53 results about "Fuzzy recognition" patented technology

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention discloses a weighing sensor test compensation method based on fuzzy recognition, and relates to the technical field of sensor test compensation, and the method comprises the following compensation steps: inputting an original reading of a weighing sensor into a calculation module, and outputting a final compensated reading by the calculation module. According to the invention, the influence of various environmental factors such as ground flatness, power supply stability, temperature, humidity, air pressure, altitude and the like on reading is comprehensively considered, preliminary compensation and comprehensive compensation based on position and time are realized, and the adaptability and measurement precision of the weighing sensor under different environmental conditions are remarkably improved; a vibration correction coefficient is introduced to effectively compensate the influence of vibration on reading, the direct influence of vibration strength is considered, the compensation strength is dynamically adjusted through a modulation factor, the compensation effect is more accurate and effective, automatic adjustment and optimization of compensation parameters can be achieved, and the method is suitable for large-scale popularization and application. Therefore, the weighing sensor can automatically adapt to a complex and changeable measurement environment.

The invention discloses a chip design and manufacturing optimization system and method based on edge reinforcement learning, and relates to the field of data analysis, and the system comprises a scene recognition module, a strategy selection module, a decision correction module and a cloud updating module. A fuzzy recognition and alarm mechanism is introduced to enhance the adaptability; inter-process linkage optimization is realized by means of a double-index strategy library and an influence gradient transmission mechanism, and the process collaboration is improved; through dynamic uploading of the state tetrad and cloud oscillation identification, the system can automatically enter cold-quiet period locking under strategy abnormity and energy consumption fluctuation, and abnormal diffusion is effectively inhibited. Meanwhile, a lightweight edge deployment and reinforcement learning mechanism enables the system to have high response, low communication and self-learning capabilities, and the system is suitable for a large-scale complex chip manufacturing environment.

The invention discloses a knowledge graph data intelligent question and answer method and system based on voice activation, and the method comprises the steps: obtaining a voice instruction inputted by a user side, and converting the voice instruction into a natural language text; semantic fuzzy recognition is conducted on the natural language text, and then whether semantic fuzzy exists in the natural language text or not is determined; if yes, generating a voice prompt problem by using a standard entity in the knowledge graph and a corresponding known attribute, obtaining a correction text fed back by a user based on the voice prompt problem, performing named entity recognition on the correction text, and extracting a main entity and a target keyword; if not, named entity recognition is carried out on the natural language text, and a main entity and a target keyword are extracted; calculating the semantic similarity between the known attribute of the standard entity corresponding to the main entity in the knowledge graph and the target keyword; and determining a user intention based on the semantic similarity, generating a query statement for executing a corresponding query operation in the knowledge graph database, and obtaining a corresponding query result.

The invention discloses a camouflage target segmentation method based on multi-scale cross-spectral feature interaction, and belongs to the technical field of computer vision. The implementation method comprises the following steps: 1, constructing a camouflage target detection network; 2, obtaining visible light and infrared dual-mode features for the network subjected to feature extraction; 3, performing multi-layer feature fusion on the visible light image features and the infrared image features; therefore, bimodal complementary information extraction is realized; 4, constructing an edge sensing network for performing detail processing on the edge of the image so as to obtain an edge prediction result; 5, training the camouflage target detection network to obtain training parameters of the camouflage target detection network; optimizing training parameters of the camouflage target detection network; 6, inputting the image pair into the training optimization camouflage target detection network to obtain a predicted camouflage target detection result; compared with the prior art, the accuracy of boundary fuzzy recognition and appearance similarity recognition of camouflage target detection is improved.

The invention provides a foreign language slang teaching device with a fuzzy recognition function and a use method, and relates to a foreign language teaching device. Comprising a mounting base, a learning machine body mounted on the mounting base and a supporting mechanism for supporting the mounting base, and one end of the learning machine body is hinged to the top of the mounting base through a pin shaft. According to the device, by driving the earphones in the storage box, the headphones can transmit speech during oral training to the speech recognition module in the learning machine body through the microphones on the earphones for recognition, and sliding of the sliding frame on the mounting base is adjusted through the adjusting mechanism; the learning machine body is pushed to turn over under the action of the connecting rod, the observation and use angle of the touch screen display is adjusted, students can observe whether their mouth shapes are correct or not through the arranged screen when doing oral language practice, the students are further assisted in oral language learning, and the teaching effect is improved.

