51 results about "Confidence distribution" patented technology

The invention relates to the technical field of data processing, in particular to a high-noise data intelligent cleaning and credibility evaluation method based on uncertainty quantization, which comprises the following steps: constructing a noise manifold model of a high-dimensional data space through a Riemannian geometry method, and extracting data intrinsic structure features and noise distribution trajectory tensor; according to the noise distribution trajectory tensor, constructing an anisotropic diffusion equation, and generating confidence distribution with data manifold constraint; the geometric invariance of a data set is kept while noise stripping is carried out in a data manifold tangent space, and noise stripping and structure keeping are achieved; calculating an intrinsic credibility index and generating a credibility evaluation map coupled with the data manifold structure; according to the method, the intrinsic characteristics of the data manifold are kept in the cleaning process, the deformation degree of the quantized data structure is calculated through the geodesic deviation, and it is ensured that the intrinsic geometrical relationship of the data is not affected by noise stripping.

The invention discloses a large model-based text classification method, which comprises the following steps of: 1, setting a task semantic constraint rule, and writing a fixed cue word template; 2, sorting classification labels and adding semantic description information; 3, semantic similarity clustering and confidence allocation are executed, and a confidence multi-granularity label prefix tree is generated; 4, dynamic constraint autoregressive decoding is executed in combination with the confidence multi-granularity label prefix tree and the large language model, and a target classification label is output; 5, constructing a BERT auxiliary discrimination module to carry out confidence mask constraint; step 6, constructing a decoding temperature control parameter and a semantic bias item to perform semantic guidance and category distinguishing control; 7, performing uncertainty self-calibration on the autoregression decoding process of the large language model; and 8, constructing semantic consistency loss between the large language model and the BERT auxiliary discrimination module. According to the method, the stability and the classification efficiency of text classification in a small sample scene are improved.

The invention discloses a multi-relation extraction error propagation optimization method and device based on data collaborative enhancement, and the method comprises the steps: generating extended training data through grammar recombination and adversarial samples, carrying out the positioning and weighted sampling of a low-frequency relation according to an entity pair relative position, and obtaining an upper relation data set; and training an upper relation classifier based on the upper relation data set, and performing prediction through the trained upper relation classifier to obtain a prediction result and confidence distribution thereof. According to the method, through text enhancement and a weighted sampling strategy for relative position positioning based on entities, the problem of unbalanced data distribution is effectively relieved, the modeling capability of the upper relation classifier for long-tail distribution is remarkably improved, and therefore systematic optimization of the upper relation classifier for low-frequency relation recognition accuracy is achieved.

The invention provides an analysis method and system for a soil microbial community structure and a medium, and relates to the technical field of ecological environment monitoring. According to the method, environmental parameters, namely pH, organic carbon, moisture and oxidation reduction potential, of a soil sample are collected and subjected to normalization processing, phylum abundance, dominant phylum information and Shannon index classification community structure categories are obtained in combination with high-throughput sequencing, the support probability of the environmental parameters for classification is calculated through a multi-term logistic regression model, and the classification result is obtained. A D-S evidence theory is utilized to fuse multi-source BPA (basic probability allocation), joint confidence distribution is output, a conflict factor threshold value is set, an artificial review prompt is triggered under a certain condition to avoid errors caused by environmental parameters, finally, to-be-detected soil parameters are input into a pre-training model, confidence distribution is output in real time, and the detection accuracy is improved. And judging the structure of the microbial community in combination with a threshold rule, and outputting health, risk, transition state or uncertainty.

The invention provides a cable fault diagnosis and dynamic maintenance method and system based on multi-domain feature fusion, and relates to the field of fault diagnosis. Time-varying weighted Clark transformation is adopted to obtain a current vector trajectory diagram based on cable current information; inputting the trajectory diagram into an improved wavelet CNN network to obtain a fault diagnosis result; the improved wavelet CNN network comprises a multi-stage feature extraction module and a feature fusion module which are connected in series, and the multi-stage feature extraction module is used for extracting low-frequency features stage by stage; a high-frequency feature enhancement branch is added behind the last-stage feature extraction module and is used for extracting high-frequency local distortion features; and obtaining a fault category and corresponding confidence distribution based on a fault diagnosis result, inputting the fault category and the corresponding confidence distribution into the large language model to obtain a semantic diagnosis report containing mechanism description, potential risks and maintenance suggestions, and performing fine adjustment on the large language model by using line identification parameters. Accuracy, robustness and interpretability of cable fault diagnosis are effectively improved, and powerful support is provided for intelligent operation and maintenance of cables.

