1691results about "Digital technique network" patented technology

The invention discloses a sampling implementation method and system suitable for motor protection quick response, belongs to the technical field of motor protection, and can increase the sampling frequency and improve the quick response of motor fault protection. Comprising the steps that motor phase current and bus voltage are collected, after anti-aliasing filtering is conducted, parallel sampling is conducted through a main ADC module and a redundant ADC module which are independently configured, and the following operations are synchronously executed based on a double-buffer-area framework: in a first buffer area, a compensation coefficient updating period is dynamically adjusted, and convergence is restrained; in the second buffer area, generating a voltage gradient prediction sequence, and compensating data abrupt change; when the harmonic frequency spectrum amplitude or the voltage gradient prediction sequence exceeds the limit, an oversampling mode is triggered, a redundant ADC module is allocated to load a multi-order digital filter, and an independent hardware acceleration unit is started to process harmonic compensation and gradient prediction in parallel; and generating a dynamic safety envelope, and if the gradient extreme value in the preset continuous window breaks through the safety envelope, generating an overvoltage fault signal.

The invention provides a high-precision motion capture real-time calibration method based on multi-sensor fusion, and the method comprises the steps: obtaining motion data and environmental parameters collected by multiple sensors, the multiple sensors comprising an inertial measurement unit and an optical sensor; constructing a dynamic calibration model based on the action data and the environmental parameters, and performing real-time error compensation and fusion processing on the sensor data; and establishing a time-varying error dynamic model, and performing real-time compensation on zero offset drift, vibration noise and electromagnetic interference of the inertial measurement unit based on the environmental parameters. According to the invention, the IMU and the optical sensor are fused, respective advantages are exerted, and motion data are comprehensively collected. And meanwhile, the time-varying error dynamic model compensates errors generated by environmental interference in real time, so that real actions can be accurately restored through action capture.

The invention discloses a self-adaptive tracking and positioning method for an engineering vehicle, and relates to the technical field of vehicle navigation and control, and the method comprises the steps: collecting position information and operation parameters, and transmitting the position information and operation parameters to a vehicle-mounted control unit through a vehicle-mounted sensor network; the position information and the operation parameters are fused through the vehicle-mounted control unit, GPS signal noise and inertial navigation accumulative errors are eliminated by adopting a Kalman filtering algorithm, real-time position trajectory data are generated, a deviation value between an engineering vehicle and a preset path is calculated by adopting a self-adaptive particle filtering algorithm in combination with a pre-stored engineering area electronic map, and the real-time position trajectory data are obtained. Inputting to the constructed neural network model, and predicting the position of the engineering vehicle at the next moment; according to the error between the predicted position and the actual position of the engineering vehicle, parameters of the neural network model are dynamically adjusted, a steering angle correction amount and a speed adjusting instruction are generated and executed through a controller, and trajectory tracking and positioning of the engineering vehicle are achieved. According to the invention, the problems of accurate positioning and trajectory tracking of the engineering vehicle in a complex environment are effectively solved.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention relates to the technical field of water resource analysis, and discloses a water resource predictive analysis method based on artificial intelligence. The method relates to the technical field of water resource analysis, and comprises the following steps: acquiring an original hydrological data set including rainfall intensity, river flow and the like through a sensing terminal, and performing multi-modal data alignment to generate a hydrological space-time tensor; constructing a dynamic water level threshold response mechanism in combination with watershed topographic features to obtain a partition water level calibration matrix; inputting the hydrological feature map into a spatial-temporal feature coupling network containing a long-short-term memory module and a spatial self-attention module to generate a hydrological feature map; constructing a multi-dimensional abnormal association tensor based on the multi-dimensional abnormal association tensor, and identifying rainfall flood event nodes by using an adaptive sliding window detection algorithm; and an optimal hydrological parameter set is obtained through genetic algorithm optimization, and the three-dimensional hydrological dynamic model is driven to establish a mapping relation chain. The method can effectively fuse hydrological data spatio-temporal characteristics, and improves the accuracy and efficiency of water resource prediction analysis.

