378 results about "Low Confidence" patented technology

The invention discloses an intention recognition method based on cross attention and multi-scale uncertainty. The intention recognition method comprises the following steps: preprocessing multi-modal data; parallel multi-modal feature coding oriented to intention recognition; the invention relates to multi-scale uncertainty perception decoding. According to the method, a parallelized multi-modal feature extraction path is constructed, and a hierarchical fusion mechanism based on cross attention is designed, so that deep semantic alignment and complementary enhancement of four types of heterogeneous information including the posture, the motion track, the global scene and the local vision of a rider are realized; the problems of incomplete feature representation and insufficient cross-modal correlation modeling caused by dependence on a single information source or adoption of a shallow fusion strategy in a traditional method are solved, so that the accuracy and robustness of intention recognition in a complex traffic scene are remarkably improved. According to the method, a multi-scale uncertainty perception decoding framework is introduced, risk early warning or context auxiliary verification is carried out on a low-confidence identification result, and the reliability of an automatic driving system in a safety critical scene is improved.

The invention provides a semiconductor chip automatic test optimization system, and belongs to the technical field of electrical variable measurement. Comprising an acquisition module used for establishing an electrical variable time sequence arranged according to a time sequence, a drift analysis module used for calculating a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value based on the impedance time sequence arranged according to the time sequence, and a contact judgment module. The evaluation module is used for constructing a contact impedance coefficient based on a short-time fluctuation amplitude value, a long-term drift slope and a high-frequency noise amplitude value in a jth sliding window, and evaluating and optimizing the contact impedance coefficient, and the data marking module is used for marking electrical variable measurement data corresponding to a test channel which is evaluated to be instable in contact as low confidence. According to the system, the ith test channel of the temperature sensor chip can be tested more accurately, the short-time fluctuation amplitude value, the long-term drift slope and the high-frequency noise amplitude value are calculated at the same time, multi-source data participates, and the test accuracy is improved.

The invention provides an image text recognition method and system based on a mask diffusion model, a storage medium and equipment, and belongs to the technical field of image or video recognition or understanding. According to the method, multi-scale visual features of an image are extracted through a visual encoder, a mask diffusion decoder is combined, a diversified mask strategy and random character replacement disturbance are adopted in a training stage, denoising loss and auto-reflection loss are calculated respectively, and a model is optimized in a combined mode; in the reasoning stage, starting from a full mask state, a complete text sequence is recovered through multi-round iterative denoising. According to the method, the one-way modeling limitation of a traditional autoregression model is broken through, all-around context-dependent modeling is achieved, an autoreversion error correction mechanism and a block low-confidence mask strategy are introduced, and the recognition accuracy and reasoning efficiency in complex scenes such as shielding and fuzzy scenes are remarkably improved. The method provided by the invention reaches a leading level on a plurality of public data sets, and has the advantages of high precision and high speed.

The invention relates to an archive credible question and answer method based on graph context retrieval and knowledge graph enhancement, and belongs to the field of computer software and artificial intelligence. According to the method, the relation between key entities and entities in a historical file is extracted by using a multi-modal large language model MLLM, and low-confidence fields are automatically identified to generate marks needing to be manually rechecked, so that the correctness of information extraction is ensured while the workload of manual rechecking is reduced, and then based on a given knowledge base, the information extraction efficiency is improved. Previous related questions and answers are retrieved according to query questions to achieve context enhancement, and finally, credible answers are generated through a large language model (LLM) in combination with extracted information in historical archives. On the premise of keeping the value of the archive voucher, the processing efficiency and the knowledge service capability are remarkably improved.

