901 results about "Root mean square" patented technology

The invention relates to the technical field of fault prediction, in particular to a centrifuge fault prediction system based on machine learning, which comprises a data channel synchronization module, a multi-dimensional feature extraction module, a state evolution index construction module, a trend aggregation trajectory recognition module and a fault section recognition module. According to the method, different types of data are synchronously aligned by a multi-channel signal segmentation processing mechanism based on a periodic state, a state evolution sequence is constructed in combination with a unified sampling structure based on a time scale, a state characteristic track is established through a multi-dimensional parameter set, a trend change index is constructed by means of a difference root-mean-square between adjacent states, and the state evolution sequence is analyzed. According to the method, the aggregation section is recognized and the trajectory deviation frequency is counted by utilizing continuous trend mutation, so that dynamic migration of the trajectory boundary and intelligent recognition of the fault section are realized, the boundary failure problem caused by static preset conditions is avoided, and the continuous prediction stability of long-period equipment and the application range under a non-standard working condition are effectively enhanced.

The invention provides a soft rock tunnel surrounding rock parameter dynamic identification method and system based on data driving, and relates to the technical field of underground tunnel mechanical parameter dynamic identification, and the method comprises the steps: obtaining multi-element tunnel surrounding rock parameters, and building a joint probability distribution model of the multi-element surrounding rock parameters based on a Copula theory; performing Monte Carlo simulation, and generating a high-dimensional parameter sample library meeting physical constraints in the parameter constraint space based on the joint probability distribution model; establishing a tunnel three-dimensional numerical model and performing automatic numerical simulation to generate multivariate response data; constructing a Kriging agent model of a Gaussian kernel function based on multivariate response data training, establishing a nonlinear mapping relation between parameter input and deformation output, constructing an inversion objective function by taking the minimum root-mean-square error of multi-measurement-point displacement as an objective, and performing inversion solution by using an adaptive particle swarm optimization algorithm to obtain inversion identification parameters, and a dynamic feedback mechanism is constructed to realize adaptive tracking of the time-varying characteristics of the surrounding rock parameters.

The invention discloses a microplastic transport simulation and risk identification method based on multi-factor coupling, which comprises the following steps: by coupling a hydrodynamic model and a microplastic transport model, comprehensively considering multiple environmental factors such as tide, water level, runoff, wind speed, wind direction and the like, constructing a wave flow coupling microplastic migration and diffusion model suitable for complex hydrodynamic conditions of a Pearl River estuary; the model is calibrated and verified through measured data, and the precision of the model is evaluated by using indexes such as a Nash efficiency coefficient and a root-mean-square error; in combination with the simulation result and the spatial distribution characteristics of the typical sensitive area, representative sections and stations are selected, and annual-scale micro-plastic concentration change analysis is carried out; and further introducing an ecological risk index to carry out regional ecological risk grade identification, and determining a micro-plastic high-risk area and influence main control factors. The method has high regional adaptability and expansibility, and scientific support and technical reference can be provided for prevention and control of microplastic pollution of estuary and coastal water.

The invention discloses a wind profile radar radial speed quality control and horizontal wind field inversion method. According to the method, through the steps of multi-mode detection splicing, signal-to-noise ratio threshold value quality control, beam consistency inspection, horizontal wind component inversion, space and time continuity inspection and the like, the quality control process of the wind field data is optimized, the precision of the horizontal wind field data is remarkably improved, and particularly, the root-mean-square error and deviation in high-level data are remarkably reduced. The core innovation comprises a mode splicing strategy based on sounding data comparison, dynamic signal-to-noise ratio threshold calculation, threshold setting of beam consistency check and a wind component compensation algorithm when a vertical beam is missing. Through experimental verification, compared with a traditional wind profile radar data processing method, the wind profile radar data processing method has remarkable advantages in data accuracy and stability.

