40 results about "One step prediction" patented technology

The embodiment of the invention discloses a digital key ranging value filtering method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring ranging information of a plurality of UWB anchor points in real time; obtaining the position change trend of the digital key according to the distance measurement information; under the condition that the effective distance measurement value does not exist, a preset target position is obtained according to the positioning result corresponding to the moment t of the previous effective distance measurement value, the position change trend and the unlocking and locking state of the vehicle terminal, and a predicted distance is obtained according to the distance relation between the effective distance measurement value at the moment t and the preset target position; obtaining a prediction duration T according to the prediction distance; in the T, when an effective distance measurement value is not detected, state one-step prediction of a Kalman filtering algorithm is executed to obtain a filtered distance measurement value, and when the effective distance measurement value is detected, the state one-step prediction is terminated, and a filtering estimation value is obtained according to the Kalman filtering algorithm to serve as the filtered distance measurement value; and obtaining a positioning result of the area outside the vehicle according to the filtered distance measurement values of the plurality of anchor points.

The invention provides a model-free current prediction control method for a permanent magnet synchronous motor based on a Lyapunov function method, and the method comprises the steps: building a q-axis control channel, constructing a discrete prediction current model without motor parameters, and introducing a parameter alternative item vector to represent the influence of the precise parameter change of the motor on a prediction model; performing first-step prediction on the current state by using the discrete prediction current model; constructing a candidate voltage set, calculating a measurable output quantity regression error by utilizing a regression error model according to the current operation state data, estimating a current parameter alternative item vector by utilizing the parameter alternative item vector self-adaptive law based on the regression error, and for each candidate voltage, calculating the current parameter alternative item vector according to the parameter alternative item vector self-adaptive law; performing second-step prediction on the current state by using the discrete prediction current model; calculating a cost function corresponding to each candidate voltage, and selecting a cost candidate voltage as an optimal control voltage; according to the method, the robustness of the system is improved, and the calculation complexity is reduced.

The disclosed techniques automatically identify cyber-security attacks and predict attack next steps. Descriptions of previously observed cyber-attack campaigns are decomposed into attack campaign steps. Real-time security incident signals are generated by cybersecurity software. Attack campaigns are identified by mapping attack campaign steps to security incident signals. Custom-generated telemetry queries are executed to determine if a missing attack campaign step occurred. A machine learning model generates embeddings for attack campaign steps, security incident signals, and telemetry query responses. A security incident signal or a telemetry query response matches an attack campaign step when their embeddings are within a defined distance. A security alert may be raised when most or all of the attack campaign steps of a particular attack campaign are matched. Attack campaign steps that are not matched to security incident signals or telemetry query results are predicted as attack next steps.

This invention discloses a distributed optimization state estimation method for asynchronous sampling rates under stochastic topology. The method is as follows: 1. Establish an asynchronous sampling rate time-varying nonlinear dynamic model with different system state update rates and measurement sampling rates; 2. Transform the dynamic model into a single-rate time-varying nonlinear dynamic model; 3. Design a state estimator under stochastic topology; 4. Calculate the state estimator at q... k The gain matrix K of the estimator at time step i (q k ) and G i (q k ); V. K i (q k ) and G i (q k Substituting this into the estimator, we obtain q. k+1 VI. Calculate the upper bound of the one-step prediction error covariance matrix; VII. [The text abruptly ends here, likely due to an incomplete sentence or missing information.] i (q k ) and G i (q k Substitute the upper bound of the one-step prediction error covariance matrix into the formula to calculate q. k+1 The minimum upper bound of the one-step prediction error covariance matrix at time step q; let q k =q k+1 Execute step three until q is satisfied. k+1 =K. This invention solves the problem that existing state estimation methods cannot simultaneously handle distributed optimization state estimation problems with stochastic nonlinearity and asynchronous sampling rates under stochastic topologies.

