Voiceprint unlocking intelligent vehicle lock system and method

Abstract

The invention relates to the technical field of vehicle lock unlocking control, in particular to a voiceprint unlocking intelligent vehicle lock system and method, and the system comprises an elastic boundary waveguide modeling subsystem, a modal resonance excitation subsystem and a motion entropy field verification body subsystem. The method comprises the steps that tubular conduction ripples of sound waves in a frame structure of the electric bicycle are collected; constructing a dynamic boundary waveguide mechanism; generating a frame structure intrinsic vibration spectrum based on the output elastic structure fingerprint; a rider needs to excite resonance at the sound production frequency of structural carrier resonance, so that energy node distribution is generated at a handlebar or a cushion; the generated energy node distribution is input into a motion entropy processor, and a riding voiceprint entropy field is generated in combination with the riding state; and by mapping parameters of the riding voiceprint entropy field to a deformation tolerance of a preset man-vehicle coupling constraint domain, when a tolerance interval is converged to a threshold value, triggering unlocking. According to the invention, a three-level authentication system of structural conduction-modal excitation-motion verification is established.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle lock unlocking control, and particularly relates to a voiceprint unlocking intelligent vehicle lock system and method. BACKGROUND

[0002] As an important tool for short-distance travel, the electric bicycle is an important part of the electric bicycle, and the lock is a key part of the electric bicycle. The lock is used frequently to ensure the safety of the electric bicycle. However, the traditional mechanical lock and the electronic lock have the problems of high rate of theft, easy loss of key and remote control safety hazard. The voiceprint recognition technology is particularly suitable for electric bicycles which need to be frequently unlocked and are cost-sensitive. However, the current voiceprint recognition technology needs to be further improved in terms of intelligence, and the voice recognition is fixed and cannot effectively perform related operations according to the user's requirements. The identity recognition accuracy of the electric bicycle is low under complex working conditions such as vibration and wind noise. SUMMARY

[0003] To achieve the above purpose, the technical scheme adopted by the present application is as follows:

[0004] In one aspect of the present application, a voiceprint unlocking intelligent vehicle lock system is provided, comprising:

[0005] A modal resonance excitation subsystem is used to generate a frame structure eigen-vibration spectrum based on an output elastic structure fingerprint. The frame structure eigen-vibration spectrum is modulated by the frame structure material resonance mode and the rider contact stiffness. The structure carrier wave resonance is excited only when the sound frequency matches the specific mode. The rider needs to excite resonance at the sound frequency of the structure carrier wave resonance, so that the energy node distribution is generated at the handlebar or the seat cushion.

[0006] A motion entropy field verification body subsystem is used to input the generated energy node distribution into a motion entropy processor, and generate a riding voiceprint entropy field in combination with the riding state. By mapping the parameters of the riding voiceprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, the unlocking is triggered when the tolerance interval converges to the threshold value.

[0007] In another aspect of the present application, a voiceprint unlocking intelligent vehicle lock method of a voiceprint unlocking intelligent vehicle lock system is provided, comprising the following steps:

[0008] When the rider of the electric bicycle makes a sound, the sound wave of the sound is collected in the tubular transmission wave of the frame structure of the electric bicycle. By analyzing the multi-phase damping oscillation formed by the sound wave at the contact point of the tubular wall of the frame structure and the rider, a dynamic boundary waveguide mechanism is constructed. The dynamic boundary waveguide mechanism converts the frame structure into an acoustic transmission line with an elastic structure fingerprint. The waveguide characteristics of the acoustic transmission line integrate the geometric characteristics of the frame structure and the biomechanical characteristics of the rider.

[0009] Generate the frame structure eigen vibration spectrum based on the output-based elastic structure fingerprint; the frame structure eigen vibration spectrum is modulated by the frame structure material resonance mode and the rider contact stiffness, and the structure carrier wave resonance is excited only when the sound emission frequency matches a specific mode; the rider needs to excite resonance at the sound emission frequency of the structure carrier wave resonance to generate an energy node distribution at the handlebar or seat cushion;

[0010] Input the generated energy node distribution into a motion entropy processor to generate a riding voiceprint entropy field in combination with the riding state; by mapping the parameters of the riding voiceprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, the unlocking is triggered when the tolerance interval converges to a threshold value.

[0011] The present application realizes biomechanical identity authentication based on human-vehicle coupling characteristics through multi-module cooperation, and its technical significance mainly lies in that the elastic boundary waveguide modeling subsystem combines the mechanical impedance characteristics of the frame structure with the biomechanical characteristics of the rider when sounding, such as contact stiffness and damping characteristics, to form a unique elastic structure fingerprint, which not only contains geometric parameters such as pipe diameter, wall thickness and included angle of the frame material, but also integrates acoustic modulation characteristics such as soft tissue damping and skeletal conduction of the rider's body organization, realizing deep coupling of physical structure and physiological characteristics. The modal resonance excitation subsystem requires the rider to accurately excite a specific resonance mode of the frame structure through the matching mechanism of the eigen vibration spectrum, which has a double anti-counterfeiting feature: on the one hand, it needs to match the natural frequency of the frame material, and on the other hand, it needs to meet the stiffness condition of the human-vehicle contact interface, such as palm pressure and sitting contact area; only when the structure resonance and biomechanical constraints are met at the same time, can a detectable energy node be formed at the specified position of the handlebar / seat cushion. The motion entropy field verification subsystem constructs a human-vehicle coupling characteristic field containing time and space dimensions by monitoring the dynamic characteristics of the spatial trajectory, intensity distortion rate and phase difference of the energy node. The innovation of the system lies in extending the static voiceprint to a dynamic entropy field, and realizing continuous identity verification in the motion state by quantifying the signal stability under wind shear interference and the nonlinear distortion caused by acceleration. BRIEF DESCRIPTION OF DRAWINGS

[0012] The accompanying drawings are included to provide a further understanding of the application, and constitute a part of the specification, which together with the embodiments of the application, are used to explain the application, and do not constitute a limitation on the application. In the drawings:

[0013] Figure 1 The voiceprint unlocking intelligent bicycle lock system block diagram provided in embodiment 1 of the present application;

[0014] Figure 2 The voiceprint unlocking intelligent bicycle lock system schematic diagram provided in embodiment 1 of the present application;

[0015] Figure 3 The elastic boundary waveguide modeling subsystem block diagram provided in embodiment 2 of the present application;

[0016] Figure 4 a motion entropy field verification subsystem block diagram provided in embodiment 9 of the present application;

[0017] Figure 5 a voiceprint unlocking intelligent vehicle lock method flowchart provided in embodiment 10 of the present application;

[0018] Figure 6 a block diagram of an electronic device provided by the present application;

[0019] Figure 7 a block diagram of a computer readable storage medium provided by the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all the embodiments.

