1530 results about "Evaluation data" patented technology

The invention provides an interactive simulation teaching processing method and device for ship electromechanical guarantee, and relates to the technical field of interactive simulation teaching, and the method comprises the steps: building a ship electromechanical full-structure model in a virtual simulation platform, digitally mapping real equipment operation parameters into virtual parts with real-time response characteristics, and carrying out the real-time simulation of the virtual parts; synchronous acquisition and normalized calibration of operation instructions, equipment feedback and environment disturbance in a teaching scene are realized, and a unified teaching simulation data set is generated. State recognition, fault simulation and operation evaluation calculation are completed through multi-layer task scheduling, and real-time teaching feedback is generated and visually displayed on a terminal. Based on difference analysis of dynamic evaluation data and a standard operation template, operation deviation and abnormity are automatically identified, and a targeted guide prompt is generated. According to the invention, unified modeling and data linkage can be carried out on student operation behaviors, equipment dynamic responses and teaching feedback results, and the problems of teaching feedback lagging and lack of pertinence in operation correction are solved.

The invention discloses an unmanned aerial vehicle assisted three-dimensional scanning well measurement system and method. The method comprises the steps of firstly collecting shaft information, adjusting equipment, planning a measurement path, enabling an unmanned aerial vehicle to collect data according to the planned path, then preprocessing the data, intelligently identifying and classifying the data, constructing a three-dimensional model, analyzing errors, evaluating the data, and finally generating a measurement report. The system comprises an unmanned aerial vehicle, a laser radar, a structured light camera, a depth camera, an IMU + UWB and a cloud processor. Compared with a traditional measurement mode, the method has obvious advantages. Through laser radar SLAM + UWB positioning, the unmanned aerial vehicle can be accurately navigated, high-precision scanning and image acquisition are realized in combination with multi-sensor fusion, and measurement blind areas are reduced. The intelligent identification and classification technology can automatically detect and classify hoistway facilities, detect wall damage and support dynamic obstacle avoidance. Various algorithms are adopted for data processing to improve the model precision, the finally generated high-precision model can be directly used for various engineering scenes, and an efficient and intelligent solution is provided for well measurement.

The invention discloses an evaluation result determination method and device, a storage medium and electronic equipment, and relates to the technical field of retrieval enhancement generation, the method comprises the following steps: obtaining an evaluation data set generated by a preset agent based on knowledge base data; obtaining a retrieval result generated by a target retrieval enhancement generation system based on the evaluation data set; obtaining a target dimension capability evaluation result obtained by performing target dimension capability evaluation on the target retrieval enhancement generation system by the preset intelligent agent based on the retrieval result; and determining a target evaluation result of the target retrieval enhancement generation system according to a plurality of capability evaluation results corresponding to different target dimensions. The technical problem of how to efficiently evaluate the comprehensive capability of the retrieval enhancement generation system is solved, and the technical effect of efficiently evaluating the comprehensive capability of the retrieval enhancement generation system is achieved.

The invention discloses a VLA model evaluation method and device, a storage medium and a program product. The method comprises the steps that an evaluation data set containing visual data, language data and action data is acquired; inputting the evaluation data set into a VLA model to be evaluated to obtain a corresponding model output result; based on the model output result, determining an index evaluation result of the VLA model corresponding to at least one evaluation dimension index; and determining a performance evaluation result of the VLA model according to each index evaluation result. Therefore, the comprehensive performance of the VLA model in multiple aspects of perception, prediction, decision making, planning, reasoning, interaction and the like can be comprehensively reflected.

The invention provides a manufacturing and processing photoetching method and system of a display panel, and relates to the technical field of photoetching processing. According to the method, the display panel substrate is subjected to cleaning, prime coating and partition division to form an imageable micro-mark covering all exposure partitions, and the thickness and the uniformity are measured after lower-layer supporting glue and upper-layer imaging glue are sequentially formed; carrying out in-situ acquisition and modeling by taking the micro-mark as an imaging object, and generating an exposure correction instruction and an overlapping energy table; performing partition exposure and real-time correction by using a digital micromirror, and completing development to obtain an anti-etching window with a steep side wall; and setting an etching window according to the exposure record and the window quality data, and executing low-damage etching and defect recovery to form stability evaluation data. According to the method, comprehensive improvement of high-precision exposure, pattern consistency and electrical stability is realized.

