468 results about "Non linear functions" patented technology

The invention discloses an AI-based bridge construction intelligent monitoring method, particularly relates to the field of data analysis, and comprises the steps of multi-modal sensing network construction, cross-domain characteristic parameter extraction, comprehensive state evaluation modeling and intelligent decision and regulation. According to the method, deformation, corrosion, vibration and other multi-physical field data are synchronously collected through a multi-modal sensing network, a cross-scale feature extraction algorithm is adopted to construct creep-corrosion collaborative factors and other composite indexes, the limitation of traditional single-dimensional parameter analysis is broken through, a dynamic evaluation model is fused with weighted integral and a nonlinear function, and the dynamic evaluation accuracy is improved. Precise early warning of risks such as bridge pier instability and cantilever cracking is achieved, the multi-field coupling effect quantification capacity is remarkably improved, an intelligent joint control mechanism automatically triggers instructions such as jacking reinforcement and damping tuning through a three-level emergency response system, a monitoring-analysis-regulation closed-loop system is constructed, the hysteresis of manual decision is effectively eliminated, and the safety and reliability of the system are improved. And all-weather self-adaptive safety guarantee is provided for bridge construction.

The invention relates to the technical field of chip testing, and discloses an LED driving chip testing method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-point temperature excitation and constant-current output synchronous collection of an LED driving chip, and obtaining electrothermal coupling test data; performing thermal resistance network decomposition on the temperature field in the chip to obtain temperature distribution characteristic parameters; performing nonlinear function fitting on the temperature-constant current coupling relation to obtain an electrothermal coupling transfer coefficient; performing four-dimensional coupling calculation on the temperature distribution characteristic parameter and the electrothermal coupling transfer coefficient to obtain four-dimensional coupling response characteristic data; and partial differential sensitivity calculation is carried out based on the four-dimensional coupling response characteristic data, and an electrothermal coupling sensitivity analysis result is obtained.According to the method, the problem of test errors caused by time desynchrony in a traditional separated temperature-current test is solved, quantitative sensitivity analysis of electrothermal coupling parameters is achieved, and the sensitivity of the electrothermal coupling parameters is improved. Therefore, the test accuracy of the LED driving chip is improved.

An adaptive variable step-size LMS filter based on a hyperbolic tangent function and its filtering method and computer equipment belong to the field of filter technology, and solve the problem that the LMS algorithm improved based on a nonlinear function requires too many manual adjustments of parameters and that the convergence speed and convergence accuracy of the traditional LMS algorithm cannot be satisfied simultaneously. The method of the present invention includes: determining the filter tap length and the number of iterations; initializing the filter parameters; obtaining the filter input signal vector; determining the instantaneous expected signal of the filter; obtaining the instantaneous output signal; obtaining the instantaneous error signal of the filter and the accumulated amount of the error signal based on the instantaneous output signal; obtaining the instantaneous step size using a step-size update formula based on a hyperbolic tangent function; and continuously iterating the filter tap weight coefficient vector to construct an LMS filter using the filter tap weight coefficient vector obtained after the number of iterations. The present invention is suitable for improving the performance of the LMS filter, and the filter of the present invention can be widely used in various signal processing fields.

The invention relates to the technical field of power system operation and control, and discloses a primary and secondary frequency modulation cooperative control system of a hybrid energy storage coupling wind generating set, which comprises a wind power generation unit, a hybrid energy storage unit and a central cooperative controller. A total power demand including inertia response and primary and secondary frequency modulation is synthesized, a primary and secondary frequency modulation cooperative distribution module generates a dynamic adjustment weight by using an S-type nonlinear function based on a frequency change rate and frequency deviation coupling relationship, and an energy relay compensation mechanism is introduced to fill a power gap in a switching process. And the SOC adaptive constraint and execution module applies direction selective boundary constraint to the state of charge, and controls hybrid energy storage to perform internal energy self-balancing in a frequency modulation dead zone. According to the invention, seamless connection of multi-time scale frequency modulation is realized, frequency secondary drop is effectively prevented, and the frequency support capability and the self-recovery capability of the system are improved.

