322 results about "Identification error" patented technology

The invention relates to the field of computer vision, and particularly provides a table recognition reconstruction method and system, a terminal and a medium, and the method comprises the steps: firstly decomposing a large-size table image into a plurality of overlapped sub-images, and carrying out the table structure detection and OCR character recognition of each sub-image through parallel recognition; then, sub-graph recognition results are integrated through a coordinate mapping and confidence coefficient weighted fusion algorithm, and boundary errors are eliminated; then, automatically distinguishing common cells based on an area clustering algorithm, merging the cells and a header region, and reconstructing a complete table logic structure; further understanding header semantics through a natural language model and repairing identification errors; and finally, realizing intelligent splicing and standardized output of the cross-page table. According to the method, the memory limitation of the traditional OCR technology is broken through, an oversized table can be processed, the recognition accuracy of a complex structure is improved, and the digitization efficiency of professional documents such as financial statements and engineering drawings is improved.

Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are incentivization systems and methods for expert curation systems that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.

Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are expert curation systems and methods that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.

Provided herein are systems and methods that improve the performance and accuracy of artificial intelligence (AI) systems and enhance real-world uses thereof. For example, provided herein are expert curation systems and methods that prevent or reduce the frequency of AI hallucinations; allow for rapid identification of errors, misinformation, and out of date information; enable faster and easier corrections; and provide accurate and actionable results.

The invention relates to the technical field of image recognition data processing, in particular to a pipeline element library creating method based on a model training and image recognition technology, which comprises the following steps of: acquiring pipeline element image data with depth information through a multispectral imaging scheme, executing Retinex algorithm illumination equalization and morphological restoration through a cascade preprocessing pipeline, and establishing a pipeline element library. A standardized data set associated with the metadata is generated. A multi-task joint learning framework is adopted to integrate ResNet-50, HRNet and Mask R-CNN networks, a bottom convolution feature extraction layer is shared, oil stain and strong reflection antagonism data synthesized by a generative adversarial network is injected, model parameters are optimized in combination with a progressive training strategy, and a lightweight MobileNetV3 model is output. Model parameters and an index structure are dynamically updated, and system self-calibration is achieved in combination with a cross-device calibration protocol and data consanguinity tracking. According to the method, through automatic data labeling, multi-task feature multiplexing and retrieval feedback closed loop, the construction efficiency of the pipeline element library is effectively improved, and the recognition error rate in a complex environment is reduced.

The invention discloses an intelligent news content error correction system based on a large model, particularly relates to the technical field of big data, and aims to extract key entity information of figures, positions and institutions in news texts and identify time sequence feature words in position description so as to identify association relationships among entities; historical job change records and latest information are obtained from multiple data sources, reliability scoring is performed through a large model, a multi-modal evidence chain with time sequence attributes is constructed, a prediction model is trained based on historical job data, the change probability of a specific job in a future time period is calculated, and space-time consistency of job information is analyzed. And in combination with the output change probability, identifying errors by comparing a multi-source evidence chain, implementing priority verification on a high-probability change event, generating a hierarchical error report, outputting a correction suggestion, updating an error correction result in real time, implementing two-factor authentication on the correction suggestion, and storing a typical job change mode and a correction scheme by creating a historical case library.

A codebase system that maintains and utilizes a dynamic mapping between a collection of tests and files within a codebase to ensure error detection and code validity. The codebase system accesses a collection of tests configured to process files within the codebase to identify errors. It then generates a mapping by executing these tests to determine the specific files each test operates on. Upon detecting updates to the test collection, the codebase system updates this mapping by re-executing the updated tests, identifying the new set of files for each test, and aggregating these updates into the existing mapping. When changes to the files in the codebase are detected, the codebase system accesses the updated mapping to identify which tests relate to the altered files. The codebase system then executes this relevant subset of tests to validate the modifications made to the files.

