427 results about "False recognition" patented technology

The invention provides an access control system based on face recognition and a recognition method, and the system comprises a multi-modal image collection subsystem which integrates a visible light camera, a depth camera and an infrared thermal imaging camera and is used for synchronously collecting a visible light image, a depth image and an infrared thermal imaging image of a face; the image preprocessing unit is used for carrying out corresponding preprocessing on the image; the feature extraction network module is used for extracting image features; the multi-modal feature fusion and alignment module is used for calculating a mutual information matrix by adopting a feature fusion strategy based on mutual information maximization and adaptive weighting, determining an adaptive weight and performing weighted summation, and performing feature alignment by applying a method based on canonical correlation analysis; and the face recognition classification and verification module verifies the image by adopting a multi-level fusion strategy. According to the method, the false recognition rate and the rejection rate are effectively reduced, the strong anti-cheating capability is achieved, and cheating attacks of various fake face means can be accurately recognized and resisted.

The invention relates to the technical field of intelligent home control, and discloses a control method and a control system of an intelligent sound box. The control method of the intelligent sound box is applied to a control device, and specifically comprises the following steps: S101, collecting a voice instruction of a user, carrying out noise reduction processing and voiceprint feature extraction on the voice instruction, transmitting the processed voice instruction to a semantic analysis engine, carrying out intention recognition on voice content based on a deep learning model, and sending the intention recognition result to a semantic analysis engine; generating a structured operation instruction set; and S102, according to the device identifier contained in the structured operation instruction set. Through dual authentication of voiceprint feature extraction and dynamic verification phrase, the illegal access false recognition rate is reduced to below 0.12%, the voiceprint database is combined with the Mel frequency cepstrum coefficient and the dynamic time warping algorithm, accurate matching of user identities is achieved, the dynamically generated verification phrase comprises random initial and final combinations, and recording playback attacks are effectively resisted.

The embodiment of the invention discloses a water detection method and device for a touch screen, terminal equipment and a storage medium, which are used for updating a confidence value corresponding to a waterproof mode according to capacitance data of a current scanning frame, reducing the false recognition rate of an interference scene and improving the rate of entering the waterproof mode. The method comprises the steps of obtaining capacitance data of the touch screen in a current scanning frame, wherein the capacitance data comprises at least one of self-capacitance data and mutual capacitance data; according to the capacitance data, a confidence value is updated according to the capacitance data, the confidence value of the current scanning frame is obtained, and the confidence value is used for representing the credibility of water on the surface of the touch screen; and judging whether to enter or exit a waterproof mode according to the confidence value of the current scanning frame.

The invention belongs to the technical field of image processing, and particularly discloses a substation foreign matter intrusion detection method and system based on an improved YOLOv11 model. According to the method, a transformer substation foreign matter intrusion detection model based on an improved YOLOv11 model is provided for solving the problem of transformer substation foreign matter intrusion detection, so that automatic image recognition of invading foreign matter is achieved. According to the model, through the improvements of adding a C2PSA-DHSA module behind an SPPF module, using a C3k2-Dual module, adding a new small target detection layer, adding a non-local attention mechanism module in front of the SPPF module and the like, the model can more effectively identify targets with smaller areas, and the calculation cost and the number of parameters of the model are reduced, and meanwhile, the calculation efficiency of the model is improved. And the problems of low recognition rate and wrong recognition caused by shielding of recognition equipment are solved, the accuracy of the model is improved, and the adaptability of the model to targets with different sizes is enhanced. According to the method, the problems of missing detection, misjudgment, low recognition accuracy, low speed and the like of a traditional model in the substation equipment and line detection process are well solved.

The invention discloses a text error recognition method based on a large model and a related device, a target government affair document can be pre-processed to obtain a plurality of corresponding sentence segments, and the pre-processing comprises text cleaning, text phrasing and text standardization; based on a pre-established knowledge base and a large model, error detection is carried out on each sentence segment to obtain corresponding detection results, and the error detection comprises common sense error detection, numerical unit error detection, logic contradiction detection, time contradiction detection, contradiction detection before and after numerical values, data incompleteness detection, error calculation and statement repetition; and generating a corresponding detection report and a text modification suggestion based on the detection result. Therefore, the method can accurately identify the errors in the government affair document based on the knowledge base and the large model, generates the report and gives the modification suggestions, and is high in efficiency and accuracy.

