31 results about "Expert analysis" patented technology

The invention discloses an interference atlas recognition method and device based on morphological characteristics, electronic equipment and a storage medium, the method and device are applied to the electronic equipment, and specifically, binarization processing is performed on an original PRPD atlas to be recognized to obtain a binarized PRPD atlas; calculating an amplitude threshold value of a maximum bottom noise signal in the binarized PRPD atlas; calculating a distribution curve of the partial discharge pixel points greater than the maximum bottom noise signal based on the amplitude threshold; calculating based on the distribution curve to obtain a phase correlation matrix and a partial discharge phase center matrix; and based on the constructed phase distribution judgment threshold, the phase correlation matrix and the partial discharge phase center matrix, judging the original PRPD atlas to obtain a recognition result. According to the scheme, preliminary screening of the interference atlas in the acquired PRPD atlas can be realized, so that the workload of subsequent expert analysis of partial discharge conditions based on the PRPD atlas is greatly reduced.

The present invention relates to the technical field of coal mine dynamic disaster monitoring and early warning, and discloses an intelligent monitoring and early warning system method for multi-dimensional integration in a mine. The system is an all-time, multi-scale, and multi-parameter coupling monitoring and early warning model and system integrating twelve major modules, namely, the original data acquisition module, mining condition analysis, data transmission module, pre-data preprocessing, expert analysis and demonstration, determination of optimal parameters, basic theory calculation, numerical simulation analysis, laboratory verification, machine learning and neural network optimization, coupling model construction, and on-site application detection. The intelligent monitoring and early warning system method for multi-dimensional integration in a mine proposed by the present invention uses multi-level means, avoiding the limitations of traditional single solutions or the subjectivity of single human interference, and can effectively prevent mine dynamic disasters to a great extent, which is of great significance for reducing the monitoring and early warning of coal-rock-gas compound dynamic disasters and the safe production of mines.

A nuclear power circulating water pump online abnormality monitoring and identification method is disclosed. In the method, multi-source time series data in the circulating water pump operation process is obtained online, data labels are given through an expert analysis module, data normalization processing is performed, and signal is randomly divided into training set and test set samples. When the input data stream only contains healthy data, a data fusion monitoring module based on a feature layer constraint long short-time self-encoder, an attention mechanism and a gate structure can fully mine the state information and potential correlation characteristics of the data, and realize state monitoring under healthy samples. With the occurrence of abnormal data in the monitoring data, an abnormality identification network is further constructed based on the encoder feature, a joint loss function based on cross-entropy, distillation loss and triple loss is constructed, and a neighboring mean value classifier is used to realize online monitoring and identification of known / unknown abnormal states. The method has good compatibility and expansibility, and pushes more accurate state monitoring results to operation and maintenance personnel.

The invention discloses a power generation equipment operation and maintenance large model fine tuning method and system based on non-stop analysis process restoration. The stepwise analysis process of'premise conclusion-contextual information-reasoning judgment 'is completely restored. Constructing a question-answer pair generation mechanism based on process restoration, and enabling the fine tuning data set to contain expert reasoning logic instead of fragmented knowledge; a LoRA fine tuning technology is combined, so that the big model simulates a thinking path of an expert to analyze a problem while learning professional knowledge, and the interpretability and migration ability of model reasoning are improved; the construction cost of professional question and answer pairs is reduced, existing operation and maintenance document resources are fully utilized, and the fine adjustment efficiency and practicability are improved.

The invention relates to the technical field of computer science and technology, and discloses a karst metal underground mine hidden disaster-caused water disaster risk evaluation method comprising the following steps: constructing a three-level evaluation index system of a karst metal underground mine comprising a target layer, a criterion layer and an index layer; analyzing the expert analysis risk level of the second-level index; correcting expert analysis risk levels to obtain membership degrees of the second-level indexes relative to different risk levels so as to construct a fuzzy relation matrix of the karst metal underground mine; calculating a first-level index weight of a first-level index corresponding to the criterion layer and a second-level index weight of a second-level index corresponding to the criterion layer, and performing fuzzy synthesis operation on the fuzzy relation matrix and the second-level index weight to obtain a first index risk probability of the first-level index; and performing fuzzy synthesis operation on the first index risk probability and the first-level index weight to obtain a comprehensive risk level corresponding to the target layer. According to the invention, the monitoring accuracy of major safety accidents caused by hidden water disasters can be improved.

