25 results about "Confidence set" patented technology

The invention discloses a multi-time-scale park integrated energy system distribution robust optimization scheduling method, which comprises the following steps of: constructing an electric heating collaborative system model taking a combined heat and power generation unit as a core, and introducing a carbon transaction mechanism; constructing a confidence set in combination with a 1-norm and an infinity-norm, and respectively making a robust start-stop plan and a flexible operation strategy in day-ahead and intra-day two-stage scheduling; a column and constraint generation algorithm is adopted to decompose the constructed day-ahead and intra-day two-stage model into a main problem and a sub-problem for repeated iterative solution, and an optimal scheduling scheme with both economical efficiency and robustness is obtained. According to the method, the uncertainty of new energy prediction is fully considered, and the coping of the park to the randomness of the new energy can be better played through processing of different time scales.

The invention relates to the field of computer information processing, in particular to a multi-source heterogeneous knowledge graph construction method and system. The method comprises the steps that ancient books, clinical literatures and traditional Chinese medicine, medicine and disease database data are accessed, and entity candidate sets are formed after field mapping extraction; calculating a name similarity, an attribute similarity and a semantic vector similarity for the entity candidate set, and inputting multiple classes of similarities into a fusion network to generate an entity alignment mapping table; constructing a remote supervision annotation set and training a relationship classification model, driving expert annotation increment update through model uncertainty, and outputting a relationship triple set and a corresponding relationship confidence set; and finally, forming a three-layer heterogeneous graph structure communicated with the syndrome type, the disease, the prescription and the medicinal components, and writing the three-layer heterogeneous graph structure into a graph database to generate a knowledge graph database. Unified modeling of entity alignment, relation extraction and relation strength quantification in the multi-source heterogeneous data is achieved, and the structural consistency in the knowledge graph construction process is improved.

The invention provides an industrial large model construction method and a storage medium. The method comprises the steps of obtaining multi-modal fusion data of an industrial field; performing scene classification on the industrial field to obtain industrial subdivided scenes; according to the multi-modal fusion data, determining a multi-modal data set of each type of industrial subdivision scene; according to the multi-modal data set, constructing a knowledge organization structure of each type of industrial subdivision scene; according to a text mode in the multi-mode data set, finely adjusting the small language model to obtain a routing center command body; the routing center command body is used for identifying a received user instruction and determining a target industrial subdivision scene confidence degree set; the target industrial subdivision scene confidence coefficient set is used for screening a target model from a combat model library by adopting an adaptive routing decision algorithm according to the target industrial subdivision scene confidence coefficient set, and generating a target answer by adopting the target model; wherein the combat model library comprises a scene model library, a standby reasoning body and a scene expert agent.

The application relates to a target tracking method and system based on classification information assistance, which comprises the following steps: firstly, calculating target motion state prediction and target existence state prediction at the current time; then, combining spatial information of sensor measurement at the current time to calculate associated spatial probability and associated prior probability of the target and the measurement; then, calculating an associated initial confidence set of the target and the measurement according to the two probabilities; then, extracting features from radar range-Doppler spectrum information by using a radar range-Doppler spectrum feature extraction model; then, calculating an associated confidence set of the target and the measurement after fusing classification confidence obtained by using a target classification model and the associated initial confidence of the target and the measurement; then, calculating an update message of the measurement according to the associated confidence set and the spatial information of the measurement; and finally, calculating motion state estimation and existence state estimation of the target at the current time according to the update message and the target motion state prediction and the target existence state prediction. The application can significantly improve the performance and robustness of target tracking.

The present application relates to the field of artificial intelligence tracking technology, and discloses a pedestrian trajectory tracking method based on artificial intelligence.The method comprises collecting multi-source sensor pedestrian movement feature data stream, extracting initial trajectory key points as tracking reference points;dividing associated historical position data into a high-confidence trajectory segment set and a to-be-verified trajectory segment set, and discarding the to-be-verified set;arranging the high-confidence set in descending order according to the number of trajectory points, selecting the first several segments as optimal trajectory segments, and calculating the proportion;comparing the proportion with a dynamic screening threshold, outputting the high-confidence set as an initial tracking result, or outputting a trajectory checking instruction generated by comparing trajectory data;in response to the checking instruction, dividing the space grid of the optimal trajectory segment, and outputting an optimization instruction in combination with historical trajectory features;updating the reference points based on the optimization instruction, and screening and outputting secondary divided trajectory data;finally, obtaining parameters to generate a tracking efficiency evaluation coefficient, and starting a parameter correction strategy when the coefficient is lower than an optimization threshold.