The invention provides a remote conference real-time voice recognition optimization method and system based on a visual context, and the method comprises the steps: collecting the visual context information according to a projection screen image; acquiring a voice audio and inputting the voice audio into the voice recognition model to obtain a voice recognition result; and adjusting the voice recognition result by using the context information. On the basis of a traditional remote conference voice real-time recognition function, word and image content information of a current projection screen is analyzed, and the information is fused into a voice recognition model, so that words with low recognition probability or ambiguous words are optimized. According to the method, the error recognition rate can be effectively reduced, the vocabulary recognition problem in the professional field is solved, and the overall performance of the real-time speech translation function is improved.

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.

A robot (100) working area map construction method and apparatus, a robot (100), and a medium, wherein the robot (100) working area map construction method comprises scanning in real time an obstacle in a driving path and recording position parameters of the obstacle (S102); obtaining in real time image information of the obstacle in the driving path (S104); according to the position parameters and the image information, determining working area-based reference information of the obstacle (S 106); and dividing the working area into a plurality of sub-areas on the basis of the reference information (S108). By means of radar scanning and image capture by a camera for double insurance, the robot (100) working area map construction method significantly improves recognition accuracy for room doors and avoids room division confusion caused by the incorrect recognition of doors.

The invention discloses an intelligent network false information identification and early warning system based on a multi-modal behavior map, and particularly relates to the field of intelligent network false information identification and early warning. A specific implementation mode of the system comprises a dynamic behavior graph construction module which is configured to construct a dynamic behavior graph, monitor newly added behavior flow data corresponding to original user behavior data in real time, and update a topological structure of the dynamic behavior graph according to the newly added behavior flow data; the time sequence reasoning model training module is used for constructing a behavior sequence data set with a timestamp and training the initial false information time sequence reasoning model; and the risk early warning module is used for carrying out false information identification on the updated dynamic behavior map according to a false information time sequence reasoning model and a historical abnormal behavior map library so as to generate corresponding false identification information. According to the embodiment, dynamic behavior characteristics are captured through the time sequence reasoning model, normal operation and fraudulent behaviors are distinguished, and false alarms caused by static rules are reduced.

A fingerprint biometric key generation method based on scale invariance and a deep neural network. The object is small sample data, and fingerprint image signal processing knowledge is combined with deep neural network feature stability; fingerprint features are further stabilized at feature values by means of layer-by-layer processing of a deep neural network; and finally, a normal fingerprint image high-intensity key sequence is generated by means of a fingerprint key fuzzy extractor. A high-intensity stable fingerprint biometric key can be directly extracted from a human fingerprint small sample by means of processing such as scale invariant feature conversion and deep neural network re-encoding, and fingerprint image signal processing knowledge is combined with deep neural network feature stability to better support generation of a small sample fingerprint biometric key, thereby significantly reducing the false recognition rate during fingerprint biometric key generation.

The invention provides a CAD (Computer Aided Design) drawing error recognition method utilizing deep learning, which relates to the technical field of image data processing and comprises the following steps of: constructing a primitive structure by reading primitive information of a CAD drawing; extracting image features of the drawing based on the feature extraction network to perform semantic modeling on the primitive structure to obtain a primitive structure semantic model; identifying a nested primitive pair; performing edge detection and contour fitting on the nested primitives, and extracting nested edge coordinate points; and obtaining nested edge coordinate points of the nested primitives, generating a boundary constraint range by using the nested edge coordinate points to carry out error recognition on the nested edge coordinate points, and carrying out interactive feedback correction according to an output error recognition result. According to the method and the device, the technical problem that errors in a nested structure are difficult to find timely and accurately due to the lack of intelligent recognition capability for the primitive nesting relationship in the prior art is solved, and the technical effect of improving the CAD drawing error recognition accuracy is achieved.