The invention relates to the technical field of data processing, and discloses an old people health state assessment data processing system based on multi-modal data, and the system comprises a medical data integration module which obtains electronic medical record data and a medical examination report through an FHIR interface, and extracts a structured health index; the cross-modal causal fusion processing module is used for fusing the monitoring data and the medical text through an image, text and image interlayer architecture; the health state evolution modeling module is used for mapping the health feature vectors into physiological function, cognitive level and athletic ability three-dimensional state indexes; an evaluation report backtracking module; and a decision output module. Through an image, text and image interlayer architecture, deep semantic fusion of multi-modal features is realized under the constraint of medical pathology rules, feature weight adaptive distribution is dynamically guided based on a medical causal atlas, a high-dimensional fusion vector retaining key pathology information is generated, the semantic integration ability of health data is remarkably improved, and the health data fusion efficiency is improved. And the reliability of discrimination and decision making is improved.

The invention provides a communication network operation accident simulation drilling method and device, and solves the problem that a standardized simulation drilling process with automatic deduction and objective quantitative evaluation capabilities is difficult to construct in the prior art. The communication network operation accident simulation drilling method comprises the following steps: acquiring historical operation data of a communication network, and generating at least one scene element data set according to the historical operation data; the scene element data set comprises disposal evaluation data; determining a scene element data set corresponding to the target accident scene, and constructing an analog simulation environment of a communication network operation accident; determining a preset timeline according to the fault injection parameter, and driving a simulation environment to evolve; and acquiring drill process data, and generating a drill evaluation result by combining with analysis of the disposal evaluation data. The invention further provides a device corresponding to the method. According to the method, historical data is converted into a structured drilling process, and measurable, reproducible and continuous optimization of the emergency capability is realized.

The invention relates to the technical field of computing power resource allocation, and particularly discloses a computing power resource allocation method and system based on multi-dimensional dynamic evaluation and a product. The method comprises the steps of obtaining evaluation index data of a to-be-scheduled task in multiple dimensions; dynamically configuring a weight value of each evaluation index according to a task attribute and a system state; the evaluation index data is fuzzified by using a preset membership function, a fuzzy evaluation matrix is constructed in combination with the weight value, and comprehensive calculation is performed through a fuzzy inference rule to obtain fuzzy comprehensive evaluation data of the task, so that an accurate evaluation result is generated; modeling a task execution process by adopting a multi-layer perceptron model, and predicting the execution performance of the task; and based on the evaluation result and the prediction result, dynamically selecting a proper strategy from a plurality of predefined task scheduling strategies, and scheduling the tasks to optimize computing power resource allocation. According to the method, the resource utilization efficiency is remarkably improved through multi-dimensional evaluation and a dynamic scheduling mechanism.

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

The invention discloses a container type battery environment monitoring system based on edge calculation, which relates to the technical field of battery environment monitoring, and is characterized in that sensors and edge nodes are arranged in cabins of a battery energy storage device to acquire internal operation environment data of the container type battery energy storage device; a pneumatic environment data set and a water vapor critical data set are formed after data processing, cross-cabin air exchange efficiency Eex is constructed for air exchange qualification evaluation, a condensation nucleation potential index Cnp is constructed when air exchange is abnormal, and a comprehensive condensation risk index Rall is generated in combination with the cross-cabin air exchange efficiency Eex for condensation risk evaluation. Instructions such as fan power adjustment, dehumidification device start and stop and power limitation are issued according to the risk level, and information labels are added to the evaluation data set and uploaded to the cloud in real time. According to the invention, the real-time monitoring of the container type battery environment, the dynamic prediction of the condensation risk and the automatic control are realized, and the safety and reliability of the operation of the energy storage system are improved.

