107 results about "Outcomes evaluation" patented technology

The invention provides a pre-stressed anchor cable construction arrangement quality evaluation method, and relates to the field of underground engineering, and the method comprises the following steps: S1, model construction: building three-dimensional numerical models of different anchor rod arrangement schemes; s2, simulation calculation and result evaluation, wherein excavation simulation calculation is executed according to the three-dimensional numerical models of different anchor rod arrangement schemes, and numerical simulation calculation results of surrounding rock deformation, stress distribution, damage and fracture area distribution and supporting structure stress are obtained; evaluating numerical simulation calculation results of different anchor rod arrangement schemes to obtain evaluation indexes, and determining an optimal scheme according to the evaluation indexes. Through the cooperation of the above structures, the method has the following beneficial effects: 1, the evaluation accuracy can be comprehensively improved, and the data is converted into a visual and quantitative evaluation result by constructing a comprehensive evaluation system and applying a data analysis model and algorithm; and secondly, by establishing an error correction model, the deviation between numerical simulation and on-site monitoring data can be accurately calibrated.

The invention discloses an agent and large language model evaluation method and device, a medium and electronic equipment, and relates to the technical field of large models and agents, and the evaluation method comprises the steps: creating an evaluation task which is used for evaluating at least one dialogue of a plurality of to-be-evaluated objects in a dialogue process from at least one evaluation dimension, the plurality of to-be-evaluated objects are used for realizing dialogue tasks in the same field; the evaluation task is executed to obtain an evaluation result, and the evaluation result is used for comparing the dialogue ability among the multiple to-be-evaluated objects; displaying the evaluation result on the first interface, and displaying a second interface; in response to the configuration operation for the evaluator of the target type in the second interface, a configured evaluator is obtained, the configured evaluator is used for evaluating the dialogue from the corresponding evaluation dimension, a user can conveniently analyze the building effect of the agents of different configurations, in addition, user-defined configuration can be carried out based on the configuration operation, and the user experience is improved. And thus, evaluators of different evaluation dimensions are obtained.

The invention discloses an examination result evaluation system in the technical field of medical examination evaluation. The system comprises a physiological dynamic recognition module, a critical state interpretation module, a process efficiency optimization module, a health trend prediction module, an examination accuracy improvement module, a result interpretation optimization module, a potential pathology mining module and a health evaluation comprehensive module. According to the method, the depth and the breadth of medical examination and evaluation are remarkably improved by introducing technologies such as a nonlinear dynamic model, fuzzy logic and a machine learning algorithm. Firstly, the application of the physiological dynamic recognition module enables the analysis of time sequence data to be more accurate, and subtle changes of physiological behaviors can be revealed, so that the capability of early recognition of diseases is improved. The introduction of the fuzzy logic shows higher flexibility and accuracy in the aspects of uncertainty and fuzziness of processing test results, and particularly provides finer support for medical decision making in the aspect of judgment of boundary values and critical states.

The invention relates to the technical field of artificial intelligence, in particular to a method and system for dynamically processing complex business data based on a large model, after data are input, standardized data are generated through cue word and code dual preprocessing; the dynamic path selection unit generates an optimal reasoning path according to an evaluation standard; the task execution module processes the data according to a selected path and flow, and if a result does not conform to expectation, path adjustment or prompt word modification is triggered; and the result evaluation module evaluates a result through the three-stage system and drives the system to iteratively optimize. According to the scheme, dynamic path selection and professional processing flow selection are carried out in a data driving mode in combination with the large model, the problems of illusion and indefinite reasoning path when the large model processes complex business data can be effectively solved, and the flexibility and efficiency of the large model in complex problem solving are improved.