The invention relates to the technical field of big data service, in particular to a chemical fiber raw material impurity spectrum intelligent analysis system which comprises a spectrum feature deconstruction module, an impurity feature modeling module, a periodic drift analysis module and a component quantification output module. According to the method, noise is suppressed through moving window scanning in combination with Savitzky-Golay filtering, peak shape features are reserved, the signal-to-noise ratio is increased, feature distortion caused by mean filtering is avoided, segments are divided through extreme value density, weak impurity recognition is enhanced in combination with a dynamic threshold value, and a multi-dimensional fusion model is established through peak width change rate and baseline offset convolution. Linear dimension reduction limitation is broken through to improve discrimination precision, dynamic time warping is used for aligning peak height difference and symmetry degree time sequence, drift and interference influences are eliminated, periodic error accumulation is solved, Kalman filtering is used for carrying out recursive optimization on compensated concentration, model lag is reduced, and a closed-loop optimization system is constructed through multi-stage feature decoupling and dynamic compensation. The sensitivity is improved; and the misjudgment rate is reduced.

A GNSS positioning slowly-varying fault detection method based on residual-SVR regression comprises the steps that an observation information sequence is acquired based on a Kalman filter, and a covariance matrix of the observation information sequence is calculated; accumulating multi-step information through a sliding window, and constructing chi-square statistics; based on the fault-free data, constructing a training set by taking an innovation sequence as input and chi-square statistics as output, and generating an innovation-statistics mapping function; and fitting a normal slope threshold value based on an SVR predicted value, carrying out least square fitting on an observation statistic curve by sliding a window in real time, and judging whether to start a slow change fault alarm or not. According to the method, the residual error sequence is directly used as model input, and the dynamic chi-square statistical magnitude is used for replacing a traditional dichotomy label, so that the detection delay is reduced; an SVR detection model based on grid search and cross validation collaborative optimization is utilized, and an optimal parameter combination of a minimum mean square error (MSE) is screened through logarithm uniform sampling, interval linear sampling and five-fold cross validation, so that the average absolute error of slowly varying fault detection is reduced.

The invention discloses an electromobile EMB system clamping force estimation method based on a non-pressure sensor, and relates to the technical field of distributed electric drive-by-wire automobiles, the viscous effect between a brake disc and a friction plate during actual braking is considered, and a clamping force dynamic stiffness model is established for predicting the clamping force; discretizing a description equation of the clamping force dynamic stiffness model, and establishing a discretized clamping force state space equation; according to the motor torque balance equation, designing a sliding-mode observer of the motor load torque for observing the load torque of the motor in real time in the braking process; according to a transmission model of the EMB system, a load torque observation value observed by the sliding mode observer in real time is converted into a clamping force observation value; and calculating an optimal clamping force estimated value at the current moment according to an optimal clamping force estimated value obtained at the previous moment by using a Kalman filtering algorithm in combination with the discretized clamping force state space equation and the clamping force observed value, so as to realize real-time optimal estimation of the clamping force. The problems that under the control of the pressure sensor, due to high temperature and high pressure during braking, the measuring precision of the pressure sensor is reduced, and even the whole pressure sensor is invalid are solved.

The invention discloses a GPS interference phased array monitoring direction-finding antenna system for a 1575 frequency band, and relates to the technical field of intelligent antennae, comprising a data integration module for integrating data of satellite ephemeris, carrier attitude and airspace coverage through a weighted average method and generating an environment parameter set, a prediction calibration module for predicting the data of the satellite ephemeris, the carrier attitude and the airspace coverage through a weighted average method and generating an environment parameter set, and a direction-finding antenna module for performing direction-finding on the environment parameter set. The phase drift prediction module is used for inputting an environment parameter set into a bidirectional LSTM network, obtaining a phase drift prediction value, carrying out nonlinear calibration on the phase drift prediction value by adopting a Kalman-particle hybrid filtering algorithm, and synchronously monitoring an IQ signal by utilizing a closed-loop feedback mechanism, and the beam generation module is used for carrying out orthogonal modulation and radio frequency up-conversion processing on the IQ signal, and outputting the processed IQ signal. According to the method, the bidirectional LSTM network is combined with the Kalman-particle hybrid filtering algorithm, so that phase drift caused by factors such as carrier vibration and the like can be accurately predicted and calibrated in real time.