The invention discloses a centrifuge liquid flow detection system based on a fuzzy recognition and classification technology, and relates to the technical field of centrifuge flow detection, and the centrifuge liquid flow detection system comprises the following steps: collecting an image set, and carrying out preprocessing and marking processing to obtain a segmentation flow rate; obtaining characteristic parameters; obtaining a preferred fuzzy model; characteristic parameters of the real-time liquid image are obtained, and flow detection is conducted on the real-time liquid; the method is used for solving the problems that in an existing method for detecting the liquid flow, when the liquid surface is irregular or liquid fluctuation exists, the accuracy and stability of data received by a sensor can not be affected, and consequently the collected liquid flow is different from the actual liquid flow; and the operation of the centrifugal machine is influenced.

The invention discloses a protection device constant value checking method and system. The method comprises the following steps: processing collected first operation constant value data to generate recognizable granulated constant value data; the centralized control station receives the data and establishes a setting value sheet identification model and a shared setting value sheet template library so as to perform data extraction and identification; defining a first fuzzy recognition rule based on the shared constant value single template library; acquiring second operation constant value data, and matching and checking the second operation constant value data through the first fuzzy recognition rule; and if the error of the matching and checking result exceeds a first threshold value, the centralized control station feeds back an abnormal result of the protection device, and checks an abnormal reason of the protection device according to the abnormal result. Aiming at comparison and check between the setting fixed value and the operation fixed value, the system fault of the power grid is reflected in time, the maintenance amount of operation maintenance personnel is reduced, the input cost of a transformer substation printer and maintenance of the transformer substation printer is reduced, carbon emission is reduced through paperless operation and inspection, and remarkable social and economic benefits are achieved.

The invention discloses an image detection and recognition method based on data analysis, and relates to the technical field of image detection, and the method comprises the steps: determining an object recognition result corresponding to a to-be-detected weight ratio as a final recognition result if the to-be-detected weight ratio with the same number as a preliminary object recognition result is greater than or equal to a weight determination threshold value through statistics; if the weight ratio to be measured is smaller than a weight judgment threshold value and only different results which can be directly recognized exist in different object recognition results, image object recognition after error adjustment is carried out on a preset brightness simulation adjustment parameter set, or when a fuzzy recognition result exists in a difference result to be measured, image object recognition is carried out on the preset brightness simulation adjustment parameter set. If not, segmenting a to-be-recognized intercepted image from the to-be-detected image, and performing image object recognition after different degrees of error adjustment on brightness simulation of an edge object and a center object in the to-be-recognized intercepted image so as to judge a final recognition result. The image detection and identification method based on data analysis provided by the invention can improve the efficiency of image detection work.

The invention discloses a tower crane bolt axial force intelligent ultrasonic detection method and system based on multi-modal data fusion. Firstly, a multi-stage influence factor system is constructed based on a fuzzy hierarchical comprehensive evaluation theory, and a high-risk region identification model is established; and calculating a regional risk value based on the model, and determining a monitoring point distribution scheme. A heterogeneous network composed of a fiber grating strain sensor and an ultrasonic sensor is arranged in a selected area, and strain and ultrasonic data are synchronously collected. And inputting the multi-modal data into a deep belief network for feature fusion, and realizing health state dynamic evaluation through a fuzzy recognition model. And based on an evaluation result, performing fatigue life prediction by adopting an improved rain flow counting method and an accumulative damage theory. And finally, data fusion display and intelligent early warning are realized through the remote monitoring platform. According to the invention, full-chain intelligent health management from risk identification, real-time monitoring to life prediction is realized.

