912 results about "Performance estimation" patented technology

The invention provides an intelligent short message scheduling method and device based on multi-dimensional dynamic optimization, and the method comprises the steps: obtaining the performance data of a plurality of short message channels, and calculating a channel health score based on a weight dynamic adjustment model; determining a scheduling strategy according to the priority identifier of the to-be-sent message, and performing channel screening and optimal matching; executing message sending and monitoring a sending state; terminal state detection is carried out on the failure message through operator base station signaling, and a decision tree model is applied to determine a retry strategy; and performing Huffman coding compression processing on the P2-level marketing messages which fail in retry, and performing batch sending in an idle window. According to the method, a comprehensive performance evaluation index and reward function model is also constructed, and parameter optimization is performed by applying a reinforcement learning algorithm. According to the invention, multi-dimensional dynamic channel scoring, intelligent retry decision making based on terminal state perception, batch processing with balanced cost-time efficiency and a closed-loop self-optimization system are realized, the short message delivery rate is obviously improved, and the invalid retry rate and the sending cost are reduced.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of defect detection, in particular to a cross-process defect root cause tracing method and system. According to the method, the data feature matrix covering multiple dimensions is formed by integrating the process parameters, the equipment state and the quality detection information, so that the performance evaluation of each process is more comprehensive, the interaction and influence paths among the processes can be clearly described by constructing the process relation graph, and the performance evaluation efficiency is improved. Meanwhile, basic data support is provided for quantifying the relation between the procedures through introduction of procedure attenuation factors, the shortest propagation path and the propagation probability of the defects can be accurately recognized by analyzing a procedure relation graph, root cause tracing of the cross-procedure defects becomes systematized in combination with construction of a knowledge graph, and the defect tracing efficiency is improved. The knowledge graph not only can effectively integrate and display data, but also is convenient for quickly positioning problems, and by utilizing an adaptive correlation analysis technology, the system can intelligently adjust an analysis model and a path and continuously optimize a defect detection and tracing process when facing new data.

The invention relates to the technical field of artificial intelligence chips, and discloses a model performance estimation method and device and computer equipment, and the method comprises the steps: determining model configuration data and candidate distributed strategies of a target model; converting the original calculation graph corresponding to the single-card deployment state based on the model configuration data and the strategy configuration data of the candidate distributed strategies to obtain a distributed overhead calculation graph corresponding to the multi-card deployment state; and performing performance estimation on the candidate distributed strategy according to the basic overhead and the additional overhead in the distributed overhead calculation graph to obtain strategy performance data of the target model under the candidate distributed strategy, thereby realizing conversion of a single-card model into a multi-card model. And based on the multi-card model, simulation calculation of the multi-card interconnection mode is realized on the premise of limited hardware resources, so that the influence of a communication operator and a topological structure corresponding to the multi-card interconnection mode in the overall operation of the model is reflected, and the upper limit of the model performance can be accurately evaluated in a simulator verification stage before silicon is applied.

The invention discloses an offshore pile foundation evaluation system construction method based on evolutionary algorithm optimization and PIML linkage. A standardized engineering data set is obtained; parameterizing the pile-soil coupling dynamics boundary condition, the pile-soil interaction specification criterion and the limit state and use state criterion, and storing the parameters as a physical constraint set; obtaining an initial performance evaluation model; generating an alpha evolutionary optimization performance evaluation model; outputting a bearing capacity-settlement relation index, a dynamic and static stiffness degradation curve index and a residual bearing capacity probability distribution index to form a structural performance index set; generating a credible structure performance index set containing confidence boundaries and extracting an influence degree sorting result; and automatically generating a structured evaluation report according to a verification result. The model output has physical consistency and engineering interpretation all the time, and the problem that a traditional pure data driving model is unreliable in result under data sparsity and environment sudden change is remarkably solved.

