24results about How to "Efficient and accurate analysis" patented technology

The invention provides a photocuring 3D printing intelligent slicing optimization method based on artificial intelligence, and relates to the technical field of photocuring 3D printing.The method comprises the steps that a target model summary file is generated by collecting a 3D printing model file and printing parameter data; performing slicing processing on model data in the target model summary file to generate model slicing data; analyzing the geometric complexity of the model by using a deep convolutional neural network, and dynamically adjusting the thickness of a slice layer based on a complexity coefficient; through multi-objective optimization of printing quality, time and material consumption, printing setting is dynamically adjusted according to an optimization result; and the printing state is monitored in real time, slice parameters are adjusted according to monitoring data, an optimization result is verified before actual printing, and slices are adjusted and optimized according to a verification result. The 3D printing model file and the printing parameter data input by the user can be automatically analyzed, the printing process is monitored in real time, the slicing parameters are dynamically adjusted, and the printing quality and efficiency are improved.

The power distribution network reliability evaluation method based on load partitioning comprises the following steps: S1, establishing an improved power distribution network reliability evaluation model of a switch element-oriented load partitioning technology, and inputting a fault parameter, a load parameter, a system topology parameter and a load branch parameter; s2, forming a system topological structure diagram according to the branch numbers of the line nodes; s3, partitioning the power distribution network according to the system topological structure diagram; s4, introducing fuse factors, and calculating block fault parameters; s5, enumerating fault blocks to form a fault consequence mode table; s6, performing power supply reliability judgment on the areas divided in the step S3, setting fuses in the required areas, and supplementing the influence of fuse branches on the load at the same time; s7, enumerating all fault blocks; s8, adopting a Bayesian network method to obtain weak links of the power distribution network; and S9, calculating a system reliability index. The reliability evaluation precision of the power distribution network is comprehensively improved, and safe and stable operation of a power system is ensured.

The invention relates to the technical field of refrigeration house operation and maintenance inventory management, and discloses a refrigeration house operation and maintenance intelligent management system and method based on a distributed system, and the system comprises a refrigeration house refrigerated goods information processing module, a refrigeration house refrigerated goods storage state analysis module, and a refrigeration house refrigerated goods operation and maintenance information feedback module. The current refrigeration time and date parameters are dynamically and accurately collected through the timing module, and data support is provided for accurately evaluating the storage state of refrigerated goods in the refrigeration house; based on a distributed refrigeration house operation and maintenance sub-platform, the current refrigeration time and date parameters are compared with the standard storage time interval of the refrigerated goods and the in-time storage time interval of the refrigerated goods, and the storage state parameters of the refrigerated goods are intelligently analyzed. Standardized operation and maintenance management of the storage state of the refrigeration house refrigerated goods based on the distributed system is achieved, and the quality and reliability of operation and maintenance management of the refrigeration house are improved.

The invention discloses a device and a method for collecting and analyzing gas produced in different thermal runaway stages of a lithium ion battery, and relates to the technical field of thermal runaway of the lithium ion battery, a closed box body for accommodating the lithium ion battery is provided with a plurality of gas collection ports, and the plurality of gas collection ports are located at different positions of the closed box body; the gas collection control part is respectively connected with each gas collection port pipeline of the closed box body, and is used for determining the thermal runaway stage of the lithium ion battery based on the surface temperature and the tab voltage of the lithium ion battery acquired by the sensing part; controlling the gas collection control part to obtain mixed gas at different gas collection ports in different thermal runaway stages to obtain the mixed gas corresponding to the different thermal runaway stages, and subsequently analyzing the mixed gas corresponding to the different thermal runaway stages. Therefore, the gas generated at different thermal runaway stages of the lithium ion battery and at different positions of the closed space can be efficiently and accurately collected and analyzed.