The invention discloses a multi-frame superposition point cloud super-resolution system for three-dimensional reconstruction, particularly relates to the field of structured light three-dimensional scanning, and is used for solving the problem that a thick shell and false details are formed due to the fact that abnormal points and geometric conflicts are mixed and fused during repeated scanning and superposition. The method comprises the following steps: collecting multi-frame structured light point clouds of the same target to generate a multi-frame point cloud set, synchronously generating a point-level confidence set, registering the multi-frame point cloud set to obtain a pose transformation result, generating a unified coordinate point cloud set, and generating a point-level registration residual set; generating a complementary sampling label on the unified coordinate point cloud set according to a fringe phase reproduction degree parameter and a line-of-sight interlayer separation parameter, outputting a complementary point set, an abnormal point set and a to-be-corrected point set, and executing attachment merging and limited fusion according to the complementary sampling label to obtain a super-resolution point cloud result. And the point-level confidence set and the complementary sampling label are updated through re-input consistency verification, and a super-resolution point cloud result subjected to verification constraint is output.

The application provides a compatible method, system and medium of a multi-voltage adapter based on Feiteng D3000, first, according to first voltage information input by the adapter, a first switch control signal is generated based on preset voltage threshold comparison logic, then, according to the first switch control signal, a corresponding power supply path is turned on to obtain second voltage information, wherein, for the second power supply path, a first voltage regulating parameter is configured based on preset voltage regulating logic to obtain the second voltage information, and finally, according to the second voltage information, first confidence information is obtained based on preset confidence setting logic to judge whether a redundant protection mechanism is triggered. Through voltage detection and logic decision, the application solves the compatibility problem of the product, and through dynamic voltage regulation and redundant protection, multiple protections are formed to improve the reliability and safety of the product.

The invention discloses an ultrasonic video ovary lesion analysis method fusing anatomical prior and spatial-temporal characteristics. Extracting features of three scales from each frame of image in the ultrasonic video sequence through the backbone network, and stacking the features of the same scale along the time dimension to obtain multi-scale frame-level features; the features of different scales are optimized and fused through a feature pyramid module, and scale enhancement features are obtained; through an anatomical prior enhancement module, generating a prediction segmentation map of the ovary and the focus according to the scale enhancement features; through a gating mechanism, obtaining anatomical prior enhancement features according to the prediction segmentation map and the scale enhancement features; performing space-time fusion on the anatomical prior enhancement features through a pixel-level time sequence attention mechanism through a space-time feature fusion module to obtain fusion features; and through the target detection head, generating a focus bounding box set and a confidence set of each frame of image based on the fusion features. The problems that a traditional single-frame detection method lacks time sequence association and background interference in a dynamic video is large can be solved.

This invention discloses an intelligent warehouse automated inventory management method and system, relating to the field of warehouse management technology. The method includes: deploying automatic identification tags at each storage location in the warehouse and constructing a digital map of the warehouse; a mobile inventory device collecting tag signals from the automatically identified tags at the current location and the real-time positioning coordinates of the inventory device itself; filtering all storage locations within a preset radius centered on the current location of the inventory device; determining the spatial association and attribution of the candidate storage location set based on the tag signals and real-time positioning coordinates to obtain a storage location attribution confidence set; performing optimal matching based on the storage location attribution confidence set to obtain the tag identification result; and updating the inventory data based on the tag identification result. This invention effectively suppresses interference caused by multipath reflection of radio frequency signals and coupling of signals from adjacent storage locations in dense metal environments through multi-constraint verification in the spatial dimension, significantly improving the accuracy of storage location identification and the reliability of inventory data while ensuring real-time performance.

The embodiment of the application provides a device safety risk identification method and system based on continuous interaction of mine agents, after the mine agent receives and analyzes device abnormal natural language description, calls analysis multi-source time sequence signal flow, outputs an initial fault hypothesis set and an initial fault confidence set, according to the confidence deviation of the initial fault confidence set relative to a preset confidence threshold, constructs a fault hypothesis space convergence strategy of the initial fault hypothesis set to call multi-source compensation signal flow and iteratively update fault root cause, until the updated fault confidence set meets the confidence threshold, and outputs a structured device fault diagnosis report. The problems that the existing mine device fault diagnosis exists cross-monitoring system data island and relies on artificial experience, which leads to deviation of fault diagnosis and delay of diagnosis result feedback response are solved. The mine device fault diagnosis achieves a second-level response, completely eliminates the artificial experience blind area and cross-system data island, and greatly reduces the operation and maintenance cost.

