66results about How to "Resolve uncertainty" patented technology

The invention discloses an early recognition method and system for a dam crest crack of a high-core-wall rockfill dam, and the method comprises the following steps: marking InSAR data, unmanned plane oblique photography data and three-dimensional laser scanning data through a spatial-temporal index structure, carrying out the abnormal value cleaning through a dynamic multi-dimensional threshold system, and carrying out the early recognition of the crack at the crest of the high-core-wall rockfill dam. A registration data set is obtained through processing of a segmented adaptive standardization processing unit and a space-time registration unit; extracting local features by utilizing probability tensor decomposition, constructing a global feature matrix by combining map entropy calculation, and obtaining a fusion feature set through multi-dimensional probability combination; constructing a multi-level early warning index based on hierarchical sparse representation, identifying a critical state by adopting a probability migration matrix, and determining an early warning level through an adaptive threshold network and probability combination discrimination; and carrying out multi-dimensional verification by adopting a combined verification matrix, and realizing system optimization and updating through a multi-objective optimization network and an incremental learning matrix. According to the method, the accuracy and reliability of early crack recognition are improved.

The invention belongs to the technical field of force / position hybrid control of mechanical arm systems, and particularly discloses a finite-time self-adaptive impedance control method and system for a flexible joint mechanical arm system. In order to solve the problems that the control effect of a mechanical arm system is affected by nonlinear and unknown system model parameters at present and derivation is complex in backstepping design, the fuzzy self-adaptive finite time impedance controller is designed based on the impedance control technology so as to achieve hybrid control over the track and force of the tail end of the mechanical arm. In the design process of the controller, a real control law is constructed to realize finite time stability of the system; and meanwhile, command filtering and fuzzy self-adaptive technologies are introduced to solve the problems of calculation explosion in backstepping design and unmodeled dynamics of a mechanical arm system. By means of the control method, the influence caused by system uncertainty can be effectively overcome, the position tracking error of the mechanical arm system is reduced, and meanwhile it can be guaranteed that all signals in a closed-loop system are bounded within finite time.

The invention relates to the technical field of medical instruments, and discloses a blood vessel ablation system. The system comprises an ablation catheter, a heating element, a pressure sensor and a radio frequency generator, pressure borne by the catheter in a blood vessel is detected in real time through the pressure sensor, pressure data are fed back to the radio frequency generator, the radio frequency generator judges the contact condition of the catheter and the blood vessel wall according to the pressure data, and then a corresponding indication signal is sent out. And injection of swelling liquid and output of radio frequency energy are controlled. The system can monitor the contact condition of the catheter and the blood vessel wall in real time, eliminate dependence on experience of doctors, achieve accurate control and improve safety and effectiveness of blood vessel ablation treatment.

The invention relates to an impurity detection system for high-purity gas and a detection control method thereof, and the impurity detection system for the high-purity gas comprises a detection path selection module, a busbar sample introduction module, a chromatography pretreatment module and a moisture detection pretreatment module, the detection path selection module is connected with confluence path units contained in the busbar sample introduction module, the chromatography pretreatment module comprises a plurality of chromatography pretreatment units, the confluence path units are respectively connected with the corresponding chromatography pretreatment units, each confluence path unit is connected with the moisture detection pretreatment module, and the moisture detection pretreatment module is connected with the detection path selection module. The detection path selection module is used for acquiring storage information in a high-purity gas storage tank, and performing corresponding chromatographic analysis steps and / or moisture detection analysis steps on the high-purity gas according to the storage information; the opportunity and parameters of gas entering the detection equipment are automatically adjusted according to the storage states of different steel cylinders, so that the accuracy of a detection result is improved.

The invention discloses a new energy power prediction correction method and system based on a mixed channel cloud model, and the method comprises the steps: obtaining historical key data, including a predicted power value and an actual power value, of new energy power prediction from different data channels, and obtaining the predicted power value and the actual power value after normalization processing through normalization processing; constructing a cloud function by using a triangular membership function based on the predicted power value and the actual power value after normalization processing; and constructing a mixed channel cloud model, generating a correction weight for the power prediction value of each time point in the target time period by a cloud function, performing automatic dynamic component optimization on the power prediction value of each time point, correcting the power prediction value of the corresponding time point, and obtaining a final new energy prediction power value. According to the method, the power prediction value can be dynamically adjusted and optimized to correct the error in prediction, and the precision, reliability and stability of the prediction result of new energy such as wind energy and solar energy can be remarkably improved.