The invention provides an MEMS navigation attitude correction method and device based on motion acceleration estimation, and belongs to the field of MEMS navigation algorithms. The method comprises the following steps: carrying out filtering and moving average processing on real-time angular velocity and acceleration data, adding Markov modeling on a motion acceleration state on the basis of a current navigation system model, and carrying out one-step prediction through state transition. And when the heading attitude filtering admission condition is met, separating the motion acceleration in the measurement matrix, and estimating the current navigation state of the carrier by using Kalman filtering. And finally, east and north mathematical platform deflection angles with motion acceleration influence deducted are used during filtering correction. According to the method, accurate modeling can be carried out on the motion acceleration under the large maneuvering condition, the attitude error correction probability of the MEMS attitude and heading system is reduced, the convergence time after a carrier is converted into stable flight from maneuvering is shortened, and the product performance and the use experience are improved.

This invention discloses a one-step snap-fit ​​prediction method for flexible flat cable based on virtual-real fusion and adaptive gradient. First, in the real operating environment of mobile phone flexible flat cable assembly, real tactile images are acquired using a tactile sensor. Then, based on the real tactile images, the Material Point Method (MPM) is used to simulate the real tactile sensation in a virtual environment, generating a virtual tactile image. Real and virtual tactile sensations together constitute a digital twin environment. By performing adaptive gradient calculation on the virtual and real tactile images in the digital twin environment, a one-step prediction model based on adaptive gradients is established. The model uses a CNN+LSTM network to predict the tactile images, generating predicted tactile images, which are then compared with ideal tactile images. Then, by encoding and decoding the tactile images, a one-step snap-fit ​​guidance strategy is obtained and applied in the real operating environment. The one-step prediction model based on adaptive gradients is trained and iterated in the digital twin environment.

The application discloses a kind of adaptive robust Kalman filtering integrated navigation methods based on MVC, mainly include: obtaining sensor real-time data;Carry out Kalman filtering state step prediction;Adaptive robust Kalman filtering estimation parameter matrix and adaptive factor are utilized;Carry out Kalman filtering measurement update;Output integrated navigation result.The application solves the problem that when system process noise is uncertain and system measurement noise is abnormal, the precision of integrated navigation result decreases or even diverges.

The present application belongs to the technical field of ship working condition prediction, and discloses a rotating speed prediction method for a ship gas-electric hybrid power system. A ship gas-electric hybrid power propeller rotating speed prediction model is constructed based on an adaptive neuro-fuzzy inference system (ANFIS); a difference between the obtained predicted rotating speed and the actual rotating speed is obtained; an improved rotating speed prediction model is constructed using the obtained difference and the used rotating speed information; and future rotating speed prediction is performed through the constructed improved rotating speed prediction model. The present application uses the initially constructed rotating speed prediction model to obtain the first-step predicted rotating speed, and provides the difference between the predicted rotating speed and the actual rotating speed for the improved prediction model; the improved rotating speed prediction model is constructed according to the obtained difference and the initial rotating speed information, so as to improve the rotating speed prediction accuracy and achieve the real-time prediction effect within a given time step.

The application discloses a kind of laser radar synthetic wind speed and wind direction processing method based on Kalman filtering, through the data detected by laser radar is calculated by Kalman filtering algorithm, specifically for the weight of sample point and sample point set one-step prediction, system state weighted mean and covariance matrix, measurement point set one-step prediction, measurement system weighted mean and covariance matrix, Kalman filtering gain and update system state value, update covariance value calculation, with prior state input and prior covariance matrix selects sample, obtains new system state weighted mean and covariance as the parameter of Kalman filtering by the nonlinear transformation of inversion algorithm, under the premise that the mean of wind speed and wind direction inversion error at hub is zero and unchanged, reduce the standard deviation of inversion error.

The invention discloses a nonlinear pipeline system state estimation method and system based on unscented Kalman filtering and a storage medium, relates to the field of network control, and aims to solve the problems of limited state estimation precision and high power consumption caused by signal attenuation, nonlinear characteristics and limited communication resources in the existing method. Comprising the following steps: 1, establishing an oil pipeline system dynamic model, and discretizing to obtain a state space model; 2, designing a dynamic event triggering mechanism, and constructing an amplification forwarding relay system to obtain amplification measurement signal data; step 3, setting initial values of state estimation and a covariance matrix; step 4, weighting 2n + 1 Sigma sampling points obtained by unscented transformation according to a weight coefficient to obtain one-step prediction and a one-step prediction error covariance Pj + 1j; 5, designing a filter according to a system phenomenon, calculating an estimation error, calculating an estimation error covariance upper bound matrix by using a matrix inequality, and solving a filtering gain matrix Kj + 1; and step 6, executing the step 4 and the step 5 until the total duration is reached.

