196 results about "Data stability" patented technology

The invention discloses a primary and secondary fusion complete ring main unit fault diagnosis method, and particularly relates to the technical field of power distribution fault diagnosis, and the method comprises the steps: collecting multi-path original data, carrying out the frequency domain and time domain combined calibration, carrying out the comprehensive evaluation according to two preset discrimination factors, namely, a data stability deviation amplitude and a multi-path waveform time deviation degree, and obtaining a fault diagnosis result. Two types of feature data sets are constructed subsequently, input data of two interference modeling networks are calculated respectively, then the interference modeling networks are input to output interference grade values, sampling precision dynamic adjustment and fault recognition strategy switching operation are executed according to the interference influence grade values, and diagnosis accuracy and stability in a complex interference environment are improved. According to the method, unified normalization processing of multi-path sensing data is realized, and the sensing accuracy of fault features is improved; through double-factor triggering and feature fusion evaluation, the stability of interference identification is enhanced; and sampling adjustment and strategy switching are executed based on the interference level value, so that the robustness and reliability of diagnosis are improved.

The invention relates to the technical field of quality monitoring, in particular to a virtual simulation and real-time decision-making system for canned fish production quality risks, which is characterized in that data stability and jump accuracy are improved by adopting a filtering mode, a key path area is screened, a conduction trunk is formed, synchronous construction of a mapping and adjusting strategy is realized, and a real-time decision-making result is obtained. By introducing action time, a control time consumption difference value and a node jump amplitude trend multi-dimensional quantitative judgment standard into an execution structure, a feedback recognition mechanism of index fluctuation is enhanced, the abnormal trend response granularity is improved in cooperation with multi-dimensional nested calculation of temperature and pressure parameters, synchronous association of key control parameters and historical operation tracks in a fault chain is complemented, and the reliability of the fault chain is improved. Through starting from a beat logic structure, data cleaning, trend grouping and traceability return are performed alternately, so that a risk propagation chain obtains collaborative modeling among a pressure difference, a temperature difference and a control instruction, and risk path response consistency and intervention precision under a beat driving condition are enhanced.

The invention relates to an offshore wind speed prediction method based on a TVFEMD-FE-TCN-Transform model, and the method comprises the steps: obtaining historical offshore wind speed data, carrying out the preprocessing of the data, decomposing the original wind speed data through TVFEMD to obtain a plurality of IMF components, improving the data stability, and carrying out the prediction of the offshore wind speed. According to the method, four types of signals including a high-frequency signal, an intermediate-frequency signal, a low-frequency signal and a trend signal are generated through reconstruction according to IMF component complexity through fuzzy entropy FE, calculation complexity is reduced, a time domain convolutional network TCN is adopted to extract reconstructed signal features, fusion is performed, the reconstructed signal features are input into Transform for wind speed prediction, meanwhile, Transform model parameters are optimized through MWOA, and the wind speed prediction accuracy is improved. Predicting the offshore wind speed by using the optimal parameter combination Transform model to obtain a final offshore wind speed prediction value; according to the method provided by the invention, the problems of insufficient signal decomposition, weak feature extraction capability and low prediction precision of a single prediction model are effectively solved, and powerful support is provided for operation and maintenance management and power grid dispatching of the offshore wind power plant under the condition that the offshore wind speed has intermittent and fluctuation characteristics.

The invention discloses a metabonomics data batch correction method based on multi-kernel learning, and belongs to the cross technical field of bioinformatics and analytical chemistry. According to the method, the multi-kernel learning technology is utilized, the advantages of different kernel functions are fused in a self-adaptive mode, a model conforming to data reality is constructed, complex drift characteristics of metabolite signals are accurately captured, and efficient and accurate normalization processing of metabonomics data is achieved. Compared with traditional data standardization methods such as SVR and LOESS, the method has the advantages that the performance is excellent in the aspect of reducing the metabolite peak intensity variability, and the data stability is remarkably improved. In the subsequent multivariate statistical analysis, the classification accuracy is greatly improved, the comparability among different batches of data is also remarkably enhanced, reliable data support can be provided for discovery of disease biomarkers, and the method plays a key role in large-scale metabonomics research.