The invention discloses a power grid Internet of Things platform personalized reconstruction construction method and system, and relates to the field of platform personalized reconstruction, and the method comprises the steps: dividing the functions of all parts of a power grid Internet of Things platform, and obtaining the weight of the functions of all parts in the whole platform based on an expert analysis method; according to the correlation degree between the different numbered platform functions, determining the correlation degree comprehensive grade of the different numbered platform functions; a lightweight transformation-increment upgrading mode is adopted, and the weight of function transformation of each part of the platform is set; on the basis of function personalized transformation cycle evaluation of each part of the platforms with different numbers, setting a personalized transformation cycle deviation coefficient of the power grid Internet of Things platform; and establishing a personalized transformation sequence model of the power grid Internet of Things platform, and screening out an optimal function transformation sequence. The method has the advantages that the optimal function transformation sequence can be obtained according to specific analysis of functions of the platform, and the risk of system crash caused by large-scale transformation is reduced.

The invention relates to the technical field of medical teaching simulation, and discloses a tracking type teaching platform based on learning model data interaction, which comprises a focus expert analysis and storage module, a training image acquisition module, an acquisition position comparison module, an analysis report generation module, a training result comparison module and a comprehensive evaluation module, the comprehensive evaluation module comprehensively evaluates a teaching training result based on comparison results of the acquisition position comparison module and the training result comparison module; according to the invention, based on the comparison of training results, the excursion frequency of the collection position is used as the proficiency degree or accuracy for examination, so that the identification and judgment ability of the student on the focus is evaluated more accurately and comprehensively, the comprehensiveness of teaching management is improved, and the teaching management can be more accurate and accurate based on a teaching training result curve. And determining a further learning direction of the student so as to carry out personalized enhancement.

The invention discloses an intelligent operation and maintenance monitoring system and method for a transformer, relates to the field of transformer monitoring, and aims to solve the problem that existing transformer matched monitoring equipment cannot perform comprehensive intelligent analysis and suggestion. The system comprises a real-time monitoring module and an intelligent safety evaluation module connected with the real-time monitoring module; the intelligent safety evaluation module is connected with an expert analysis module and a spatial digital twinborn construction module, and the real-time monitoring module and the expert analysis module are connected with a periodic management and control module. The cycle management and control module comprises an archive recording unit for recording the full life cycle of the transformer and an information construction unit for constructing full life cycle information according to the recorded archive; the invention further comprises a corresponding system. The transformer operation data and health state can be dynamically displayed and analyzed through the spatial dimension digital twinborn model, operation and maintenance personnel can find problems in time, and intelligent diagnosis and active operation and maintenance analysis of the transformer operation state are achieved.

This application relates to a method, apparatus, electronic device, and storage medium for detecting abnormal server behavior, applied in the field of network security technology. The method includes: real-time acquisition of system call sequences during server process execution; dividing the system call sequence into N runtime phase subsequences, and further dividing the runtime phase subsequences corresponding to the target process's runtime phases into process activity subsequences; segmenting the system call sequence into multiple unidentified subsequences; inputting each unidentified subsequence into a process semantic abstraction model to obtain the named entity recognition result corresponding to each system call in the unidentified subsequence; aggregating and overwriting the named entity recognition results of multiple unidentified subsequences to obtain the named entity recognition result corresponding to each system call in the system call sequence; if unidentified named entities are included, determining that the system call sequence is abnormal, and sending an alarm message. This application can extract process behavior, facilitating expert analysis of abnormal events.

The invention provides a human-in-the-loop Text2SQL iterative data labeling method fused with a large model. The method comprises the following steps: firstly, carrying out training and fine tuning on a generative model by utilizing a pre-constructed manual labeling training sample; performing same-semantic diversification expansion on the training sample by using the expansion model; inputting the plurality of extended training samples into a generative model to obtain a first SQL query statement; for each first SQL query statement, reward and punishment assignment is carried out on model parameters of the expansion model according to an expert analysis result and a reinforcement learning mechanism to determine a target training sample; performing manual annotation on the second SQL query statement, and performing incremental training on the generation model by utilizing a manual annotation result to obtain a second SQL query statement; and when the verification result of the second SQL query statement shows that the query expectation is not met, manually marking the target training sample circularly until the verification result meets the query expectation. The efficiency of data annotation is improved through man-machine cooperation and reinforcement learning.

1. Name of the product in this design: Graphical User Interface for Food Analysis in Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Electronic devices are designed in a conventional way, so other views are omitted. 6. Purpose of the graphical user interface: Used for AI food expert analysis; the main view adds a food identification result page, clicking "AI" displays the interface change state (Figure 1) during food expert analysis, and after the analysis is completed, the interface change state (Figure 2) displays the results of the food expert analysis, clicking "More Nutrients" displays the interface change state (Figure 3).

