37 results about "Automated algorithm" patented technology

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

The invention discloses a method and system for measuring and identifying a high crack of a house wall, and belongs to the technical field of buildings, and the method comprises the steps: collecting complete wall crack point cloud data, carrying out the geometric feature extraction of the wall crack point cloud data, and dynamically adjusting the deployment density of an environment and a strain sensor. And completing the prediction of the future width of the crack through the prediction model, completing the classification of the crack risk level based on the predicted value and the structure standard, and generating a final maintenance strategy according to the crack dynamic expansion condition, the risk level and the construction resource configuration rule. According to the scheme, by introducing dynamic data fusion and prediction analysis, through continuous data collection and environment change analysis, pre-judgment and intelligent decision making of the future development trend of the crack are achieved, the damage level of the crack is evaluated through an automatic algorithm, the system can generate personalized repair plans under different crack conditions, and the repair efficiency of the crack is improved. The repairing efficiency is improved.

The invention relates to the technical field of computer science and industrial engineering, in particular to a tool workshop scheduling method, which comprises the following steps of: acquiring production characteristics of a current tool workshop, generating an optimal batch scheduling strategy according to the production characteristics of the current tool workshop based on a preset evolutionary strategy, and automatically designing an algorithm by utilizing a preset large model, and calculating the minimum completion time and the maximum completion time of the optimal batch scheduling strategy, and completing tool workshop scheduling according to the minimum completion time and the maximum completion time based on the optimal batch scheduling strategy. Therefore, the problems of high workshop scheduling complexity and the like caused by strong coupling of batch division and working procedure sorting, strict constraint between working procedures and high resource networking degree in workshop scheduling are solved, an automatic algorithm design framework based on a large model is introduced, the expert algorithm design time is shortened, and the workshop scheduling efficiency is improved. Therefore, the efficiency and feasibility of aircraft tooling production scheduling are improved.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and / or to specify the coordinates / locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.

The invention discloses an automatic algorithm configuration method based on deep reinforcement learning in evolutionary computation. The method comprises the following steps: determining a problem set; obtaining initial data for constructing a Markov decision process; defining a state vector of each individual in a time step in an evolutionary computation algorithm by using an individual state vector according to the initial data; constructing feature vectors of population features, individual features and development features according to the initial data; defining a continuous action space according to the initial data, and jointly controlling the selection of all individuals on hyper-parameters; based on whether a better solution is found or not, a reward mechanism is set, and maximization of expected benefits is achieved; and the deep reinforcement learning agent uses an attention mechanism to control dynamic hyper-parameters of the algorithm, so that a Markov decision process is realized, and automatic algorithm configuration is completed. According to the method, direct mapping from high-dimensional sensing information to continuous action space output is realized, configuration of individual exploration and development tradeoff is dynamically adjusted, and a balance mechanism of exploration and development is optimized.

The application discloses an ancient landslide dam extraction method based on optical images, and specifically comprises the following steps: step one, image acquisition; step two, basic element indication; step three, ancient landslide interpretation; step four, river blocking analysis; step five, dam interpretation; step six, parameter calculation and verification; and step seven, database construction. The application relates to the technical fields of remote sensing interpretation and ancient disaster identification. The ancient landslide dam extraction method based on optical images combines the ancient landslide range outlined on the images with direct interpretation signs of ancient dams and indirect inference methods, can preferably extract the ancient dams, and is supplemented by field sampling verification. Compared with an automatic algorithm identification mode, the ancient landslide dam extraction correctness is greatly improved, and the time and labor of pure field investigation are effectively avoided, and the efficiency is greatly improved.

This invention belongs to the field of dental medical technology, specifically relating to an automatic positioning method, electronic device, and storage medium for dental template teeth. The method includes: acquiring a patient's dental arch model, the cervical margin of a restoration, and a template tooth model; determining the dimensional characteristics of the template tooth; performing preliminary overall matching of the template tooth; extracting the tooth portions from the dental arch and fitting a dental arch curve; calibrating the position and size of the template tooth based on the dental arch curve, including determining the target position and orientation of the restoration based on the cervical margin and the dental arch curve for restored teeth, and determining and locating the missing tooth based on the projection of adjacent restorations, and scaling it according to the morphology of the cervical margin or the arc length of the missing tooth interval. This invention achieves precise initial positioning and adaptation of the template tooth through an automated algorithm, solving the problems of cumbersome and inefficient manual positioning in existing technologies.

