103 results about "Evaluated data" patented technology

This application discloses an unsupervised adaptive method and system for evaluating insurance claims images. The method includes: normalizing multimodal claims images; extracting feature vectors from a Siamese network encoder, bringing features of different modalities under the same case ID closer together and pushing features of images under different case IDs further apart, achieving case-level cross-modal semantic alignment; constructing a structured evaluation database storing historical image feature vectors and business dimension labels; generating evaluation results for new images through a similarity retrieval database; fusing contrast loss and reconstruction loss, and automatically adjusting the dynamic balancing parameter γ based on the retrieval matching degree or reconstruction error to achieve adaptive evolution of the encoder incremental updates. This application eliminates the need for manual annotation, solves the problems of semantic fragmentation in multimodal data and model update lag, and improves evaluation accuracy and efficiency.

The application provides a kind of Internet of Things empowerment's unmanned plane global inspection data management method and system, it is related to fire danger monitoring technical field, the method includes: when determining as high fire danger grade, corresponding unmanned plane inspection task is generated according to the position and range of early warning area, unmanned plane inspection terminal is controlled from unmanned plane airport autonomous take-off, flies to target early warning area according to preset inspection route and real-time collection fire-related data, correlation analysis is carried out to inspection data, fire danger factor data and fire danger grade evaluation data, fire development trend is deduced through intelligent analysis algorithm, and inspection report and decision suggestion are generated.The application not only improves the accuracy and response efficiency of forest grassland fire danger early warning, but also realizes the whole-process response closed loop from fire danger monitoring and early warning to fire comprehensive analysis and disposal.

This application discloses a model evaluation method, apparatus, medium, and product, belonging to the field of computer science. The model evaluation method of this application identifies initial data to obtain first candidate data and the corresponding data type; filters the first candidate data based on the corresponding data type; inputs the filtered data into at least two target models to obtain second candidate data output by each target model; and evaluates the corresponding target models based on the second candidate data output by each target model to generate evaluation results. This application improves the accuracy of data selection and reduces labor costs by identifying initial data and filtering first candidate data; inputting the filtered data into multiple target models and evaluating the corresponding target models based on the second candidate data output by each target model enables a more comprehensive evaluation of the overall capabilities of the target models, making the evaluation data more realistic.

When using spectroscopic diode lidar to detect a gas of interest such as methane (CH4), detecting high concentration pathlength (CPL) values of the gas of interest may not actually indicate a plume or leak. In areas such as a refinery, where ambient gas levels may be routinely high, absorption from ambient gas measured over a long pathlength may produce the same CPL value as a much closer gas leak, leading to a false positive. By collecting and using lidar distance information in tandem with the CPL values determined from the lidar system spectroscopic measurements, the statistical properties of the dataset can be evaluated, and ambient concentrations of the gas estimated. A lidar dataset with revised values of CPL that adjusts for these measurements of ambient concentration may be created to provide better reliability for analysis and in for triggering alarms for potentially dangerous gas concentrations and leaks.

The application discloses a fire-fighting electrical equipment fault detection device and method based on data collection, which comprises the following steps: collecting target equipment operation data, target climate and geographical environment data and target human operation factor data in real time; evaluating the health degree of a target sensor to obtain target sensor health degree evaluation data; calculating target environment and human comprehensive influence data; obtaining target equipment parameter correction data according to the target environment and human comprehensive influence data; calculating target equipment normal mode similarity and target equipment fault mode similarity; calculating target equipment comprehensive fault score; evaluating the target fire-fighting electrical equipment according to the target equipment comprehensive fault score, and outputting target equipment fault evaluation result data and a processing scheme. The application can prevent misjudgment caused by detection equipment self-fault problems, can identify complex faults, reduce the false negative rate, quickly locate fault causes, and formulate accurate early warning and maintenance strategies.