The invention provides an automatic detection method and system for a landslide settlement risk area. The method comprises the following steps: acquiring satellite data of a target area; based on the satellite data, adopting a small baseline integrated aperture radar interferometry technology to obtain a surface deformation rate diagram of the target area; based on the earth surface deformation rate map, identifying an earth surface landslide settlement risk area in the target area through a pyramid scene analysis network; according to the method, interference measurement analysis is carried out on satellite data by adopting the small baseline integrated aperture radar interference measurement technology, so that interference signals can be overcome, and the surface deformation rate extracted by the small baseline integrated aperture radar interference measurement technology can be automatically identified through an improved pyramid scene analysis network; and the calculation amount can be reduced in the process of identifying the land surface settlement risk area, so that the cost of asking experts for analysis each time is saved, and the large-scale landslide monitoring efficiency and reliability are improved.

The invention discloses a college purchase review expert recommendation method and device, computer equipment and a storage medium. Obtaining feature vectors, review features and recommendation suggestions of the purchase review experts through the text information and the historical review items of each purchase review expert; according to the purchase project name and the to-be-purchased project, obtaining to-be-purchased project characteristics, recommended expert types and to-be-purchased project feature vectors; and according to the similarity between the purchase review expert feature vector and the to-be-purchased project feature vector, recalling a first number of purchase review experts matched with the to-be-purchased project and corresponding review features and recommendation suggestions from a purchase review expert database, and obtaining a target recommendation list. According to the invention, the recall and sorting strategies are adopted, and the real-time recommendation of the review experts of the to-be-purchased project of the college is realized through the cooperation of the project analyzer, the expert recall device, the expert analyzer and the expert sorter. According to the method, the hit rate and the average precision index are remarkably improved.

The invention relates to the technical field of computers, in particular to an Internet credit region fraud identification model construction method. Comprising the steps of constructing a risk tag system, and performing feature engineering preprocessing; dividing the preprocessed data set into a model development sample and an out-of-time test sample; fraud association features are mined, the correlation between variables and regional fraud tags is analyzed by applying chi-square test, and information values are calculated to screen out variables with significant correlation and strong predictive ability; deriving expert features, and generating feature crossover and time window statistic derived variables; performing variable screening and optimization; configuring risk weights to the screened variables; carrying out model training and optimization; carrying out model verification and calibration; deploying a production environment; and carrying out model full-life-cycle management, and regularly injecting new credit data and expert analysis experience. According to the technical scheme, a fraud identification model with high predictability, high interpretability and good adaptability can be constructed.

The present disclosure provides for a customized topical formulation energized with quantum frequencies. In at least one aspect, the topical formulation contains a base formulation and a booster formulation. In another aspect of the topical formulation, the topical formulation is created by collecting data about a user and applying artificial intelligence (AI) and optimization models to the data to arrive at the custom and / or optimized topical formulation for the user.

The patent application provides a kind of surface landslide subsidence risk area automatic detection method and system, comprising: the satellite data of target area is collected;Based on satellite data, using small baseline set aperture radar interferometry technique, obtain the ground deformation rate graph of target area;Based on ground deformation rate graph, through pyramid scene analysis network, identify the surface landslide subsidence risk area in target area;The present application adopts small baseline set aperture radar interferometry technique is to satellite data is interferometric analysis, therefore can overcome interference signal, through the improved pyramid scene analysis network of the present application not only can the ground deformation rate extracted by small baseline set aperture radar interferometry technique be automatically identified, also can reduce the amount of calculation in the process of identifying ground subsidence risk area, therefore not only save the expense of each time please expert analysis, also help to improve the efficiency and reliability of large-scale landslide monitoring.

The present disclosure provides for a customized topical formulation energized with quantum frequencies. In at least one aspect, the topical formulation contains a base formulation and a booster formulation. In another aspect of the topical formulation, the topical formulation is created by collecting data about a user and applying artificial intelligence (AI) and optimization models to the data to arrive at the custom and / or optimized topical formulation for the user.

This application discloses an event evolution reasoning and prediction method based on cognitive-driven intelligent generation. First, it utilizes a domain-wide language model to extract expert knowledge points, analysis processes, analytical perspectives, and logical reasoning from event research reports, constructing an interactive thought chain. It then extracts causal reasoning and constructs a causal reasoning network structure. Cognitive logical knowledge is integrated with relevant facts of the event to be predicted and embedded into a prompt template to construct a prompt model. This allows the domain-wide language model to generate event evolution reasoning and prediction results based on expert analysis dimensions and processes. Finally, conflict detection is performed on event elements through closed-loop evidence chain analysis, and credible conclusions are selected to obtain credible prediction conclusions. By constructing prompt templates to guide the large model through cognitive logical learning and combining multi-level credible verification and screening, the technical problem of excessive prediction errors caused by illusions generated by the large model is solved, improving the accuracy and credibility of event evolution prediction.

