676 results about "Identification error" patented technology

A method to be executed on a computing device comprising (i) accessing and / or modifying a database to be automatically curated, (ii) optionally accessing additional data or information sources for further useful data or information, (iii) using one or more pre-trained large language models (LLMs) accessed via API or other connections, issuing prompts and retrieving prompt-answers and executing database curation requests that specify database curation tasks to be performed on at least one sub-structure of the database, the tasks comprising (a) a database enrichment task to compute new data records to be inserted into the database sub-structure, (b) a database verification task to verify, using the one or more LLMs, data contained in the sub-structure, and identify incorrect data, (c) a database update, and (d) a null-value or a missing value replacement task. The requested tasks are automatically performed via a computation comprising an adaptively generated prompt sequence.

A feature extraction measurement and deviation correction device and method based on an intelligent target. The present invention comprises intelligent target design, intelligent target recognition, target center point high-precision extraction and positioning, accurate measurement and real-time deviation correction. The present invention designs an intelligent target device that is easily recognized and capable of implementing quality inspection and self-circulation monitoring, provides a feature extraction and sub-pixel matching algorithm, and establishes a setting out data and real-time monitoring target online fusion and evaluation analysis system, provides an intelligent distribution and coordination mechanism based on a target instruction, effectively improves the measurement efficiency, saves the labor cost, reduces the target recognition error rate, achieves automatic recognition and automatic deviation correction of the target, and ensures high-precision measurement of the coordinates of a center point of the target, thereby effectively ensuring the precision of fieldwork measurement.

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.

The invention discloses a voice rejection method, a vehicle-mounted voice interaction system and a vehicle. The voice rejection method comprises the following steps: acquiring multi-mode perception information; determining a current dialogue scene in the vehicle according to the multi-mode perception information; determining a target rejection threshold according to the current dialogue scene in the vehicle, and adjusting the rejection threshold of a rejection model to the target rejection threshold; and performing rejection judgment based on the rejection model after the rejection threshold adjustment to obtain an identification result, comprehensively determining the dialogue scene in the vehicle through the multi-modal sensing information, and adjusting the rejection threshold of the rejection model according to the dialogue scene, so that the rejection model can dynamically adapt to the dialogue scene to execute rejection judgment. Therefore, the recognition accuracy of the rejection model can be effectively improved, and the situations of false wake-up and recognition errors of the voice interaction system in a complex dialogue environment are reduced.

Systems and methods are provided to for identifying a mis-set, mis-calibrated, or malfunctioning thermostat. The method can include receiving ambient environmental data from at least one sensor monitoring an asset; identifying aberrative behavior in the ambient environment data; obtaining a complement of the aberrative behavior; determining a segment of normal behavior in the complement; identifying a mis-set subsequence in the segment; generating a report documenting the mis-set subsequence; and transmitting the report to a user device.

The invention discloses an S-wave seismic phase first arrival identification method based on actually measured blasting-induced particle vibration signals, and relates to the field of negative effect monitoring and safety evaluation of blasting vibration induced by rock blasting excavation, and the method comprises the steps: determining a blasting scheme based on a scanning result; arranging a blasting vibration sensor; acquiring actual measurement waveforms of blasting vibration at different blasting center distances; constructing an S-wave seismic phase first arrival recognition function; and obtaining the first arrival moments of the S-wave seismic phases of the actually measured vibration waveforms at different explosion center distances. Compared with the prior art that the recognition precision of the S wave of the blasting vibration signal under the engineering scale is low and the like, by comprehensively utilizing the differences of the S wave, a noise signal, a P wave and a P wave tail wave in the aspects of energy, frequency, polarization characteristics and the like, recognition parameters such as the ratio of transverse energy to total energy, the polarization degree and the deflection angle are introduced, and the transverse energy serves as a weight coefficient; an S-wave recognition function suitable for actual measurement of blasting vibration is provided, and the technology has the advantages of small recognition error, high efficiency and the like.

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 the technical field of territorial resource supervision, and provides an automatic law enforcement monitoring method and device based on a remote sensing image, and a storage medium. The automatic law enforcement monitoring method comprises the following steps: extracting at least one pattern spot area from a remote sensing image of a monitoring area; predicting based on pattern spot data corresponding to the pattern spot region to obtain a predicted use type; under the condition that the predicted use type is not matched with the planned use type and the predicted use type needs to be verified, obtaining a verification image corresponding to the pattern spot region; and under the condition that the pattern spot data is matched with the verification image, determining that land use abnormity exists in the pattern spot area. In the technical scheme, the pattern spot data can be further verified through the verification image under the condition that the predicted use type determined based on the pattern spot data is not matched with the planned use type, so that the influence of remote sensing image acquisition and identification errors on an abnormal judgment result can be reduced, and the accuracy of the abnormal judgment result is improved. And thus, the accuracy and reliability of automatic law enforcement monitoring can be improved.

