46 results about "Accuracy improvement" patented technology

The invention relates to the technical field of state monitoring, in particular to an energy state monitoring deduction method and system based on digital twinning, and the method comprises the following steps: obtaining a power value and a direction to construct a chain structure map, marking a mutation point to extract a linkage path, screening out an abnormal path, screening an available index, and combining state behaviors to form an evolution trajectory. And comparing the measured data to generate a mapping result. According to the method, through cooperative calculation of the multi-period node power change direction and the response time difference, the energy transfer path relation is defined by utilizing the construction of an inter-node chain transmission structure, and the node coincidence frequency is superposed in the multi-path sudden change behavior time sequence intersection statistics to realize the judgment and elimination of the abnormal path; path availability screening is realized based on response direction consistency of terminal nodes in residual paths, and accuracy improvement of energy flow anomaly identification, continuity guarantee of path deduction results and verification of equipment response states are realized.

The invention provides a video depth forgery detection method based on space-time inconsistency and frequency domain analysis, the forgery detection method constructs a video depth forgery detection network of an unknown environment image, and the video depth forgery detection network comprises a space-time inconsistency module, a frequency domain analysis module and a video level classifier. The space-time inconsistency module captures space-time features in video frames, through a double-flow cooperation mechanism integrating space-time inconsistency modeling and frequency domain artifact analysis, the space-time features in videos are efficiently captured, dynamic information interaction between key frames is achieved, and therefore an enough feature basis is provided for deep counterfeiting detection. And the frequency domain analysis module combines a deep neural network with time-frequency analysis and maps video frames to a frequency domain so as to realize end-to-end identification of an unknown tampering mode and provide support for understanding of fine-grained content and improvement of classification precision.

The invention belongs to the technical field of computers, and particularly discloses a computer system performance prediction method and device, electronic equipment and a medium. Predicting resource utilization rate prediction information of each hardware resource at the next time of the historical time period; determining a comprehensive performance evaluation index of the computer system based on the resource utilization rate prediction information of each hardware resource and target resource utilization rate information of each hardware resource in a static performance peak state; and predicting the performance state of the computer system at the next time by using the comprehensive performance evaluation index, and determining a performance state prediction result of the computer system. By means of the method and device, the precision of computer system performance prediction can be effectively improved, and the accuracy of prediction results is improved.

The invention discloses a straw incineration area smoke recognition method based on a deep learning algorithm, and relates to the technical field of intelligent recognition and environment monitoring cross, and the scheme uses a programming language python and a deep learning framework pytorch to construct a straw incineration area smoke recognition model. The method comprises the following steps: constructing sample data by using Jilin No.1 high-resolution remote sensing image data and Sentinel-2A multispectral remote sensing data, pre-processing the sample data, dividing the obtained remote sensing image data, training a deep learning model by using a training sample, and calculating the accuracy of the deep learning model according to a loss function convergence condition and a precision improvement condition of the verification sample. Selecting optimal parameters for the whole network to obtain an optimal model; and carrying out precision evaluation on a prediction result by adopting four indexes including the precision rate, the recall rate, the average precision and the F1 score, and analyzing the overall performance of the model. Experiments prove that the method shows excellent performance on multi-source remote sensing satellite images and provides key technical support for environment remote sensing monitoring.

The invention provides a train arrival and departure information high-precision and accuracy improving method based on an external interface system, which comprises the following steps: step 1, determining the type of the external interface system which at least comprises a railway dispatching center interface and a station terminal interface, and acquiring train arrival and departure information through the external interface system, the train arrival and departure information comprises train number numbers, planned arrival time, planned departure time, actual arrival time, actual departure time and stop station numbers; 2, carrying out data preprocessing on the train arrival and departure information obtained in the step 1; 3, performing multi-interface data comparison on the preprocessed train arrival and departure information; step 4, abnormal data occurring in the multi-interface data comparison is verified; and step 5, outputting the high-precision train arrival and departure information passing the verification. According to the method, a brand new algorithm is established, so that the method adapts to various operation and fault scenes, and ATS server information can be correctly sent to an external system.