The invention discloses a visible light and infrared fusion target detection method and system for an all-time unmanned aerial vehicle scene, and belongs to the technical field of unmanned aerial vehicle aviation and intelligent image processing. The method comprises the following steps: analyzing an original visible light image to calculate average brightness and root-mean-square contrast, and quantifying scene illumination conditions; distortion correction and registration are carried out on the original image; a parallel backbone network is adopted to extract visible light and infrared twinborn feature maps, the number of channels allocated to two modal features is dynamically adjusted through 1 * 1 convolution according to illumination condition parameters, and then a dual-light fusion network containing a gated convolution block is utilized to perform weighted fusion; and target prediction is carried out through the progressive feature pyramid network. Through an illumination adaptive fusion strategy and targeted data enhancement, the precision and robustness of target detection in a complex all-day scene are significantly improved, and the method is suitable for real-time target detection tasks of the unmanned aerial vehicle.

The invention provides an Internet of Vehicles broadcast frame radio frequency fingerprint identification method based on LMMSE channel estimation. The method comprises the following steps: obtaining subcarrier data in a resource grid based on an obtained signal of a physical side link broadcast channel; performing root-mean-square delay expansion and channel autocorrelation matrix construction based on the subcarrier data, and combining priori signal-to-noise ratio regularization and time domain windowing operation to obtain a channel estimation value; initial radio frequency fingerprint features are obtained through the channel equalization and the channel estimation value, and the improved neural network and the initial radio frequency fingerprint features are utilized to carry out classification identification on the Internet of Vehicles equipment. Influences of noise and channels on fingerprints are effectively considered, and the purpose of extracting fingerprints of different devices in a complex environment more accurately is achieved.

The invention relates to a rainfall nowcasting method based on U-KAN grading loss weighting and frequency self-adaption, which comprises the following steps: (1) carrying out quality control and screening on a radar puzzle, and establishing a data set; (2) dividing a training set, a verification set and a test set, and standardizing; (3) constructing a U-KAN model, selecting training parameters and inputting data: combining a traditional Unet structure with a KAN network to construct the U-KAN model; then performing model training to obtain a prediction result; the prediction result is restored to the original magnitude through destandardization; (4) introducing a loss function based on a root-mean-square error and grade weighting in a model training stage, and performing post-processing on model output by adopting a frequency deviation correction method; (5) integrating and averaging the forecast products processed by the two complementary strategies, and recording the forecast products as U-KANE; and (6) predicting a rainfall result in the next three hours by using radar echo data in the past one hour, and outputting a rainfall short-term and imminent forecast result by the U-KANE.

The invention provides a slope displacement monitoring method and system based on reinforcement learning and enhanced Kalman filtering, and the method comprises the steps: carrying out the preprocessing of displacement data collected by Beidou, and carrying out the abnormal value elimination, missing value interpolation and time consistency inspection; establishing a Kalman filtering model containing displacement and speed state vectors, and initializing a process noise covariance matrix Q and an observation noise covariance matrix R as initial filtering parameters; q and R matrixes are dynamically optimized through a PPO reinforcement learning algorithm, and parameter self-adaptive adjustment is achieved; carrying out displacement trend analysis on the filtered output data, marking abnormal trend data by adopting a statistical test and trend inflection point recognition algorithm, and feeding back a root-mean-square error of the abnormal trend data to a PPO algorithm to carry out parameter readjustment; data stage changes are analyzed based on a sliding window technology, independent experience playback buffer areas are set for data in different stages in PPO, and associated updating of filtering parameters is achieved. According to the invention, the precision and reliability of slope displacement monitoring are improved.

The invention relates to the technical field of electroencephalogram signal processing, and provides a brain-computer AI glasses electroencephalogram signal multi-dimensional quality dynamic detection method, which comprises the following steps: preprocessing collected electroencephalogram signals; performing task frequency band component analysis on the channel signals subjected to the matrix rank analysis, and calculating a power proportion of a target frequency band; performing maximum gradient analysis on the channel signal, and calculating the maximum gradient value of the channel signal; performing root-mean-square analysis on the channel signal, and calculating a root-mean-square value of the channel signal; performing fluctuation amplitude analysis on the channel signal, and calculating a peak-to-peak value of the channel signal; performing brain hemisphere symmetry analysis on the left and right homologous channel signals, calculating the correlation and mean square error of the channel signals, and setting a dynamic threshold according to the frequency band of the channel signals; obtaining an electrode impedance value, and mapping the electrode impedance value into an impedance score through a preset exponential attenuation function; the final quality score of the channel is obtained, and dynamic detection and real-time score updating of the electroencephalogram signal quality are achieved.

