be improved. In addition, the method is also beneficial to promoting the technical progress and industrial upgrading of the eel processing industry, and in short, the invention provides an efficient, reliable, economical and practical technical scheme for solving the challenges faced by the current eel processing industry.

The utility model discloses a distance-adjustable cantilever type automatic box nailing machine which comprises a conveying belt, a portal frame is fixedly installed on the outer wall of the conveying belt, a hydraulic cylinder is fixedly installed in the center of the top in the portal frame, a box body is arranged at the lower end of the hydraulic cylinder, a threaded rod is arranged at the position, close to the bottom, in the box body, and the threaded rod is connected with the portal frame. A motor is fixedly installed at the position, close to the center, of the top in the box body, and the motor is fixedly connected with an output shaft through the output end on one side of the motor. According to the distance-adjustable cantilever type automatic box nailing machine, through the design of a threaded rod, a motor, a transmission wheel, a guide rod, a guide block, threaded plates, an extension rod, a pointer and scale marks, the motor drives the threaded rod to rotate, synchronous movement of the two threaded plates on the threaded rod is achieved according to the threaded connection principle, then the distance between the two nailing devices is accurately adjusted, and the nailing efficiency is improved. The transmission mode is stable and reliable, the adjusting precision is high, and the accuracy of nailing operation is ensured.

The embodiment of the present invention discloses an application identification method, device, electronic device and storage medium. The method includes: obtaining the download link data of the application to be identified, and determining the download link IP data corresponding to the download link data; extracting the IP data features of the download link data according to the download link IP data; in the case where it is determined that the IP data features are not included in the IP filing whitelist database and are not included in the IP blacklist database, determining the application installation package data of the application to be identified according to the download link data; extracting the static features of the application to be identified according to the application installation package data, and in the case where it is determined that the static features of the application to be identified meet the abnormal static feature library comparison conditions, determining that the application to be identified is an abnormal application. The technical solution of the embodiment of the present invention can quickly and accurately identify abnormal applications, and improve the recognition efficiency and recognition accuracy of abnormal applications.

The embodiment of the invention discloses a medical image processing method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a target thyroid ultrasound image; the target thyroid ultrasound image is input into a thyroid image processing model which is trained in advance, a thyroid contour area of the thyroid ultrasound image and at least one reference thyroid nodule marking box are determined, and the thyroid image processing model which is trained in advance is obtained based on improved yo11 model training; and determining a target thyroid nodule labeling frame in the at least one reference thyroid nodule labeling frame according to a position relationship between the thyroid contour region and the at least one reference thyroid nodule labeling frame, and displaying the target thyroid nodule labeling frame in the target thyroid ultrasound image. According to the technical scheme of the embodiment of the invention, the thyroid nodule in the thyroid ultrasound image can be quickly and accurately identified, and objective reference can be provided for clinical diagnosis.

The invention discloses an electronic product aging test system, and relates to the technical field of intelligent cat doors, and the system comprises an initial test classification module, an aging performance analysis module, an intelligent cat door model evaluation module and a production quality grade evaluation module, the initial test classification module obtains the model of a current batch of intelligent cat doors, and distributes the model to an initial test platform; after the test is finished, the aging performance analysis module evaluates the aging performance of each intelligent cat door, the performance of the products in different aspects can be accurately evaluated, potential quality problems can be found in time, the intelligent cat door model evaluation module counts the number of unqualified products, and the intelligent cat door model evaluation module is used for evaluating the quality of the products. And analyzing the production early warning scheme. And the production quality grade evaluation module evaluates the production quality grade of the qualified product and further analyzes a production line maintenance scheme, so that the aging performance test, quality evaluation and production scheme analysis of the intelligent cat door are realized, and the product quality and the production stability are guaranteed.

The invention discloses a method and device for monitoring the smelting process of a vacuum induction furnace based on machine vision, and relates to the technical field of vacuum induction furnace monitoring, a visual monitoring technology is adopted to obtain a smelting image of a key position around the vacuum induction furnace, and whether a lens is shielded or not is recognized through a lens shielding judgment model based on a convolutional neural network; the smelting state of a steel ingot is recognized through a smelting state recognition model based on a convolutional neural network, and when a smelting period is entered, the smelting state recognition model based on target detection is adopted to carry out visual mode recognition on a smelting image so as to recognize abnormal types such as dross, furnace wall scrap steel and molten steel splashing; and information such as the number of abnormal working conditions, starting time, ending data and duration time is recorded. The smelting condition of the vacuum induction furnace can be automatically monitored and accurately recognized, the reaction is rapid, tracing is convenient, and a foundation is laid for improving the smelting efficiency and precision.

This application discloses a data processing method, apparatus, device, storage medium, and program product, applicable to artificial intelligence scenarios. The method includes: acquiring target text detection data; performing character-to-image conversion on the target text detection data to obtain target visual image information corresponding to the target text detection data; performing visual feature extraction processing on the target visual image information to obtain target image hidden features corresponding to the target visual image information; performing text vectorization processing on the target text detection data to obtain target text features corresponding to the target text detection data; and performing semantic fusion processing on the target text features and the target image hidden features to obtain a quality probability parameter corresponding to the target text detection data. Using this application, text quality can be identified quickly and accurately.

The invention discloses an intelligent path planning and cargo side throwing method based on an ROS and an intelligent trolley. The method comprises the steps that firstly, a downsuction type suction cup support with a camera is printed and installed at the tail end of a mechanical arm of the intelligent trolley; using a Dijkstra-based global path and a TEB-based local path to navigate the intelligent trolley to a sorting table for parallel correction so as to enable the intelligent trolley to be parallel to the sorting table; if the arm extension length of the mechanical arm of the intelligent trolley is smaller than the needed grabbing extension distance, similar triangular path planning correction is conducted on the posture of the trolley; a mechanical arm is controlled to stretch out by a specific distance towards a specific angle to conduct deflection angle grabbing, and goods two-dimensional code two-stage recognition is conducted through a two-dimensional code enhancement and recognition algorithm based on a deep neural network; and finally, according to the delivery positions of all the goods, an optimal navigation path is planned, the lateral delivery direction is judged, and target goods are grabbed and delivered. According to the method, the precision and efficiency of the cargo grabbing process are optimized, and the overall performance of the intelligent path planning and cargo side throwing method is effectively improved.

This invention provides a pressure recognition method, apparatus, device, and storage medium, belonging to the field of artificial intelligence technology. The method includes: acquiring labeled physiological signal samples and physiological signal samples to be identified from a target user; inputting the labeled and unlabeled physiological signal samples into a Siamese network to obtain a first depth feature corresponding to the labeled physiological signal sample extracted by a first sub-network of the Siamese network and a second depth feature corresponding to the unlabeled physiological signal sample extracted by a second sub-network of the Siamese network; and, based on the difference between the first and second depth features, using a trained pressure recognition model to obtain the pressure intensity recognition result corresponding to the unlabeled physiological signal sample. This invention, by predicting the pressure intensity difference between two physiological signal samples, achieves the identification of unlabeled physiological samples from the same user using only a single labeled physiological signal sample with pressure intensity annotation.