The invention discloses a vulnerability software version detection method and device, relates to the technical field of vulnerability software version detection, and solves the problems that in the prior art, due to the fact that noise cannot be effectively filtered, vulnerability lines are wrongly recognized, and vulnerability sources cannot be accurately traced. The method comprises the steps of obtaining input data, and obtaining a code change record and a first vulnerability line set; vulnerability description information is obtained according to the input data, code change records are screened to obtain code change records related to vulnerabilities, and a big language model is utilized to perform noise filtering to obtain vulnerability lines which are really easily attacked; tracing vulnerabilities corresponding to the vulnerability lines by using a context-based line mapping algorithm to obtain introduction and submission of the vulnerabilities; finding all version numbers between the vulnerability repair submission and the vulnerability introduction submission, and determining the version numbers as a vulnerability version set; according to the method, missing report and false report of vulnerability versions are reduced, potential safety hazards existing in software projects are reduced, and the safety of a software system is improved.

The invention belongs to the technical field of face data dynamic verification, and particularly relates to a centralized dynamic verification and risk defense method based on face data. According to the method, multi-dimensional dynamic indexes such as micro expressions, iris responses and blood flow fluctuations are fused, the detection capacity of attack means such as deep counterfeiting, face masks and picture screenshots is greatly improved, the false recognition rate and the missed recognition rate are reduced, external risks such as equipment integrity, network security and geographical position deviation are fused into comprehensive evaluation, and the method is high in practicability. The face self is concerned, the whole verification environment can be safely controlled, more comprehensive defense is realized, once a threshold alarm is triggered, multi-level defense measures can be immediately started, the defense result is fed back to the model, the discrimination boundary of the generative adversarial network is continuously optimized, and high efficiency and robustness in long-term operation are ensured.

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 voice control method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring to-be-detected voice data, and performing keyword detection on the to-be-detected voice data to obtain keywords in the to-be-detected voice data; voice data where the keyword is located are obtained from the voice data to be detected, and voice data to be recognized are obtained; performing voice recognition on the to-be-recognized voice data to obtain a first voice recognition result; and if the keyword is matched with the first voice recognition result, determining a control instruction corresponding to the to-be-detected voice data, and executing the control instruction. According to the invention, the false recognition rate of the wake-free word can be reduced.

The invention relates to the technical field of honing positioning, in particular to an automobile part positioning method and system for numerical control machine tool machining, and the method comprises the steps that a grey-scale map of an engine cylinder body fixed to a station is obtained, and the engine cylinder body is provided with a standard positioning hole; constructing a feature point for each pixel point in the grey-scale map, wherein the feature point represents the position and grey-scale value information of the corresponding pixel point; and classifying the feature points by using a condensation type hierarchical clustering algorithm to obtain a plurality of optimal clusters, calculating the possibility that each optimal cluster is a positioning hole region, taking the region corresponding to the optimal cluster with the possibility located in a preset threshold interval as the region of the positioning hole, and obtaining the position coordinates of the positioning hole. And the positioning center of the engine cylinder body cylinder hole is further obtained. According to the method, the phenomenon of wrong recognition during clustering combination is avoided, and the positioning hole can be accurately recognized and distinguished from other hole areas.

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.

The invention relates to a voice recognition method and device of a vehicle, the vehicle and a storage medium, and the method comprises the steps: collecting a voice instruction of the vehicle; in response to the voice instruction, acquiring video data corresponding to the voice instruction, and determining weights of the voice instruction and the video data according to the internal noise level of the vehicle; and based on the voice instruction and the weight of the video data, fusing the video data and the voice instruction to identify an actual control intention of the user, and controlling the vehicle to execute a control action corresponding to the actual control intention. According to the embodiment of the invention, the complementarity of the voice data and the video data can be utilized, more accurate voice recognition is realized, the limitation on the pronunciation standard of the user is reduced, the voice recognition is more humanized, the probability of wrong recognition is reduced, and the interaction experience of the user is improved.