The invention provides avionic equipment based on high-precision inertial navigation and a signal processing method, relates to the technical field of aviation navigation control, and aims to evaluate the selection of a driving sensor by using environmental complexity, optimize the acquisition quality of inertial data such as angular velocity and acceleration, pixel coordinates and specific optical data in real time, reduce redundancy and improve the precision of navigation. Spectral decomposition and adaptive filtering are utilized, signals are dynamically cleaned according to environment noise of different frequency bands, key features are reserved, high-dimensional feature vectors are generated through spatial-temporal feature reconstruction, the expression ability of low-quality data is enhanced, fusion weights are dynamically adjusted based on the signal-to-noise ratio and consistency indexes, predictive error correction is combined, and long-term drift is eliminated. Hardware support ensures that processing delay is less than 10ms, anti-interference shielding maintains data stability, dependence of traditional navigation on external signals is broken through, long-time high-precision navigation is realized, the method is particularly suitable for aviation secret tasks and civil aviation flight in severe weather, and task success rate and safety are improved.

The invention discloses a method and system for reducing data errors of a dynamic glucometer, and the method comprises the steps: collecting a current signal of fluid between subcutaneous tissues in real time through an implantable sensor, and synchronously recording the working environment parameters of the sensor; carrying out denoising and normalization processing on the current signal, and carrying out temperature compensation and drift correction on the signal according to a working environment parameter; extracting time sequence features and frequency domain features from the signals processed in the step S2; training a machine learning model based on the time sequence feature data and the frequency domain feature data, wherein the machine learning model is used for predicting the blood glucose concentration and identifying abnormal data points; correcting a model prediction result through residual analysis and weighted adjustment, and performing secondary correction on abnormal data points in combination with event data input by a patient; and outputting the optimized blood glucose data to a terminal display device in a time sequence curve form. Through the process, the influence of abnormal state data on blood glucose monitoring is reduced, and the data stability is improved.

The invention relates to the technical field of water level data processing, in particular to a water level data acquisition and processing system based on the Internet of Things, which comprises a trend identification module, a sampling frequency modulation module, a data encapsulation module, an exception elimination module and an instruction linkage module. According to the method, trend recognition is achieved by analyzing the continuous water level change trend, the sampling frequency is dynamically adjusted, so that the sampling interval is matched with the actual water level fluctuation state, the response sensitivity of water level data and the timeliness of data acquisition are improved, and a uniform packaging format is constructed by adopting a timestamp index mechanism and a bidirectional mapping structure; the integrity of a water level data structure and the convenience of index management are enhanced, sudden change breakpoints are recognized through the water level amplitude difference, data are stored in a segmented mode, the data stability and the emergency situation recognition capacity are improved, and the self-adaptive capacity of water level information processing, the maintaining level of data continuity and the coping efficiency of sudden abnormity are overall improved.

The invention discloses a live pig slaughtering whole process management method and system based on a segmentation model, and relates to the technical field of live pig slaughtering management. Fat content and skeleton parameters are obtained through zoning measurement, a prediction result of meat distribution is constructed in combination with real-time environment data, the recognition accuracy and data stability are improved, cutting precision is guaranteed through structural precision in combination with production line capacity and equipment load information, serial numbers are subjected to iterative sorting optimization through a genetic algorithm, and the cutting efficiency is improved. Production line conflicts and resource waste are reduced, the adaptability and dynamic cooperation capability of the whole processing system are enhanced, the beat matching efficiency is improved through path continuity, the cutting deviation and error propagation are reduced, the segmentation sequence is adjusted in combination with equipment load change and conveying rhythm, and data-driven closed-loop management and control from posture recognition to cutting execution stages is achieved; and the precision management, the cutting consistency and the production flow robustness of the slaughter link are effectively improved.