The application provides a kind of finish rolling temperature drop control method and system, comprising: obtaining the first detection temperature of current intermediate blank at the exit of rough rolling last pass, current gauge roller bed temperature drop self-learning coefficient, the second detection temperature at the entrance of finish rolling;According to the first detection temperature, determine the first temperature calculation value at the entrance of finish rolling, adjust the roller bed temperature drop self-learning coefficient according to second detection temperature;According to the second detection temperature, determine the second temperature calculation value at the exit of finish rolling, adjust the interstand temperature drop self-learning coefficient according to the measured temperature at the exit of finish rolling;According to the new roller bed and interstand temperature drop self-learning coefficient, adjust the first temperature calculation value and the second temperature calculation value of the next intermediate blank of the same gauge, to improve the accuracy of the first temperature calculation value and the second temperature calculation value.The application can effectively improve the temperature calculation accuracy of intermediate blank finish rolling entrance and exit, improve the intermediate blank head finish rolling stability and the quality control accuracy of finished strip head thickness.

This invention discloses an adaptive small object detection method based on an attention mechanism, belonging to the field of computer vision technology. The method includes: acquiring a dataset; constructing a feature enhancement module; constructing an adaptive sparse convolutional detection head; constructing an adaptive small object detection model based on an attention mechanism, adding the feature enhancement module to the backbone network, and using the adaptive sparse convolutional detection head to replace the original detection head; and determining the final small object detection model. This invention constructs an adaptive sparse convolutional head, dynamically adjusting the use of the detection head corresponding to different FPN layers through a channel attention mechanism, effectively balancing model complexity and detection accuracy, improving the accuracy of small object detection while maintaining computational efficiency; and constructs a feature enhancement module, utilizing global average pooling to fuse global contextual information, enhancing the model's ability to represent the features of small objects, and improving the model's detection performance for objects of different sizes in complex environments.

The application discloses a self-distillation action quality evaluation method based on position awareness and reward feedback, which firstly acquires an action quality evaluation data set, obtains space-time features through a multi-scale space-time feature extraction backbone network based on a deep three-dimensional convolution; secondly, the space-time features are respectively input into corresponding time-channel attention modules to obtain enhanced space-time features; the enhanced space-time features are reconstructed to a standard feature dimension space through a feature reconstruction module, and adaptive weights are obtained by an adaptive weight generator; finally, the reconstructed space-time features are respectively input into an action category classifier and a shared regression head, and a loss is calculated by combining the adaptive weights for training; in the inference stage, the action category classifier and the adaptive weight generator are removed to obtain an action quality evaluation result. The application fuses bottom-level detail features and high-level abstract representations, guarantees the precision, improves the operation efficiency and enhances the overall knowledge distillation effect.

The invention discloses a method and a system for improving the mounting precision of a side valve based on 3DE. The method comprises the following steps: collecting input parameters related to the side valve; establishing a first parameterized geometric model of the broadside valve, and mapping the input parameters into the first parameterized geometric model; on the basis of the characteristics output by the first parameterized geometric model, an interface calibration model is adopted to compare and adapt the actual geometric data of the welding part with the data of the broadside valve so as to measure and adjust the welding part; based on the characteristics output by the first parameterized geometric model and the actual geometric data of the welding part, a broadside valve precision model is used for evaluation; and generating a broadside valve installation map by using the evaluation result. According to the invention, the installation precision can be improved, the welding process can be optimized, the data transparency and safety can be enhanced, and the working efficiency can be improved.

The application discloses a kind of based on wind power equipment manufacturing process quality prediction method and system.Method includes: obtaining process parameter data, equipment state data and parts detection data and so on Multi-source heterogeneous data and pre-processing, construct multivariate quality characteristic data set;Using the method based on CCO-JMD-SE to feature decomposition, screening and reconstruction, extract key quality feature sequence;The hybrid quality prediction model combining bidirectional long short-term memory network and support vector machine is constructed, and the model hyperparameter is optimized and trained using particle swarm optimization algorithm;Real-time data are input into model to obtain quality prediction value, and the prediction value is mapped into assembly quality index by quality measurement method based on QLF-SNR, and the quality grade is determined according to grading threshold and the disposal strategy is triggered.The application realizes quality evaluation quantification, multi-source data deep fusion, prediction accuracy improvement and key process accurate control.

