119results about How to "Avoid error accumulation" patented technology

The invention discloses a child massage teaching auxiliary system based on artificial intelligence, and particularly relates to the technical field of massage auxiliary teaching, a Kalman filtering technology is adopted to effectively filter out large-amplitude interference in hand motion data, time-frequency analysis is carried out in combination with fast Fourier transform or wavelet transform, and frequency domain characteristics of microfibrillation signals are accurately captured; through fine micro-tremor signal analysis, noise interference is removed, the fine change of micro-tremor of the hands of a student is accurately identified, and the evaluation precision of the massage action stability is remarkably improved; and the integrated learning progress management module automatically adjusts teaching contents according to the learning progress and skill mastering conditions of the students, provides real-time feedback and forward excitation, provides excitation when the students have good performance, prompts and optimizes follow-up actions in real time when deviation occurs, helps the students to continuously improve skills, avoids error accumulation, and effectively improves the teaching effect.

The invention relates to the technical field of spectral analysis, and discloses a glucose solution concentration detection method and system based on a near infrared spectrum. The method comprises the following steps: acquiring transmission light intensity of a sample and a reference liquid by using a narrow-band near-infrared LED light source, calculating absorbance characteristics, screening a dynamic wavelength set based on sensitivity, and constructing a concentration prediction model to realize accurate detection of concentration. Compared with the prior art which depends on single wavelength or lossy detection, especially under the condition of temperature and humidity fluctuation or unstable light source power, the technical problem that high-precision concentration measurement cannot be realized is solved. High-precision glucose concentration detection under complex environmental conditions is realized, so that the problem of error accumulation caused by environmental interference is avoided, the detection precision, stability and environmental adaptability of the system are improved, and the reliability and accuracy of glucose solution concentration detection are improved.

The invention discloses a lithium battery combined health state estimation and service life prediction method, which comprises the following steps of: 1, predicting the health state and the residual service life of a lithium battery by carrying out pulse test on the lithium battery and establishing a Thevenin model at the aging normal operation stage of the lithium battery; 2, after the lithium battery enters a deep aging stage, performing an EIS test on the lithium battery to obtain impedance spectrum data, and correcting the Thevenin model into a variable order RC model; and 3, estimating the health state of the deep aging stage by using the impedance spectrum data and parameters identified by the variable order RC model, and when the prediction error of the two parameters is smaller than an error threshold value, considering that a battery health state and residual life prediction model suitable for the deep aging stage is successfully established. Otherwise, adjusting the order of the variable-order RC model to reduce the error. According to the invention, the reliability of lithium battery life prediction and estimation in a complete life cycle can be ensured, so that the control complexity and cost of a test working condition can be effectively reduced.

The invention discloses a same commodity normalization method and system based on a large model, a storage medium and computer equipment. The method comprises the following steps: obtaining commodity titles and self-filling commodity codes corresponding to a plurality of commodities; clustering the commodity titles to determine a commodity class, determining a target commodity code corresponding to the commodity class according to the self-filling commodity code of each commodity in the commodity class, and filtering the commodities, of which the self-filling commodity codes belong to a commodity code cluster, in each commodity of the commodity class according to the commodity code cluster to which the target commodity code belongs, commodities corresponding to the commodity codes in the commodity code cluster are the same; and determining a first to-be-normalized commodity of which the self-filling commodity code does not belong to the commodity code cluster in each commodity of the commodity class, and according to a commodity title corresponding to the first to-be-normalized commodity and a standard title in a standard library, performing same-commodity normalization on the first to-be-normalized commodity through a large model so as to determine a correct commodity code corresponding to the first to-be-normalized commodity. And efficient and accurate same-product normalization of the commodities is realized.

The invention discloses a crank guide rod transmission pneumatic needle suction type small-particle-size bean sprout seed automatic sowing device which comprises a vacuum air chamber, a swing air cylinder, a seed conveying pipe, a guide rod, a fixing frame, a pneumatic vibration seed disc and a seedling disc. The device adapts to seeds of different sizes, thicknesses and shapes, and adsorption failure or seed damage is avoided; according to the shape characteristics of irregular leaf-shaped seeds, the specific suction needle caliber, hole shape and air flow distribution are designed, the adsorption efficiency is improved, and it is ensured that the seeds can be stably adsorbed; an airflow channel is optimally designed by virtue of airflow dynamics, so that uniform acting force on the leaf-shaped seeds in the adsorption process is ensured, falling or dislocation of the seeds caused by non-uniform adsorption positions is avoided, the seeding requirements of seeds of different types, sizes and shapes are met, and the universality and the service life of equipment are improved.