The invention relates to the technical field of electrical testing, in particular to a battery testing method and system, and the method comprises the steps: obtaining a voltage data sequence and historical log data of each cell in a to-be-tested battery pack; determining a dynamic instability score of each battery cell according to the parameter volatility index of each battery cell and the spatial correlation weight between each battery cell and the adjacent battery cell; on the basis of the historical log data, analyzing a repeated cumulative effect of all historical damage events corresponding to each battery cell, and determining a historical damage degree of each battery cell; performing deep fusion on the dynamic instability score and the historical damage degree through a nonlinear function to obtain a comprehensive risk index of each battery cell; and based on the extreme values and distribution of the comprehensive risk indexes of all the cells, determining a fault degree capable of representing the overall health risk of the battery pack, and determining a health test result of the battery pack according to the fault degree. According to the method, the accuracy and reliability of battery pack testing are improved.

The invention discloses a self-adaptive safety control method of a nonlinear MIMO system under intermittent DoS attack, which comprises the following steps: establishing a nonlinear MIMO system model suffered from intermittent DoS attack, and the system model is in a strict feedback form and comprises an unknown nonlinear function and bounded external disturbance; the DoS attack causes periodic interruption of a communication link from a sensor to a controller, so that actual measurement data is unavailable in an attack activation period; designing a switching type adaptive state estimator; coordinate transformation is carried out by introducing a filter, and a system error is decomposed into a tracking error and a dynamic surface filtering error; a Lyapunov function is constructed; performing stability analysis in stages; designing an adaptive law to update unknown parameter estimation values of the FLS; and a final event triggering controller is designed, and the event triggering controller is combined with a virtual controller to track an error compensation term and an FLS approximation term. The problem that the state cannot be measured is solved.

The invention discloses a nonlinear multi-agent system sampling consistency control method and system and a medium, and the method comprises the following steps: S101, obtaining system model parameters, including a nonlinear function # imgabs0 #, a pulse gain # imgabs1 #, a sampling period # imgabs2 #, a communication topological graph # imgabs3 # and a leader-follower kinetic equation; s102, verifying an assumed condition; s103, constructing a Lyapunov function # imgabs4 #, and analyzing the stability of an error system in combination with pulse disturbance; s104, deriving a consistency condition through a Lyapunov method and a pulse theory, calculating key parameters, and determining a pulse gain and a sampling period constraint; s105, selecting parameters meeting conditions; s106, constructing a sampling controller containing pulse disturbance; and S107, implementing the controller and performing numerical simulation. According to the invention, the anti-pulse disturbance capability is strong: through the design of the pulse gain # imgabs5 #, the instantaneous interference of an input channel is effectively counteracted, and the robustness of the system is improved; a sampling data control strategy is adopted, a control signal is updated only at a discrete sampling moment, and the communication burden is reduced.

The invention relates to a vehicle and unmanned aerial vehicle combined scheduling method for wide-range low-cost inspection. The method comprises the following steps: acquiring prior information; modeling the unmanned aerial vehicle inspection problem of each target area according to the prior information to obtain a mixed integer non-convex optimization problem with the goal of minimizing the weighted sum of the total execution time and the energy consumption of all the inspection unmanned aerial vehicles; performing linearization on a non-convex bilinear term in the mixed integer non-convex optimization problem, and performing discretization processing on a nonlinear function by adopting piecewise linear approximation; an approximate mixed integer linear programming problem is obtained and solved, and an unmanned aerial vehicle scheduling strategy is obtained; modeling according to the unmanned aerial vehicle scheduling strategy and the prior information to obtain an inspection vehicle path planning problem taking the comprehensive driving cost as a target; the routing inspection vehicle path planning problem is converted and modeled into a Markov decision process, a routing inspection vehicle is used as an intelligent agent, a state, an action and a reward function are defined, and a routing inspection vehicle scheduling strategy is obtained. Therefore, combined inspection of the inspection vehicle and the unmanned aerial vehicle is realized, and the inspection range is expanded.

The invention provides a nonlinear data generation and processing method based on light quantum and a light quantum computer, and the method comprises the steps: obtaining a nonlinear function and attribute information of a nonlinear layer; according to the frequency spectrum number and the attribute information of the nonlinear function, constructing an initial light quantum circuit; measuring the initial light quantum circuit, and determining a target function corresponding to the initial light quantum circuit according to a measurement result; loss information of the target function is determined, whether the initial light quantum circuit is applied or not is determined according to the loss information, and if yes, the initial light quantum circuit serves as a target light quantum circuit; and performing operation on input data of the nonlinear layer based on the target light quantum circuit. According to the method, the low-energy-consumption characteristic of photon calculation, the nonlinear characteristic of light quantum calculation and the general programmable capability of electronic calculation can be fused, and high-energy-efficiency calculation can be carried out on input data of a nonlinear layer. In addition, the method also has the advantages that the complexity of a quantum circuit is smaller, and the participation of a nonlinear optical device is not needed.

