170 results about "False recognition" patented technology

The invention relates to the technical field of wireless, and provides a target sensing method, device and equipment, a storage medium and a program product, the target sensing method is applied to network equipment, and the method comprises the following steps: determining type information of a sensing target in a sensing result based on the sensing result and RID information under the condition that the sensing result and the RID information exist; wherein the sensing result is obtained by performing target sensing by the network equipment; the RID information is sent by the RID receiver. According to the invention, under the condition of determining that the sensing result and the RID information exist, the RID information can be used as the auxiliary information to assist the base station to carry out data processing in the sensing result, and the type information of the sensing target in the sensing result is accurately determined, so that the sensing result and the RID information are fused, and the sensing accuracy is improved. The method can solve the problem that the general sensing integrated system is easy to mistakenly recognize the target type when detecting the target, thereby improving the credibility of the detection result, and guaranteeing the reliability of the general sensing integrated system.

The invention relates to a data correction method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring an original structured statement and original annotation information; determining knowledge data corresponding to the original structured statement in a target knowledge database based on a metadata query engine; the knowledge data comprises graph relation metadata and semantic vector metadata; according to the data correction large model, the graph relation metadata and the semantic vector metadata, error recognition is carried out on the original structured statement and the original annotation information, data correction is carried out on error data obtained through error recognition, and a target structured statement and target annotation information are obtained. By adopting the method, the accuracy of structured statement and annotation error correction can be improved.

The invention discloses a living body collateral state monitoring method and a related device, and relates to the field of data processing, and the method comprises the steps: obtaining the acoustic features, image features and positioning features of a monitored object, recognizing the scene of the environment where the monitored object is located at present, and determining the target weight distribution matched with the scene. And carrying out weighted fusion on the obtained acoustic features, image features and positioning features of the monitoring object based on the target weight distribution to obtain multi-modal fusion features. And finally, identifying the abnormal state of the monitored object based on the multi-modal fusion feature. According to the method, the complementary effect of the acoustic features, the image features, the positioning features and other dimension features is utilized, the recognition accuracy is improved, and the false recognition rate is reduced. Moreover, the scheme can identify the scene of the current environment and automatically adjust the weight distribution of each modal feature according to the scene characteristics to generate the unique fusion feature vector, and the robustness of the model to the environment is improved.

A system and method for automated defective data record resolution is disclosed. The method is performed by a remote digital claim remediation service operating within a distributed network of computers. The method includes receiving adverse digital claim data comprising a plurality of data fields, where at least one data field contributes to an adverse action against a target digital claim. A feature extraction model processes the claim data to extract defect feature data. A machine learning model then computes defect inferences, identifying likely defect types and associated confidence values. The method further evaluates these inferences against predefined routing criteria, routing the claim data either to a remediation or disposal queue. For remediable claims, a machine learning model generates remediation inferences, which are applied to adapt and correct the claim. The defect-free claim is then resubmitted in real-time or near real-time, accelerating claim processing and enhancing system efficiency.

The invention relates to the technical field of BIM intelligent design and deep learning, in particular to a graph structure representation and model error recognition method and device of a BIM model, and the method comprises the steps: extracting semantic features, spatial features and topological features among various types of components according to the component information of different types of components in the BIM model; generating corresponding graph structure representation based on the semantic features, the spatial features and the topological features, and constructing a training data set by using the graph structure representation; and training the pre-training graph neural network model by using the training data set to obtain a trained training model, and carrying out transfer learning by using the trained training model to identify an error identification target of the BIM model by using a transfer learning model. Therefore, the problems that in the related technology, specific complex semantics, space and topological features of the BIM bring challenges which are difficult to neglect, and the BIM cannot be perfectly suitable for a pre-training and transfer learning model of the features of the BIM are solved.

The invention belongs to the technical field of medical rehabilitation, and discloses a rehabilitation treatment monitoring system based on multi-modal sensing and AI evaluation. A multi-dimensional skeleton key point confidence evaluation mechanism is established by integrating three types of sensing data of visual skeleton tracking, an inertial measurement unit and pressure distribution, and potential tracking interruption risk frames and error recognition candidate frames are recognized in real time. And a multi-modal fusion correction model is adopted to carry out accurate compensation on error recognition, missing data are reconstructed in combination with a skeleton motion mode memory bank and a bidirectional time sequence prediction algorithm, and a continuous and complete all-day skeleton motion sequence is generated. On the basis of the calculation, the time-phased activity level index and the rehabilitation action completion degree score of the patient are calculated, and an objective and quantitative rehabilitation evaluation basis is provided. According to the invention, the stability and continuity of skeleton tracking in a complex rehabilitation environment are improved, so that a medical team can obtain a real and complete all-day activity portrait of a patient, accurately position a rehabilitation bottleneck, formulate a personalized treatment scheme and promote functional recovery of the patient.

