85results about How to "Break through limitations" patented technology

The application relates to the technical field of data analysis, and discloses a karst fissure identification-based open-pit mine blasting parameter optimization method and system. The method comprises the following steps: obtaining karst fissure distribution characteristic data through multi-source detection, establishing a three-dimensional fissure network topology structure, analyzing static breaking agent inflation stress propagation rules, performing multi-objective optimization solving based on stress propagation direction and intensity distribution to obtain an initial blasting parameter combination, and obtaining dynamic adjustment blasting parameters through real-time tracking and regulation according to fissure dynamic evolution in the blasting process. The application solves the problem that static blasting parameters cannot be real-time regulated according to the dynamic evolution state of the fissure network in the blasting process, resulting in deviation between the blasting effect and the expected target.

This application discloses an automatic sauerkraut filling machine in the field of food packaging technology, including a base, a support column, a top seat, and multiple filling components. The support column is connected between the base and the top seat. The base has a notch, and a packaging machine is connected to the notch. The packaging opening of the packaging machine is located at the notch, and a packaging bag is connected to the packaging opening. A motor is located at the bottom inner part of the base, and the output shaft of the motor is connected to the support column. A turntable is connected to the top of the base and is connected to the support column. Multiple filling components are all located on the turntable. Multiple drive components are connected to the support column to drive the filling components from the turntable through the support column to the bottom of the top seat. Each filling component includes a slider, a connecting column, and an electric telescopic rod. The slider is connected to the turntable and is connected to the drive components. A locking component for fixing sauerkraut is connected to the slider. The connecting column is connected to the top of the slider, and the electric telescopic rod is connected to the top of the connecting column.

The utility model discloses a kind of tablet number grain filling devices, belong to pharmaceutical equipment technical field, driving mechanism is installed on tray, the output end of driving mechanism is connected with measuring disc through tray axis, tray is obliquely set on base by supporting mechanism, a plurality of tablet discharge ports are provided on measuring disc, a plurality of circumferential array arranged medicine containing area are provided on measuring disc, the position of medicine containing area corresponds with tablet discharge port position, scraper is connected with tray by bunker fixing block, scraper is attached to measuring disc, barrel-shaped bunker is provided on tray. The present application adopts obliquely set tray and matched measuring disc, scraper, can accurately control tablet quantity, improve production accuracy. Dust suction port and broken tablet discharge port can clean impurities and broken tablets in time, ensure medicine quality. Driving mechanism stably provides power, ensure that device continues to operate.

The application discloses an in-situ self-generating ceramic particle reinforced low-tungsten-content composite material, and a preparation method of the in-situ self-generating ceramic particle reinforced low-tungsten-content composite material.The low-tungsten-content composite material is composed of the following components in percentage by mass: W 59% to 69%, Ni 21% to 28%, Fe 9% to 12%, and ceramic reinforcing phase 1%.The tungsten content in the material system is greatly reduced, the toughness of the bonding phase is significantly increased, the plasticity and processing performance are improved, the W atoms in the bonding phase are difficult to diffuse to the surface of other W grains during the sintering process, the long-range migration is blocked, the W grain growth is effectively inhibited, the size of the in-situ generated ceramic particles is small, the distribution is uniform, the interface between the metal matrix and the ceramic particles is pure and firm, the strong dispersion strengthening and fine-grain strengthening effects can be produced, and the application of the low-tungsten-content composite material is suitable for the fields of aerospace, radiation shielding and precision instruments.

This invention discloses a sequencing technique for enriching mutations after library construction. The co-priming amplification (Co-PCR) technique of this invention couples a mutation probe to a short P5 / P7 primer. This short primer has a low melting temperature (Tm) and cannot hybridize and amplify on its own at the reaction temperature. Only when the mutation probe hybridizes to the mutation site can it assist the coupled short P5 / P7 primer in hybridizing to the adjacent P5 / P7 sequence at the end of the pre-library, initiating amplification. Therefore, this Co-PCR technique not only amplifies a specific mutation in a specified gene but also amplifies from both ends of the original pre-library, replicating the complete sequence information of the pre-library, including sequencing adapters, indexes, and sequencing primers. The amplified products can be directly sequenced after purification. By optimizing the co-primers, Co-PCR enrichment amplification of five sites in the pre-library was achieved, resulting in nearly 100-fold enrichment.