The invention provides a financial risk dynamic prediction method based on deep learning, and relates to the field of financial risk dynamic prediction. The financial risk dynamic prediction process provided by the invention comprises the steps of constructing a financial risk prediction data set, calculating a deviation absolute value sequence of a factor data time sequence, and fusing and mapping the time sequence and the deviation absolute value sequence into a high-dimensional embedded vector sequence through a weighted nonlinear function; constructing a finite span directed graph by taking each time step as a graph node; introducing a structure resonance factor; carrying out weighted coding and weighted fusion on a path to form an aggregation context vector; calculating a risk score; overall feature representation of factor data is extracted in combination with a bidirectional long-short-term memory network, and finally a financial risk prediction value is calculated by using a full-connection neural network. According to the method, local deviation and global time sequence information are fused, and the accuracy of financial risk prediction is improved.

The invention discloses a correction method for saturation spectral line intensity in a laser spectrum, and relates to the technical field of spectral data analysis, the method comprises the following steps: identifying saturation characteristics in the spectrum, determining a saturation point based on an intensity threshold, and recording the original intensity of the saturation point; determining a target spectrum peak wavelength range containing a saturation point; eliminating saturation points from the wavelength range of the target spectrum peak, and retaining unsaturation points; fitting the unsaturated point through a nonlinear function to obtain the maximum fitting strength; if the maximum fitting strength is lower than the original strength, performing strength correction by adopting a linear extension mode by taking an unsaturated point as a reference, and otherwise, performing strength correction directly based on a fitting model; and outputting the corrected spectral intensity data, and calculating the maximum intensity of the spectral peak and the integral area of the spectral peak. According to the method, the accuracy of saturation judgment and the reliability of a correction result are improved, and a direct intensity data basis and core derivative parameters are provided for quantitative analysis.

The invention discloses a product forging data intelligent management system and method based on 5G cloud computing, and relates to the field of intelligent manufacturing. According to the system, multi-source data such as temperature, stress, load and images are collected through a process data module and weighted labeling is carried out; the multi-model reasoning module selects support vector regression, a random forest and a convolutional neural network for parallel prediction according to data features; the prediction fusion module generates a unified prediction result by adopting a nonlinear function based on confidence score; the cloud-edge collaborative scheduling module determines cloud or edge deployment in combination with task complexity, edge computing power and a network state; the parameter optimization module adopts an EPQ-Opt algorithm to optimize process parameters based on energy consumption prediction and residual score; and the security guarantee module adjusts the strategy through consistency detection and robustness evaluation and guarantees data security. The method significantly improves the prediction precision and response speed, reduces the energy consumption, enhances the system safety, and is suitable for the field of high-end forging and intelligent manufacturing.

The invention discloses a multi-dimensional layered current limiting method based on spatio-temporal feature fusion. The method comprises the following steps: acquiring a hardware limit speed, and generating a global quota based on a reinforcement learning model; calculating a variable coefficient, and if the variable coefficient exceeds a preset critical value, introducing a conservative coefficient and generating a weight factor through nonlinear function mapping to suppress the micro network jitter; constructing a priority factor matrix based on the service priority and the service type, and obtaining a service priority factor; correcting the average bandwidth of the node by integrating the multi-dimensional dynamic weight factor and the service priority factor to obtain a suggested speed, inputting a historical performance index into the LSTM model to predict the load of the next period, and if the historical performance index exceeds a critical value, triggering connection migration before executing current limiting to realize active avoidance; otherwise, comparing and selecting the minimum value of the global quota, the suggested speed and the hardware limit speed as the final current-limiting speed. The problems of current limiting strategy lag and wide oscillation can be solved, and the bandwidth utilization rate and the system stability are improved.

A data storage device has an inference engine that can infer a read threshold based on a non-linear function of inputs that reflect current memory and data conditions. The read threshold can be used in reading a wordline in the memory. Using a machine-learning-based approach to infer a read threshold can provide significant improvement in read threshold accuracy, which can reduce bit error rate and also improve latency, throughput, power consumption, and quality of service.