The invention belongs to the technical field of intelligent equipment control, and discloses a dynamic data intelligent control system and method. Comprising the steps of collecting multi-dimensional operation state data of a moving object in real time; historical multi-dimensional operation state data are collected, and the entropy value of a moving object is calculated; preliminarily evaluating whether the moving object has resistance abnormity or not according to the entropy value of the moving object; if the initial resistance abnormity exists, extracting a dynamic change rate index of the moving object; based on the multi-dimensional operation state data of the moving object, the dynamic change rate index and a preset resistance evaluation model, an evaluation result is output, and the evaluation result comprises normal resistance or abnormal resistance; if the resistance is determined to be abnormal, generating a resistance regulation and control strategy through a swarm intelligence optimization algorithm according to the multi-dimensional operation state data of the moving object, and executing and feeding back the data; quantitative and accurate friction reduction of the moving object can be achieved, and the fault risk of the moving object is reduced.

The invention relates to the technical field of ship construction and artificial intelligence, in particular to a CGPINNS-based ship construction cost efficient prediction method, which comprises the following steps: step 1, data acquisition: collecting ship design parameters, construction materials, process, labor cost and other data; 2, data preprocessing, cleaning, denoising and data normalization are carried out, and the data quality is improved; 3, a CGPINNS model construction module replaces a derivative item in gradient calculation by using central difference and is fused into a ship construction physical equation; step 4, after the model training module is trained by an Adam algorithm, the cost prediction module outputs a prediction result and a confidence interval; and 5, comparing the predicted cost with the actual cost by a result evaluation and feedback module, and if an error exceeds a threshold value, optimizing the model by the system. According to the method, indexes such as L2 relative errors are better, physical information is effectively fused, the generalization ability is high, accurate cost prediction can be provided for ship construction enterprises, scientific decision making can be assisted, and economic benefits can be improved.

The invention discloses a method for identifying early-stage diabetic patients based on physiological and biochemical indexes. The method comprises the following steps: collecting data; analyzing and preprocessing data; a BP neural network model is adopted, and the model comprises an input layer, six hidden layers and an output layer; model training: dividing the source data into a training set and a test set according to the ratio of 7: 3 for model training, the training round epoch is 70, the batch size is 16, and in the training process, 10% of the training data is used as a verification set for evaluating the performance; and evaluating a result. By comprehensively considering the multi-dimensional clinical data of the patient, the classification model capable of accurately identifying the risk of the early diabetes mellitus of the individual is developed, and the accuracy and reliability of early diabetes mellitus identification are improved; and a doctor can be assisted to formulate personalized prevention and treatment strategies through the identification result of the model.

The invention discloses a multi-modal large model reasoning system which comprises an input module, an input preprocessing module, a load balancing module, a parallel computing module, an evaluation feedback module and an output module. The input module is used for receiving external input information; the input preprocessing module is used for preprocessing input information in the input module and performing iteration according to feedback given by the evaluation feedback module; the load balancing module is used for monitoring and scheduling system hardware and distributing system resources; the parallel computing module is used for reasoning the task to generate a reasoning result; the evaluation feedback module is used for receiving the reasoning result generated by the parallel computing module and performing verification feedback and result evaluation on the reasoning result; and the output module is used for outputting one or a combination of more of characters, pictures, voices, videos and codes. The invention further discloses a multi-modal large model reasoning method and application, and the multi-modal large model reasoning method has a wide application scene.

The embodiment of the invention discloses an automatic test method and system for an intelligent cockpit large model, and the method comprises the steps: uploading a to-be-tested case document, and generating a test input signal according to the to-be-tested case document; receiving the test input signal, executing a test task for the test input signal, and generating a test response result; collecting a test response result, wherein the test response result comprises a voice recognition result, a human-computer interaction interface screenshot and a system operation log; performing semantic comparison and scoring on the test response result through a judgment large model, and outputting a related index corresponding to the test response result; and generating a test result evaluation report according to the related indexes corresponding to the test response result, and outputting a typical error case list according to the test cases which do not reach the standard in the test result evaluation report. According to the method, the accuracy, the efficiency and the reproducibility of the intelligent cabin model test can be improved.