The invention discloses a virtual power plant system integrating multiple resources and a flexible scheduling method of the virtual power plant system, and relates to the technical field of virtual power plant management. Analyzing photovoltaic fluctuation characteristics, energy storage response capability indexes and stability indexes of the load power data corresponding to various types of data, and performing normalization processing; establishing a resource interaction tensor based on the collected data, fusing the power grid frequency deviation and the line load, and constructing a collaborative optimization model containing time delay compensation; updating the power grid frequency and the line load rate with a fixed time window, dynamically solving the collaborative optimization model, and outputting a load regulation quantity and an energy storage target scheduling instruction; and obtaining the power grid impedance characteristics and load distribution parameters of the area to be loaded with the control instruction, analyzing the scene deviation degree of the area, and correcting the scheduling value based on the scene deviation degree. The scheduling precision is improved, and the complex power grid compatibility is improved.

The invention discloses a high arch dam global deformation analysis method and system fusing InSAR and measuring point data, particularly relates to the technical field of radar interference deformation monitoring, and is used for solving the problem of inaccurate shielding failure compensation. According to the method, data stability is guaranteed by obtaining InSAR image data and extracting sight line direction displacement in combination with coherence indexes, a key measuring point deformation sequence is collected, an InSAR result is calibrated based on stable reference points, data consistency is improved, a projection correction coefficient is constructed by using a dam body three-dimensional structure model, the sight line displacement is inversely solved into structural normal displacement, and the accuracy of the dam body three-dimensional structure model is improved. Obtaining an initial deformation value of the real structure meaning; according to the method, a spatial coupling map of measuring points and InSAR pixels is constructed for a shielding failure area, deformation compensation of the failure area is completed based on an edge weight, finally the deformation compensation result is remapped to a three-dimensional structure model, measuring point data restoration is performed on a high arch dam, and structural plane-level deformation visualization and trend analysis are realized more quickly and more accurately.

The invention is suitable for the technical field of semiconductor testing, and provides a data acquisition method and device based on a DDIC chip, and testing equipment, and the data acquisition method comprises the steps: receiving a test pattern, controlling the DDIC chip to output a corresponding gray scale voltage signal, and achieving the comprehensive testing of the response capability of the chip in a full gray scale range; secondly, a target acquisition link composed of functional units including impedance conversion, single-ended to differential conversion, programmable gain amplification, filtering, analog-to-digital conversion and the like is utilized, so that the signal driving capability and the anti-interference capability are effectively improved, meanwhile, high-frequency noise and nonlinear distortion are reduced, and the basic quality of sampled data is guaranteed; on the basis, sampling optimization processing is carried out on the sampling data based on the FPGA unit, and the data stability is improved. According to the scheme, through high-precision transmission of the signal link, the data acquisition precision of the DDIC chip is improved, and then the technical requirements of high-precision DDIC chip testing are met.

The invention discloses a method for determining the demulsification speed of emulsified asphalt, and relates to the technical field of online detection and intelligent monitoring of road engineering materials, and by integrating a lightweight time sequence prediction model and a self-adaptive processing mechanism, the practicability and reliability of the determination of the demulsification speed of the emulsified asphalt are remarkably improved; in the aspect of portability, an edge model based on a gating circulation unit structure is adopted, so that the dependence on computing resources is reduced, the method can be operated on handheld equipment, the problem that traditional laboratory equipment is heavy is avoided, and construction personnel can conveniently and quickly deploy the equipment on site; the real-time performance is enhanced, the adaptive sliding window dynamically adjusts the window length and step length according to the data stability to ensure that the inference frequency is matched with the demulsification process, and the model can immediately respond to environmental changes such as temperature fluctuation and output a demulsification speed estimated value without obvious delay in combination with an online learning mechanism, so that the paving and compacting opportunities are guided; and construction interruption is reduced.