The invention discloses a robot welding auxiliary control system and method in the technical field of arc welding robots, the system comprises a robot controller and a cloud function module cluster which are in two-way communication connection, and the cluster comprises a process reasoning module, a program generation module, a program simulation module and an expert analysis module. The process reasoning module generates initial welding process parameters; the program generation module generates a welding program according to the welding structure model, the workstation model and the initial welding process parameters; the program simulation module simulates a welding program and downloads the program and a result to the robot controller for an operator to adjust; and the expert analysis module is used for analyzing all programs and equipment data and transmitting a finally confirmed welding program to the robot controller and the process reasoning module. According to the method, the problem of insufficient precision caused by geometric deviation between a workpiece object and a model can be effectively solved, and intelligent generation and self-adaptive adjustment of a welding program are realized.

Various embodiments of the present technology provide an integrated platform that provides acceleration tools that can be used across multiple lifecycle phases of an industrial automation system to assist users in various lifecycle phases of an industrial automation system. In accordance with various embodiments, the integrated platform can take historical designs and provide various acceleration actions (e.g., initial designs, answering specific questions, expert analysis, etc.) for a current system. Various embodiments can use a common, cross-platform data file that links activity and efficiently provides needed information to a user. Some embodiments provide and manage reviews of layouts or designs by experts (e.g., individuals and expert systems) to aid in identifying needed changes to the design or system.

The invention discloses a road line selection method and system based on space-air-ground internal intelligence integrated study and judgment, and relates to the technical field of road line selection, and the method comprises the steps: employing the InSAR and remote sensing image interpretation technology to carry out the preliminary recognition and screening of the geological hazard hidden danger of a corridor zone from a large scale range; an airborne laser radar technology is adopted to carry out detailed investigation on geological disasters of the corridor from a small-scale range; checking geological disasters around the preliminary line by adopting comprehensive survey means of geological survey, geophysical prospecting test and drilling verification; risk evaluation is carried out on geological disaster hidden danger points in the preliminary line corridor zone range, and a risk evaluation model is constructed; a plurality of suggested lines are given after the lines are optimized by a geological engineer and a line engineer, and an optimal planning line is calculated by adopting an intelligent expert research and judgment model. The technical problem that in the prior art, due to dependence on single geological disaster data, scientificity and objectivity of line selection are limited is solved.

The invention provides an energy science and technology intelligence brain construction method fusing machine intelligence and expert intelligence, and the method is characterized in that the method comprises the following steps: obtaining energy science and technology data; processing and analyzing the energy science and technology data based on a preset machine intelligence algorithm model to obtain energy science and technology analysis data; acquiring expert analysis information based on the energy science and technology analysis data; constructing an energy science and technology knowledge graph based on the energy science and technology analysis data and the expert analysis information; and obtaining and outputting energy science and technology intelligence information based on the energy science and technology knowledge graph. The corresponding energy science and technology intelligence information is output for the user, so that intelligent analysis related to energy science and technology is carried out, and decision-making related to energy science and technology is assisted.

The application discloses a geomorphology professional question answering service method and system based on geomorphology features. The method acquires geomorphology documents through multiple channels and preprocesses to construct a standardized corpus, encodes text semantics into high-dimensional vectors using vector embedding technology, and then realizes semantic similarity calculation and retrieval based on vector distance measurement to construct a geomorphology knowledge vector database. The LangChain framework is combined to configure a geology expert system prompt template, simulate expert analysis thinking process, form a geomorphology large model, and provide geomorphology professional question answering services covering geomorphology feature, cause process and evolution mechanism interpretation for users. The application significantly improves the accuracy of geomorphology professional knowledge retrieval and the quality of question answering, and provides intelligent professional technical support for geomorphology research and education.

The invention provides a task template generation method and device based on reverse engineering, equipment and a medium. The method comprises the steps of obtaining an example document, analyzing the example document, extracting a plurality of text blocks, and associating visual layout metadata for each text block; constructing a document tree based on content semantics and visual layout metadata of the text blocks, and performing hierarchical clustering on text contents in the document tree to generate a logic step sequence; identifying named entities in the full text of the example document, replacing the named entities with parameterized variables, and generating a parameter mapping table based on the corresponding relationship between the parameterized variables and the named entities; according to the method, the logic step sequence is associated with the parameter mapping table, the task template is generated, the expert analysis normal form can be extracted from diversified documents in a robust mode, the highly reusable and parameterized task template is generated, the efficiency, the normalization and the intelligent level of research work are remarkably improved, and the method has wide application prospects in the field of knowledge automation.