The invention discloses a data scarcity industrial vertical field-oriented knowledge-guided retrieval enhancement generation method, belongs to the technical field of natural language processing and industrial intelligence crossing, and can improve the retrieval accuracy and generation reliability of a large language model in an industrial scene. According to the method, a'sparse vector + dense vector 'mixed knowledge base is constructed, and general knowledge and industrial field texts are fused; after the model generates an initial text, judging whether external retrieval is needed or not through multi-dimensional evaluation token confidence; extracting attention weights for the low-confidence tokens, and screening key tokens to generate a retrieval query; dynamically adjusting the weight of a retriever based on a BGE-M3 model, and optimizing a retrieval result through reordering; the retrieval knowledge is converted into a context with an index, and a prompt template is constructed to generate a correction value iteration calibration text; and finally, optimizing the text format, and generating a structured response meeting industrial requirements. The method solves the problems of lack of professional knowledge of large language models in the industrial field, shallow retrieval and generation fusion, lack of knowledge calibration mechanisms and the like.

The invention provides a bridge structure vibration long-term monitoring algorithm and system based on computer vision, and relates to the technical field of beam monitoring. In the prior art, there is no vision-based bridge monitoring algorithm with high precision, strong robustness and shielding self-recovery capability. According to the method, matching point confidence is calculated, and matching points with low confidence scores are removed; calculating the displacement of the bridge at the plurality of feature points, and generating vibration time sequence data; the inherent frequency and the corresponding modal shape are extracted and compared with the reference frequency range and the reference modal shape, the monitoring result is obtained, shielding self-recovery is carried out according to the environmental interference influence, the precision and reliability of bridge monitoring are improved, and the robustness of bridge monitoring activities in a complex scene is improved.

The invention discloses a character recognition method and system based on a large model and an OCR technology, and relates to the technical field of character recognition, and the method comprises the steps: extracting picture information, carrying out the unified preprocessing, generating a text detection box of a character region through a DBNet lightweight text detection model, and obtaining a text position; quickly identifying characters in the textbox by using a lightweight OCR model to obtain text content, and generating an identification information group; carrying out average value calculation on the confidence coefficient of the lightweight OCR model result, and analyzing the overall confidence coefficient; and for the result with low confidence coefficient, inputting the corresponding identification information group into the multi-modal large model, and carrying out secondary identification. According to the method, the text position set and the text character string sequence are combined into the identification information group, so that the effect of structured storage of detection and identification results is achieved, subsequent information retrieval and multi-modal fusion analysis are facilitated, and the effect of optimizing the subsequent processing efficiency and precision is achieved through the combination of the confidence coefficient screening step.

The invention provides a blind area early warning method and device and a storage medium, and the method comprises the steps: carrying out the multi-dimensional recognition of a vehicle road scene, obtaining the data of a plurality of sensors, determining the environment type of the road scene where a vehicle is located, adjusting the original weight of each sensor based on the environment type, obtaining a first weight corresponding to each sensor, and carrying out the adjustment of the first weight. Through recognition of environment types, weights are dynamically distributed based on indexes related to environment scenes in a non-linear mode so as to adapt to complex scenes. The sensor data is fused according to the first weight, the influence of the data source with low confidence on the fusion processing is dynamically reduced, blind area early warning is performed based on the fusion result, and the false alarm rate is effectively reduced. In addition, according to dynamic weight adjustment of the indexes related to the environment, the response speed of system execution control is increased.

The invention relates to a multi-spectral diagnosis result credibility evaluation method. The method comprises the following steps: synchronously acquiring three-spectral images and environmental parameters of target equipment; the collected three-spectrum image is preprocessed; inputting the preprocessed image into the improved model to obtain a prediction probability distribution vector of fusion features and three-spectral-band branches; obtaining a prediction category and uncertainty entropy; carrying out confidence score calculation; judging whether to trigger a feedback condition; according to the method, the complementary characteristics of three spectrums of visible light, infrared light and ultraviolet light are utilized, and the CBAM attention module is introduced, so that key characteristics under different spectrums are enhanced, and the sensing ability of the model to an abnormal region is improved; the method achieves the prediction result confidence evaluation based on a Monte Carlo Dropout reasoning mechanism, effectively recognizes a low-confidence result, gives a diagnosis conclusion, can also evaluate the confidence, is suitable for deployment in key scenes such as a high-pressure valve hall, and facilitates the improvement of the overall safety and stability of a system.