The invention discloses a test evaluation method and device for an industrial park integrated management system. The method comprises the following steps: acquiring a performance index data set of the industrial park integrated management system; the performance index data set comprises a performance index test data subset of each subsystem; the performance index test data subset comprises a test data sequence of each performance index; pre-processing the performance index data set to obtain a to-be-evaluated data set; and performing test evaluation processing on the to-be-evaluated data set to obtain a performance evaluation result value of the industrial park integrated management system.

The invention discloses a digital twinning-based building full life periodicity performance evaluation method and device, and belongs to the field of building construction.The evaluation method comprises the following specific steps that I, in the building planning and design stage, a building foundation digital model is constructed, meanwhile, various construction data are collected in real time through a multi-modal sensor, and block chain storage is established; iI, performing real-time sensor data preprocessing and updating based on the building basic digital model so as to perform multi-physics field coupling simulation, and dynamically updating and predicting a building state; the method overcomes the limitation that traditional simulation is large in calculation amount and difficult to update in real time, improves evaluation reliability, achieves dynamic calibration and precision enhancement, enables material aging and repair prediction to be more accurate, provides a solid basis for durability evaluation, improves the economy and reliability of the whole life cycle of a building, and has a wide application prospect. And a complete closed loop of coverage planning-construction-use-maintenance is formed, so that the performance evaluation can be continuous, dynamic and traceable.

Provided in the embodiments of the present application are a large-model performance assessment method and a related device. The method comprises: acquiring structured text description of a test material, wherein the structured text description comprises information of the test material in a plurality of dimensions; inputting the structured text description and a test question into a large language model, so as to acquire an evaluation true value; inputting the test material and the test question into a large model under test, so as to acquire an answer under test of the test question; and inputting the evaluation true value, the test question and the answer under test into the large language model, so as to acquire an assessment result for the answer under test.

The invention discloses a robot task dynamic scheduling optimization method for an annular warehousing system, and particularly relates to the technical field of intelligent warehousing system scheduling. Task basic information is collected to construct a task state table, and robot operation states are collected to construct a robot state table R; constructing a task-robot matching graph based on T and R, and calculating a matching priority score by adopting a graph neural mechanism; generating an initial scheduling scheme according to a game optimization and state driving strategy; path conflict detection is carried out in combination with a ring topology structure and traffic rules, and a task execution path is optimized through a sliding time window and a local disturbance rearrangement algorithm; a scheduling simulation engine is constructed to perform performance evaluation, if an evaluation result does not reach a preset threshold value, structure reconstruction is performed on the scheduling graph based on a residual feedback mechanism, and the method has the advantages of being high in real-time performance, low in conflict rate and high in resource utilization efficiency and is suitable for high-reliability intelligent scheduling in a multi-robot intensive task scene.

The invention discloses a verification system and method for a material formula through a confidence interval, and relates to the technical field of material informatics and intelligent research and development decision. Comprising a data acquisition module used for acquiring candidate formula basic data, historical experiment basic data, environment associated data, material recessive data and equipment state data and preprocessing the acquired data; according to the method, the final error value is obtained through fusion, the adjusted final credible interval is constructed, the problems that an existing material performance prediction tool can only output a point prediction result and lacks a stable credible interval, and engineers are difficult to assess that performance reaches the standard and actually and successfully grasp are solved, the coverage rate is verified through the verification set, the error scale is scaled, and the reliability of the material performance prediction tool is improved. It is ensured that the credible interval meets the preset coverage requirement, successful mastering of performance standard reaching can be quantified, an engineer does not need to depend on experience judgment any more, and the accuracy of performance evaluation is improved.

The invention discloses a multi-element coupling heat supply monitoring system based on a meteorological condition prediction model, belongs to an industrial control system, realizes deep coupling of meteorological data and an equipment operation state, and solves a fusion error problem caused by fixed meteorological element weight in traditional monitoring to a certain extent. By establishing the efficiency evaluation model containing the environment compensation mechanism, the interference of external temperature fluctuation on equipment performance evaluation is eliminated, and the accuracy of the operation efficiency index is improved. A multi-dimensional health assessment system is adopted to integrate equipment maintenance data and real-time operation parameters, potential fault risks can be recognized in advance, and data support is provided for equipment preventive maintenance.