The invention relates to a tunnel deformation prediction method based on multi-source monitoring data fusion, and belongs to the technical field of tunnel deformation prediction, and the method comprises the following steps: S1, an early-stage preparation stage; s2, a data collection and analysis stage; s3, a model establishment and prediction stage; and S4, a result evaluation and application stage. According to the tunnel deformation prediction method based on multi-source monitoring data fusion, through accurate deformation prediction, an engineer is helped to carry out optimization design on a tunnel structure and select proper supporting measures before construction, unnecessary material waste and cost increase are avoided, and through a continuous monitoring and immediate feedback mechanism, the tunnel deformation prediction accuracy is improved. The method has the advantages that the method is simple and easy to implement, precious time is won for emergency evacuation and response measures, data support is provided for making a tunnel maintenance plan through accumulation and analysis of long-term monitoring data, maintenance work is precise and efficient, and the advantages of being high in prediction efficiency and prediction accuracy are achieved.

The invention discloses a large language model agent system and method and related equipment. The large language model agent system comprises an input analyzer module, a strategy generation module, an execution controller module, a task result evaluation module, an reflection module and a strategy rewriting module, and a self-adaptive optimization closed loop for task execution is formed through the reflection module and the strategy rewriting module. Wherein the reflection module can generate a report containing failure reasons, and the problem of failure diagnosis is solved; the strategy rewriting module optimizes a plan according to a report, adjusts tool calling, and realizes self-learning and strategy reconstruction; the closed-loop design forms a complete cognitive process, and an input analyzer guarantees the stability of prompt. According to the system, the task completion rate, the execution stability and the scene adaptability are remarkably improved, the robustness and the error tolerance in a multi-step complex task are particularly enhanced, and the key blank of an existing language model agent system in the aspects of self-reflection and self-optimization is filled.

The present invention provides a method for evaluating the quality of anchor construction layout of a main powerhouse, which relates to the field of underground engineering and includes the following steps: S1, model construction: establishing a three-dimensional numerical model of different anchor layout schemes; S2, simulation calculation and result evaluation: performing excavation simulation calculations based on the three-dimensional numerical models of different anchor layout schemes, obtaining numerical simulation calculation results of surrounding rock deformation, stress distribution, distribution of damaged and fractured areas, and stress of support structures; evaluating the numerical simulation calculation results of different anchor layout schemes, obtaining evaluation indicators, and determining the optimal scheme based on the evaluation indicators. The coordination between the above structures has the following beneficial effects: First, it can comprehensively improve the accuracy of the evaluation, through the all-round integration of geological survey data, in-depth analysis of the surrounding rock lithology, joint and fissure direction, and ground stress distribution, and comprehensive consideration of the physical properties of the anchor itself, and closely combining with key indicators of the construction link.

The invention discloses a project similarity evaluation method based on semantic comprehension and multi-dimensional feature comparison, and the method comprises the steps: obtaining the project text data of a current declaration project, carrying out the semantic analysis and semantic mode understanding processing, extracting the text semantic features according to the semantic mode, and obtaining the project semantic features; historical declaration project data is obtained to carry out key text feature extraction processing, a project management model is constructed according to key text features and corresponding project declaration stages, and project semantic features are input into the model to carry out multi-dimensional semantic feature comparison processing. And evaluating the item similarity according to a multi-dimensional feature comparison result to obtain a repeated item approval evaluation result of the item, performing feature independent comparison processing on the item semantic feature of each sub-item of the current declaration item, and performing independent evaluation on the sub-item similarity to obtain a sub-item similarity evaluation result of the current declaration item. The method and the device have the effects of effectively evaluating the item similarity and improving the accuracy of item similarity evaluation.

The invention provides a fine tuning method of a preset model, and a question answering method and device based on the preset model, and relates to the technical field of artificial intelligence. The fine tuning method comprises the following steps: rewriting a sample problem by utilizing a preset model to obtain a retrieval problem; performing retrieval based on the retrieval problem to obtain a retrieval result for the retrieval problem; performing reflection on the retrieval result by utilizing a preset model to obtain a reflection result aiming at the retrieval result; under the condition that the reflection result represents that the retrieval result meets a preset answer generation condition, generating an answer of the sample question based on the retrieval result by utilizing a preset model; respectively evaluating the retrieval result and the answer to obtain a process evaluation score for the retrieval result and a result evaluation score for the answer; and performing fine tuning on parameters of a preset model based on a training sample constructed from the sample question, the answer, the retrieval result, the process evaluation score and the result evaluation score.

