61 results about "Differential algorithm" patented technology

The invention discloses a dam termite image recognition method based on multi-modal fusion and dynamic feature enhancement. The method comprises the steps that image data, environmental parameters and vibration signals of a dam monitoring area are collected in real time through a multi-modal sensor network; carrying out the preprocessing of the image data, and carrying out the target detection through an improved ACP-YOLOv5s model; processing the continuous video frames by adopting Gaussian background modeling and a difference algorithm to generate a binary mask graph representing dynamic activities of termites, and separating a moving target area from a static background; and the target detection result and the dynamic mask graph are fused, the collected environment parameters and vibration signals are combined, the termite damage level is calculated through an AI engine, and graded early warning or automatic killing response is triggered. The method breaks through the limitation of traditional single-mode detection. And a multi-dimensional hazard grade evaluation model is established, so that the automation level of dam termite prevention and control is greatly improved.

The invention discloses a regulation and control system and method based on heat energy of an energy storage cabinet, and belongs to the technical field of intelligent heat management. Temperature data in the energy storage cabinet are collected, a temperature distribution matrix is constructed, and a thermal gradient vector and an overheating risk area are extracted based on a difference algorithm; further establishing a thermal power load mapping model, and dynamically generating an air circulation path optimization diagram; the opening angle of a flow deflector and the rotating speed of a fan are adjusted in combination with an optimized path, and self-adaptive regulation and control of a thermal field in the cabinet body are achieved; meanwhile, a machine learning model is trained by utilizing historical operation data, an optimal regulation and control strategy is output, and an execution layer is driven to realize collaborative response of a fan and a flow deflector; the method has the advantages of high regulation and control precision, high response speed, low energy consumption and the like, and is suitable for heat management requirements of various types of energy storage cabinets.

The present invention relates to the fields of PID control and Kalman filtering, and disclosed is an improved PID control method based on Kalman filtering. The present invention comprises: in the process of acquiring a target value of an expected signal, taking the difference between the target value and a current feedback quantity of a controlled object to obtain an error value, acquiring an output of PID from the error value, applying the output of PID to the controlled object to obtain a noisy output signal, then, after Kalman filtering, feeding the output back to PID, continuously iterating to make the feedback quantity approach the target value, and finally obtaining a stable output signal. Variable-speed integration and integral separation algorithms are added to an integral term of PID to change an integration speed on the basis of a system deviation, and an incomplete differential algorithm and a low-pass filter are added to a differential term of PID to prevent high-frequency interference. According to the present invention, the optimal estimation of a system state is obtained by means of prediction of a current state and updating of observation data, thereby reducing the influence of measurement errors and noise, and improving the performance and stability of a PID controller.

The invention relates to the technical field of debugging systems, in particular to an adaptive debugging system and method for touch screen FW parameters, and the system comprises a signal acquisition module, a trend triggering module, a temperature compensation calculation module, a window generation module and a parameter output module. According to the invention, the edge of the driving voltage is carefully collected, the sliding difference algorithm is introduced to filter out high-frequency noise, meanwhile, real-time sampling of environment temperature and baseline capacitance adjustment are combined, the influence of external variables on signals can be dynamically corrected, and accurate judgment of capacitance fluctuation is improved by utilizing periodic delay deviation and regression trend analysis. By means of main frequency feature extraction and zero crossing point sudden change detection, threshold setting can be refined, self-adaptive window expansion can be achieved, touch signal response is kept sensitive and consistent all the time, adjustment and optimization can still be automatically completed even under the condition that the temperature changes frequently or signal interference is complex, parameter drift and debugging failure are effectively prevented, and user experience is improved. And the parameter automatic matching efficiency and the finished product consistency in batch production are improved.

The invention relates to the technical field of metal composite materials, in particular to a metal composite material production process dynamic simulation method based on digital twinning. Comprising the following steps: constructing a digital twin model comprising a physical entity layer, a virtual model layer and a connection layer, and establishing a Fourier neural operator model embedded with a Navier Stokes equation and a heat conduction equation constraint; real-time molten pool monitoring data are mapped into model input through a connecting layer, and advanced time step deduction is executed by using a deep learning model to predict the state of a molten pool; based on the differentiable characteristic of a physical information neural operator, calculating the gradient of a prediction state and an ideal target by using an automatic differential algorithm, generating a process parameter field through reverse iteration updating, and discretizing the process parameter field into a galvanometer control instruction; and when the risk of aggregation of the reinforced phase particles is predicted to exist, automatically generating a swing scanning strategy to realize active suppression. The method gives consideration to simulation precision and real-time performance.