The present invention discloses a scheduling method for coupled operation of transactions and spot transactions of a multi-energy system, which solves the problem that the existing method cannot predict the trend of electricity prices in the future, and further cannot plan the power generation plan of the electricity spot market in advance when the uncertainty and intermittent fluctuations of renewable energy appear. The method comprises: obtaining the day-ahead spot transaction related data, loading the day-ahead spot transaction related data in real time, the electricity price prediction model outputs the electricity price prediction result and the transaction risk value corresponding to the electricity price prediction result, and the comprehensive optimization scheduling model calculates the predicted output value of the thermal power unit and the predicted output value of the new energy based on the electricity price prediction result and the transaction risk value; the present invention can effectively cope with the uncertainty and intermittent fluctuation of renewable energy by constructing the electricity price prediction model and the comprehensive optimization scheduling model for coordinated cooperation, and ensure that the multi-energy system can operate stably under different energy supply conditions.

The invention relates to a service-hidden multipoint secure transmission method and system, and belongs to the technical field of network security. An identification key distribution management system deploys an independent software shield for each gateway device as an identification key for system communication, and every two gateway devices execute the following point-to-point communication for data encryption transmission: a device B verifies a single packet authentication message serving as a communication request initiator based on identification key information of a device A; after the single packet authentication is passed, the A and the B carry out identity authentication key negotiation based on identification key information of the A and the B to obtain a data transmission shared key; and A and B construct an encrypted UDP channel based on the shared key to realize data encryption transmission. According to the method, the secure communication transmission channel based on the complex network environment of the Internet is constructed for data encryption transmission, so that the security of data transmission can be ensured, the attack from an enemy opposite side is avoided, and the problems of uncertainty and anonymity in the prior art are solved.

The invention relates to the technical field of seismic data processing, in particular to a seismic waveform inversion method and system based on deep learning image registration, and the method comprises the steps: carrying out the least square migration based on small offset observation data and a background velocity field, and constructing a reflection coefficient model; performing Born forward modeling according to the reflection coefficient and the background velocity field to obtain a simulated seismic record image; acquiring a seismic record deformation field by adopting deep learning image registration based on the simulated seismic record image and the seismic observation image; calculating a normalized deformation field, and judging whether the normalized deformation field meets a preset condition or not; if yes, terminating the iteration process; otherwise, executing the next step; and according to the seismic record deformation field and the accompanying seismic source, updating the background velocity field through a conjugate gradient algorithm, and repeating the steps. According to the method, high efficiency and high precision of deep learning are utilized, and the problem that a cycle jump problem which is prone to occurring in waveform matching falls into a local minimum value is avoided.

The invention discloses a water-rich tunnel stress mechanism analysis method based on physical-data dual drive. A data driving model is introduced, and the model can capture various complex phenomena and change trends possibly occurring in the running process of the water-rich tunnel through analysis of actual observation data, so that the stress condition is predicted more accurately. By adopting the dual-drive method, the defects of a single model are overcome, stress analysis under complex working conditions can be coped, and a new method is provided for tunnel design and construction. According to the method, the theoretical basis of a physical model and the data capturing capacity of a data driving model are combined, and a more comprehensive and accurate analysis method is formed.

The invention discloses a user interface mutual locking method and system, and relates to the technical field of machine configuration. Comprising the steps that a table is established in a database in an upper computer server, and a user account list, a UI machine IP address list and master control information are stored in the table; acquiring a current user account, and if the current user account is stored in the user account list, feeding back the IP address of the UI machine with the master control authority of the user to the user interface; and if the current user account is not stored in the user account list, storing the current user account, the corresponding UI machine IP address and the main control into a database table in the upper computer server, and feeding back the UI machine IP address to the user interface. And when the control authority is switched, password verification is carried out, so that the safety is ensured. And meanwhile, each user interface can continuously receive the IP address information of the UI machine currently having the master control authority of the upper computer server, so that the situation that the same equipment is controlled by a plurality of user interfaces at the same time is avoided.