The invention relates to the technical field of air conditioner copper pipes, in particular to a multi-station synchronous correction control system for an air conditioner copper pipe. Comprising a copper pipe straightening unit used for straightening a to-be-treated copper pipe; the detection unit is used for collecting structural parameters of the to-be-treated copper pipe; the central control unit comprises a first processing module and is used for establishing a copper pipe analysis model; the first processing module is further used for setting a primary correction strategy according to the structure parameters of the copper pipe to be processed and the copper pipe analysis model; the first processing module is further used for setting working parameters of the copper pipe correction unit according to the primary correction strategy; the second processing module is used for establishing a linkage monitoring model; the second processing module is further used for judging whether a compensation correction instruction is generated or not according to the linkage monitoring model. By analyzing the structural parameters of the to-be-treated copper pipe, the correction efficiency of the system on the air conditioner copper pipe is improved. And a linkage monitoring model is additionally arranged, local deviation in the correction process of the to-be-treated copper pipe is early warned in time, and error accumulation is prevented.

The invention belongs to the technical field of remote sensing image processing, provides a remote sensing image blocking effect elimination method fusing adaptive weight and an anchoring mechanism, and aims to eliminate the blocking effect from a global level, take radiation consistency as a constraint condition, introduce the adaptive weight to carry out differential adjustment on constraint intensity of different overlapped regions, and improve the blocking effect elimination efficiency. Meanwhile, an anchoring mechanism is adopted, stabilization constraint is carried out on an overall solution space, finally, a global equation set is constructed, an iterative least square optimization strategy is used for solving, image radiation consistency and visual continuity are integrally ensured, and the quality of remote sensing images is improved.

The invention discloses an automatic control device for the press-fitting height of a pin shaft, which relates to the technical field of component control devices and comprises an upper mounting seat, wherein the bottom of the upper mounting seat is rotatably connected with a shaft rod, the bottom of the shaft rod is fixed with a mounting disc, and a positioning seat for placing the part to be press-fitted is arranged below the mounting disc; a punching component is mounted on the mounting disc, and the punching component is used to punch holes in the part to be press-fitted; a pressing component is also mounted on the mounting disc, and the pressing component is used to place and position the pin shaft; a measuring component is also mounted on the mounting disc, and the measuring component is used to measure the length of the pin shaft extending out of the part to be press-fitted; a conversion component is mounted on the bottom of the upper mounting seat, and the conversion component is used to drive the mounting disc to rotate, so as to switch the punching, pressing and measuring processes, and drive the operation of the punching component, the pressing component and the measuring component; the error accumulation caused by the difference in equipment accuracy between different processes is effectively avoided, the excess tolerance rate is reduced, and the error adjustment becomes simple and quick.

This invention discloses a navigation method and system for offshore wind power booster stations based on multimodal information interaction, belonging to the field of artificial intelligence and robot navigation technology. It achieves visual-language modality alignment through the CLIP model, constructing an end-to-end navigation parameter generation model. This model can adaptively generate AGV motion parameters based on natural language commands and scene images, enabling efficient and accurate inspection of offshore wind power booster stations. By establishing a multimodal dataset and associating language commands with AGV motion parameters, this invention solves the error accumulation problem caused by multi-stage processing in traditional methods, improving navigation accuracy and adaptability. Furthermore, this invention employs a multi-task loss function optimization model, including contrastive loss and motion parameter regression loss, enabling the AGV to adaptively adjust motion parameters according to environmental changes, thus improving the efficiency and safety of offshore wind power booster station inspections.