The invention relates to the technical field of vibration construction methods, in particular to a collision object recognition method in the vibration process. According to the method, the position and collision force data are comprehensively utilized, prediction is performed in combination with the neural network model, and high-precision recognition of the type of the collision object is achieved. According to the method, the position and collision force of the collision object are monitored in real time, the neural network model is dynamically updated through the gain matrix and the covariance matrix, and the accuracy of collision object type prediction is improved. According to the technology, the safety and efficiency in the construction process can be remarkably improved, accidental shutdown or damage caused by wrong recognition of collision objects is reduced, and powerful technical support is provided for vibration construction.

The invention discloses an artificial intelligence model evaluation method and device and a storage medium, and the method comprises the steps: obtaining to-be-evaluated questions, the to-be-evaluated questions are selected from a target question bank, and the target question bank is formed by a plurality of questions passing the question quality inspection based on evaluation framework information and standard information; inputting a to-be-evaluated question into the evaluation model to obtain a target SQL and a target DSL; performing database query on the target SQL and a standard SQL associated with the to-be-evaluated question to obtain a target answer and a standard answer; and determining an evaluation result and a question positioning result based on the target answer, the standard answer, the target DSL and the standard DSL. According to the method, the questions are generated and subjected to quality inspection based on the evaluation framework information and the standard information, the high-quality evaluation questions are automatically constructed, the evaluation efficiency is improved, the evaluation result and the problem positioning result are determined based on the target answer, the standard answer, the target DSL and the standard DSL associated with the to-be-evaluated question, and reasons of wrong answers can be automatically recognized.

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 provides a chip digital layout generation method, device and equipment and a readable storage medium. The method comprises the steps of extracting contour features of a layer structure and an interlayer connection structure of a chip from a physical layout of the chip; generating a circuit netlist of the chip based on the contour features; wherein the content of the circuit netlist comprises the position information and the size information of the electronic components in the chip and the connection relation between the associated electronic components; on the basis of the circuit netlist, automatic placement of PDK devices of all electronic components can be achieved, and the digital layout of the chip is generated by further combining the metal layer pattern layer and the through hole layer pattern layer extracted from the physical layout. Through the implementation of the scheme of the invention, contour feature extraction, layer operation and device placement can be efficiently completed, so that a required digital layout is obtained; excessive manual intervention is not needed, and the identification error rate is low.

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 provides a financial statement data error correction and standardization processing method and device, equipment and a storage medium. The method comprises the steps of performing text error correction processing on target OCR text data corresponding to financial statement image data based on a text error correction model to obtain error-corrected text data; the text error correction model is formed by finely adjusting the mask language model based on different accounting criterion text data in advance; performing accounting subject standardization processing on the error-corrected text data by adopting an accounting subject standardization model to obtain standardized accounting subject data; the accounting subject standardization model is formed by training a text classification model based on a standardization data set in advance, and the data set is formed according to all accounting criterion text data and similar-form character data. According to the method, the manual participation degree and the recognition error rate of financial statement data processing can be reduced, the error correction capability and the generalization capability of the model used for financial statement data processing can be improved, and then the accuracy and the effectiveness of a financial statement data processing result are improved.

The embodiment of the invention provides a crowd gathering recognition method. The method comprises the following steps: acquiring a to-be-recognized image of a target area; performing identification processing based on the to-be-identified image to obtain the number of personnel in the to-be-identified image and a personnel identification frame; when the personnel number is greater than a preset first personnel number threshold value, performing first expansion processing based on the personnel identification frames to obtain a personnel expansion frame corresponding to each personnel identification frame; performing fusion processing based on the personnel expansion frame to obtain at least one fusion region; and based on the fusion region, determining whether crowd gathering exists in the target region. The first expansion processing can reduce identification errors caused by personnel postures or partial shielding, a more accurate personnel expansion frame is obtained, fusion processing is carried out based on the personnel expansion frame, the spatial relation between personnel can be effectively identified, a fusion area is obtained, and the fusion efficiency is improved. Therefore, whether crowd gathering exists in the target area can be accurately judged according to the fusion area, and the accuracy of crowd gathering recognition is improved.

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.