The invention discloses a source domain irrelevant cross-domain cardiac beat recognition method and system for pseudo-label mining, and relates to the technical field of pseudo-label learning, and the method comprises the steps: obtaining electrocardiogram data with cardiac beat labels in a source domain, inputting a pre-established source domain model for pre-training, and obtaining a pre-trained source domain model; acquiring unlabeled electrocardiogram data of a target domain, and screening and classifying the data of the target domain based on a pre-trained source domain model and a preset category threshold to obtain data with high false label confidence and data with low false label confidence; using the source domain model to initialize target domain model parameters, revising a strategy based on a pseudo label of local and global semantic perception, updating the pseudo label of the low-confidence data, and combining the high-confidence data to obtain updated pseudo-labeled target domain data; target domain data subjected to data augmentation pseudo labeling are input into a target domain model, a target domain model optimization total loss function is calculated, and cross-domain cardiac beat intelligent recognition irrelevant to a source domain is achieved.

The invention provides a vehicle target automatic labeling method and system based on deep learning. The method comprises the steps of obtaining video stream monitoring data collected by a road camera; based on the video stream monitoring data, utilizing a pre-trained vehicle identification model to automatically label a vehicle target in the video stream monitoring data to obtain a preliminary labeling result; performing uncertainty evaluation on the preliminary labeling result through an adversarial sample generation strategy to obtain an uncertainty score; when the uncertainty score does not exceed a preset threshold value, taking the preliminary labeling result as a final labeling result of the vehicle target; according to the invention, by using the pre-trained convolutional neural network model, large-scale video stream data can be quickly processed and preliminary annotation can be completed, so that the dependence on manual annotation is reduced; uncertainty evaluation is carried out on the preliminary labeling result through an adversarial sample generation strategy, errors or low-confidence-coefficient areas possibly existing in the labeling result can be effectively recognized, and the accuracy of the final labeling result can be guaranteed.

The invention discloses a self-adaptive text extraction method and system based on artificial intelligence, and the method comprises the steps: carrying out the analysis of the document structure entropy of an example document set, quantifying the noise density, geometric distortion degree and background complexity of the example document set, and carrying out the self-adaptive selection of a preprocessing assembly line intensity grade according to the above; dynamically configuring image preprocessing parameters and AI recognition model parameters, and generating a recognition engine instance to output a preliminary recognition text; after regularized coarse screening extraction is carried out based on key field description, a multi-candidate generation strategy is started for low-confidence-coefficient candidate text fragments, a multi-person cooperative verification process is triggered for lower-confidence-coefficient fragments, finally all the fragments are processed through a text standardization module, and structured text extraction information is output. According to the method, accurate adaptation of processing intensity is achieved through document quality quantitative evaluation, the extraction accuracy and system robustness of complex heterogeneous documents are effectively improved through a multi-level confidence coefficient verification mechanism, and the identification error risk caused by image quality fluctuation or rule solidification is reduced.

The invention relates to the technical field of AI vision, in particular to a sesame seed candy forming defect real-time detection method and device based on AI vision. The method comprises the following steps: respectively collecting multimode images of qualified sesame seed candies, generating a qualified characteristic fingerprint set, and calculating sugar body light transmission uniformity and sesame adhesion density as a domain parameter set; constructing a probability distribution model, and setting an anomaly judgment threshold value and a domain parameter early warning threshold value; collecting a multi-mode image of the to-be-detected sesame seed candy in real time, extracting a to-be-detected feature fingerprint, and calculating the light-transmitting uniformity of a to-be-detected candy body and the sesame adhesion density; calculating a comprehensive abnormal score, and judging a defect; and capturing a low-confidence sample based on the comprehensive anomaly score, obtaining an artificial correction feedback sample, and updating a probability distribution model, an anomaly judgment threshold value and a domain parameter early warning threshold value by utilizing the feedback sample through online incremental learning. According to the invention, the detection cost of a high-yield production line can be reduced, and the model deployment period is shortened.