The invention discloses a multichannel electrophysiological signal acquisition system and an acquisition method thereof, and the multichannel electrophysiological signal acquisition system comprises a power supply module electrically connected with a controller, an analog front-end chipset, a filter module and an analog-to-digital conversion module; the analog front-end chip set is used for acquiring an analog signal and preprocessing the analog signal; the pre-processed analog signal is converted into a digital signal through a filter module and an analog-to-digital conversion module, and the digital signal is transmitted to a controller; the filter module is used for filtering and denoising; the timer module connected with the controller generates a sampling frequency, transmits the sampling frequency to the analog-to-digital conversion module, and provides a time sequence signal for the analog-to-digital conversion module. The invention discloses a multi-channel electrophysiological signal acquisition system, which solves the problem of synchronous sampling time sequence control of a high-density electrode array and eliminates signal phase deviation caused by clock jitter among multiple channels under the constraint of maintaining the total power consumption of the system. And real-time power frequency interference suppression is realized.

The invention relates to the technical field of narrow-band filters, and discloses an ultra-low power consumption narrow-band filter implementation method based on a digital phased array system, and the method comprises the following steps: S1, collecting input signal data, carrying out the adaptive preprocessing, and generating preprocessed signal data; s2, performing digital beam forming processing based on the preprocessed signal data to generate digital beam forming data; s3, performing narrowband filtering parameter matching processing according to the digital beam forming data to generate narrowband filtering parameter data; in the narrowband filtering processing process, disturbance of environmental electromagnetic interference to a target signal is identified and eliminated in real time through adaptive noise suppression and a multi-dimensional signal preprocessing mechanism, so that the system can guarantee the pointing precision of digital beam forming, the pointing offset problem in the space signal capturing process is avoided, the accuracy of narrowband signal extraction is improved, and the accuracy of narrowband signal extraction is improved. And the reliability of filtering output and the signal fidelity are enhanced.

The invention discloses a wide-range weak feature scene measurement system based on multi-sensor fusion and structured light scanning, and relates to the technical field of wide-range scene scanning, in the system, an extended Kalman filtering algorithm is adopted, fusion is carried out based on positioning data provided by an inertial measurement module and a positioning sensing module, and a wide-range weak feature scene is obtained. And the distance measurement information provided by the laser distance measurement module is combined to correct the fused positioning data, so that the precision and robustness of subsequent point cloud registration are ensured. Then, according to position information and attitude information of the binocular structured light scanning camera during scene scanning, calculating a relative rotation matrix and a relative displacement matrix of each frame of scanning point cloud data relative to the first frame of scanning point cloud data, and performing coarse registration; in the coarse registration process, all frames of point clouds can be converted into a unified coordinate system by directly utilizing global pose information, and the efficiency of coarse registration is greatly improved. And finally, extracting laser marking point clouds projected by the non-contact laser marking device in each frame of scanning point cloud data, and performing iterative precise registration on each frame of scanning point cloud data by using color features and geometric distribution rules of the extracted laser marking point clouds. In conclusion, high-precision, high-efficiency and high-robustness point cloud registration in a large-scale weak feature scene is realized, and the method can be used for realizing high-precision non-contact measurement.