The invention discloses a priori mask optimization method based on similarity measurement. The overall process is as follows: foreground mask extraction based on a fusion prototype, background mask extraction based on a gating mechanism, and foreground mask optimization based on loop feedback. A trainable weighted fusion channel is established between a support prototype and a query prototype, the semantic deviation problem of traditional single prototype matching is solved, a background probability confidence coefficient matrix and cyclic consistency check are combined, cross-sample positioning of false positive prediction is achieved, temperature parameters are dynamically adjusted in the mask correction stage, and the accuracy of the temperature correction is improved. Compared with a fixed temperature strategy, the method is more suitable for confidence distribution characteristics of complex scenes; and the segmentation mask is optimized by fusing prototype similarity measurement and a cyclic feedback mechanism, so that false positive prediction is effectively reduced, and the distinguishing precision of the foreground and the background is improved.

The invention relates to the technical field of refrigeration house energy-saving control, and provides a refrigeration house energy-saving control method based on deep reinforcement learning, which comprises the following steps: dividing a refrigeration house according to a multi-scale grid, collecting data in real time, weighting and aggregating according to spatial characteristics, calculating spatial semantic codes and confidence indexes of each grid, and obtaining a standardized and traceable state vector; real-time energy consumption, grid temperature space gradient, long-term exposure economic loss, event condition triggering and weighted summation form a reward function, and a deep strategy network is adopted to train parameters in an accumulated reward mode; constructing a grey box and residual dynamic prediction model, shrinking the original temperature and humidity constraint, solving the prediction model through scenario under the shrinkage constraint, and carrying out weighted fusion on the depth strategy obtained by training and the solution of the prediction model according to confidence; and calculating a statistical value in the sliding window, constructing confidence distribution of strategy return, triggering a hierarchical response according to a detection result, and inputting all triggers into an audit log for threshold self-calibration.

Provided herein are systems for determining a location of a gross target volume of a patient. In some examples, systems can include one or more processors that are configured to obtain image data associated with a plurality of images of a lesion of a patient. For each image, the one or more processors can be configured to backproject points representing the lesion into the 3D space to determine a plurality of distribution confidence values for a subset of voxels within the three-dimensional space. The one or more processors can be configured to determine a three-dimensional confidence distribution based on confidence values from the plurality of distribution confidence values corresponding to each voxel of the 3D space and determine a position of the lesion within the 3D space based on the 3D confidence distribution.

The invention discloses a semi-supervised medical image segmentation method based on high-value region adaptive learning, and the method comprises the steps: constructing a semi-supervised medical image segmentation model based on high-value region adaptive learning, and the model comprises two parallel networks which have the same structure and independently update parameters; the two parallel networks output an original prediction result and a disturbed prediction result, and different value regions, namely a reliable and unstable region and an unreliable and unstable region, are screened out based on confidence distribution of the disturbed prediction result; training a reliable and unstable area through a confidence-guided cross prototype consistency learning module, and training an unreliable and unstable area through a dynamic teacher competition teaching module; and training the model by using the training set, selecting an optimal weight based on the verification set, inputting the test set into the model with the optimal weight, and outputting a result. Through the differential learning strategy, the method is accurately focused on the high-value region of the medical image, and the segmentation effect of the model in the key region of the image is remarkably improved.

The invention discloses a power distribution system intelligent management method based on big data, relates to the technical field of big data, and improves the accuracy and timeliness of power grid fault diagnosis. According to the method, a plurality of core point locations are set for historical operation data collected in different operation and maintenance periods and corresponding to the same operation and maintenance sub-period, the confidence degree of each core point location is obtained, and then standard operation data intervals of various types of operation data are obtained according to confidence degree distribution; the method comprises the following steps: intercepting abnormal historical data fragments under various fault item name combinations from historical operation data through a standard operation data interval, establishing an abnormal data vector combination according to the abnormal historical data fragments under different fault item name combinations, and judging whether the real-time operation data is abnormal or not through the standard operation data interval. And matching an abnormal data vector combination according to the type and the quantity of the real-time operation data judged to be abnormal, and further judging a fault item currently existing in the power distribution sub-area.