The invention relates to the field of transformers, in particular to a transformer state comprehensive judgment method and system based on a digital gas relay and a medium. The invention provides a weight adaptive fuzzy recognition comprehensive state evaluation method combining an industry score deduction criterion and a wooden barrel effect on the basis of data such as temperature, pressure, gas volume and gas production rate of a digital gas relay and online monitoring data such as oil chromatography, iron core grounding current, partial discharge and vibration. And early warning of major faults is realized. The action reliability of the digital gas relay is improved, and the alarming and tripping accuracy of the gas relay is improved.

The invention relates to the field of character and image recognition, in particular to a multi-mode intelligent dictation and correction system and method based on dynamic calibration, and the system comprises an acquisition module for obtaining a writing test image based on voice feedback and carrying out character recognition, a feature splitting module for constructing a feature observation group and labeling a structural unit, the layout analysis module selects a recombination structure by sliding a window frame and sets a fuzzy identification tag based on the prior probability of spatial distribution features, and the identification module compares contour features through region mapping to determine difference types. And the calibration module realizes context restoration and dynamic calibration by removing fuzzy blocks, performing mapping comparison with a sample database and replacing contour features. According to the method, the recognition accuracy of complex handwriting such as scribbling and stroke adhesion is improved, and on this basis, the local image content is calibrated through a replacement mechanism based on context restoration, so that the accuracy and reliability of the correction process are improved.

The present invention discloses a chip design and manufacturing optimization system and method based on edge reinforcement learning, which involves the field of data analysis and includes a scene recognition module, a strategy selection module, a decision correction module, and a cloud update module. The present invention realizes robust recognition of the equipment operating status through a state-aware feature channel and a confidence scoring mechanism, and introduces a fuzzy recognition and alarm mechanism to enhance adaptability; with the help of a dual-index strategy library and an impact gradient transmission mechanism, it realizes inter-process linkage optimization and improves process synergy; through the dynamic upload of state quadruple and cloud-based oscillation identification, the system can automatically enter a cool-down period lock under policy anomalies and energy consumption fluctuations, effectively suppressing the spread of anomalies. At the same time, lightweight edge deployment and reinforcement learning mechanisms enable it to have high response, low communication and self-learning capabilities, suitable for large-scale complex chip manufacturing environments.

The application discloses a hybrid vehicle energy management device, which comprises a driving style recognition module, a typical road condition recognition module and an energy management adaptive module; the driving style recognition module is used for acquiring driving style reference data in a preset time period, inputting the driving style reference data into a driving style fuzzy recognition model, and acquiring a current driving style output by the driving style fuzzy recognition model; the typical road condition recognition module is used for acquiring driving characteristic parameters of a vehicle in a preset time period, calculating driving characteristic similarities corresponding to various typical road conditions according to the driving characteristic parameters and various corresponding driving characteristic parameters of the various typical road conditions, and taking a typical road condition corresponding to a maximum driving characteristic similarity as a current road condition; and the energy management adaptive module is used for acquiring the current driving style and the current road condition, formulating an electric quantity balance point according to the current driving style and the current road condition, and selecting a proper vehicle driving mode according to a relationship between a current residual electric quantity and the electric quantity balance point.

This invention relates to a DNN fuzzy recognition model training method. The method involves extracting features from input sample objects using an object-oriented DNN fuzzy recognition model, mapping these features to obtain the object's classification probability distribution, and mapping these classification probabilities to membership degrees of fuzzy sets in a fuzzy sample model library. The model is trained by calculating a loss function value based on the classification probability distribution using sample annotations. After one generation of training, the model is adjusted by calculating a loss function value based on the fuzzy set proximity using sample annotations. If the result of either loss function calculation does not meet the preset loss value requirement, the samples in the fuzzy sample model library are randomly shuffled and reordered, and the calculation is iteratively repeated until the results of both loss function calculations meet the preset loss value requirement. This invention also relates to a method and apparatus for identifying hydrological and meteorological elements using a model trained using the above method. This method can simulate crew members automatically identifying hydrological and meteorological elements, improving the accuracy of identification and significantly reducing the workload of crew members.