A calculation method for a transformer winding considering a frequency-dependent loss, comprising: according to simulation requirements, defining key parameters calculated by using a finite-difference time-domain (FDTD) algorithm; constructing an equivalent flat wire model in an FDTD grid; calculating frequency-dependent internal impedance per unit length of a flat wire; and on the basis of a vector fitting method and a time-domain convolution calculation method, substituting the frequency-dependent internal impedance into a time-domain equation of the FDTD algorithm for updating and iterative solving, so as to obtain an electromagnetic field calculation result. In the present invention, on the basis of the vector fitting method and time-domain convolution calculation, the impact of the frequency-dependent characteristics of an electromagnetic field distributed in a conductor on an electromagnetic transient process is accurately considered during the FDTD time-domain solving, so that the calculation accuracy and efficiency of conventional FDTD algorithms and even conventional cross-scale modeling techniques can be significantly improved, thereby carrying out the targeted electromagnetic transient analysis on the reliability, safety, and predictability of transformers, and achieving the early detection of potential faults and defects, or formulating targeted maintenance plans and repair schemes.

The invention relates to the technical field of industrial data management. The data management method, system and device based on the industrial digital base and the medium are provided, the method comprises the steps that block chain evidence storage is conducted on version hash, micro-differential packet hash and an obtained operator certificate, an on-chain evidence storage block is generated, distributed storage processing is conducted on a micro-differential packet, and an off-chain differential packet storage entity is obtained; when it is detected that the data is abnormal, abnormal version positioning is carried out based on the record-level blood relationship chain, and an abnormal version sequence is determined; on the basis of a predefined inverse operation mapping relation, performing reverse playback on a micro-differential packet corresponding to the abnormal version sequence to realize historical data state recovery; and carrying out signature verification on the incremental service pack, carrying out dynamic loading processing after the verification is passed, updating a preset blood relationship extraction rule and a preset difference algorithm, and generating a hot update strategy, so as to achieve the technical effects of improving the data blood relationship tracing precision, reducing the storage redundancy overhead and enhancing the credibility of an evolution process.

The invention provides a process GPU-oriented real-time fault tolerance and task migration system and method, and relates to the technical field of distributed computation.The method comprises the steps that a training task state in a GPU video memory is captured, difference data is generated through an incremental difference algorithm, and the difference data is stored in a multi-level storage system after being subjected to lightweight compression; meanwhile, GPU node health indexes are collected in real time, node states are analyzed through a time sequence prediction model, and a migration process is triggered when fault risks are predicted or preemption early warning is received; during migration, the task is paused, a final increment check point is generated, check point data is quickly transmitted to a target healthy GPU node through an RDMA network, a complete context state is reconstructed, task execution is recovered, and real-time fault tolerance and seamless migration of the GPU task are achieved. According to the method, the continuous operation reliability and the resource utilization efficiency of the GPU cluster in the long-time training task are improved.

The application discloses a kind of data mirror persistent backup systems of power grid, it is related to electric power system technical field, including data acquisition module, data preprocessing module, difference data synchronization module, data integrity check module, data mirror module, data storage module, control and management module;Data acquisition module is used to collect the operation data of power grid in real time;Data preprocessing module carries out cleaning, formatting and compression processing to the data collected, prepares for subsequent data synchronization;Difference data synchronization module calculates and identifies the change part of data since last synchronization by difference algorithm, and only synchronizes the change part data.The application greatly reduces the data transmission amount when real-time topology reconstruction by only synchronizing the changed data, reduces the backup time at the same time, improves the efficiency of power grid data backup, optimizes the synchronization process of data, reduces the storage space waste caused by full backup, and guarantees the accuracy of backup data by hash value comparison mode.