[0021] Hereinafter, the terms "first", "second", and the like are used only for description convenience, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second", and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0022] In the present application, unless otherwise explicitly specified and limited, the term "connection" should be understood broadly, for example, "connection" can be a fixed mechanical connection, or a detachable mechanical connection, or integrated; or "connection" can be direct connection, or indirect connection through intermediate medium. In addition, unless otherwise explicitly specified and limited, the term "coupling" should be understood broadly, for example, "coupling" can be direct electrical connection, for example, physical contact and electrical conduction between two components, or can be understood as electrical connection between different components through solid lines such as copper foil or wire of printed circuit board (PCB) in line structure to transmit electrical signals; or "coupling" can be indirect electrical connection between two components through intermediate medium; or "coupling" can be electrical connection between two components through space / non-contact, for example, capacitive coupling between two components to transmit electrical signals.

[0023] In the embodiments of the present application, the orientation terms such as "up", "down", "left", "right" and the like can include but are not limited to the orientation defined by the relative placement of the components in the drawings. It should be understood that these directional terms can be relative concepts, and they are used for relative description and clarification, which can change accordingly according to the change of the placement of the components in the drawings.

[0024] Embodiment 1:

[0025] As shown in Figure 1 , the embodiments of the present application provide a voiceprint unlocking intelligent bicycle lock system (the principle is referred to the attached Figure 2 , which comprises:

[0026] The elastic boundary waveguide modeling subsystem is used to collect the tubular transmission wave of the sound wave when the rider of the electric bicycle makes a sound in the frame structure of the electric bicycle; a dynamic boundary waveguide mechanism is constructed by analyzing the multi-phase damping oscillation of the sound wave at the contact point of the tubular wall of the frame structure and the rider; the dynamic boundary waveguide mechanism converts the frame structure into an acoustic transmission line with an elastic structure fingerprint, and the waveguide characteristics of the acoustic transmission line integrate the geometric characteristics of the frame structure and the biomechanical characteristics of the rider;

[0027] The modal resonance excitation subsystem is used to generate a frame structure intrinsic vibration spectrum based on the output elastic structure fingerprint; the frame structure intrinsic vibration spectrum is modulated by the frame structure material resonance mode and the rider contact stiffness, and only when the sound frequency matches a specific mode, the structure carrier wave resonance is excited; the rider needs to excite resonance at the sound frequency of the structure carrier wave resonance, so that the energy node distribution is generated at the handlebar or the seat cushion;

[0028] The motion entropy field verification body subsystem is used to input the generated energy node distribution into a motion entropy processor, and generate a riding voiceprint entropy field in combination with the riding state; by mapping the parameters of the riding voiceprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, when the tolerance interval converges to a threshold value, the unlocking is triggered;

[0029] The riding voiceprint entropy field comprises: the migration trajectory of the energy node in the frame space, the distortion rate of the node intensity with acceleration, and the phase difference between the wind shear sound and the structure resonance.

[0030] In the above-mentioned embodiment, the voiceprint unlocking smart car lock system of this embodiment achieves biomechanical identity authentication based on the coupling characteristics of human and vehicle through the collaboration of multiple modules. Its technical significance lies in the following: the elastic boundary waveguide modeling subsystem combines the mechanical impedance characteristics of the frame structure with the biomechanical characteristics of the rider's voice, such as contact stiffness and damping characteristics, to form a unique elastic structural fingerprint. This fingerprint not only includes geometric parameters of the frame material, such as tube diameter, wall thickness, and angle, but also integrates the acoustic modulation characteristics of the rider's human tissue, such as soft tissue damping and bone conduction, achieving deep coupling between physical structure and physiological characteristics. The modal resonance excitation subsystem, through the matching mechanism of the intrinsic vibration spectrum, requires the rider to accurately excite specific resonant modes of the frame structure, thus providing dual anti-counterfeiting features: on the one hand, it must match the natural frequency of the frame material, and on the other hand, it must meet the stiffness conditions of the human-vehicle contact interface, such as palm pressure and sitting contact area. Only when both structural resonance and biomechanical constraints are met can a detectable energy node be formed at the specified position of the handlebar / seat. The motion entropy field verification subsystem constructs a characteristic field for human-vehicle coupling that encompasses both spatial and temporal dimensions by monitoring the spatial trajectory, intensity distortion rate, and phase difference of energy nodes. The system's innovation lies in expanding the static voiceprint into a dynamic entropy field. By quantifying signal stability under wind shear interference and nonlinear distortion caused by acceleration, it enables continuous authentication in motion.

[0031] In summary, this embodiment establishes a three-tiered authentication system: structural conduction, modal excitation, and motion verification. This upgrades traditional voiceprint recognition to a multi-dimensional authentication system that incorporates frame mechanical characteristics, riding dynamics, and environmental factors. Without the need for additional sensors, the frame itself serves as a waveguide, enabling embedded biometric collection while maintaining mechanical strength. This solves the problem of reliable identification of e-bikes in complex operating conditions such as vibration and wind noise.

[0032] Example 2:

[0033] like Figure 3 As shown, based on Example 1, the elastic boundary waveguide modeling subsystem provided by the embodiment of the present invention includes:

[0034] The multi-phase damped oscillation feature extraction module is used to extract the biomechanical damping fingerprint of each contact point by capturing the asymmetric propagation delay of the ripples in the three-sided tubular structure of the frame.

[0035] The biomechanical damping fingerprint includes: the acoustic energy attenuation slope of the seat cushion contact area, the ripple reflection phase shift of the handlebar point, and the frequency dispersion distortion rate of the pedal contact surface;

[0036] The topological constraint waveguide synthesis module is used for inputting the biomechanical damping fingerprint into a frame geometry constraint field according to the spatial topological relationship of the frame structure; the dynamic boundary conduction mechanism is generated by fusing the biomechanical damping fingerprint, the upper tube and the vertical tube included angle to determine the longitudinal waveguide curvature and the lower tube and the rear fork length ratio to generate the transverse harmonic suppression coefficient; and the waveguide path constraint of the sound wave under the elastic boundary condition is quantified.