The application discloses a kind of optimization distributed filtering method of target tracking system under work cycle scheduling strategy, the method is as follows: one, the dynamic model of target tracking system with state constraint and probability quantization is established;Two, distributed filter design is carried out to dynamic model under work cycle scheduling;Three, the upper bound of one-step prediction error covariance matrix is calculated;Four, filter gain matrix is calculated;Five, it is obtained in distributed filter by substituting, whether the total length of sensor network is reached, if, then execute six, if, then end operation;Six, the upper bound of filtering error covariance matrix is calculated;Let, execute two, until meet. The application solves the problem that existing distributed filtering method cannot simultaneously process sensor network with state constraint, probability quantization and work cycle scheduling, leading to the problem of reduced filtering performance.

The invention provides a double-view feature matching method based on a paired motion field, and the method comprises the following design steps: 1, for a given image feature matching image pair, employing an SIFT algorithm to extract feature points; 2, designing a pairwise motion vector field construction module; 3, designing a feature fusion module; and 4, further predicting the probability of each matching pair serving as a real matching point (namely an inner point) by using the initial matching pair processed by the constructed paired motion vector field and the feature fusion module. And 5, taking the obtained probability set and the corresponding set as input, and estimating an essential matrix by using a weighted eight-point algorithm. And F, iteratively executing the step C to the step E for five times, calculating cross entropy loss according to a predicted classification result and a real category result in each iteration, calculating regression loss in combination with a predicted essential matrix and a real essential matrix to guide network training, and finally obtaining a double-view feature matching model with optimal performance.

The application discloses an optimization method, system and device for predicting uplink throughput based on RSRP and a medium. The method comprises the following steps: constructing a data set comprising RSRP values and corresponding uplink throughputs; constructing an RSRP and uplink throughput mapping relationship to obtain a mapping model; performing multi-step prediction on the time sequence of RSRP based on an improved LSTM model, wherein the improved LSTM model comprises an encoder, an attention mechanism layer, a decoder and a full connection layer; the attention mechanism layer is used to calculate the correlation weight between the current time hidden state and all hidden states of the encoder at each step of the decoder prediction, and generate a weighted sum as the input of the decoder at the current time according to the correlation weight; and the value of the future time slot RSRP is predicted by using the improved LSTM model, and the uplink throughput is predicted in combination with the mapping model. The application predicts the uplink throughput by using the 5G network parameter RSRP based on the deep learning technology, so that the resources can be allocated in advance to meet the demand of the current system on the communication performance.

The application discloses a coordinate system transformation fusion filtering tracking method and system for a dual-base station radar, and relates to the technical field of radar target tracking. The method comprises the following steps: firstly, establishing state and observation equations, and determining an initial state of a target based on prior information in a Cartesian coordinate system. Then, a target motion state is obtained through one-step prediction, sigma points are generated by using U transformation, and a mean value and a covariance of state space prediction are calculated and constructed. Next, measurement data and prediction data are fused by using a Kalman filter to obtain optimal state estimation and a covariance matrix. Finally, the updated state estimation is converted back to the Cartesian coordinate system based on U transformation, and the process is repeated until the tracking is completed. The application can avoid the nonlinear filtering problem in the updating process, and improves the robustness and precision of tracking.

This invention discloses a multi-sensor information fusion target tracking method with a stochastic variable parameter matrix. The method includes the following steps: 1. Establishing a dynamic model of the tracking target in a multi-sensor target tracking system; 2. Designing the predictor and estimator structures; 3. Calculating the upper bound Θ of the one-step prediction error covariance matrix of the i-th sensor at time k+1. i,k+1|k 4. Calculate the estimated gain matrix K of the i-th sensor at time k+1. i,k+1 And the fusion estimation of the tracking target, part five, K i,k+1 Substituting into step two, we obtain the state estimate of the target tracked by the i-th sensor at time k+1. We then determine whether k+1 has reached the estimated total duration MN. If k+1 < MN, proceed to step six; if k+1 = MN, the process ends after calculating the fusion estimate. Step six: Calculate the upper bound of the estimation error covariance Θ. i,k+1|k+1 Let k = k + 1, and continue in step 2 until k + 1 = MN is satisfied. This invention can effectively estimate the target state and has good robustness.

