22 results about "Method evaluation" patented technology

The invention discloses an ultrasonic defect detection method based on AI identification, and aims to solve the problems that in the prior art, a half-wave height method cannot automatically distinguish true and false defects, and an AI algorithm is large in calculation amount, unstable in result and the like in defect quantitative evaluation. The method comprises the steps of firstly obtaining and preprocessing an ultrasonic detection image; thirdly, carrying out preliminary coordinate positioning and size evaluation on the suspected defects by adopting a national standard half-wave height method; then, carrying out authenticity judgment on the defect by utilizing an AI identification algorithm; and finally, integrating an AI judgment result and a half-wave height method evaluation result, and determining the real position and size of the defect. Compared with the prior art, the method has the advantages that the advantages of the two methods are effectively combined, automation, high efficiency and high accuracy of defect detection are realized, manual intervention and misjudgment are remarkably reduced, superiority is particularly shown in complex scenes such as thin plate detection, hardware does not need to be transformed, and good practicability and compatibility are achieved.

The invention discloses an AI-based schizophrenia clinical thinking training simulation system, which relates to the technical field of artificial intelligence, and is characterized by comprising the steps of constructing a dynamic knowledge base, receiving and preprocessing multi-modal input, performing standardized mapping by extracting keywords, and then performing judgment of a rule layer and a semantic layer. A student question method and a standard answer method in the dynamic knowledge base are sent into a model for response generation, then four-dimensional quantitative scoring and clinical operation multi-dimensional scoring are carried out, error codes and positioning sentences thereof are output, and finally personalized training courses are recommended to students; and hot-updating the dynamic knowledge base continuous optimization model. According to the method, four dimensions of the ICD-10 diagnostic standard are converted into a quantitative scoring algorithm capable of being automatically executed, quantitative assessment of clinical operation specifications is integrated, a diagnosis and operation double-track evaluation system is constructed, the problems that a traditional method is lack of evaluation standards and high in subjectivity are solved, and the evaluation efficiency is improved. Therefore, the evaluation of the clinical thinking ability of the medical students becomes objective, comprehensive and evidence-based.

The application relates to the technical field of regional data detection, and relates to a regional reconstruction method and system based on multi-source data fusion, which comprises the following steps: evaluating a sequence of regional detection methods to obtain a sequence evaluation value set, constructing a multi-modal detection data set based on a regional detection event, counting a detection method type set, performing method evaluation on the detection method type set based on the multi-modal detection data set to obtain a method evaluation value set, performing optimization iteration on the detection method type set based on the method evaluation value set and the sequence evaluation value set to obtain an optimal detection method sequence, obtaining an optimal detection data set by using the optimal detection method sequence, performing three-dimensional reconstruction on a target region based on the optimal detection data set to obtain a reconstructed region. The application can improve the quality of regional detection data and the reconstruction accuracy, and reduce the time and energy consumption of the detection process.

This invention provides a method, system, and storage medium for simulating and evaluating atmospheric parameters in a FADS system. The method utilizes status words to determine the correctness of pressure measurement data at each pressure measurement point, sets faulty pressure measurement points and removes their corresponding pressure measurement data, obtains new pressure measurement data by configuring a state matrix, and obtains atmospheric parameter calculation values ​​under different conditions by selectively adding error terms and selectively using filters to process the data. By comparing the error between the calculation values ​​and theoretical values, the parameters of the algorithm model are adjusted to obtain atmospheric parameter calculation values ​​with acceptable errors. Finally, the algorithm model is evaluated based on these calculation values. This invention addresses the technical problems in existing technologies where the impact of algorithm error introduction and faulty pressure measurement point isolation on the accuracy of atmospheric parameter calculation is not readily assessable.

