273results about How to "Efficient integration" patented technology

The invention relates to a distribution network maintenance topology identification system based on multi-source data fusion, and the system achieves the effective integration and precise processing of multi-source power distribution network data through the cooperative work of a multi-source data collection module, a data preprocessing module and a measurement generation module, and remarkably improves the temporal-spatial resolution of the state quantity of a distribution network. According to the method, a confidence evaluation system is constructed, and an algorithm confidence evaluation system is used for carrying out confidence scoring on an output identification structure, so that the uncertainty of an identification result can be quantified, and the availability and reliability of the system in an actual complex environment are greatly improved; through seamless connection and data interaction between a database and a marketing system and a scheduling system, the applicability and effectiveness of the system in an actual maintenance scene are ensured, accurate and reliable topology identification support can be provided for power distribution network maintenance, and the maintenance efficiency and the power supply reliability are improved; the method has the advantages of being high in self-adaption, accurate and reliable.

The application discloses a multi-modal wind turbine electromechanical transient modeling method based on artificial intelligence, relates to the technical field of new energy power generation modeling, and comprises the following steps: using a causal discovery algorithm to perform causal correlation mining and dependent path identification on a standardized space-time data cube, constructing a causal topology graph, and performing cross-modal feature fusion through a space-time graph attention network to generate a high-order feature tensor; dividing the high-order feature tensor into a meta-learning task pool according to different machine types and environmental conditions, and training a cross-machine type parameterization framework by using a double-layer optimization strategy; constructing a composite working condition generator based on the trained cross-machine type parameterization framework, and performing constraint through a causal regularization loss function to form an extended test working condition set; and using a multi-modal deep reinforcement learning algorithm to optimize control parameters of the extended test working condition set to obtain optimized control parameters. The application lays a foundation for realizing electromechanical transient modeling with high robustness and high generalization ability through accurate causal modeling and cross-modal fusion.

The application discloses a tunnel or mine gushing water space-time prediction method and system coupled with a water power numerical model, and the method comprises the following steps: based on the identified and verified underground water numerical model, outputting multi-source data, complementing the missing measured data, quantifying the difference between fault and normal stratum permeability characteristics and coupling to the data system, and incorporating the tunnel or mine excavation space data. An LSTM-isolation forest-K nearest neighbor regression coupled model is constructed, and a multifunctional module is configured to realize common training of multi-scene data. The pretreated multivariate time series data is divided into a training set and a test set, hidden features are extracted through the coupled model, abnormal detection results are fused, a residual correction model is trained synchronously, and the hyperparameters and weights are adaptively optimized according to the multi-project prediction error feedback. Based on the trained coupled model, a window rolling strategy is adopted to carry out synchronous gushing water space-time prediction, residual correction is combined, and prediction data meeting the engineering precision is output. Reliable technical support is provided for engineering construction safety control.

The application discloses a kind of multi-modal medical image segmentation method, device, computer equipment and storage medium, comprising: obtaining the multi-modal medical image of the same examinee and pre-processing and spatial registration;The target segmentation concept is standardized and analyzed to generate prompt;Using basic segmentation model obtains multiple candidate segmentation results of each modality;Constitute credibility gating model, assess the credibility score of each candidate result and carry out screening, obtain the credible instance of each modality;Establish the corresponding relationship between cross-modal credible instance to form instance group;Each instance group is executed evidence weighted fusion and conflict resolution, and the final segmentation result of each target is obtained;Output multi-instance segmentation result and corresponding instance-level uncertainty index.The application can effectively reduce the false detection and result fluctuation of basic model in multi-modal image, improve the consistency and reliability of cross-modal segmentation, and provide robust segmentation result for clinical screening.

