423 results about "Identification error" patented technology

The invention relates to the technical field of abnormity early warning, in particular to a transformer load abnormity early warning method and system, and the method comprises the following steps: obtaining an electrical parameter sequence and correcting a phase, constructing a current and power change rate sequence, extracting a load fluctuation trend factor, calculating a disturbance growth rate and judging whether the disturbance growth rate crosses a boundary, and recognizing a power gradient abrupt change feature. And judging whether periodic overlapping exists or not, and outputting a combined abnormity early warning identification sequence. According to the invention, by introducing phase difference and time base offset calculation, constructing a phase-aligned electrical parameter sequence group, and analyzing a periodic rate mean value and an integral quantity, disturbance trend evaluation is realized, a power gradient sequence is used to identify a section mutation interference signal, and a joint anomaly identification mechanism is constructed on two dimensions of a period and a section. The method achieves the composite judgment of the load disturbance trend and sudden change interference, effectively improves the accuracy and response time efficiency of load abnormity early warning, and reduces the recognition error risk caused by hidden fluctuation or local sudden change.

The embodiment of the invention relates to the technical field of natural language processing, and provides a text error correction method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining a to-be-corrected target text, and carrying out the semantic boundary recognition, and obtaining all semantic boundaries; segmenting the target text into a plurality of clauses, distributing IDs according to the appearing sequence of each clause, and generating structured data with sequence IDs; inputting the structured data into a language model, and calling a target domain knowledge base corresponding to the target text as a constraint condition to perform semantic analysis, and identifying an error type; adjusting an error correction path based on the error type so as to re-identify error information, the error information including error content, the error type and the error position; and recombining a plurality of clauses based on the sequence ID, the error content, the error type and the error position, and generating an error correction result containing the error position, the error type and the correction suggestion. Therefore, collaborative detection and correction of grammar, semantic and logic errors are realized.

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.

The invention discloses a multi-sensor fusion-based people flow dynamic identification method and system, relates to the technical field of intelligent traffic, and is used for solving the problems of people flow perception distortion and shielding tracking interruption in a complex environment. The method comprises the following steps: firstly, constructing an environment interference matrix, and dynamically distributing sensor weights according to a radar electromagnetic attenuation rate, thermal imaging illumination abrupt change and a piezoelectric mechanical noise factor; when the thermal imaging density weight is too low and the radar speed weight is greater than a threshold value, triggering piezoelectric gait verification and outputting calibration fusion data; mapping the fusion data to a dynamic grid, calculating three indexes of density entropy, velocity mutation and flow direction consistency, generating a congestion vector, and marking a risk boundary; for a shielding target, generating a virtual track by combining the final known position, speed and density distribution, and optimizing model parameters when a re-identification error exceeds a limit; and finally, a hierarchical dredging instruction is judged and triggered through the virtual track and the risk area space overlapping, and the gate is dynamically adjusted based on the congestion vector to release.

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 relates to the technical field of motor control, in particular to a permanent magnet synchronous motor online parameter identification system and a compensation method thereof, comprising a data acquisition module, a deep transfer learning parameter identification module, a parameter optimization module, a temperature compensation module and a controller parameter integration module. The deep transfer learning parameter identification module establishes mapping and function relations between characteristic factors and motor parameters through a teacher network, a transfer network, a memory network and an output layer, processes time sequence information and outputs a parameter identification result, and the parameter optimization module finely adjusts the parameters based on a particle swarm optimization algorithm. The temperature compensation module carries out temperature compensation on the optimized parameters according to a temperature distribution model, the controller parameter integration module generates control parameters, and the motor control module generates torque control signals according to the control parameters, so that fusion processing of multi-physical field characteristics is realized, and the precision and adaptability of parameter identification are greatly improved; and the d-q axis inductance identification error is reduced by more than 70%, and the millihenry-level precision is achieved.