[0037] The living body coupling transmission line generation module is used for field coupling of the dynamic boundary conduction mechanism and the real-time parameters of the seat cushion pressure distribution and the hand holding stiffness of riding; the attenuation flux of the conduction matrix is modulated by the seat cushion pressure gradient, and the stress refractive index of the waveguide path is remodeled to dynamically calibrate the conduction matrix according to the hand holding stiffness, and finally the elastic structure fingerprint acoustic transmission line with living body adaptability is output.

[0038] In the above embodiment, the elastic boundary waveguide modeling subsystem of the embodiment realizes accurate modeling and dynamic optimization of the sound wave conduction characteristics of the bicycle frame structure through the cooperative work of multiple modules. Specifically, the multiple-phase damping oscillation characteristic extraction module quantifies the biomechanical damping characteristics of the seat cushion, handlebar and pedal contact points by capturing the asymmetric sound wave conduction delay in the three-edge tubular structure of the frame, and provides input parameters for the system, including the attenuation slope, phase shift and frequency dispersion distortion rate. The topological constraint waveguide synthesis module generates a dynamic boundary conduction mechanism based on the frame geometry constraint field, such as the upper tube-vertical tube included angle, the lower tube-rear fork length ratio and the biomechanical damping fingerprint. The path constraint of the sound wave conduction under the elastic boundary condition is quantified, including the longitudinal waveguide curvature and the transverse harmonic suppression coefficient. The coupling transmission line generation module dynamically calibrates the attenuation flux and stress refractive index of the conduction matrix by fusing the real-time parameters of riding, such as the seat cushion pressure gradient and the hand holding stiffness, and finally outputs the elastic structure fingerprint acoustic transmission line adapted to the current riding state.

[0039] In summary, the embodiment accurately characterizes the sound wave conduction characteristics of the frame structure under dynamic load by coupling acoustic waveguide modeling and biomechanical parameters, and provides a quantitative basis for frame vibration suppression, material optimization or riding posture adjustment. The modules form a closed loop: characteristic extraction, topological constraint modeling, real-time parameter coupling and transmission line generation, realizing full-link acoustic conduction simulation from static geometry to dynamic load.

[0040] Embodiment 3:

[0041] On the basis of embodiment 1, the modal resonance excitation subsystem provided by the embodiment of the application comprises:

[0042] A biomechanical spectrum modulation component is used to input the elastic structure fingerprint into the contact stiffness field, and extract the living resonance modulation factor through real-time feedback of the cyclist's palm pressure gradient and the saddle deformation depth. It contains the dynamic stiffness coefficient of the handlebar grip point area, the damping attenuation rate of the saddle contact interface, and the energy reflection phase of the pedal force point.

[0043] An intrinsic vibration field synthesis component is used to couple the living resonance modulation factor with the inherent properties of the frame material. By coupling the living resonance modulation factor, the Young's modulus of the frame tube material, and the stress distribution of the welding point, a three-dimensional field convolution of the harmonic suppression domain is derived, and the frame structure intrinsic vibration spectrum with human-vehicle coupling characteristics is synthesized. The resonance peak position is dynamically shifted by the biomechanical parameters.

[0044] A carrier resonance excitation component is used to excite stress standing waves inside the frame when the rider's voice frequency matches the main modal of the frame structure intrinsic vibration spectrum. The standing wave forms a high stress antinode density at the handlebar stand pipe and a low energy node at the saddle support point. Through the spatial coupling of the high stress antinode density and the low energy node, an energy node distribution is formed, and its topological structure is determined by the frame geometry and the living modulation factor.

[0045] A dynamic resonance verification component is used to monitor the stability of the stress standing wave in real time. The gradient of the antinode density and the spatial consistency of the high stress antinode density generate the resonance confidence coefficient. The time-domain correlation of the anti-node energy attenuation rate and the low-energy anti-node generates the energy convergence factor. When the product of the resonance confidence coefficient and the energy convergence factor exceeds the critical threshold, it is determined that the energy node distribution is effective.

[0046] In the above embodiment, the modal resonance excitation subsystem of the embodiment realizes accurate regulation and verification of the resonance characteristics of the bicycle frame structure during riding through the synergistic effect of multiple components. The specific function decomposition is as follows: the biomechanical spectrum modulation component extracts the resonance modulation factor, dynamic stiffness coefficient, damping attenuation rate, and energy reflection phase through the palm pressure gradient of the rider, seat deformation depth, and pedal force point feedback, and provides dynamic parameter input of human-vehicle interaction for the system. The intrinsic vibration field synthesis component couples the resonance modulation factor with the inherent properties of the frame material, Young's modulus, and stress distribution at the welding point to generate a three-dimensional field convolution of the base frequency dispersion and the harmonic suppression domain, and outputs the frame intrinsic vibration spectrum modulated by the dynamic biomechanical parameters, and the resonance peak position changes with the riding state. The carrier wave resonance excitation component excites the stress standing wave inside the frame when the rider's sound frequency matches the main modal of the frame intrinsic vibration spectrum, forms a spatial distribution of high stress antinode density at the handlebar riser and low energy node at the seat support point, and its topological structure is determined by the frame geometry and the resonance modulation factor. The dynamic resonance verification component calculates the resonance confidence coefficient and the energy convergence factor by monitoring the antinode density gradient and the time domain energy attenuation rate of the stress standing wave; when the product of the two exceeds the threshold, it is determined that the current energy node distribution meets the resonance stability requirements.

[0047] In summary, the embodiment dynamically combines the intrinsic vibration spectrum of the human-vehicle coupling through the coupling of biomechanical parameters and frame material properties, and excites a controllable stress standing wave under acoustic excitation. Finally, through the real-time verification mechanism, the stability of the resonance energy distribution is ensured, providing a quantitative basis for active control of frame vibration, optimization of resonance frequency, or evaluation of structural safety. The components form a closed loop link: parameter modulation, vibration spectrum synthesis, standing wave excitation, and dynamic verification, realizing the whole process regulation from biomechanical input to resonance characteristic output.