The invention discloses a picture recognition management system based on video monitoring, and relates to the technical field of image management. The image processing unit is used for collecting image features of the basic data and carrying out gray processing on the image features, the image recognition management system based on video monitoring can enhance the image features by adjusting the foreground and background of a gray image, detail optimization can be carried out when character feature recognition blurring exists in a live broadcast image, and the image recognition efficiency is improved. The quality of the live broadcast picture is enhanced, and continuous adjustment is performed on the enhanced image features, so that the situation that the attention of the live broadcast room is not on a product and public opinions are caused due to sudden picture adjustment can be effectively avoided, a good live broadcast picture effect can be ensured, the attention of the live broadcast room can be reduced, and the user experience is improved. And the adjustment sequence of the plurality of image pictures is analyzed under the delimited rule, so that the pictures can be managed in a unified manner, and the value maximization of live broadcast picture optimization is ensured.

The invention discloses a blurred image recognition method and device, computer equipment and a storage medium. A to-be-recognized original image is obtained. And inputting the original image into a pre-trained image classification model, analyzing the original image by the image classification model, and outputting an original image type corresponding to the original image. Calculating a plurality of Laplacian operators corresponding to the original image and a first Laplacian variance corresponding to each Laplacian operator; and calculating a mean value of the plurality of first Laplacian variances corresponding to the original image as an original mean value. And obtaining a contrast mean value of the clear image corresponding to the original image type in a preset clear image library. If the original mean value is smaller than the contrast mean value, determining that the original image is blurred. Compared with an existing universal fuzzy recognition method, the method has the advantages that the preprocessing step of image rotation convenience and the preposition step of image classification are added, in the fuzzy recognition process, for each subdivision category, an independent Laplacian variance threshold value is adopted for judgment, and the fuzzy recognition accuracy is greatly improved.

The embodiment of the invention relates to the technical field of artificial intelligence, and discloses a method and device for interacting with character customer service, electronic equipment and a storage medium. The method comprises the following steps: acquiring user input data, and preprocessing the user input data to obtain target input data; inputting the target input data into a target language representation model generated by pre-training to obtain prompt and supplementary suggestion information input to the user; performing fuzzy recognition on the user input data to obtain fuzzy input data; according to prompt and supplementary suggestion information corresponding to the fuzzy input data, uncertainty measurement of model identification is carried out; and when the uncertainty measurement result meets a model uncertainty optimization condition, performing model optimization on the target language representation model, performing character customer service interaction by adopting the optimized target language representation model, and performing character customer service interaction by optimizing the model when the fuzzy input data cannot be accurately recognized by the model. The fuzzy input data processing performance of the model can be improved, and the recognition accuracy is improved.

The invention relates to the related field of document data engines, in particular to a document data engine method and system, which realizes high-robustness field extraction capability by combining a template engine with an AI model, and adapts to multiple types of unstructured documents; field splitting / combination is intelligent, and manual intervention is reduced. Automatic mapping of standard fields and alignment of platform fields are achieved, the adaptation relation between an industry field standard library and multi-system fields is constructed, and the fields can be directly pushed to multiple service systems from original samples; field data quality and consistency guarantee capability are improved, and output fields are more accurate due to field-level difference comparison and format restoration functions; the AI completion and fuzzy recognition mechanism solves the problem that fields are not covered or uncertain in the traditional recognition technology. The system has four characteristics of standardization, rule driving, model assistance and dynamic updating, and the manual operation intensity and the business risk are remarkably reduced.

The present invention relates to the technical field related to factory manufacturing, and specifically includes a method and device for intelligent selection of factory automation parts, including: establishing an intelligent selection platform, including input, adjustment, and fuzzy recognition ports; the platform integrates user input data to generate application scenarios, and corrects and compensates within a preset time; activates fuzzy recognition, collects fuzzy and historical data, parses and reconstructs a database, matches parts, and completes intelligent selection, which solves the technical problems of a wide variety of parts involved in factory automation and a lack of an adaptive optimization mechanism, resulting in poor flexibility in part selection and affecting the overall processing and manufacturing efficiency of the factory, and integrates data from different sources, realizes efficient automatic selection through a fuzzy recognition port and a historical matching call function, and performs automatic compensation and correction according to a priority database to improve the accuracy of selection.