A method executed by a computing device includes interpreting a request to generate a non-fungible token (NFT) for an object distributed ledger to produce baseline content that includes an obligation provider identifier (ID) and an outcome recipient ID. The method further includes verifying, validity of the baseline content. When the baseline content is valid, the method further includes determining whether a beneficial improvement to a present estimate of a triggered outcome is provided when a hypothetical change to the obligation provider ID is made. The method further includes an outcome evaluation and establishing NFT content based on the outcome evaluation to include the baseline content. The method further includes causing generation of a new block affiliated with the NFT via a blockchain of the object distributed ledger, where the new block includes the NFT content.

The embodiment of the invention relates to the field of artificial intelligence and chemical process optimization, and discloses a chemical process collaborative optimization method and device based on a distributed training model deployment system, and the method comprises the steps: generating an initial process scheme through a second chemical process special training model in response to a target compound generation instruction; obtaining an execution result of executing the initial process scheme by the edge device, and generating execution result evaluation data based on the execution result; performing optimization training on the second chemical process special training model based on the execution data of the initial process scheme and the execution result evaluation data to obtain an optimization process scheme meeting preset evaluation data and corresponding optimization model parameters; and when a feedback instruction is received, sending the target process scheme and / or the target model parameters to a cloud end, so as to update the general chemical training model and / or the first chemical process special training model. And the optimization process scheme and the optimization model parameters are selectively sent to the cloud, so that knowledge sharing is realized and privacy controllability is ensured.

The invention relates to a clearing result evaluation method and device for an electric power spot market, computer equipment, a computer readable storage medium and a computer program product. The method comprises the steps of collecting electric power index data of an electric power spot market; establishing an index system of the to-be-evaluated object based on the power index data; according to the index system, obtaining the connection degree between each evaluation index and the state level by using the clearing result index layer evaluation model; according to the connection degree between each evaluation index and the state level, utilizing a clearing result target layer evaluation model to obtain a comprehensive basic probability distribution value of each evaluation index under different state levels; and according to the comprehensive basic probability distribution value, performing evaluation operation to obtain a state level of a clearing result of the to-be-evaluated object of the electric power spot market. By adopting the method, the evaluation weight can be adaptively adjusted, and an evaluation system of a multi-dimensional index network is fused, so that the transparency of a clearing result is improved.

The invention provides an intelligent reading control system, which is applied to the technical field of intelligent reading and comprises an acquisition module used for acquiring multi-modal original data in a reading process of a user in real time; the extraction module is used for extracting depth features of the multi-modal original data, generating behavior cognition feature vectors and calculating feature fusion coefficients; the evaluation module is used for outputting an evaluation result and evaluation confidence; the adaptation module is used for performing deep semantic analysis on the reading material, constructing a knowledge graph and calculating semantic association strength; based on the evaluation result, the evaluation confidence and the semantic association strength, generating multi-type and multi-level adaptive difficulty questions; the recommendation module is used for constructing a user ability portrait based on an evaluation result, calculating an interest matching degree and recommending personalized books, learning tasks and auxiliary materials; the intervention module is used for judging whether intervention is needed or not based on a preset intervention triggering threshold value, so that the learning efficiency of the user is improved.

This invention discloses a method and system for automatically extrapolating dynamic test schemes based on one-click planning rules. In the test planning and design phase, based on preset one-click planning rules, a dynamic test scheme including test scenarios, test factors, and test samples is automatically generated, and automated rules and equipment action modes are configured for the simulation extrapolation process. In the test execution phase, according to the generated dynamic test scheme, simulation resources are automatically scheduled to execute the simulation extrapolation without human intervention. During the extrapolation process, based on the automated rules and equipment action modes, command issuance, autonomous equipment behavior response, data collection, and result evaluation are automatically completed, forming a fully automated simulation extrapolation closed loop. This invention enables a top-down automated process, forming a complete solution for the entire process of automated simulation extrapolation in the "human-in-the-loop" environment, helping users solve various problems encountered in the construction of current "human-in-the-loop" simulation models.