The invention discloses a man-machine text data set construction method and system for AI generated text detection, and relates to the technical field of text detection.The method comprises the steps that a human text is obtained and preprocessed, and metadata annotation is conducted on the preprocessed human text; selecting a plurality of language models, generating an intelligent text by configuring different generation parameter combinations, and performing metadata annotation on the intelligent text; and fusing the human text and the intelligent text subjected to metadata labeling, constructing a structured human-computer text data set, and dividing the human-computer text data set. According to the method, high-quality and large-scale data collection of the data set is ensured through data cleaning, quality screening and source labeling, rich learning samples are provided for the model, misjudgment caused by the quality problem of the existing data set is avoided, the accuracy rate, the precision rate and the recall rate can be improved, the risk of misrecognition is reduced, and the recognition efficiency is improved. And the reliability of detection results is enhanced.

The invention discloses a children voice expression error recognition and correction method based on comparative learning, and the method comprises the steps: carrying out the preprocessing of an inputted children voice signal, obtaining a logarithmic Mel spectrum feature sequence, and converting the logarithmic Mel spectrum feature sequence into voice semantic coding features through an improved Transform encoder; on the basis of an age-adaptive positive and negative sample selection mechanism, voice features are optimized by using a comparative learning method, and an enhanced voice representation vector is obtained; constructing a multi-modal fusion network, combining an enhanced voice vector and BERT language model features, realizing adaptive fusion through multi-head cross attention and a gating mechanism, adopting a bidirectional LSTM to design an error positioning module to recognize the position and the type of a pronunciation error, and using a joint loss function to execute end-to-end training; standard correction audio is generated according to the error type, and correction guidance is provided for children. According to the invention, high-precision recognition, positioning and correction of children's speech expression errors are realized, and technical support is provided for children's language development.

The invention provides an application packaging processing method, a storage medium and electronic equipment, and is applied to the technical field of software application. According to the method, a full-process closed-loop mechanism of the front stage, the middle stage and the rear stage of packaging is constructed; before packaging, application codes and key parameters are subjected to security detection; in the packaging process, grammar and semantic errors are deeply recognized through a regular engine, an abstract syntax tree and data flow analysis, and a diagnosis report is generated; and after packaging, executing an automatic test by utilizing the matched test case to obtain an application test result. Based on the diagnosis report and the test result, whether repackaging is needed or not is intelligently analyzed, packaging parameters and test cases are adjusted according to the error types and fed back to a safety detection link in a circulating mode, automatic error recognition and self-adaptive optimization are achieved, and therefore the safety and reliability of application packaging are effectively improved, and the development risk is reduced.

A method or system for enhancing vehicle identification accuracy. The system identifies a gap in a training dataset for training a license plate identification model. The gap represents underrepresented visual characteristics in misidentified license plates. A guidance prompt is generated based on the visual characteristics of the misidentified license plate. Condition embeddings are then generated from the guidance prompt, which are used to condition a diffusion model to create synthetic license plate images. The diffusion model is trained to receive a real license plate image, encode it into a vector, apply forward diffusion to add noise, and then apply reverse diffusion to remove the noise, resulting in a denoised vector that represents a synthetic license plate image conditioned by the condition embeddings. The denoised vector is then decoded to produce the synthetic license plate image. The synthetic images are then used to retrain the license plate identification model.

The present application relates to the technical field of face recognition, and particularly relates to a recognition method and system based on a dynamic face database, a computing device and a storage medium, comprising a face tracking server configured to acquire face information obtained by a channel camera and a regional camera, and to merge the face information obtained by the channel camera and the regional camera; a central face server configured to store face information left by a user when registering remotely, and to respectively identify face information collected by the face tracking server from the channel camera and the regional camera, and to send the identification results to a channel face server and a regional face server; the present application reduces the number of face databases by using face tracking and face recognition technology, forms a face server with multiple face databases, improves the recognition efficiency by accurately simplifying the face databases, and reduces the false recognition rate of face recognition.