The invention relates to the technical field of motor control, in particular to a control and parameter intelligent optimization method for an anti-impact high-precision PMSM (permanent magnet synchronous motor) feeding servo system, which comprises the following steps of: S1, establishing a physical structure and a dynamic model of a PMSM driving feeding system; s2, designing a variable gain fractional order super-spiral sliding mode controller VGFSTSMC, realizing finite time convergence of position tracking errors by dynamically adjusting and controlling gain coefficients, and inhibiting system jitter; s3, designing an adaptive sliding mode disturbance observer ASMDO, and estimating and compensating unknown disturbance in real time through adaptive gain adjustment and an integral sliding mode surface; s4, identifying a system Stribeck friction model based on a least square method, and inputting the system Stribeck friction model as a friction feedforward compensation FFC to a current loop to reduce the disturbance uncertainty of the system; s5, dynamically adjusting and optimizing control parameters of the VGFSTSMC and the ASMDO by adopting an optimization algorithm so as to minimize a root-mean-square value RMSE of a position tracking error and a maximum instantaneous error; and S6, VGFSTSMC, ASMDO and FFC are combined with an optimization algorithm, and the PMSM is driven to realize high-precision position control.

The invention discloses a self-adaptive threshold SAMP reconstruction method for a power quality disturbance signal. According to the method, a compression observation value is obtained by constructing a random Gaussian observation matrix, and sparse representation is carried out on an original signal by using discrete Fourier transform. In the iterative reconstruction process, the spectrum amplitude difference is introduced for the first time to serve as an adaptive termination basis, and automatic adaptation of different noise levels and different disturbance characteristics is achieved in combination with a dynamic threshold update function. According to the method, the problems of traditional SAMP sparseness overestimation and redundant iteration are effectively avoided, and the calculation load is remarkably reduced. Compared with an OMP method, an original SAMP method and the like, the method has the advantages that the number of iterations can be reduced by 30%-60%, the reconstruction signal-to-noise ratio is increased by 2-5 dB, the root-mean-square error is reduced by 10%-25%, higher robustness and real-time performance are achieved in power quality disturbance signal reconstruction, and the method is quite suitable for scenes such as compressed sampling, edge calculation and high-speed signal reconstruction in a power quality monitoring system.

The invention provides a rolling bearing digital twinning dynamic evolution method and system based on continuous learning, and belongs to the technical field of bearing life prediction. The method comprises the following steps: processing a bearing monitoring signal through short-time Fourier transform to generate standardized time-frequency data; constructing health indexes based on the index degeneration function and dividing health levels; training a life prediction model by using the extended LSTM network and taking the time-frequency data as input; real-time data is collected through a fixed time window to predict the life, and when the root-mean-square error of a predicted value and an actual value exceeds the limit, the edge device is triggered to upload new data; evaluating parameter importance in combination with a Fisher information matrix, and dynamically adjusting a regularization intensity updating model; and monitoring the data standard deviation in real time, triggering shutdown when the data standard deviation exceeds the limit, otherwise, predicting the remaining life by updating the model, and stopping when the remaining life reaches the threshold value. According to the method, dynamic evolution of the digital twin model is realized through continuous learning, and the industrial equipment state monitoring and predictive maintenance capability is effectively improved.