This application provides a method, apparatus, storage medium, and program product for constructing hallucination assessment data, relating to the field of computer technology. It aims to increase the coverage of hallucination assessment data, clarify the classification of hallucination types, and improve the quality of the hallucination assessment data. The method includes: extracting at least one knowledge triple based on a knowledge document of a target domain; generating a first question-and-answer pair based on the knowledge triple; generating a hallucination response to a hallucination question and a hallucination type label corresponding to the hallucination response based on the knowledge triple and the first question-and-answer pair; and constructing hallucination assessment data based on at least one of the knowledge triple, the first question-and-answer pair, a second question-and-answer pair, and the hallucination type label.

The application provides an enterprise green transformation data processing method, device, equipment, medium and product, and relates to the technical field of energy. The method is applied to an electronic device and includes the following steps: a first interface is called to obtain a plurality of evaluation index data of enterprise green transformation, the plurality of evaluation index data including first category data and second category data; a plurality of pre-configured quantification functions are called to perform standard quantification processing on the plurality of evaluation index data, to obtain a first evaluation value corresponding to each evaluation data, the quantification function being related to a relative benchmark sub-function and / or an internal index sub-function; the quantification function is also related to a normalization sub-function, so as to convert the plurality of evaluation index data into a standardized result that can be compared under a unified scale; and the plurality of first evaluation values corresponding to the plurality of evaluation index data are subjected to weighted processing, to obtain a second evaluation value, the second evaluation value being used to evaluate the effect of enterprise green transformation. The application can improve the accuracy and efficiency of enterprise green transformation data processing.

This application provides a method, apparatus, electronic device, and storage medium for correcting hallucination assessment data, relating to the field of computer technology, and is used to improve the reliability of hallucination assessment data. The method includes: generating hallucination assessment data for a target domain based on knowledge documents of that target domain; validating the hallucination assessment data based on a verification model set; and correcting invalid items in the hallucination assessment data if invalid items exist.

This invention provides a method and apparatus for photoelastic stress reconstruction based on physically constrained deep learning, relating to the fields of experimental mechanics and optical mechanics. The method involves acquiring images of the interference fringe state of a transparent specimen under load to obtain multi-channel tensor input data; using a physically enhanced convolutional neural network model for feature extraction and processing, and performing differential calculations to obtain full-field stress component output data; visualizing and rendering the full-field stress component output data to obtain reconstructed visual data; comparing the full-field stress component output data with reference benchmark data, calculating error indices, and recording process time to obtain accuracy and efficiency evaluation data; and simultaneously integrating and displaying the reconstructed visual data and the accuracy and efficiency evaluation data to obtain a comprehensive evaluation and reconstruction result. This invention solves the inherent limitations of traditional phase unwrapping and stress separation techniques.

The present application relates to the technical field of tunnel anti-seismic, in particular to a kind of across fault tunnel post-earthquake function recoverability rapid evaluation system, it includes: using remote sensing and GIS technology integration technology, through satellite remote sensing obtains post-earthquake tunnel region topography, surface displacement and surrounding environment data, through unmanned aerial vehicle aerial photography supplements ground observation blind area, using image recognition extracts across fault tunnel earthquake damage detail parameter, will tunnel function recoverability be divided into three levels, utilize historical earthquake damage repair case library establishment modular repair strategy library, using natural language processing technology will evaluation data automatically generate post-earthquake function recoverability evaluation report, through multi-department collaborative feedback mechanism improves report content, obtains final evaluation report.The present application solves the problem that post-earthquake information acquisition of across fault tunnel is not comprehensive and efficient, structural damage diagnosis is not accurate, evaluation report generation is complicated and collaborative is poor.

Techniques are provided for database control using machine learning based prediction. One method includes obtaining first and second sets of designated database metrics characterizing active sessions and waiting sessions, respectively, of a database workload for one or more designated time intervals; evaluating an amount of work performed by the active and waiting sessions of the database workload within a designated time interval; applying historical workload data of the database workload to a machine learning model to obtain a forecasted amount of work performed by the database workload within a subsequent designated time period; and initiating an automated action using the forecasted amount of work. The active sessions may utilize one or more database resources and the waiting sessions may execute in response to an occurrence of a designated event.