The invention relates to a thermal simulation numerical simulation method of a multi-physics field architecture with CFD as a core, and belongs to the technical field of cross of computational fluid mechanics and heat transfer science. The method comprises the following steps: constructing a geometry and a conformal / non-conformal grid, presetting a model library, establishing association between a region and a physical field through physical group division, confirming a boundary and initial parameters, and screening a core simulation model; dividing an independent control volume based on a core numerical model, converting an area component and a volume component through integral operation, discretizing a boundary flux to construct a parameter matrix, then solving an equation set, and synchronously optimizing the grid quality; calculating a boundary flux and matching a discrete format, a turbulence model and a corresponding multi-physical field coupling model; original data are screened and processed, and cloud picture results such as temperature and flow field are generated through visualization. Accurate simulation of a multi-physics coupling scene is achieved, the precision and stability of thermal simulation under complex working conditions are improved, and efficient and reliable technical support is provided for engineering design optimization and performance evaluation.

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

The invention provides a code retrieval method and device for AI programming and related equipment, and the method comprises the steps: constructing a double-layer embedded vector model which comprises a basic semantic coding layer for parameter freezing and a plurality of independent lightweight adaptation layers which coexist; monitoring implicit feedback data generated by interaction of a user in the target code library to extract positive and negative sample pairs, and mining difficult negative samples based on a current model to form a training sample set; when the cumulative number of the samples reaches a preset trigger threshold value, optimizing and updating at least one lightweight adaptation layer parameter by utilizing a preset comparison loss function, and applying elastic weight constraint to key parameters of the lightweight adaptation layer parameter to obtain an updated double-layer embedded vector model; and performing multi-dimensional performance evaluation on the updated model, and when an evaluation result meets a preset publishing condition, deploying the model to a code retrieval service. The problems that in the prior art, cross-domain is difficult, the adaptation cost is high, and catastrophic forgetting is likely to happen are effectively solved, and zero-labeling self-evolution of the model is achieved.

The invention discloses a metering performance evaluation method and system for an intelligent electric energy meter, a medium and a product, and the method comprises the steps: employing a standard meter for synchronous operation for a to-be-evaluated intelligent electric energy meter, so as to obtain a field test value of a basic error of the intelligent electric energy meter, inputting the environment detection data and the operation time of the intelligent electric energy meter to be evaluated into the trained basic error prediction model corresponding to the model of the intelligent electric energy meter to be evaluated so as to obtain a model prediction value of a basic error; the basic error prediction model comprises an input layer, a one-dimensional CNN layer, an LSTM layer fused with a time attention mechanism, a full connection layer and a basic error output layer, and the metering performance level of the intelligent electric energy meter is determined according to a field test value and a model prediction value of a basic error. The invention aims to realize metering performance evaluation of the intelligent electric energy meter in a typical environment so as to realize higher operation efficiency and solve the bottleneck problem of long-term dependence on modeling and local feature extraction.

The invention discloses a low-speed unmanned vehicle artificial intelligence decision and performance evaluation system, and belongs to the technical field of low-speed unmanned vehicle scheduling, and the system comprises a generation module which is used for generating a scene novelty index through monitoring the instantaneous offset of an environment parameter; the topology module is used for constructing a dynamic scene topology according to the scene novelty index, and the dynamic scene topology comprises nodes and edge weights generated based on the scene novelty index; the decision-making module is used for converting the edge weight into a decision-making rule according to the dynamic scene topology and generating a decision-making strategy; the performance calculation module is used for tracking the deviation between the vehicle motion parameter and the control instruction during the execution of the decision strategy to calculate a performance disturbance coefficient; and a topology calibration module. The method can effectively adapt to unrecorded edge scenes such as sudden congestion and severe weather superposition, improves the generalization ability of a system to complex scenes exceeding a pre-training range, and reduces decision errors caused by insufficient scene adaptation.