The present application relates to the technical field of precipitation forecast, solves the problem that the prior art is prone to underestimate the peak value of precipitation or produce false alarm under strong convective weather, and particularly relates to a short-impending precipitation forecast method fusing GNSS and water conservancy rain measurement radar, GNSS observation data and water conservancy rain measurement radar base data in a monitoring area are acquired, pretreated, corresponding atmospheric precipitable water distribution field and radar reflectivity factor distribution field are generated, based on a preset time sliding window, time sequence characteristics of the atmospheric precipitable water distribution field and the radar reflectivity factor distribution field are extracted, and the two are superimposed in channel dimension to construct a multi-channel spatio-temporal fusion input tensor. The present application overcomes the defect that the traditional extrapolation method rapidly decays with the prolongation of the forecast time, significantly improves the prediction accuracy of the rainstorm center intensity and the falling area, reduces the missed report and underestimation of the precipitation event, and solves the problem of low prediction accuracy of the traditional linear extrapolation algorithm under strong convective weather.

The invention relates to the technical field of fiber bragg grating inscribing, in particular to a reflectivity-based real-time monitoring inscribing single-wavelength continuous weak grating system, and a to-be-inscribed fiber is used for bearing the inscribed fiber and executing the inscribing task of the grating; the light path inscribing system is used for inscribing a single-wavelength weak grating on the optical fiber to be inscribed; the broadband light source is used for detecting reflected light of the single-wavelength weak grating; the precise 3D transmission platform is used for fixing and moving an optical fiber to be inscribed to a continuous inscribing position; the spectrograph is used for measuring spectral data reflected by the optical fiber; the data collector is used for collecting reflected optical signal data in real time and carrying out signal processing and analysis; the display terminal is used for displaying reflected spectral data and grating inscribing quality information in real time, the accuracy and efficiency in the optical fiber single-wavelength weak grating inscribing process are improved through a dynamic feedback mechanism, continuous grating inscribing is supported, and meanwhile high inscribing quality and high inscribing efficiency are met.

The invention provides a side wall positioning and mounting method and device for an assembly trolley of an overlapped assembly type subway station, and belongs to the technical field of underground engineering construction. The method comprises the following steps: an environment perception and initial positioning step: scanning a construction environment, and determining spatial positions of a side wall and an H-shaped steel groove relative to a preset BIM model through a target identification and point cloud registration technology; a transverse moving and coarse positioning step: planning a path based on a positioning result, and controlling the assembly trolley through real-time pose estimation and visual tracking to enable the side wall to move to a position above the target; an accurate positioning and leveling step: obtaining deviation and real-time attitude through visual detection and multi-sensor fusion technology, and driving an adjusting mechanism to carry out accurate alignment and leveling; a gap detecting and filling step: after the side wall falls in place, measuring the shape of the gap and guiding filling construction; according to the invention, global absolute position calibration is realized, error accumulation is avoided, construction safety is ensured through non-contact measurement, and positioning precision and reliability are ensured by using real-time feedback control.

The invention discloses a strip network railway base station data processing method and system. The method comprises the following steps: obtaining observation data of a plurality of IGS base stations; obtaining observation data of a plurality of standard reference stations in a preset range around the strip network railway reference station, wherein the standard reference stations comprise a national station, a provincial station and a Chinese tectonic environment monitoring network station; and based on the observation data of the IGS reference station, combining the observation data of the standard reference station to perform hierarchical calculation on the plurality of railway reference stations to obtain space coordinate data of each railway reference station. Observation data of an IGS base station and standard base stations such as surrounding national stations and provincial stations are obtained, and a rich data basis is provided for railway base station coordinate measurement and calculation. A hierarchical resolving mode is adopted, the coordinate precision is improved, error accumulation is avoided, and the result reliability is enhanced; the method can adapt to different precision requirements and complex environments, considers local differences, and meets the requirement of a strip network railway for high-precision coordinate data.

The application provides a pressure regulating hydrogen storage device, a pressure regulating hydrogen storage method and a composite hydrogen delivery system, which can deliver hydrogen under pressure for a long time and meet the filling demand of hydrogen for hydrogenation load with large capacity. The pressure regulating hydrogen storage device comprises a hydrogen delivery pipeline, a pressure regulating hydrogen storage unit and a temperature regulating liquid bath unit. The pressure regulating hydrogen storage unit comprises a first pressure regulating hydrogen storage tank group filled with a first metal hydride hydrogen storage material and a second pressure regulating hydrogen storage tank group filled with a second metal hydride hydrogen storage material, which are sequentially connected in series along the hydrogen delivery direction of the hydrogen delivery pipeline. The platform pressure of the first metal hydride hydrogen storage material at room temperature is less than that of the second metal hydride hydrogen storage material at room temperature. The temperature regulating liquid bath unit comprises a first temperature regulating bath chamber containing the first pressure regulating hydrogen storage tank group, a second temperature regulating bath chamber containing the second pressure regulating hydrogen storage tank group and a temperature regulating liquid delivery tank in circulation communication with the first temperature regulating bath chamber and the second temperature regulating bath chamber, respectively.