This invention relates to the field of image recognition and discloses a method and system for monitoring aquatic environments based on image recognition. The method includes: data normalization of a multi-source image dataset to obtain a standardized image data sequence; feature deconstruction of the standardized image data sequence to obtain multi-scale feature data, and semantic category mapping to obtain environmental element identification result data; dynamic evolution analysis of the environmental element identification result data to obtain element-level spatiotemporal evolution trajectory data; network construction of the element-level spatiotemporal evolution trajectory data to obtain dynamic relationship graph data, and anomaly topology extraction of the dynamic relationship graph data to obtain environmental evolution feature data; dynamic risk assessment of the environmental evolution feature data to obtain comprehensive assessment data; and structured encapsulation of the comprehensive assessment data to obtain a comprehensive monitoring report. This invention can improve the monitoring efficiency of an image recognition-based aquatic environment monitoring method.

Certain aspects of the disclosure provide a method for generating an evaluation dataset. The method may include accessing a plurality of vector embeddings associated with a plurality of documents, wherein each vector embedding corresponds to a document; clustering the plurality of vector embeddings into a plurality of clusters; selecting, from each cluster of the plurality of clusters, a subset of vector embeddings of the plurality of vector embeddings according to a sampling strategy; for each respective vector embedding of the subset of vector embeddings: generating a seed question based on content of the portion of the document associated with the respective vector embedding; transforming the seed question into a transformed question of a question type different from the seed question; and associating the transformed question with the respective vector embedding as a ground truth label; and forming an evaluation dataset comprising the transformed questions and their associated ground truth labels.

The utility model belongs to the field of magnetic resonance imaging, concretely relates to a kind of test cell for artifact quantification measurement in magnetic resonance environment, including test cell body, adjustable positioning mechanism and fixed mechanism;Adjustable positioning mechanism is multi-axis adjusting mechanism, adjustable positioning mechanism sliding assembly in the lateral wall of test cell body;The end of adjustable positioning mechanism into test cell body inside installs sample one, and the spatial position of sample one in test cell body is adjusted by adjustable positioning mechanism;Fixed mechanism is fixedly assembled in the inside bottom surface of test cell body, and sample two is installed on fixed mechanism.Compared with prior art, the utility model solves the quantitative measuring device of the image artifact produced under a group of standard scanning conditions in the test device of artifact evaluation in prior art, and the detection of artifact imaging parameter of different materials in MR is realized in the scheme, to ensure that effective artifact evaluation data is output.

A system may utilize a large language model (LLM). The system may train and test agents via an LLM by using the LLM to generate a set of scenarios and a set of training data and then training the agents using the training data and executing the agents accordingly. The system may also perform evaluations of data within a data table via LLMs and evaluations of prompt templates using a set of data from the data table. In one example, the system may receive a set of instructions for data evaluation, evaluate the set of data via the LLM, and obtain a set of output data from the LLM. In another example, the system may receive a prompt template for the LLM, execute the prompt template via the LLM, obtain a set of output data from the LLM, and evaluate the performance of the prompt template via the LLM.

Embodiments can relate to techniques for comparing and evaluating data from disparate data sources and / or data streams. The technique can involve receiving data from at least two disparate data sources and / or data streams (the data including object movement information). The technique can involve abstracting data and generating data sets representative of an attribute of an object, generating normalized data sets, aggregating normalized data sets based at least in part on time windows spanning between attributes of abstracted data, which can be stored. The method can involve retrieving data from the aggregated data sets and comparing the retrieved data to benchmark data representative of at least one or more of a standard, a pattern, or a statistic of an attribute of at least one or more objects. An output representative of the comparison can be generated.

This invention provides a non-contact online detection method for pig body size and weight based on 3D machine vision, belonging to the field of machine vision technology. This invention collects 3D point cloud data, RGB image data, and posture change data of pigs; constructs a point cloud perception reliability index to evaluate data quality and performs adaptive optimization when requirements are not met; further, it obtains a standard posture point cloud model through key point detection, posture alignment, surface reconstruction, and non-rigid correction, and calculates body size parameters such as body length, body height, and chest circumference; performs posture evaluation and compensation based on the posture standardization deviation index; constructs a dynamic weight prediction model to calculate weight, and verifies and corrects the results online through a weight prediction stability judgment mechanism. This method achieves high-precision, non-contact, real-time detection of pig body size and weight, improving detection accuracy and system stability.

