344results about How to "Improve evaluation efficiency" patented technology

The invention discloses a data-driven cross-domain intelligent asset knowledge reasoning and value evaluation method and system, and relates to the technical field of intelligent assets, and the method comprises the steps: extracting multi-domain data features of intelligent assets based on a deep learning model, and constructing a cross-domain intelligent asset knowledge graph; analyzing a user evaluation target based on a pre-trained language model, retrieving related assets in a knowledge graph, and performing knowledge reasoning by using an attention mechanism and a graph reasoning algorithm; and on the basis of the value evaluation model of reinforcement learning training, a final evaluation value and an evaluation report containing a confidence interval are generated in combination with a time sequence prediction algorithm. According to the method, multi-source heterogeneous data can be effectively fused, knowledge reasoning and value evaluation of cross-domain intelligent assets are realized, and the evaluation efficiency and accuracy are improved.

The invention provides a power transmission and transformation project ecological influence space-time boundary determination method and system based on multi-source remote sensing information deep fusion interpretation, and belongs to the technical field of power transmission and transformation project management. The method comprises the steps of data acquisition, data preprocessing and multi-source data integration, multi-scale space-time analysis, ecological influence index construction, remote sensing ecological index model construction, ecological influence space-time boundary determination, dynamic monitoring and early warning system platform establishment and long-term monitoring and evaluation. By integrating satellite remote sensing, unmanned aerial vehicle aerial photography and ground monitoring data and combining an image fusion algorithm, a principal component analysis method and a random forest algorithm technology, the method is beneficial to promoting accurate evaluation of the influence of the power transmission and transformation project on the ecological environment, visualizes the space-time boundary of the ecological influence, breaks through the limitation of a traditional evaluation method on time and space coverage, and improves the evaluation efficiency of the power transmission and transformation project. And a scientific method system for analyzing and evaluating the ecological influence of the power transmission and transformation project is further improved.

The invention discloses a multi-language translation method and system, and relates to the field of electrical digital data process.The method comprises the steps that cultural feature information and semantic structure information are obtained; splitting the source language text into a plurality of semantic units based on the semantic structure information; retrieving cultural connotation information of which the association degree with the semantic unit is greater than a preset threshold value; determining that the semantic unit is a culture sensitive semantic unit; extracting a target language expression mode based on the cultural connotation information; performing semantic combination on the target language expression mode and standard translation results of other semantic units except the culture sensitive semantic unit in the source language text to obtain a translation result; judging whether the semantic coherence of the translation result meets a preset condition or not; if the preset condition is met, outputting a translation result; and if the preset condition is not met, re-executing the step of semantic combination. The accuracy of the cultural connotation and the emotional color contained in the transmission source language is improved, and then the cultural adaptability of the translation result is improved.

The invention discloses a big language model evaluation system and method, and belongs to the technical field of big language model evaluation. The system comprises a data preparation module, a basic performance evaluation module, an efficiency evaluation module, a robustness and multi-mode evaluation module, a distributed task scheduling module and a feedback optimization module. Through the system, the technical effect of multi-dimensionally, efficiently and automatically evaluating the large language model is achieved.

The invention discloses an LLM-based graduation design thesis intelligent evaluation system. The system comprises a data input module, a preprocessing module, an LLM evaluation module and an evaluation result output module. Text data of graduation papers are input into the data input module, the data input module is connected with the preprocessing module, the text data, processed by the preprocessing module, of the graduation papers are input into the LLM evaluation module, and the evaluation result output module is connected with the LLM evaluation module. According to the system, a large language model is utilized to perform multi-dimensional evaluation on graduation theses submitted by students, including but not limited to content quality, logic structure, language expression, innovation, academic specification and the like. According to the LLM-based undergraduate thesis evaluation system, the LLM can adapt to thesis evaluation requirements in different subject fields through a fine adjustment mode.