[0048] Embodiment 4:

[0049] Based on embodiment 3, the dynamic resonance verification component provided by the embodiment of the application comprises:

[0050] The spatial consistency quantization sub-component is used to monitor the stress standing wave energy field on the surface of the frame in real time, extract the instantaneous antinode density distribution in the handlebar riser area, perform spatial convolution on the instantaneous antinode density distribution and the high stress antinode density template pre-generated by the carrier wave resonance excitation component, obtain the geometric center offset vector of the antinode cluster, measure the topological distortion rate of the antinode spacing, and generate the antinode spatial fidelity through vector synthesis of the geometric center offset vector and the topological distortion rate. The scalar value reflects the spatial consistency degree of the actual distribution and the theoretical template.

[0051] The time domain attenuation correlation subcomponent is used for synchronously collecting the anti-node energy time sequence of the seat support point, performing third-order differentiation on the attenuation curve to extract the energy curvature feature, performing time domain correlation on the energy curvature feature and a pre-stored low-energy anti-node reference model, comparing the time domain positioning deviation of the energy valley, and obtaining the curvature similarity of the attenuation slope; and the anti-node time domain convergence factor is derived through the weighted fusion of the time domain positioning deviation and the curvature similarity.

[0052] The dynamic confidence fusion subcomponent is used for inputting the antinode space fidelity into the frame deformation constraint field, adjusting the confidence weight in real time according to the riding posture, generating a space verification tolerance coefficient of the handlebar torque in steering, and generating a time domain compensation factor of the seat cushion displacement on a bumpy road; and the resonance confidence coefficient is generated through field coupling operation of the antinode space fidelity, the space verification tolerance coefficient and the time domain compensation factor.

[0053] The energy convergence judgment subcomponent is used for correlating the anti-node time domain convergence factor with the riding energy dissipation ground state, modulating the attenuation reference line of the wind speed gradient, and compensating the environmental disturbance of the tire ground noise generation; and the energy convergence factor is output through dynamic calibration of the anti-node time domain convergence factor, the attenuation reference line and the environmental disturbance compensation.

[0054] The double-factor combined triggering subcomponent is used for performing tensor product operation on the resonance confidence coefficient and the energy convergence factor in the motion constraint space, mapping the space distribution of the confidence coefficient to the geometric constraint space, and converting the time domain change of the convergence factor to the energy dissipation constraint; when the intersection volume of the geometric constraint space and the energy dissipation constraint exceeds a critical threshold, a stress standing wave topology locking signal is generated, and it is judged that the energy node distribution is effective.

[0055] In the above embodiment, the dynamic resonance verification component of the embodiment realizes accurate control of the frame vibration energy through multi-dimensional collaborative detection. The spatial consistency quantization subcomponent establishes a spatial dimension evaluation reference for the antinode distribution, the time domain attenuation correlation subcomponent constructs a time domain evaluation standard for energy attenuation, and the two form orthogonal detection dimensions to ensure that the vibration energy distribution meets the spatial topology requirements and the time domain attenuation characteristics at the same time. Through real-time adjustment of the space verification tolerance coefficient and the time domain compensation factor, the system has the ability to adapt to changes in riding posture and road excitation, and maintains the dynamic stability of the verification standard. Field coupling operation realizes interactive verification of the stress standing wave field, the mechanical load field and the displacement field, tensor product operation unifies the geometric constraint and the energy constraint to the motion constraint space, and a multi-dimensional parameter correlation model is established. The intersection volume threshold triggering mechanism converts continuous detection quantities into discrete locking signals, and quantifies the overlap degree of the geometric constraint space and the energy dissipation constraint to objectively judge whether the vibration energy distribution reaches the ideal resonance state.

[0056] In summary, the embodiment outputs a binary decision result topology locking signal or distribution invalidity with clear physical meaning through a four-level processing flow of spatial distribution detection, time domain attenuation analysis, environmental disturbance compensation and multi-field coupling calculation, thereby providing a deterministic decision basis for frame resonance control.

[0057] Embodiment 5:

[0058] On the basis of embodiment 4, the dual-factor combined trigger subassembly provided by the embodiment of the application comprises:

[0059] The spatial discretization module is configured to input the resonance confidence coefficient into a frame topology constraint field, map the coefficient value to a discrete confidence density in the handlebar vertical pipe region according to the spatial grid distribution of the frame tubular structure, and form a confidence propagation vector along the frame upper pipe according to the density gradient; the discrete confidence density and the confidence propagation vector are converted into a geometric constraint space through vector field integration, and the geometric constraint space is a set of frame physical regions with confidence exceeding a threshold value;

[0060] The time domain discretization module is configured to couple the energy convergence factor with a riding motion time axis, extract an attenuation reference point of the energy convergence factor on an acceleration time-varying curve, generate an energy dissipation time window according to a tire grounding period, and generate an energy dissipation constraint through time domain convolution of the attenuation reference point and the energy dissipation time window, the energy dissipation constraint representing a compliant attenuation path of resonance energy in the time domain;

[0061] The four-dimensional constraint body construction module is configured to extend the geometric constraint space along a time dimension, determine a time expansion coefficient of a spatial voxel according to a frame deformation rate, and modulate a time-varying curvature of a spatial boundary according to a wind resistance gradient; and a dynamic geometric constraint four-dimensional body is constructed through the geometric constraint space, the time expansion coefficient and the time-varying curvature.

[0062] The dissipation path embedding module is configured to embed the energy dissipation constraint into the four-dimensional body, align the dissipation path with a time axis of the four-dimensional body to generate a reference dissipation channel, compare an actual resonance energy attenuation trajectory with the reference dissipation channel to generate a path offset, and update an effective constraint volume of the four-dimensional body through coupled operation of the reference dissipation channel and the path offset.

[0063] The topology locking trigger module is configured to calculate a space-time intersection of the dynamic geometric constraint four-dimensional body and the effective constraint volume, extract a stable standing wave proportion of an intersection volume, measure an energy leakage rate of a volume boundary, and generate a stress standing wave topology locking signal when the stable standing wave proportion is greater than the energy leakage rate multiplied by a safety factor.