The application relates to the technical field of intelligent management of transformer substations, in particular to a modular prefabricated cabin transformer substation intelligent comprehensive management system with multi-source data integration and collaborative management functions.The system comprises a multi-source data acquisition unit, an edge intelligent processing unit is used for locally preprocessing, feature extraction and abnormal early warning of collected transformer substation multi-source heterogeneous data, and through a lightweight AI algorithm and a mixed communication protocol carried by an edge computing terminal module, data noise reduction, abnormal identification and equipment health degree evaluation are realized; and a data integration and collaboration unit.Through an adaptive sampling strategy and a multi-modal feature fusion mechanism of the multi-source data acquisition unit, multidimensional data such as power parameters, dynamic environment, fire safety and security are integrated into a monitoring system, feature layer correlation analysis is realized by relying on Jousselme distance and Dempster synthesis rules, the evaluation limitation of traditional schemes with single equipment and single-dimensional data is broken through, and a more comprehensive state basis is provided for equipment health degree judgment.

A method and an apparatus for assessing a risk caused by improper use of a cluster data resource, an electronic device and a storage medium are provided. A high-risk diagnosis indicator in a storage assessment dimension and a high-risk diagnosis indicator in a computation assessment dimension are obtained. The amount of high-risk diagnosis indicators in the storage assessment dimension that are satisfied in the use process of the data storage resource and the amount of high-risk diagnosis indicators in the computation assessment dimension that are satisfied in the use process of the data computation resource are counted, and are recorded as high-risk indicator trigger quantities. Risk on the data storage resource is assessed based on the high-risk indicator trigger quantity in the storage assessment dimension, and risk on the data computation resource is assessed based on the high-risk indicator trigger quantity in the computation assessment dimension.

To provide a data evaluation system, data evaluation method, and program that can evaluate data that does not have an explicit relationship with other data. [Solution] The data evaluation system according to this disclosure comprises a management unit that manages the history information of one or more data to be evaluated; an extraction unit that extracts related information of the history data, including at least one of the source data or derived data of the data to be evaluated, based on the history information; and an evaluation unit that evaluates the data to be evaluated based on the extracted related information.

The application discloses a kind of forest and grass ecological monitoring devices, belong to forest and grass ecological technical field, including for overall support's stand, the right side of the stand is fixedly connected for adjusting telescopic component, the right side of telescopic component is fixedly connected for enclosing fence's fence component, the inside of fence component is provided for adjusting position's adjusting component.The application is by being provided with stand, telescopic component, fence component, adjusting component, high-definition camera, monitoring component, base structure, screw structure and push structure, solve the single solidification of current sample method specification parameter, only can match the investigation scene of specific range, poor adaptability, the corner design of rectangular structure is easy to form visual blind area, leading to survey personnel when sample detection appears observation omission, this human error directly influences the accuracy of vegetation quantity statistics, finally cause grassland ecological evaluation data to produce systematic deviation.

The present application belongs to the technical field of ammunition testing, and particularly relates to an ammunition integrated testing automatic evaluation system and method, aiming to provide a general automatic evaluation framework for ammunition integrated testing, comprising: (1) a testing data management module, which converts original data into application data with practical significance and saves and maintains the application data; (2) an evaluator module, which evaluates data to obtain evaluation results and conclusions; and (3) a testing evaluation management module, which manages the operation of the testing data management module and the evaluator module according to different testing types. Compared with the prior art, the present application replaces visual inspection with automatic diagnosis, reduces the workload of people in batch production, reduces the probability of false detection, improves work efficiency, and reduces production cost. The evaluation type, evaluation requirement and output report of the present application can be customized again according to a project without changing the software.