The embodiment of the invention provides a multi-site collaborative electromagnetic spectrum situation prediction method based on multi-source data fusion and an attention mechanism, and the method comprises the steps: 1, collecting spectrum observation data, spatial data and meteorological data of multiple sites, and carrying out the data preprocessing; step 2, based on prior knowledge and GIS data, calculating a theoretical field intensity value of a known emission source at each monitoring station by using a radio wave propagation model; step 3, constructing a multi-branch fusion neural network MFNN based on 3D-CNN, LSTM, GCN and MLP, extracting joint feature representation of multi-source data in the step 1 and the step 2, and introducing an adaptive attention mechanism to perform weighted fusion on each branch feature; and 4, generating a predicted value of a future frequency spectrum state through a prediction output layer, and performing data inversion to obtain a physical power value to realize channel state inference. Structured information capable of directly driving a decision is output through data inversion and channel state inference.

This invention provides a nonlinear time-series estimation method for the state of health (SOH) of lithium batteries, comprising: collecting feature data during the charge-discharge cycle of the lithium battery and generating a multi-channel standardized time-series dataset; extracting features and compressing dimensions of the data through an encoder; reconstructing and denoising the data through a decoder to output sequence features; mining the global correlation between feature sequences to obtain a context vector; further obtaining the SOH estimation result and synchronously updating the parameters. This method effectively suppresses model overfitting while introducing an improved KL divergence to prevent posterior collapse, achieving high-precision and robust estimation of the SOH of lithium batteries.

The application discloses a large language model test learning method, device, equipment and medium, test data is input into a large language model with low rank adaptive parameters, the prediction score of each token in the vocabulary at each generation step is obtained; the token-level evidence quality of the generation step is extracted based on the prediction score, and the token-level cognitive uncertainty of the generation step is calculated; the token-level cognitive uncertainty is smoothed, the stable area of the generation step is determined according to the smoothed uncertainty, and the mask of the model update window is constructed; the mask entropy loss is calculated based on the mask, and the low rank adaptive parameters are updated through back propagation. The application can solve the problem of adaptive drift in model updating in related technologies.

The invention discloses an event camera video reconstruction method and system based on active aperture modulation, and the method comprises the steps: introducing an aperture modulation strategy for the first time, and reconstructing an initial frame with good quality by periodically adjusting the opening and closing of an aperture and actively triggering a dense global event signal; the problem of low background reconstruction quality caused by sparse events in a static region in the prior art is solved, and good prior information is provided for subsequent dynamic scene reconstruction. By constructing a forward-reverse bidirectional network, rich intensity information is provided for a static scene, and the problems of background disappearance and error accumulation in long-time operation in a traditional method are effectively solved. And meanwhile, high-time-resolution capture of a dynamic region is kept, and high-fidelity and high-dynamic-range video reconstruction is realized.

The application relates to the field of wind power prediction, and discloses a wind power prediction method and system based on DIRMO and a differentiated target function. In order to solve the defects that a single model is used for prediction in the existing multi-step power prediction, time series modeling and feature processing cannot be simultaneously considered, the prediction accuracy is reduced, the model robustness is insufficient, and the search efficiency is low, the power and wind speed data after preprocessing are denoised, normalized and feature-extracted to obtain multi-scale features; the normalized power and wind speed data are subjected to time series modeling to generate a preliminary prediction result in the future; the multi-step prediction task is divided into several groups, and a multi-output problem is converted into a single-output problem for training and prediction; the hyperparameters of a LightGBM model are automatically optimized, the optimized hyperparameters are used for training the LightGBM models of the groups, and the final power prediction is carried out based on the preliminary prediction result of the GRU and the multi-scale features. The application is mainly used for predicting wind power.