The invention relates to a partial discharge type classification method and system based on a multi-algorithm model, and the method comprises the steps: obtaining the time series data of a partial discharge event through a YOLO algorithm, and carrying out the preprocessing; taking the time sequence data as the input of the PatchTST model to obtain a discharge prediction result; inputting the discharge prediction result into a classifier for discharge type classification to obtain a classification result; wherein the training steps of the PatchTST model are as follows: segmenting the preprocessed time sequence data into Patches with the same size, and extracting the features of the Patches by using the PatchTST model; carrying out feature mapping on each Patch by using a trainable linear parameter matrix Wp; using the Patch features subjected to feature mapping to train a Patch TST model; and a self-supervised learning method is adopted to randomly cover a part of time sequence data and train a PatchTST model to reconstruct the covered data. Compared with the prior art, in-depth analysis of the partial discharge detection system on the development trend and type classification of the discharge event is realized.

The invention relates to a self-adaptive control method for an electrically-controlled electric gear shifting executing mechanism, and aims to overcome the influence of uncertain problems such as parameter perturbation and external interference in the gear shifting process and quickly and accurately complete the gear shifting action. The method comprises the following steps: firstly, modeling a gear shifting execution mechanism according to a working principle of the gear shifting execution mechanism; secondly, considering the uncertainty of external interference and the parameters of the gear-shifting direct current motor, adding a sliding mode control algorithm, optimizing a membership function of fuzzy sliding mode control through a genetic algorithm, and realizing the self-adaptive adjustment of sliding mode control gain and following the actual response characteristics of the gear-shifting direct current motor; and finally, a fuzzy PI controller is designed to carry out adaptive adjustment on the duty ratio of the gear-shifting direct current motor, and accurate tracking of the displacement of the gear-shifting execution mechanism is completed. According to the method, the tracking precision, the response speed, the anti-interference capability and the like of the control of the gear shifting execution mechanism are improved through the online self-adaptive adjustment of the control parameters.

The application discloses an expression recognition method and system based on uncertainty learning, comprising the following steps: S1, obtaining a face image to be recognized; S2, preprocessing the input face image to obtain a standardized face image; S3, initializing a deep learning training condition; S4, training an expression recognition model by using a plurality of modules, including a face feature extraction module, a multi-dimensional feature classification module, a latent label distribution learning module and an attention consistency module, and updating model weight parameters; and S5, obtaining an output expression category of the face image through the expression recognition model. The expression recognition method and system based on uncertainty learning solve the uncertainty problem of face expressions in a wild environment by combining a latent label distribution and an attention consistency method, improve the accuracy of expression recognition by mining the latent label distribution, and enhance the interpretability of the model by using a generated attention map method.

The application discloses a sealing ring position distribution description method under a multi-scale assembly factor of a flared joint, establishes a multi-scale manufacturing and assembly factor fusion model containing a matching angle, a radial circle runout and a half-flared angle; a reference coordinate system is established with the center of a circle intersecting a tapered surface and a cylindrical surface in the flared pipe as an origin, a macroscopic matching angle is represented by rigid body rotation, a mesoscopic radial circle runout is represented by a radius of a point on a generatrix of the tapered surface of the pipe joint, and a mesoscopic half-flared angle is represented based on a spherical coordinate; a coordinate of a contact point at the top of the edge of the flared pipe is located, a local analytical model is established to represent an intermediate parameter angle changing with the matching angle, and finally, a relative contact position of a contact area of the flared pipe and the pipe joint is determined as an initial boundary condition of a microcosmic finite element contact model. The application can provide an accurate initial contact state and boundary condition for a contact model with a microcosmic morphology, and improve the prediction accuracy of sealing performance.