The invention discloses a tunnel construction quality evaluation system and method. The evaluation system comprises a data acquisition module, a quality evaluation standard system, an early warning and notification module and a data management module. Wherein the data acquisition module is used for acquiring construction vehicle data, construction site sensors and site photographing; the quality evaluation standard system comprises drilling quality evaluation, back break evaluation, primary support thickness evaluation, anchor rod construction quality evaluation, waterproof and drainage pipe quality evaluation, steel bar binding quality evaluation, secondary lining quality evaluation and primary support flatness evaluation; the early warning and notification module integrates early warning signals from all the evaluation modules; and the data management module is responsible for storing data generated by each independent module and executing cross-module data analysis. According to the tunnel construction quality evaluation system, by integrating advanced monitoring equipment, an automatic algorithm and comprehensive data analysis, the quality control effect in the tunnel construction process is remarkably improved.

The invention discloses an industrial park groundwater pollutant screening and identification method, which comprises the following steps: firstly, scoring the source strength, toxicity and path of pollutants through a comprehensive scoring method, and carrying out preliminary screening according to the scoring result to determine preliminarily concerned pollutants, thereby being beneficial to narrowing the screening range and reducing unnecessary evaluation work; the evaluation efficiency is improved. Meanwhile, the ITD index model constructed based on the three dimensions of source intensity, toxicity and path can more comprehensively consider the potential risk of the pollutants, and a scientific basis is provided for an evaluation result. The toxicity test result is introduced, so that the evaluation result is more accurate, and experimental data support is provided for the evaluation result. The method can provide scientific basis and technical support for underground water pollution prevention and control in industrial parks. According to the comprehensive evaluation result, corresponding risk prevention and control measures are provided for the optimal control pollutants in the list, the underground water pollution risk of the industrial park is reduced, and the environment and human health are protected.

The invention discloses a traditional Chinese medicine planting site selection evaluation method, system and device based on big data and a medium, and relates to the technical field of data processing. The method comprises the following steps: acquiring a traditional Chinese medicine category of to-be-planted traditional Chinese medicine and soil data and meteorological data of a plurality of candidate planting areas; based on the plant growth parameters corresponding to the traditional Chinese medicine categories, establishing a planting area evaluation standard; performing evaluation processing on the soil data and the meteorological data of each candidate planting area according to a planting area evaluation standard to obtain an environmental adaptability index; receiving the multispectral image of each candidate planting area, determining the vegetation coverage and water and soil loss rate of each candidate planting area, and calculating an environmental risk index; and determining corresponding site selection priorities in combination with the environmental adaptability indexes and the environmental risk indexes, and taking the candidate planting area with the highest site selection priority as a target site selection area of the to-be-planted traditional Chinese medicine. By implementing the technical scheme provided by the invention, the accuracy of traditional Chinese medicine planting site selection evaluation can be improved.

The invention discloses a software testing method and system based on a large model, and relates to the technical field of software testing, and the method comprises the following steps: step 1: describing a code review rule, and expanding specifications required to be checked: firstly, listing the check specifications required to be satisfied by a code, and according to a thinking chain and a zero- / feed-shot example, establishing a list of the check specifications required to be satisfied by the code; the check rules in the list are described as reasoning instructions of the model according to the check key points and the check steps through combination of one or more technologies of context learning; 2, code slicing: performing structure analysis and dependency analysis on the tested software project: firstly, slicing the code according to a function by utilizing a code analysis tool; according to the software testing method and system based on the large model, convenient project management, user-defined configuration and high-automation-degree test execution can be implemented on a software evaluation task, and the evaluation efficiency is effectively improved.

A large model agent generation content quality evaluation method based on double baselines comprises the steps that data generated by human beings is acquired, preprocessing and semantic segmentation are carried out, and data samples after semantic segmentation are formed; performing theme extraction on the data samples after data segmentation to form a theme list; the content of the data forming the theme list is generated through an intelligent agent workflow and the content of the data forming the theme list is directly generated through a large model, and an evaluated sample and a contrast sample are obtained; comparing and scoring the data sample after semantic segmentation, the evaluated sample and the contrast sample; selecting a sorting algorithm to carry out score conversion on the graded and compared samples; and performing statistical verification and result analysis. According to the method, a comprehensive, reliable and efficient solution is provided for quality evaluation of the generation content of the large model agent. The method not only can accurately evaluate the content quality, but also provides powerful support for continuous optimization of an intelligent agent system.