This invention discloses a method and apparatus for predicting the performance degradation of integrated circuit power supplies, relating to the field of integrated circuit technology. The method includes: smoothing and denoising the first health index data of the collected sample power supply using a Kalman filter algorithm to obtain a historical health index dataset; truncating the historical health index dataset into multiple first data sequences of preset window lengths, determining the true remaining service life label corresponding to each first data sequence, and generating a supervised learning dataset; determining the initial parameters of an LSTM-Attention prediction model based on a particle swarm optimization algorithm and the supervised learning dataset; iteratively training the model to obtain a target LSTM-Attention prediction model; and predicting the remaining service life of the collected second health index data of the power supply to be monitored based on the target LSTM-Attention prediction model, enabling early warning and switching processing of the power supply before a serious fault occurs.

The invention discloses a multifunctional carrying device and method for a building damper, the device comprises a fixing unit, a carrying unit and an auxiliary unit, the carrying unit comprises a moving part, a supporting part arranged on the moving part, a traction part for carrying the damper and a lifting part for controlling the carrying device to ascend and descend; the traction component comprises a platform plate, a haunching plate, a hinge and a folding plate, one end of the folding plate is connected with the hinge, the lifting component comprises a lifting plate, an adjusting component, a movable rod and a connecting rod, the adjusting component is used for being inserted into a gear hole to adjust the lifting plate, one end of the movable rod is arranged on a nut, the other end of the movable rod is arranged on the lifting plate, and the connecting rod is arranged in the horizontal direction of a screw. The lifting component is in butt joint with the damper to be carried and integrally matched with the traction component, and carrying of the damper is achieved. Meanwhile, the tedious process that in the traditional installation process, the folding plate needs to be lifted and transported manually and effortlessly to be aligned with the installation position is simplified, and error accumulation caused by too many intermediate links is also avoided.

The utility model relates to mechanical processing technical field, concretely is a kind of self-adapting shaft sleeve inner and outer circle synchronous processing device, including rack, main shaft system, inner circle processing unit and outer circle processing unit, the rack is used to support overall structure;Main shaft system is installed on rack, include shaft sleeve chuck and driving equipment, wherein driving equipment is made of case, driving motor, speed reducer, transmission wheel and driving wheel, workpiece rotation is realized by belt drive, inner circle processing unit includes adjusting track assembly one and milling cutter one, adjusting track assembly one is made of advancing track one, offset track one and lifting track one, cooperation gear drive device and screw drive device realize three-dimensional accurate positioning;Milling cutter one is equipped with cylinder and milling cutter, can complete high-precision inner circle processing, outer circle processing unit structure symmetry, including the same adjusting track assembly two and milling cutter two of structure, for synchronously completing the processing of shaft sleeve outer circle.

This invention discloses a radar echo extrapolation method based on dynamic weight loss, comprising: (1) constructing a dynamic loss function based on the prediction results of an existing short-term forecast model, wherein the existing short-term forecast model is a model that uses radar echo extrapolation to predict precipitation intensity; (2) collecting radar echo data and establishing a radar echo extrapolation dataset, dividing the radar echo extrapolation dataset into a training set and a validation set according to a certain ratio; (3) inputting the training set data into the short-term forecast model for model training, obtaining the dynamic loss function to optimize the short-term forecast model, and inputting the validation set data into the optimized short-term forecast model to obtain the prediction results. This invention effectively improves the prediction performance of current mainstream deep learning models based on radar data and effectively alleviates the cumulative error generated by existing radar echo extrapolation methods.

The invention provides a robot control method and device based on audio driving and a robot, and relates to the technical field of robot motion control. The method comprises the following steps: acquiring audio data, and extracting audio features of the audio data; inputting the audio features into a pre-trained audio motion alignment module for audio motion alignment processing, and outputting audio style latent variables subjected to motion prior alignment; obtaining a motion content latent variable corresponding to the current task semantics; inputting the audio style latent variable and the motion content latent variable into a pre-trained diffusion student strategy network, and generating a joint action instruction of the robot; and sending the joint action instruction to a joint driver of the robot so as to drive the robot to execute an action synchronous with the audio data. According to the embodiment of the invention, through end-to-end direct mapping, multi-stage error accumulation is avoided, and high-fidelity and low-delay synchronous control of the robot action and the audio data is realized.