A computer-aided method for determining permissible topological states (Z) of a power grid (10) is given, wherein the power grid (10) comprises a plurality of network nodes (N1-N5) and a plurality of switches (S1-S5) arranged between the network nodes (N1-N5), wherein the method comprises the following steps: a) Providing a first subset (M1) of the switches (S1,S2) which must be closed during the regular operation of the power grid (10), b) Providing a second subset (M2) of switches (S3-S5) which may be open during the regular operation of the power grid (10), c) Generating a multitude of incidence matrices (B), each representing a topological state (Z) of the power grid (10), - where for the respective topological state (Z) the closed switches (S1-S4) of the first (M1) and second (M2) subset are represented by entries in the incidence matrix (B), - where the individual incidence matrices (B) differ with respect to the entries corresponding to the closed switches (S3,S4) from the second subset (M2), - wherein the generation of the incidence matrices (B) is computer-aided using an automated algorithm, d) Checking the multitude of generated incidence matrices (B) with regard to at least one predefined boundary condition (RB1) as well as e) Forming a first set (Z1) of admissible topological states (Z) from the tested incidence matrices (B) that satisfy the predefined boundary condition (RB1). Furthermore, a corresponding operating procedure for a power grid (1) and a corresponding computer program product are specified.

The invention relates to the technical field of intelligent maintenance and artificial intelligence crossing of civil aviation, and provides a civil aircraft health management method and device based on a large model agent. According to the method, through combination of natural language interaction and an automatic algorithm, the workload of glue code writing and parameter debugging is reduced, the PHM system development threshold is lowered, and non-algorithm professional engineers can quickly construct an analysis process; task planning and algorithm calling are autonomously completed, the process execution efficiency is optimized, and the task processing period is shortened.

The invention belongs to the technical field of underwater acoustic signal processing, and particularly relates to an active sonar man-machine cooperation multi-target tracking method. Comprising the following steps: carrying out balanced denoising processing on background noise, and automatically detecting a threshold value to obtain azimuth and distance information of a target; meanwhile, the detection threshold value is manually adjusted through manual intervention; establishing a track for the detection point through an automatic algorithm and distributing a track ID; meanwhile, through manual intervention, suspected target points are manually recorded; calculating the proximity of each detection point and each track through an automatic algorithm, and carrying out track association according to a proximity matrix; meanwhile, through manual intervention, track ID affiliation is manually corrected; according to the invention, a hybrid tracking mode of'automatic-based and manual-assisted 'is formed, a full-automatic tracking algorithm is used for processing a conventional process, an abnormal decision is manually intervened, a high-reliability man-machine cooperative active sonar target tracking system is established, and the tracking robustness in a complex environment is improved by dynamically switching a manual / automatic mode.

The application relates to the technical field of robots and discloses a method for remotely manually repositioning coordinates of an indoor robot, which comprises the following steps: step 1, identifying a positioning loss state, and the platform sends an action: when the robot cannot maintain reliable positioning, the platform timely sends an alarm, and after an operator receives and confirms the alarm, the repositioning process is started; step 2, constructing a new map: a new map construction mechanism is set; step 3, calculating a target pose: the operator manually aligns the new map and the old map to obtain the target pose; and step 4, repositioning: the robot realizes internal updating according to the target pose. The application combines intelligent guidance of an automatic algorithm and flexible decision-making of manual operation, effectively overcomes the defects that pure automatic repositioning is prone to failure in a large range loss or a complex environment and falls into a local optimum, and simultaneously avoids the problems that traditional manual intervention needs on-site operation and is low in efficiency.

This invention provides a device and method for acquiring a three-dimensional model of abdominal organs, comprising: a coil support assembly, an MRI scanner, and an image processing device; the coil support assembly is clamped and fixed to the side of the examination bed to support the abdominal coil and reduce pressure on the abdomen of the user; the MRI scanner is positioned above the abdomen of the user, with the axis of the examination positioning light aligned with the midpoint of the axis of the coil support assembly, and a magnetic resonance imaging (MRI) scan sequence is acquired by scanning with the MRI scanner; the image processing device is communicatively connected to the MRI scanner, acquires the MRI scan sequence, and obtains a three-dimensional model of the abdominal organs based on the MRI scan sequence. This invention achieves precise extraction of abdominal organs at all levels through multi-region seed point strategies and adaptive threshold segmentation, overcoming the inefficiency and subjective limitations of traditional manual segmentation, and ensuring the reliability of the results through automated algorithms, thus providing support for the image analysis of abdominal organs.