The invention discloses a news industry classification method and device based on large language model active learning, relates to the technical field of text classification, and can remarkably reduce the manual annotation cost while ensuring the news text classification precision. According to the scheme, the method comprises the following steps: calling a large language model to classify each news text for multiple times for a plurality of news texts to obtain at least one corresponding tag; based on a majority voting mode, dividing the plurality of news texts and the corresponding labels into high-confidence samples and low-confidence samples; performing label labeling on the low-confidence sample to obtain a labeled sample; and taking the labeled sample and the high-confidence sample as a training set, carrying out iterative training on the classification model by using the training set until the model precision of the classification model meets a preset condition, obtaining a target classification model, and carrying out prediction classification on the news text by using the target classification model.

The invention provides an end-side adaptive document structure understanding method, which comprises the following steps of: uniformly rendering and normalizing a to-be-analyzed document, and outputting a page-level pixel grid and basic metadata; executing lightweight layout analysis and region classification to obtain a bounding box, a reading sequence and a region type label of each region in the page; each document area is routed to a corresponding special analysis channel for parallel analysis, and each analysis channel outputs a structured intermediate result and confidence; performing consistency verification and completion reasoning on intermediate results output by each channel, and generating a traceable verification evidence chain for low-confidence fragments; all channel results after verification are fused into a unified document-level structured output; and for a new document type or a continuous low-confidence mode, starting an adaptive process of a parameter efficient fine tuning technology to generate a channel-level increment weight packet, and updating model parameters of an analysis channel. The method has the beneficial effect that parallel accurate analysis of different elements such as tables, formulas, texts and the like can be realized.

The invention relates to the technical field of semantic processing, and discloses a semantic processing system and method for home elevator intelligent customer service, and the system comprises a data collection module, a semantic processing module, a response generation and optimization module, and a communication and log module. The method comprises the steps of collecting natural language request information input by a user through voice, performing semantic analysis and intention recognition on preprocessed information based on a deep learning model, and synchronously recording an interaction log of a whole link according to an intention recognition result and the emergency degree and priority of optimized response content. In order to solve the problems that a traditional keyword matching system is poor in semantic comprehension ability and prone to misjudgment of user intentions, the context and real intentions of natural language requests can be deeply understood by introducing a semantic analysis model based on deep learning and combining a confidence degree evaluation mechanism, and when the system recognizes the low confidence degree condition, the user intentions can be accurately judged. And multiple rounds of clear conversations can be automatically triggered or interaction channels can be switched.

The present invention relates to the technical field of computer vision, and provides a pseudo-label filtering-based online domain change continual learning method and system. The method comprises: acquiring a pre-trained model, using the pre-trained model to predict changing target-domain data, and generating a pseudo-label for online adaptation; deriving a lemma for threshold-based pseudo-label filtering in online domain change continual learning on the basis of binary classification, and designing a threshold setting principle in the online domain change continual learning on the basis of the lemma; using the designed threshold setting principle to filter a pseudo-label having a low confidence level in model prediction, and introducing a class prior alignment method to encourage the model to perform fair prediction on an unknown-domain sample; and using the filtered pseudo-label to update and optimize the model to obtain a classification prediction result in the online domain change continual learning. In the present invention, an adaptive threshold capable of adapting to a CTTA process is established, thereby ensuring the quality of pseudo-labels.