The application provides a kind of shipboard aviation equipment composite material damage D-S evidence theory nondestructive evaluation method, belongs to the field of shipboard aviation equipment nondestructive evaluation, it includes the following steps: at time, the triaxial damage area characteristics and triaxial vibration frequency characteristics of the composite material structure of shipboard aviation equipment are synchronously acquired and stored;The triaxial damage area characteristics and triaxial vibration frequency characteristics at time are compared respectively, and the corresponding triaxial damage area change degree and triaxial vibration frequency change degree are obtained;The identification framework of D-S evidence theory is established, and the triaxial mass function of the composite material structure is calculated respectively;According to the created D-S damage judgment model, the classification judgment and early warning of the composite material damage of shipboard aviation equipment are carried out.The application can realize the real-time classification evaluation of the dynamic damage of the composite material component of shipboard aviation equipment, and provide data support for the condition-based maintenance of shipboard aviation equipment.

The application discloses a self-adaptive multi-modal data fusion bridge health monitoring method and system, generates a response sample database through establishment of a bridge nonlinear dynamics equation, pre-processes bridge sensor data and trains a deep learning model to extract damage features, and self-adaptively optimizes model parameters; based on model output, real-time and historical data are combined to perform bridge health evaluation, and a monitoring strategy is dynamically adjusted or early warning is triggered; an edge computing device is deployed to perform pre-processing and damage identification, and long-term trend analysis is realized in cooperation with a cloud platform; macro-structure damage and micro-crack data are fused through cross-scale feature extraction; an artificial intelligence algorithm is used to generate a bridge health evaluation result, a bridge maintenance and repair scheme and a decision-level early warning signal. The application combines nonlinear dynamics modeling, self-adaptive deep learning, edge cloud collaborative computing and cross-scale analysis, improves complex damage identification precision and monitoring intelligent level, and is suitable for large-scale traffic network bridge monitoring management.

The invention provides a polarization image defogging method based on a neural network, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a plurality of fogged polarization images, and generating a polarization image package; constructing a neural network defogging model; and based on the constructed neural network defogging model, carrying out defogging processing on the foggy polarization image in the generated polarization image packet to obtain a fogless image. According to the image defogging method, the deep learning and the polarization imaging technology are combined, the problem that the calculation complexity of the polarization imaging technology is high can be effectively solved, the overall generalization ability and defogging level are improved, and better robustness and high adaptability can be shown in the face of different types of fog and different degrees of haze.

The utility model provides a contact assembly, a load switch and an electric meter, and relates to the technical field of low-voltage electrical appliances, the contact assembly comprises a static contact plate and a movable contact plate which is driven to move along a first direction and is contacted with or separated from the static contact plate, and at least two contact points are formed on the movable contact plate. The static contact plate is in contact with or separated from the moving contact plate to form at least two breakpoints, the opening distance of at least one breakpoint is smaller than the opening distance of other breakpoints, the breakpoint with the minimum opening distance is an arc contact, the other breakpoints are main contact contacts, the arc contact and the main contact contacts are arranged in series, and when the moving contact plate is separated from the static contact plate, the arc contact and the main contact contacts are connected in series. And the arc contact is separated relative to the main contact. According to the contact assembly, the load switch and the electric meter provided by the invention, the problem that the position of an arcing point is uncertain in the prior art is solved, and the number of arc extinguish chambers is reduced.

The present invention discloses a high-resolution assimilation inversion system and method for methane emission sources, including a data preprocessing module, a regional atmospheric transmission mode module, a methane emission source diagnosis module, an ensemble Kalman smoothing module, a sub-sector accounting module of assimilated sources, and an output module. The ensemble forecast methane concentration field under the regional atmospheric transmission mode is obtained through the regional atmospheric transmission mode module; the methane emission source is constructed through the methane emission source diagnosis module; the methane assimilation source is obtained through the ensemble Kalman smoothing module; the bottom-up methane emission inventory is used as a priori estimation through the sub-sector accounting module of the assimilated source, and the assimilated emissions of each department are obtained on the basis of the methane assimilated source. The present invention has the advantages of high estimation accuracy, improved spatiotemporal resolution, etc., and can solve the uncertainty problem of bottom-up priori emission source accounting.