The embodiment of the invention discloses a wrongly written character processing method, device and equipment, and the method comprises the steps: receiving text data of a target domain input by a user, the text data comprising special words of the target domain; performing word segmentation processing on the text data through a preset word segmentation strategy aiming at a target field, and performing identification processing aiming at special words of the target field on segmented words corresponding to the obtained text data to obtain target special words in the segmented words corresponding to the text data; carrying out wrongly written character recognition processing on the target proprietary word through a proprietary word error recognition model for the target field to obtain a first recognition result, and carrying out wrongly written character recognition processing on segmented words, except the target proprietary word, in the segmented words corresponding to the text data through a general word error recognition model to obtain a second recognition result; and based on the first recognition result and the second recognition result, determining wrongly written character information contained in the text data through fusion processing of the two different recognition results.

A user is assisted in performing a voice operation appropriately.A situation determination section determines a situation. A state control section controls a voice command appropriate for the determined situation to put the voice command into a receivable state. For example, the user is informed of what the voice command in a receivable state is, by means of display or voice output. The user can utter a voice command without performing a user action to prevent false recognition, such as the utterance of a wake word. This reduces the troublesomeness and burden of the user.

The application discloses a dynamic image feature capturing method based on deep learning, and relates to the technical field of image recognition. Through optimization of a deep convolutional neural network model, a convolutional layer in the deep convolutional neural network model which does not work or has a negative effect on a recognition result is removed, so that the calculation amount of the deep convolutional neural network model is reduced, and the accuracy of the deep convolutional neural network model is further improved on the basis of having completed training. Meanwhile, after a technical personnel deletes a feature marking frame of an error recognition through a tool, corresponding contrast video frames and original video frames are input as training samples of the deep convolutional neural network model, so that the recognition accuracy of the deep convolutional neural network model is more accurate.

This invention relates to the field of artificial intelligence education technology, specifically to a method and system for reviewing and providing feedback on physics, chemistry, and biology lab reports based on a multimodal large-scale model. The method includes: acquiring lab report question data, student answer data, and scoring standard data; vectorizing the question data and retrieving scoring criteria from a scoring knowledge base; selecting either a text-based or image-based scoring process based on the answer type, and calling a fine-tuned multimodal large-scale language model for scoring reasoning; calling an error identification module and a teaching suggestion generation module based on the scoring results; summarizing the complete review results and calculating the joint confidence level; and determining whether to push the results for manual review based on the confidence level. This invention can improve the accuracy, fine-grained understanding, and quality of teaching feedback in lab report review, enabling the self-evolution of the review system.

The invention discloses a human body behavior recognition method and system based on a fused thermodynamic diagram statistical scene, and relates to the technical field of human body behavior recognition. The method comprises the following steps that an obtained human motion video is preprocessed, key skeleton points are extracted, scene objects are recognized, thermodynamic diagrams are generated for the extracted key skeleton points and the scene objects, and the thermodynamic diagrams comprise multiple skeleton point thermodynamic diagrams and multiple static object thermodynamic diagrams; and based on the extracted key skeleton points and the identified scene objects, calculating contact scores between the skeleton points and the corresponding scene objects, and quantizing the key skeleton points and the corresponding scene objects through contact score values. According to the method, the behavior judgment threshold value is improved through the preset weight, the weight of the noise action which does not want to be recognized mistakenly is set to be low, so that the weighted result is reduced, at the moment, the unwanted result is output with a small probability, the false recognition rate is reduced, and the effect of improving the recognition precision of the specific action needing to be recognized is achieved.

The method and system for detecting the humidity of needle-shaped tea based on image recognition disclosed in this application obtains the humidity detection results of the images of needle-shaped tea to be detected. When the number of images of the to-be-detected that fall within the range of the fixation processing value is greater than a first predetermined value, the corresponding humidity morphology is determined as the humidity morphology of the needle-shaped tea. Based on the morphology, clustering is performed on the humidity detection result library of the images of the to-be-detected needle-shaped tea to form a set of needle-shaped tea images corresponding to the humidity morphology. Data configuration filtering is performed to determine whether it belongs to a balanced or unbalanced configuration, and the fixation processing range value corresponding to the humidity morphology is determined. This solution only needs to obtain the current humidity detection result of the needle-shaped tea, and can directly determine the humidity morphology of the needle-shaped tea through the processing range value. It can be achieved without running a complex model, which is more efficient and accurate. Furthermore, it does not require the use of intermediate features such as color and brightness type, which reduces computational complexity and further reduces false recognition or missed recognition.