The invention relates to the technical field of ground resistance detection, in particular to a single-core cable ground resistance detection method capable of resisting strong power frequency interference. High-frequency signals are injected in a non-contact mode through a coupling type capacitor clamp, background voltage and current signals are collected firstly, and injection frequency far away from power frequency and harmonic waves is selected through Fourier analysis; cancelling background noise by adopting a normalized least mean square adaptive filter to generate an enhanced signal; the signal is down-converted to a base band through digital quadrature demodulation, a Kalman filter estimates the amplitude and the phase in real time, and the state and noise parameters are periodically calibrated through a compressed sensing algorithm; and finally, loop impedance is calculated according to the voltage and current amplitudes and the phase difference to obtain grounding resistance. The method has the advantages of being non-contact, high in safety, strong in anti-interference capability, high in measurement precision and the like, and is suitable for monitoring the grounding state of the cable in a complex electromagnetic environment.

The invention relates to the technical field of equipment full-life-cycle management, in particular to an equipment full-life-cycle tracing and management and control system based on digital threads. Comprising a digital thread generation unit which is used for generating a dynamic digital thread penetrating through the whole life cycle of technical improvement purchasing, operation maintenance and decommissioning of equipment; a full life cycle data integration unit; and a business process management and control unit. According to the method, a dynamic digital thread which runs through the whole life cycle of technical improvement purchasing, operation overhaul and decommissioning of equipment is constructed, and a multi-dimensional data anchoring algorithm fused with weighted Bayesian estimation is combined, so that probabilistic association matching between heterogeneous physical data in each stage and a corresponding physical scene is realized; and a unique device identifier is bound to form dynamic association of physical state-data record-time node, so that the problems of fragmentation of data in the whole life cycle of the device and lack of association between the data and a physical scene in the prior art are solved.

The invention discloses an interactive multi-model underwater maneuvering target tracking method and system based on a pure azimuth second-order EKF. The method comprises the following steps: establishing a discrete state space model of a pure azimuth target tracking system; performing second-order linearization on the pure azimuth measurement model; calculating mixed input of a sub-filter corresponding to each model in the model set of the interactive multi-model algorithm; estimating a target state through parallel filtering of the azimuth-pure second-order EKF sub-filter corresponding to each model in the model set; updating the probability of each model in the model set; and combining the estimation results of all the sub-filters to obtain a final estimation result of the target state. According to the method, the target tracking precision can be improved, the problem of highly nonlinear measurement of the UUV on a target detection and tracking system through a passive sonar is effectively solved, good real-time performance is achieved, and real-time tracking of the UUV on the motion state of the underwater maneuvering target is achieved through adaptive matching of the real motion of the target through the interactive multi-model algorithm.

An electric linear actuator has a housing extending along a longitudinal axis. A spindle is provided in the housing and extends along the longitudinal axis. A rod is coaxially coupled to the spindle and extends from a proximal end located in the housing to a distal end located outside the housing. The rod is movable along the longitudinal axis in response to rotation of the spindle. A circuit board is located in the housing adjacent the proximal end of the rod. The circuit board includes a linear inductive sensor having a linear axis oriented parallel to the longitudinal axis. The linear inductive sensor senses an actual position of the proximal end of the rod.

Disclosed in the present invention are a delay compensation filtering method for suppressing high-frequency measurement noise and a related device. The method comprises: obtaining a real signal having time delay and amplitude distortion acquired by an inertial measurement unit on a quad-rotor unmanned aerial vehicle, and performing time delay compensation processing on the real signal to generate an initial filtering result; using an iterative filter having an angle truncation function to perform iterative optimization on the basis of the initial filtering result, and introducing an angle-limited constraint to truncate a signal having a large angle variation to maintain a smooth trajectory; and the output of the iterative filter being subject to boundary constraints centered on the initial filtering result, and if a filtering result output by the iterative filter exceeds a boundary, triggering a boundary processing mechanism to limit the filtering result within the boundary to obtain an underlying low-frequency real signal. The present invention effectively improves the signal quality, reduces the negative impact of delay caused by delay of a linear low-pass filter, and improves the smoothness and accuracy of signal processing and data transmission.