The invention discloses a confidence evaluation method and device for a large language model, equipment and a storage medium, and the method comprises the steps: obtaining a cue word set which comprises a plurality of cue words; sequentially inputting a plurality of cue words in the cue word set into the to-be-evaluated large language model to obtain a target sequence generated by the to-be-evaluated large language model based on each cue word, and obtaining the confidence coefficient of each target sequence according to the confidence coefficient of each lexical element in each target sequence; and obtaining a generation number of the target sequences, determining a confidence degree distribution result of the to-be-evaluated large language model according to the confidence degrees of all the target sequences when the generation number reaches a preset number, and generating an evaluation result of the to-be-evaluated large language model according to the confidence degree distribution result. Therefore, the problems that it is difficult for a user to effectively monitor the open source large language service model and it is difficult to provide evidence that the service model has degradation behaviors are solved, and the service quality of the open source large language model can be monitored on the user side.

The application discloses an uncertainty knowledge graph reasoning method based on semi-supervised confidence distribution learning, comprising the following steps: converting the triple confidence in the uncertainty knowledge graph training data into a confidence distribution; simultaneously learning the embedding of the uncertainty knowledge graph on the labeled data and the pseudo-labeled data generated by a pseudo-labeled data generator by using a relation learner based on confidence distribution learning; generating high-quality pseudo-confidence distribution labels for unlabeled data by using the pseudo-labeled data generator; iteratively training the relation learner based on confidence distribution learning and the pseudo-labeled data generator by using meta-self-training until the two converge; and inputting the data to be completed into the trained relation learner based on confidence distribution learning to perform reasoning, thereby achieving the completion of the uncertainty knowledge graph. The application can capture the supervision information of a small number of confidence or unseen confidence in the labeled data and is suitable for the scene where the triple confidence distribution of the uncertainty knowledge graph is unbalanced.

The present invention relates to systems and methods for determining a location of a gross target volume of a patient. In some examples, a system can include one or more processors configured to obtain image data associated with a plurality of images of a lesion of a patient. For each image, the one or more processors can be configured to backproject points representative of the lesion into a 3D space to determine a plurality of distribution confidence values for a subset of voxels within the three-dimensional space. The one or more processors can be configured to determine a three-dimensional confidence distribution based on the confidence values from the plurality of distribution confidence values corresponding to each voxel of the 3D space and determine a location of the lesion in the 3D space based on the three-dimensional confidence distribution.

The invention discloses a secondary equipment state evaluation method based on an evidence theory. According to the method, the application advantages of the evidence theory in comprehensive evaluation of secondary equipment are considered and analyzed. Firstly, equipment health states are divided; and secondly, determining an information fusion model according to the index characteristics of each level, fusing evidences by adopting an ER rule and an ER algorithm for different index levels, and finally obtaining a comprehensive evaluation model of the equipment state. Thirdly, determining a model input quantity, acquiring evidence weight by adopting an improved G1 method, acquiring confidence distribution of evidence by adopting a model based on a ridge-shaped membership function, and acquiring evidence reliability by adopting an evidence distance; and finally, determining the final state of the equipment according to the degree of support of the equipment to each state obtained by the evaluation model and the maximum membership principle. The method is suitable for comprehensively evaluating the secondary equipment, and the health state of the secondary equipment can be more accurately evaluated, so that the method can be used for guiding the analysis design, operation maintenance and other work of the secondary equipment.

The invention relates to a scene data classification method, a scene data application method, electronic equipment, a vehicle and a medium. The classification method comprises the following steps: executing a first classification task on scene data to obtain first confidence distribution data; a first confidence coefficient and a first category corresponding to the first confidence coefficient are obtained from the first confidence coefficient distribution data, and the first confidence coefficient is the highest confidence coefficient in the first confidence coefficient distribution data; a second confidence coefficient is obtained from the first confidence coefficient distribution data, and the second confidence coefficient is a second highest confidence coefficient in the first confidence coefficient distribution data; obtaining a first certainty index value representing the certainty degree of the first confidence coefficient according to the first confidence coefficient and the second confidence coefficient; and under the condition that the first deterministic index value is greater than or equal to a preset threshold value, determining the first category as a classification result of the scene data.