The invention discloses an intelligent evaluation method and device for an RAG system based on a large model, and computer equipment. The method comprises the following steps: acquiring a knowledge base source file required by a to-be-evaluated RAG system; analyzing and converting the knowledge base source file, creating a structured document block set, and executing preprocessing operation to obtain an intermediate knowledge base; automatically generating an evaluation data set including the question, the answer and the document block of the corresponding evidence according to the intermediate knowledge base based on LLM; performing performance evaluation on the to-be-evaluated RAG system by using an NLP evaluation index and an LLM-based multi-dimensional scoring mechanism based on the evaluation data set to obtain an evaluation result; and generating a report according to the evaluation result, and outputting the report. By implementing the method provided by the invention, the performance evaluation and iterative optimization of the RAG system can be comprehensively and accurately improved at low cost.

The invention relates to the technical field of course supervision, in particular to a course quality supervision analysis method and system based on big data. The method comprises the following steps: acquiring course data of a course teaching platform to carry out course teaching planning design, and generating course teaching planning data; obtaining course teaching log data of the course teaching platform to perform learning attention deviation evaluation and course response evaluation during teaching of each course, and generating response evaluation data corresponding to teaching of each course; and obtaining historical teaching information data to carry out course teaching result analysis and course teaching quality evaluation, carrying out course structure adjustment processing on the course teaching planning data, generating course teaching planning data after structure adjustment, and carrying out updating processing on the course data of the course teaching platform. According to the invention, real-time assessment of course quality and course optimization are realized through a whole-process supervision mechanism of behavior deviation, course response and teaching achievements.

The invention discloses an Agent-based education evaluation method and system, and relates to the technical field of artificial intelligence and education evaluation, and the method comprises the steps: obtaining historical evaluation data and knowledge graph labeling data of students, constructing a student knowledge state matrix, calculating a mastering probability value and a forgetting attenuation coefficient of each knowledge point, and generating an evaluation question recommendation sequence; inputting the question text and the student answering text into a multi-Agent collaborative evaluation model to generate a multi-dimensional scoring result; dynamically updating the knowledge point mastering probability value of the student in the continuous answering process, calculating the mastering degree change rate of each knowledge point node, constructing an adaptive difficulty adjustment function, and generating a question difficulty parameter and a knowledge point coverage range parameter of the next round of evaluation; and taking the question difficulty parameter and the knowledge point coverage range parameter as constraint conditions, screening a candidate question set meeting the conditions from a question bank, and generating a self-adaptive evaluation path and a capability diagnosis report. The invention also discloses a method.

The invention discloses an intelligent monitoring method for an annealing process of a wound transformer iron core, and particularly relates to the technical field of intelligent monitoring. The method comprises the following steps: acquiring temperature data and magnetic field intensity data generated in an annealing process of a wound transformer iron core, and carrying out time domain synchronization processing and abnormal data labeling; identifying an iron core micro-deformation area and a corresponding temperature anomaly area according to the synchronous data, and extracting temperature anomaly fluctuation characteristics; analyzing a dynamic coupling relationship between the temperature abnormal fluctuation characteristic and the iron core stress change, and outputting characteristic data of an iron core magnetic field and stress interaction; synchronously analyzing the interference intensity of abnormal temperature fluctuation on the annealing heat distribution uniformity, and outputting temperature disturbance intensity data; and constructing a coupling anomaly evolution model, generating iron core annealing anomaly evaluation data, quantifying a quality fluctuation risk in an annealing process, and generating intelligent early warning information. The monitoring precision and reliability of the iron core annealing process are effectively improved, and the annealing quality of the transformer iron core is guaranteed.