The invention discloses a charging pile thermal runaway intelligent protection method and system based on edge calculation, and relates to the technical field of charging pile thermal runaway protection. Comprising the following steps: S1, collecting thermal runaway multi-mode sensing data in real time, and carrying out data preprocessing; a charging pile thermal runaway multi-mode abnormal state is judged, and an abnormal feature data packet is generated; s2, multi-modal abnormal feature vectors are constructed, the thermal runaway risk probability is evaluated, and thermal runaway risk grading protection of the charging pile is carried out; s3, the response effect of thermal runaway risk grading protection is quantified, and thermal runaway risk grading protection is adjusted; and S4, monitoring and feeding back the thermal runaway risk, and optimizing algorithm parameters and a thermal runaway risk grading protection strategy. The problems that an existing charging pile thermal runaway protection system is difficult to adapt to complex working conditions, multiple in false alarm and missing alarm, lagging in response and insufficient in protocol compatibility and data stability, and consequently the thermal runaway risk is difficult to recognize and protect timely and accurately are solved.

The invention relates to the technical field of cloud data synchronization, in particular to a diabetes management-oriented cloud data synchronization method, which comprises the following steps: based on a blood glucose monitoring record, unifying a time zone and verifying a device serial number, generating a terminal version identifier, comparing a cloud version to screen missing entries, and identifying time abnormality and a device source. And forming a version difference mark, judging a missing range and a conflict identifier, updating a synchronization state after sorting, and counting the number of synchronization entries. According to the method, the problems of additional recording of historical data and version conflicts in data synchronization are effectively solved by introducing a dynamic rollback mechanism and timestamp difference comparison, clock differences between devices are automatically detected and adjusted in the synchronization process, timely updating and consistency of data are ensured, invalid synchronization operations are reduced through an intelligent conflict resolution mechanism, and the synchronization efficiency is improved. The accuracy and timeliness of data in diabetes management are ensured, and the data stability and consistency in a multi-terminal environment are improved.

The invention discloses a data processing method and system of a chromatographic instrument, and relates to the technical field of data processing, and the method comprises the following steps: collecting whole-course original detection signals and external interference signs generated in a chromatographic analysis process, carrying out time alignment under a unified time scale, generating an interference rhythm index, and calculating the interference rhythm index; the reference is used as a unified reference for subsequent signal comparison; and based on the interference rhythm index, performing phase drift comparison on the acquired signal, calculating the phase difference between the detection signal and the interference signal, drawing a beat frequency wandering graph, extracting candidate signal segments of pseudo-period interference according to beat frequency change, and generating a pseudo-period candidate segment list. According to the invention, by establishing the interference rhythm index and comparing the interference rhythm index with the phase drift, the time decoupling of the detection signal and the interference signal is realized, and the sampling anti-interference capability is improved; and through dynamic control of a false peak characteristic fingerprint draft and a misjudgment reason table, signal rhythm correction and peak identification optimization are completed, so that the detection accuracy and the data stability are improved.

The invention discloses a data fusion dynamic operation state evaluation method and device, and relates to the technical field of operation state evaluation methods, and the method comprises the steps: collecting original data from a plurality of sensors, monitoring equipment or an external system, and carrying out the normalization processing and abnormal value elimination operation of the collected original data, obtaining a preprocessed standardized data sequence; based on the preprocessed standardized data sequence, the dynamic confidence value of each data source at the current moment is calculated, the dynamic confidence value is comprehensively determined by three factors of historical consistency, environmental adaptability and data stability of the data sources, and a dynamic weight value corresponding to each data source is obtained; based on the dynamic weight value, performing weighted fusion operation on the standardized data sequence in a set time window, introducing a time decay factor to enhance the attention on recent data, and obtaining a fused state feature vector; and taking the fused state feature vector as an input.

The invention relates to the technical field of fault detection, in particular to an ultralow-temperature refrigerator refrigeration fault detection method. Information is collected through the information sensing layer, external sensing data and refrigerating system data are output, and the defect of a single external sensor is overcome; the external sensing unit and the refrigeration sensing unit respectively perform targeted acquisition, and when an external sensor is abnormal, fault detection can be realized through refrigeration system data, so that the stability and reliability of detection are improved, and the problem of detection failure caused by sensor faults is avoided; according to the method, the data stability index is obtained by identifying the external sensing data, the data health signal is obtained by performing health assessment on the data stability index, the external sensing abnormal total value and the refrigerating system abnormal total value are comprehensively analyzed, abnormal levels are divided, the corresponding fault alarm strategy is generated, the fault severity degree can be matched, and the fault alarm accuracy is improved. False report and missing report are reduced, and safe operation of the refrigerator is guaranteed.

