8results about How to "Disambiguation" patented technology

The invention relates to the technical field of underwater target detection, and discloses an underwater environment real-time lightweight target detection method and a readable storage medium, and the method comprises the steps: improving a standard YOLOv11n model, employing a C3-CPMSFA module, fusing global context information and multi-scale local features, and combining with a partial convolution strategy to achieve efficient feature extraction; feature processing is expanded to a frequency domain by using a C2SFA module, space-frequency joint adaptive sensing is realized by using a dynamic frequency domain mask, and back scattering noise is accurately filtered out; and semantic noise reduction and small target enhancement are realized through multi-scale feature progressive fusion and depth separable convolution by using an auxiliary head. According to the method, the accuracy and robustness of underwater small target detection are remarkably improved while light weight and real-time performance are kept.

The application discloses an online medical intelligent doctor guiding system based on text graph embedding and belongs to the technical field of medical doctor guiding. Through fine-grained medical entity recognition and ambiguity resolution on online medical consultation texts, fine-grained entities can be accurately recognized, and the problem of fine semantic granularity can be solved. Through matching of a medical dictionary and context, the ambiguity generated by the entity can be effectively eliminated. Through a dynamic updating mechanism, the latest medical knowledge can be ensured to be incorporated into the graph, and the knowledge can be prevented from being outdated. From text preprocessing to graph construction, a complete link is formed, which can provide a high-quality knowledge base for subsequent graph embedding and doctor guiding reasoning. The entity vector obtained through processing contains graph structure, deep semantics and clinical logic, and can effectively solve the defects of single mode of traditional embedding. Through a constraint loss function, the vector can be ensured to conform to the path rules of authoritative clinical guidelines, clinical logic consistency can be realized, and vectors that violate medical common sense can be avoided.

The application relates to a data index identification method and device, equipment and a storage medium, and relates to the field of data processing. The method comprises the following steps: based on historical data, analyzing and processing each data field in a plurality of data fields to determine at least one first data field; based on a preset dimension matching rule, analyzing and processing each data field to determine at least one second data field; based on a preset metric matching rule, analyzing and processing each data field to determine at least one third data field; and based on the at least one first data field, the at least one second data field and the at least one third data field, determining the type of each data field. Thus, the dimensions and metrics of the data index can be automatically identified based on the historical data, the preset dimension matching rule and the preset metric matching rule, and the technical problem that a large amount of manpower is consumed and the efficiency is low when the dimensions and metrics of the data index are manually identified can be solved.

The invention discloses an accounting system for carbon emission of domestic animals in a family pasture, and relates to the technical field of carbon emission. The invention relates to a household pasture livestock carbon emission accounting system, which comprises a parameter database module, a seasonal feeding day number dynamic calculation module, a daily ration dry matter intake and nutritional ingredient accounting module, an IPCCTier2 execution core emission calculation module, a module for uniformly converting into carbon dioxide equivalent and summarizing, and a batch processing and result output module. The precision of micro-scale accounting is fundamentally improved, fine distinguishing and dynamic accounting of seasonal feeding modes and different livestock life stages are achieved by introducing localized parameters, the deviation of a traditional Tier 1 method is overcome, the standardization and efficiency of scientific research are enhanced through the model, and the method is suitable for large-scale popularization and application. An input and output interface and an end-to-end automatic calculation chain eliminate ambiguity possibly generated when researchers interpret and splice complex formulas by themselves, and the comparability between different research results is guaranteed.

The invention provides an algorithm exercise knowledge point automatic labeling method and system based on multi-agent cooperation, and belongs to the field of artificial intelligence and educational information, and the method comprises the steps: S1, inputting a to-be-labeled algorithm exercise into a plurality of labeling expert agents, and obtaining a preliminary knowledge point labeling set outputted by each labeling expert agent; s2, jointly inputting the preliminary knowledge point labeling set and a predefined standard knowledge point set into an alignment expert agent to obtain an alignment knowledge point labeling set; s3, inputting the aligned knowledge point labeling set into a review expert agent to obtain a knowledge point labeling result; and S4, taking the knowledge point labeling result and the to-be-labeled algorithm exercises as input, and repeatedly executing the steps S1 to S3 until the maximum number of iterations is reached, so as to obtain a final knowledge point labeling result. According to the method, comprehensive and accurate marking of the knowledge points is realized through accurate distinguishing of the knowledge points and deep analysis of complex problem solving logic.

This invention discloses a method for staging TUG (Turn-Off) test actions based on a kinematically constrained Hidden Markov Model (HMM), relating to the fields of medical rehabilitation assessment and action recognition technology. The method includes: acquiring and preprocessing human IMU data collected by an IMU sensor during a timed stand-up-walk test; extracting the slope of linear regression of acceleration and the maximum absolute value of angular velocity using a sliding window, and fusing them to generate observation features; simultaneously calculating the human torso tilt angle through integration; discretizing the observation features to generate an observation sequence; constructing a HMM containing six TUG action states; introducing angle interval matching weights to improve the Viterbi algorithm for decoding the observation sequence; correcting the path probability by judging the angle matching degree; and finally outputting the action state sequence to achieve automatic six-stage staging of the TUG test. This invention improves the accuracy of action staging and reduces computational complexity by introducing kinematic prior knowledge to constrain the HMM decoding process.

The invention discloses a processing method and device for project features in an engineering quantity list, a medium and electronic equipment, and the method comprises the steps: obtaining a to-be-processed target engineering quantity list, and determining a project feature group associated with the target engineering quantity list; wherein the project feature group is associated with a job type; determining a keyword template corresponding to the job type; wherein the keyword template is a data structure defined with keyword extraction rules under a specific job type; the keyword template comprises a field list and a format specification; performing keyword extraction on the project feature group based on a keyword template to obtain feature keywords corresponding to the project feature group; and determining a target cost item corresponding to the target project quantity list based on the project feature group and the feature keyword corresponding to the project feature group. According to the invention, key information extraction is carried out on the project features in the bill of quantity, parameter integrity, structure normalization and information accuracy can be ensured, and the accuracy of cost item determination can be improved.

The application discloses a gas pipeline fault diagnosis method and system based on knowledge reasoning, relates to the field of knowledge graphs, and comprises the following steps: obtaining high-quality symptom data through dynamic monitoring and data preprocessing, and constructing and continuously updating a fault diagnosis knowledge graph; extracting a standardized fault feature vector based on the graph, performing preliminary diagnosis and confidence evaluation by using a hybrid knowledge reasoning engine, combining case matching and secondary reasoning verification results and accurately positioning; continuously correcting rules and the graph through on-site verification, realizing closed-loop optimization and performance improvement, and forming a continuously self-improving pipeline fault intelligent diagnosis system.The application has the advantages that: through accurate collection and preprocessing of multi-source data, knowledge graph support and a hybrid reasoning engine, accurate fault determination and positioning are realized, and the performance can be continuously improved through closed-loop optimization, thereby effectively guaranteeing the safe operation of the pipeline.