The application discloses a lining cloth dyeing control method and system based on artificial intelligence, and is used for the control field, and the method comprises the following steps: real-time monitoring of multi-source data in the lining cloth dye vat, capturing the video stream inside the lining cloth dye vat; using a Gaussian mixture variational autoencoder model to extract features from the multi-source data, obtaining multi-source feature data; obtaining the changing moment of the fabric state in the video stream, and extracting the fabric state change feature data according to the observed fabric color change and the heterogeneity of the dye distribution in the video stream; fusion of multi-source feature data and fabric state change feature data; introducing a model predictive control layer, predicting the result to adjust the dyeing parameters in real time; establishing an intelligent feedback mechanism. The Gaussian mixture variational autoencoder model is trained and optimized through the small-batch stochastic gradient descent and back propagation algorithm, important features are learned and captured from the data.

The invention discloses a coke residue feature detection method and system based on image and pressure alignment, and solves the problems that the subjectivity is high, the accuracy is difficult to guarantee and the physical and psychological health of detection personnel is influenced when coke residue feature detection is manually carried out in the prior art. The method comprises the steps of collecting a coke residue image and pressure data, performing preprocessing to form image input and pressure input, and constructing sample data; a neural network model is constructed, image features of image input are extracted by the image coding layer, pressure features of pressure input are extracted by the pressure coding layer, and the image features and the pressure features are fused and then are subjected to coke residue classification through a classifier; constructing a model comprehensive loss function; inputting sample data for training to obtain a coke residue classification model; and deploying the model, and inputting the to-be-detected coke residue image and the pressure data into the model to obtain a coke residue classification result. According to the method, the neural network is fully utilized, the image and the pressure are effectively fused, information complementation and integration are realized, and the classification detection precision of the coke residues is effectively improved.

The invention discloses an integrated intelligent monitoring system and a hierarchical isolation method and equipment based on a security domain, and relates to the technical field of industrial automation system integration, the method comprises a centralized data bus, a modular function service and a service interface, the centralized data bus is used for receiving original real-time data acquired from bottom equipment; the modularized function service is used for designing each function service of the discrete manufacturing workshop into a mutually independent and pluggable module form, and each function service subscribes to required data from the centralized data bus; and the service interface is used for publishing the generated new service data back to the centralized data bus after the function service consumes the subscription data, so that other function services can subscribe and consume. According to the invention, the problems of architecture chaos, data islands and security risks in multi-subsystem integration can be effectively solved.

The application discloses a kind of joint dynamic sliding window blind detection methods for non-cooperative unmanned aircraft downlink signal, comprising: receiving array observation data, and utilizing sliding window to divide array observation data into local array data block;Each local array data block is executed and includes: establishing the original hypothesis condition and alternative hypothesis condition of this local array data block;First likelihood function under the original hypothesis condition is constructed, and first noise power under the original hypothesis condition is estimated according to first likelihood function;Second likelihood function under the alternative hypothesis condition is constructed, and second noise power and graph signal under the alternative hypothesis condition are estimated according to second likelihood function;According to the first and second likelihood function, the expression of likelihood ratio detection statistic calculation formula is constructed, and first, second noise power and graph signal are substituted into the expression of likelihood ratio detection statistic calculation formula to calculate the likelihood ratio detection statistic, when the likelihood ratio detection statistic is greater than detection threshold, judge graph signal in corresponding sliding window.

This invention discloses an interaction method, device, vehicle, and storage medium for a vehicle driver's cockpit, belonging to the field of vehicle interaction technology. The method includes: collecting a multi-dimensional sensor dataset corresponding to a target user based on multi-dimensional sensors installed in the target vehicle; determining driving emotion features corresponding to the target user based on the multi-dimensional sensor dataset; determining an interaction control strategy corresponding to the target user based on the driving emotion features; and controlling the interaction devices in the driver's cockpit according to the interaction control strategy. By extracting driving emotion features, determining corresponding interaction control strategies based on these features, and controlling the interaction devices to provide different interaction modes according to the user's emotion features, this invention achieves effective fusion of multi-modal emotion recognition, deeply integrating emotion recognition with cockpit interaction, forming a complete closed loop from emotion perception to interaction enhancement, improving the user's experience in the driver's cockpit, and enhancing driving safety.