The present invention discloses an information fusion method for slope stability estimation. The present invention monitors the changes in groundwater level and surface crack width based on liquid level meters and crack meters, combines them into characteristic variables, uses a clustering algorithm to obtain a reference value set of the characteristic variables, and constructs a corresponding slope stability grade reference confidence distribution. After obtaining the monitoring sample online, the reference confidence vector is fused according to the activation degree to obtain the fused stability confidence distribution, and finally the stability of the slope is determined according to the confidence maximization principle. The present invention constructs the influencing factors affecting the stability of the slope into a multidimensional characteristic variable, and the characteristic variable will match all reference value sets to obtain a reference confidence distribution. The reference confidence distribution is corrected and recursively fused to achieve slope stability estimation. This method improves the limitations of traditional assessment and alarm methods that rely on single monitoring data and can only match local reference values and cannot fully reflect the movement state of the slope.

The invention discloses a source-domain-free transfer learning target detection method based on double-adapter pseudo tag generation, and the method comprises the steps: firstly initializing a double-teacher model and a student model, and carrying out the data enhancement of a target domain image; introducing a confidence adapter, dynamically adjusting a confidence screening range through statistical analysis according to confidence distribution characteristics of each category, and generating candidate pseudo tags; introducing a category imbalance adapter, and fusing a confidence historical threshold and a current threshold according to the frequency of each category to obtain a current final confidence threshold; and finally, supervising student model training by using the screened pseudo labels, and updating double-teacher model parameters through index moving average to realize passive domain migration of the model. According to the source-domain-free target detection method based on double-adapter pseudo tag generation, the problems that pseudo tag confidence distribution is inconsistent and category samples are unbalanced are effectively relieved, and pseudo tag quality and cross-domain detection performance are remarkably improved.

The invention provides a face image classification method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of artificial intelligence and the field of financial science and technology. The method comprises the steps that a to-be-classified face image is acquired, multi-layer feature information of the face image is extracted based on a pre-trained depth feature extraction model, and the multi-layer feature information comprises feature mapping results output by different layers of the depth feature extraction model; performing fusion processing on the multi-layer feature information to obtain fused feature representation; the fusion feature representation is input into a classification model, multiple face categories and corresponding confidence distribution are output, a dynamic classification threshold value is calculated according to the confidence distribution, and the dynamic classification threshold value is used for adjusting classification boundaries of the multiple face categories; and obtaining the category of the face image based on the plurality of face categories and the dynamic classification threshold.

The present application relates to a credible evidence reasoning method, apparatus and equipment for multi-source data fusion, which constructs a multi-source decision information system. In this multi-source decision information system, a support matrix is designed to quantify the correlation between multi-source data attributes, and the concepts of similarity and distance are combined to evaluate the relationship. First, multi-source data of different scales are converted into confidence distributions and given probabilistic meanings, so that the generated evidence can be applied to multi-scale data fusion. Then, a credible evidence reasoning rule is constructed, in which the credibility of the evidence is determined by its weight and reliability. By integrating multi-source data samples of different scales, the confidence distribution of the results is derived. This solves the problem of difficulty in determining the confidence distribution of evidence.

The application discloses a network performance evaluation method based on dynamic evidence reasoning rules, which comprises the following steps: dynamically adjusting the reference value of a test index based on the change of a network state; using the adjusted reference value to convert monitoring data into a confidence distribution form, converting the test index into a corresponding evidence form according to the mapping relationship of IF-Then; fusing the evidences corresponding to multiple indexes based on the evidence reasoning rules to obtain an evaluation result; and optimizing the model parameters by using an optimization algorithm through setting the constraint conditions and optimization targets of parameters. The application further discloses a network performance evaluation device based on dynamic evidence reasoning rules. The network performance evaluation method based on dynamic evidence reasoning rules can more accurately reflect the actual performance of a network in different scenarios according to the change of the network state, and meets the evaluation requirements of different network environments and business types.