[0064] In the above embodiments, the dual-factor combined trigger subassembly system of the present embodiment realizes precise control of the resonance energy of the bicycle frame through multi-dimensional constraint coupling and dynamic evaluation mechanism. The confidence distribution model of the frame structure is established through the spatial discretization module, and the time domain discretization module constructs the time reference of energy attenuation, which together forms a four-dimensional constraint body, three-dimensional space + time dimension. This collaborative constraint unifies the physical characteristics of the frame and the energy characteristics of the motion in the four-dimensional parameter space. The dissipation path embedding module dynamically adjusts the effective volume of the four-dimensional constraint body by comparing the reference dissipation channel with the actual energy trajectory in real time. The feedback mechanism enables the system to adapt to the energy distribution changes during cycling, maintaining the real-time effectiveness of the constraint conditions. The topological locking trigger module introduces the quantitative comparison of stable standing wave proportion and energy leakage rate. When the system meets the standing wave stability condition, the stable standing wave proportion > energy leakage rate x safety factor, the locking signal is triggered. This judgment standard realizes the calculable evaluation of the frame resonance state.

[0065] In summary, the present embodiment covers the whole process of energy generation, propagation and dissipation from spatial confidence mapping, time domain attenuation modeling, four-dimensional constraint construction, dynamic volume adjustment and final trigger judgment. Through the mathematical representation of the dynamic response characteristics of the frame structure, a quantifiable engineering solution is provided for the resonance control of electric bicycles and sports equipment.

[0066] Embodiment 6:

[0067] Based on embodiment 5, the topological locking trigger module provided by the present embodiment comprises:

[0068] The locking signal space decoding submodule is used to input the stress standing wave topological locking signal into the frame structure dynamics field. The analysis signal includes the antinode density authentication vector of the handlebar riser area and the anti-node attenuation authentication scalar of the saddle support point. The energy node space anchor point is generated through the antinode density authentication vector and the anti-node attenuation authentication scalar, and the coordinates of the anchor point are uniquely determined by the frame geometry constraint;

[0069] The motion entropy field parameter binding submodule is used to couple the energy node space anchor point with the riding voiceprint entropy field output by the motion entropy field verification body subsystem. The anchor point position is mapped to the node migration trajectory of the entropy field, and the anchor point strength is associated with the node distortion rate of the entropy field. The entropy field constraint energy node is generated through the differential homeomorphism transformation of the node migration trajectory and the node distortion rate.

[0070] The human-vehicle coupling domain verification submodule is used to map the entropy field constraint energy node to the preset human-vehicle coupling constraint domain. The node generates biomechanical fitness in the constraint domain, and the distortion rate and the constraint domain boundary form a dynamic tolerance gradient. When the biomechanical fitness > dynamic tolerance gradient x safety factor, the energy node biological authentication is output.

[0071] The unlocking decision fusion submodule is used for four-dimensional association of the effective constraint volume of the energy node biological authentication and the two-factor combined trigger subassembly: the authentication time limit covers the energy dissipation time window, and the biological characteristics penetrate the geometric constraint space; when the effective action time range of the energy dissipation and the time and space distribution volume of the biological characteristic parameter in the geometric constraint space jointly satisfy the condition of a critical value, energy node distribution effective authentication is generated.

[0072] In the above embodiment, the topology locking trigger module system of the embodiment realizes accurate cooperative control of bicycle structure dynamics and riding biomechanics through multi-stage physical field coupling and dynamic constraint verification. The locking signal space decoding submodule converts mechanical vibration signals into quantifiable topology parameters, a nodal density vector and a nodal decay scalar, and establishes a mathematical model of the frame structure dynamics field, providing an initial spatial reference for subsequent energy node positioning. The motion entropy field parameter binding submodule introduces the dynamic characteristics of the voiceprint entropy field, and couples static structure parameters and dynamic riding characteristics, node migration trajectory / distortion rate through differential homeomorphism transformation, so that the energy node has time-varying adaptability. The human-vehicle coupling domain verification submodule establishes a biomechanics constraint boundary, and through real-time calculation of the dynamic relationship between the degree of fit and the tolerance gradient, ensures that the energy node distribution meets the ergonomics requirements. The unlocking decision fusion submodule constructs a four-dimensional time and space verification system, time window + geometric space + biological characteristics + effective volume. When the multi-dimensional parameters reach the critical coupling strength in the continuous time and space domain, the system determines that the topology locking state is established.

[0073] In summary, the embodiment realizes real-time topology optimization and safety locking of the stress distribution of key components of an electric bicycle through triple coupling of the structure dynamics field, the voiceprint entropy field and the biomechanics constraint domain, and its verification mechanism meets the requirements of mechanical structure stability, motion dynamic adaptability and ergonomics safety. The final output of the energy node distribution effective authentication marks the completion of the whole chain verification of the system from physical signal collection to multi-dimensional safety decision.

[0074] Embodiment 7:

[0075] On the basis of embodiment 6, the unlocking decision fusion submodule provided by the embodiment of the application comprises:

[0076] The time limit coverage quantization unit is used for inputting the time limit interval of the energy node biological authentication into the riding motion time axis, comparing the authentication start and end points with the boundary of the energy dissipation time window to generate a coverage depth, deriving a time limit strength factor from the authentication pulse density in the time window, and generating a time coverage authentication amount through the product of the coverage depth and the time limit strength factor;

[0077] A spatial flux quantization unit is configured to map the biometric parameters of the energy node biometric authentication to a geometric constraint space, and the distribution of the biomechanical features in the spatial grid forms a biometric flux, and the gradient change of the flux along the tubular structure of the frame generates a spatial permeability; and the spatial flux authentication quantity is generated by field integration of the biometric flux and the spatial permeability;

[0078] A four-dimensional reference body construction unit is configured to use the effective constraint volume of the double-factor combined trigger subassembly as a reference, modulate the space-time expansion coefficient of the strain time-varying field of the frame, and generate a phase calibration vector of the riding biological rhythm; and the dynamic four-dimensional reference body is constructed by the effective constraint volume, the space-time expansion coefficient, and the phase calibration vector;

[0079] An authentication quantity fusion unit is configured to couple the time coverage authentication quantity and the spatial flux authentication quantity in the reference body, expand the time quantity along the time axis of the reference body into an authentication time manifold, project the spatial quantity into the spatial grid of the reference body to form an authentication space skeleton, and generate a fusion authentication four-dimensional body by space-time interweaving of the authentication time manifold and the authentication space skeleton;

[0080] A critical volume determination unit is configured to calculate the intersection of the fusion authentication four-dimensional body and the dynamic four-dimensional reference body, extract the space-time volume density of the intersection region, integrate the density to generate an effective authentication volume, and generate the energy node distribution effective authentication when the effective authentication volume is not less than a critical volume threshold.