The invention discloses a soil water content inversion method based on improved combination roughness, and relates to the field of remote sensing, and the method comprises the steps: synchronously obtaining a Sentinel-1 radar image and a Sentinel-2 optical image, and extracting different polarization backscattering coefficients, local incident angles and normalized water body indexes through preprocessing; the method comprises the following steps: generating a bare soil simulation backscattering coefficient data set, removing vegetation scattering contribution by using a water cloud model, obtaining a real bare soil backscattering coefficient, constructing a training set and a verification set containing actually measured soil water content, constructing a lookup table based on double models, and calculating the water content of the bare soil by minimizing a root-mean-square error between simulation and the real backscattering coefficient. Global search is carried out in a preset parameter space to determine an optimal earth surface root mean square height and a correlation length, a novel polynomial combination roughness is constructed, a physical correlation between the roughness and a backscattering coefficient is established, a dual-polarization empirical equation set is constructed, and simultaneous solution is carried out after parameters are optimized according to a criterion; according to the method, a more reasonable inversion result of the soil water content in a large range can be obtained.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention provides a bearing fault diagnosis method based on a one-dimensional local binary pattern and a Hankel matrix. The bearing fault diagnosis method comprises the following steps: acquiring a discrete vibration signal; performing first-order differential operation on the discrete vibration signal to obtain a differential signal; performing inherent time scale decomposition on the differential signal to obtain an inherent rotation component signal; performing quantization and signal reconstruction on each inherent rotation component signal by taking a root mean square as a quantization criterion of a one-dimensional local binary mode method to obtain a decimal feature signal; constructing a Hankel matrix of the decimal characteristic signal and performing signal reconstruction according to a covariance matrix of the Hankel matrix; performing spectral analysis on the reconstructed signal, calculating the fault characteristic frequency of the bearing, and then judging the state and the fault type of the bearing through a frequency component obtained through spectral analysis and the fault characteristic frequency of the bearing obtained through calculation. According to the bearing fault diagnosis method, noise can be effectively suppressed, the bearing fault feature information can be effectively extracted, and the bearing state and the fault type can be accurately identified.

The invention relates to the technical field of artificial intelligence and electric power system prediction, and discloses a wind power short-term output prediction method based on multi-modal data, and the method comprises the steps: obtaining the multi-modal data, such as historical output, numerical weather forecast, actually measured weather of an anemometer tower, landform and fan operation state; performing sliding window segmentation on the output sequence and identifying a mutation interval; calculating a local optimal alignment path of each mode in the mutation interval based on a dynamic time warping algorithm; non-uniform resampling is carried out in this way, and a time-synchronized multi-modal alignment feature sequence is generated; and inputting a hybrid neural network formed by a gating circulation unit and an attention mechanism, and outputting a high-precision output prediction value in the next 15 minutes. The system comprises corresponding function modules. According to the method, through dynamic time alignment and cross-modal feature fusion, the wind power short-term prediction precision is remarkably improved, the root-mean-square error in a sudden change scene is reduced by 23.7%, and reliable support is provided for power grid dispatching.

The invention discloses a power distribution network transient characteristic prediction method based on supervised learning, and relates to the technical field of power distribution network state prediction, and the method comprises the steps: collecting historical operation data through a power distribution network monitoring system, carrying out the data preprocessing, and obtaining standardized multi-dimensional time series data; carrying out transient feature extraction, constructing a high-dimensional feature set, and carrying out feature dimension reduction according to a transient event tag to generate a feature subset; inputting the feature subset into a mixed supervised learning model of a gradient boosting decision tree GBDT and a long short-term memory network LSTM for joint training to obtain a transient feature prediction result; and calculating a root-mean-square error according to the transient characteristic prediction result and the real-time monitoring observation value of the power distribution network, and dynamically adjusting hyper-parameters of the supervised learning model based on a Bayesian optimization algorithm. According to the method, the detection accuracy can be improved, the calculation complexity can be reduced, and the discrimination capability and the time sequence prediction capability of the model are considered.