The application is a target detection and parameter estimation method of a sensing integrated system, aiming to solve the target detection and fractional Doppler and time delay parameter estimation problems of an OTFS sensing integrated system, and the method comprises the following steps: step one, a sensing integrated signal is generated at the sending end; step two, the receiver receives the echo signal and processes it; step three, a threshold map is generated through an integer time delay and Doppler detection network based on a convolutional neural network to perform target detection; and step four, fractional Doppler and time delay indexes are estimated. The target detection and parameter estimation method of the OTFS sensing integrated system based on deep learning has the following advantages: compared with the commonly used CFAR detection algorithm, the detection performance of the integer time delay and Doppler detection network is better. In addition, compared with the differential algorithm considering only integer Doppler and time delay and considering fractional Doppler and time delay, the relative distance and speed estimated by the fractional estimation network are more accurate.

The application provides a power plant DCS data acquisition interface automatic configuration method and system. The method comprises the following steps: obtaining a production process diagram file from a DCS host computer, analyzing and extracting measuring point information based on a DCS type; generating a structured dictionary sequence through lexical analysis, and generating a measuring point list according to a preset rule to screen target measuring points; comparing the measuring point list state by using a Myers difference algorithm to dynamically update the interface program acquisition range; transmitting the measuring point list, the process diagram and real-time data to a management information layer (MIS) across a safety zone, and synchronously checking the consistency based on a digital twin model. The application realizes automatic extraction of measuring points by using regular expressions and DOM analysis technology, solves the problems of low efficiency of manual configuration, lagging update and cross-zone data island by combining dynamic key encryption and incremental update mechanism, significantly improves the accuracy of measuring point configuration and synchronization real-time performance, and provides safe and reliable digital support for power plant production monitoring and information management.

The application belongs to the technical field of computers, and provides a PDF document comparison method and system based on intelligent differential merging. The method is as follows: the visual fingerprint sequences of a source PDF document and a target PDF document are calculated; the visual fingerprint sequences of the two PDF documents are compared through a Myers differential algorithm, a difference sequence is generated, all deletion operation sequence regions adjacent to insertion operation sequence regions are identified, and a candidate page pair is constructed; pre-filtering is performed on the candidate page pair, a similarity score is calculated, and based on a threshold judgment and a greedy strategy, the best match is locked, the deletion operation node and the insertion operation node are combined and replaced by a modification operation node; finally, spatial quantization index technology is used to compare internal objects, and the final comparison result is output. The application can not only accurately identify the subtle changes of the PDF document, but also reduce the comparison complexity, improve the comparison efficiency, and meet the real-time comparison needs of users.

The invention relates to a differential cloud platform and a vehicle software differential upgrading method, and the platform comprises a first storage unit, a differential engine service, a differential algorithm service and a second storage unit. The first storage unit is used for storing a difference algorithm mirror image file; the second storage unit is used for storing a vehicle software package file; the difference engine service is used for obtaining the difference algorithm mirror image file from the first storage unit in response to an input difference request so as to start the difference algorithm service based on the difference algorithm mirror image file; and the differential algorithm service is used for obtaining the vehicle software package file from the second storage unit and carrying out differential processing on the vehicle software package file through a differential algorithm program to generate a differential software package, so that the problem that the adaptation difference of different differential software architectures in platform integration cannot be shielded is solved.

This invention discloses a guidance and control method for high-dynamic aircraft based on a fusion differential method. To improve the system's response speed and continuous accuracy, this method designs a fusion differential algorithm that includes nonlinear and linear terms to ensure rapid response in the initial response stage and smooth tracking in the subsequent tracking stage during line-of-sight angular velocity estimation. This allows for timely and accurate acquisition of the line-of-sight angular velocity required for guidance and control. Furthermore, based on a gravity-compensated proportional guidance method, the method designs the required overload for the high-dynamic aircraft. The required overload derives control commands according to the transfer ratio of different aircraft and transmits them to the servo mechanism. The servo mechanism then controls the servo motor to control the aircraft's flight, achieving higher-precision guidance and control.