The present invention discloses a coal-rock-gangue sorting method and system based on multi-source data fusion analysis, which belongs to the field of coal-rock-gangue sorting, performs the same load loading test on rock blocks and coal blocks, monitors the strain of coal blocks and rock blocks, monitors the heat released during loading, tests different types of coal, rock, and gangue, analyzes the spectral absorption peak characteristics, associates different strains, released heat, and different absorption peak characteristics with corresponding types of coal, rock blocks, and gangue, respectively establishes strain multi-classification models, heat multi-classification models, and spectral multi-classification models, respectively uses the above models to sort and identify the mixture of coal, rock, and gangue, and determines the final sorting result based on the dynamic weighted fusion of three sorting results. Combined with the apparent characteristics, physical properties, and composition differences of coal, rock, and gangue, deep-level decision fusion is carried out to solve the uncertainty problem caused by a single sorting method, and ensure the accuracy and reliability of the sorting results.

The invention provides an urban sewage treatment abnormal condition intelligent feature extraction method based on a sparse self-encoding network, and the method achieves the intelligent diagnosis of the abnormal condition in the urban sewage treatment process. Aiming at the challenge that in the sewage treatment process, the residence time is uncertain due to inflow fluctuation, so that detection data and the inflow state are difficult to accurately associate, the method comprises the following steps: firstly, calculating time lag through hydrodynamics; secondly, homogenizing data distribution by constructing a data feature sparse matrix; and finally, establishing an abnormal working condition feature extraction model based on the sparse self-encoding network, thereby realizing intelligent diagnosis of the abnormal working condition in the urban sewage treatment process, improving the detection precision, and providing technical support for the sewage treatment industry.

The invention provides an improved position control method for a direct-drive electro-hydrostatic actuator, and belongs to the field of control. The method comprises the steps that firstly, nonlinear characteristics of the direct-drive electro-hydrostatic actuator system are considered, a state space model of the system is established and reasonably simplified into a strict feedback form, a power unit of the direct-drive electro-hydrostatic actuator system is a permanent magnet synchronous motor, and a hydraulic pump is a gear pump; secondly, the obstacle Lyapunov function is combined with a backstepping controller, a constraint boundary is introduced, and the output constraint performance of the system is guaranteed; and finally, the parameter disturbance is adjusted online in real time by establishing the adaptive rate of the uncertain parameters, and stability analysis is carried out. According to the direct-drive electro-hydrostatic actuator position control method, the problem of uncertainty of system parameters in the direct-drive electro-hydrostatic actuator position control process can be solved, and the control precision and robustness of a direct-drive electro-hydrostatic actuator control system are improved.

The invention discloses a heavy-load train cooperative tracking control method and system based on coupler and buffer uncertainty, and relates to the field of heavy-load train cooperative control safe driving, and the method comprises the steps: building a multi-mass-point dynamic model of a heavy-load train; establishing a heavy haul train system equation with an actuator fault model; establishing a system dynamic error model; converting the system dynamic error model into a system state space equation; obtaining an error transformation state-space equation according to the error variable after filtering transformation; according to the error transformation state-space equation, a cyclic small gain theorem and a linear matrix inequality are adopted to obtain sufficient conditions of the stability of the heavy-load train; constructing a distributed self-adaptive cooperative controller of the heavy-load trains to ensure the safety of each heavy-load train, and realizing the asymptotic tracking of the locomotive to the expected speed and displacement curve of the locomotive and the consistent tracking of the truck to the expected speed and displacement curve of the truck based on the minimum safety distance; according to the invention, cooperative tracking control of the speed and displacement of the heavy-load train is realized.

The invention discloses a ship maintenance progress prediction method based on multi-source data fusion and machine learning, and the method comprises the following steps: 1, obtaining multi-source data, and constructing a multi-source heterogeneous data set; 2, preprocessing and feature engineering processing are carried out on the multi-source heterogeneous data set, and the multi-source heterogeneous data set is converted into feature data needed by machine learning model input; 3, constructing a prediction model based on LightGBM and quantile regression, and training the prediction model through the feature data; 4, predicting the ship maintenance progress by using the trained prediction model; and 5, updating the prediction result of the ship maintenance progress in real time through Bayesian updating based on the progress performance index. According to the method, the project static attribute, the repair project network, the historical performance, the real-time resource state, the material supply information, the environmental factors and the streaming progress data are systematically integrated, the feature vector comprehensively describing the project state is formed, and the problems of complexity and uncertainty of the ship repair project are solved.