The invention relates to the technical field of air pollution detection, in particular to a flue gas detector and a calibration detection method thereof, and the flue gas detector comprises a concentration fluctuation analysis module, an abnormal data screening module, an error interval collection module, a parameter trend analysis module and a period stability judgment module. According to the method, abnormal data and equipment drift influence are dynamically screened, an error change interval is actively collected, and through linkage comparison of multi-source parameter trends and separation of working condition disturbance, instrument abnormality and periodic offset signals, automatic screening of concentration data and operation parameters under multiple links is achieved, feature recognition and abnormal partition are associated, and the accuracy of the system is improved. The sampling effectiveness, the data stability and the instrument parameter change trend under the continuous detection working condition are managed in a layered mode, a traceable dynamic calibration chain is formed, the smoke detection consistency and the data exception handling capacity in the complex emission environment are remarkably improved, and an intelligent judgment mechanism of data grading rejection and trend aggregation is perfected.

The invention belongs to the technical field of medical image processing, and provides a multi-modal medical image analysis system, medium and equipment for gastrointestinal disease recognition, and the technical scheme is as follows: extracting multi-scale texture features and structural features of a gastrointestinal endoscopy color image and a gastrointestinal endoscopy OCT depth image respectively, and fusing to obtain a cross-modal feature sequence; based on the feature sequence added with the position code, learning a spatial corresponding relation of different modal data on blood vessel morphology and gastrointestinal tract wall interlayer structure; obtaining a multi-scale global context feature sequence and a local detail feature sequence based on the aligned cross-modal blood vessel and gastrointestinal tract wall interlayer structure features, and fusing the multi-scale global context feature sequence and the local detail feature sequence to obtain fused multi-modal features; obtaining an adjusted dense deformation field based on the fused multi-modal features; and performing coarse registration and fine registration based on the adjusted dense deformation field to obtain a registered gastrointestinal endoscopy color image and a registered gastrointestinal endoscopy OCT depth image. The anti-interference capability is strong, and the data stability is high.

The invention discloses a measurement system and method based on dynamic distance measurement adjustment, and belongs to the technical field of three-coordinate measurement, and the system comprises a three-coordinate machine main body, a dynamic distance measurement module, a data fusion module, a coordinate system calibration module and a data processing module. The dynamic distance measuring module divides data and adjusts measuring head parameters, the data fusion module outputs accurate data after preprocessing and denoising, the calibration module selects three points to establish a system, and the processing module monitors data fluctuation and generates a result; starting a system to collect initial contour, distance and shaft speed data of the workpiece; classifying, associating and analyzing the data; dynamically adapting sampling parameters; performing multi-source data fusion optimization; selecting a reference point, establishing a system and calculating coordinates of a to-be-measured point; monitoring data stability, and generating a three-dimensional model and a measurement result of the workpiece; according to the method, errors are reduced by dynamically adjusting parameters, optimizing data fusion and reliably calibrating.

The invention discloses a data recovery method and electronic equipment, and relates to the technical field of storage systems.The data recovery method comprises the steps that a virtual disk array is created according to virtual disks, and the virtual disks in the virtual disk array are divided into data disks and verification disks; writing the write-in data of the write-in request into a data disk, obtaining verification information according to the data written into the data disk, and writing the verification information into a verification disk; and if the abnormal data exists, performing data reconstruction on the abnormal data according to the data except the abnormal data in the virtual disk array and the verification information to obtain repaired data. The problems that a data error correction mode of a storage system is single, only underlying data processing abnormity can be sensed, but abnormal data cannot be recovered can be solved. According to the method, when any virtual disk in the virtual disk array has abnormal data, the abnormal data can be recovered and corrected by means of data reconstruction, and the data stability and data security of a storage system are guaranteed.