The present disclosure provides a health information generation method, a storage medium and an electronic device, the method comprising: acquiring multi-modal health data of a user, the multi-modal health data comprising at least: basic information data and physiological information data, the basic information data being used to represent personal basic characteristics of the user, and the physiological information data being used to represent physiological state of the user; determining health suggestion information of the user according to the multi-modal health data; and outputting the health suggestion information. In the health information generation method provided by the present disclosure, the device can generate health suggestion information for the current user based on the real-time acquired multi-modal health data. The fusion analysis of the health data of the user in different modes is realized, and the limitation of only processing a single index in the related technical solutions is broken through, and more real-time, scientific and comprehensive health suggestion information can be provided for the user.

This invention relates to the field of renewable energy output prediction technology, and discloses a method, equipment, and medium for renewable energy output prediction based on multidimensional index correlation analysis. The method acquires historical renewable energy output data and preprocesses the data; establishes a multidimensional index system and performs correlation analysis and screening of the multidimensional indicators to determine key influencing indicators; the correlation analysis includes analyzing the linear and nonlinear relationships between the indicators and renewable energy output; constructs a hybrid prediction model based on convolutional neural networks and long short-term memory networks, using the preprocessed data corresponding to the key influencing indicators as input to train and predict future renewable energy output data; wherein, convolutional neural networks are used to extract the interaction features between multidimensional indicators, and long short-term memory networks are used to capture time-series features. This invention improves prediction accuracy and provides reliable technical support for grid connection of renewable energy with high penetration rates.

This invention discloses a method, device, equipment, and medium for monitoring high physiological stress based on rPPG. First, a video sequence containing the user's face is acquired, and the region of interest (ROI) sequence is extracted and input into a deep learning network model. A spatiotemporal encoder extracts preliminary spatiotemporal physiological feature sequences from these sequences. Then, relying on an adaptive frequency-aware decoder, multi-scale parallel processing and dynamic feature fusion are performed on these preliminary spatiotemporal physiological feature sequences to output feature representations of abnormal high-frequency rhythmic events. Finally, a state determiner, combined with the abnormal high-frequency rhythmic event identification results, directly outputs a binary classification result indicating whether the user is in a state of high physiological stress. This invention effectively solves the problem of abnormal high-frequency rhythmic signal loss caused by existing fixed filters, achieving high-precision identification of abnormal high-frequency rhythmic events and accurately determining the user's state of high physiological stress.

The application provides a kind of mitochondrial function optimization additive for cell culture, preparation method and application, belong to cell culture technical field.The additive includes core active ingredient, auxiliary component and carrier, and the core active ingredient includes D-Arg-Cha-Lys-Phe-NH2 Polypeptide, ubiquinone-10 phosphate, reduced glutathione and alpha-lipoic acid.The application forms a multi-target core active system through the synergy of the four, realizes the triple synergistic protection of stabilizing mitochondrial cardiolipin, maintaining mitochondrial morphology and membrane potential, and reducing mitochondrial ROS, which can significantly improve cell culture quality.When applied, the mitochondrial cardiolipin content can be increased by more than 50%, the mitochondrial abnormality ratio can be reduced to less than 12%, the membrane potential positive cell ratio can be increased to more than 86%, the mitochondrial ROS level can be reduced by more than 40%, and the cell proliferation rate can be increased by more than 25%, which is suitable for in vitro culture of various eukaryotic cells, and the preparation process is simple and has no cytotoxicity.

This invention provides a deep learning-based intelligent analysis method for cardiovascular medical images, comprising the following steps: S1, heterogeneous data fusion and acquisition of multimodal medical images and physiological signals; S2, intelligent extraction and quantitative analysis of vascular structural features based on deep learning; S3, hemodynamic parameter modeling and risk region segmentation based on fused structural features; S4, texture feature mining and intelligent identification of lesion types in high-risk areas; S5, cardiovascular disease risk stratification prediction based on multi-dimensional feature fusion; S6, federated learning optimization and closed-loop update of the model. This invention's deep learning-based intelligent analysis method for cardiovascular medical images achieves, for the first time, heterogeneous fusion of medical images, millimeter-wave radar-PPG physiological signals, and clinical molecular data, and achieves precise spatiotemporal alignment through GPS-controlled crystal oscillators, overcoming the limitations of single image analysis and providing a more comprehensive feature foundation for subsequent feature extraction and risk prediction.