The present invention relates to the field of target detection technology, and in particular to a target detection model based on multi-scale receptive field feature fusion. The present invention designs a new module CspRepSPP, uses depthwise convolution to replace the maximum pooling in the SPPF module, performs convolution operations on each channel of the feature map through depth convolution, effectively solves the information loss problem existing in the original module, and further reduces the computational complexity of the module during the reasoning process through structural reparameterization and CSP (CrossStagePartial). Compared with SPPF, the new module CspRepSPP has a faster computing speed and greater accuracy improvement in the reasoning stage. At the same time, inspired by the multi-scale receptive field feature fusion idea of ​​SPP, the present invention designs a new feature extraction module ImprovedBottleNeck, which replaces the traditional BottleNeck in YOLOv5, so that the target detection model fuses features of receptive fields of different sizes at each stage, further improving the detection accuracy of the model without additional computational complexity.

The invention provides a training method of a fraud recognition model, a fraud recognition method and related equipment, and relates to the technical field of artificial intelligence, and the training method of the fraud recognition model comprises the steps: obtaining training data; performing feature extraction on the plurality of training samples to obtain a tag feature vector and a plurality of first feature vectors; performing feature screening on the plurality of first feature vectors to obtain N first feature vectors; performing feature dimension reduction on the N first feature vectors according to the association weight value corresponding to each first feature vector to obtain M first feature vectors; and inputting the M first feature vectors into a preset fraud identification model, and performing iterative training on the fraud identification model based on the tag feature vector to obtain a trained target fraud identification model. In the embodiment of the invention, the fraud recognition model is trained by applying the first feature vector strongly related to the fraud behavior, so that the recognition precision of the model is improved, and the accuracy of a fraud recognition result is improved.

The invention relates to the technical field of power equipment monitoring, and discloses a transformer iron core evaluation method, which comprises the steps of obtaining multi-source data and historical fault current grounding data of a to-be-detected transformer, and forming a multi-dimensional comprehensive score evaluation system by combining trend abnormal value analysis, two-factor correlation calculation and historical fault similarity evaluation, finally, the iron core evaluation level is determined based on the comprehensive score, the interference tolerance of a monitoring system is effectively enhanced, the false alarm phenomenon caused by false fluctuation is reduced, early warning of iron core faults is achieved through time sequence trend mining and early evolution law recognition, an optimal governance window is provided for operation and maintenance personnel, and the operation and maintenance efficiency is improved. Through a standardized data processing flow and a working condition differentiation evaluation mechanism, comprehensive evaluation is carried out in combination with key parameters such as operation load and oil temperature, the accuracy and practicability of evaluation results are remarkably improved, the monitoring precision of the operation state of the transformer iron core is enhanced, and the power supply reliability of a power grid is improved. And the large-area power failure risk caused by iron core faults is reduced.

The application discloses a model training method, an intrusion detection method, a device, equipment, a medium and a product. The method comprises the following steps: acquiring first parameters obtained by training a first model by using local data of each client device in at least one training group; for the at least one training group, aggregating the first parameters associated with each training group to obtain second parameters corresponding to each training group; and updating third parameters corresponding to a second model according to the second parameters and the number of times that each training group participates in updating the second model within a preset time, until the second model converges, thereby obtaining the trained second model. According to the application, joint training can be performed without uploading local data, the data security of the client device can be ensured, local training can be performed by using the computing capacity of each client device, the convergence speed and precision of model training can be improved, and the accuracy of the network intrusion detection result can be improved.

The invention discloses a finite element simulation strain correction method suitable for a display backboard stamping process, and relates to the technical field of stamping strain correction, and the finite element simulation strain correction method comprises the following steps: obtaining geometric characteristics and stamping process parameters of a display backboard to be analyzed, and constructing a finite element model; historical stamping process processing data of display backboards of the same model are collected, and corresponding process parameters and environment data are recorded; the method comprises the following steps: mapping historical process parameters and processing data to form a training data set, performing finite element analysis based on the training data to obtain simulated strain data, calculating the relative difference between the simulated strain data and historical data, analyzing the correlation between the simulated strain data and the process parameters, environmental data and residual stress, and screening the parameters meeting the correlation requirements to obtain the simulated strain data. And a regression equation is established by utilizing a regression algorithm, the simulated strain data of the to-be-analyzed display is corrected, and the method improves the simulation precision and improves the adaptability to the change of the stamping process.