The invention provides a gushing prediction method and system for grouting engineering, and belongs to the technical field of tunnel and underground engineering construction safety monitoring. The method comprises the following steps: collecting grouting pressure, flow, slurry viscosity and micro-seismic event data; calculating a slurry resistance index and a micro-seismic event aggregation density; inputting the initial gushing risk index into a pre-trained machine learning model to obtain an initial gushing risk index and a confidence score; according to whether the confidence score is lower than a preset threshold, selecting direct early warning or entering a parameter correction path; when the confidence coefficient is low, dynamic correction factors are generated based on scores and risk changes, the calculation weight of the slurry resistance index is adjusted, corrected parameters are obtained, and secondary prediction is carried out; and integrating the initial prediction result and the secondary prediction result to obtain a final risk index, and executing graded early warning. According to the invention, the accuracy and reliability of gushing risk prediction are improved through a confidence-driven double-path decision and parameter on-line adaptive correction, and a constructed early warning mechanism.

The invention relates to the technical field of deep transfer learning, and discloses a semi-supervised multi-source-domain generalization fault diagnosis method and system based on mutual information, and the method comprises the steps: obtaining fault vibration signal data of a manual fault test bed bearing, and obtaining labeled source domain data, unlabeled source domain data and unknown target domain data; the method comprises the following steps: extracting deep features of label source domain data and label-free source domain data, classifying the deep features, and generating a preliminary false label and confidence of the label-free source domain data; screening the preliminary pseudo tags based on an adaptive threshold strategy, determining high-confidence pseudo tags, and correcting low-confidence pseudo tags to obtain an updated source domain feature set; and training a double-branch classifier by using the updated source domain feature set, and predicting a fault category label of unknown target domain data. The data of the target domain does not need to participate in training, and the unknown target domain can be diagnosed only by using the source domain data, so that the accuracy of fault diagnosis is improved.

The invention relates to a multi-agent cooperation-based language model reasoning resource scheduling method and system, and the method achieves the intelligent simulation of multi-view iterative thinking in a complex reasoning task through the construction of a multi-agent system which is clear in division of labor and is provided with a special knowledge base. View limitation and decision deviation of a single model in long-sequence and multi-step reasoning are effectively overcome; depending on the fusion of the general capability of the large language model and the task special knowledge base, the professionality and accuracy of the reasoning result are improved. Meanwhile, the problems of resource waste, redundant calculation and unstable convergence caused by cognitive overload in the cooperation process are solved by combining multi-round dynamic cooperation with a convergence mechanism of cognitive load perception and a dynamic token number limitation and low-confidence branch pruning strategy; therefore, on the premise that the reasoning quality is guaranteed, the utilization efficiency of computing resources is remarkably optimized, peak value occupation is reduced, the overall reasoning time delay is shortened, and reliable technical support is provided for efficient and stable deployment of a large language model in a complex task.

The invention relates to the field of electrical energy, and discloses an intelligent anomaly identification and recovery method for electrical energy time sequence data. Comprising the steps of collecting and preprocessing multi-dimensional time sequence data of a power terminal; local and long-term features are extracted by combining a periodic attention mechanism through a feature extraction module fusing a time convolutional network and a Transform; a residual self-supervised classifier is used for identifying multiple types of anomalies such as missing, jumping, drifting and sudden rising; the abnormal segments are repaired in a context interpolation and period fitting combined mode, and dynamic weighted reconstruction is achieved through a deformable time window; after restoration, a credibility scoring mechanism based on residual amplitude, period consistency and classification confidence is introduced, high-risk marking is carried out on low-confidence fragments, and manual rechecking or system correction is triggered; and self-adaptive updating of the model is realized through historical feedback.

The invention discloses a data processing method and device, equipment and a medium. Comprising the steps that a training sample set is obtained, the training sample set comprises a plurality of sample pairs, each sample pair comprises an image sample and a text sample, and through a to-be-trained model, prediction is carried out based on the image samples and the text samples to obtain an output lexical element sample sequence, determining a first target lexical element with relatively high confidence and a second target lexical element with relatively low confidence from a plurality of output lexical elements contained in the output lexical element sample sequence; filtering the loss value corresponding to the first target lexical element to obtain a first target loss value; performing upper limit constraint on the loss value corresponding to the second target lexical element to obtain a second target loss value; and training based on the first target loss value and the second target loss value to obtain a visual language model. According to the technical scheme, the reliability of data processing in a visual language model scene is improved.