The invention relates to the field of material science and engineering, and discloses a big data-driven intelligent gold bonding wire system performance prediction method, which comprises the following steps: constructing a gold wire physical model of a gold bonding wire, defining a data-driven network architecture of the gold bonding wire, and determining a data-driven model of the gold bonding wire; defining a GANs network of the intelligent gold bonding wire system, analyzing the gold wire feature tuple of the gold bonding wire, and training the gold wire physical model and the data driving model to obtain a trained gold wire physical model and a trained data driving model; analyzing physical model performance and driving model performance of the training gold wire physical model and the training data driving model, and coupling the training gold wire physical model and the training data driving model to obtain a mixed gold wire analysis model; and capturing a performance factor dependency relationship of the gold bonding wire, analyzing a potential performance coefficient of the gold bonding wire by using the mixed gold wire analysis model, and constructing a performance analysis report of the gold bonding wire. The performance analysis effect of the gold bonding wire can be improved.

The invention relates to the technical field of mill lining plate wear monitoring, and discloses a mill lining plate intelligent wear monitoring system and method based on big data, the system comprises a mill lining plate wear monitoring parameter acquisition module, a mill lining plate wear failure state evaluation module, and a mill lining plate wear monitoring information feedback module; accurately matching the wear failure state spatial three-dimensional model library required by the monitored mill lining plate according to the type of the mill lining plate product by combining the wear failure state spatial three-dimensional model library of different types of mill lining plate products stored based on big data with an intelligent search algorithm and the type identification parameters of the mill lining plate product; intelligent evaluation of the wear failure state of the mill lining plate is carried out based on mill lining plate space three-dimensional model parameters in combination with an intelligent recognition algorithm and a mill lining plate product wear failure state space three-dimensional model library which is customized and matched according to the type of the mill lining plate product, and the wear failure state of the mill lining plate is efficiently and accurately analyzed.

The present invention relates to the technical field of network data transmission fault monitoring, and discloses a distribution gateway fault monitoring method and system based on Internet of Things data collection, wherein the system comprises a distribution gateway Internet of Things communication fault identification module, a distribution gateway Internet of Things communication network object matching module, and a distribution gateway Internet of Things communication fault processing module; the Internet of Things communication network objects searched around the distribution gateway are connected through autonomous communication and the Internet of Things communication network speed parameters are collected online synchronously; the maximum network speed parameters of the Internet of Things communication network objects near the distribution gateway are accurately analyzed by using an intelligent recognition algorithm, and at the same time, the best Internet of Things communication network object parameters around the distribution gateway corresponding to the maximum network speed parameters of the Internet of Things communication network objects near the distribution gateway are searched according to the Internet of Things communication network object number, so as to accurately screen out the best Internet of Things communication network objects around the distribution gateway under the fault state of the distribution gateway.

The invention relates to the technical field of machine vision, in particular to a teaching project assessment system based on posture perception, which comprises a video acquisition module; the data extraction module is used for extracting examination images and key examination video segments; the data processing module is used for acquiring the outline and key assessment action point positions of the assessed person and constructing lines of the key assessment action point positions; the evaluation module is used for judging whether the key evaluation point position posture of the examinee meets a preset standard or not, verifying whether the key evaluation point position posture of the examinee meets the preset standard or not, determining an optimization mode of a key evaluation action standard image, judging whether the key evaluation action posture process in the key evaluation video segment meets the preset standard or not, and if yes, judging whether the key evaluation action posture process meets the preset standard. The attitude analysis module is used for checking whether the key assessment action process meets a preset standard or not; comprising a first storage unit and a second storage unit, the intelligent analysis of the examination posture is realized, and the accuracy of the examination system is improved.

The invention discloses a method and system for resisting network storm by updating a white list in real time, and the method comprises the steps: carrying out the flow statistics of an input message, judging whether the message is a subscription message stage or a storm message stage according to a preset flow range, and enabling the message in the storm message stage to comprise a subscription message and a storm message; classifying messages in a storm message stage according to the generated white list, filtering storm messages, and retaining subscription messages; wherein the generation process of the white list comprises the following steps of: processing and analyzing an input message in parallel by adopting an FPGA (Field Programmable Gate Array) to obtain a characteristic value of the message, counting the characteristic value, and establishing a characteristic value statistical table; and analyzing according to the characteristic value statistical table to obtain a change model of the characteristic values, and writing the characteristic values and the change model into a white list. According to the invention, a CPU (Central Processing Unit) does not need to analyze the message, and the network storm can be resisted in a dynamic message environment.