The invention relates to a method for detecting a coal seam interface while drilling, relates to the technical field of geological exploration, overcomes the limitation of a single physical field by using multi-modal fusion of electromagnetic waves, sound waves and gamma rays, remarkably improves the detection precision, greatly reduces the identification error of a thin coal seam compared with a traditional method, and improves the identification success rate of a complex structural region (fault and dirt band). Preliminary response within 1s is realized through underground edge calculation, decision lag is compressed to 1.5 s through ground cooperative processing, and leap of data real-time performance and predictive capacity is realized; the front 0.5-1.5 m prediction function gains time for track adjustment, and the risk of penetrating out of the coal seam is reduced. The multi-sensor redundancy design still keeps the signal-to-noise ratio not less than 20dB in a strong noise environment, the detection success rate is greatly improved when mud invasion is serious, and the environmental adaptability is high. The coal seam drilling rate is increased, invalid footage is reduced, the single well comprehensive cost is reduced, and economic benefits are remarkably improved.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention discloses an electrolytic capacitor pin defect visual inspection device, and relates to the technical field of visual inspection devices. Through cooperation of the first electric telescopic rod, the material guide plate and the triangular plate, the conveying angle and position of the capacitor can be adjusted conveniently, and the flexibility of capacitor conveying path adjustment is improved; the analysis module carries out continuity judgment and length comparison on the gray blocks, the length edge of the capacitor in the image can be accurately recognized, even if part of stains exist or the gray blocks are not continuous due to uneven illumination, the complete length edge can be obtained through extension processing, edge recognition errors caused by local defects of the image are effectively avoided, and the image quality is improved. The position judgment precision of the capacitor is improved; by dynamically calculating the deviation angle and correspondingly adjusting the exposure time and gain, the detection device can automatically adapt to different skew states of the capacitor, manual intervention is not needed, and the automation degree of detection and the universality of the device are improved.

The invention relates to the technical field of water meters, in particular to a character wheel-pointer type intelligent camera shooting water meter high-precision reading method and system. Comprising the following steps: positioning a character wheel area in a water meter image by adopting a rotating target detection model, and automatically correcting dial plate deflection by analyzing a deflection angle and geometrical characteristics of the character wheel area; and extracting a pointer and a pointer end region based on the corrected image, and calculating and obtaining a pointer reading by utilizing a long side slope of a pointer end bounding box. And finally, based on the pointer reading and the character wheel region, constructing a convolutional neural network classification model containing a carry state, and realizing digital reading by combining a dual-threshold carry correction algorithm, thereby realizing composite water meter reading. According to the method provided by the invention, high-precision reading can be realized under the influence of any deflection angle, indication error and mechanical carry precision difference, the processing speed reaches 74 milliseconds / frame, and the average pointer angle identification error is lt; and the method is particularly suitable for water meter automatic reading scenes in complex environments.

The invention discloses a laser broken line rapid identification method for a spinning process, relates to the field of spinning detection, and is used for solving the problem of high identification error rate in a multi-material blending and frequent line changing environment. A multi-channel optical behavior characteristic matrix is formed by constructing a broken line recognition mechanism based on material perception and light intensity behavior characteristic collaborative modeling, collecting light intensity response data of yarn channels, constructing a dynamic detection sequence and extracting characteristics such as shielding amplitude gradient and recovery time delay. A yarn material identifier is introduced, a spectral response template is matched, a broken yarn judgment threshold group is generated, whether shielding sudden change and delay recovery characteristics are met or not is judged in combination with a detection sequence, a broken yarn event is accurately recognized, and recognition parameters are corrected in real time according to the shielding edge change rate and fluctuation stability in light intensity changes before and after yarn breakage. The dynamic optimization of the broken line judgment condition is realized, and the recognition precision and the response speed are improved.

This invention discloses a boom identification method, a processor, a boom identification device, and a computer-readable storage medium. The boom identification method includes: obtaining the output current I of the Nth boom segment. N2 The Xth arm receives the output current I from the (X+1)th arm. (X+1)2 As the input current I of the Xth arm segment X1 and the input current I X1 The process is performed to obtain the output current I of the Xth arm segment. X2 For the output current I 12 The process involves processing to obtain identification parameters for each boom segment. Based on these parameters, the current boom segment type and connection relationship are determined, and the correctness of these parameters is then assessed. The boom identification method, processor, device, and computer-readable storage medium of this invention can identify both the boom segment type and the connection sequence of each segment, ensuring that the correct boom segment type is used and connected in the correct order. This reduces safety accidents caused by boom segment identification errors, thereby minimizing casualties and property damage.

The invention relates to the technical field of image processing, and provides a pipeline robot pipeline defect identification method and device for pipeline safety overhaul, and the method comprises the steps: obtaining continuous frames of pipeline inner wall images through the driving of a pipeline robot in a pipeline; extracting a plurality of feature points from each frame of pipeline inner wall image to obtain a plurality of matched feature point pairs in two adjacent frames of pipeline inner wall images; based on the distribution of the edges of the two adjacent frames of pipeline inner wall images and other edges, obtaining the crack possibility of each edge in each pipeline inner wall image; the crack possibility difference of the same edge in the pipeline inner wall images of the adjacent scales is analyzed, and the interval regularity of all the edges is obtained; obtaining the gray uniformity of each edge; the crack authenticity of each edge is obtained; and acquiring the crack density of each pipeline inner wall image, and carrying out pipeline defect identification and alarm based on the crack density. The invention aims to solve the problem of recognition errors caused by the fact that internal defects of a pipeline are similar to texture features.