This invention relates to a method for fusion of heterogeneous knowledge in the power industry, comprising: obtaining first triples containing entity relations from the ontology layers of at least two knowledge graphs; classifying the entity relations in the first triples into synonym relations and inclusion relations according to relation aggregation prompts by a language model; merging entity relations belonging to synonym relations; obtaining second triples containing entity attributes or relation attributes from the ontology layers of at least two knowledge graphs; classifying the attributes in the second triples into synonym attributes and non-synonymous attributes according to attribute aggregation prompts by a language model; merging entity attributes or relation attributes belonging to synonym attributes; obtaining entities from the instance layers of at least two knowledge graphs and dividing them into several data blocks; determining whether there are duplicate entities according to entity alignment prompts by a language model; and merging duplicate entities.

The application belongs to the technical field of natural language processing, and particularly relates to an aspect-level sentiment classification method based on a graph attention network, which comprises the following steps: obtaining word embedding representation of context text in which an aspect word is located; dynamically adjusting the weight of a context word according to the relative position of the context word and the aspect word, and obtaining context semantic features; aggregating syntactic information through an improved graph attention network to obtain syntactic features of the text; using a deep cross network to fuse the syntactic features of the text and the context semantic features to obtain final feature representation; and performing sentiment prediction on the final feature representation through a full connection layer to obtain the sentiment polarity distribution of the aspect word in the text. The application solves the problem of feature information loss that may occur in a multilayer network of the graph attention network, and considers the position information of the context word when extracting semantic features, so that the syntactic features and the context semantic features are fully fused, thereby improving the accuracy of aspect-level sentiment classification.

This invention provides a method and apparatus for correcting sea surface temperature (SST) predictions based on spatiotemporal axial attention. The method includes inputting target SST data into a target SST prediction correction model, comprising a convolutional input layer, an encoder, and a decoder. The convolutional input layer performs feature extraction, temporal encoding, and positional encoding on the target SST data to obtain a target feature vector. The encoder performs attention calculations on the target feature vector in three dimensions to obtain a first target output vector. The decoder outputs the target SST prediction result based on the first target output vector and historical prediction values ​​output by the decoder. Temporal and positional encoding are performed during the model input stage to enhance the representation of temporal and positional information contained in the original data. By performing attention calculations on the target feature vector in the spatiotemporal, longitude, and latitude dimensions respectively by the encoder, effective fusion of features in different dimensions is achieved, further improving feature representation capabilities and increasing the accuracy of SST prediction.

The present application relates to a kind of lake reservoir type chlorophyll concentration prediction method, device and medium, method includes the following steps: obtaining the water quality, water dynamics and weather of the online automatic monitoring data of lake reservoir including forecast point and upstream point;The online automatic monitoring data is preprocessed, and the data after processing is obtained;The data after processing is carried out feature extraction to obtain the water quality feature and water dynamics index feature of forecast point and upstream point, and construct cumulative illumination feature;Upstream water quality feature, water dynamics index feature and illumination feature are input into the fusion prediction model pre-trained, to obtain upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result, using error reciprocal method upstream transport chlorophyll prediction result and time series influence chlorophyll prediction result are fused;The chlorophyll prediction result obtained by fusion is output.Compared with prior art, the present application has the advantages of high accuracy, strong stability and the like.

The invention relates to the technical field of medical instruments, and particularly discloses a multifunctional medical physiotherapy robot which comprises a cabinet, an operation screen and a six-axis robot, and a quick-change base is mounted on an output shaft of the six-axis robot. The core is that the quick-change base can be quickly detached and provided with four functional modules, namely a low-frequency pulse spray massage head, a radio frequency thermal therapy massage head, a shock wave massage head and a negative pressure massage head. Each massage head is connected with the execution end through a structure comprising a flexible connecting piece so as to adapt to the curved surface of the human body and compensate motion errors. Through the quick change mechanism and the central control system, integration and programmed collaborative application of multiple advanced physiotherapy technologies on one device are achieved, the problems that an existing device is single in function and poor in collaboration are solved, and the device has the advantages of being accurate in treatment, convenient and fast to operate and wide in adaptation disease range.