The invention discloses a sample testing system of a GaAs chip, which belongs to the technical field of chip testing, and comprises a source measuring unit for applying bias voltage to a sample; the tunable light source module provides a stable single-mode light beam with continuously adjustable wavelength; the optical coupling module couples the single-mode light beam to a sample; the photoelectric detection and conversion module is used for collecting photocurrent signals of the sample under single-mode light beams with different wavelengths and converting the photocurrent signals into voltage signals; the data acquisition and processing module acquires each voltage signal and the corresponding wavelength and draws a photocurrent spectrum curve; and the band gap energy analysis module analyzes the band gap energy of the current sample according to the photocurrent spectrum curve. The light source wavelength is controlled to detect the light current change of the chip after photoelectric response, the light current change is converted into the voltage signal change which is obvious in data and convenient to record to draw the light current spectrum curve, and the band gap energy of the chip is efficiently and accurately analyzed.

The invention relates to the technical field of feed water heater operation performance analysis, and provides a nuclear power feed water heater operation performance analysis method which comprises the following steps: calculating a pipe side state according to pipe side measuring point data of a feed water heater, calculating states of a feed water heater shell side and a drain outlet pipeline, and calculating heat exchange performance of a condensation zone. Comprising condensation zone heat transfer efficiency and condensation heat transfer coefficient. The operation performance and the interface thermodynamic state of the nuclear power plant feed water heater can be efficiently and accurately analyzed.

The invention relates to the technical field of oil well program control systems, and discloses an off-grid oil well intelligent control system and method, the system comprising an oil well drilling behavior self-checking module, an oil well drilling fluid control parameter analysis module, and an oil well drilling control execution module; the system dynamically feeds back formation pressure parameters at the drill bit through a pressure measuring device, combined with an intelligent recognition algorithm and scientifically preset drilling fluid density parameters required for different formation pressures, to perform intelligent analysis of the drilling fluid density required for balancing the formation pressure at the drill pipe and drill bit position in the oil well; the system adopts a drilling pressure torque measurement pup joint to collect real-time formation working condition information at the drill bit online, combined with target drilling fluid density parameters, intelligent search algorithms and scientific storage of different drilling fluid densities and drilling fluid types corresponding to formation working conditions, to perform precise and efficient analysis of the drilling fluid type required for balancing the formation pressure at the drill pipe and drill bit position in the oil well, thereby improving the drilling fluid parameter control precision and accuracy of the oil well control system.

The present invention relates to the field of materials science and engineering, and discloses a big data driven intelligent gold bonding wire system performance prediction method, comprising: constructing a gold wire physical model of the gold bonding wire, defining a data driven network architecture of the gold bonding wire, and determining a data driven model of the gold bonding wire; defining a GANs network of the intelligent gold bonding wire system, analyzing the gold wire feature tuple of the gold bonding wire, training the gold wire physical model and the data driven model, and obtaining a training gold wire physical model and a training data driven model; analyzing the physical model performance and driving model performance of the training gold wire physical model and the training data driven model, coupling the training gold wire physical model and the training data driven model, and obtaining a hybrid gold wire analysis model; capturing the performance factor dependency of the gold bonding wire, analyzing the potential performance coefficient of the gold bonding wire using the hybrid gold wire analysis model, and constructing a performance analysis report of the gold bonding wire. The present invention can improve the performance analysis effect of the gold bonding wire.