The invention provides a self-adaptive state prediction control method for improving the stability of a grid-forming type new energy shafting, and belongs to the technical field of wind power generation, and the method comprises the steps: determining an extended state observer based on a nonlinear function and a closed-loop system; obtaining a virtual torque compensation item by using the extended state observer, and feeding back the virtual torque compensation item to a virtual rotor motion equation of the virtual synchronous machine to perform additional power compensation; in the additional power compensation process, when the rotating speed of the draught fan is lower than a preset safety value, proportional integral differential control is set to zero through a rotating speed protection module, the wind power virtual synchronous machine system is controlled to exit additional power compensation, and the active supporting system is controlled to operate stably; in the stable operation process of the active support system, the rotating speed of the fan is recovered to the optimal value through the rotating speed recovery module, and the influence caused by unreasonable parameters is reduced and the dynamic stability of the wind power VSG grid-connected system is effectively enhanced by estimating the imbalance between the external disturbance power and the internal state quantity and performing tracking compensation in the controller.

The invention provides a heterogeneous multi-agent system control method under a DoS attack based on an event triggering strategy. Robust cooperative control under a complex attack environment is realized through the following steps: S1, performing state description and topological structure modeling on a heterogeneous multi-agent system; s2, carrying out approximation on an unknown nonlinear function in the system; s3, restraining the error dynamic state through a performance function, and ensuring that the system response meets the preset precision; s4, designing a time-varying threshold function, and dynamically adjusting a trigger condition to reduce communication burden; s5, designing an anti-attack controller in combination with event triggering and a Lyapunov theory; s6, designing an adaptive law to carry out online estimation on disturbance; and S7, testing synchronization precision, communication efficiency and anti-attack performance based on a physical platform. According to the method, deep fusion of the event triggering mechanism and the preset performance constraint is realized for the first time, and the self-adaptive dynamic threshold strategy is introduced, so that malicious interference of DoS attacks on a communication link is effectively dealt with, and it is ensured that the heterogeneous nodes can still meet preset performance indexes in an attack scene.

The invention discloses a self-adaptive finite time fuzzy safety control method for a flexible joint mechanical arm, which comprises the following steps that a flexible joint mechanical arm state space model influenced by spoofing attack is established, and a control signal comprises an unknown time-varying parameter and an unknown bounded attack function; introducing a Nussbaum function meeting a preset limit integral property; designing a control method based on a finite time stability principle, and ensuring that a system state converges to a bounded residual set within finite time; a fuzzy logic system is adopted to approach an unknown nonlinear function in the mechanical arm model; defining an error variable through coordinate transformation, and constructing a parameter which exponentially decays along with time; constructing a Lyapunov function containing the exponential decay parameter, and designing a virtual control function, a controller and a parameter adaptive law based on the function; and the Nussbaum function is constructed to be used for processing the unknown time-varying parameters. According to the invention, the influence of spoofing attacks on state signals can be reduced.

The invention relates to the technical field of block chains, and discloses an intelligent power distribution method and system for realizing power supply equipment based on a block chain, and the method comprises the steps: constructing a block chain network of the power supply equipment, analyzing a dynamic mapping relation between input data and output data, and determining a discrete nonlinear function of the input data and the output data; determining a target function required by the power supply equipment, determining power distribution control parameters of the power supply equipment, and integrating a data driving control model of the power supply equipment; calculating an electrical distance between blockchain nodes, and dividing the blockchain network into a plurality of power distribution control areas; determining an area dynamic control mechanism of the multiple power distribution control areas, and determining a coordination control algorithm of the multiple power distribution control areas; and integrating the data driving control model, the regional dynamic control mechanism and the coordination control algorithm into a power distribution control module of the power supply equipment, and executing intelligent power distribution of the power supply equipment. The power distribution efficiency and stability of intelligent power distribution of the power supply equipment can be improved.

The invention relates to the technical field of data signal processing, and discloses a data equalization system and method, and the method comprises the steps: inputting an input signal into an improved equalization network in response to a signal compensation request, carrying out the affine transformation of the input signal through a neuron, and obtaining a transformed signal, according to a trainable coefficient of a B-spline primary function learned during affine transformation, a nonlinear activation function is determined, then weighted summation is carried out on transformed signals, nonlinearity is introduced through local interpolation and recursive definition of the B-spline primary function in the weighted summation process, nonlinear mapping signals are obtained, and then through judgment and error code detection, the non-linear mapping signals are obtained. And outputting the balanced data signal meeting the error rate threshold. According to the method, the input signal is primarily processed through affine transformation, nonlinear mapping is carried out by using the nonlinear activation function based on the B-spline primary function, and the complex high-dimensional signal processing problem is decomposed into the combination of a plurality of low-dimensional nonlinear functions in the process, so that the calculation complexity is reduced.