The invention relates to the technical field of cement-based composite cementing material preparation, in particular to a low-carbon environment-friendly cement-based composite cementing material and a preparation method thereof.The preparation method comprises the steps that an initial forming object is subjected to regional division according to position characteristics so as to obtain real-time monitoring results of all target monitoring regions, determining the actual state of each target monitoring area according to the real-time monitoring result; analyzing the actual state to determine a single mixing amount set of each target monitoring area; integrating the single mixing amount sets to obtain a target mixing amount set, and sequentially performing stirring and pouring molding according to the target mixing amount set to obtain a target molded object; in the stirring process, the initial stirring parameters are adjusted according to the actual stirring degree in combination with the overall analysis result, so that actual stirring parameters are determined; and in the pouring forming process, the initial pouring parameters are adjusted according to the bubble generation rate in combination with the overall analysis result, so that the actual pouring parameters are determined.

The invention relates to the technical field of speech recognition effect evaluation, in particular to a speech recognition evaluation method based on artificial intelligence. The method comprises the following steps: acquiring to-be-evaluated voice data, analyzing the to-be-evaluated voice data to obtain a voice fluency index, a voice clarity index and a voice accuracy index, and calculating to obtain a first voice quality index of the to-be-evaluated voice data based on the voice fluency index, the voice clarity index and the voice accuracy index; inputting the to-be-evaluated voice data into the voice content recognition model, outputting a prediction text of the to-be-evaluated voice data, and performing comparative analysis on the prediction text and the labeled text to obtain a second voice quality index of the to-be-evaluated voice data; and obtaining a voice recognition evaluation result corresponding to the to-be-evaluated voice data based on the first voice quality index and the second voice quality index. According to the invention, the evaluation efficiency and accuracy of the speech recognition effect can be improved.

The invention discloses a circuit breaker contact wear degree intelligent evaluation system, and belongs to the technical field of circuit breaker contact wear evaluation, the circuit breaker contact wear degree intelligent evaluation system comprises a camera, a data collector, a server and a display, the server comprises a circuit breaker type analysis module, a signal processing module, a wear evaluation module, a wear prediction module and a health monitoring module, the circuit breaker type analysis module, the signal processing module, the wear evaluation module, the wear prediction module and the health monitoring module are in communication connection in sequence; the display comprises a data display module; according to the invention, the wear degree of the contact head of the circuit breaker can be intelligently evaluated, and meanwhile, the system also has the functions of wear degree prediction, residual use frequency calculation and wear frequency early warning, so that the maintenance and management efficiency of the circuit breaker can be greatly improved, and the equipment fault risk is reduced.

The invention relates to the technical field of clustering analysis, and particularly discloses a fruit growth monitoring system, which comprises an acquisition module for acquiring a target value at a time point, acquiring target data based on the target value, and generating a target point; the clustering analysis module is used for setting a clustering radius and clustering the target points according to the density of the target points in the clustering radius; the clustering optimization module is used for adjusting a clustering cluster according to the theoretical gravity center and the maximum distance when the density of the target points in the overlapping region meets the condition; and the result module obtains the standard points and judges the growth state of the fruits based on the standard points. According to the invention, the efficiency of monitoring the growth state of the fruits can be improved.

The invention discloses a supplier quality evaluation method, and relates to the technical field of data quality analysis, and the method comprises the steps: obtaining a data initial quality evaluation label of each supplier; counting an uploaded information quality processing comprehensive data quality evaluation value of each type of suppliers; determining an initial quality detection constraint period of each type of suppliers and performing quality tracking detection; obtaining quality tracking detection data, and analyzing and processing to obtain a quality tracking monitoring result; according to the method, the data quality of the data uploaded by the suppliers is analyzed, the availability of the quality evaluation data is ensured, the suppliers whose initial quality evaluation is unqualified are screened, and the quality of the suppliers is evaluated to be qualified. Quality evaluation resources are concentrated on suppliers whose initial quality evaluation is qualified, customized quality tracking detection is carried out on each type of suppliers, the quality tracking detection efficiency and result are improved, and the quality detection constraint period is dynamically adjusted in time.