[0081] In the above embodiment, it is not said that the energy node biological authentication efficiency in the riding process is accurately evaluated and controlled by multi-dimensional quantification and dynamic modeling. The time-effect coverage quantization unit establishes a time-dimension authentication evaluation model, and accurately quantifies the time-dimension authentication strength through the distribution characteristics (coverage depth x time-effect intensity factor) of the energy node authentication pulse on the riding time axis; the space-penetration quantization unit constructs a three-dimensional space authentication evaluation model, and completely depicts the space-dimension authentication effect based on the penetration characteristics (biological penetration flux x space penetration rate) of biomechanical characteristics in the frame geometric space. Both of them constitute the time-space separation quantization basis of authentication efficiency. The four-dimensional reference body construction unit innovatively takes the double-factor constraint volume as the reference framework, reflects the influence of frame dynamic deformation through the time-space expansion coefficient generated by strain field modulation, and combines the phase calibration vector of riding rhythm to construct a four-dimensional (3D space + time) dynamic reference system that can adapt to the change of riding state. The reference body provides a standardized coordinate system for subsequent authentication fusion. The authentication amount fusion unit uses manifold expansion and space projection technology to convert discrete time coverage authentication amount into continuous time manifold, and reconstructs the space-penetration authentication amount into a gridized space skeleton, and generates a fusion authentication body with complete four-dimensional characteristics through a time-space interlacing algorithm. This fusion method maintains the time-space topological relationship of the original authentication data. The critical volume determination unit introduces a time-space volume density integral algorithm, analyzes the intersection area between the fusion authentication four-dimensional body and the dynamic reference body, and converts the authentication effectiveness determination into a quantifiable time-space volume comparison (effective authentication volume to critical threshold). This determination method overcomes the limitations of traditional single-dimensional threshold determination.

[0082] In summary, the embodiment realizes a complete processing flow from discrete authentication data acquisition, time-space dimension separation quantization, dynamic reference construction, four-dimensional fusion, and volume determination, and finally outputs an energy node distribution effectiveness judgment with clear physical meaning, providing a core decision basis for precise control of the riding biological energy system.

[0083] Embodiment 8:

[0084] Based on embodiment 7, the critical volume determination unit provided by the embodiment of the application comprises:

[0085] The time-space reference alignment sub-unit is used for synchronizing the time axis of the dynamic four-dimensional reference body with the motion time field of the fusion authentication four-dimensional body: the frame strain rate generates a time axis stretching factor, and the riding acceleration gradient derives a space grid calibration coefficient; the time-space reference normalization is performed on the double four-dimensional bodies through the time axis stretching factor and the space grid calibration coefficient;

[0086] The stress wave interference detection subunit is used for exciting a virtual stress wave in a normalized space-time domain, a reference body boundary emits a reference constraint wave front, and an authentication body surface generates an authentication response wave; and an intersection domain initial boundary is extracted through interference fringes of the reference constraint wave front and the authentication response wave.

[0087] The tubular structure constraint strengthening subunit is used for inputting a frame tubular topological field to the intersection domain initial boundary, generating a spatial anchor stress field at a frame triangle vertex, and modulating a boundary constraint strength through a pipe wall thickness distribution; and a structure strengthening deformation is performed on a boundary through a spatial anchor stress field and a boundary constraint strength, so as to form a stable boundary conforming to frame mechanics.

[0088] The energy density field generation subunit is used for constructing a space-time energy grid in a strengthened boundary, assigning a constraint energy level to a reference body grid point, and carrying an authentication energy flow by an authentication body grid point; and a space-time volume density field is generated through energy level coupling operation of the constraint energy level and the authentication energy flow.

[0089] The biological rhythm integration subunit is used for integrating a space-time volume density field along a riding biological rhythm, driving a density longitudinal integration through a pedaling cycle, and modulating a transverse density aggregation through a steering frequency; and an effective authentication volume is output through rhythm synchronous integration of the density longitudinal integration and the transverse density aggregation.

[0090] In the above embodiment, the critical volume determination unit of the embodiment realizes accurate quantitative evaluation of an intersection region of a fusion authentication four-dimensional body and a dynamic four-dimensional reference body through multi-level physical field coupling and dynamic integration algorithm. The space-time reference alignment subunit performs dynamic synchronous calibration on the double four-dimensional bodies through frame strain rate and riding acceleration gradient, eliminates space-time reference deviation caused by frame deformation and motion state change, and ensures that subsequent interference detection is performed in a unified space-time coordinate system. The stress wave interference detection subunit preliminarily determines a space-time boundary of the intersection region through interference fringes of the reference constraint wave front and the authentication response wave, avoids calculation error of a traditional geometric intersection algorithm, and improves boundary positioning accuracy. The tubular structure constraint strengthening subunit performs mechanical optimization on the initial intersection boundary based on the frame tubular topological field, so that the initial intersection boundary conforms to actual stress characteristics of the frame, and avoids judgment distortion caused by idealized boundary assumption. The energy density field generation subunit establishes a space-time energy grid in a strengthened boundary, generates a space-time volume density field reflecting authentication energy distribution density through coupling operation of a constraint energy level and an authentication energy flow, and provides a high-precision data source for subsequent integration. The biological rhythm integration subunit introduces a riding biological rhythm as an integration driving factor, reflects periodic authentication energy accumulation through longitudinal integration, and adapts to energy distribution changes caused by dynamic steering through transverse aggregation, and finally outputs an effective authentication volume conforming to actual riding characteristics.

[0091] In summary, this embodiment implements a complete judgment process from space-time reference calibration, interference boundary extraction, structure enhancement, energy density calculation, and rhythm integration, ensuring the physical rationality and calculation accuracy of critical volume judgment, and providing the final quantitative basis for the effectiveness of energy node authentication.

[0092] Example 9:

[0093] like Figure 4 As shown, based on Example 1, the motion entropy field verification body subsystem provided by the embodiment of the present invention includes:

[0094] The dynamic spatial constraint generation module is used to input the frame structure deformation field of the energy node migration trajectory, generate spatial constraint anchors at the trajectory turning points, and derive dynamic constraint boundaries from the topological relationship between the anchors. The spatial constraint anchors and dynamic constraint boundaries are used to construct the spatial skeleton of the human-vehicle coupling constraint domain.