The invention discloses an energy-based economic key element linkage prediction method and system, and relates to the technical field of economics and prediction modeling, and the method comprises the steps: determining and collecting a data source, and carrying out the preprocessing of the obtained data; exporting key business data based on the multi-dimensional data model, and constructing an energy consumption data and economic growth prediction model; training an energy consumption data and economic growth prediction model, and performing result evaluation and model optimization; and economic growth and energy prediction are carried out by using the optimized model, and analysis is carried out according to a prediction result and a corresponding decision is provided. By constructing the data cube model, data from different fields can be efficiently integrated, multi-dimensional comprehensive analysis is realized, and the comprehensiveness of the prediction model is improved; according to the method, the optimized model is utilized, a more flexible nonlinear modeling method is adopted, the complex relation between economic growth and energy consumption is better captured, and the prediction accuracy is improved.

The invention provides a model evaluation method and device. The method comprises the steps that a scoring system determines a first score of a first reply and a first explanation of the first score according to the first evaluation question, a first reference answer of the first evaluation question and the first reply of the first evaluation question, and the first reply is the first reply of a to-be-evaluated model to the first evaluation question; the first reference answer is a preset correct answer for the first evaluation question; the first explanation is used for representing a reason for giving the first score; the scoring system outputs the first score and the first explanation to an evaluation person or a receiving module; the first score and the first explanation are used for determining an evaluation result, and the evaluation result is used for evaluating the accuracy of replying the first evaluation question by the to-be-evaluated model. By adopting the method, the scoring reason can be explained while the reply to the to-be-evaluated model is scored, so that the efficiency of generating a complete evaluation result is improved.

The invention discloses a power industrial control network simulation verification scene generation method based on LLM, and the method comprises the steps: collecting paired input data and label data based on a power industrial control network topology to generate a training data set, and converting the training data set into a model input vector through preprocessing and vector coding; constructing a generation model based on a large language model by using the vectorized training data set, and obtaining an output result of a directed graph matrix containing network topology and alarm information through the trained generation model; evaluating the alarm information generation capability and the matrix generation capability according to the output result to obtain an evaluation result; and converting a square matrix and a vertex array output by the generative model into a directed graph form according to an evaluation result, and constructing a virtual simulation scene in a graph node mapping mode. According to the invention, the problems of single and fixed network scene required by simulation verification of the electric power industrial control network, incapability of rapid adjustment and the like can be solved.

This invention provides a resource scheduling method, apparatus, device, storage medium, and computer program product, relating to the field of communication technology. The method includes: responding to a task offloading request sent by a terminal device, publishing the task to be offloaded carried in the task offloading request to a nearby MEC device; receiving an evaluation result of the task to be offloaded sent by the nearby MEC device; the evaluation result is used to characterize whether the nearby MEC device has the capability to process the task to be offloaded; if, based on the evaluation result, it is determined that the task to be offloaded needs to be migrated, at least one target MEC device is determined according to the energy consumption information and latency information of the MEC device, and the task to be offloaded is scheduled to the target MEC device. In this invention, tasks do not need to be offloaded from a cloud central node to an edge device; instead, the local edge device accepts the task request, employs a distributed computing strategy, and autonomously completes resource allocation, achieving decentralized resource scheduling.

The embodiment of the invention discloses an artificial intelligence management method and device, a computer, a storage medium and a product, and the method comprises the steps: receiving a model task issued by a task issuing node, and sharing the model task to N artificial intelligence models; n is a positive integer; the N artificial intelligence models are independently trained; obtaining candidate task execution results of the N artificial intelligence models for the model task, and sending the N candidate task execution results to a result evaluation node, so that the result evaluation node performs result detection on the N candidate task execution results; and obtaining a result evaluation node, and for a target task execution result consensus by the N candidate task execution results, feeding back the target task execution result to the task release node. According to the invention, the security of the artificial intelligence model can be improved.