The invention discloses a borehole stratum handwritten record information identification method. The method comprises the following steps: S1, constructing a basic sample set of borehole stratum handwritten records; s2, training an information identification basic model of the hand-written record of the drilling stratum; s3, constructing a project-level sample set of the hand-written record of the drilling stratum; s4, training a project-level identification model of the hand-written record of the drilling stratum; s5, constructing a stratigraphic record space nested confusion text dictionary; s6, identifying and automatically correcting the handwritten record picture of the drilling stratum information; s7, manually checking an identification result; s8, adding the recognition text corrected in the step S7 and the corresponding handwritten record photo to the item-level sample set in the step S3, training the item-level recognition model, and adding an error recognition text and a corrected text to a confusion text dictionary; and S9, executing S6 and S7 for a next to-be-identified task to obtain a final identification result. According to the method, the recognition accuracy and the working efficiency of the handwritten record of the drilling stratum are remarkably improved.

This application relates to a method, system, device, and medium for text recognition in e-books. Through the collaborative work of Transformer and CNN, it accurately segments text regions within complex shape contours while preserving overall structure and detailed features, eliminating interference from background patterns. By defining a precise recognition range through shape segmentation, it avoids missed or incorrect text recognition caused by text box positioning deviations in traditional methods, thus improving the accuracy of text extraction from e-book illustrations.

The invention provides an olefin hazardous gas loading operation safety monitoring method, device, equipment and medium, and belongs to the technical field of industrial safety monitoring. The method comprises the following steps: acquiring an olefin loading image, and inputting a behavior model to obtain a violation probability; and if the probability exceeds a preset value, matching the image with a misinformation image library (i.e., an image which is wrongly recognized as the violation in history) corresponding to the target violation category, and only when the misinformation image library is not matched with all misinformation images in the library, judging that the violation is real and outputting the target violation category. According to the method, through a dual judgment mechanism of preliminary screening of the behavior detection model and accurate comparison of the historical false alarm image library, the false alarm rate of olefin gas loading safety monitoring is remarkably reduced, and the alarm accuracy is improved.

The present application relates to the cross technical field of speech signal processing, respiratory physiological monitoring and artificial intelligence, and specifically discloses a respiratory-speech pause signal analysis system and method. The present application synchronously collects speech and respiratory signals, extracts language-adapted pause parameters and physiological indexes, performs time sequence alignment and correlation analysis, and then fuses a prediction model and a true-false recognition model for intelligent analysis, thereby solving the problems that traditional heart-lung function detection relies on professional equipment and cannot be remotely and non-contactly monitored, and that existing speech analysis lacks a physiological coupling mechanism, leading to the inability to identify synthetic speech and insufficient cross-language adaptation, and realizing the dual ability improvement of non-contact respiratory physiological state evaluation and speech fraud recognition.

The present invention relates to a computer-implemented method to identify extremum locations of wave signals. The method comprises steps of obtaining a displacement series comprising displacement of each time frame of the wave signal, determining a total state series which maximizes a penalized total path as an optimized state series based on the displacement series, and determining positions of turning points of the wave signal based on the optimized state series. A transition penalty is introduced to the penalized total path to suppress false identification of noise-derived local extrema as turning points. A dynamic programming (DP) algorithm may be used to perform the computer-implemented method.

The embodiment of the invention provides a voice processing method of a vehicle, the vehicle and a computer readable storage medium, and relates to the technical field of vehicle technology and voice processing.The method comprises the steps that the seat riding state of the vehicle is monitored, and a monitoring result is obtained and used for reflecting whether a riding object exists on any seat in the vehicle or not; in response to the fact that no riding object exists on the first seat in the vehicle, the first audio collection device is adjusted based on an audio signal suppression strategy, and the audio signal suppression strategy is used for increasing the sensitivity threshold value of the first audio collection device or closing an audio input channel of the first audio collection device; in response to an object on a second seat in the vehicle, performing voice recognition processing on a first audio signal acquired by a second audio acquisition device to obtain a character recognition result; and displaying the character recognition result in a display area of the vehicle. According to the invention, the technical problem of false wake-up and false recognition in the voice recognition technology in the related technology is solved.