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 discloses a voice navigation model training method and device, a navigation method and a vehicle-mounted navigation system. The method comprises the following steps: constructing a language database comprising a point-of-interest core word library, an easily confused word mapping list and a corrected sentence pattern template library for correcting navigation points of interest; calling a specified model to generate an annotation data set based on a pre-constructed cue word project and a language database; inputting the labeled data set into a pre-constructed navigation model for iterative training to obtain a target navigation model; the target navigation model is used for performing initial interest point recognition and initial interest point correction on a voice navigation instruction output by a user and determining a target interest point. Therefore, the core word library provides a point-of-interest vocabulary basis, and the easy-to-confuse word mapping list effectively solves the problem of recognition errors caused by non-standard pronunciation of a user and the like. Meanwhile, due to the introduction of the corrected sentence pattern template library, the interest points can be corrected, the navigation failure caused by the input error of the user is reduced, and the navigation precision is improved.

The invention relates to a circuit diagram-to-netlist generation method and system based on deep learning and a product. The circuit diagram-to-netlist generation method comprises the following steps: S1, identifying devices in a circuit diagram through a YOLOv8 model to obtain position information and category of each device; identifying a straight line segment in the circuit diagram through a probabilistic Hough transform algorithm to obtain a starting point coordinate, an end point coordinate and an orientation corresponding to the straight line segment; and S2, generating corresponding text description information according to the position information, the category, the starting point coordinate, the end point coordinate and the orientation. According to the method, by effectively integrating multi-modal information and combining image and text data, comprehensive understanding of a circuit diagram is provided, the recognition accuracy and robustness are improved, even if a single component has a recognition error, the influence of noise on the whole model can still be reduced through final information fusion, and the recognition accuracy and robustness are improved. Therefore, misjudgment and missed judgment in recognition are reduced, and the recognition accuracy of the element and the connection relation is remarkably improved.

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 image processing, and provides a reviewing method and device, electronic equipment and a storage medium, and the method comprises the steps: carrying out the text recognition of a homework image, obtaining the test question information and answer information of each test question in the homework image, and carrying out the test question answering through employing an answer model based on the test question information, obtaining a standard answer text corresponding to each test question; on the basis of the test question stem in the test question information, the student answer text in the answer information and the standard answer text, the test question is reviewed by applying the reviewing model constructed based on the large model to obtain the reviewing result of each test question, so that automatic test question answering and homework reviewing are realized, and the test question reviewing efficiency is improved. According to the method and the system, the text recognition model is adopted, answering and reviewing are performed by means of the answering model and the reviewing model, and recognition errors introduced by text recognition can be compatible, so that the influence of the recognition errors on a reviewing result can be well avoided, the reviewing accuracy is improved, the application range is widened, and the advantages of a question bank mode and an open set mode are achieved at the same time.

The invention discloses a YOLO-OCR highway engineering quantity intelligent hitching system, which relates to the technical field of highway engineering quantity and comprises a drawing engineering quantity analysis module, a structured data correction module, an engineering component intelligent hitching module and a standardized list generation module. The method comprises the steps of receiving drawing image data containing an engineering quantity table, positioning and identifying a table area to generate preliminary structured data, repairing identification errors based on context rules, associating engineering quantity information with candidate component nodes in a knowledge graph by calculating similarity, and generating a standardized contract list according to pricing specifications. And engineering quantity intelligent hooking and list generation are realized. According to the method, accurate engineering quantity recognition is achieved through the mixed visual model and semantic verification, intelligent component hooking is completed by means of the knowledge graph, data source backtracking and model self-adaptive learning capabilities are achieved, and engineering management efficiency and data accuracy are improved.