The application relates to a zero-sample prototype expansion method and system for relieving a hubness problem and compound fault diagnosis, and belongs to the field of mechanical fault diagnosis. State signal samples of bearings of different types and under different working conditions are collected through a bearing fault test bed, corresponding fault attribute prototypes are designed, prototype expansion kernel functions are used to map the prototypes to a high-dimensional space, the distance between the designed attribute prototypes is expanded, a neural network feature extractor is designed, single fault signal samples and corresponding fault prototypes are input for training, compound fault signals are input into the trained feature extractor to obtain attribute features, the corresponding compound fault types are inferred according to the distance, and a designed measurement method is used to evaluate the hubness problem. The application can effectively relieve the hubness problem, improve the accuracy of compound fault diagnosis, and realize rolling bearing fault diagnosis for compound faults only by using single fault data.

A system is provided.SOLUTION: A system comprising: means for receiving a query; means for analyzing the received query to identify query content; means for accessing an in-house system to obtain relevant information based on the identified query content; means for generating an answer to the query using a generative AI model based on the obtained information; means for evaluating the reliability of the generated answer; means for determining whether the answer is appropriate based on the evaluation results and escalating to a human representative if not appropriate; means for sending a final answer to the user; and means for storing the query and answer in a log.SELECTED DRAWING: Figure 1

The application discloses an ultrasonic detection defect method based on AI recognition, aiming at solving the problems that the half-wave height method in the prior art cannot automatically distinguish true and false defects, and that the AI algorithm has large calculation amount and unstable results in defect quantitative evaluation. The method first acquires and pre-processes an ultrasonic detection image; then, adopts the national standard half-wave height method to preliminarily position coordinates and evaluate the size of the suspected defect; then, uses an AI recognition algorithm to determine the true and false of the defect; finally, comprehensively determines the real position and size of the defect according to the AI judgment result and the half-wave height method evaluation result. Compared with the prior art, the application effectively combines the advantages of the two methods, realizes the automation, high efficiency and high accuracy of defect detection, significantly reduces manual intervention and misjudgment, especially in complex scenes such as thin plate detection, and has good practicability and compatibility without the need of modifying hardware.

A system is provided.SOLUTION: A system, comprising: means for obtaining a dataset; means for preprocessing the dataset; means for dividing the dataset into a training dataset and a test data; means for learning the dataset using a regression model; means for making a prediction using the test data; means for evaluating the prediction; and means for visualizing the evaluation.SELECTED DRAWING: Figure 1

The invention discloses a method, a device, a medium and equipment for quickly evaluating oil and gas share reserves. The method comprises the steps that according to data input and output standards of various evaluation tools, collected data of a geological block to be evaluated are converted into basic data meeting integrated import requirements, and the basic data comprise static data, dynamic data and economic parameters; respectively inputting the static data and the dynamic data into the selected static method evaluation tool and the dynamic method evaluation tool corresponding to the static data, comparing the evaluation results of the static data and the dynamic data, and rechecking and adjusting the specified parameters of the static method evaluation tool and the dynamic method evaluation tool if the comparison result does not meet the preset requirement; inputting the dynamic data into the adjusted dynamic method evaluation tool to obtain a yield profile; and inputting the yield profile and the economic parameters into an economic evaluation tool to obtain oil and gas share reserves. Data selection and data conversion are automatically realized, evaluation results are compared to adjust specified parameters of an evaluation tool so as to ensure that the evaluation results are accurate, and rapid evaluation of oil and gas share reserves is realized.

The invention discloses a demand response potential customer intelligent recommendation system and method based on multi-dimensional feature fusion, and belongs to the technical field of power demand side management. The system comprises a multi-source data acquisition module, a feature engineering module, a potential evaluation model and a value maximization recommendation module. Multi-dimensional information such as load characteristics, response willingness, industry characteristics and the like is creatively fused, high-precision potential evaluation is realized by adopting an improved random forest algorithm, and a recommendation list is generated in combination with long-term value prediction. According to the method, the problems of inaccurate evaluation, low recommendation value, cold start of new customers and the like of a traditional method are solved, and the accuracy and efficiency of demand response customer screening are remarkably improved. Experiments show that compared with a traditional method, the evaluation accuracy of the method is improved by more than 25%, and the average life cycle value of signed customers is improved by 30%.