The invention discloses an RGB-T semantic segmentation method and system based on multi-attention guidance and hierarchical fusion, and relates to the technical field of image processing. The system is composed of a double-flow encoder, a discriminative local texture perception unit, a semantic-driven cross-modal fusion unit, a semantic enhancement unit and a multi-scale layered refinement decoder, and efficient fusion and analysis of multi-modal features in a complex traffic scene are achieved. According to the discriminative local texture perception method, saliency features are learned through multi-attention guidance and a self-adaptive gating mechanism, accurate modeling of shallow texture information is focused, and the distinguishing ability of a region of interest and a target edge is improved; according to the semantic-driven cross-modal feature fusion method, efficient aggregation of global contexts is realized through high-level semantic guidance and cross-modal feature interaction, and feature complementarity is enhanced, so that the semantic-driven cross-modal feature fusion method has significant advantages in analysis of small targets, long-distance targets and boundary regions. The decoder adopts a progressive fusion mode, an additional edge detection module does not need to be added, and the overall segmentation precision is improved.

The present invention relates to the field of peritoneal dialysis monitoring technology, and specifically to an intelligent monitoring method, system and device for automated peritoneal dialysis. The method obtains various physical parameters of each kidney patient's examination and various dialysis data of each automated peritoneal dialysis; determines similar patients of each kidney patient based on the differences in physical parameter changes between each kidney patient and each other kidney patient; obtains the special degree of each kidney patient of the current automated peritoneal dialysis based on the differences in various dialysis data between each kidney patient of the current automated peritoneal dialysis and each of its similar patients each time they undergo automated peritoneal dialysis, and then determines the current treatment plan for each kidney patient. The present invention determines similar patients for each kidney patient, and then accurately and efficiently determines the current treatment plan for each kidney patient based on similar patients, thereby ensuring that kidney patients receive timely treatment.

The invention provides a passive sonar network monitoring system based on an underwater unmanned aerial vehicle group. The passive sonar network monitoring system comprises a plurality of unmanned aerial vehicles distributed underwater and a sonar monitoring module for sonar monitoring underwater, the fixing module is used for matching and fixing the sonar monitoring module and the unmanned aerial vehicle; and the control station is arranged on the water and is used for analyzing and processing sound wave signals obtained by monitoring of the sonar monitoring module. The control station disclosed by the invention realizes efficient and accurate underwater sound source monitoring and analysis by receiving and filtering various sound wave signals, extracting a target frequency spectrum band by utilizing Fourier transform, analyzing the signals in detail to determine the type of a sound source, summarizing position information and monitoring time of different sensors, splicing the monitoring time and generating a moving track.

The present invention discloses an analysis method based on control system operation data, by first splicing the independent variables to be analyzed into a character string, and then selecting reference characters from the character string in sequence to arrange the operation data, the data that are as identical as possible can be arranged together, thereby facilitating the specific calculation of the subsequent operation data matrix and the specific calculation of the distance between them, and the data to be analyzed can be quickly generated, so that the operation data can be analyzed accurately and efficiently. The present invention also provides a device, equipment and storage medium, which also have the above-mentioned beneficial effects.

The invention discloses a hybrid expert multi-classification method and system combined with an embedded double-model organization structure, is applied to the technical field of data processing, and constructs a scientific value sentence recognition model by performing deep learning on a pre-training model based on a data distribution consistency threshold and a scientific value sentence training corpus. Based on a hybrid expert mechanism, a scientific value sentence multi-classification model is built, and the scientific value sentence multi-classification model comprises an academic value expert sub-model, an application value expert sub-model, an innovation value expert sub-model and a gating network. And packaging the scientific value sentence recognition model and the scientific value sentence multi-classification model into a scientific value sentence detection double model to obtain scientific and technical literatures. And inputting the scientific and technical literature into the scientific value sentence detection double model to obtain a scientific value sentence comprehensive classification result. The technical problems that in the prior art, when an existing scientific literature value sentence classification method is used for processing scientific literatures, the processing efficiency is low, and the accuracy is difficult to guarantee are solved.