Even if an AI performs well on an evaluation dataset, it may not achieve the same level of accuracy in real-world applications. In such cases, confidence in the AI ​​diminishes, highlighting the need for a mechanism to develop reliable AI. [Solution] In model comparison analysis mode, the CPU 201 of the client terminal 101 acquires the inference results of the first AI model and the inference results of the second AI model for each of the multiple images (S1002-S1005), and controls the system to compare and display the inference results of each AI model for each image in order of the image in which the number of correctly detected objects among the inference results of each AI model increased (S1006, S1009, S1010).

We provide the system. [Solution] Information review means for receiving data and reviewing the format and content of said data, A malware detection means for detecting viruses in the data, A supplier evaluation means for evaluating data providers and determining their reliability, A security monitoring means that monitors the security status of the entire system and detects anomalies, A data encryption method that enhances security by hashing transaction data, Approval management means for approving or rejecting data-based transactions, A system that includes this.

Computer-implemented method (105) for determining a performance value of a data set (T) for training and / or testing a machine learning system (60), wherein the data set (T) comprises a plurality of inputs (x) and an output (y) corresponding to each input (x), wherein the input (x) characterizes at least one value of a sensor signal (S), and the output (y) comprises a classification and / or a regression result with respect to the input, wherein the method comprises the following steps: • Determining (101) a binning of the values ​​of the sensor signals (S) of the majority of the inputs (x), wherein inputs (x) are assigned to different bins of the binning according to the values ​​of the sensor signals (S) encompassed by the inputs (x); • Assigning (102) the outputs (y) to the bins according to the assignment of the inputs (x) corresponding to the outputs (y). • Determining (103) a first quality value for a plurality of bins of the binning, preferably all bins of the binning, wherein the first quality value characterizes an average or median of second quality values, wherein each second quality value characterizes a deviation with respect to an output (y) associated with the bin from correspondingly desired outputs; • Determining the quality score of the data set, where the quality score corresponds to a numerically largest quality score of the first quality scores or a quantile of the first quality scores.

The application provides a salary automatic payment system based on a smart contract, relates to the technical field of computer data processing, and first introduces an adaptive context anomaly detection mechanism based on group statistical characteristics, dynamically identifies anomalies by evaluating the relative position of data points in the group distribution, and effectively solves the high misjudgment and missed judgment problem of data cleaning in a complex correlation scene. On this basis, the scheme encapsulates the calculation process containing sensitive salary logic in an off-chain security sandbox, only generates a zero-knowledge proof of calculation without private information and a clear payment list. The on-chain smart contract does not need to touch the original data or the calculation model, and only needs to prove the validity through mathematical verification to confirm the correctness of the calculation result, thereby realizing the trustless verification of complex salary calculation logic and the automatic and accurate distribution of funds on the premise of completely shielding commercial secrets and personal privacy.

The application discloses a method and device for scheduling a seat resource, an equipment and a storage medium, relates to the technical field of resource scheduling, and comprises the following steps: hot spot identification is performed on multi-dimensional operation data to obtain hot spot data of the multi-dimensional operation data; a dynamic programming algorithm is used to split a seat resource scheduling decision task into a plurality of subtask packages which are executed in parallel, and a scheduling decision path is determined; scheduling is performed based on the hot spot data and the scheduling decision path to obtain real evaluation data; and the real evaluation data and expected evaluation data are compared and analyzed to obtain comparison and analysis results. Through hot spot identification and dynamic programming parallel decomposition, the application obtains the processing of subtasks, improves the speed of data query and resource scheduling, and improves the efficiency of seat resource scheduling.

This application discloses a method and apparatus for confidence level assessment. The method includes: constructing a repeating permutation of an N-digit binary string; constructing a first mapping relationship between decimal values ​​and confidence levels based on the repeating permutation; and specifying that the decimal values ​​in the first mapping relationship are related to the confidence levels in the repeating permutations. N Each binary string is mapped one-to-one, where N is an integer greater than or equal to 2. The data to be evaluated is obtained and converted into a decimal comparison value. The data to be evaluated is N-digit binary data. A decimal value matching the decimal comparison value is determined based on the first mapping relationship. The confidence level corresponding to this decimal value is set as the confidence level of the data to be evaluated. According to the embodiments of this application, the confidence level generation process is simple, and the real-time output of the confidence level is good.