The invention discloses a clinical test quality evaluation method and system based on artificial intelligence, and relates to the technical field of clinical test quality evaluation. Through a series of automatic data processing and model evaluation processes, automatic operation is basically realized from data acquisition to final report generation, manual complex evaluation calculation on a large amount of data is not needed, and the evaluation efficiency is greatly improved; evaluation is carried out through an artificial intelligence algorithm and a rule-based expert system, so that the influence of subjective factors on an evaluation result is effectively reduced, and the consistency and accuracy of the evaluation result are improved; according to the method, multiple types of high-dimensional data are processed through an artificial intelligence algorithm, key feature subsets are extracted through feature extraction and screening, a trained deep learning model is used for evaluation, the method adapts to constantly changing large-scale and high-dimensional data conditions by constantly optimizing model performance, and the requirement for efficient and accurate evaluation is effectively met.

The invention relates to the technical field of data analysis and evaluation, in particular to a color fastness evaluation method and system, and the method comprises the following steps: setting test conditions to simulate external factors encountered in the actual use or processing process of textiles; under the simulated test condition, treating the textile sample for a preset time; acquiring a multispectral image of the textile by adopting a multispectral imaging technology, and capturing color change of the textile; developing a self-adaptive image enhancement algorithm, and performing preprocessing; comparing spectral information of the multi-spectral image of the textile before and after processing based on chromatic aberration evaluation analysis so as to quantify the color fastness; analyzing the multispectral image after image enhancement by using a deep learning technology, extracting color attenuation features, and comprehensively evaluating a color fastness score; according to the color fastness evaluation method, basic color difference information is considered, advanced features such as textures, edges and local color modes of the textile are further included, and the accuracy and reliability of color fastness evaluation are remarkably improved.

The invention discloses a pre-splitting blasting half-wall porosity intelligent identification system and method, and the system comprises a front-end display platform and a rear-end operation system which carry out data interaction through a computer, and the operation result information is displayed and checked through the front-end display platform; the front-end display platform consists of a menu bar, a toolbar, a display window and a command line; the rear-end operation system mainly comprises a half-wall hole image database, an image denoising module, an image segmentation module, a data statistical analysis module, an identification calculation module and the like, half-wall hole photo images are collected on steps on the two sides of the platform through the unmanned aerial vehicle oblique photography technology with or without the RTK function, a three-dimensional image database marked with half-wall hole information is established, and the three-dimensional image database is used for storing the half-wall hole information. An intelligent half-wall porosity recognition algorithm based on computer deep learning is developed, accurate recognition of the half-wall porosity is achieved, the traditional presplitting blasting quality evaluation efficiency can be greatly improved, and the method can be widely applied to slope presplitting blasting effect evaluation in the fields of surface mines, water conservancy and hydropower, transportation and the like.

The invention belongs to the technical field of ecological environment protection, and particularly relates to a method and system for determining a biodiversity key area based on the population number in an area range, and the method comprises the steps: collecting and analyzing key data, such as an environment influence coefficient, a species diversity index and a population density value; and combining the established threat factor quantification standard and the weight distribution system, and adopting a scientific calculation model to obtain the threat coefficient of each sample region. Then, through setting a reasonable threat coefficient threshold value, screening out a key region of biodiversity protection; spatial analysis is carried out by utilizing a GIS technology, distribution and mutual relations of the key areas in geographic space are determined, and priority ranking is further carried out according to threat degrees and protection values. The invention aims to provide accurate, rapid and resource-focused decision support for biodiversity protection.

The invention provides a shale oil fracturing effect evaluation method and device, a terminal and a storage medium, and belongs to the technical field of petroleum engineering. According to the method provided by the embodiment of the invention, the fuzzy comprehensive evaluation method is utilized to perform fuzzy processing on the basic parameters such as the target reservoir parameters and the target engineering parameters, so that the values of the basic parameters are more representative and are not certain values, and therefore, an evaluation result close to the reality can be obtained, and the accuracy of a calculation result is improved; and optimizing the shale oil fracturing effect evaluation method. Moreover, by fitting the function model of the main control factors and the yield and utilizing the function model to evaluate the fracturing effect, the model construction process is simplified, and the evaluation efficiency is higher.