The invention is suitable for the technical field of forensic dentition science, and provides an automatic forensic dentition science individual identification method based on a 3D computer vision algorithm, and the method comprises the steps: carrying out the filtering, denoising and down-sampling processing of an initial 3D dentition model, so as to remove the noise and reduce the data size; segmenting a dental crown region as a region of interest through an automatic algorithm, and removing gingiva and palatal plica soft tissues; and performing rigid registration on the segmented ROI model by adopting an iterative nearest point algorithm, and calculating a root-mean-square error and a registration fitting degree after registration. According to the method, the steps of filtering, denoising and down-sampling preprocessing are added before model segmentation, the 3D dental model segmentation, registration and comparison method is designed based on an open source 3D vision algorithm, full-process automatic processing from model preprocessing, model segmentation, model registration to model comparison is achieved, the registration and comparison precision can be guaranteed, and meanwhile the registration and comparison efficiency is improved. Registration efficiency is greatly improved, computing resources are saved, and subjective errors caused by manual segmentation of a model are avoided.

This invention discloses a method for rapid and automatic identification of structural surfaces based on high-precision point cloud data. To address the problem of slow regional growth and significant influence of the threshold of the growth criterion when segmenting structural surfaces, the concept of fuzzy boundaries is introduced to reduce the impact of the threshold on regional growth. Simultaneously, a pre-allocated search space accelerates the segmentation of regional growth, resulting in a rapid and accurate method for segmenting structural surfaces. While ensuring accurate identification of structural surfaces, this method rapidly extracts geological information about structural surfaces and improves the efficiency of obtaining this information through an automated algorithm.

This invention proposes a system for generating intraoperative neurological status monitoring reports. It utilizes automated algorithms to process and analyze data in real time, generating reports based on electromyographic signals during and before surgery, as well as nerve confirmation and comparative analysis. The system eliminates the need for manual report writing, significantly improving report generation efficiency. Furthermore, the system eliminates the need for specialized technicians to manually monitor and capture signals and perform extensive subsequent data analysis; the processing center automatically interprets the signals in real time, alleviating the burden of manual data processing.

Mechanisms are provided for automatically determining a speaker or writer's viewpoint, intensity, veracity and intended audience of natural language text. Language understanding functions including question answering, information search and retrieval, text generation, machine translation, transcription, summarization and decision-support are more accurate when these indirectly articulated elements of intent are understood. The functions use explicitly coded and curated commonsense knowledge propositions that form a knowledge graph data structure to interpret these elements of intent using contextual, subtextual and extratextual cues. These mechanisms operate on input text that has already undergone lexical disambiguation and provide a framework for identifying bias, deception and literary devices including simile, metaphor, analogy, allegory, personification, hyperbole, metonymy, sarcasm, diplomacy, intimidation and allusion.

A thermodynamic process and system for material modification including software procedures and methodologies comprising specialized thermodynamics processes. The system applies thermal bonding and division through thermal contrasting in nuclear technology, medicine, computing, and military operations. The process uses heat to bond fibers while employing contrasting temperatures to maintain different thermal zones in confined areas for specific results. The system emphasizes absolute containment to prevent contamination and organizes disordered cells, atoms, and mechanisms. Both automated algorithms and operator technicians provide oversight to verify data within the system database using current and real-time information.

The present invention discloses a glue coating detection method and device based on laser projection video recognition. The detection method includes the following steps: S1, laser projection line acquisition and recognition; S2, determination of the recognition area; S3, binarization of the recognition area; S4, detection of the glue state. Through video image processing and automatic algorithms, the present invention realizes automatic monitoring and analysis of the glue coating process, breaking away from the high cost and low efficiency of traditional manual methods and significantly improving work efficiency. Utilizing the principle of glue refraction, the continuity and shape of the glue can be accurately detected, avoiding errors that may arise from human judgment, achieving a high degree of automation and increasing reliability. Through real-time video monitoring and image processing, it can promptly detect whether the glue line is abnormally interrupted, and automatically identify the interruption location and distance, so that glue can be quickly replenished to ensure product quality.