The invention relates to the technical field of smart power grids, in particular to a detection signal design method and device for multi-injection-point and multi-load-node identification, and the method comprises the steps: calculating the static sensitivity of a plurality of to-be-identified load nodes corresponding to each target equipment injection node in a plurality of target equipment injection nodes based on steady-state operation data, establishing a detection signal optimization model taking a reactive detection signal as a decision variable by taking the total minimum load model parameter identification error of a plurality of to-be-identified load nodes as a target and combining static sensitivity, and introducing reactive energy constraints and signal structure constraints of a plurality of target equipment injection nodes and solving; generating an optimal reactive power detection signal waveform of each target equipment injection node, performing evaluation by using a target evaluation index, when an evaluation result meets a preset constraint condition, injecting the optimal reactive power detection signal waveform into the system, and after a response is generated, performing identification on a load model parameter by using the response, and obtaining an identification result of each load node to be identified.

The invention discloses a network security entity identification method based on fine-grained denoising diffusion boundary detection, and relates to the field of network security, in particular to a network security entity identification method. The objective of the invention is to solve the problems that effective generalization is difficult to realize, network security entity features are difficult to fully extract and the boundary identification error rate is high due to insufficient targeted research of the existing network security entity identification method. The method comprises the following steps of: 1, acquiring marked network security entity data; 2, constructing an entity recognition model based on fine-grained denoising diffusion boundary detection, and obtaining a trained entity recognition model based on fine-grained denoising diffusion boundary detection; and 3, obtaining to-be-detected network security entity data, inputting the to-be-detected network security entity data into the trained entity recognition model based on the fine-grained denoising diffusion boundary detection, and outputting the category of the entity by the trained entity recognition model based on the fine-grained denoising diffusion boundary detection.

The invention relates to the technical field of document processing, and discloses a document analysis inspection-free method and system, electronic equipment and a storage medium, and the method comprises the following steps: inputting a document, identifying layout elements, and adding element-level inspection-free marks to the layout elements meeting a preset credible condition; extracting text, image and path information, and recombining the text, image and path information into structured memory data according to a reading sequence; executing multi-source information fusion and rule matching, and adding a structure-level inspection-free mark to a chapter structure division result meeting a preset credible condition; attribute labeling: adding an attribute-level inspection-free mark to the text block meeting a preset credible condition; and executing post-processing operation, adding a processing-level inspection-free mark to a post-processing result meeting a preset credible condition, and storing the post-processing result as a standard structured document with the inspection-free mark. According to the method, identification errors caused by prejudice or limitation of a single model are avoided, the identification accuracy and the processing efficiency can be improved, manual intervention is reduced, and the automation level is improved.

The invention relates to the technical field of power system modeling and simulation, in particular to a load model identification error analysis method, device and equipment and a storage medium, and the method comprises the steps: obtaining the prediction output and the measurement output of a preset load model, and building a target function based on the error between the prediction output and the measurement output; decomposing the measurement output to obtain an actual output and a noise component of a preset load model; and based on the objective function and the actual output, establishing a linear model between the identification error of the preset load model and the noise component through a first-order approximation method, and performing linear regression according to the linear model to obtain an analysis result of the identification error. Therefore, by constructing the theoretical model of the load model identification error, the problems that the identification error is difficult to predict, the data processing strategy selection lacks theoretical guidance and the like in related technologies are solved, and a theoretical basis is provided for selecting an optimal load modeling data processing strategy.

The invention discloses an unformatted scale bill intelligent conversion method and system based on image recognition, relates to the technical field of character intelligent recognition, and solves the technical problems that the accuracy of subsequent OCR recognition is unstable due to the fact that the inclination angle detection precision is low and noise filtering and contrast enhancement effects are limited by scenes. According to the method, table line detection and text line analysis are adopted for the structured / no-table scale bill, the inclination angle detection precision is improved, layered denoising and adaptive contrast enhancement are achieved, the text recognition accuracy in a complex scene is improved, PaddleOCR is dynamically switched based on the Chinese proportion, a low-confidence region recheck mechanism is combined, the recognition error rate is reduced, and the recognition efficiency is improved. According to the technical scheme, a scale document field dictionary and a relation graph are constructed, intelligent term matching and error correction are achieved, the recognition problem of uncommon words and industry exclusive vocabularies is solved, a hierarchical rule system of basic verification, association verification and compliance verification is adopted, format, logic and industry / enterprise compliance full dimensions are covered, and the verification coverage rate is increased.