The invention discloses an adversarial background generation method and device for a dual-induction error recognition attack, and the method comprises the following steps: 1, carrying out the physical attack modeling of an adversarial background based on the dual-induction error recognition attack, generating the adversarial background through a non-shielding strategy and a dual-induction loss function, and placing the adversarial background under a target; step 2, protected target training data collection and damage model training; step 3, obtaining a target mask through instance segmentation and binaryzation, and generating a physically adaptive enhanced confrontation background; and step 4, adversarial sample detection, loss calculation and background updating. According to the method, the problems of physical attack robustness reduction caused by insufficient utilization of image background information and difference of cross-domain training tests and attack flexibility limitation caused by a multi-dependence single-induction attack mode in an existing attack resisting method are solved.

This invention discloses a method for constructing a virtual psychological supervisor based on multi-agent collaboration, belonging to the fields of digital mental health training and artificial intelligence technology. By constructing a multi-agent collaborative framework of "error-prone counselor agent - error-sensitive patient agent - error-correcting supervisor agent," and combining iterative optimization mechanisms guided by validators, high-quality "consultation dialogue - supervision feedback" aligned data pairs are generated. Based on data optimization of a large language model, a virtual psychological supervisor with error recognition, professional feedback generation, and skills assessment capabilities is ultimately constructed. This invention solves the problems of insufficient training data, inadequate feedback professionalism, and subjective assessment standards in traditional psychological counseling supervision, achieving safe, efficient, standardized, and scalable psychological counselor skills supervision, significantly improving supervision accuracy and training efficiency.

The application belongs to the field of circuit design, and particularly relates to a subgraph matching method based on an enhanced VF3 algorithm, a storage medium and equipment. The method first generates a type of graph structure data of a pattern circuit and type II graph structure data of the pattern circuit and a target circuit according to a netlist file, and establishes a mapping between nodes in different graph structure data. The type II graph structure data takes element ports as nodes, and takes electrical connection relationships between nodes, signal transmission directions and control logic as edges. Then, key nodes in the pattern circuit are identified and the priority of the remaining nodes is calculated. A priority queue is generated in which the key nodes are in front and sorted by priority. Finally, the priority queue is taken as a node search sequence, and a subgraph matching algorithm is used to match nodes in the pattern circuit and the target circuit in the type II graph structure data, so as to determine the pattern circuit contained in the target circuit. The application solves the problem of low search efficiency and error identification of the existing subgraph matching scheme.

The application provides a crowd statistical method and device based on confidence probability, comprising: obtaining an original picture for crowd quantity statistics; obtaining a feature map and a generated map corresponding to the original picture through encoding and decoding based on the original picture; inputting the original picture, the feature map and the generated map into a preset confidence probability model to obtain a confidence probability density map; inputting the feature map into a preset prediction neural network to obtain a prediction probability density map corresponding to the feature map; determining a real density map based on the confidence probability density map and the prediction probability density map, and performing crowd quantity statistics according to the real density map. In one aspect, the application reduces the data training model through encoding and decoding; on the other hand, the application screens the prediction probability density map by using the confidence probability density map, reduces the occurrence of false recognition, and thus improves the accuracy of crowd quantity statistics.

This application provides a training method for a fingerprint recognition network, a fingerprint recognition method, an apparatus, and a medium. The training method for the fingerprint recognition network includes: determining fingerprint feature matching maps based on a first initial fingerprint image and a second initial fingerprint image; forming a training sample set from multiple fingerprint feature matching maps; and training an original neural network based on the training sample set to obtain a fingerprint recognition network. The technical solution provided by this application solves the problem of difficulty in distinguishing whether two different fingerprint images belong to the same finger, thus reducing the false acceptance and false recognition rates of fingerprint recognition.

This application discloses a method, apparatus, device, and storage medium for determining head movements, relating to the field of head-mounted device technology. The method includes: converting initial angular velocity data collected by a gyroscope to a target coordinate system to obtain target angular velocity data; determining the target temporal features corresponding to the target angular velocity data, wherein the target temporal features include at least one of a first temporal feature corresponding to the first angular velocity data, a second temporal feature corresponding to the second angular velocity data, and a third temporal feature corresponding to the third angular velocity data; and matching the target temporal features with preset temporal features corresponding to each type of head movement to determine the target head movement type. Therefore, by extracting the temporal features of each of the three-axis angular velocity data and matching them with preset temporal features corresponding to various head movements, the detailed differences in head movements can be comprehensively captured, significantly improving the accuracy of head movement recognition and reducing the false recognition rate of similar movements.