This invention relates to the technical field of agricultural production management and discloses a collaborative management method and system for agricultural production processes based on multiple intelligent agents. The method intelligently and accurately selects the agricultural production guidance agents needed for user-side agricultural production based on user-side agricultural production stage location data, user-side agricultural production process analysis data, artificial intelligence algorithms, and standard agricultural production stage information for different agricultural production guidance agents. Furthermore, it intelligently formulates guidance suggestions for user-side agricultural production problems based on agricultural production problem characteristic data, user-side agricultural production stage location data, user-side agricultural production process analysis data, user-side agricultural production guidance agent characteristic data, and in conjunction with agricultural management platforms and internet platforms. This enables the comprehensive and reliable formulation of highly scenario-based and operable agricultural production guidance suggestion data based on multiple agricultural production guidance agents, achieving intelligent and convenient agricultural production management for users.

The application discloses a training method, a simulation method, equipment and a medium of a simulation model of combustion fracturing crack propagation. The training method of the simulation model of combustion fracturing crack propagation based on a graph neural network comprises the following steps: obtaining combustion fracturing crack propagation simulation parameters; generating a triangular mesh geometric model according to the geometric model parameters; performing numerical simulation of combustion fracturing crack propagation according to the generated triangular mesh geometric model and the combustion fracturing engineering parameters to obtain displacement data and crack state data of the triangular mesh geometric model at multiple time steps; constructing a graph structure and determining features according to the displacement data and the crack state data of the triangular mesh geometric model at the multiple time steps; taking graph data of the graph structure as a training data set according to the constructed graph structure; and training a first graph neural network model constructed in advance according to the training data set.

The present invention belongs to the field of vehicle-bridge system dynamics analysis, and specifically relates to a vehicle-bridge coupling analysis method based on the ABAQUS platform that considers road surface unevenness. The initial model of the vehicle and bridge is established using the ABAQUS software, and the model is output as an .inp file. A function of the road surface elevation with the vehicle's forward distance is generated in the MATLAB software, and the ordinate values ​​of the road surface components in the initial bridge model are replaced to generate a new .inp file for vehicle-bridge system calculation and analysis. The present invention utilizes the powerful dynamic solution function of ABAQUS to intuitively establish a vehicle-bridge coupling model, avoiding the need to write a large amount of dynamic equation codes. At the same time, the factor of road surface unevenness is considered in subsequent analysis, thereby ensuring the accuracy of numerical calculations.

The invention provides a route generation method and device, electronic equipment, a storage medium and a computer program product, and relates to the technical field of artificial intelligence, and the method comprises the following steps: in the embodiment of the invention, performing data extraction on acquired tourism data by using a large model, and determining a plurality of routes; and determining a target route based on the similarity between the related data corresponding to the plurality of routes and the demand data corresponding to the current user. Through the scheme in the embodiment of the invention, a personalized target route can be provided.

The present application provides an environmental status analysis method and system based on smart agriculture, which obtains prior farmland environmental progress data and knowledge information of multiple reference environmental status label decision networks, and then generates a specific focused environmental progress knowledge vector for each environmental status label. Then, by integrating the knowledge information and environmental characteristic pattern information of different reference environmental status label decision networks, special network knowledge information is generated for each environmental status label, and then integrated to configure a target environmental status label decision network for predicting the environmental status of a specific farmland area. Thus, the prior farmland environmental progress data and multi-network information are combined to improve the accuracy and adaptability of environmental status analysis, which helps to manage farmland more accurately, thereby optimizing agricultural production and achieving sustainable development of smart agriculture.

Systems and methods for determining the concentration of a plant component in a developed food product prior to or during the manufacture of the food product are disclosed. The method may include: receiving, by a computing system, protein data of a plant component; retrieving at least one model, the at least one model having been trained to generate an output indicative of a desired concentration of a plant component to be used for developing the food product, the at least one model comprising at least one of a pH model, a temperature model, and a protein content model; providing the received protein data as input to the model; receiving an output from the model; and generating instructions for developing the food product, the instructions including a desired concentration range of the plant ingredient.