The invention discloses a vehicle queue preset time output feedback safety control method and system, and belongs to the field of intelligent network connection multi-vehicle queue control, the control method comprises the following steps: designing a non-linear vehicle queue model, designing a fuzzy observer, combining with a fuzzy logic system to approximate an unknown non-linear function in the vehicle queue model, and calculating the preset time output feedback safety of the vehicle queue. Outputting an estimated displacement, an estimated speed, and an estimated acceleration of the following vehicle; mapping the error transformation of the finite time domain to an infinite time domain to obtain infinite time domain error transformation; designing a Lyapunov function, and performing derivation on the Lyapunov function to finally obtain a following vehicle brake input signal; designing a false data injection FDI attack model for injecting an attack signal into the following vehicle brake input signal to obtain an input with an attack; the invention provides a cooperative control architecture based on a time domain mapping mechanism, and tracking errors under any initial condition can be asymptotically converged to zero within preset time.

The invention relates to a virtual inertia-damping cooperative adaptive control method for a voltage source type direct-driven permanent magnet wind turbine generator. The method comprises the following steps: constructing a voltage source type direct-driven permanent magnet wind turbine grid-connected equivalent model; constructing a wind turbine generator frequency response model based on a dynamic coupling relationship between virtual inertia and virtual damping; collecting a frequency signal of a common connection point of a power grid, and carrying out collaborative optimization processing on the frequency signal by adopting a composite filter to obtain a virtual power increment; the active power reference value is introduced into an active power control loop of the grid-side converter; and controlling the power grid side converter after the additional power reference value is introduced by adopting a rotational inertia and damping coefficient adaptive control method based on a nonlinear function. According to the method, frequency steady-state deviation compensation and dynamic change rate correction can be realized, the frequency stability under wide-range disturbance can be improved through segmented nonlinear adjustment, and the requirements of unit active inertia response and power grid frequency stability are met.

The invention provides a level protection-oriented network security configuration checking method, which comprises the following steps of: constructing a knowledge graph associated with a security control item and a technical implementation mode by relying on a standard document and configuration data, and implementing a control item logic dependency and semantic weight reconstruction model; security configuration of assets is automatically collected and semantically mapped to map nodes, configuration deviation is recognized, a risk influence path template is matched in combination with asset importance and service context, semantic propagation path influence intensity is dynamically calculated, and an accumulated risk influence value of configuration deviation is generated in an aggregation mode; according to the method, risk level judgment is carried out by adopting a piecewise nonlinear function, a structured risk assessment record is output, risk identification and reinforcement suggestion closed loop are realized, and the hierarchical management and control and automatic compliance response capability of security configuration deviation in a network environment is improved.

The invention discloses a heat dissipation system and method for a sealed motor, and belongs to the technical field of sealed motors and transmission devices. The system comprises a motor unit, a multistage gearbox unit, a liquid cooling unit, an air cooling unit, a plurality of groups of sensors and a controller. The liquid cooling unit is integrated in the motor outer cover, and the air cooling unit is installed on the motor radiating ribs. According to the method, the total heating power and the variable target temperature of the system are dynamically calculated by collecting the current, the temperature and the rotating speed of the motor and gear signals of the gear box in real time, the required heat dissipation power is calculated in real time through a nonlinear function based on the temperature difference, and then heat dissipation resources of the air cooling unit and the liquid cooling unit are cooperatively distributed with the optimal energy efficiency ratio. According to the invention, feedforward prediction temperature control of dynamic working conditions is realized, the problems of heat dissipation lag, low energy efficiency and overheating risk of the sealed motor under complex load and multi-gear operation are solved, and the reliability, adaptability and overall energy efficiency of the system are significantly improved.

The invention discloses a design method and an implementation circuit of a discrete local active memristor. The design method comprises the following steps: determining a discrete iterative operation model of the discrete local active memristor; determining a modular framework of the discrete local active memristor comprising a nonlinear function module; and selecting a nonlinear function module to complete the design of the discrete local active memristor. Through the determination of the discretization iteration operation model and the modularization framework of the discrete type local active memristor, the design of the discrete type local active memristor and the realization of the corresponding circuit are realized, and the reliability, the accuracy and the adaptability are higher.