This application provides a digital grading method and apparatus for paper-based assignments. The grading method includes: performing adaptive partitioning on an assignment image containing student information to obtain a question area and an answer area; the answer area includes an objective filling area and a subjective writing area, wherein the subjective writing area includes a handwritten text area and a handwritten formula area; based on the content characteristics of the target area, calling a detection and recognition model that matches the content characteristics to obtain the recognition result of each answer area; for fuzzy recognition results of the subjective writing area, introducing the writing style characteristics of the current student information to correct the fuzzy recognition results, and updating the recognition results of the subjective writing area based on the correction results; comparing the final recognition results of each answer area with the assignment answers to generate and output the grading results. The grading method and apparatus of this application form a fully automated process from image input to grading result output, requiring no manual intervention and achieving effective automatic grading.

The invention relates to an elevator car scheduling method, device and system based on a spatial mode. The elevator car scheduling method based on the spatial pattern comprises the steps that target detection is conducted on an image when a car stops so as to extract object information, and the occupied capacity is estimated based on an object mobility rule; whether a historical scene is matched or not is judged in combination with the case library, if yes, a corresponding target function is directly selected, and if not, traffic flow prediction is conducted through object information of the lift car, lift car direction information and historical data, fuzzy recognition is conducted through the fuzzy rule library, and a current traffic mode is determined; then, an objective function is selected according to a use mode or a traffic mode of a historical case, optimization solution is carried out in combination with the current occupied capacity, and an optimal lift car scheduling scheme is generated. According to the elevator car scheduling method based on the spatial pattern, the problems of inaccurate scheduling and low efficiency caused by neglecting the capacity factor are effectively solved.

The invention relates to the technical field of self-healing and repairing assistance, and discloses a self-healing and repairing assistance system based on a four-start system, and the system comprises a four-dimensional data collection module which collects the initial four-dimensional data of a user; the four-dimensional data analysis module is used for carrying out independent scoring and correlation analysis on the initial four-dimensional data and determining the short plate dimension of the user; the self-healing repair scheme generation module is used for generating a self-healing repair scheme according to the short plate dimension and the intervention strategy library; the scheme feedback adjustment module is used for acquiring feedback data of the user for executing the self-healing repair scheme and dynamically adjusting the self-healing repair scheme; and the repair effect evaluation module is used for obtaining the final-period four-dimensional data of the user, comparing the final-period four-dimensional data with the initial four-dimensional data, and determining the repair evaluation effect of the self-healing repair scheme. The technical bottlenecks of multi-dimensional cutting, fuzzy recognition, strategy stiffness, fault assessment and the like in traditional growth intervention are solved, and the functions of active prevention, accurate repair and system collaboration are achieved.

The invention discloses a secondary confirmation method for opening and closing positions of an isolating switch of a photovoltaic power station based on fuzzy recognition, which comprises the following steps of: 1, shooting the isolating switch of the photovoltaic power station by using a high-precision camera to obtain images of three states of the opening and closing positions of the isolating switch; 2, marking three state target areas of the opening and closing position of the disconnecting switch of the photovoltaic power station in the image by using a marking method, and constructing a fuzzy function and a fuzzy set of three states of the disconnecting switch of the photovoltaic power station; 3, performing fuzzy clustering operation on the current photovoltaic power station disconnecting switch image and the marked three state images by using a clustering algorithm in fuzzy recognition; 4, identifying the opening and closing position of the disconnecting switch in the current image according to the principle of proximity, and completing the secondary confirmation work of the opening and closing position of the disconnecting switch of the photovoltaic power station; the problem of secondary confirmation of the opening and closing positions of the disconnecting switch of the photovoltaic power station is effectively solved.

Methods and systems are described herein for identifying the location and nature of any blur within one or more images received as a user communication and generating an appropriate correction. The system utilizes a first machine learning model, which is trained to identify blurred components of inputted images and determine whether the blurred components are located in portions of the inputted images comprising textual information. The system may apply a corrective action selected by the first machine learning model, which may comprise stitching blurred images together to a sharp product image and / or some other method appropriate for rectifying images received.