The application provides a warehouse receipt supervision method and platform based on Internet of Things, and relates to the technical field of warehouse management, which comprises the following steps: deploying Internet of Things equipment in the warehouse, collecting the state information of warehouse goods in real time, preprocessing the collected data, and generating a warehouse receipt; a unique initial hash value is generated for each warehouse receipt, and each time a new warehouse receipt is generated, the warehouse receipt is combined with the current hash value; the application deploys Internet of Things equipment in the warehouse, collects the state information of warehouse goods in real time, generates a warehouse receipt, and realizes real-time processing and verification of the warehouse receipt information through a dynamic hash chain algorithm combined with a block chain, and then stores the warehouse receipt information in the block chain, thereby ensuring the authenticity and integrity of the data; the verification rules and business logic of the warehouse receipt information can be defined through an automatic algorithm, and the data verification and updating operations are automatically executed, which facilitates subsequent query, monitoring and auditing; the overall supervision process is automatically operated in cooperation with the algorithm, thereby saving a large amount of manpower, material resources and financial resources.

The invention relates to the technical field of robots, and discloses an indoor robot remote manual coordinate repositioning method, which comprises the following steps that: step 1, a positioning loss state is identified, and a platform makes an action: when a robot cannot maintain reliable positioning, an alarm is sent to the platform in time, and an operator starts a repositioning process after receiving and confirming the alarm; 2, constructing a new map: setting a new map construction mechanism; step 3, calculating a target pose: manually aligning the new map and the old map by an operator to obtain the target pose; and 4, repositioning is carried out, wherein internal updating is achieved through the robot according to the target pose. According to the method, intelligent guidance of an automatic algorithm and flexible decision of manual operation are fused, the defects that pure automatic repositioning is prone to failure and falling into local optimum in a large-range loss or complex environment are effectively overcome, and meanwhile the problems that traditional manual intervention needs field operation and is low in efficiency are avoided.

The present invention discloses a deep learning-based movie image object recognition and automatic color restoration system, which relates to the field of image recognition technology and includes: an image input module for receiving movie image frames to be restored; an object recognition module for identifying specific objects appearing in the image frames based on a deep learning algorithm; a color mapping module for color restoration of the identified objects; a restoration processing module for restoring the identified and colored objects based on an automated algorithm, restoring their color and details; and an image output module for outputting the restored movie image frames. The present invention proposes automatic recognition and color restoration of objects in images, avoiding the tedious and inefficient traditional manual restoration process. Through a deep learning model, the system can intelligently restore the color and details of objects based on their features in the image. Through feature extraction and object detection using a multi-layer convolutional neural network, the system can efficiently identify specific objects in images.

The application discloses a kind of gas sensor array adaptive front fusion detection method, belong to sensor intelligent detection technical field, comprising: data set preparation and pre-processing;Based on the construction of neural network model of deep learning and MASK operator;The training of neural network model based on deep learning and MASK operator;Actual concentration value and array sensor device state are detected using the trained neural network model described above.The application integrates array sensor and interference gas sensor into calculation, the input data is the original data collected from each sensor, and the output data obtained after algorithm fusion analysis is the gas concentration value and the working state of each sensor device, realizing the end-to-end information extraction in true sense;The successful construction of the algorithm helps to promote the construction of gas sensing system general algorithm framework and automated algorithm optimization.

The invention discloses an EN50399 standard-oriented flame burning area envelope line digital detection method and device. The method comprises the following steps of: 1, reading and preprocessing an image; step 2, contour detection and light-emitting plate rectangle screening; step 3, quadrant division and target confirmation; 4, performing perspective transformation and image splicing; step 5, low R channel square positioning is carried out to establish a real coordinate system; 6, carrying out dark area contour fitting and feature extraction; the device comprises three modules, namely a core control module, an image acquisition module and a calibration reference module. According to the method, through standardized hardware calibration and automatic algorithm processing, manual naked eye recognition and manual marking are replaced, the problem of judgment threshold difference caused by personnel experience in a traditional method is solved, and the reproducibility of a detection result is high; and moreover, expensive equipment is not needed, the portability is good, and the method is suitable for field detection scenes of combustion equipment production enterprises, quality inspection institutions and certification laboratories.

The present invention discloses an adaptive post-fusion detection method for a gas sensor array, which belongs to the field of intelligent sensor detection technology, including data set preparation and preprocessing; construction and training of a neural network model based on deep learning, compensation correction algorithm and MASK operator; and use of the above-mentioned trained neural network model to detect the actual concentration value and the state of the array sensor device. The present invention adopts a post-fusion algorithm, firstly performs calibration compensation and data fusion based on sensors of the same type, and then finally fuses the fusion results of each type of sensor. The fusion algorithm performs staged fusion according to the strength of the correlation between different data, which is conducive to controlling the complexity of the algorithm, improving the interpretability and execution efficiency of the algorithm, and reducing the requirements for the size of the data set during the model training process. The successful construction of this type of algorithm will help promote the construction of a universal algorithm framework for gas sensing systems and automated algorithm optimization.