The invention discloses a man-machine collaborative auditing and optimizing method for AI (artificial intelligence) generated content oriented to the field of literature and blog. The man-machine collaborative auditing and optimizing method comprises the following steps: S1, content acquisition and personalized constraint preprocessing: acquiring literature and blog field related content generated by an AI model; if the content is customized by a specific object, inputting an exclusive digital resource library and a style guide of the corresponding object into an AI generation model as constraint conditions; s2, multi-dimensional automatic evaluation: calling a multi-dimensional content evaluation model to carry out automatic analysis on the content, and outputting a quality score vector containing a plurality of evaluation dimension scores; s3, man-machine task dynamic allocation and collaborative auditing: calculating the comprehensive confidence of the content based on the quality score vector, and dynamically allocating the low-confidence content to the matched artificial auditor according to the confidence and the professional portrait of the auditor; and an auditor can trigger traceability verification based on the authoritative literature and blog mapping knowledge domain during auditing. According to the method, the AI generation content in the literature and blog field can be better audited and optimized.

The invention relates to a fruit maturity nondestructive intelligent detection method and system based on high-frequency dynamic excitation and acoustic vibration signal analysis. The method comprises the following steps: controlling an excitation device to apply high-frequency transient mechanical excitation to a fruit; synchronously collecting a vibration response signal on the surface of the fruit and an internal sound wave signal; extracting dynamic feature vectors including formant characteristics, sound wave attenuation coefficients and time domain attenuation constants from the sound vibration signals; inputting the feature vector into a time sequence convolutional network model to obtain a final maturity grade and a judgment confidence coefficient; and starting an active learning mechanism for a low-confidence sample, and continuously optimizing the model through manual annotation and incremental learning. By detecting the internal physical characteristics of the fruits, the defects that the traditional visual and tactile methods are interfered by surface states and the internal maturity is difficult to judge are overcome, and rapid, lossless and high-precision intelligent detection of the maturity of the fruits with self-evolution capability is realized.

The invention discloses a self-updating method and device of a financial risk control model, a storage medium and a terminal, relates to the technical field of data processing, can be applied to the field of financial risk control, and mainly aims at solving the problem that the model recognition accuracy is low due to the fact that an existing financial risk control model is not timely updated. The method mainly comprises the following steps: acquiring anomaly prediction scores and confidence coefficients of different newly-added samples obtained in a process of performing anomaly identification on newly-added sample data in a production environment by a financial risk control model; extracting a low-confidence sample from the newly added samples according to the abnormal prediction score and the confidence; performing clustering processing on the low-confidence samples, and constructing an updated training sample set according to target samples extracted from each cluster; and performing incremental learning update training on the financial risk control model based on the training sample set, so as to continue to execute anomaly recognition of subsequent sample data based on the financial risk control model completing update training. The method is mainly used for updating the financial risk control model so as to improve the timeliness of model updating.

The invention relates to the technical field of geological exploration and three-dimensional modeling, and discloses a geological structure three-dimensional measurement system based on inertial navigation. According to the system, an inertial measurement data flow is processed through a resolving module, the instantaneous space attitude and the relative displacement vector of a measurement carrier are obtained, and a basic three-dimensional geologic structure model is constructed. The quantization module calculates the confidence coefficient of a resolving result in real time and generates a space-time uncertainty distribution field; and the model correction module performs error propagation analysis by using the distribution field, and performs iterative optimization of geometric morphology on a low-confidence region in the model. The system imports lithology, formation and structure interpretation data from the outside, and associates the data as attributes to the model. And the result packaging module packages the optimized model, the association attribute and the uncertainty distribution field into a composite three-dimensional geological survey result file. According to the method, dynamic quantification and visualization of measurement uncertainty are realized, the model can be autonomously optimized, and the reliability and precision of inertial navigation three-dimensional geological modeling are improved.