This disclosure relates to the field of oil and gas exploration technology, and in particular to a method, apparatus, equipment, medium, and product for suppressing anomalous noise. The method includes: performing effective signal inversion on seismic data based on the L1 norm to obtain an initial expression for the inverted effective signal; and using a mathematical differential algorithm to obtain a regularization term for calculating the effective signal; confirming the expression for the inverted effective signal based on the initial expression and the regularization term; and solving for the inverted effective signal using the Lagrange multiplier method based on the expression for the inverted effective signal to remove anomalous noise and obtain the effective signal of the seismic data. By performing effective signal inversion on seismic data using the L1 norm, anomalous noise in the seismic data can be accurately identified and removed, while minimizing damage to the effective signal, effectively improving the signal-to-noise ratio of the seismic data, and avoiding damage to the effective signal due to excessive denoising.

The invention belongs to the technical field of gesture somatosensory interaction, and particularly relates to a gesture somatosensory interaction method based on fused vision, inertial touch and multi-point touch information, edge detection and a three-frame difference algorithm are combined, so that an obtained image has good contour information, and a part of'cavity 'phenomenon is made up; and meanwhile, the improved three-frame difference method is supplemented by fusing the Gaussian mixture background difference algorithm, so that the target image has continuous contour and relatively complete internal information, the gesture recognition rate is improved, and the false detection rate is reduced.

The invention discloses a three-column horizontal rotation type disconnecting switch opening and closing state monitoring method and device, and the method comprises the steps: S1, scanning a disconnecting switch working place through a laser radar, and obtaining the point cloud data of a disconnecting switch; s2, designing a point cloud difference algorithm based on an octree to process the disconnecting switch point cloud data to obtain difference four-dimensional point cloud data; s3, designing an isolating switch point cloud discrimination strategy based on semantic segmentation, and processing the differential four-dimensional point cloud data to obtain semantic enhanced four-dimensional point cloud data; s4, generating a kinematic skeleton of the conductive arm of the disconnecting switch, and resolving the motion trend of the disconnecting switch; and S5, determining the opening and closing state of the isolation switch. By adopting the technical scheme of the invention, the rapid and accurate fitting of the kinematic skeleton of the conductive arm is realized, and then the monitoring and judgment of the opening and closing states of the three-column horizontal rotary disconnecting switch are completed.

The application belongs to the technical field of industrial automation program control and digital logic control system, and relates to a logic control system for instantaneous flow of a continuous fluid conveying node, comprising: a signal acquisition module for acquiring a discrete flow signal sequence, a trend deduction module for extracting flow evolution characteristics based on a difference algorithm and generating a leading prediction flow parameter through Taylor series truncation operation in combination with a constant term representing pipe network lag; and an adjustment rhythm solving module for solving a parameter hedging instruction according to a deviation amount of the leading prediction flow parameter and a target value. The application hedges physical phase lag induced by fluid medium movement inertia through a leading prediction mechanism, eliminates time sequence misplacement of adjustment action and flow state evolution, cooperates with a logic degradation operator for discrete variance fluctuation to suppress high-frequency noise amplification effect, and ensures closed-loop adjustment stability of the conveying node under nonlinear complex disturbance working conditions.

The invention discloses a frequency tracking method, system and device of an ultrasonic therapeutic apparatus and a medium, and belongs to the technical field of ultrasonic therapeutic apparatus control. The method comprises the following steps: monitoring a phase difference between a current signal and a voltage signal of the transducer; when the phase difference deviates from a preset threshold value, executing the following operation to determine the driving frequency of the transducer; determining an optimal solution through a particle swarm optimization algorithm; based on the optimal solution, determining a driving increment of the driving frequency through a proportional integral differential algorithm; and determining the driving frequency of the transducer based on the driving increment and the current driving frequency. And the value of each parameter in the PID algorithm is determined through the particle swarm optimization algorithm, so that the problem that the resonant frequency is difficult to lock quickly due to the fixed parameter value in the existing PID algorithm is solved.

This invention discloses a method and system for deduplication, archiving, and traceability management of cloud documents for office knowledge bases, relating to the field of semantic processing technology. The method and system include the following steps: S1, event-driven data collection of document objects to obtain the original dataset and perform normalization processing; S2, generating multi-layer fingerprints, performing candidate duplicate retrieval and alignment, and evaluating archiving duplication; S3, generating differential units and evaluating intent strength to construct an indexing strategy; S4, constructing a version causal relationship network, generating archiving evidence packages, and performing consistency verification. This invention effectively improves the accuracy of deduplication and archiving of office knowledge base cloud documents, archiving throughput efficiency, and version traceability reliability, solving the problem that document version differential algorithms generate redundant difference descriptions for complex editing operations and are difficult to reverse merge.