The utility model provides a friction coefficient detection device for thixotropic slurry, and relates to the technical field of pipe jacking engineering. The device comprises a moving device, the moving device comprises a pulley structure and a moving belt, the moving belt is arranged along the circumferential wall of a roller of the pulley structure to form a circulating moving system, a test sample structure and a standard test piece are arranged on the moving belt, the test sample structure is fixedly connected with the moving belt, and the standard test piece is arranged on the moving belt. The bottom of the standard test piece is in contact with the top of the test sample structure; the force measuring structure is connected with the standard test piece through a fixing device, when the pulley structure is moved, the moving belt and the test sample structure are driven to move, and friction is generated between the test sample structure and the standard test piece. According to the structure, the problems of inaccuracy and uncertainty of mud performance evaluation in engineering calculation and actual operation processes are solved.

This invention proposes a method for updating lane-level dynamic information in high-precision maps based on crowdsourced data. The method includes: collecting trajectory information and road image data from multiple sources, with a high-precision map embedded within; constructing a single-stage line-anchor detection model using a convolutional neural network; performing lane line perception detection on the road image data based on the single-stage line-anchor detection model; outputting lane line perception information; using an image alignment compensation algorithm to register and fuse road image data from consecutive frames during vehicle movement, and inputting this data into a target detection model for recognition, outputting traffic cone perception information; and projecting the lane line perception information and traffic cone perception information onto the high-precision map's geographic coordinate system using camera parameters. This method effectively suppresses missed and false detections in single-vehicle perception by combining low-cost crowdsourced data with a multi-vehicle evidence fusion mechanism, improving the accuracy and real-time performance of lane-level dynamic information updates, and meeting the high-precision requirements of autonomous driving for high-precision maps.

The invention discloses an approval process configuration method, system and device, a storage medium and a program product, and the method comprises the following steps: packaging a process engine into a WebAPI for terminal service calling through a workflow service module, and providing a BPMN-based workflow designer through the workflow service module; constructing an approval flow service module based on the workflow designer, defining an approval flow conforming to real businesses through the approval flow service module, and converting the approval flow into a BPMN definition; the examination and approval flow service module calls a flow engine through a WebAPI to analyze and process the BPMN definition so as to realize the operation of real business; wherein the terminal service comprises an approval flow service module. According to the method, visual definition and combination processes of various users can be met, and the demand of online circulation of actual business of an enterprise is met.

The invention provides a model and a method for predicting the water-gas ratio and the water yield of formation water of a fracture-vug type water-producing gas well. The model establishment method comprises the following steps: screening out abnormal fluctuation data from gas field production dynamic data to obtain daily output data; calculating the accumulated gas production and formation water-gas ratio of the water-producing gas well according to the daily output data; establishing a formation water-gas ratio calculation model of the water-producing gas well, wherein the model is a mathematical relationship between the formation water-gas ratio and cumulative gas production and contains a plurality of undetermined indexes; the accumulated gas production of the water-producing gas well and the water-gas ratio of the formation water are used for fitting to determine the value of each index, and then a prediction model of the water-gas ratio of the formation water is obtained. The water yield prediction model is established based on the formation water-gas ratio model. According to the prediction method, the future daily water yield can be predicted according to the water yield prediction model. According to the method, the labor cost of a large number of professionals is effectively reduced, the timeliness and convenience are improved, and the method has good applicability and popularization prospects in field practical application.

The present application discloses a pedestrian re-identification method based on a pedestrian re-identification model, a computer device and a computer-readable storage medium. The method comprises: optimizing a preset deep model by randomly selecting pedestrian images to be trained, presetting feature extraction modules, presetting local correlation attention modules and presetting overall correlation attention modules to obtain optimized pedestrian features of the pedestrian images to be trained and optimized pedestrian features with overall correlation, generating a pedestrian re-identification model to obtain pedestrian recognition results of the pedestrian images to be tested, learning the dependencies between pedestrian images and the dependencies within images through the deep model, and integrating the dependencies between images and the dependencies within images into a network structure, learning the robust features of pedestrian images, overcoming the uncertainty problems caused by the variability of the environment, pedestrians, etc., and improving the accuracy of pedestrian re-identification search.