The invention relates to the technical field of electrical performance monitoring, in particular to an electrochemical energy storage residual life estimation method and system. According to the method, by applying multi-stage voltage step excitation and capturing the maximum offset of the instantaneous current, the high-sensitivity characteristic reflecting the electrochemical dynamic characteristic in the energy storage monomer can be obtained, the limitation that a traditional method depends on the hysteresis index of capacity fading for evaluation is avoided, early insight of performance change is achieved, and meanwhile, the performance of the energy storage monomer is improved. The environment temperature is collected in real time, the preset correction coefficient is used for correcting the dynamic response characteristics, the interference of working temperature fluctuation on performance evaluation is effectively stripped, the consistency and comparability of characteristic data under different environment conditions are ensured, and then the deviation spectrum of the performance characteristics evolved along with time is constructed, so that the performance evaluation accuracy is improved. And the change sequences which are continuous and consistent in direction are specially identified, the irreversible decline trend caused by internal aging is accurately locked, and the influence of random noise and short-term fluctuation is filtered.

The invention provides a rotating mechanical system performance evaluation and optimization method and system based on system negentropy conversion rate, and the method comprises the following steps: carrying out the numerical simulation of a flow field of a rotating mechanical system through a computational fluid mechanics method, and obtaining the parameters of the flow field; calculating a viscous dissipation entropy yield, a turbulent dissipation entropy yield and a wall entropy yield in the fluid domain based on the flow field parameters; the total entropy yield of the system is obtained through the volume fraction and the wall area fraction of the whole fluid domain; converting the input power of the rotating machinery into an equivalent negentropy flow, and calculating the negentropy conversion rate of the system for evaluating the effective utilization degree of the input power; and evaluating the performance of the rotating machinery system based on the negentropy conversion rate, and identifying a key area of energy loss. According to the method, the negentropy conversion rate result is applied to structural design optimization, running state diagnosis and energy efficiency adjustment of the rotating machinery, and online calculation and dynamic visualization can be realized in combination with real-time monitoring data.

Disclosed are apparatuses, systems, and methods, for generative artificial intelligence analysis and improvement. The systems and methods may analyze a plurality of outputs produced by a first generative AI model for using a plurality of input options for performing each task of a plurality of tasks. The system may then compare first performance data reflecting a first subset of input options selected from the plurality of input options used by the first generative AI model for at least one task of the plurality of tasks and second performance data reflecting a second subset of input options used by a second generative AI model for the at least one task. Based on a comparison of the first performance data and the second performance data, the systems and methods may generate a recommendation related to a use of the first generative AI model.

The invention relates to an energy storage converter performance evaluation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring performance index parameters of the energy storage converter under a plurality of evaluation dimensions; the evaluation dimensions at least comprise electrical performance, thermal performance, mechanical performance, environmental adaptability and control performance; performing correlation analysis processing on correlation degrees among the evaluation dimensions based on the performance index parameters to generate a correlation matrix; vectors in the incidence matrix are used for representing the correlation degree between every two evaluation dimensions; and performing statistical analysis and quantitative evaluation processing on the incidence matrix to obtain a performance evaluation result for the energy storage converter. By adopting the method, the accuracy of energy storage converter performance evaluation can be improved.

The invention relates to the technical field of charging management, and discloses an intelligent charging method and system based on a charging pile, and the method comprises the steps: collecting an identifier of a charging object connected with the charging pile, calling the historical charging data and safety evaluation information, obtaining a user charging instruction, and combining with a charging pile performance evaluation model, performing gradient analysis on the charging instruction to generate a test plan and a subsequent plan, performing testable charging according to the test plan, collecting test data to construct a test information flow, analyzing the test information flow to form a feedback factor matrix, obtaining guide information in combination with safety evaluation information, and substituting the guide information into the subsequent plan to realize final stable charging operation. According to the method, by introducing multi-dimensional data analysis and gradient control, a personalized and dynamic charging strategy is realized, the charging efficiency and safety are improved, the service life of the battery is prolonged, the risks of overcharging, overheating and the like are reduced, and the problem that in the prior art, a charging pile lacks individual difference adjustment of a charging object is solved.