The utility model discloses a general vehicle full -color night -vision auxiliary driving system, including multi -objective camera, individual helmet, car machine big screen and panoramic video processor, multi -objective camera all with panoramic video processor electricity is connected with individual helmet, car machine big screen, the multi -objective camera is used for gathering the image of vehicle surrounding environment, individual helmet is provided with head -mounted display, and the head -mounted display of individual helmet is used to present the image of vehicle surrounding environment after the processing of panoramic video processor, car machine big screen is used for presenting the image of vehicle surrounding environment after the processing of panoramic video processor on the vehicle. The utility model solves the problem that the imaging color of traditional vehicle night -vision auxiliary equipment is single, the detail is not clear, cannot provide panoramic visual information, is difficult to effectively integrate with vehicle system, helps the driver to be more accurate in judging the road condition under the night and complex environment, and improves driving safety and convenience.

The present application provides a kind of coal mine with the integrated equipment of digging anchor, it includes body, supporting device and adjustment component;Supporting device includes support frame and guard plate component, support frame is connected with body and can be rotated around the straight line of the width direction of body relative to body, support frame includes the first frame body and second frame body connected, second frame body can be rotated around the straight line of the length direction of body relative to first frame body, guard plate component is arranged in second frame body;Adjustment component includes first driver, the output end of first driver is drivingly connected with second frame body, first driver is used to drive second frame body to rotate relative to first frame body, so that guard plate component adapts to the inclination angle of roadway roof.The present application can realize the efficient integration of tunneling and supporting by the cooperative work of body, supporting device and adjustment component, so that the equipment can flexibly adapt to various complex conditions of coal mine roadway, provide a safer and more efficient solution for coal mining.

The application discloses a liquid droplet fusion microfluidic method based on a liquid droplet fusion microfluidic device regulated by focused surface acoustic waves, and the device comprises an interdigital transducer, two focusing arc electrodes arranged on the interdigital transducer, and a microfluid channel system bonded on the upper part of the interdigital transducer. The focusing arc electrodes are matched with the microfluid channel system, and the microfluid channel system is provided with a first collection outlet joint, a first dispersed phase inlet joint, a continuous phase inlet joint, a second dispersed phase inlet joint and a second collection outlet joint. The focusing surface acoustic waves generated by the interdigital transducer and the liquid droplet microfluid channel structure are used to flexibly realize the fusion control of liquid droplets with different sizes in the microfluid channel. The symmetrical structure is adopted to enhance the durability and repeatability of the microfluid device. The method is not only suitable for the fusion control between microdroplets, but also suitable for the fusion control between microbubbles and between microdroplets and microbubbles. The surface acoustic waves are non-contact and have good biological compatibility, and thus meet the needs of the liquid droplet fusion technology in the fields of biochemistry and medical treatment.

This application provides a data association method, apparatus, electronic device, and storage medium. The method includes acquiring a set of measurement tracks and a set of system tracks at the current time. The strategy track set includes a visual track subset, a forward radar track subset, and a side-rear radar track subset. The method involves matching the visual track subset and the system track set according to a first association rule to determine system-visual data pairs. It also involves matching the forward radar track subset and the system track set according to a second association rule to determine system-forward radar data pairs. Furthermore, it involves matching the side-rear radar track subset and the system track set according to a third association rule to determine system-side-rear radar data pairs. Finally, it involves updating the system track set based on the determined system-visual data pairs, system-forward radar data pairs, and system-side-rear radar data pairs.

The invention discloses an automobile demand prediction method and system based on large model time sequence cross-modal alignment, and belongs to the technical field of automobile demand prediction. The automobile demand prediction method based on large model time sequence cross-modal alignment comprises the following steps: acquiring time sequence data and prompt data related to a hydrogen fuel cell automobile; inputting the time sequence data into the time sequence coding branch in a reverse embedding manner to generate a time sequence embedding representation; inputting the prompt data into an LLM enhanced coding branch for coding, and generating a prompt embedding representation; and calculating a channel-level similarity matrix between the time sequence embedding representation and the prompt embedding representation, aggregating to the time sequence embedding representation based on the channel-level similarity matrix, generating an aligned cross-modal feature, and generating a demand prediction result of the hydrogen fuel cell vehicle based on a decoder. According to the method, multi-source heterogeneous information is effectively fused, the precision and robustness of demand prediction of the hydrogen fuel cell vehicle are improved, and the capability of responding to emergencies caused by external factors is enhanced.