The embodiment of the invention relates to the technical field of information processing, and discloses a document recognition method, device and equipment and a storage medium, and the method comprises the steps: obtaining a to-be-recognized text image and a cue word which is used for prompting to-be-recognized information in the to-be-recognized text image; recognizing the to-be-recognized text image based on a preset orientation recognition algorithm to obtain a to-be-adjusted angle; rotating the to-be-recognized text image based on the to-be-adjusted angle to obtain a target text image; and inputting the target text image and the cue word into the multi-modal large model to obtain a first text. The orientation of the target text image input to the multi-modal large model is correct, so that the high learning ability of the multi-modal large model is fully utilized to perform document recognition. The problem that error superposition is easy to occur when the OCR is used for recognizing the document image is avoided, and the problem that the accuracy is low when a multi-mode large model is used for recognizing the document image with the wrong orientation is also avoided. And the accuracy of document identification is improved.

The invention discloses an industrial data identification method and device, equipment, a medium and a product, and relates to the technical field of data identification, and the method comprises the steps: obtaining a display image of industrial equipment; preprocessing the display image to obtain a preprocessed image; performing image segmentation processing on the preprocessed image to obtain a sub-image, and determining a target area containing character information from the sub-image; and identifying the target area to obtain text data, and correcting the text data to generate industrial data corresponding to the industrial equipment. According to the method, a data acquisition gateway does not need to be additionally arranged on the industrial equipment, the problem that the operation efficiency of the equipment is influenced is avoided, the text data of the industrial equipment can be accurately identified, identification errors are effectively reduced, and the identification accuracy of the industrial data is further 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.

The invention relates to the technical field of information security identification, and particularly discloses a multi-modal data artificial intelligence security classification identification method in combination with transfer learning, which comprises the following steps of: in a process of constructing a multi-modal behavior data set, acquiring file access frequency and network connection frequency in real time; fluctuation characteristic values are extracted through fast Fourier transform and a K-means clustering algorithm and used for evaluating internal threat and external attack risks faced by the system, the two types of characteristics are fused into a comprehensive characteristic vector, the comprehensive characteristic vector serves as input to construct a transfer learning model based on a support vector machine, intelligent recognition of the safety state of a target object is achieved, and the safety of the target object is improved. In order to improve the recognition accuracy, a proportional-integral-differential control mechanism is introduced into the system, the sampling period and the detection granularity are dynamically adjusted according to recognition errors, a closed-loop feedback optimization structure is formed, effective integration of multi-source heterogeneous behavior data, cross-scene migration of the model and online self-adaptive adjustment are achieved, and the recognition accuracy is improved. And the intelligent level of the security identification system is 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.

Systems, methods, and other embodiments associated with error resolution and auto-correction are described. In one embodiment, a method includes, for a selected application that was executed by the computing system, accessing a system error log and identifying error messages that occurred during execution. Skipped data records that were not processed are identified. A resolving function is executed to resolve a selected error type that is associated with a group of skipped data records by: identifying an error pattern from an error message and matching it to a database of observed error patterns. In response to a match, retrieving a corrective action that is assigned to the observed error pattern and executing the corrective action to resolve the error on the group of skipped data records. The skipped data records are re-submitted and reprocessed where previous error should be resolved.

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 concrete crack measurement, and particularly discloses an automatic concrete crack width measuring instrument which comprises a supporting bottom plate and a plurality of moving wheels arranged at the bottom of the supporting bottom plate, and further comprises a measurer used for measuring and detecting a crack on a concrete wall surface; according to the device, multiple functions such as height adjustment, angle adjustment, rapid installation and lubrication are integrated in a compact structure, a worker only needs to push the device, the measurer can be moved according to the action of the moving wheels, and compared with a mode that the worker holds the measurer to measure a concrete wall surface, the device is more convenient to use, and the working efficiency is improved. Therefore, the working fatigue of a worker for measuring the concrete wall surface is correspondingly reduced, and meanwhile, under the condition that long-distance measurement is performed or a target point is not easy to aim, the problem of angular deviation or target point identification error caused by the shaking effect of the arm of the worker when the worker holds the measurer in a hand is avoided, and the working efficiency is improved. Therefore, the working efficiency of measuring the concrete wall surface by a worker is improved.

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.