This invention provides a comprehensive carbon blending control system and method for sintering. The system includes a batching detection unit, a mixture detection unit, a sintering process detection unit, an FeO detection unit, and a main control unit. The method monitors multi-dimensional parameters in real time and establishes a multi-dimensional dynamic coupling correction strategy for influencing factors such as total Fe content, moisture, particle size, material layer thickness, and sintering endpoint temperature. Combined with intelligent algorithm prediction, dual-closed-loop collaborative control, and precise compensation of feed rate, the method achieves quantitative output of solid fuel feed rate through a comprehensive calculation formula. This invention achieves dynamic optimization and precise control of carbon blending, stabilizes the FeO content of sintered ore, improves sintered ore quality and blast furnace smelting efficiency, balances energy consumption optimization and cost control, and enhances the automation and intelligence level of sintering carbon blending control.

The present application relates to the technical field of Pickleball, and specifically relates to an intelligent wearable system for monitoring sports posture. The system synchronously collects multi-axis acceleration and angular velocity original signals before and after the player hits the ball through the inertial measurement unit built in the wearable device, generates time series after time window interception and normalization processing; removes noise and baseline drift by wavelet transform; fuses time domain and frequency domain features to construct a high-dimensional comprehensive feature vector; realizes preliminary classification of actions through a long short-term memory network, refines similar action types by combining a convolutional neural network, and finally outputs accurate classification results through a support vector machine; dynamically optimizes model parameters based on a real-time feedback mechanism, and enhances local feature representation through a channel attention mechanism. The system can accurately identify more than fifteen Pickleball special technical actions, has high robustness and self-adaptive ability, can provide real-time posture specification guidance for athletes, and helps to improve training efficiency and prevent sports injuries.

This invention proposes an ADCP echo signal denoising method based on overlapping segmentation DRSN-LSTM-CNN, belonging to the field of deep learning technology. This invention addresses the inherent limitations of existing ADCP signal processing methods when dealing with nonlinear, non-stationary, and low signal-to-noise ratio signals, and the lack of deep adaptation and optimization of existing machine learning methods for ADCP signal characteristics. The steps of this invention include: S1: Segmenting the noisy ADCP signal into noisy segments using a sliding window; S2: Inputting each segment into a DRSN module; S3: Performing a weighted average on the signal output from the DRSN module; S4: Sequentially inputting the signal into a bidirectional LSTM network and a CNN network, outputting the denoised ADCP signal.

This application relates to the field of medical data processing technology and discloses a method and system for screening infertile patients with polycystic ovary syndrome (PCOS). The method includes: acquiring data on the number of antral follicles, anti-Müllerian hormone (AMH) concentration, and ovarian volume from multiple patient monitoring cycles; performing feature extraction and normalization to obtain a set of normalized feature vectors; determining the synchronous relationship between the number of antral follicles and ovarian volume based on the vector set, and identifying signs of asynchronous changes; combining hormone concentrations for joint grouping to obtain the distribution of abnormal indicator clusters and identifying ovarian function stage markers; performing sequence matching based on the stage markers of continuous cycles and historical medical records to generate an ovarian function progression trajectory curve; extracting inflection point features from the curve and performing causal association rule mining to construct a logical chain between abnormal indicators and clinical outcomes; and determining conditions layer by layer according to the logical chain to output the final screening results. This application improves the accuracy of screening infertile patients with PCOS.

A spatial decision-making suggestion generation method and system based on LLM and a geographic knowledge base, relating to the field of data processing technology, is disclosed. This method first establishes a knowledge base containing spatial geometric data of geographic entities. When a user's natural language decision query is received, a large language model is used for semantic parsing to extract the decision objective, spatial entities, and constraints. The system retrieves candidate knowledge from the knowledge base through semantic similarity calculation and evaluates the spatial compliance confidence level of each candidate based on the spatial geometric data and constraints. Subsequently, the candidate knowledge is hierarchically labeled according to confidence level to construct structured text, which is then injected into the large language model through a preset template. Finally, the system generates a spatial decision report based on the injected structured information. Implementing the technical solution provided in this application can improve the accuracy of spatial decision-making suggestions.

This invention belongs to the field of communication and network control technology. To address the problems of poor adaptability in non-ideal observation environments and the inability of a single optimization objective to meet various differentiated service requirements, and to overcome scheduling failures caused by network jitter or node failures leading to data loss, this invention provides: a time-sensitive network traffic scheduling method based on causal data completion. This method collects real-time traffic data from the current time-sensitive network (TSN) and constructs an irregular observation matrix; it uses a state reconstruction module based on neural Granger causality to complete the missing data in the observation matrix; it extracts robust spatiotemporal features with noise resistance from the completed observation matrix using a variational graph attention network; and it inputs these robust spatiotemporal features into a reinforcement learning decision network based on hierarchical optimization. Under the hard real-time constraint of priority critical time-triggered TT flow, it optimizes the best-effort BE flow transmission efficiency, ultimately generating a gated control list for execution. This invention is primarily applied in design and manufacturing applications.