The invention discloses a key parameter long-time-sequence prediction method for a complex process industry, and the method comprises the steps: collecting multivariable sensor data in the process industry, and constructing a high-dimensional long-time-sequence prediction data set; constructing a PatchConvRNN prediction model by combining time slice embedding, dimension decoupling convolution, depth separable convolution and a recurrent neural network based on a sequence-to-sequence normal form; a point value-statistical mixed loss function is adopted, the point prediction precision, the sequence mean value and the standard deviation consistency are optimized at the same time, a prediction model is trained in combination with an optimization algorithm, and network model parameters are adjusted; and comprehensively evaluating the prediction model through a root-mean-square error, an average absolute percentage error and a standard deviation average absolute error. According to the method, high-precision prediction and fluctuation maintenance of the key time sequence variables under the complex working condition of the industrial process are achieved, and powerful support is provided for quality control and predictive maintenance of the production process.

The invention relates to the technical field of attitude determination of a photoelectric pod of an unmanned aerial vehicle, in particular to an MEMS-IMU dual-mode correction attitude determination method for the photoelectric pod of the unmanned aerial vehicle. Comprising the following steps: establishing a damping system second-order transfer function model, and outputting an attenuation coefficient matrix; based on MEMS-IMU three-axis motion parameters, distinguishing a large maneuver turning state and a linear flight state; respectively compensating a roll angle, a pitch angle and a course angle of the MEMS-IMU by using the main inertial navigation according to different states; converting the corrected attitude angle into a quaternion, and optimizing and outputting a fused quaternion through a gradient descent method; taking the fused quaternion as an input, constructing a seven-dimensional state space model, and outputting an error compensation amount through Kalman filtering; dynamically adjusting the main inertial navigation weight according to the accelerated speed root mean square; and outputting a final attitude angle in combination with the error compensation amount and the weight, and verifying the final attitude angle through GNSS (Global Navigation Satellite System) position inversion. The method has the advantages that cross interference is avoided; therefore, the method is suitable for the photoelectric pod scene of the unmanned aerial vehicle.

According to the power cable fault diagnosis method based on multi-feature fusion, accurate diagnosis of partial discharge, an early fault and a short-circuit fault of a power cable is realized by comprehensively considering feature parameters such as partial discharge, temperature, current and voltage. Samples are obtained by adopting a simulation analysis method, cable fault identification is realized through an artificial intelligence algorithm, and a rapid and accurate method is provided for cable fault identification and positioning. For cable fault type identification, the CNN-BiLSTM-based cable fault identification method is provided, and the method comprises the following specific steps: firstly, obtaining fault current of cable monitoring points M and N, taking the root mean square of the current after the fault as a feature value, inputting the feature value into an artificial intelligence algorithm, training through the artificial intelligence algorithm, and finally carrying out algorithm iteration to obtain an optimal solution; and a cable fault type is obtained through algorithm identification. In order to consider fault types under different conditions, different fault types of the cable are identified by using an artificial intelligence algorithm.

The invention provides a traffic flow prediction method combining a dynamic graph neural network and a Mama mechanism. The future short-term traffic flow is predicted by using historical traffic data. According to the method, firstly, normalization preprocessing is carried out on traffic state data collected by multiple sensors, a training sample is generated by adopting a sliding window, and a traffic flow value in the next one hour is predicted according to data in the past 24 hours. On the basis of the model structure, a time modeling module composed of multiple layers of MambaBlocks is constructed and used for capturing historical time sequence dependence; constructing a spatial modeling module of dynamic graph convolution, and combining a static adjacency matrix of the road network with a learnable adaptive adjacency structure to extract spatial association; and finally, the outputs of the modules are fused, and a prediction result is obtained through a prediction output module. In the training process, a Huber loss function is used as an optimization target, and evaluation indexes such as a mean absolute error (MAE), a root mean square error (RMSE) and a mean absolute percentage error (MAPE) are used for evaluating the performance of the model. According to the method, the traffic space-time dynamic characteristics are effectively mined, and the long-range dependence modeling capability and the prediction precision are improved.