The invention relates to the technical field of low-altitude safety, and provides an aircraft optimal trajectory screening method and system based on adjustable phase quantum search. Candidate trajectories are generated according to flight mission constraints, cost values are calculated, and Boolean markers are distributed based on cost threshold values to identify high-quality solutions; performing iterative search and measurement on the balanced quantum superposition state of the coding track index by utilizing a Grover quantum search algorithm based on the adjusted search phase to obtain an output index; and mapping the index into a track, obtaining an optimized track through classic local optimization, adaptively adjusting the phase parameter and the cost threshold value based on performance evaluation, and repeatedly executing until an end condition is met, thereby determining an optimal track. On-line reconfiguration of a strategy is realized by introducing an adjustable phase mechanism, a feedback closed loop is established to realize adaptive optimization of parameters, and the real-time requirements of scenes such as urban air traffic control and unmanned aerial vehicle clusters are met.

The invention provides a large geothermal heat exchange station efficient construction system and method based on a BIM technology. The scanning device is used for constructing a three-dimensional model of the geothermal heat exchange station through space scanning; the pre-assembly module is used for generating a BIM model with a precision control rule based on a drawing and a professional model, supporting standard value / standard update value double-precision grades, and automatically triggering precision switching by a user instruction or a conflict detection result; the conflict detection module is used for identifying building, structure, equipment and pipeline collision problems; the performance analysis module is used for generating analysis data such as fluid mechanics and thermal performance; the fusion module is used for aligning the conflict information and the performance data by using the fusion matrix to generate a high-precision analysis result; and the machine learning model is used for generating a performance evaluation result and a design optimization instruction based on historical data, and dynamically adjusting the model precision. Through dynamic precision control, multi-source data fusion and machine learning closed-loop optimization, the problems of low cooperation efficiency, frequent design change and operation and maintenance disjunction in traditional construction are solved.

The invention provides a data heterogeneous integration method under an AI platform, and belongs to the technical field of digital data processing.The data heterogeneous integration method comprises the steps that feature recognition is conducted on multi-source heterogeneous data through a data fingerprint technology, the data contribution rate is calculated, and an overall alignment index matrix and a partial alignment index matrix are constructed; a semantic alignment optimization model based on a Transform architecture is adopted to carry out deep semantic matching to construct a standardized data set, and a data AI training feedback model is combined with a sparse sampling function and a Gaussian kernel smoothing function to construct a simplified data set to carry out shallow training. And the performance of the complete model is rapidly evaluated by using a performance estimation function, a feedback signal is converted into a standardized feedback signal through a feedback signal generation function, an optimization process is monitored through an iterative index calculation function, and a closed-loop optimization process is formed, so that the technical problem that self-adaptive optimization integration is difficult to realize in a multi-source heterogeneous data integration process is solved.

The invention discloses a shield segment heterogeneous modeling crack and limit simulation method. The method comprises the following steps: constructing an aggregate three-dimensional model database; generating a heterogeneous aggregate accumulation model; establishing a concrete shield segment three-dimensional model; defining a model material constitutive relation and a damage criterion; setting an equivalent field staged loading working condition and a boundary condition; grid division and nonlinear static simulation solution are carried out; and constructing a crack response monitoring and output module, and executing judgment of a structure limit state. High-precision construction and multi-stage loading simulation of a shield segment model are realized by reconstructing a concrete microstructure and steel bar arrangement information and combining a field loading force value and a boundary condition; and a nonlinear material constitutive and damage evolution criterion is introduced, the whole-process response of the crack is captured, a limit state criterion is set, and automatic identification and damage form judgment of the bending resistance limit load are completed. According to the method, a technical path capable of being popularized is provided for shield segment structure performance evaluation, ultimate bearing prediction and a crack control strategy.