A method for determining the association and continuity combination of top-side-rock structural surfaces based on unsupervised learning is proposed. This method involves investigating the structural surfaces of the roof and side-rock masses in underground engineering projects to obtain geometric and statistical information. A unified encoding is performed to construct a joint feature set of roof-side-rock structural surfaces. An unsupervised density-based clustering algorithm is used for initial cluster analysis of the joint feature set. A swarm intelligence-based Lüperfox optimization algorithm is introduced to optimize key parameters of the DBSCAN clustering algorithm. A Latin hypercube sampling method is used to improve the population initialization process of the Lüperfox optimization algorithm, resulting in better DBSCAN hyperparameters and optimized structural surface clustering results. The method determines the structural surface system affiliation of structural surfaces in different locations, quantitatively calculates the spatial continuity and combination relationship of structural surfaces, and outputs the spatial association determination results and continuity levels of the roof-side-rock structural surfaces. This method enables intelligent identification and quantitative analysis of the spatial relationship between roof and side-rock rock structural surfaces.

The application relates to the technical field of energy storage system monitoring and control, in particular to a multi-parameter early warning monitoring system and method for thermal runaway of energy storage batteries; the system comprises a data acquisition module, an early warning analysis module, a decision control module and an execution feedback module; the data acquisition module acquires state parameters and health parameters; the early warning analysis module establishes a three-level risk assessment mechanism based on the state parameters and the health parameters, and outputs a graded early warning signal; the decision control module generates a protection instruction based on the graded early warning signal, including: an optimized cooling instruction for first-level protection, a composite trigger instruction for second-level protection and a forced start instruction for third-level protection; the execution feedback module executes the protection instruction and monitors the execution state of the protection instruction; the three-level risk assessment mechanism is optimized based on the execution state; the application realizes early pre-control of thermal runaway of energy storage batteries, improves the accuracy of early warning, and enhances the synergy and reliability of the protection system.

The utility model discloses an industrial wastewater biochemical system sludge treatment system, it belongs to industrial wastewater treatment field, it will fluid power cavitation, thermal effect and alkaliolysis effect system integration, break through the limitation of single technology, realized the high -efficient sludge disintegration under low energy consumption. It mainly includes sludge pump, the sludge pump is connected with the pretreatment unit through valve no.

The present application relates to the technical field of fluid measurement, and discloses a rock fracture seepage velocity detection method and system based on optical fiber sensing, which comprises the following steps: constructing a sensing detection network based on optical fibers and auxiliary heat sources of a sensing monitoring network; injecting a thermal pulse into rock fractures based on the sensing detection network to obtain a thermal disturbance field; exciting the rock fractures based on the thermal disturbance field; demodulating sensing optical fiber signals to obtain multi-physical field coupling response data; jointly analyzing the multi-physical field coupling response data based on a multi-physical field coupling forward model to obtain seepage characteristic strain components; performing velocity inversion on the seepage characteristic strain components based on the spatial distribution of the multi-physical field coupling forward model to obtain fracture seepage velocity; and performing difference integration on the fracture seepage velocity to generate seepage velocity distribution monitoring results of the rock fractures.

The utility model discloses a kind of anti-UV fabric straight transverse tensile tester, comprising: fixed frame, with moving frame, and the protective box being set to the bottom of moving frame, moving frame is fixedly connected with protective box, moving frame is polygonally arranged;Driving mechanism, driving mechanism is set to the inside of protective box, driving mechanism is fixedly connected with protective box, driving mechanism is used to provide power for equipment, and stretching mechanism, stretching mechanism is slidably connected in moving frame inside, driving mechanism top and stretching mechanism bottom meshing, provide power for stretching mechanism, stretching mechanism top is used to fix the fabric to be detected;So that the twelve groups of stretching rods distributed in the circumferential direction of annular frame can independently or cooperatively complete stretching action in any direction, especially through the linkage control of multiple stretching rods, the composite direction pulling force that fabric can be simulated in wearing process.

This invention discloses a data-driven intelligent prediction method, system, and cable for cable life, relating to the field of intelligent operation and maintenance technology for power equipment. The method includes collecting multi-dimensional historical operating sequence data of the cable, such as current, voltage, partial discharge, temperature, and ambient temperature and humidity; generating a comprehensive cable state feature sequence through multi-source heterogeneous data fusion technology; extracting a degradation feature vector from this data, based on aging mechanisms, including accumulated load fatigue, insulation degradation index, and thermal stress damage degree; inputting this vector into a pre-trained model for time-series evolution simulation, outputting the probability distribution of remaining life and key failure time nodes; and generating a preventative maintenance decision-making scheme including maintenance windows, operation priorities, and inventory warnings. This method, through mechanism-driven and probabilistic prediction, achieves more accurate assessment and risk quantification of cable life, providing a direct decision-making basis for proactive operation and maintenance.