The invention discloses a multi-modal target fusion and evaluation method, which comprises the steps of performing alignment preprocessing on multi-source observation data to generate multi-source time alignment observation data and multi-source sensor state data; executing single-source target detection and initial threat estimation, and constructing single-source target state data and initial target threat evaluation data; based on the single-source target state data, the initial target threat assessment data and the multi-source sensor state data, executing multi-sensor target track association and fusion by applying threat consistency constraint, and generating multi-sensor fusion target model data; and based on the multi-sensor fusion target model data and the multi-source sensor state data, executing a multi-sensor collaborative scheduling decision by applying threat reduction income evaluation, and determining multi-sensor scheduling scheme data. According to the method, deep coupling of association and scheduling on the threat assessment task is realized, and the association accuracy and the scheduling decision effectiveness are improved.

A computer-implemented system and method for evaluating an AI agent associated with an enterprise computing environment by aggregating a plurality of AI agents associated with the enterprise computing environment, and evaluating, with an agent evaluation unit, a selected AI agent of the plurality of AI agents using evaluation data, wherein the evaluation data includes operational behavior data and trustworthiness data. The agent evaluation unit can be configured to determine a total agent evaluation score for the selected AI agent from a plurality of category specific evaluation scores. The agent specific categories can have associated therewith a category evaluation score and the agent specific categories can include categories associated with the operational behavior and the trustworthiness of the selected AI agent. An AI-based intervention can be applied in response to the total agent evaluation score.

The invention discloses an AI-based digital project performance evaluation data processing method and system, and relates to the technical field of artificial intelligence and digital project management, and the method comprises a multi-source data collection module which is used for obtaining data; the entity alignment module is used for establishing an association relationship among cross-system data entities; the dynamic index generation module is used for dynamically adjusting the evaluation index weight by utilizing a reinforcement learning framework; the efficiency prediction module is used for modeling a task dependency relationship according to the time sequence diagram convolutional network and outputting a delay risk probability; and the visual interface is used for displaying the data. According to the method, cross-system entity alignment, dynamic index weight adjustment, task delay risk prediction and root cause analysis are realized through AI technologies such as BERT semantic matching and a graph neural network, visual data display and interaction functions are provided by virtue of a visual interface, the problems of data dispersion, index static state, risk lag and the like in traditional project evaluation are solved, and the project evaluation efficiency is improved. And the decision support capability of project management is improved.

The invention discloses a method for evaluating the code generation capability of a fine-tuned credential large model, and relates to the technical field of artificial intelligence, and the method comprises the steps: S1, constructing an evaluation data set which is constructed according to a half-and-half rule, S2, executing a model generation test, and based on the evaluation data set, carrying out a model generation test; the method comprises the following steps of S1, adjusting a code output result, S2, calling the adjusted credential large model to generate a corresponding code output result, S3, carrying out multi-dimensional evaluation analysis, S4, forming a comprehensive evaluation result, and calculating the credibility 7 degree of the large model code generation capability for guiding model performance optimization and subsequent iteration improvement; according to the fine-tuned evaluation method for the code generation capability of the credential large model, evaluation deviation caused by a single index is effectively reduced, and the obtained quality credibility is more practical and explanatory and can be used as a decision basis for supporting large model optimization strategy formulation and parameter fine tuning.

Disclosed herein are methods and systems that use artificial intelligence techniques for determining an allocation of an asset based, at least in part, on analyzing electronic images of the asset. In an embodiment, an AI model can be executed on a received data packet comprising a set of electronic images to generate a set of attributes. In an embodiment, the AI model can identify a segment of the asset associated with the electronic image, generate an attribute comprising a description of quality of the segment of the asset, and determine whether the electronic image is associated a subset of the electronic images. In an embodiment, the set of attributes and an indication of the subset with user input about the asset can be aggregated to generate evaluation data. In an embodiment, a computer model can be executed to determine an allocation of the asset to a class.