The invention provides an FI-DAC system peak nonlinear amplitude-frequency error pre-calibration method based on SVR-LWL, belongs to the technical field of amplitude-frequency error calibration, and solves the problems that the fitting precision of passband signals is reduced and the calibration effect is affected due to the fact that a traditional amplitude-frequency error calibration method is difficult to meet the actual application requirement. Comprising the following steps: step 1, pre-distortion processing is carried out on a target input signal of an FI-DAC system, ideal frequency division processing is carried out on the target input signal after pre-distortion processing by adopting a linear phase FIR digital filter, and a high-frequency sub-path signal and a low-frequency sub-path signal are obtained; 2, performing frequency mixing, filtering, analog-to-digital conversion and combination operation on the high-frequency sub-path signal and the low-frequency sub-path signal, and inputting the signals into an analog link to generate a broadband signal; and step 3, carrying out dynamic calibration on amplitude-frequency errors caused by nonlinear characteristics based on an amplitude-frequency error pre-equalizer in the analog link, generating a calibrated target signal waveform table, and outputting the calibrated target signal waveform table.

The invention discloses a multi-channel AFE signal acquisition method and system based on adaptive adjustment, and the method comprises the steps: synchronously acquiring multi-channel analog signals through a multi-channel parallel AFE module, and configuring an independent programmable gain amplifier and an anti-aliasing filter for each channel; extracting time-frequency domain characteristic parameters of each channel signal in real time; dynamically generating an optimal adjustment strategy based on the time-frequency domain characteristic parameters, and adaptively adjusting the gain coefficient, the filtering bandwidth and the sampling rate parameter of each channel through a closed-loop feedback mechanism; and abnormal signal detection is realized by adopting an inter-channel cross validation algorithm, and automatic isolation of a fault channel and intelligent switching of a redundant channel are triggered. Through dynamic parameter intelligent adjustment, multi-channel cooperative fault tolerance and energy efficiency optimization architecture, the problems of signal distortion, response lag and high power consumption in a traditional multi-channel signal acquisition system are solved, and the signal fidelity and the system reliability are remarkably improved.

The invention relates to a response performance test method and system of a microswitch. The method comprises the following steps: inputting a multi-pulse trigger sequence to a microswitch contact and collecting contact response original data; performing digital filtering processing on the contact response original data to obtain contact response time sequence data; comprehensive response time parameters including contact mechanical action starting time, electrical initial contact time and electrical stable contact time are calculated according to the contact response time sequence data; performing temperature compensation and contact rebound characteristic extraction on the comprehensive response time parameter to obtain contact rebound characteristic evaluation data; according to the contact rebound characteristic evaluation data and the comprehensive response time parameter, the response performance stability of the microswitch under the high-frequency operation condition is calculated, and a reliability evaluation result is generated. According to the invention, the problem of time sequence measurement error caused by the influence of contact jitter in the traditional test is effectively solved, and the accuracy and consistency of contact response time measurement are remarkably improved.

The invention relates to a house building construction stage carbon emission dynamic estimation method and system based on full life cycle data, and the method comprises the steps: dividing a construction region based on a BIM model, laying a multi-source sensor network, fusing static BIM data and a dynamic Internet of Things monitoring flow, building a four-dimensional space-time label data set, and carrying out the construction stage carbon emission dynamic estimation based on a double-layer dynamic correction model. Kalman filtering is utilized to eliminate instantaneous interference, LSTM transfer learning is combined to predict mechanical efficiency attenuation, a dynamic correction coefficient matrix of a mechanical aging rate, operator skills and environmental factors is embedded, and a carbon emission value is calibrated in real time; when abnormal emission is detected, a key pollution source is traced and positioned through a graph neural network, a three-dimensional thermodynamic diagram is generated in the digital twinborn model, and component-level carbon flow analysis and early warning are performed, so that the carbon emission estimation precision is remarkably improved, the abnormal traceability time consumption is shortened, and the timeliness is remarkably improved.