The invention relates to the technical field of unmanned aerial vehicle control, and discloses an unmanned aerial vehicle rotor pulling force-based load weight measurement method, which comprises the steps of obtaining a plurality of original lift force measurement values generated by each rotor in a set sampling period when an unmanned aerial vehicle is in a stable flight state, and forming an original lift force value set corresponding to each rotor; performing data cleaning and validity verification on each lift original value set, and calculating to obtain an initial lift value of each rotor after removing abnormal values; and calculating a global lift reference value according to the initial lift values of all the rotors. According to the method, multi-sensor data can be effectively fused, and the influence of factors such as flight attitude and aerodynamic change is dynamically compensated in calculation, so that stable and reliable weight estimation can be provided when the unmanned aerial vehicle hovers, cruises and even executes certain maneuvering actions, and key data support is provided for flight control, endurance evaluation and task planning.

This invention relates to a method and system for automated generation of TSN network configurations based on a reflective agent. The system comprises a network state perception and intent reflection module, a YANG model configuration parameter and structure decision module, and a YANG model intelligent generation module. The network state perception and intent reflection module is responsible for structurally perceiving network state and resource information. The YANG model configuration parameter and structure decision module integrates multi-source knowledge and contextual information, intelligently generating deployable TSN scheduling parameters and YANG configuration structures based on business needs. The YANG model intelligent generation module automatically generates the corresponding YANG configuration model based on the optimized results, performs syntax and semantic verification, and then distributes it to the target device, achieving automated deployment of network configurations. This invention significantly improves the intelligence level and deployment automation capability of TSN network configuration by introducing reflective reasoning and feedback optimization techniques.

The application discloses an edge resource allocation method and device based on long-term value fairness and overlapping coalition game, a medium and equipment, and belongs to the technical field of mobile edge computing and resource scheduling. The method builds a distributed crowdsourcing system architecture, establishes an ESP multi-dimensional resource capability, a dynamic task flow and a reputation evaluation model, models the resource collaboration problem as an overlapping coalition game, builds a value loss virtual queue, converts it into a single time slot optimization problem by using a Lyapunov drift plus penalty framework, and designs a distributed potential game algorithm guided by rights and interests to realize the collaborative optimization of system efficiency and long-term fairness.

The invention provides an intelligent cabin probe data processing method and device and computer equipment, relates to the technical field of intelligent cabin monitoring, and aims to realize full coverage of structural probe data categories of an intelligent cabin, efficient processing of high-frequency probe data and no loss of key probes and provide a system-level probe processing framework and interface support for data burying points. The scheme comprises the following steps: capturing a multi-source event of the intelligent cabin system; generating different first probe data according to different multi-source events, and caching the first probe data into a probe processing framework; implementing a cache and pop-up strategy on the first probe data by using the probe processing framework, popping up second probe data, and distributing the second probe data to a data burying point control; and uploading the second probe data to the cloud by using the data burying point control.

The utility model discloses a multifunctional cat climbing frame belongs to pet feeding field. The utility model discloses cat climbing frame, include: first platform, first platform bottom four around be equipped with support spare, and the accommodation space of the cat litter basin that can be placed is formed below first platform. Second platform, second platform is supported above first platform. The accommodation opening is formed to the forward side below first platform, and the lateral surface is equipped with the connecting plate, and the mounting hole site is equipped on the connecting plate. And the front and back extension width of second platform does not exceed the 2 / 3 of first platform front and back width. The utility model can be adapted cat litter basin collocation use, make full use of the upper space of cat litter basin original position, do not extra occupy the space of placing, and realize the effective integration of multifunctional space distribution, and provide the hole board for fixing the storage other pet supplies, further save space, greatly improve the convenience of user's use.