This invention relates to the field of die casting process control technology, specifically to a temperature control method and system for die casting, comprising: acquiring real-time multi-source temperature data of the die casting process; performing noise filtering and timestamp alignment on the real-time multi-source temperature data to form time-series data of the die casting temperature field; dynamically extracting key temperature feature vectors through spatial consistency verification; inputting the vectors into an improved temperature field prediction model, the temperature field prediction model being based on a finite difference algorithm, discretizing Fourier's law of heat conduction into difference equations between grid nodes to establish a physical foundation model, and superimposing a deep learning network on the model to output residual correction quantities, thereby predicting the evolution of the mold and casting in future time periods; establishing a dynamic optimization control model; generating an optimized set of control parameters for each heating and cooling circuit of the mold and the molten metal heat preservation device; and calculating and issuing dynamic temperature control commands to the actuator based on the optimized set of control parameters.

The application discloses a trajectory optimization method for a parallel robot based on an improved dream optimization algorithm, and relates to the technical field of trajectory optimization of parallel robots, aiming to obtain a smooth running trajectory of a robot in a high-speed running process and improve the motion performance of the parallel robot in the high-speed running process.The application constructs a target function according to the total time and total impact required when the parallel robot runs according to a specified trajectory, constructs a constraint condition according to the running requirements of the speed and acceleration of the parallel robot, initializes a population of the dream optimization algorithm by means of a good point set and reverse learning, improves a dream sharing strategy by adopting a differential algorithm, and improves the local optimization ability of a crossover mutation algorithm, thereby improving the quality of a solution and obtaining an optimal solution of the target function.The target function takes into account the running time and impact of the robot, improves the running speed of the robot, and guarantees the stability of the running process of the robot.

The invention relates to the technical field of industrial automation control, in particular to an industrial process closed-loop feedback adjustment method based on instrument operation data deviation, and the method comprises the steps: calculating an inherent lag parameter through cross-correlation analysis, and constructing a phase alignment reference instruction sequence of time sequence alignment; calculating an excitation intensity index of the instruction by using a sliding window, and calculating a response intensity index of the measured value based on a second-order difference algorithm; constructing a nonlinear coupling model to quantify an excitation response mismatch index; and dynamically adjusting a feed-forward gain based on the excitation response mismatch index, and introducing an instruction trend correction term to generate a phase-advanced reconstruction feedback signal. On the premise that hardware cost is not increased, fault features are accurately recognized, data phases are repaired in real time, and closed-loop control stability is guaranteed.

The present application relates to a kind of LED wire bonding point cloud classification methods based on deep learning, comprising the following: obtaining target LED three-dimensional point cloud;Respectively, the first feature vector is obtained by the feature extraction based on PointNet to the target LED three-dimensional point cloud, and the second feature vector is obtained by the normal vector estimation based on normal differential;The first feature vector and second feature vector are spliced, and the complete point cloud feature is obtained;Based on the complete point cloud feature, the action is carried out through fully connected network, and point cloud classification is completed.The normal differential algorithm is used to estimate the normal vector of point in the present application, and the vector is fused as the feature vector of point into the deep learning network based on PointNet, which improves the accuracy of point cloud classification.The classified and extracted wire bonding point cloud can be further used for three-dimensional defect detection, so as to realize and improve the accuracy of LED wire bonding defect detection with three-dimensional characteristics.

The invention provides a three-dimensional transient thermal simulation method and device, and relates to the technical field of thermal conduction. The three-dimensional transient thermal simulation method comprises the following steps: establishing a three-dimensional physical model of a real object, and determining an internal region and a boundary region of the three-dimensional physical model; determining a first spatial derivative of the internal region by adopting a high-order center finite difference method, and determining a second spatial derivative of the boundary region by adopting a method of combining the high-order center finite difference method and a high-order non-center finite difference method; an explicit generalized high-order finite difference algorithm is adopted, based on the first space derivative, the second space derivative and the first transient temperature of the real object at the first moment, the second transient temperature of the real object at the second moment is determined, and the time step length between the second moment and the first moment is determined in a self-adaptive mode according to the constraint condition; and iteratively executing the step of determining the second transient temperature, and performing three-dimensional transient thermal simulation on the real object according to iteration results at different moments to obtain a three-dimensional transient temperature field of the real object.