The present application belongs to the field of pile foundation static load test, and relates to multi-sensor fusion technology, which is used to solve the problem that the prior art cannot realize real-time sensing of the load distribution uniformity of a heaped load body, and specifically relates to a pile foundation static load test heaped load precision analysis method based on multi-sensor fusion, which comprises the following steps: sensor arrangement for the pile foundation static load test; data acquisition and preprocessing for the pile foundation static load test; after the pile foundation static load test starts, a collection time point T i is set i , and heaped load parameter collection is performed; the present application realizes multi-dimensional real-time monitoring and dynamic analysis of the heaped load precision of the pile foundation static load test, so that heaped load abnormalities can be found and located in time, load transmission deviation from the design axis is avoided to prevent bearing capacity data distortion, meanwhile, the hidden risks after test interruption are quantitatively evaluated to provide an objective basis for subsequent scheme adjustment, the test precision can be more comprehensively and accurately controlled, and the reliability of the pile foundation bearing capacity test result is improved.

The application discloses a text2sql accuracy improvement method and system based on a multi-path retrieval mode link, belongs to the natural language processing and database query technical field, and comprises the following steps: collecting and structuring metadata to construct a data knowledge graph, constructing an NLP dictionary and a domain knowledge base and vectorizing storage; receiving a user question and utilizing a large model to combine domain knowledge to perform semantic expansion and ambiguity elimination; recalling metadata nodes from a vector library through three parallel channels of original input, rewritten questions and NLP word segmentation; grouping the recalled nodes, searching and constructing multiple "path subgraphs" representing potential correlation in the knowledge graph; introducing a dynamic weight adjustment mechanism to calculate the correlation score of each path subgraph with the user query and select the path subgraph with the highest score, extract the correlation contained in the path subgraph, and provide the accurate mode link result to a subsequent SQL generation module, so that the accuracy and efficiency of text to SQL conversion are effectively improved.

This invention relates to the field of static testing technology, and discloses a support and boundary simulation device and method for static testing of a long straight cylindrical structure. The invention employs six-degree-of-freedom statically determinate constraints to constrain the rigid body displacement of the cylindrical structure without restricting the elastic deformation of the main beam. Active loads are applied to the outer docking fixture, while passive balancing loads are applied to the constraint points of the inner docking fixture. This ensures the accuracy of the boundary load simulation at the end face of the cylindrical structure while reducing the restriction on the elastic deformation of the cylindrical structure by the constraints. Furthermore, the change in constraint reaction force can be monitored in real time during the test loading process, improving the safety and reliability of the test. This invention optimizes the cantilevered fixed-support constraint of the current conventional testing method to a hinged constraint at both ends of the long straight cylindrical structure, shortening the deformation at the maximum deformation point of the long straight cylindrical structure. This improves the accuracy of test loading and structural assessment, enhances the safety of the test, and reduces the complexity of the test loading system.

The invention relates to the technical field of data processing, and provides a product recommendation method and device, electronic equipment and a readable storage medium. The method comprises the following steps: performing expert network weighting processing on attribute feature data of a to-be-recommended product to obtain an expert network vector corresponding to the attribute feature data; performing residual connection processing on the expert network vector to obtain a cascade cross fusion vector; performing deep fusion processing on the attribute feature data to obtain a deep fusion vector corresponding to the attribute feature data; performing joint mapping processing on the cascade cross fusion vector and the deep fusion vector to obtain a demand prediction value of the to-be-recommended product; and recommending the to-be-recommended product on the basis of the demand prediction value, so that the demand prediction precision is improved, the accuracy and individuation degree of product recommendation are improved, the efficiency of high-order feature interactive modeling is improved, the expression ability of the features is enhanced, and the richness of the features is deepened.