The invention provides a printed circuit board surface defect intelligent detection method and system, and the method comprises the steps: firstly collecting a batch-level defect classification original confidence sequence and synchronous process state parameters, extracting the mean value, variance, change rate and other dynamic characteristics of confidence, and fusing the dynamic characteristics with process parameters to obtain a data fusion model; inputting a pre-training time sequence model to predict the confidence coefficient trend of the next batch; the classification threshold value of the key defect category is dynamically adjusted through the deviation between the actual confidence coefficient mean value and the predicted confidence coefficient mean value, non-linear function self-adaptive mapping is adopted for the adjustment coefficient, and self-adaptive optimization of the classification model threshold value along with quality fluctuation is achieved; when the production line is in a stable stage, threshold adjustment is automatically frozen. The method improves the accuracy of defect identification and the adaptability of the production line.

The embodiment of the invention provides a nonlinear function calculation method and a special function calculation unit (SFU). The method comprises the following steps: determining a to-be-fitted function and a fitting interval corresponding to the to-be-fitted function; based on the to-be-fitted function and the fitting interval, configuring segment parameters and linear parameters corresponding to the to-be-fitted function; and performing fitting calculation based on the piecewise parameters and the linear parameters to obtain a first target function corresponding to the to-be-fitted function, and completing nonlinear function calculation based on the first target function. According to the invention, the problems of low precision, poor performance and low efficiency of SFU processing in the prior art are solved, and the effect of improving the SFU processing precision, efficiency and performance is achieved.

The invention provides an IGBT junction temperature prediction model construction method and system, and relates to the technical field of power electronic device thermal management, and the method comprises the steps: obtaining an initial data set representing the transient thermal response relation between an IGBT chip and an NTC temperature measurement point in an IGBT power module where the IGBT chip is located; training a neural network model based on the initial data set, and learning and representing a nonlinear function relationship between time and thermal impedance; inputting a preset new time sequence into the trained neural network model, and generating a time-continuous and noise-suppressed optimized thermal impedance data set; and based on the data set, performing parameter fitting on a preset lumped parameter thermal network model to identify thermal resistance and thermal capacity parameters of the model so as to complete construction of the junction temperature prediction model. According to the method, the neural network is used as a data optimization tool in a physical model parameter identification process, and accurate and convenient online prediction of the IGBT junction temperature is realized only by using the temperature signal of the NTC temperature measurement point of the module.

The invention belongs to the field of active frequency support of power electronic equipment in a network-construction-type flexible direct-current power transmission system, particularly discloses a control parameter adaptive design method and system of a network-construction-type flexible direct-current converter, and aims to solve the problems of insufficient system inertia support and oscillation caused by fixed parameters in a wide-range disturbance scene. The method comprises the steps that a receiving-end converter extracts the deviation value, the change rate and the acceleration of the frequency of a power grid by monitoring the electrical quantity of a grid-connected point in real time, and the system operation state is divided into three modes of steady-state maintenance, fluctuation early warning and emergency response based on threshold judgment; calculating a virtual inertia coefficient by using a saturated nonlinear function, and cooperatively solving a damping coefficient in combination with small signal stability analysis; and establishing a virtual inertia-damping parameter feasible region and outputting the virtual inertia-damping parameter feasible region to a receiving end converter station controller. The limitation of traditional fixed parameter design under the disturbance working condition is broken through, the frequency supporting capacity of the system can be remarkably improved, and the stability problem caused by frequent parameter change is avoided.

In some implementations, a radio frequency (RF) sensing node may receive a non-linear frequency-modulated (NLFM) configuration from a configuring node of a wireless network, where the NLFM configuration is indicative of a set of one or more parameters of a non-linear function describing time-varying frequency characteristics of an NLFM signal to be used by the sensing node for RF sensing. In addition, the device may perform an RF sensing function with the sensing node in accordance with the NLFM configuration.

The invention relates to the technical field of mobile payment, in particular to a dynamic adjustment method for a mobile payment password-free limit, which comprises the following steps: when a payment terminal initiates a payment request, acquiring a network environment, an equipment unlocking mode, an SIM card state, system integrity and a discrete state of an application environment in real time, and establishing an equipment security state vector; according to the invention, multi-dimensional acquisition is carried out on the network environment, the equipment unlocking mode, the SIM card state, the system integrity and the discrete state of the application environment, and the security state vector is constructed, so that the equipment security can be subjected to structured description before the transaction is initiated, and the security fluctuation characteristics during the operation of the equipment are captured; by means of a security product attenuation value generated by a security state vector and in combination with a highest password-free quota reference value set by a user, a comprehensive equipment security score is output in a nonlinear function form, and quantitative filtering of equipment credibility is realized in a previous stage of quota calculation.