The invention provides a multiphase filtering sampling method suitable for satellite communication. The method comprises the following steps: receiving an original modulation signal and converting the original modulation signal into a digital signal sequence; extracting a signal feature vector and generating a processing configuration parameter; performing orthogonal decomposition to generate I / Q signal pairs and performing oversampling; initializing a multi-phase filter and generating an optimal filter coefficient; carrying out phase calibration and filtering; determining an interpolation coefficient and executing multi-stage integer interpolation and mixed precision fractional interpolation; configuring a dynamic cache system; phase reordering and synthesis are executed; spectral analysis is performed and optimization parameters are generated. According to the invention, high-precision signal processing is realized, the adaptive capability and the efficient resource utilization characteristic are realized, and the performance of a satellite communication system is remarkably improved.

The invention relates to the technical field of charging pile operation and maintenance systems, and particularly discloses a charging pile operation unmanned inspection and automatic operation and maintenance system which comprises a data acquisition module, a state initial judgment module, a fault diagnosis module and an automatic operation and maintenance module. An unmanned three-dimensional inspection network is constructed through an unmanned aerial vehicle and an Internet of Things sensor, accurate fault diagnosis is realized in combination with multi-modal feature fusion, a dynamic operation and maintenance priority is generated and a differential strategy is formulated based on a machine learning model, and a complete operation and maintenance closed loop of acquisition, diagnosis, execution, feedback and optimization is formed at the same time. According to the method, the inspection real-time performance and the fault recognition accuracy are greatly improved, the operation and maintenance resource configuration efficiency is optimized, the operation and maintenance cost is reduced, the equipment downtime can be shortened, the charging experience of a user is improved, the operation and maintenance capability can be continuously iteratively optimized through data accumulation, and powerful support is provided for stable and reliable operation of a charging pile network.

The invention discloses an engine rotating speed signal filtering processing method, device and equipment and a medium, and relates to the technical field of engine rotating speed signal processing, and the method comprises the steps: obtaining a rotating speed signal, and calculating the period of the rotating speed signal, the average time length of a high level in one period, and the period change rate; determining whether a processing position corresponding to the rotating speed signal is a gear normal position or a gear tooth missing position; and filtering interference signals at different processing positions by adopting different filtering strategies. The method is used for solving the problem that in the prior art, the filtering effect of interference signals in engine rotating speed signals is poor, and effective filtering of the interference signals in the engine rotating speed signals is achieved.

The invention relates to the technical field of data processing, in particular to a multi-channel controllable power converter for realizing filtering processing and a filtering processing method, and the method comprises the steps: obtaining the signal delay time and voltage signal data of the multi-channel controllable power converter in a current monitoring time period and a historical monitoring time period; according to peak value distribution and frequency distribution of the voltage signal data in each historical monitoring time period, analyzing a noise interference degree, and combining the noise interference degree and the difference of a pulse width parameter change trend to obtain a synchronization characteristic index; according to the signal delay time of the current monitoring time period and the historical monitoring time period, combining the synchronization characteristic index to obtain a filtering weight; and decomposing the voltage signal data in the current monitoring time period according to the filtering weight, and filtering in combination with Gaussian characteristics of decomposed signal components to obtain filtered voltage signal data. The filtering processing result has a good processing effect, and the stability of power supply output is ensured.

The invention discloses an analysis method, system, medium and equipment adaptive to faults of a water-optical storage network source system, and relates to the technical field of power system fault analysis. The system comprises a multi-source data acquisition and preprocessing module, a fault feature extraction and classification module, a fault positioning module, a fault influence evaluation module and a dynamic updating and self-adaption module; the method comprises the following steps of multi-source data acquisition and preprocessing, fault feature extraction and classification, fault positioning and influence evaluation, and dynamic updating and adaptive adjustment. The invention provides a fault analysis method adaptive to a water-light storage network source power system, various faults in the complex power system can be comprehensively and accurately analyzed, and rapid and accurate fault type identification, fault position positioning and fault influence range evaluation are realized by comprehensively considering the characteristics of each component of the system. Powerful support is provided for timely and effective fault processing, and stable and reliable operation of a power system is guaranteed.