The invention relates to the technical field of industrial control, in particular to a self-adaptive control method for vibration of collective doffing spindles, which comprises the following steps of: firstly, optimizing and generating a non-standard flexible motion track capable of inhibiting vibration from the source by establishing an electromechanical joint dynamical model, and meanwhile, constructing a disturbance model library and prospectively analyzing a PLC (Programmable Logic Controller) instruction, so that the vibration of the collective doffing spindles is inhibited; generating a predictive compensation signal; the predictive compensation signal is used for realizing double feed-forward control; the servo feed-forward gain of the manipulator is dynamically adjusted by analyzing the energy characteristics of the predictive compensation signal, so that time-varying stiffness control is realized; then a spindle seat actuator is driven to conduct active damping so as to dissipate disturbance energy; on this basis, the system further predicts residual vibration after compensation, and calculates an inverse waveform signal based on an inverse dynamic model for accurate offset; and finally, correcting each model on line by adopting a PLC (Programmable Logic Controller) time sequence phase locking algorithm, comprehensively applying predictive control, feed-forward compensation and self-adaptive correction, and realizing high-precision and whole-process suppression on spindle vibration.

The invention discloses a maximum correlation entropy Kalman filtering method based on a rational kernel function, and belongs to the technical field of signal processing. The method specifically comprises the following steps: 1) constructing a linear system equation and a measurement equation; step 2) selecting a kernel width of a rational quadratic kernel function, and initializing a system state and a covariance; 3) according to a system equation, updating one-step prediction of a state and a covariance; (4) the state value is initialized again at the fixed point iteration starting moment; 5) performing system model deformation according to the initial system and the measurement equation to obtain an error vector after deformation; 6) according to a concept based on a weighting criterion and entropy, defining a cost function by using an error vector; 7) for the cost function, solving an optimal solution of a state estimation value according to a maximum correlation entropy principle; and step 8) estimating the variance of a posterior estimation value. Compared with the existing GSKF, HF and MCKF algorithms, the method provided by the invention has the advantage that the accuracy of state estimation and the robustness of estimation are greatly improved.

This invention proposes a multi-step fault prediction method for chemical processes based on a knowledge-enhanced graph Transformer. The steps are as follows: constructing a multivariate time series and processing it with a sliding time window to obtain input samples; constructing a mechanism constraint graph and assigning weights to node pairs to form a prior adjacency matrix; constructing a learnable adjacency matrix based on the input samples and fusing it with the prior adjacency matrix; inputting the node feature matrix into a graph convolutional network to obtain a spatial feature matrix; adding a position encoding vector to the input samples and inputting them into the Transformer module to obtain the temporal dependency feature matrix for each time step; concatenating the inputs into a gating network and performing weighted fusion to obtain a fused feature matrix; using the obtained fused feature matrix to generate the next prediction, and using the prediction results and historical sequences for inference to achieve multi-step prediction. This invention significantly improves the early fault identification and warning capabilities of industrial processes under complex operating conditions while maintaining model interpretability and operational stability.

The invention discloses a permafrost refrigeration system control method based on a cold load, and belongs to the technical field of permafrost protection measures, and the method comprises the following steps: 1, predicting the cold load of a permafrost protection work site; secondly, a refrigerating system control module based on the cooling load is constructed. Intelligent control over the operation process of the refrigerating system is achieved through an intelligent algorithm, and the dual purposes of permafrost temperature control and refrigerating system energy saving are achieved. The permafrost cold load is predicted based on a machine learning model, a refrigeration system control module based on the cold load is constructed, the control module effectively integrates geological information and meteorological information of different time scales, the dynamic adaptability to key influence factors of permafrost degradation is improved, and the advantages of being high in generalization ability, energy-saving, efficient and the like are achieved; and according to the cold load prediction and feature recognition result, the control strategy of the refrigeration system is adjusted in time, a control instruction is generated, and therefore the permafrost intelligent protection method with the environment self-adaption function is achieved.