The application provides a method and device for identifying dead farmed animal individuals in a video based on a ViT technology, a storage medium and a terminal, and belongs to the technical field of computers. The method is applied to a terminal. The terminal selects key frames from a short video segment containing dead farmed animals, and synthesizes pseudo-color three-channel images from the key frames. The pseudo-color three-channel images are data-labeled to label dead farmed animal individuals and non-dead farmed animal individuals in the images, and a training sample set is obtained. The training sample set is input into a ViT target detection model to train the model. The scheme converts target detection in a video into target detection in an image, thereby reducing the time cost of labeling work. Whether the farmed animals are alive or not is inferred by distinguishing the motion and stillness of the animals in the short video segment. When there are tens of thousands or even millions of animals in a large-scale farm, the automatic algorithm can replace the frequent patrol cost of manual work.

The present application relates to the technical field of tire design, and particularly relates to a tire type rubber outer contour automatic conversion method, system and program product based on coordinate conversion. The method automatically reads and processes the outer contour feature point coordinates of the conversion object and the flip object through a program, calculates the distance of the feature points to the upper contour, and generates new conversion coordinates. Then, the program automatically draws the upper and lower parts of the new contour, and connects them through an automatic algorithm to form a complete conversion contour. The present application significantly improves the design accuracy and work efficiency, reduces manual operation and human error, and avoids the problems of missing points and repeated points caused by manual measurement. It has strong adaptability and can handle the automatic conversion of irregular contours, and is suitable for different design software platforms. By reducing the work burden of designers and improving the intelligent level, the present application provides an efficient and accurate automatic solution for tire type rubber design, and has a wide application prospect.

An embodiment of the present invention provides a coating control method for semiconductor process equipment and semiconductor process equipment, the method comprising: obtaining abnormal discharge information within a target time; adding an increment corresponding to the abnormal discharge information to the compensation process time; comparing the executed process time plus the target time with the process setting time, and continuing the process stage and executing the next target time process, or updating the process time according to the updated compensation process time, performing the compensation process stage, executing the next target time process, or terminating the process, based on whether the process compensation stage has been reached and / or whether the abnormal discharge information meets preset requirements. The impact of abnormal discharge on coating is automatically incorporated into the entire process through an automated algorithm, and the impact of abnormal discharge on coating film thickness or quality can be automatically compensated, thereby improving the automation level of the machine, avoiding the problem of reduced production capacity due to abnormal discharge, and saving production costs.

Systems and methods are disclosed for automatically performing motion correction in dynamic positron emission tomography scans, such as dynamic positron emission tomography myocardial perfusion imaging studies. An automated algorithm can be used. The algorithm can use simplex iterative optimization of a count-based cost-function customized to different dynamic phases for performing frame-by-frame motion correction.

The invention provides a flexible real-time regulation and control method based on an automatic algorithm and a related device, and the method comprises the steps: determining a target authority of a target user, and determining first access data according to the target authority; determining m reusable modules according to a preset reference algorithm, and obtaining demand parameters of a target user; combining the m reusable modules according to the demand parameters to obtain a first target algorithm; determining a target algorithm adjustment parameter of the first target algorithm according to the first access data; adjusting the first target algorithm according to the target algorithm adjustment parameter to obtain a second target algorithm; and processing the first access data according to a second target algorithm to obtain target data. The modular algorithms are flexibly combined, so that the algorithms can be quickly adapted according to different service scenes, and the flexibility and the adaptivity of data processing are improved.

The application discloses a kind of flame scorching area envelope digital detection method and device for EN50399 standard, method includes: step 1, image reading and pre-processing;Step 2, contour detection and light-emitting plate rectangular screening;Step 3, quadrant division and target confirmation;Step 4, perspective transformation and image splicing;Step 5, low R channel square positioning establishes real coordinate system;Step 6, dark area contour fitting and feature extraction;Device includes three modules, respectively: core control module, image acquisition module and calibration reference module.The application replaces artificial naked-eye identification and manual marking by standardized hardware calibration and automated algorithm processing, solves the problem of determination threshold difference caused by personnel experience in traditional method, and the detection result has strong reproducibility;And without expensive equipment, good portability, applicable to the on-site detection scene of combustion equipment production enterprise, quality inspection organization and certification laboratory.