According to the multi-workpiece correction value determination method and device, the electronic equipment and the storage medium provided by the invention, rapid and centralized configuration of the multi-workpiece position parameters is realized through the position configuration interface, the operation process is greatly simplified, and the measurement information and the correction value of the coordinate measurement equipment for measuring the measurement points on the reference workpiece are obtained; the measuring points on the other workpieces are measured based on the clamping positions of the other workpieces and the measuring information of the measuring points on the reference workpiece so as to obtain the correction values of the other workpieces, the positions of the other workpieces are accurately determined based on the reference workpiece, correction is carried out in combination with the measuring information, the correction precision is improved, and the measurement accuracy is improved. And the working efficiency is improved.

This invention provides a finishing mill specification change control method and system. The method includes: within a single finishing mill work roll change cycle, if the historical number of rolled strips of the corresponding specification after the specification change is zero, then obtaining learning values ​​matching the target final rolling temperature, width, and thickness of the strip after the specification change from a short-cycle data table; wherein the short-cycle data table is used to record the learning values ​​within a single finishing mill work roll change cycle; and performing a finishing mill pre-setting calculation for the first strip after the specification change based on the matching learning values, thereby improving the accuracy of the pre-setting parameters for the head of the first strip after the specification change. This invention can effectively improve the control accuracy of the final rolling temperature and thickness of the head of the first strip after the specification change, and improve the quality control accuracy of the first strip after the specification change.

The application discloses a kind of test result evaluation systems in the technical field of medical examination evaluation, system includes physiological dynamic identification module, critical state interpretation module, process efficiency optimization module, health trend prediction module, test accuracy improvement module, result interpretation optimization module, potential pathology mining module, health evaluation comprehensive module.In the present application, by introducing nonlinear dynamics model, fuzzy logic, machine learning algorithm and other technologies, the depth and breadth of medical test evaluation are significantly improved.First, the application of physiological dynamic identification module makes the analysis of time series data more accurate, and can reveal subtle changes in physiological behavior, thereby improving the ability of early disease identification.The introduction of fuzzy logic shows higher flexibility and accuracy in dealing with the uncertainty and fuzziness of test results, especially in the judgment of boundary values and critical states, providing more delicate support for medical decision-making.

The present application relates to the field of steel rolling, and proposes a rail bottom width adjustment method, system, device and medium, the method comprising: obtaining historical data of rail production through VR simulation, and constructing an expert experience model based on the historical data; integrating the expert experience model in a simulation platform, and simulating the working condition of rail adjustment; in response to the deviation of the bottom width exceeding a preset value, and the upper leg tip thickness and the lower leg tip thickness being greater than the head thickness, adjusting the horizontal roller and the bottom vertical roller of the rolling mill; in response to the deviation of the bottom width exceeding a preset value, and the upper leg tip thickness and the lower leg tip thickness being less than the head thickness, adjusting the horizontal roller or the bottom vertical roller of the rolling mill. The present application aims at the deviation problem of the rail bottom width, realizes high-precision control of the rail bottom width, ensures the stability of the rolling process, and significantly improves the control precision of the rail bottom width; improves the timeliness of adjustment, and ensures the standardization of steel rolling adjustment; not only solves the problem of low calculation efficiency in manual adjustment, but also avoids errors caused by human factors.

Described herein are techniques for improving language model performance through collaborative interaction between specialized and general-purpose models. A specialized model with fewer than ten billion parameters undergoes supervised fine-tuning on domain-specific data and generates initial outputs. These outputs are refined by a general-purpose model having over one hundred billion parameters and advanced reasoning capabilities. The framework implements preference tuning where outputs from both models are evaluated to generate preference triplets that optimize the performance of the specialized model. This approach achieves significant accuracy improvements while maintaining data privacy and computational efficiency.

The invention discloses an equipment fault detection method and a related device, relates to the technical field of artificial intelligence, and aims to separate the detection opportunities of various equipment parts and consumables through different detection periods, combine daily period detection with detection opportunity evaluation, improve the detection efficiency and reduce the resource consumption, and improve the detection efficiency by configuring a visual weight sequence. The influence degrees of various indexes are flexibly adjusted, the adaptability and interpretability of fault detection are enhanced, the unified health degree evaluation model is used for processing multi-source state sequence data, and automation and accuracy improvement of fault prediction are achieved.