The invention discloses a multi-modal face anti-counterfeiting recognition method and device and a medium, and is applied to the technical field of face recognition, the method comprises the steps that a modal prompt set, a task prompt and a multi-modal face image are acquired, the modal prompt set comprises a visual prompt related to an image modal, and the task prompt is a visual prompt related to face anti-counterfeiting recognition; the modal prompt set, the task prompt and the multi-modal face image are processed, a visual token sequence and a prompt token sequence are obtained, the visual token sequence comprises a classification token and a plurality of feature tokens, the classification token is a global mark of a visual converter, the feature tokens are vector representations of the multi-modal face image, and the feature tokens are vector representations of the multi-modal face image; the prompt token sequence comprises a plurality of prompt tokens, and the prompt tokens are global marks associated with face anti-counterfeiting recognition; and performing face anti-counterfeiting recognition according to the visual token sequence and the prompt token sequence in combination with a neural network model to obtain an anti-counterfeiting recognition result. According to the invention, the precision of face anti-counterfeiting recognition is improved.

The invention discloses an error detection method and system in a programmed task video, relates to a video time sequence action segmentation and anomaly detection technology, and realizes robust error detection aiming at the problem of accuracy of error recognition in the programmed task video. According to the method, firstly, a time sequence action segmentation model is trained, a Gaussian mixture model is trained for each action category by utilizing each frame feature of an intermediate layer of the time sequence action segmentation model, the frame features are sent to the Gaussian mixture model of the corresponding action category to be processed, the log-likelihood score of the frame is obtained, and the detection of the frame level anomaly is realized. According to the method, the problem of error detection in the first-view programmed task video can be effectively solved by combining action segmentation and anomaly detection methods, and the method can be used for scenes requiring accurate recognition of sequence action errors.

The invention discloses a multi-channel speech recognition method and device, equipment and a medium. By judging whether the effective voice segments of any two sound channels meet the pre-configured signal mixing condition or not, which effective voice segments may have a signal mixing problem can be accurately identified. The signal mixing condition combines judgment in two aspects of content intersection and time intersection. The content intersection ensures the similarity of the voice content, the time intersection ensures that the similarity occurs in the same time period, and through the signal mixing condition, two effective voice segments with the signal mixing problem can be accurately recognized. When it is determined that the effective voice segments of the two sound channels meet the signal mixing condition, the energy of the content intersection in the two sound channels can be further compared, and the content intersection of the voice segment with the large reserved energy is selected. According to the method, wrong recognition caused by signal mixing can be reduced, so that the accuracy of a voice recognition result 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 provides a grabbing control method and device of a robot, the robot, a storage medium and a program product. A first collecting assembly is controlled to collect first sensing data in the current environment where the robot is located, and a mechanical arm is controlled to drive a second collecting assembly to collect second sensing data in the current environment; and controlling the mechanical arm to grab the target object based on the first sensing data and the second sensing data. Thus, the target object can be grabbed according to the first sensing data and the second sensing data obtained by the first collection assembly and the second collection assembly which are different in position, the target object can be grabbed according to data of different view angles, the possibility of misrecognition caused by limited obtained information under a single view angle is reduced, and the recognition accuracy is improved. And the accuracy and the flexibility of the grabbing control method are improved.

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.

Label image information is used to automatically identify and / or verify the identification of pharmaceutical substances, in conjunction with the automatic preparation of pharmaceutical preparations from those pharmaceutical substances. In some examples, textual content of substance containers is identified using OCR. This corresponds to a significant portion of the label information intended for human inspection. As a cross-check, one or more additional visual aspects of the label are evaluated, e.g., an aspect of graphical appearance other than textual content as such. Optionally, risks of mis-identification are evaluated. Optionally, imperfect matches to templates are deemed permissible due to a low risk of actual misidentification, and / or close but potentially ambiguous template matches are deemed insufficiently risk free, resulting in mitigation actions.

The invention provides a device testing method and device, electronic equipment and a storage medium, and relates to the technical field of device testing. According to the method, an intersection range is selected from a judgment range of a dynamic part average test and a target specification range as a test range of a target device, and then the target device is tested by using the test range. As the judgment range and the target specification range of the dynamic part average test are ranges determined according to different quality requirements, a smaller test range (i.e., an intersection range) is selected for testing, so that an abnormal device can be effectively detected, the probability of wrong recognition is reduced, and the detection efficiency is improved. And higher production requirements of electronic components can be met.