9results about How to "Highly structured" patented technology

The invention provides a multi-modal large model construction and operation method and system for reservoir dam safety. The method comprises the following steps: constructing a training set, wherein the training set comprises a single sample; multi-modal data as model input: sensor data arranged on the dam, historical hidden danger records, engineering technology documents formed in various stages of planning, design, construction and operation of the dam, information resources related to dam safety, geometric and material information of the BIM model, timestamps and spatial position information; the probability value of occurrence of the hidden danger event serving as the training label is obtained; the training set is adopted to train a multi-mode pre-training model based on a Transform framework; performing real-time prediction and early warning on potential safety hazards of the dam; and calculating the actual influence degree, the comprehensive risk score, the dynamically set early warning threshold and the emergency measure suggestion of the hidden danger event according to the probability value of whether the hidden danger event occurs or not output by the model. The scientificity and effectiveness of dam safety monitoring are comprehensively improved.

The invention provides a method for creating a structured document based on a deep learning model, and relates to the technical field of data processing, and the method comprises the steps: obtaining basic text information collected by a user, obtaining text data, fragmenting the text data, obtaining text fragmented data, and creating a structured document according to the text fragmented data. Analyzing the structures of the sentences in the paragraphs, determining keywords in the sentences, analyzing the logical relationship between the sentences in the paragraphs, associating the sentence keywords in the paragraphs, analyzing the association degree between the paragraphs in the chapters, identifying core keywords of the chapters, obtaining a chapter keyword data set, and analyzing the field involved in the text. The method comprises the steps of extracting field information of a text to obtain a text keyword data set, constructing a structured document model based on a graph neural network model, and inputting the text keyword data set and text data into the structured document model to obtain a structured document. According to the method, the structured document can be constructed through the deep learning model.

The invention belongs to the technical field of carbon black post-treatment, and particularly relates to a method for preparing high-structure-degree carbon black through plasma sintering. In order to solve the problem that the structural degree of furnace-process carbon black is limited by a reaction furnace in the prior art, the invention provides the method for preparing the high-structural-degree carbon black by utilizing plasma sintering, and the structural degree of the carbon black is improved by utilizing a two-step method. Part of the aromatic hydrocarbon compound is left at the contact part of the carbon black to provide a carbon source for the next step of micro-sintering; in the second step, high-temperature plasma is adopted for heating, the carbon source can be rapidly carbonized in a short time, the micro-sintering phenomenon is generated among the carbon black, the size of a carbon black aggregation structure is increased, and therefore the structural degree is improved, and compared with traditional high-temperature heat treatment, the method can reduce energy consumption and consumed time in the treatment process to a greater extent.

The invention relates to the technical field of data processing, in particular to a construction method and system for a neural rehabilitation evaluation model. The method comprises the following steps: acquiring multi-modal data of a neurological rehabilitation patient; performing feature extraction according to the multi-modal data of the neurological rehabilitation patient to obtain multi-modal feature data of the neurological rehabilitation patient; performing data classification on the multi-modal feature data of the neurological rehabilitation patient to obtain multi-modal feature classification data; and marking the multi-modal feature data of the neurological rehabilitation patient by using the multi-modal feature classification data to obtain multi-modal feature marking data of the neurological rehabilitation patient so as to carry out neurological rehabilitation evaluation model construction auxiliary work. According to the method, the model capable of comprehensively and objectively evaluating the rehabilitation progress of the neurological rehabilitation patient is constructed in an auxiliary manner, so that a scientific basis is provided for rehabilitation treatment.

A simulation method and related equipment for machining process training are disclosed. The method includes: acquiring engineering data and process data of the target workpiece; extracting features from the engineering data and combining them with the process data to construct a case resource library; performing semantic association analysis between the pre-set knowledge resource library and the machining cases in the case resource library to generate a process knowledge network; constructing a virtual training environment based on the process knowledge network; collecting interaction data of learners in the virtual training environment and evaluating the interaction data to obtain evaluation results; and generating guidance animations based on the evaluation results and the process knowledge network. This application can improve the learning efficiency of simulation training.

The application discloses a kind of multi-modal event primitive extraction, coding and collaborative generation method and system for smart engineering construction scene, the system includes multi-modal data acquisition module, edge preprocessing module, event candidate extraction module, time-space alignment module, subject association module, event primitive coding module, multi-source fusion generation module and primitive output module. Through edge pre-processing, candidate event extraction, time alignment, space mapping, subject association, unified coding and multi-source fusion to video, environmental parameters, device status, positioning information, wearable terminal data and job log data and other multi-modal raw data, generate standardized event primitives, and output to risk identification, knowledge graph and early warning module. The present application can reduce the transmission pressure of raw data, improve the unified semantic expression ability of multi-source heterogeneous data, the fusion accuracy and the subsequent risk analysis efficiency, suitable for hoisting, high altitude, closed space and warehousing logistics and other smart engineering construction scenes.

The application provides an enterprise credit intelligent analysis method and system fusing a knowledge graph and a dialogue engine, and relates to the technical field of enterprise credit risk analysis.The application firstly acquires multi-source credit records of a target enterprise and associates source information fields to construct an enterprise credit knowledge graph; analyzes a credit query request to obtain a credit conclusion unit and a graph query condition; searches a candidate fact set and forms an evidence chain data package; groups according to a preset conflict rule and calculates a confidence score based on a preset scoring rule to determine a main conclusion and an alternative conclusion and generate a conflict explanation, which is output to a dialogue engine to generate a dialogue reply.The application associates the credit conclusion with the corresponding evidence chain, introduces a conflict grouping and confidence sorting mode, and improves the structured expression ability and conclusion reliability of the enterprise credit analysis result.

The application discloses a tunnel disease detection image data processing method and system and a readable storage medium, relates to the technical field of tunnel engineering detection image data processing, and comprises the following steps: S1, data acquisition; S2, automatic classification of original photos; S3, mileage key frame extraction; S4, key frame compression; S5, mileage interpolation and standardized renaming; S6, disease detection and parameter extraction; S7, disease result typing and merging; S8, three-level splicing; and S9, report backfilling. The application has the advantages that the disease detection result is directly converted into engineering parameters and written into a report template, the degree of result structuring is improved, difference merging rules are set according to disease types, and the accuracy of disease item deduplication and integration is improved; and through three-level splicing and a direction adaptation mechanism, a long-width spread map and a multi-camera panoramic map with stake number annotations can be output.

The invention discloses a project state evaluation method and device, electronic equipment and a storage medium, and relates to the technical field of artificial intelligence or other related fields, and the method comprises the steps: regularly collecting project related data of a target project from a specified data source, and obtaining multi-source project data; determining a project stage of each piece of multi-source project data according to the project standard flow table, and generating a project stage label; the multi-source project data and the project stage labels are input into an evaluation model, the evaluation model outputs an evaluation result, the evaluation model is optimized in advance based on stage weights, the evaluation model is used for analyzing the input data in combination with a fuzzy set theory, and project state probability distribution obtained through analysis serves as the evaluation result. Through the project progress and risk assessment method and device, the technical problem that the project progress and risk assessment accuracy is low due to the fact that limitation exists when project full-life-cycle data is processed and complex project condition changes cannot be adapted in the related technology is solved.