A model deployment tuning system (MDTS) receives a trained ML model, specified constraints, and model evaluation data and applies a plurality of model utilization techniques to the trained ML model to produce a plurality of useable model versions of the trained ML model. The MDTS executes each of the plurality of useable model versions of the trained ML models on a plurality of different compute instance types using the model evaluation data to produce model evaluation results for a plurality of different combinations. The MDTS filters the model evaluation results based on the specified constraints to indicate one or more of the different combinations satisfying the specified constraints. The MDTS deploys one of the plurality of useable model versions of the trained ML model to a compute instance types according to a selected combination satisfying the specified constraints.

The invention relates to the field of clinical diagnostics, and discloses an elderly NSC emergency risk layering method and device and a medium, and the method comprises the steps: S1, obtaining multi-dimensional evaluation data of an elderly NSC patient; s2, adopting a preset risk mapping rule to convert the risk values into single risk values of a unified scale; s3, dividing the single risk value into a plurality of risk sub-models, and calculating a dimension risk score of each risk sub-model; s4, based on a preset fusion strategy, integrating the risk sub-model and the single risk value interaction effect, and calculating to obtain a total risk score of the patient; and S5, determining the risk level of the patient according to the total risk score, and outputting a clinical diagnosis and treatment suggestion. According to the method, multiple biomarkers and key clinical parameters are integrated, a multi-dimensional combined risk assessment model is constructed, and the potential pathological state of a patient can be reflected more comprehensively.

The invention belongs to the technical field of petroleum and natural gas engineering, and particularly relates to a priori knowledge embedded unsupervised learning geology-engineering dessert comprehensive evaluation method, which comprises the following steps: S1, obtaining an evaluation data set of a research area, and preprocessing and standardizing the evaluation data set; s2, dividing the evaluation data set into a positive index and a negative index according to the prior knowledge, constructing positive and negative ideal points through the prior knowledge, and fusing to generate an unsupervised evaluation data set; s3, clustering is carried out on the unsupervised evaluation data set by adopting principal component analysis and an incremental online k-means algorithm; and S4, according to category labels obtained by clustering, determining geological-engineering dessert grades by adopting a standardized variable distance grading evaluation method, and carrying out segmented clustering optimization and perforation position optimization on the geological-engineering dessert grades. According to the method, a supervised evaluation effect is achieved through an unsupervised algorithm, comprehensive evaluation of the dessert of the fractured horizontal well section is achieved, and the problem of unlabeled data evaluation is effectively solved.

The invention discloses a coastal corrosion geological environment structure corrosion resistance evaluation method and system, and relates to the technical field of building structure corrosion resistance evaluation. The method comprises the steps of data acquisition, coast environment corrosion factor data is acquired by arranging a sensor module, related data of a building structure is acquired at the same time, and the accuracy of assessment data and assessment results is effectively improved by considering multiple factors into the corrosion resistance assessment of the building structure in the coast region; meanwhile, a coupling corrosion rate correction model is constructed, corrosion factor data monitored in real time and real-time data of the building structure are combined, the purpose of evaluating the corrosion resistance of the building structure in the coastal area according to real-time changes of corrosion factors in the tidal period is achieved, the real-time performance of an evaluation result is guaranteed, and the evaluation efficiency is improved. And the accuracy of the evaluation data is further improved.

The invention relates to the technical field of psychosomatic health management, in particular to a psychological immune system evaluation system and intervention method for cancer patients. And the data acquisition module is used for acquiring and fusing the immune biochemical indexes of the patient, the continuous physiological time sequence signals generated by the wearable device, the digital psychological assessment data and the clinical treatment stage marks, and outputting a structured multi-dimensional time sequence data set through time alignment and feature extraction operation. Through multi-modal data fusion and integrated machine learning analysis, the psychosomatic health risk of the patient can be predicted based on the continuous physiological and psychological data change trend of the patient, and corresponding intervention measures can be triggered before the patient has obvious clinical symptoms by outputting the quantized risk coefficient and the multi-stage early warning signal, so that the accuracy of the patient is improved. Therefore, an intervention mode is promoted to be converted from traditional intervention after evaluation to predictive and preventive intervention.