We provide the system. [Solution] Methods for collecting data from past talent contests, A means for analyzing the aforementioned data to extract language structure and evaluation trends, A generation means for generating a new script based on the analyzed data, A method for selecting performers to use the generated script and for them to practice together, An evaluation means that records and analyzes the aforementioned practice process and provides feedback, A system that includes this.

The invention provides a model quality evaluation method and a related device, and relates to the technical field of model prediction control. The method comprises the following steps: after a first control model of a target production system operates for a preset duration, acquiring operation condition data of the target production system according to an input variable and an output variable of the first control model; wherein the first control model is used for performing predictive control on the target production system; obtaining a second control model according to the operation condition data and a preset system identification method; and performing quality evaluation on the first control model according to the first control model and the second control model. According to the technical scheme provided by the invention, the problems that the evaluation result is greatly influenced by human subjectivity and the accuracy of the evaluation result is difficult to guarantee in a model quality evaluation method in the prior art can be solved.

The invention discloses a large language model evaluation method and device, electronic equipment, a computer readable storage medium and a program product, and relates to the technical field of large language models.The method comprises the steps that evaluation benchmark data in the financial field is acquired; based on the assessment reference data and a preset assessment task, generating an assessment task set for the target large language model; inputting the evaluation task set into the target large language model through a plurality of preset prompt strategies to obtain an output result of the target large language model; and based on the evaluation dimension and the quantitative index associated with the evaluation task set, performing multi-dimensional analysis on the output result, and determining the comprehensive performance of the target large language model. Through combination of structured cross-modal data, multi-type evaluation tasks, multi-prompt strategies and multi-dimensional quantitative analysis, a set of complete and standardized large language model financial field evaluation process is formed, and the comprehensive performance of the model can be comprehensively, objectively and accurately judged.

The application provides a phenotype prediction method and system based on feature reduction and generalized inverse technology, which adaptively fuses linear and nonlinear effects, constructs a new model of adaptive fusion of linear and nonlinear effects of prediction factors for crop phenotype prediction, and evaluates the importance of different effects on the phenotype through the method of adaptive weight adjustment, so as to realize the function of comprehensively considering the influence of linear main effect and nonlinear relationship in crop phenotype prediction. When solving the model, the feature reduction is performed on the training data set, and the model is quickly and effectively solved by using the generalized inverse, so that the determination efficiency of the phenotype value of biological materials is improved. The learning set and the test set obtained by randomly dividing the experimental data set for multiple times are used for model learning, so that the system error of the model is reduced, and the stability of the phenotype prediction result is improved. The effectiveness of the application for phenotype prediction is verified based on DNA molecular marker genotype data and metabolite-based intermediate omics data.

The present invention provides a method for monitoring column performance and operating a chromatography column by applying a generalized linear model to system suitability parameters (SSPs) to assess how quickly the column is changing over time and whether the column stationary phase needs to be replaced. The method leads to faster identification of column failures, helping to maintain high separation quality and consistent analytical results for analytical and preparative chromatography methods. Columns evaluated and / or monitored by the method, as well as products resulting from the use of these columns and methods, are also provided.

The invention discloses a training method and device of a peritoneal function evaluation model, which can be applied to the field of artificial intelligence, and comprises the following steps: acquiring retina image data and clinical data of a target user, and fusing the two kinds of data to form multi-modal fusion data; the multi-modal data fusion mode provides richer and more comprehensive information input for the peritoneal function evaluation model, so that the model can capture subtle differences of peritoneal function states more accurately. And performing iterative training on the peritoneal function evaluation model by using the multi-modal fusion data, and adjusting network parameters in each iterative process until the loss function reaches the minimum value. Through iterative training and optimization of the loss function, it is ensured that the model can accurately learn the mapping relation between the features in the fusion data and the peritoneal function state, the accuracy and generalization ability of the model are improved, and therefore accurate evaluation of the peritoneal function transfer ability is achieved. The problem that a traditional method is inaccurate in evaluation is solved.