Arrangements for providing improved anomaly detection in time series data are provided. A computing platform may receive data from one or more servers. The data may be analyzed using natural language processing to identify features in the data. A machine learning model currently deployed in a production environment may be copied and updated based on the features. The production model may be executed to generate production model outputs and the updated model may be executed to generate updated model outputs. The outputs may be compared and, if no or insufficient differences exist, the production model may be maintained in the production environment. If differences exist and are sufficient, an accuracy improvement associated with the updated machine learning model may be determined. If the accuracy improvement meets a threshold, the updated machine learning model may be deployed to the production environment. If not, the production machine learning model may be maintained.

The invention discloses a reliability modeling method based on a mechanism data hybrid model, and relates to the technical field of data processing. The reliability modeling method based on the mechanism data hybrid model comprises four steps of data collection, modeling path selection, parameter calculation and result output. According to the reliability modeling method based on the mechanism data hybrid model, model modeling is carried out under the conditions of no test data and existence of test data, various technical paths are designed according to the full degree of the test data, and the technical paths comprise the conditions of no test data, existence of the test data but insufficient test data and full test data; reliability modeling and test data volume decoupling are realized; the problem of insufficient test data is solved through data and mechanism fusion, and the generalization ability of the model is improved; dynamic correction can be achieved, prior and test data are fused through a Bayesian method, and the confidence interval precision is improved; the industrial adaptability is wide, multiple types of equipment such as pumps, valves and filters are supported, and the universality is high.

The invention provides a multi-source data-driven intelligent early warning method and device for a safety state of foundation pit construction, and the method comprises the steps: inputting a monitoring data sequence into a pre-created prediction model, and obtaining a deformation prediction value of each time point in a future preset time period; according to the construction plan of the foundation pit, key construction stages and planned completion time of the key construction stages are obtained, and control intervals with fixed duration are set before and after time nodes of all the key construction stages; performing mechanical analysis on the key construction stage, and generating a numerical simulation prediction result corresponding to the time point in the control interval; determining the difference between the numerical simulation prediction result and the deformation prediction value corresponding to the same time point, and correcting the deformation prediction value based on the difference to obtain a corrected prediction result; and the corrected prediction result is compared with a preset grading early warning threshold value, the safety level of foundation pit construction and a corresponding early warning signal are determined, the prediction precision of the sensitive area in the construction stage is improved, and the prediction lag phenomenon caused by process conversion is eliminated.

The present application relates to a space laser radar forest structure data correction method and system, and belongs to the technical field of space laser remote sensing and forest resource monitoring. In view of the four types of errors of GEDI data, such as geometric positioning deviation, slow variation distortion, terrain widening effect and crown structure influence, the present application adopts hierarchical layout strategy to layout UAV control points, establishes the same point relationship through waveform registration and calculates the weight; based on the regular grid control points, the overall geometric correction is carried out; based on the encryption control points, the local distortion correction is carried out; the physical compensation model is established to compensate the terrain widening effect; the statistical regression model is established based on the crown structure parameters and GEDI signal quality index to correct the crown height residual error; finally, the high-precision crown height convergence result is obtained through spatial smoothing processing, and the corrected data is output. The present application realizes the gradual accuracy improvement from the whole to the local and from the geometry to the height, has low cost, high precision and wide application range.

The application particularly relates to a precision improvement method for aero-engine performance digital twinning, which comprises the following steps: establishing a compensation model; setting two output channels to respectively output two output values; comparing and calculating the two output values with corresponding performance prediction target values respectively to obtain two loss parameters; iteratively inputting the loss parameters into the basic digital twinning model and the compensation model respectively until the error converges; and predicting the aero-engine performance parameters, and correspondingly inputting the monitoring data of the engine into the converged basic digital twinning model to obtain target performance parameter prediction values. The purpose is to gradually eliminate errors through the coupling training of the compensation model and the basic model, directly couple the compensation effect of the compensation model into the basic digital twinning model without increasing the complexity of the basic model, improve the prediction precision without increasing the prediction time, and ensure the real-time performance.