The invention discloses a water pumping test intelligent analysis system and method, relates to the technical field of hydrogeological exploration, and aims to solve the problems of low analysis efficiency and poor accuracy of a water pumping test analysis system in the prior art. According to the system, a test configuration module is used for determining target test configuration, and a data input module is used for data input and data analysis to obtain target original data; the intelligent preprocessing module is used for performing data preprocessing on the target original data to obtain preprocessed data; the multi-algorithm calculation engine is used for calculating the preprocessed data by adopting multiple calculation methods to obtain target hydrogeological parameters; the visual analysis module is used for generating a plurality of target charts based on the target hydrogeological parameters; the result evaluation module is used for generating a result comparison table for multiple calculation methods and determining the permeability grade for the target original test data; therefore, the analysis efficiency and accuracy of the water pumping test are improved.

The invention discloses a quality evaluation method for a test data set of a vehicle function point, a server and a computer readable storage medium. The method comprises the steps of performing evaluation processing on a test data set based on a plurality of preset evaluation dimensions, and determining an evaluation result of the test data set in each preset evaluation dimension. And then, based on a preset quality evaluation rule, according to an evaluation result, evaluating the quality of the test data set. Thus, through multi-dimensional quantitative evaluation of the test data set, the quality of the test data set can be comprehensively reflected, and the quality level of the test data set can be visually known, so that the performance of the vehicle-mounted dialogue system can be verified by using the high-quality test data set, the performance level of the vehicle-mounted dialogue system is guaranteed, and the test efficiency is improved. And the user experience is improved.

The invention relates to a method for realizing a resource adaptive pressure test based on load scaling. The method comprises the following steps: load modeling; constructing a distributed test cluster; service simulation and Mock construction are carried out; load prediction; self-adaptive pressurization is carried out; and evaluating a result. The invention also relates to a system and a device for realizing the resource adaptive pressure test based on load scaling, a processor and a computer readable storage medium thereof. By adopting the method, the system and the device for realizing the resource adaptive pressure test based on the load scaling, the processor and the computer readable storage medium thereof, the resource utilization rate is greatly improved, the test efficiency is greatly improved, manual intervention is reduced through an automatic test process, and the test period is shortened. The environment adaptability is enhanced, and the method has good universality for different types of service systems. The cost effectiveness is outstanding, the dependence on a special high-performance test environment is greatly reduced, the existing low-configuration equipment is fully utilized, and the hardware investment and the maintenance cost are saved.

The application discloses a kind of based on temperature control curve's forging heat treatment process optimization method, it is related to data processing technical field, including, based on process task data packet, temperature control performance prediction model is constructed and reverse solution is executed, generates core temperature control reference curve;To core temperature control reference curve executes heat response mapping, obtains furnace temperature control trajectory, and obtains online core tracking control scheme by heat treatment risk timing evaluation;Online core tracking control scheme is executed, process data is recorded synchronously, and combined furnace temperature control trajectory is passed through heat history inversion operation, obtains execution temperature control curve;Based on execution temperature control curve, temperature control performance prediction model is optimized, and obtains process quality evaluation result by performance deviation evaluation.The application is by constructing temperature control performance prediction model and executing reverse solution and feedback optimization, improves the calculation consistency of process parameter design and the quantifiable and iterative optimization ability of result evaluation.

The invention relates to the technical field of intelligent monitoring, and discloses a system operation state monitoring method based on multi-source information mining and organic fusion, which comprises the following steps: acquiring internal operation state data and external operation state data, calibrating the internal operation state data and the external operation state data by using a sensor calibration algorithm based on artificial intelligence; the method comprises the following steps: extracting internal operation state data and external operation state data, performing organic fusion to form a comprehensive data set, performing correlation analysis on the comprehensive data set, mining a correlation relationship between the internal operation state data and the external operation state data, and deriving key variables influencing the operation state of a monitoring target system; and comparing the real-time data of the key variables with a preset normal operation range to obtain a comparison result, evaluating the current operation state of the monitoring target system based on the comparison result, obtaining an evaluation result, and when the evaluation result exceeds a preset standard value, triggering an early warning mechanism to prompt a user to take corresponding measures. According to the invention, the monitoring precision of the monitoring target can be improved.