The invention provides an unmanned area road identification method and system based on a convolutional neural network, and relates to the technical field of data identification, and the method comprises the steps: obtaining a plurality of road scene images; road semantic segmentation and semantic uncertainty analysis are carried out on the plurality of road scene images based on a convolutional neural network, and road space structure optimization and geometric uncertainty analysis are respectively carried out on a plurality of road scene image segmentation region sets by using a geometric consistency mechanism; and based on the plurality of pixel-level road probability-semantic confidence degree sets, the plurality of road space structure sets and the plurality of road geometric consistency coefficient sets, carrying out dual-uncertainty cooperative credible region screening, and carrying out network graph structure topology on the initial road space structure set to obtain a road recognition result. The technical problem of insufficient recognition accuracy and reliability of a depopulated area road recognition method in the prior art is solved. The technical effect of improving the accuracy and reliability of road recognition in the unmanned area is achieved.

The present application relates to the field of computer information processing, and particularly relates to a multi-source heterogeneous knowledge graph construction method and system. The method comprises the following steps: accessing ancient books, clinical literature, and data of traditional Chinese medicine, drugs and disease databases, and forming an entity candidate set after field mapping extraction; calculating the name similarity, attribute similarity and semantic vector similarity of the entity candidate set, and inputting the multiple types of similarity into a fusion network to generate an entity alignment mapping table; constructing a remote supervision annotation set and training a relationship classification model, driving expert annotation incremental update by model uncertainty, and outputting a relationship triple set and a corresponding relationship confidence set; finally forming a three-layer heterogeneous graph structure connected by syndrome type, disease, prescription and drug components, and writing into a graph database to generate a knowledge graph database. The present application realizes unified modeling of entity alignment, relationship extraction and relationship strength quantification in multi-source heterogeneous data, and improves the structural consistency of the knowledge graph construction process.

This invention provides a method and apparatus for automatically segmenting racket-swinging sports videos. The method includes: decomposing the racket-swinging sports video stream into video frames and inputting them into a motion recognition model to obtain a confidence set of the athlete's actions on each first-type video frame; inputting the confidence sets of each first-type video frame into a keyframe discrimination model to obtain the action video frame type to which each first-type video frame belongs; segmenting the sports video stream according to the positions of each serve start video frame and rally end video frame in the sports video stream; and combining them to obtain a set of valid rally videos for racket-swinging sports. This invention can automatically segment racket-swinging sports videos, improving the segmentation efficiency of racket-swinging sports videos.

The invention provides a source load coordinated planning method considering multi-energy complementation and direct current delivery, and relates to the technical field of power system planning. The method comprises the following steps: constructing a source-load coordination expansion planning model by taking the minimum total cost of a power system as a target; introducing the confidence set into the source-load coordination expansion planning model, and determining the constraint of the confidence set; establishing a two-stage source load coordination distribution robust planning model based on the source load coordination expansion planning model introducing the confidence set; and solving the source-load coordinated distribution robust planning model by adopting a column constraint uploading algorithm, and generating a planning scheme. The method can give full play to the dynamic regulation and control capability of the hydropower station and the pumped storage power station, and improves the multi-energy complementary benefit; and the power transmission flexibility can be improved by optimizing the power of a direct-current transmission line.

This invention discloses a lane line tracking method, apparatus, terminal device, and storage medium. The method includes: acquiring a current set of lane lines and a set of historical lane lines corresponding to each current lane line; performing a lane line confidence subset acquisition action on each historical lane line set to obtain a lane line confidence set; and obtaining a lane line tracking result by combining the current set of lane lines with the historical set of lane lines based on the lane line confidence set. The lane line confidence subset acquisition action includes: for any current lane line, acquiring a set of distances between matching sampling points and a set of angles between matching sampling points for each historical lane line, based on the current lane line and each historical lane line; obtaining a lane line distance confidence based on the set of distances between matching sampling points; obtaining a lane line angle confidence based on the set of angles between matching sampling points; and obtaining a lane line confidence subset based on the lane line distance confidence and the lane line angle confidence to improve the accuracy of lane line tracking.