This invention provides a parking space status prediction method based on frequency perception and hybrid expert analysis, belonging to the field of data processing technology. Specifically, it includes: extracting time-domain features; converting the time-domain features to the frequency domain, extracting the highest frequency point, performing frequency domain masking and recombination to obtain the highest frequency component, then repeating the frequency domain masking and recombination M-1 times for the frequency components of the mask to obtain M frequency components; calculating the similarity between the remaining M-1 frequency components and the highest frequency component, and calculating the weight of each frequency component accordingly; inputting the highest frequency component into the corresponding expert module in the time domain after undergoing inverse discrete cosine transform; inputting the remaining frequency components into the corresponding expert modules in sequence according to their corresponding weights after undergoing inverse discrete cosine transform; and fusing the M prediction results according to the weights of their corresponding frequency components to obtain the final parking space status prediction result. This invention improves prediction efficiency and accuracy.

The invention relates to an intelligent procurement risk prediction and abnormal behavior detection method. The method comprises the following steps: constructing a multi-dimensional procurement risk index system; the indexes are classified, and a quantitative scoring model and a Boolean judgment model are included; screening the indexes based on a feature importance algorithm; and based on a dynamic weighted aggregation model, fusing objective data and expert analysis suggestions, carrying out weight distribution and risk value calculation on the simplified index set, and outputting a comprehensive risk value of the purchase project. The bid invitation risk dynamic prediction method has the beneficial effects that the bid invitation risk dynamic prediction is realized by constructing a comprehensive algorithm, the actual benefits are reflected in multiple dimensions of risk prevention and control, efficiency improvement, cost saving, management optimization and the like, and a remarkable business value closed loop is formed.

The present application relates to the technical field of membrane operation, and particularly relates to a modeling method of a RO membrane risk diagnosis model, comprising: establishing a marker event library of RO membrane operation, and marking according to the time sequence of each marker event input; constructing a basic model according to RO membrane operation data, water quality biochemical test data, expert analysis diagnosis logic and general judgment rules; inputting the preprocessed data set into the basic model to obtain a risk diagnosis result, wherein one timestamp corresponds to one risk diagnosis event in the risk diagnosis result; according to the corresponding timestamp, the marker event in the marker event library is called, and one risk diagnosis event corresponds to at least one marker event; comparing the risk diagnosis event with the corresponding marker event to obtain a deviation result, and iteratively processing the model according to the deviation result until the deviation result meets the requirement, and obtaining an optimized RO membrane risk diagnosis model. The present application improves the risk diagnosis efficiency and operation management efficiency of the RO membrane.

This invention provides a human-in-the-loop Text2SQL iterative data annotation method that integrates large models. First, a generative model is trained and fine-tuned using pre-constructed manually annotated training samples. Then, an extended model is used to semantically diversify the training samples. Next, multiple extended training samples are input into the generative model to obtain a first SQL query. For each first SQL query, the model parameters of the extended model are assigned reward / penalty values ​​based on expert analysis results and reinforcement learning mechanisms to determine the target training sample. This sample is then manually annotated, and the results are used to incrementally train the generative model to obtain a second SQL query. When the verification result of the second SQL query does not meet the query expectations, the target training samples are repeatedly manually annotated until the verification result meets the query expectations. This method improves the efficiency of data annotation through human-computer collaboration and reinforcement learning.

Systems, methods, and computer program products are disclosed herein. A method comprises receiving a dataset comprising a plurality of text entities; determining one or more candidate immutable tokens from the dataset; determining one or more immutable tokens from the one or more candidate immutable tokens, based on a predetermined rule or a subject matter expert analysis; generating synthetic data, using a large language model, wherein the large language model is instructed to maintain the one or more immutable tokens; and filtering the generated synthetic data based on compliance with the one or more immutable tokens and the associated rules.

The invention relates to the technical field of fish health monitoring, and discloses a non-invasive Chinese sturgeon health state prediction method, which comprises the following steps of: acquiring excrement samples of a plurality of Chinese sturgeons in a suction manner, respectively performing physical characteristic detection and biochemical characteristic detection on the excrement samples, and constructing a data set; dividing respective proportions of physical features and biochemical features through expert analysis, then analyzing detection results of the physical features, making an expert score table to obtain corresponding scores and weights, and training a random forest model by using detection results of the biochemical features; and predicting the health state of the Chinese sturgeons to be detected by using the formulated expert score table and the trained random forest model.