The invention discloses a face gear planetary transmission system dynamic uniform load test platform and test method, and belongs to the field of gear transmission, and the test platform comprises a rack, a drive motor, a step-up gear box, a face gear planetary transmission system, a magnetic powder brake, a torque sensor, a slip ring, a stress strain test system and the like. The face gear planetary transmission system comprises an input face gear, a planet wheel, a planet carrier, a fixed face gear and the like. According to the dynamic uniform load test, stress values at the tooth root of the face gear and the tooth root of the planetary gear are measured mainly through strain gauges, and the corresponding relation between the tooth root stress and the meshing force at the gear meshing point is obtained through finite element simulation. And calculating the dynamic uniform load coefficient according to the dynamic meshing force root-mean-square values and the average values obtained at the tooth roots of the face gear and the planetary gear. By testing and analyzing the dynamic uniform load coefficient, the load distribution characteristic of the face gear planetary transmission system can be accurately revealed, a scientific basis is provided for structural parameter optimization, meshing performance improvement and reliability design of the face gear planetary transmission system, and then accurate and high-performance design of the transmission system is achieved.

The invention discloses a construction method of a multi-underwater robot cooperative control system, which relates to the field of underwater robots, and establishes a multi-parameter coupled data acquisition and preprocessing mechanism by using a Doppler current profiler ADCP, an inertial measurement unit IMU and controller software. A combined algorithm of sliding median filtering and Kalman filtering is adopted, and data disturbance caused by environment noise and attitude drift is effectively removed; and through abnormal point triple standard deviation determination and double adjacent point interpolation completion, stable correction of sampling data is realized. A standardized data set obtained after normalization processing unifies parameter scales of different physical magnitudes, so that the local average flow velocity, the flow velocity fluctuation root mean square and the average acceleration can be directly compared in a feature space. Therefore, according to the method, the high-consistency expression of the water flow disturbance characteristics in time and space is realized, so that the synchronous measurement precision of multiple underwater robots in a non-uniform flow field is remarkably improved.

The invention discloses a self-piercing riveting simulation prediction and parameter optimization method and related equipment, and relates to the technical field of mechanical connection numerical simulation, and the method comprises the steps: building a material failure model according to material failure information; setting boundary conditions of the self-piercing riveting joint forming simulation model according to test working conditions to obtain section data and a joint model of the self-piercing riveting joint; establishing a mechanical property simulation model of self-piercing riveting according to the mechanical property sample based on the joint model, and setting boundary conditions of the mechanical property simulation model; calculating according to the mechanical property simulation model to obtain a load displacement curve; optimizing parameters of the material failure model by taking the undercut amount and the bottom thickness size error of the cross section and the root mean square error of the load displacement curve as optimization targets; the optimized parameters are substituted into the self-piercing riveting joint forming simulation model and the mechanical property simulation model, corresponding boundary conditions are set for simulation, and a simulation result is obtained. According to the method, the simulation precision and the process optimization efficiency are improved.

The invention provides a vibration nonlinear signal energy analysis method and system based on variational mode decomposition, and relates to the technical field of signal processing, and the method comprises the following steps: obtaining a monitoring signal of a vibratory roller, determining a corresponding state based on root-mean-square data of the monitoring signal, and determining the state of the monitoring signal; performing segmentation processing on the monitoring signal based on the state corresponding to the monitoring signal to obtain a signal segmentation result; performing variational mode decomposition processing on the signal segmentation result to obtain at least two second mode components; performing Hilbert transformation processing on the second modal component, and combining the instantaneous frequency and the instantaneous amplitude of each modal component obtained through transformation to obtain frequency spectrum information of the monitoring signal; and performing marginal spectrum calculation and integration on the frequency spectrum information to obtain the total energy of the monitoring signal. According to the method, interference signals can be efficiently identified and eliminated in the vibration signals, so that the precision and robustness of compaction quality evaluation are improved.

The invention discloses a circuit breaker fault diagnosis system and method, and particularly relates to the technical field of circuit breaker diagnosis. According to the invention, through the root-mean-square value, kurtosis and zero-crossing frequency of the time-domain signal and the dominant frequency and high-frequency energy ratio parameters of the frequency domain, and by combining the stroke length, the average displacement speed and the root-mean-square error of the displacement time curve, the mechanical difference evaluation index of the circuit breaker is obtained, and the multi-dimensional evaluation of the mechanical state of the circuit breaker is realized. According to the method, initial similar cases are screened through early warning depth values, parameters are converted into coordinate points in a rectangular plane coordinate system, and the similarity between a current fault and an abnormal case is quantified through a line distance value and an Euclidean distance, so that subjective judgment errors are avoided, and the accuracy of abnormal reason estimation is improved; the problems that in the prior art, existing fault diagnosis mostly depends on single parameter analysis, and the fault of the circuit breaker cannot be rapidly positioned by combining a multi-parameter analysis result and historical data are solved.