[0095] The distortion rate reference field construction module is used to couple the distortion rate of node strength with acceleration and riding dynamics. The time-varying acceleration curve modulates the time domain baseline of the distortion rate, and the frame stress distribution generates a spatial reference gradient. The deformation tolerance reference field is formed through the time domain baseline and spatial reference gradient.

[0096] Phase difference tolerance injection module, which is used to adapt the phase difference between wind shear sound and structural resonance to the environment, decompose the environmental disturbance component of the phase difference using the wind speed vector, extract the structural stability component using the frame vibration mode, and generate the phase tolerance vector through field superposition;

[0097] The four-dimensional tolerance volume synthesis module is used to fuse the spatial skeleton, reference field, and tolerance vector in the riding space-time domain. The spatial skeleton is expanded along the time axis into a constraint domain four-dimensional volume. The reference field and tolerance vector are interwoven to generate a tolerance modulation field, and the dynamic deformation tolerance interval is output;

[0098] The entropy field convergence judgment module is used to monitor in real time the evolution of the dynamic deformation tolerance interval, the drift convergence rate of the spatial constraint anchor point, the gradient stability of the distortion rate reference field, and the oscillation attenuation coefficient of the phase tolerance vector; unlocking is triggered when the drift convergence rate × gradient stability × oscillation attenuation coefficient ≥ the critical convergence threshold.

[0099] In the above-mentioned embodiment, the motion entropy field verification body subsystem of this embodiment is to construct a multi-dimensional coupled dynamic tolerance control system, achieving precise deformation field management through the synergy of the following five modules: The dynamic spatial constraint generation module establishes the spatial topological skeleton of the human-vehicle system and provides a structured spatial benchmark for subsequent modules through anchor trajectory turning analysis and dynamic generation of constraint boundaries. The distortion rate reference field construction module converts riding dynamics parameters into time-deformation benchmarks and quantifies the deformation tolerance threshold of the frame structure under different working conditions through acceleration modulation and stress gradient mapping. The phase difference tolerance injection module analyzes the coupling effect of environmental disturbances and structural vibrations, generates anti-interference compensation vectors through vector decomposition and modal superposition, and improves the system's robustness to external excitations. The four-dimensional tolerance body synthesis module integrates the four-dimensional parameters of space, time, mechanics, and environment to generate dynamically adaptable tolerance intervals, achieving precise definition of the deformation domain under all riding conditions. The entropy field convergence judgment module objectively monitors the system stability state through the product criterion of multi-parameter coupling analysis (drift, gradient, oscillation coefficient), ensuring that the unlocking operation only occurs within the safety threshold of the mechanical entropy value convergence.

[0100] Example 10:

[0101] like Figure 5 As shown, based on Examples 1 to 9, the voiceprint unlocking smart car lock method provided by the embodiment of the present invention includes the following steps:

[0102] Step S100: When an electric bicycle rider makes a sound, the tubular conduction ripples of the sound waves within the frame structure of the electric bicycle are collected; a dynamic boundary waveguide mechanism is constructed by analyzing the multiphase damped oscillations formed by the sound waves at the contact points between the tube wall of the frame structure and the rider. The dynamic boundary waveguide mechanism transforms the frame structure into an acoustic transmission line with an elastic structural fingerprint, and its waveguide characteristics integrate the frame structure geometry and the rider's biomechanical characteristics.

[0103] Step S200: Based on the output elastic structure fingerprint, an intrinsic vibration spectrum of the frame structure is generated. The intrinsic vibration spectrum of the frame structure is modulated by the resonant mode of the frame structure material and the rider's contact stiffness. The structural carrier resonance is excited only when the sound frequency matches a specific mode. The rider needs to resonate at the sound frequency of the structural carrier resonance to generate energy node distribution at the handlebars or seat.

[0104] Step S300: Input the generated energy node distribution into the motion entropy processor and generate a cycling soundprint entropy field based on the riding state; map the parameters of the cycling soundprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, and trigger unlocking when the tolerance interval converges to a threshold;

[0105] The riding voiceprint entropy field includes: the migration trajectory of the energy node in the frame space, the distortion rate of the node intensity with acceleration, and the phase difference between the wind shear sound and the structure resonance.

[0106] In the above embodiment, the present embodiment realizes identity authentication through the coupling of the acoustic characteristics of the frame structure and the biological characteristics of the rider; the frame of the electric bicycle is used as a sound wave transmission medium, and the geometric characteristics and material resonance modes of the triangular structure are used to establish an acoustic transmission channel with individual specificity of the rider. When the rider speaks, the sound wave forms a multi-phase damping oscillation modulated by the biomechanical parameters in the frame, which is converted into an elastic structure fingerprint containing the coupling characteristics of the rider and the vehicle through a dynamic boundary waveguide mechanism. The fingerprint excites the structure carrier resonance of a specific mode in the intrinsic vibration spectrum of the frame, and forms energy nodes with spatial and temporal distribution characteristics at key parts of the vehicle body. Through the motion entropy processor, parameters such as the migration trajectory of the energy node, the intensity distortion, and the phase difference of the environmental noise are analyzed in real time, a multi-dimensional feature space of the riding voiceprint entropy field is constructed, and finally the unlocking decision is realized through the deformation tolerance matching of the human-vehicle coupling constraint domain. The frame structure and the human characteristics are used as an integral and indivisible part of the overall acoustic system, and the synergistic effect of structural resonance and biomechanics is used to realize high-security non-contact authentication.

[0107] Figure 6 A block diagram of an exemplary electronic device suitable for implementing embodiments of the present application is shown.

[0108] The electronic device can include a central processor / microprocessor / master control chip, etc.; a storage medium coupled to the central processor / microprocessor / master control chip, etc., and storing computer executable instructions therein for performing the steps of various methods of embodiments of the present application when executed by the processor.

[0109] The central processor / microprocessor / master control chip, etc. can include but is not limited to, for example, one or more processors or microprocessors, etc.

[0110] The storage medium can include but is not limited to, for example, random access memory (RAM), read only memory (ROM), flash memory, EPROM memory, EEPROM memory, registers, computer storage media (such as hard disks, floppy disks, solid state drives, removable disks, CD-ROMs, DVD-ROMs, Blu-ray discs, etc.).

[0111] In addition, the electronic device can also include (but not limited to) a data bus, an input / output bus / external bus / device bus, etc., a display, and an input / output device (such as a keyboard, a mouse, a speaker, etc.), etc.