The invention discloses a privacy protection federal basic model continuous learning method for a new energy main body power system. Comprising an initialization stage, a stage of communication reliability evaluation and dynamic selection of a new energy main client, a privacy protection processing stage and a model aggregation updating stage, intelligent node screening is realized through communication reliability scoring and data stability analysis, and the problem of unstable communication between virtual power plants is effectively overcome. Label distribution alignment optimization and differential privacy protection are innovatively combined, and privacy security of new energy main body client communication is ensured through adaptive noise injection and gradient level privacy budget distribution.

The invention relates to the technical field of image recognition control, in particular to a linear motion module accurate control method and system based on AI vision, and the method comprises the following steps: obtaining an interference fringe image and extracting a gray path, analyzing central point displacement to generate a coordinate sequence, repairing an interference feature position, and aligning the image and a control time sequence to generate a synchronization time table. The mapping structure response forms a set of control output configurations. According to the method, a peak path is constructed through interference fringe gray scale information in an image frame, central point pixel displacement is extracted, a coordinate migration sequence is constructed, fine-grained capture and tracking of an execution state are realized, an external interference area is identified according to image gradient change, vector repair is carried out, and image data stability and feature reliability are guaranteed. And aligning the repaired image response with a control instruction time sequence, constructing an impulse response lag statistical mechanism, realizing accurate mapping adjustment of the control frequency, and improving the control precision and response consistency in high-frequency displacement execution.

The invention relates to the technical field of automatic control, in particular to a trace oxygen analyzer multi-node cooperative verification system which comprises a disturbance recognition module, a pressure flow correction module, a node scheduling module, a response evaluation module and a performance trend module. And combining the flow change of the mass flow meter to sort time sequence data in a partitioned manner. According to the method, the disturbance performance of each node is summarized in real time by relying on dynamic acquisition of disturbance characteristic factors, node pressure and flow parameters are actively linked, scheduling optimization is executed according to the real-time response performance of the node, collaborative serial number distribution is realized by analyzing node response interval sequence fluctuation, and the scheduling optimization is realized by means of a periodic stability comparison rule. Cooperative degradation trend indexes are extracted and monitored in time, continuous observation of physical disturbance and performance trend of verification nodes is enhanced, and verification accuracy, early warning capability and data stability in a multi-equipment-node scene are improved.

The invention discloses a nonvolatile memory device based on CrMoGe2S6 / Ga2O3 heterostructure tensile strain and a construction method, and belongs to the technical field of nonvolatile memory devices. Comprising the following steps: vertically stacking a single layer of Ga2O3 and a single layer of CrMoGe2S6, and constructing two kinds of CrMoGe2S6 / Ga2O3 heterostructures; performing biaxial tensile strain on the CrMoGe2S6 / Ga2O3 heterostructure, calculating a magnetic ground state and an energy band of the CrMoGe2S6 / Ga2O3 heterostructure under different strains, and screening out the CrMoGe2S6 / Ga2O3 heterostructure which is represented as a ferromagnetic state; and on the basis of the screened CrMoGe2S6 / Ga2O3 heterostructures, two CrMoGe2S6 / Ga2O3 heterostructures with the same tensile strain are selected to construct the nonvolatile memory device. Ferroelectric Ga2O3 is introduced to serve as a switch, the electronic property of CrMoGe2S6 is controlled by the polarization state of Ga2O3, the Ga2O3 which is electrically polarized upward P does not change the semiconductor characteristic of CrMoGe2S6, and the Ga2O3 which is electrically polarized downward P enables the CrMoGe2S6 to present semimetallic property; therefore, the non-volatile electric control of the CrMoGe2S6 between the antiferromagnetic semiconductor and the semimetal is indirectly realized; the characteristic is especially important for a memory device, and can significantly reduce energy consumption and improve data stability.