The invention relates to the field of parameter identification, and provides a photovoltaic model parameter identification method and system based on an improved artificial bee colony algorithm, and the method specifically comprises the steps: obtaining a to-be-identified photovoltaic model, and constructing an optimization objective function which is based on a root-mean-square error; initializing an artificial bee colony algorithm and performing terrain complexity evaluation to obtain a terrain type, the terrain complexity evaluation being based on the population fitness difference matrix; and according to the terrain type, adaptive search is carried out to obtain a final identification result, and the adaptive search is based on a smooth terrain processing mechanism and a rugged terrain processing mechanism. According to the method, the defect of a traditional artificial bee colony algorithm facing a complex terrain area is avoided, and the accuracy and efficiency of photovoltaic model parameter identification are improved.

The invention discloses a subway passenger flow prediction method, system and device considering subway-bus transfer, and a medium, and the method comprises the following steps: obtaining the historical passenger flow of a subway station in a certain region within a period of time and the historical passenger flow of a corresponding bus station around each subway station, and forming a data set; dividing the data set into a training set and a test set according to a time sequence; constructing a dynamic space-time decomposition and fusion network, wherein the dynamic space-time decomposition and fusion network comprises a time sequence decomposition module, a dynamic graph convolution module, a gated cross-modal attention fusion module and a dual-path space-time adaptive fusion module; and training the dynamic space-time decomposition and fusion network by using the training set and the test set, and predicting the subway passenger flow volume by using the optimized dynamic space-time decomposition and fusion network by using a mean absolute error, a root-mean-square error and a weighted mean absolute error percentage as performance evaluation indexes. The prediction precision is improved, and the practicability and generalization ability of the model are enhanced.

The invention provides a multi-objective structure optimization method and system suitable for a wheel excavator. Comprising the steps that a three-dimensional model which has the same actual size as an experimental test prototype of the wheel excavator and comprises a cab, an upper frame and other related parts is established; and comparing frequency domain response curve results obtained by experimental tests and verifying the accuracy of vibration performance prediction of the finite element model. Sensitivity analysis is carried out, and parts finally used for follow-up multi-target optimization are screened out according to the influence degree on the performance of the multiple targets; and performing test design according to the screened parts to obtain sample points, and importing the sample points into a finite element model for simulation to obtain a sample response data set. And fitting the agent model according to the sample data, optimizing by using a genetic algorithm to obtain an optimal solution, and sorting according to the magnitude of an acceleration root mean square value to select an optimal scheme. And importing the optimal scheme obtained according to the steps into a finite element model, and comparing the optimal scheme with an original scheme to verify the rationality of a new optimization scheme.

The invention discloses a root mean square value RMS detection method and system, and belongs to the technical field of measurement and control. Wherein a to-be-detected voltage signal is preprocessed by the filter amplification circuit, a square wave signal synchronous with a sine wave zero crossing point is generated through the zero crossing comparison circuit, a signal period boundary is identified, and an integral number of complete and continuous signal periods are extracted in real time through the period detection module; the cycle information identified by the cycle detection module is transmitted to the data storage and processing module, meanwhile, the sampling signal of the high-speed ADC module is transmitted to the data storage and processing module, and sine wave data sampled by the high-speed ADC module is subjected to point-by-point square operation; the square values in a complete period are accumulated and summed, and an original sampling sequence is not stored; fIR digital filtering is carried out on the RMS values of continuous C periods through a digital filtering module; and the dynamic response performance of the system under different frequency bands is remarkably enhanced while the measurement precision is improved.