The invention provides a financial statement intelligent standardization processing method and system, and the method comprises the steps: constructing a hierarchical standard knowledge base which comprises a standard item name reference system and a feature vector; by taking the standard knowledge base as a reference, matching an original financial statement item name with the standard item name by adopting a weighted sequence comparison algorithm to realize preliminary matching; a matching result is optimized by adopting rule matching of context awareness; and constructing a mapping relation table of the original items and the standard items, and converting the original financial statements according to the mapping relation table to finish standardized processing of the financial statements. The problems of format differences, wrongly written characters, OCR identification errors and the like of financial statement item names can be effectively solved, the matching accuracy rate reaches 95% or above, the processing efficiency is improved by 20 times, the labor cost is remarkably reduced, and reliable technical support is provided for automatic processing and analysis of financial data.

The invention discloses a vehicle-mounted user intention recognition method and system based on a large model, and relates to a large language model. The method comprises the steps that a double-tower retrieval model DREM is constructed; constructing an intention function library containing a vehicle-mounted control function; encoding each intention function text in the intention function library by using an intention encoding encoder to obtain an intention vector set and establish an index; encoding a user query text into a query vector by using a query encoding encoder; obtaining an intention function associated with the query vector based on a hybrid retrieval strategy; wherein the mixed retrieval strategy is subjected to semantic similarity retrieval and keyword matching retrieval; constructing a prompt template according to a user query text and the obtained associated intention function; reasoning by using the large model according to the prompt template to obtain a vehicle-mounted user intention recognition result; aiming at low user intention recognition accuracy caused by ASR voice recognition noise in a vehicle-mounted environment, the fault-tolerant capability of the system for ASR recognition errors is improved.

The invention discloses a line laser anti-welding noise center point extraction and weld joint point identification method, which is applied to the field of welding quality detection and machine vision measurement. The problems of low extraction precision, poor robustness, difficulty in welding seam point positioning and the like are solved for welding spatter, light reflection and background noise influences. According to the technical scheme, the method comprises the following steps: carrying out Otsu adaptive threshold segmentation, connected domain analysis, morphological opening operation and Gaussian filtering denoising; positioning an interference abnormal line and extracting a preliminary central point by a normal centroid method, and further smoothly extracting a detailed central point in an abnormal region by fitting straight lines of 10 front and back central points to assist judgment; and by calculating a second-order difference value, fitting front and back 50 points at the maximum difference value to identify welding seam points, and optimizing a track. The method can be applied to welding seam positioning and contour detection, the accuracy and stability in a complex noise environment are improved, the welding seam point recognition error is smaller than or equal to 0.2 mm, and the actual requirement is met.

The present disclosure describes a method and apparatus for an error remediation recommendation process based on multiple data sources. Aspects of the disclosure may provide for processing of a knowledgebase to identify error remediation recommendations, processing of a chat log to identify error remediation recommendations, identification of errors from log data, determination of a corresponding error remediation recommendation from the knowledgebase and / or the chat log, and causing display of output indicating a corresponding error remediation recommendation. Further aspects described may also provide for training of an artificial neural network to determine a corresponding error remediation recommendation. Further aspects described may also provide for a browser extension which scrapes the Document Object Model (DOM) of a web page to identify errors.

The invention discloses a video end-to-end delay accurate measurement method and a system thereof, and belongs to the technical field of video processing, a multicolor frame sequence is generated through a delay box and is output to a matched display, after being acquired by a camera of a tested device, the multicolor frame sequence is displayed at a display end, and a high-speed camera acquires a tested video and transmits the tested video to a signal processing unit. The signal processing unit achieves the technical purpose of achieving full-link high-precision delay measurement from video signal generation to final display on the premise that the structure of detected equipment is not changed through RGB average value comparison, transition frame detection, unique frame sequence identification ID matching and end-to-end delay accurate calculation, frame-level time synchronization is achieved, and the measurement accuracy is improved. Identification errors caused by camera exposure change, ambient light interference and display color deviation can be effectively suppressed, so that the measurement stability and accuracy are remarkably improved.