The invention discloses a feature classification method and system based on an alimentary canal internal image, and relates to the technical field of image recognition, and the method comprises the steps: collecting an alimentary canal image in real time through an endoscope, and carrying out the preprocessing; based on a position estimation network formed by a convolutional neural network and a time sequence encoder, the image sequence is mapped to a standardized alimentary canal template, and endoscope position evaluation is achieved; the attention weight of each sample is calculated according to the time sequence similarity of the similar positions, and individual adaptive optimization of the image recognition model is completed; outputting an identification result, severity and confidence distribution of a focus type through an image identification model, constructing a graph structure model taking the focus type as a node, performing information propagation in a graph structure, and deducing focus probability distribution of an undetected area; and an inference result is fed back to the attention weight of the classification output layer, so that dynamic self-learning and global optimization of the model are realized. According to the method, the accuracy and stability of alimentary canal image recognition are remarkably improved.

The invention discloses a photoelectric angle measurement device performance trend prediction method considering causal correlation. The method comprises the following steps: constructing a performance trend prediction model; acquiring monitoring data of the preset index at a plurality of moments through combination of actual measurement and prediction; converting the monitoring data into a confidence distribution form according to the performance level; calculating a conditional mixing correlation coefficient according to the causal correlation of the subsystems; calculating the index weight and reliability of the preset index, converting the preset index into a confidence distribution form of the evidence, and determining an evidence fusion sequence; the final basic probability quality of each subsystem is calculated through evidence fusion, so that model parameters are preliminarily determined; and constructing an optimization objective function, and obtaining monitoring data of a preset index again to carry out model optimization training to obtain a trained photoelectric angle measurement device performance trend prediction model used for predicting the performance trend of the target photoelectric angle measurement device. According to the method, the causal correlation between the subsystems is incorporated into the performance trend prediction model, so that the prediction precision can be improved.

This invention discloses a method and system for super-resolution reconstructed image confidence calibration based on distribution matching. The method includes: generating pixel-level reconstruction error maps of the super-resolution reconstructed image and the real high-resolution image; obtaining the error statistical distribution after normalization and constructing an inverse distribution; generating multiple sets of confidence maps corresponding to the half-integral interval widths according to a preset step size; extracting the confidence statistical distribution of each set; calculating the JS divergence and cosine similarity of the bi-distribution; determining the optimal half-integral interval width index corresponding to the two indices; and generating the final calibration confidence map based on the optimal half-integral interval width. This invention overcomes the limitations of the commonly used assumption of uniform distribution of super-resolution reconstruction confidence. By using distribution matching to achieve adaptive optimization of the half-integral interval width, it makes the confidence distribution strongly correlated with the actual reconstruction error, significantly improving the accuracy and reliability of confidence calibration. It is applicable to various biomedical super-resolution reconstruction imaging scenarios.

The invention discloses a semi-supervised welded joint health state assessment method based on credibility enhanced BRB. The method comprises the following steps: taking welding joint data as an input sample, performing forward reasoning through a confidence rule base, and generating multi-class confidence distribution representing the health level of a welding joint and internal credibility; after posterior calibration is carried out on the confidence distribution, welding process external quality observation information and the internal credibility are fused, and the total credibility used for welding quality risk control is obtained; meanwhile, based on the historical calibration set data of the welding joint, a health level prediction interval with a preset coverage probability guarantee is generated for the current welding joint sample through conformal prediction; and finally, outputting a quality risk decision in combination with the total credibility and the health level prediction interval. Through microparameterization and evidence reasoning of the credibility enhancement BRB and enhancement of the two ends of the output layer and the training normal form, the practicability and reliability of health state assessment of the welding joint can be improved.

The application discloses a kind of based on medical image multimodal data fusion collection method and system, comprising: obtaining the medical data of at least two modalities of patient and its time stamp, and acquisition parameter is obtained, including at least two kinds in image quality, physiological state before examination, biological variation, clinical event intervention, data volume factor;According to acquisition parameter, the time axis confidence distribution function of each mode is constructed, at least including one of time decay, acquisition quality, data volume, clinical event intervention cliff and maximum prediction window truncation subfunction;Each modality data is backtracked and / or predicted, generates time migration data and distribution confidence;Each mode is assigned weight, the confidence of each time point is weighted sum, the time point corresponding to maximum value is the best fusion time point, the migration data or original data of this time point is fused and collected.The application significantly improves the reliability of multimodal data time alignment and the accuracy of fusion result.