The invention discloses a manufacturing defect-oriented multi-scale composite material mechanical property prediction method, and belongs to the technical field of composite material mechanical property analysis. The method comprises the following steps: based on a multi-scale representative volume element (RVE) theory, respectively constructing parameterized models containing pores and fiber dislocations in a microscopic scale and a mesoscale; the equivalent elastic parameters under each scale are calculated through a finite element numerical homogenization technology, and an agent model is constructed in combination with an artificial neural network, so that the calculation cost is remarkably reduced, and the high-efficiency cross-scale prediction of the rigidity and strength performance of the composite material is realized. The method overcomes the defect of defect modeling of a traditional model, has high precision and high efficiency, and is suitable for performance evaluation and optimization design of composite material structures in the fields of aerospace, rail transit and the like.

Systems and methods are disclosed herein for evaluating the performance of a question-answering model. In an example system, a set of prior question-answer pairs is obtained. In an example, each prior question-answer pair comprising a question and an associated answer that was generated previously. Each prior question-answer pair is provided to a LLM to obtain an evaluation score for the prior question-answer pair. In an embodiment, the evaluation score contains a value indicative of a quality of the answer to the question. An evaluation model is trained using features and labels, where the features are based on each prior question-answer pair and the labels are based on the evaluation score for each prior question-answer pair. When a current question-answer pair is obtained (e.g., for evaluation), the evaluation model is applied to the current question-answer pair to generate an evaluation score.

The invention discloses a bridge structure crack real-time monitoring and progress analysis system based on deep learning, and belongs to the technical field of bridge structure health monitoring. The system comprises a time sequence image acquisition and multi-modal data fusion module, a self-adaptive crack feature extraction and identification module, a dynamic evolution tracking and trend prediction module and a closed-loop feedback optimization and early warning decision module, and innovatively introduces feature fusion of time sequence consistency constraint and mutual information maximization based on Riemannian manifold. Deep fusion of multi-modal data is realized; a multi-scale convolutional neural network and an attention mechanism are adopted to accurately extract crack features; predicting a crack development trend by using a long short-term memory neural network and Bayesian inference; a closed-loop feedback mechanism is designed to dynamically optimize system parameters, the four modules are deeply coupled to form a complete closed loop of data forward transmission, performance evaluation and parameter reverse feedback, real-time monitoring, dynamic tracking and intelligent early warning of cracks are realized, and a scientific decision basis is provided for bridge safety management.

The invention belongs to the technical field of underground structure prediction, and particularly discloses a tunnel settlement prediction method and system based on experience information and data driving, and the method comprises the steps: receiving influence factor data of multiple aspects of tunnel settlement, and carrying out the sampling based on the influence factor data, and obtaining a tunnel settlement data set; constructing a neural network model, and fusing the settlement development rule as a constraint condition into the loss function to obtain a total loss function fusing empirical information loss and data loss; performing hyper-parameter optimization on the neural network model by adopting a Bayesian optimization algorithm to determine an optimal hyper-parameter; training the neural network model, and updating the neural network model by using the total loss function to obtain a trained prediction model; and performing tunnel settlement prediction performance evaluation on the prediction model, and performing interpretability evaluation on the prediction model by adopting an SHAP interpretation method. According to the invention, the accuracy and efficiency of the model prediction result can be improved.

The invention discloses a target tracking model based on an enhanced target detection algorithm, and relates to the technical field of real-time target tracking. A CD-YOLO detector is constructed; a robust multi-target tracker is constructed; constructing a collaborative detection-tracking optimization mechanism; evaluating and optimizing the performance; according to the method, a deformable convolution and coordinate attention mechanism is introduced into YOLOv10, so that the perceptual ability of the model to geometric deformation and spatial information is enhanced; the CD-YOLO and the enhanced StrongSORT tracker are combined, so that the detection precision and the tracking stability of the system in complex scenes such as dense crowds, serious shielding and variable visual angles are remarkably improved; a collaborative optimization mechanism between detection and tracking is established, so that a detection result and tracking feedback can be mutually enhanced, a closed-loop robust tracking process is formed, and the overall precision and stability are remarkably improved on the premise that the real-time performance of the system is not affected.