This invention provides an automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data, comprising: Step 1, preprocessing imaging logging data; Step 2, constructing sandstone and conglomerate lithology sample data; Step 3, constructing an automatic identification network structure for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 4, training and validating the automatic identification network for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 5, conducting application tests of the automatic identification method for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data. This automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data takes one-dimensional logging data and two-dimensional imaging logging data as input, and utilizes the constructed combined identification network model to effectively fuse multi-dimensional logging data, achieving high-precision automatic identification of sandstone and conglomerate lithofacies and lithology.

This invention discloses a plant protection drone for precision seeding, belonging to the field of agricultural drone technology. It includes a central body and four arc-shaped arms that can move relative to the central body. Each arm is equipped with a power unit and rotors. Driven by a deformation drive mechanism, the arms can switch between a cruising state (forming a circular shape) and an operational state (arching inward to form a three-dimensional structure). In the cruising state, the rotor planes are coplanar, resulting in high aerodynamic efficiency; in the operational state, the rotor planes are raised and moved away from the seeding device, effectively isolating the downwash airflow from interfering with the seeded material, significantly improving seeding accuracy and uniformity. This invention solves the technical problem of seeding drift caused by rotor wind fields in existing plant protection drones.

The invention relates to the technical field of artificial olfaction and intelligent sensing, and discloses a gas recognition method and system based on a physical-data dual-drive framework. The method comprises the following steps: acquiring a macroscopic dynamic sensing signal triggered by target gas on a sensor array containing atomic-scale catalytic active sites, and a microscopic physical descriptor for representing an atomic-scale electron interaction rule between gas molecules and the catalytic active sites; and the data branch network and the physical branch network are respectively input into the data branch network and the physical branch network for feature extraction and mapping, cross-modal fusion is carried out through a feature fusion sub-module to obtain joint feature representation constrained by a physical rule, and accordingly, a gas category identification result is output. According to the method, the microcosmic physical descriptors are fused into the model reasoning logic, so that the recognition precision of the structural similar object is effectively improved.

The invention belongs to the technical field of explosive detection, and particularly relates to a shallow-buried explosive detection method and system based on multi-modal fusion, and the system comprises a magnetic signal obtaining unit, a sound signal obtaining unit, a processing unit and an output unit. And multi-modal signal synchronous acquisition, feature extraction and fusion discrimination are realized under a unified time frame, so that the recognition stability of shallow-buried explosives under complex environmental conditions is improved.

The application provides an RGB-D image feature collaborative fusion method based on transfer learning and width learning, and comprises the following steps: obtaining an RGB-D data set, performing preliminary training through a neural network, and performing retraining in the data set after modifying the structure; after feature extraction, performing correlation analysis and fusion on RGB image features and depth image features; and using width learning to classify and identify the fused features. The application can reasonably fuse the features of RGB images and depth images, ensure that the feature information of color images and depth images can complement each other, improve the running speed of the system by using width learning, and finally make the classification result have higher accuracy and reliability.

The invention discloses a hot-rolled plate structure toughness prediction method and system combined with an impact energy change curve, and belongs to the technical field of metal material structure performance evaluation.The method comprises the steps that an impact test is conducted on a hot-rolled plate sample, and an impact force-displacement curve and an energy absorption-time curve are collected in real time; preprocessing the impact force-displacement curve and the energy absorption-time curve, and extracting characteristic parameters; acquiring hardness distribution data of the hot-rolled plate sample, acquiring hardness gradient data by using the hardness distribution data, and taking the hardness gradient data as a correction factor of the curve characteristics; constructing a structure toughness prediction model based on the characteristic parameters and the correction factors, and predicting the toughness grade and the structure state of the hot-rolled plate; and outputting the toughness grade and the structure state of the hot-rolled plate. According to the method, the limitation that traditional metallographic detection consumes too long time and an impact test only provides a single value is solved, double evaluation results can be obtained within 30 seconds after the impact test, and the method is suitable for rapid online quality monitoring and process adjustment of hot rolling production.