The present invention discloses a system and method for healthcare diagnostics using foundation models with uncertainty triage, designed to deliver reliable, explainable, and safety-assured diagnostic outcomes across multimodal clinical data. The invention integrates a foundation model processor pretrained on diverse medical datasets with an uncertainty estimation processor configured to quantify epistemic and aleatoric uncertainties in diagnostic predictions. A triage control unit dynamically classifies cases into high, medium, and low-confidence categories based on computed uncertainty indices, ensuring that only high-confidence cases are automatically finalized, while uncertain or ambiguous cases are routed for clinician review. The system further incorporates a feedback adaptation processor that recalibrates model parameters and uncertainty thresholds based on expert feedback, maintaining alignment with clinical reliability standards over time. Implemented as a hardware-integrated diagnostic device, the invention supports real-time inference, secure data handling, and interpretability visualization through uncertainty heatmaps and attention overlays.

The invention discloses a natural resource monitoring multi-source heterogeneous spatio-temporal data fusion verification system and method, relates to the technical field of geographic information systems and spatial data processing, and is used for solving the problems of low fusion precision of multi-source heterogeneous data and difficulty in conflict processing. The system performs standardized packaging on the same geographic element of different data sources through an evidence packaging module to generate a standard evidence unit containing element type claim and data source credibility weight; the intelligent judgment module corrects the evidence probability based on the DS evidence theory in combination with the positioning precision, calculates the conflict degree and outputs a high-low confidence coefficient judgment result, so that the fusion reliability is improved; the semantic coupling module performs spatial topology and service semantic verification on the judgment result to generate a type recommendation sequence; the knowledge updating module dynamically adjusts data source credibility and semantic rule weight according to user confirmation information, a self-learning mechanism is established, and efficient conversion from multi-source heterogeneous data to high-credibility monitoring information is achieved.

The invention discloses an unformatted scale bill intelligent conversion method and system based on image recognition, relates to the technical field of character intelligent recognition, and solves the technical problems that the accuracy of subsequent OCR recognition is unstable due to the fact that the inclination angle detection precision is low and noise filtering and contrast enhancement effects are limited by scenes. According to the method, table line detection and text line analysis are adopted for the structured / no-table scale bill, the inclination angle detection precision is improved, layered denoising and adaptive contrast enhancement are achieved, the text recognition accuracy in a complex scene is improved, PaddleOCR is dynamically switched based on the Chinese proportion, a low-confidence region recheck mechanism is combined, the recognition error rate is reduced, and the recognition efficiency is improved. According to the technical scheme, a scale document field dictionary and a relation graph are constructed, intelligent term matching and error correction are achieved, the recognition problem of uncommon words and industry exclusive vocabularies is solved, a hierarchical rule system of basic verification, association verification and compliance verification is adopted, format, logic and industry / enterprise compliance full dimensions are covered, and the verification coverage rate is increased.

The invention discloses a cognitive function screening system and method based on an MMSE (Minimum Mean Square Error) prediction model, and relates to the technical field of data analysis, the method comprises the following steps: collecting physical examination index data, removing missing records, adopting a multiple interpolation method for interpolation, and obtaining multiple sets of complete data sets; a continuous prediction model is constructed, MMSE continuous prediction values are obtained, and a sensitivity analysis report is generated; constructing a first-stage classification model, adaptively dividing an optimal threshold combination, and dividing a sample into a high-confidence region, a to-be-discriminated region and a low-confidence region; if the sample size of the to-be-discriminated region is higher than a preset training threshold value, constructing an enhanced feature set, and constructing a second-stage classification model; when the prediction probability reaches the optimal re-discrimination threshold value, the classification result in the first stage is corrected, and otherwise, the classification result is maintained; if not, maintaining the classification result; and integrating the classification results to obtain a final classification result of all the samples.