The invention provides an automatic PDF (Portable Document Format) document desensitization system and method, electronic equipment and a computer program product, and the system comprises a data acquisition module which is used for acquiring a to-be-desensitized PDF document and a desensitization rule corresponding to at least one target desensitization object; the rule and document analysis module is used for analyzing the PDF document to be desensitized and identifying the type of a target desensitized object based on an image identification model; the multi-modal content analysis engine module is used for analyzing the specific content of the target desensitization object in the PDF document according to the identified type of the target desensitization object; the multi-modal data desensitization module is used for performing desensitization processing according to the analyzed specific content and a desensitization rule corresponding to the target desensitization object; and the PDF reconstruction module is used for reconstructing the desensitized content into a desensitized target PDF document. According to the method and the device, the desensitization error recognition error rate and the probability of incomplete desensitization are reduced, and the sensitive information desensitization efficiency is improved.

The invention discloses a mutual inductor operation error on-line monitoring system, and belongs to the technical field of detection and analysis. The method is used for solving the technical problems of secondary load and error characteristics of the mutual inductor in the existing scheme. Electrical characteristics and environmental parameters are fused through fuzzy neural reasoning, so that the identification error of error characteristic parameters can be effectively reduced; wavelet denoising and momentum item gradient descent are combined, so that the parameter updating stability and the system robustness can be effectively improved under sensor noise and temperature drift; by correcting a secondary load coupling dynamic model in real time, the goodness of fit of secondary load-error nonlinear mapping can be effectively improved; through real-time compensation of the magnetic control reactance, dynamic error suppression is realized, and ratio error drift and angular error drift caused by secondary load change can be effectively reduced; and the reactance-error mapping relation is corrected by using the dynamically updated output error characteristic parameters, so that the overall error suppression capability of modeling, identifying and compensating a closed loop can be effectively improved.

The utility model belongs to the technical field of scanners, and discloses a tracking type scanner. The tracking type scanner comprises marking modules, each marking module comprises a first marking part and a second marking part, the multiple first marking parts are sequentially and annularly arranged along the periphery of a first shaft, the first marking parts are obliquely arranged relative to the first shaft, and the second marking parts are arranged in an annular area defined by the multiple first marking parts; the center of the second marking part and the first shaft are arranged in a staggered mode, and / or a preset angle is formed between the axis of the second marking part and the first shaft, and the preset angle is not equal to 0. According to the utility model, the problems that in the prior art, due to the fact that the marking modules are distributed in a central symmetry mode, identification errors of the marking point modules are possibly caused, and errors of judgment from a certain marking point to another marking point module are possibly caused can be solved, and the identification accuracy is improved.

The invention discloses a solid tire quality detection method based on infrared and deep learning, and relates to the technical field of tire quality detection, and the method comprises the following steps: in the high-speed operation process of a solid tire, collecting a current infrared image, and extracting a high-gradient thermal texture reference point based on a phase correlation operator; and based on the extracted hot texture reference point, mapping a reference point track in a previous frame of image to the current image through adaptive optical flow tracking, and generating a hot texture original displacement vector field. According to the method, real inter-frame displacement identification is realized through hot texture reference point extraction and optical flow tracking, high-frequency disturbance is suppressed in combination with frequency domain filtering, image position reference is unified through weighted fusion and standardized offset mapping, and meanwhile, low-quality frame input is effectively judged and controlled by introducing continuous verification and dynamic weight regulation. The consistency and robustness of image splicing are integrally improved, the defect recognition error is remarkably reduced, and the detection stability and accuracy of the deep learning model in the industrial environment are guaranteed.

The invention provides a voice robot voice receiving, recognition and error correction method, which comprises the following steps: constructing an intention stability sensing unit, and realizing dynamic judgment on the state of a deliberate map in combination with multi-granularity semantic coding and sliding window variance analysis; when it is detected that the intention is unstable or multiple intentions compete, a parallel intention matching mechanism is adopted, and the accuracy of intention optimization is improved through confidence, semantic continuity and activation frequency fusion; through context significance weight and pruning strategies, historical semantic information is effectively managed, low-contribution content is compressed, and resource overhead is reduced; gating network parameters are continuously optimized on line, the intention recognition effect is improved in real time, the continuity and stability of dialogue intention modeling are enhanced, and the recognition accuracy and the system resource efficiency are both achieved.

The invention discloses a millimeter wave detection complex scene analysis method and a millimeter wave detection complex scene analysis system, relates to the technical field of millimeter wave analysis, and is used for solving the problem that the probability of misjudging a target state by a system is increased due to a target speed identification error. Calculating a radial speed of a target object; acquiring acceleration and tension data of a conveyor belt; calculating acceleration power spectral density and tension change rate; evaluating the running state of the conveyor belt; and finally, the target actual speed is obtained by combining the correction speed and the impact factor, the object state is judged according to the minimum speed resolution threshold value of the millimeter wave radar, a conveyor belt control instruction is output, and stable operation of the system is guaranteed.