The invention provides a water and soil loss prediction evaluation method and system, a storage medium and electronic equipment. The method comprises the following steps: constructing a corresponding digital prediction process processing module and a digital prediction parameter database based on a preset prediction process; constructing a data set containing low-resolution DEM data and DOM data and high-resolution DEM data and DOM data, and constructing a water and soil loss prediction model based on the data set; and performing water and soil loss prediction evaluation based on the prediction process processing module, the digital prediction parameter database and the water and soil loss prediction model. According to the method, high-resolution data and conventional-resolution data can be compatible for water and soil loss prediction and evaluation, water and soil loss prediction and water and soil conservation effect evaluation work are refined in space, and the overall evaluation efficiency can be effectively improved.

The invention belongs to the cross technical field of remote sensing data processing and soil environment monitoring, and discloses a multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method, and the method comprises the following steps: obtaining different types of remote sensing data in a target area, and constructing a multi-source data set; the acquired multi-source remote sensing data are preprocessed, data noise and system errors are eliminated, and data consistency is ensured; performing multi-source remote sensing data fusion by adopting a spectrum-polarization-texture collaborative fusion strategy in combination with a multi-scale decomposition and weighted fusion model; based on the fusion data, constructing a machine learning inversion model, and inverting soil quality key indexes; and carrying out comprehensive evaluation on the soil quality by adopting an analytic hierarchy process-matter element extension model to realize grading of the soil quality. By adopting the multi-source remote sensing collaborative soil quality index inversion and comprehensive evaluation system and method disclosed by the invention, rapid, accurate, large-area and dynamic monitoring and comprehensive evaluation of soil quality are realized.

The invention discloses a language model evaluation method and device, electronic equipment and a computer readable storage medium. The method comprises the steps of obtaining at least one question answer pair in a preset vertical field; the question-answer pairs comprise questions and answers corresponding to the questions; performing anthropomorphic processing on the target question through a natural language processing model to obtain at least one question text of the target question in different question modes; the target question is a question in any question answer pair; questioning a question answering model corresponding to a preset vertical field based on the at least one question text, and obtaining at least one reply correspondingly output by the question answering model; and performing quality evaluation on the question answering model according to the answer corresponding to the target question and the at least one reply to obtain a quality evaluation result of the question answering model. Therefore, the efficiency and accuracy of evaluating the question answering model can be effectively improved, and meanwhile, the question answering model can be comprehensively evaluated.

The invention relates to the field of image detection, in particular to a multi-dimensional data shrimp group health automatic evaluation method, which comprises the following steps of: arranging a main monitor and an auxiliary monitoring cluster above a shrimp group to be monitored, shooting the shrimp group to be monitored, forming a corresponding health evaluation image, numbering according to a shooting time sequence, and pre-learning to obtain a health evaluation result. The health learning model learns the main assessment pre-data, generates a bubble number change diagram, compares the bubble number change diagram with a number threshold value, generates a corresponding health assessment result according to a comparison result, and learns the auxiliary assessment pre-data to generate an auxiliary judgment diagram when the health assessment result is unqualified; and performing weighted average calculation on the bubble number change diagram and the auxiliary judgment diagram according to a preset proportion, comparing an evaluation average value with a number threshold value, generating a final health evaluation result, and automatically evaluating the health condition of the shrimp group by utilizing machine learning, thereby improving the evaluation efficiency and accuracy.

The invention provides an urban green land ecological benefit assessment method and system, and belongs to the technical field of ecological environment. The urban green land ecological benefit assessment method comprises the following steps: determining an establishment principle of an assessment system, and measuring and improving the value of an urban green land by taking scientificity, comprehensiveness and operability as references; determining a green land ecological benefit evaluation target, determining a purpose and an application range of an evaluation system, and determining corresponding evaluation key points and indexes according to different evaluation targets; determining green land ecological benefit evaluation content, defining an evaluation range and content, and determining the purpose and application range of an evaluation system. According to the method, the purpose and the application range of the evaluation system can be determined by determining the green land ecological benefit evaluation target, corresponding evaluation key points and indexes are determined according to different evaluation targets, the evaluation pertinence is improved, the problem is accurately focused, resource allocation is optimized, and the evaluation efficiency is improved. The explicit assessment goals may help determine which aspects require more resource investment.