The application discloses a renewable energy power generation uncertainty tracking method based on a recursive Gaussian mixture model, comprising the following steps: 1, obtaining a deterministic prediction curve of renewable energy power generation, forming a joint observation set containing predicted and actual power generation; 2, setting the cluster number as k, performing a k-means algorithm to obtain k clusters; 3, based on the k clusters, calculating initial parameters of each Gaussian component in the Gaussian mixture model; 4, updating the Gaussian mixture model parameters by using an expectation maximization algorithm to obtain starting optimal parameters; 5, constructing a recursive updating probability model, and based on newly-arrived samples, recursively updating the probability model; 6, using the updated Gaussian mixture model, calculating the conditional probability distribution of the actual power under the condition of the latest predicted power, so as to realize real-time quantification and tracking of the renewable energy power generation uncertainty.

The invention provides a document image correction method and device based on depth guidance, equipment and a medium, and relates to the technical field of document image correction, and the method comprises the steps: obtaining a color image and a depth image of a to-be-corrected document; splicing the color image and the position prompt image to form a multi-channel input tensor; inputting the multi-channel input tensor into the corrective neural network model to obtain a dense displacement field; wherein the correction neural network model comprises a plurality of cascaded geometric attention blocks, and each geometric attention block is configured to generate a rotation position code and a decomposition attention mask of depth perception based on the depth image, and perform geometric self-attention calculation of row-column decomposition on the feature map by using the code and the mask; and finally, sampling the color image according to the displacement field to generate a corrected document image. Geometric modeling is guided through depth information, a row-column decomposition attention mechanism is adopted, the complex deformation correction precision is remarkably improved, the calculation complexity is greatly reduced, and high efficiency and robustness are achieved.

The present application relates to the technical field of underground engineering ventilation and heat and humidity environment control, and discloses a tunnel heat exchange real-time prediction method and system based on physical guidance machine learning, comprising: obtaining standardized simulation data related to tunnel heat exchange and target tunnel measured data and preprocessing to obtain first time series data; based on air enthalpy and annual cycle, day cycle time characteristics, the first time series data is subjected to physical enhancement feature construction to obtain model input features including encoder input features and decoder input features; an LSTM-Seq2Seq-Attention prediction model is constructed, and a two-stage training strategy of simulation data pre-training and target tunnel measured data fine-tuning is used to train the prediction model; the trained prediction model is used to output tunnel outlet temperature and humidity prediction results at multiple future time points at one time; according to the comparison result of the prediction result and the same period measured data, effective measured samples are screened to obtain a model update data set, and the prediction model is periodically fine-tuned and dynamically updated.

The application discloses a seal identification method, device and equipment and a storage medium. The method comprises the following steps: performing feature identification on a to-be-identified image to obtain first associated information of a seal, wherein the first associated information comprises seal text features and seal position information; encoding the first associated information to generate second associated information of the seal, wherein the second associated information comprises encoded seal text features and seal position information; and generating a seal identification result according to the second associated information. The seal text features and the seal position information are obtained by performing feature identification on the to-be-identified image, the second associated information of the seal is generated by encoding the seal text features and the seal position information, and finally, the seal identification result is generated by decoding the second associated information. The method does not require manual participation, reduces the workload of manual work, reduces the maintenance cost, avoids error accumulation, improves the seal identification efficiency, and improves the accuracy of the generated seal identification result.

The utility model relates to the technical field of material distributing devices, and provides an automatic sorting device for an automatic detector, which comprises a first collecting bin and a second collecting bin which are oppositely arranged, the material distributing part is rotationally arranged between the first collecting bin and the second collecting bin and provided with a first part and a second part which are arranged in a lifting mode, and a containing groove is formed between the first part and the second part and used for providing support for materials; the material distributing piece is provided with a first working position and a second working position, and the first working position is used for receiving materials; when the material distributing piece rotates to the second working position, if the first part descends, the materials fall into the first collecting bin, and if the second part descends, the materials fall into the second collecting bin; compared with a traditional sorting mode depending on a multi-degree-of-freedom mechanical arm or a clamping structure, the automatic sorting mode for the materials has the advantages that the operation steps of grabbing, moving, releasing and the like of the materials are omitted, the treatment period of a single material is effectively shortened, and the operation efficiency of the whole equipment is improved.