The invention belongs to the technical field of ecological restoration evaluation, and provides a geological ecological restoration evaluation method and system for geological disaster prevention and control, electronic equipment and a storage medium. The method comprises the steps of research data acquisition, geological disaster type setting, initial evaluation, data preprocessing, model construction and training, risk determination before restoration, information acquisition after restoration, geological disaster risk prediction and geological ecological restoration evaluation. According to the method, the model is trained by using the remote sensing image, the sound wave data and the initial evaluation data, the evaluation result is predicted through the remote sensing image and the sound wave data, the risk evaluation difficulty and time are reduced, in addition, remote sensing and sound waves are combined, meanwhile, surface features and structural features are extracted, and the prediction effect of the model is optimized; through a multi-mode proximity perception model, the perception capability of the surrounding environment is improved, and the prediction precision of a single block is improved; and through the repair effect evaluation rule, a repair effect evaluation mechanism is optimized.

The invention relates to the technical field of data processing, and discloses a patient rehabilitation trajectory prediction and personalized nursing plan generation method and system. The method comprises the steps of collecting multi-dimensional function evaluation data to calculate a rehabilitation rate index sequence, recognizing a rehabilitation stage and configuring a feature weight vector, predicting a multi-dimensional rehabilitation trajectory through a rehabilitation stage gating sequential network, constructing a nursing task dependency network based on prediction data, and generating a personalized nursing plan through multi-objective optimization. According to the method, the accuracy of rehabilitation track prediction and the individuation degree of the nursing plan are improved, and comprehensive optimization of rehabilitation benefits, safety risks and resource consumption is realized.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

The invention discloses an intelligent driving evaluation method and equipment. The method comprises the following steps: acquiring environment perception data recorded by a real vehicle and a corresponding recorded vehicle pose; performing data transformation on the environment perception data according to a pose difference between the vehicle model pose and the recorded vehicle pose so as to generate transformation perception data corresponding to a vehicle model view angle; generating a prediction track based on the transformation perception data, iteratively updating the pose of the vehicle model according to the prediction track, and recording dynamic evaluation data; the dynamic evaluation data comprises an iteratively updated vehicle model pose, transformation perception data and a prediction track; and determining an evaluation performance index of the intelligent driving system based on the dynamic evaluation data. Therefore, a dynamic closed-loop evaluation mechanism under a real data condition is constructed, self-adaptive association of perception input and a vehicle motion state is realized, and an intelligent driving system can generate continuous decision feedback in an evaluation process, so that the time sequence consistency and stability of the system in dynamic control are accurately reflected.

Disclosed are an auxiliary decision-making method and system for urban subway waterlogging risk disposal based on a Bayesian network. The method includes: acquiring waterlogging data and basic data of a target city subway, preprocessing the waterlogging data, obtaining first evaluation data and second evaluation data from the preprocessed waterlogging data, performing risk assessment on the first evaluation data and the second evaluation data according to risk degrees to obtain a risk value, adjusting the risk value according to the monitoring data to determine a risk degree, and constructing an auxiliary decision-making model for waterlogging risk disposal according to the risk level to optimize the auxiliary decision-making for waterlogging risk disposal. The method not only can improve the accuracy of auxiliary decision-making for urban subway waterlogging risk disposal, but also has good interpretability, and can be directly applied to the auxiliary decision-making system for urban subway waterlogging risk disposal.

The invention provides a Web application-based slow denial of service attack path acquisition method and device and a medium. The method comprises the following steps of: constructing a program analysis chart of a target Web application program; extracting all program execution paths from an entry node to an exit node based on the graph; calculating the weight of the path by analyzing the key characteristics of the path, and screening candidate paths of which the weight is higher than a threshold value; and further through path state value evaluation, data flow continuity detection and type compatibility detection, finally determining a target path which may have a slow denial of service attack risk. According to the method, a potential attack path is efficiently identified through a static analysis technology, accurate positioning is provided for Web application security protection, and the risk that a system is attacked is reduced.