This invention relates to fecal microbiota transplantation (FMT) formulations for treating lung cancer, their screening methods, and applications. The invention establishes a microbiome-based quantitative screening system based on the characteristics of the lung cancer immune microenvironment, achieving a precise match between donor screening criteria and tumor-specific immune regulation needs. This overcomes the limitations of traditional FMT formulation preparations that rely on universal health standards, providing reliable support for clinical applications. Furthermore, the invention develops a combined application strategy of FMT formulations and immune checkpoint inhibitors, which can significantly improve disease control rates and prolong progression-free survival in lung cancer patients, providing an innovative solution to overcome drug resistance challenges in lung cancer immunotherapy.

This application provides a distributed node phase synchronization method, electronic device, computer-readable storage medium, and program product. The method includes: receiving the output current of a node, a first square wave signal, and a second square wave signal, wherein the phase of the first square wave signal is the same as the phase of the output voltage of a first node, and the phase of the second square wave signal differs from the phase of the output voltage by a preset angle; obtaining the actual power angle of the first node based on the output current, the first square wave signal, and the second square wave signal; calculating the difference between the actual power angle and the target power angle; and adjusting the phase of the output voltage based on the difference when the difference is greater than a preset threshold, until the difference is less than or equal to the preset threshold. This solution can achieve synchronization of voltages at all transmitting and receiving ends, overcoming the limitations of traditional single-path control.

The application relates to a hydrogeological structure analysis method based on multi-source data fusion and three-dimensional modeling, and belongs to the technical field of hydrogeological analysis. The method comprises the following steps: obtaining multi-source data of a hydrogeological structure and performing preprocessing to obtain standardized multi-source data of the hydrogeological structure; integrating the standardized multi-source data of the hydrogeological structure to obtain key multi-source data of the hydrogeological structure; the key multi-source data of the hydrogeological structure comprises special geological body spatial distribution data, underground facility-stratum spatial relationship data and stratum-structure three-dimensional framework data; an initial three-dimensional geological model is constructed based on the key multi-source data of the hydrogeological structure, and the initial three-dimensional geological model is dynamically updated to obtain a dynamic three-dimensional geological model; a representation method of the dynamic three-dimensional geological model is optimized to obtain a high-precision three-dimensional model; and mechanism analysis and correlation mining are carried out based on the high-precision three-dimensional model. The application can improve the accuracy of hydrogeological structure analysis.

The application discloses a kind of " attitude-knowledge-skill " three-dimensional target-oriented operating room nurse OSCE multi-modal data intelligent evaluation method and system.The core of this method is: by deploying camera, microphone, motion and physiological sensor and other multi-source equipment in OSCE examination station, the behavior, voice, operation and physiological data of examinee under simulated operation scene are synchronously collected;The system first presets the basic weight of three-dimensional evaluation dimension according to the type of examination task, and then through the unique dynamic weight self-adaptive mechanism, the performance state of examinee is analyzed in real time, when specific states such as " skill bottleneck ", " knowledge deficiency " or " attitude stress " are identified, the weight of each dimension is dynamically adjusted, the intelligentization and individualization of evaluation strategy are realized;Subsequently, the weighted features are analyzed in depth using the multi-modal fusion model based on attention mechanism, and finally the fine quantification score, diagnostic feedback report and individualized improvement suggestion of the three dimensions of attitude, knowledge and skill are output.

This invention relates to the field of process control technology, specifically to an intelligent control system for thin-film lithium niobate etching processes based on machine learning. The system includes: a process sensing and acquisition module that collects thin-film lithium niobate etching process parameters, etching effect detection data, process environment data, and etching equipment status data in real time; a machine learning processing center that integrates and analyzes the data, using machine learning algorithms to uncover potential patterns, correlations, and anomalies; a process visualization and interactive platform that presents a real-time heatmap of etching parameter distribution, etching defect location, and etching effect trends, automatically switching interface layouts according to different process scenarios; an intelligent process decision engine that dynamically generates etching parameter adjustment strategies and process emergency response plans; and an etching parameter adjustment unit that adjusts the process parameters of the thin-film lithium niobate etching equipment and updates the control status. This solves the problems of poor adjustment response and low etching yield in existing technologies.