The invention discloses a low-efficiency sea use comprehensive evaluation method based on multi-dimensional analysis, and relates to the technical field of marine resource management and evaluation, and the method comprises the steps: collecting sea use benefit data, and processing the collected data through a superposition analysis method; performing percentage assignment on the processed sea utilization benefit data; constructing a multi-level index system through an analytic hierarchy process, and calculating a target data weight value through a Delphi method; calculating a dimension score according to the assignment and the weight value of the sea utilization benefit data; and calculating a comprehensive score according to the dimension score, and performing grading evaluation on the sea use project. According to the technical scheme, efficient and accurate low-efficiency sea use evaluation can be made for sea use projects, and the method has high application value in the aspects of enhancing sea area space management and control and promoting ocean resource conservation and utilization.

The invention discloses an intelligent crane examination system based on vision, relates to the technical field of image processing, and solves the technical problems that a traditional crane examination system mainly depends on manual evaluation, is low in efficiency and is easily influenced by human subjective factors, so that the evaluation result is not fair and accurate enough. Identity information of examinees is verified; collecting a mark image in the process that the examinee transports the moving goods from the starting position to the preset warehousing position through the crane; on the basis of the mark image and the assessment indexes of the crane, obtaining various assessment indexes of the examinee; scoring each examination index of the examinee to obtain an examination result of the examinee; according to the method, the examination results of the examinees are obtained by automatically scoring the various examination indexes of the examinees, so that the evaluation efficiency is improved, subjectivity and unfairness possibly caused by artificial scoring are avoided, and the objectivity and accuracy of the evaluation results are ensured.

The invention provides a deep learning method and system based on a station-city fusion space energy consumption model, and the method comprises the steps: analyzing a core energy consumption system of a core space region in a station-city fusion three-dimensional network space through employing a space region analysis method, and constructing a station-city fusion three-dimensional network energy consumption model of the core energy consumption system; constructing a carbon emission mechanism model through a station-city fusion three-dimensional network energy consumption model, defining a loss function of the carbon emission mechanism model according to a deep learning neural network structure of an initial input variable and an output variable in the carbon emission mechanism model, and performing iterative training on the loss function by adopting an iterative optimization function to obtain a carbon emission mechanism model; and outputting a deep learning neural network agent model until the loss value is lower than an allowable value, and finally evaluating the green operation performance of the station-city fusion three-dimensional network space by adopting the deep learning neural network agent model according to specification requirements to obtain an evaluation result. Compared with the prior art, the method is higher in evaluation efficiency and higher in reliability.

The invention discloses a real-time optimal power flow method based on a generative pre-training model (GPT) and deep reinforcement learning, relates to the technical field of intelligent regulation and control of a power grid, and utilizes technical breakthrough in the field of large language models to directly model non-quantifiable natural language stipulations as targets and constraints in an OPF problem. The method comprises the steps of establishing comprehensive performance indexes, setting contexts and problems, converting GPT agent feedback into reward signals, carrying out iterative sampling, outputting GPT agent results in a JSON format and importing the GPT agent results into a DRL agent, carrying out offline training and online application and the like. Three deep neural networks are used in the DRL process, a cost function is optimized through a primitive-dual method, a series of constraint conditions including language specification constraints are set, and the method can be applied online to solve the OPF problem in real time.

The invention discloses an intelligent evaluation method and system for a green building, and relates to the technical field of building evaluation, and the method comprises the steps: inputting obtained building data into a preset energy consumption prediction model, so as to obtain a building energy consumption value; comparing the building energy consumption value with a set energy consumption threshold value to obtain an evaluation label; inputting the building energy consumption value into a preset green building rating model based on the evaluation label to obtain a green building grade; the energy consumption prediction model is constructed to predict the energy consumption value of a certain building, and then the energy consumption value and the energy consumption threshold value are compared and analyzed, so that whether the building meets the basic requirement for evaluating the green building or not is preferentially judged, on the basis of existing model evaluation, invalid analysis of the model is reduced, and the evaluation efficiency is improved. Therefore, the evaluation efficiency of the model on a plurality of buildings is optimized, the accuracy and reliability of rating are effectively improved, and meanwhile, the complexity of historical data analysis is reduced.