The invention discloses a hydraulic structure safety state evaluation method based on multi-level fuzzy recognition, and relates to the technical field of hydraulic engineering safety monitoring, and the method comprises the following steps: pre-constructing a multi-level evaluation index system which comprises a target layer, a criterion layer and an index layer; by introducing trapezoidal and triangular membership functions, the limitation that a traditional accurate mathematical method cannot describe the continuous safety level is broken through, and the evaluation result is made to better conform to the gradual change rule of underwater engineering damage development. Meanwhile, scattered local data is converted into a unified global safety level through fuzzy comprehensive operation, the data utilization rate and evaluation efficiency are greatly improved, a whole-process scientific support from risk identification, level judgment to maintenance scheme selection is provided for engineering managers, the probability of occurrence of safety accidents is remarkably reduced, and the engineering management efficiency is improved. Reliable technical guarantee is provided for safety management of the whole life cycle of the water conservancy project.

The application discloses a neurosurgery operation scene intelligent understanding method, which is realized through a trained neurosurgery operation scene intelligent understanding network; the network comprises a time coding layer and a space coding layer, and the output end of the space coding layer is connected with at least one fuzzy attention module; the time feature output by the time coding layer and the fuzzy space feature output by the fuzzy attention module sequentially pass through the cross-modal fusion operation of a decoding layer and the dimension reduction processing of a feedforward neural network, and output an operation stage recognition prediction result; the fuzzy attention module evaluates and quantifies the reliable degree of the space visual feature at each position in the feature sequence output by the space coding layer, dynamically adjusts the attention weight and gives the space visual feature to form a fuzzy space feature; the method effectively improves the recall rate and Jaccard coefficient while maintaining high accuracy and high precision, and proves that the method has the effect of significantly optimizing the fuzzy recognition and prediction accuracy.

This invention relates to the field of speech recognition technology, and discloses a speech recognition method, apparatus, device, storage medium, and computer program product. The method includes: constructing a hash mapping table; acquiring the speech to be recognized and recognizing it using a preset speech recognition model to obtain a conventional recognition result; performing precise and fuzzy recognition on the speech to be recognized based on the hash mapping table to obtain precise and fuzzy recognition results; and calculating the target speech recognition result based on the conventional, precise, and fuzzy recognition results. This invention solves the technical problem of poor speech recognition performance in specialized fields or specific industries by calculating the target speech recognition result using the conventional recognition result obtained from the preset speech recognition model and the precise and fuzzy recognition results obtained from the hash mapping table.

The invention relates to the technical field of voice recognition, in particular to an LED multi-dimensional information issuing method based on voice intention recognition, which comprises the following steps of: decoding an input voice signal to generate a decoded word graph, positioning a preset LED multi-dimensional information semantic slot position in the decoded word graph and extracting similar competition paths; and calculating to obtain a high-confidence ambiguity candidate set. When voice signals are processed, similar candidate word combinations can be extracted from an ambiguous recognition path by constructing a decoding word graph and positioning LED multi-dimensional information semantic slots, posterior probability differences are further calculated to form an ambiguous candidate set, and a clear distinguishing basis is introduced in a scene with similar recognition precision but semantic conflicts, so that the recognition accuracy of the voice signals is improved, and the recognition accuracy of the voice signals is improved. And fuzzy recognition caused by path divergence in the word graph is relieved. The clarified questions embedded with the competition words are synchronously presented in a voice and LED dual-channel form, so that the user feedback is more targeted, and the ambiguity clarification efficiency is improved.

The invention discloses an instruction data processing system and method based on multiple scenes. According to the instruction data processing system and method, a basic knowledge system is constructed based on preset entity definition data and entity relation data through a data system module; instruction input of a target object is obtained through a data recognition module, and recognition processing is performed on the instruction input based on the basic knowledge system to obtain a recognition result; according to the embodiment of the invention, multi-scene recognition and recognition in a single scene are adopted, then clear recognition and fuzzy recognition are adaptively determined, scene clustering, heuristic labeling, high-frequency words and sentences and generalization processing are utilized to realize the instruction recognition method, and by constructing the data system module and designing the multi-scene recognition module, the recognition efficiency is improved. The system can better understand diversified and complex user input, the accuracy of entity recognition is improved, and the method can be widely applied to the technical field of data processing.