The invention discloses a commercial building central air conditioner energy consumption prediction system and method based on data analysis, and relates to the technical field of data analysis, and the method comprises the following steps: obtaining multi-source data related to the commercial building central air conditioner energy consumption; preprocessing the data; processing the time sequence feature data, generating time sequence feature importance weights, and screening key time sequence features; processing the static feature data, generating a static feature importance weight, and screening key static features; splicing the key time sequence features and the key static features, and constructing an energy consumption prediction model; predicting key time sequence characteristic data, and further predicting energy consumption data; and defining energy consumption cost and numerical value / category type key time sequence feature conversion cost, solving by using a mixed integer optimization algorithm in combination with constraint conditions by taking total cost minimization as a target, and outputting an optimal target state of the key time sequence features. The method can effectively improve the condition that the energy consumption optimization scheme in the prior art is difficult to effectively utilize influence factors.

The application provides a trust-aware security distributed tracking method for an interactive multi-target system and belongs to the technical field of state estimation. The application has the following steps: a nonlinear random state space model is established; a memory attack detection mechanism based on trust awareness is designed, an attack detection function is constructed based on measurement residuals, and a limited memory window is used to realize real-time detection of whether the measurement data is attacked; three states of the measurement data are divided, and a measurement credibility factor is constructed; weighted fusion processing is performed to obtain effective measurement information; a security distributed state estimator is constructed to calculate one-step prediction of the system state; an upper bound of a one-step prediction error covariance matrix of each target state is recursively calculated; an estimator gain matrix of each target is calculated; the estimation of each target state is updated by using the gain matrix, and the upper bound of the estimation error covariance matrix is recursively calculated until a preset termination time. The application can improve the security, robustness and reliability of the system in a complex network environment.

The application discloses a SLAM method and system based on a robust adaptive unscented Kalman filter multi-sensor information fusion, which comprises the following steps: generating symmetric distribution sampling points according to system parameters and calculating corresponding weights, substituting the sampling points into a nonlinear function to complete one-step prediction, obtaining one-step prediction values of states and covariance through weighted summation, generating new sampling points based on the prediction results to perform observation prediction, and synchronously calculating an observation correlation covariance matrix; meanwhile, multi-step sub-processes are performed, including sampling point processing, weight application, operation monitoring and other operations, a dynamic noise estimation and adaptive sensor weight distribution mechanism is designed, a robust adaptive filter framework is combined, multi-source sensor complementary information is fully integrated, and abnormal data interference is effectively resisted, so that the positioning accuracy and robustness of the SLAM system in a complex environment are improved, and the application is suitable for real-time positioning and map construction scenes of autonomous mobile devices.

The invention provides an SQL injection attack detection method and device and a computer program product, and the method comprises the steps: constructing an SQL injection attack integration model and an SQL injection attack big language model, and determining a first prediction probability result of a to-be-queried SQL statement through the SQL injection attack integration model; and when the confidence score of the first prediction probability result is not high, jointly determining a prediction result by the SQL injection attack integration model and the SQL injection attack large language model through a weighted voting mechanism. Therefore, the semantic understanding of the model on the SQL statement can be improved. And only when the confidence score is not high, the SQL statement to be queried is handed over to the SQL injection attack big language for further prediction, so that computing resources needing to be added can be reduced to a certain extent, and the resource utilization rate is improved.

The application discloses a text prediction method and device, a computer readable storage medium and an electronic device. It relates to the field of artificial intelligence, and the method comprises: obtaining a target image, wherein the target image comprises a plurality of characters; inputting the target image into a target prediction model, and predicting the plurality of characters in the target image by the target prediction model to obtain a target character sequence, wherein the target prediction model is used for step-by-step prediction of the plurality of characters, there are a plurality of initial identification sequences in each step of prediction, a target score of the initial identification sequence is determined according to the sequence length and the target probability of the initial identification sequence, and the target character sequence is determined from the plurality of initial identification sequences according to the target score, wherein the target score is positively correlated with the sequence length. The application solves the technical problem of low prediction accuracy when a beam search algorithm is used for text prediction in related technologies.