To reduce the burden required for acquiring an image that shows physiological activity.SOLUTION: A learning apparatus includes a model learning unit which updates a function image estimation model for estimating, based on image data of an appearance image which indicates a shape and composition of a biological tissue, image data of a function image which indicates physiological activity of each part of a biological tissue that satisfies an image condition for designating a biological tissue, until a predetermined end condition is satisfied, using a pair of image data of a function image of a biological tissue that satisfies the image condition and image data of an appearance image of the biological tissue.SELECTED DRAWING: Figure 1

This invention discloses an artificial intelligence-based power standard data formulation system, specifically relating to the field of data management. It includes a preprocessing module, a coupling analysis module, an optimization training module, an evaluation module, a correction module, and a verification module. The coupling analysis module constructs a parameter correlation matrix based on effective training samples through mutual information entropy analysis, partial correlation coefficient analysis, and correlation degree definition. The evaluation module uses the optimized and trained AI model to output parameter standard reference values ​​and combines kernel density estimation methods to evaluate the initial interval of the standard parameters. This invention significantly improves the efficiency and accuracy of power standard data formulation, providing a scientific basis for power system equipment selection, maintenance cycle planning, and load control optimization. It helps optimize power resource allocation, reduce operating costs, and improve system safety and stability, resulting in significant economic and social benefits.

The present application relates to a kind of sewer network system operating condition quick diagnosis and evaluation method and system, the method is through field investigation information, through sewage system operating condition diagnosis and evaluation method, rainwater system operating condition diagnosis and evaluation method, pipe network operating defect diagnosis and evaluation method, plant network system operating condition diagnosis and evaluation method, plant network system matching diagnosis and evaluation method evaluation different pipe network operating condition, quickly understand the present situation of pipe network system Running state, provide preliminary basis for decision, with low cost, short cycle.In addition, the prior art method is mostly suitable for pipe network defect diagnosis, the technical scheme provided in the present application can not only be used for pipe network defect diagnosis using limited field investigation information, but also applicable to the evaluation of sewage, rainwater, combined flow and other different pipe network operating condition, plant network system operating condition, plant network system matching and the like.

The invention discloses an inerting effect evaluation method for aluminum-magnesium dust, and belongs to the technical field of inerting system evaluation. The technical problem that in the prior art, a scientific and reliable evaluation system is lacked in inerting material screening is solved. The method comprises the following steps: firstly, classifying the inerting material according to a substance composition form and constructing a database, then establishing a calculation model based on a weight method, and calculating the comprehensive efficiency of the inerting material; and finally, constructing an analysis and evaluation report of the inerting material, and analyzing and evaluating the inerting performance of the inerting material by combining professional theoretical analysis. According to the invention, the core physicochemical properties of the inerting material are systematically analyzed, complex experimental tests are replaced or reduced by low-cost theoretical evaluation, and the efficiency of different inerting materials is quickly and scientifically predicted and compared; through evaluation of a weight method, qualitative judgment of the inerting material can be converted into quantitative results, and a basis is provided for risk management and control of an existing system.

The invention particularly relates to a water quality prediction and evaluation method based on multi-source data, which relates to the technical field of water quality monitoring and comprises the following steps: analyzing data quality data to obtain a space-time deviation coefficient; analyzing the environment quality data to obtain an environment difference coefficient; performing comprehensive processing on the two obtained coefficients to obtain an uncertain index; and controlling a sampling strategy and a power management state of the sensor based on the uncertainty index. According to the method, a two-dimensional evaluation system of data quality space-time integrity and environmental influence is constructed, and the core problem of one-sided evaluation of a traditional method is solved; on the aspect of data quality, sensor density comparison, time sparseness quantification and local coverage anomaly analysis are integrated, data space-time defects are accurately described, and misjudgment of a single index is avoided; in the environment influence level, the influence of dominant environment fluctuation on the water quality is captured by combining the real-time change rate of environment parameters and the similarity of historical water quality events, and hidden risks with stable real-time data but approaching a risk threshold can be identified.