A computer-implemented method (100) for calibrating that an amount of uncertainty in an industrial process (1a) and / or an electrical network (1b) will exceed a given threshold, the method comprising the steps of: providing (110) an initial desired value; providing (120) a predictive model (2) configured to predict future values of the quantity from the values of the quantity; a history of past values of measurements of the quantity is provided (130). Determining (140), for this history, a difference between a value predicted by the prediction model and a measured past value as a residual (3); determining (150) an estimated probability density function (4) of the residuals (3); determining (160), based at least in part on the history and / or residual (3), a confidence set to which the estimated probability density function (4) belongs, said confidence set comprising a probability density function that is equivalently reasonable given the history and / or residual (3); and determining (170) a new value based at least in part on the dimensions of the confidence set such that if less than is exceeded, it is ensured that the initial desired value is not exceeded even if the residual should be represented according to any probability density function in the confidence set instead of the estimated probability density function (4).

The application discloses a construction site BIM modeling method and system based on three-dimensional laser scanning, and relates to the technical field of three-dimensional modeling.The method comprises the following steps: in a construction site, three-dimensional laser scanning is performed to obtain three-dimensional laser data, laser data clustering is performed to obtain clustered laser data clusters; based on a preset construction structure, the clustered laser data clusters are iteratively fitted to obtain a fitting structure confidence set, and a matching construction structure is screened; according to construction progress information of the construction site, the fitting structure confidence set is combined to configure a low confidence proportion, and a low-confidence clustered laser data cluster is screened and obtained; the low-confidence clustered laser data cluster is combined with the nearest clustered laser data cluster respectively, preset occlusion combined structure fitting is performed, an occlusion structure confidence set is obtained, a matching occlusion combined structure is screened and obtained, BIM modeling is performed in combination with the matching construction structure, and a modeling result is obtained.The application effectively improves the BIM modeling precision of a construction site.

The application discloses a general scientific research entity corpus construction method and device based on scientific literature and a medium, relates to the technical field of natural language processing and knowledge engineering, and comprises the following steps: building an extensible model pool; automatically pre-annotating a scientific literature abstract set for general scientific research entities to obtain an extracted general scientific research entity set and a confidence set; filtering and screening the confidence set to obtain a difficult sample set; extracting the difficult sample set to obtain a large language model pre-annotation result set; and obtaining a large language model correction annotation result set and a large language model correction degree set. The application solves the technical problems that the existing general scientific research corpus construction process is imperfect, the model lacks an effective feedback optimization mechanism, and the scientific research entity extraction efficiency is low and the accuracy is insufficient, and achieves the technical effects of realizing a high-quality general scientific research entity corpus construction process and improving the entity extraction efficiency and accuracy.

The invention relates to the technical field of bearing fault diagnosis, in particular to a low-speed heavy-duty bearing fault feature mining method and system based on broadband modal decomposition, and the method comprises the steps: collecting an original signal through a multi-channel vibration sensor, and carrying out the preprocessing and broadband modal decomposition to extract a target energy distribution matrix; respectively calculating a first matching degree, a second matching degree and a third matching degree by utilizing the typical energy distribution matrix, the reference energy fluctuation interval set and a deep learning model; and combining the multi-dimensional weight set with a comprehensive matching formula to generate a target matching confidence coefficient set, and finally identifying a target fault mode corresponding to the maximum matching confidence coefficient. The precision, the robustness and the real-time performance of fault diagnosis of the low-speed heavy-duty bearing under the complex working condition can be remarkably improved.

The invention provides a self-adaptive control method and system based on multi-parameter perception, and relates to the technical field of intelligent control, and the method comprises the steps: obtaining sensing measurement data and unstructured object description information, and forming an observation sequence containing sensing measurement data time sequence segments and associated object description information thereof; generating model input data based on the observation sequence, inputting the model input data into a pre-training semantic inference model to obtain object semantic output, and determining a semantic tag set and a credibility set according to the object semantic output; and determining a control prior parameter set based on the semantic tag set and the credibility set, and further generating a control vector based on the control prior parameter set and the observation sequence. Robust processing of uncertainty of object attributes is realized by introducing semantic tags and credibility constraints, strategy deviation and instability caused by misjudgment of the object attributes can be suppressed under the condition that multi-source sensing data fluctuates along with time, and consistency and robustness of control results are improved.