The invention discloses a satellite-borne microwave radar high-speed target steady-state tracking method. The method comprises the following steps: carrying out radar emission signal waveform design optimization; performing forward data screening on the input data of the filter; initializing a distance filter; carrying out one-step prediction by a distance filter; distance filter decoupling one-step estimation; initializing a speed filter; carrying out one-step prediction by a speed filter; performing one-step estimation of a speed filter; and updating the use speed of the RCMC. According to the method, the filter is independently designed for the distance and the speed, the coupling distance brought by intra-pulse Doppler is corrected in real time, the compensation speed is synchronously updated, and accurate distance measurement and speed measurement of a high-speed target with a certain dynamic state are achieved through less calculation.

The application discloses a kind of based on data analysis's commercial building central air conditioning energy consumption prediction system and method, it is related to data analysis technical field, the present method includes the following steps: obtaining the multi-source data related to commercial building central air conditioning energy consumption;Data is preprocessed;Processing time sequence characteristic data, generate time sequence characteristic importance weight, and filter key time sequence characteristic;Processing static characteristic data, generate static characteristic importance weight, and filter key static characteristic;Splice key time sequence characteristic and key static characteristic, construct energy consumption prediction model;Predict key time sequence characteristic data, further predict energy consumption data;Define energy consumption cost and numerical / classification type key time sequence characteristic conversion cost, with total cost minimization as target, combined constraint condition is solved using mixed integer optimization algorithm, output key time sequence characteristic optimal target state.The present application can effectively improve the situation that energy consumption optimization scheme is difficult to effectively utilize influencing factor in prior art.

The application discloses an indirect air cooling system operation regulation method considering both freezing prevention and economy, which comprises the following steps: firstly, preprocessing and DBSCAN clustering analysis are performed on historical operation data, and a corresponding relationship among load, environmental conditions, circulating water flow and back pressure is determined by combining a steam turbine variable working condition calculation model, a condenser heat balance model and an air cooling tower theoretical calculation model; secondly, the best back pressure value in the whole working condition domain is solved according to a best back pressure calculation model; further, the integrated database is normalized, and a best back pressure prediction model and a louver opening degree combination prediction model are constructed based on an integrated learning algorithm of Bayesian optimization; and finally, the best operation back pressure of the indirect air cooling unit is predicted according to real-time data, and the best louver opening degree combination considering both freezing prevention and economy is further predicted. The method can realize the collaborative optimization of the economy and safety of the cold end system, significantly improve the operation efficiency and freezing prevention capacity of the indirect air cooling system, and has important engineering application value.

Embodiments of the present application disclose a kind of digital key ranging value filtering method and device, electronic equipment and storage medium, comprising: real-time acquisition of the ranging information of multiple UWB anchor points;According to the ranging information, the position change trend of digital key is obtained;In the absence of valid ranging value, according to the positioning result corresponding to the time t of the last valid ranging value, the position change trend and the unlocking state of the car end, a preset target position is obtained, the distance relationship between the valid ranging value of time t and the preset target position is obtained to obtain the predicted distance, and the predicted duration T is obtained according to the predicted distance;When no valid ranging value is detected within T, the state step prediction of Kalman filtering algorithm is executed to obtain the filtered ranging value, and when valid ranging value is detected, state step prediction is terminated and the filtering estimation value obtained according to Kalman filtering algorithm is used as the filtered ranging value;According to the filtered ranging value of multiple anchors, the out-of-vehicle area positioning result is obtained.

An anti-eavesdropping distributed fusion filtering method for the multi-rate nonlinear system over sensor network includes: Step 1, establishing a dynamic model for the multi-rate nonlinear system over sensor network; Step 2, transforming the multi-rate nonlinear system dynamic model into a single-rate nonlinear system dynamic model through the prediction compensation strategy; Step 3, designing an anti-eavesdropping distributed fusion filter; Step 4, calculating an upper bound on the one-step prediction error covariance (tk+1|tk); Step 5, deriving the local distributed filter parameter Ki(tk+1); Step 6, deriving the selection matrix Lij(tk+1); Step 7, substituting Ki(tk+1) and Lij(tk+1) into Step 3 to obtain the fusion filter {circumflex over (x)}CI(tk+1|+tk+1); Step 8, solving for the upper bound on the local filtering error covariance (tk+1|tk+1). The method solves the problem that the existing fusion filtering method cannot simultaneously deal with the filtering problem for multi-rate nonlinear systems with eavesdroppers and fading measurements, thereby improving the accuracy of the filtering performance.