The invention relates to the field of signal processing, and provides a signal processing method and system for a reconnaissance unmanned aerial vehicle, and the method comprises the steps: carrying out the signal decomposition of a first intermediate frequency signal obtained through the frequency mixing of an unmanned aerial vehicle signal, so as to obtain a set of signal component intrinsic mode functions; a signal analysis and processing algorithm based on deep learning is adopted to carry out waveform semantic feature extraction on the intrinsic mode function of each signal component, so that a filtered first intermediate frequency signal is automatically generated based on the significant dynamic aggregation representation of the waveform semantic feature of each signal component in a global domain; the method can dynamically adapt to the change of a signal environment and extract more representative features so as to improve the flexibility of signal processing. Compared with a traditional fixed parameter filter, the method can better deal with interference and noise in a complex environment, enables the final signal to be clearer, and improves the signal processing quality and automation level of the reconnaissance unmanned aerial vehicle.

The invention discloses an IIR (Infinite Impulse Response) filter device and a filtering method thereof, and the IIR filter device comprises a head data module which stores current input data and two pieces of input historical data; the filtering module is used for storing output data of each stage through a first position, corresponding to each stage of filtering, of the intermediate data module, exchanging addresses of the first position and a second position, reading current input data and two pieces of input historical data from a specified position through the calculation module, and outputting the data; two pieces of output historical data are read from the intermediate data module and stored in the tail data module, data are output after filtering, and lower-level filtering is triggered; the tail data module is used for storing the output data of the level of filtering as the input data of the lower level of filtering when any level of filtering is completed; wherein the designated position of the first-stage filtering is a head data module, and the designated positions of the other-stage filtering are tail data modules. According to the method, the data reading and writing time is shortened, the calculation pipeline is prevented from being interrupted, the required data storage is reduced by half, and the required clock frequency is reduced.

The invention provides an indoor positioning method and system based on dynamic mode switching and data fusion, and relates to the technical field of visible light indoor positioning, and the method comprises the following steps: deploying a VLC transmitting device and an auxiliary positioning point, constructing a VLC fingerprint database, carrying out dynamic signal detection and mode conversion, carrying out VLC fingerprint positioning, carrying out IMU data processing, and carrying out main positioning mode data fusion. VLC signal intensity distance estimation, ultrasonic ranging, IMU relative displacement estimation and standby positioning mode data fusion are carried out. According to the invention, by deploying the visible light communication transmitting device and the auxiliary positioning point, the strength and the stability of the VLC signal are monitored in real time, and dynamic positioning mode switching is realized through fuzzy logic reasoning. In the main positioning mode, VLC fingerprint matching is combined with IMU data, and accurate positioning is carried out through a Kalman filtering algorithm; and when the VLC signal is weak or unstable, a standby positioning mode is switched, and accurate positioning is carried out by fusing data through a particle filter algorithm according to VLC signal intensity distance estimation, ultrasonic ranging and I MU data.

The embodiment of the utility model provides a numerical control attenuator with temperature compensation, which comprises at least one of an attenuator circuit, a first voltage generation circuit and a second voltage generation circuit, and is characterized in that the attenuator circuit comprises a first transistor, a resistance attenuation network and a second transistor; a first voltage generation circuit for generating a first control voltage having a first relationship with the temperature; a second voltage generation circuit for generating a second control voltage having a second relationship with the temperature; the first switch is used for outputting a first control voltage or a first turn-off voltage to a grid electrode of the first transistor; the second switch is used for outputting a second control voltage or a second turn-off voltage to the grid electrode of the second transistor. According to the embodiment of the invention, the insertion loss of the numerical control attenuator and the change of the attenuation amount along with the temperature can be improved, so that the numerical control attenuator can maintain stable performance at different temperatures.