The invention discloses a production process quality intelligent monitoring method and system based on deep learning driving, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly obtaining a multi-mode quality inspection index state of a target product and a plurality of to-be-matched index state types, and extracting state features to obtain corresponding feature vectors; calculating state confidence distribution according to the multi-modal feature vector; obtaining a relation topological structure of each composite abnormal quality inspection condition; performing embedding processing on the category feature vector by combining confidence distribution and a topological structure to obtain a depth category feature vector; and finally, determining a target index state category as a quality monitoring result according to the matching coefficient, thereby realizing intelligent monitoring on the quality of the production process.

The invention discloses an intelligent monitoring method and system for power equipment and a storage medium, and relates to the technical field of intelligent operation and maintenance and fault prediction of the power equipment, and the method comprises the following steps: carrying out the time alignment and feature fusion of obtained multi-source data through a space-time attention mechanism, and constructing a state representation tensor; performing semantic factor deconstruction on the state representation tensor, outputting an equipment anomaly probability score, and obtaining fault type confidence distribution; inputting the equipment anomaly probability score and the real-time monitoring data into a physical information neural network to generate first data; generating a fault evolution path according to the first data in combination with the fault type confidence distribution; according to the method, deep fusion of multi-source data and intelligent prediction of a fault evolution path are realized by introducing a method of combining a space-time attention mechanism and a physical information neural network, and the problems of low utilization efficiency of multi-modal data and low fault prediction precision in the prior art are solved through fusion of semantic factor deconstruction and a confidence mechanism.

The invention provides a big data mining method and system based on implantation sub-packaging process development, and relates to the field of data processing, and the method comprises the steps: carrying out the process parameter detection based on a to-be-mined packaging process development data set to obtain first confidence distribution, carrying out the process parameter detection through a detection neural network to obtain second confidence distribution, in the detection process, in combination with the detection performance of the detection neural network, the problem of inaccurate detection caused by the inherent classification capability in the detection mining of the pre-trained neural network is relieved, and the detection precision is improved. Based on the packaging process development data set to be mined and the target process parameters, performing type mapping on the target process parameters to obtain third confidence degree distribution and fourth confidence degree distribution so as to obtain target process parameter types; in the type mapping process, the classification performance of the type mapping neural network is combined again, inaccurate detection in classification mining of the pre-training neural network is relieved, and the type mapping precision is improved.

The application discloses a source-free domain migration learning target detection method based on double adapter pseudo-label generation, first, a double teacher model and a student model are initialized, and data enhancement is performed on target domain images; a confidence adapter is introduced, according to the confidence distribution characteristics of each category, the confidence screening range is dynamically adjusted through statistical analysis, and candidate pseudo-labels are generated; a category imbalance adapter is introduced, according to the frequency of each category, the final confidence threshold value is obtained by fusing the confidence historical threshold value and the current threshold value; finally, the student model is trained by using the screened pseudo-labels, and the double teacher model parameters are updated through the exponential moving average, so that the source-free domain migration of the model is realized. The source-free domain target detection method based on the double adapter pseudo-label generation effectively alleviates the problems of inconsistent pseudo-label confidence distribution and category sample imbalance, and significantly improves the pseudo-label quality and cross-domain detection performance.

The present application relates to the technical field of automatic driving, and more particularly to a multi-sensor fusion automatic driving environment perception method and system. The method comprises: performing intra-modal feature extraction on multi-modal perception data to obtain intrinsic feature representation, and projecting to a unified semantic space to obtain cross-modal features; decoupling the cross-modal features into a causally related feature subset and an environment-dependent feature subset, detecting feature stability boundaries through generative adversarial perturbation and calculating confidence distribution to obtain causally related features with uncertainty quantification; calculating reliability weights using the environment-dependent feature subset and weighting and calibrating the causally related features; constructing a dynamic spatio-temporal causal graph based on the calibrated causally related features and performing causal reasoning propagation to obtain an environment state estimate; and constructing a hypothetical scenario branch through counterfactual reasoning mechanism and generating a control decision. The method improves the robustness of environment perception and the reliability of decision-making.