The embodiment of the invention provides a new media content personalized generation method and system based on a user portrait, and belongs to the technical field of new media content generation, single-point determination estimation of a cross-platform user portrait is converted into multi-anchor point confidence set estimation by constructing a shadow anchor point portrait, and confidence constraint generation is introduced in a generation stage. In this way, under the scene that cross-platform behaviors are inconsistent and the portrait uncertainty is high, personalized contents which are consistent in cross-platform theme and meet different platform context constraints can still be output. And meanwhile, through a cross-platform consistency verification and trigger type correction mechanism, a collaborative process of generation, verification, backflow updating and re-planning / regeneration is realized, so that the cross-platform content operation efficiency and the user experience are improved. Therefore, the problem that when a cross-platform user portrait has identity drift and uncertainty, a traditional method is difficult to stably generate cross-platform consistent personalized content conforming to the context of each platform is solved.

The invention discloses a construction site BIM modeling method and system based on three-dimensional laser scanning, and relates to the technical field of three-dimensional modeling. The method comprises the following steps: in a construction site, carrying out three-dimensional laser scanning to obtain three-dimensional laser data, and carrying out laser data clustering to obtain clustered laser data clusters; based on a preset construction structure, performing iterative fitting on the clustered laser data cluster to obtain a fitting structure confidence coefficient set, and screening a matched construction structure; according to construction progress information of a construction site, a low-confidence proportion is configured in combination with the fitting structure confidence degree set, and a low-confidence clustering laser data cluster is obtained through screening; and the low-confidence-coefficient clustering laser data clusters are combined with the nearest clustering laser data cluster, preset shielding combination structure fitting is carried out, a shielding structure confidence coefficient set is obtained, a matched shielding combination structure is obtained through screening, a matched construction structure is combined, BIM modeling is carried out, and a modeling result is obtained. According to the method, the BIM modeling precision of the construction site is effectively improved.

The application discloses a vehicle longitudinal interaction intention estimation method, an electronic device and a storage medium, and belongs to the technical field of intelligent driving. The method comprises the following steps: acquiring original observation data of surrounding traffic participants of a vehicle, and fusing state information of each traffic participant; screening a target traffic participant having a longitudinal interaction relationship with the vehicle; constructing a target intention prior distribution matched with a current traffic scene based on historical longitudinal behavior data of the target traffic participant; determining an observation likelihood for each type of longitudinal interaction intention based on the state information of the target traffic participant; determining a target posterior probability corresponding to each type of longitudinal interaction intention through a hierarchical Bayesian model based on the target intention prior distribution and the observation likelihood; and generating an intention confidence set based on the target posterior probability corresponding to each type of longitudinal interaction intention, so as to be used for vehicle behavior decision. The application improves the stability and reliability of the vehicle in estimating the intention of the surrounding traffic participants in the longitudinal interaction scene.

The invention discloses a multi-mode intelligent routing inspection and early warning method and system for a new energy line, belongs to a routing inspection and intelligent operation and maintenance technology for a new energy transmission line, and is suitable for state perception and risk identification of new energy transmission lines such as wind power, photovoltaic and the like. The method comprises the steps of multi-modal data acquisition, unified time and space alignment, physical prior modeling, feature fusion, defect identification, health scoring, dynamic threshold generation, work order feedback and the like. The fusion robustness is enhanced by introducing physical consistency prior, dynamic early warning is realized by adopting generalized Pareto distribution and a confidence set mechanism, and closed-loop processing is completed in combination with a knowledge graph and digital twinning. According to the invention, the inspection identification accuracy, the early warning adaptability and the operation and maintenance response efficiency are improved.

The invention relates to an air conditioner and a control method thereof, a storage medium and a program product. The method comprises the steps that multi-source information of a target animal is obtained; the multi-source information comprises radar data, image data and sound data; determining a first confidence coefficient set according to the multi-source information; the first confidence coefficient set comprises N preset state confidence coefficients; each preset state confidence coefficient is used for representing the probability that the target animal is in each preset state in the preset state set; n is the number of preset states contained in the preset state set; the preset state set comprises a heatstroke state, a hot state, a cold state and a sleep state; and on the basis of a preset adjusting rule, according to the determined first confidence coefficient set, air conditioner operation parameters are adjusted so as to improve the comfort level of the target animal. According to the method, the adjusted operation parameters of the air conditioner can more accurately adapt to the actual environment requirements of the target animal, so that the comfort improvement effect brought by the air conditioner to the target animal is improved.