The invention belongs to the technical field of positioning, and discloses a GNSS and 5G fusion positioning method and system for an intelligent terminal, and the method achieves the joint smoothing of pseudo-range observation data through introducing a GNSS carrier phase and Doppler frequency shift in combination with auxiliary observation values such as a 5G TOA observation value. The observation continuity and the data stability in a low signal-to-noise ratio and dynamic environment can be remarkably improved. According to the method, the clock error of the intelligent terminal is effectively eliminated by using an inter-satellite single-difference and inter-base-station single-difference method, the clock error drift of the intelligent terminal is effectively inhibited while a low-complexity lightweight fusion positioning model is constructed, the positioning precision is improved, the dependence on the hardware synchronization capability of the intelligent terminal is reduced, and the positioning accuracy is improved. The method improves the positioning precision, continuity and environmental adaptability in a dynamic scene and a shielding environment, and is suitable for intelligent terminal positioning tasks in a complex urban environment, a shielding region and a mobile scene.

The present invention provides an avionics device and a signal processing method based on high-precision inertial navigation, which relate to the field of aviation navigation control technology. The invention uses environmental complexity assessment to drive sensor selection, optimizes the acquisition quality of inertial data such as angular velocity and acceleration, and pixel coordinates, and optical data such as specific data in real time, reduces redundancy, utilizes spectral decomposition and adaptive filtering, dynamically cleans signals for environmental noise in different frequency bands, retains key features, uses spatiotemporal feature reconstruction to generate high-dimensional feature vectors, enhances the expressiveness of low-quality data, dynamically adjusts fusion weights based on signal-to-noise ratio and consistency index, combines predictive error correction to eliminate long-term drift, ensures processing delay is less than 10ms with hardware support, and maintains data stability through anti-interference shielding. The invention breaks through the dependence of traditional navigation on external signals, realizes long-term high-precision navigation, is particularly suitable for aviation covert missions and civil aviation flights in severe weather, and improves mission success rate and safety.

The utility model discloses a rapid satellite alignment device based on a Beidou compass, which comprises a main control module, a double-receiving mechanism, a signal fusion processing module, an angle adjusting mechanism and a mounting bracket, and is characterized in that the angle adjusting mechanism and the main control module are mounted on the mounting bracket; the double-receiving mechanism and the signal fusion processing module are installed on the angle adjusting mechanism, and the master control module is provided with a signal processing unit and a control unit. According to the method, errors possibly caused by a single signal can be reduced by providing redundant data, so that the accuracy of beam pointing is improved, interference can be effectively eliminated by double receiving of the same satellite signal, the anti-interference capability of a system in a complex environment is enhanced, and data stability is ensured.

The present application relates to water level data processing technical field, specifically to water level data acquisition and processing system based on internet of things, system includes trend identification module, sampling frequency modulation module, data encapsulation module, abnormal elimination module, instruction linkage module.In the present application, through analyzing continuous water level change trend, trend identification is realized and sampling frequency is dynamically adjusted, so that sampling interval and actual water level fluctuation state are matched, response sensitivity of water level data and timeliness of data acquisition are improved, time stamp index mechanism and bidirectional mapping structure are used to construct unified encapsulation format, integrity of water level data structure and convenience of index management are enhanced, sudden break point is identified through water level amplitude difference and data is stored in segments, data stability and sudden situation identification ability are improved, and adaptive ability of water level information processing, maintaining level of data continuity and response efficiency to sudden abnormality are overall improved.

The present application provides a kind of spun yarn product elongation at break detection method, comprising the following steps: sample preparation and pretreatment step carries out sample form measurement and mathematical modeling step;Stretching test equipment calibration and parameter setting step;Stretching test execution and data acquisition step;Elongation at break calculation and preliminary analysis step;Stress, strain relationship analysis step;Data stability analysis step;Outlier detection and processing step;Result comprehensive analysis and report generation step;The present method realizes the accurate analysis of yarn form and stress, strain relationship, improves the accuracy of test result;Data stability and outlier detection analysis are carried out, ensure the reliability and stability of test data;For the performance evaluation and improvement of yarn provides strong support.The present method has feasibility and practicality, can significantly improve the efficiency and accuracy of spun yarn product elongation at break detection.