The application discloses a method for improving time series prediction effect, integrates and accumulates a certain length of historical time series, or integrates and accumulates after a certain algebraic treatment, and is called a historical integral sequence; a group of fixed length historical integral sequences are used as prediction model inputs, and a sequence of one or more time points is output after calculation of the prediction model, and is called a future integral sequence prediction value; the future integral sequence is subjected to difference calculation to obtain a future time series prediction value. The application is used for realizing faster modeling and more accurate prediction in a time series prediction process. In combination with an integral difference algorithm and a deep learning model, the method reduces model complexity caused by multiple sub-sequences, has clear physical meaning, and improves the accuracy of time series prediction.

The invention discloses an intelligent garbage classification method based on multi-modal sensing and AI recognition, and the method comprises the steps: obtaining the information of a person approaching equipment, carrying out the real-time analysis through a lightweight neural network model, and recognizing and distinguishing a real user from an environment disrupter; continuously comparing the difference between the current scene image frame and the reference scene by adopting a background difference algorithm, judging whether a release behavior exists or not, and awakening the gravity sensor to enter a standby state when the effective release behavior is judged; collecting image data of the thrown object; collecting weight data of the thrown object; collecting spectral data of the thrown object; inputting the three types of modal data into a D-S evidence theory model and outputting a weighted probability distribution vector containing all garbage categories; a three-level confidence policy is adopted to execute delivery, and a data packet containing twelve-dimensional features is generated in each decision. According to the invention, the operation efficiency of the whole garbage classification and recovery is improved, the daily average handling capacity reaches 800 times, the throwing response time is shortened to 0.5 second, and the number of clearance vehicles is reduced by 40%.

The invention provides a differential package generation method, a differential upgrading method, electronic equipment and a vehicle, and relates to the technical field of vehicle end software upgrade.The differential package generation method is applied to a server and comprises the steps that an identification file sent by a client is received, and the identification file comprises first identification information corresponding to each storage partition in the client; for each storage partition, determining whether the first identification information and the second identification information corresponding to the storage partition are consistent or not; wherein the second identification information is calculated and determined by the server side according to a configuration source file corresponding to the storage partition on the server side. If the first identification information is inconsistent with the second identification information, calculating a difference package through a difference algorithm according to a target upgrading file corresponding to the configuration source file, storing a preset default value in a header file of the difference package, and sending the difference package to the client, so that the client performs difference upgrading after receiving the difference package, and the client performs upgrading according to the difference package. And the success rate of differential upgrading is improved.

The application discloses a kind of electromagnetic valve characteristic parameter acquisition method and system.The disclosed scheme is based on second-order difference algorithm to decouple wave peak and wave trough search and screening process, ensure that only single factor influences the search of wave peak and wave trough characteristic point at the same time, and adopt the method of data mapping, fill the gap between original data and filtered data, so that the electromagnetic valve characteristic parameter is extracted from original data in accurate way, solve the time misjudgment problem caused by phase shift and signal characteristic reduction.

The invention discloses a student career planning management method and system based on big data, and relates to the technical field of education big data analysis, and the method comprises the steps: collecting multi-source data of students, constructing a weighted feature matrix, carrying out the dimension reduction processing through employing UMAP, dividing a gray scale image into three classes through employing a method for maximizing the inter-class variance among the three classes, defining the variance in a weighted cluster, and carrying out the calculation of the multi-source data. Carrying out iterative optimization by using an improved difference algorithm; constructing an adjacent matrix, distributing an initial community, optimizing community division by using an F2 modular function, deducing a dimension reduction tree by using a single cell trajectory to generate a pseudo time trajectory, and constructing a dynamic potential portrait; a weighted feature matrix is combined with a UMAP dimension reduction technology to process multi-source high-dimensional data, clustering of a three-class inter-class variance method and a DBSCAN clustering algorithm is maximized, a single cell trajectory inference technology dimension reduction tree is used to generate a pseudo time trajectory, a dynamic potential portrait of a student is constructed, and accurate evaluation of the system on the development potential of the student is enhanced.