[0112] The central processor / microprocessor / master control chip, etc. can communicate with external devices through wired or wireless networks (not shown) via the I / O bus.

[0113] The storage medium can also store at least one computer-executable instruction for performing the steps of the various functions and / or methods in the embodiments described in the present technology when executed by the central processor / microprocessor / main control chip, etc.

[0114] In one embodiment, the at least one computer-executable instruction can also be compiled or composed as a software product in which one or more computer-executable instructions are executed by the processor to perform the steps of the various functions and / or methods in the embodiments described in the present technology.

[0115] Figure 7 A schematic diagram of a computer-readable storage medium according to an embodiment of the present application is shown.

[0116] As shown in Figure 7 instructions, for example, computer-readable instructions. When the computer-readable instructions are executed by the processor, the various methods described above can be performed. The non-transitory computer-readable storage medium includes, but is not limited to, for example, a volatile memory and / or a non-volatile memory. The volatile memory can include, for example, a random access memory (RAM), a cache, and / or the like. The non-volatile memory can include, for example, a read only memory (ROM), a hard disk, a flash memory, and / or the like. For example, the non-transitory computer-readable storage medium can be connected to a computing device such as a computer, and then, when the computer-readable instructions stored on the non-transitory computer-readable storage medium are executed by the computing device, the various methods described above can be performed.

[0117] In several embodiments provided by the present application, it should be understood that the disclosed apparatus and method can be implemented in other manners. For example, the described apparatus embodiments are merely schematic. The division of the units is merely a logical function division. There can be another division manner for the actual implementation, for example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections can be indirect couplings or communication connections through some interfaces, devices or units, and can be electrical, mechanical or in other forms.

[0118] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they can be located in one place, or can be distributed on multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the embodiments.

[0119] In addition, each function unit in each embodiment of the present application can be integrated in one processing unit, or each unit can be physically present separately, or two or more units can be integrated in one unit. The integrated unit can be realized in the form of hardware or in the form of a software function unit.

[0120] If the integrated unit is realized in the form of a software function unit and sold or used as an independent product, it can be stored in a computer readable storage medium. Based on this understanding, the technical solutions of the present application or the entire or part of the technical solutions that essentially contribute to the prior art can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes a plurality of instructions for executing all or part of the steps of the embodiments of the present application by a computer device (which can be a personal computer, a server, or a network device, etc.). The aforementioned storage medium includes a U disk, a mobile hard disk, a read-only memory (English full name: Read-Only Memory, English abbreviation: ROM), a random access memory (English full name: Random Access Memory, English abbreviation: RAM), a magnetic disk or an optical disk, and various media that can store program codes.

[0121] The above embodiments are only used to illustrate the technical solutions of the present application, not to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that the technical solutions recorded in the foregoing embodiments can be modified, or some technical features can be replaced by equivalents; and these modifications or replacements do not make the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application.

Claims

1. A voiceprint unlocking smart car lock system, characterized by: Include: The modal resonance excitation subsystem generates the frame structure's intrinsic vibration spectrum based on the output elastic structure fingerprint. The frame structure's intrinsic vibration spectrum is modulated by the frame structure material's resonant modes and the rider's contact stiffness. The structural carrier resonance is only excited when the sound frequency matches a specific mode. The rider must resonate at the sound frequency of the structural carrier resonance to generate energy node distribution at the handlebars or seat. The motion entropy field verification subsystem is used to input the generated energy node distribution into the motion entropy processor and generate the riding soundprint entropy field in combination with the riding status; by mapping the parameters of the riding soundprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, unlocking is triggered when the tolerance interval converges to the threshold.

2. The voiceprint unlocking smart car lock system according to claim 1, characterized in that: Modal resonance excitation subsystem, including: The biomechanical spectrum modulation component is used to input the elastic structure fingerprint into the contact stiffness field and extract the living body resonance modulation factor through real-time feedback of the rider's palm pressure gradient and the seat cushion deformation depth; The intrinsic vibration field synthesis component is used to couple the living body resonance modulation factor with the inherent properties of the frame material. The intrinsic vibration spectrum of the frame structure with human-vehicle coupling characteristics is synthesized by generating the fundamental frequency dispersion using the living body resonance modulation factor and the Young's modulus of the frame tubing and deriving the three-dimensional field convolution of the harmonic suppression domain using the stress distribution of the weld points. The carrier resonance excitation component is used when the rider's voice frequency matches the main mode of the frame structure's intrinsic vibration spectrum: the specific frequency sound wave excites stress standing waves inside the frame. The standing waves form high stress antinode density at the handlebar stem and low energy antinodes at the seat support point. The energy node distribution is formed through the spatial coupling of high stress antinode density and low energy antinodes. The dynamic resonance verification component is used to monitor the stability of stress standing waves in real time. The spatial consistency of the antinode density gradient and the high stress antinode density generates a resonance confidence coefficient; the time domain correlation of the anti-node energy decay rate and the low-energy anti-node derives the energy convergence factor; when the product of the resonance confidence coefficient and the energy convergence factor exceeds the critical threshold, the energy node distribution is judged to be valid.

3. The voiceprint unlocking smart car lock system according to claim 2, characterized in that: Dynamic resonance verification components, including: The spatial consistency quantification subcomponent is used to monitor the stress standing wave energy field on the frame surface in real time, extract the instantaneous antinode density distribution in the handlebar stem area, spatially convolve the instantaneous antinode density distribution with the high-stress antinode density template pre-generated by the carrier resonance excitation component, obtain the geometric center offset vector of the antinode cluster, and measure the topological distortion rate of the antinode spacing. The antinode spatial fidelity is generated through the vector synthesis of the geometric center offset vector and the topological distortion rate. Its scalar value reflects the degree of spatial consistency between the actual distribution and the theoretical template. The time-domain attenuation correlation subcomponent is used to synchronously collect the anti-node energy time series of the seat support point and perform third-order differential analysis on its attenuation curve to extract the energy curvature characteristics; The energy curvature feature is correlated with the pre-stored low-energy anti-node reference model in the time domain, and the time domain positioning deviation of the energy valley value is compared to obtain the curvature similarity of the attenuation slope; The anti-node time domain convergence factor is derived through the weighted fusion of time domain positioning deviation and curvature similarity; The dynamic confidence fusion subcomponent is used to input the spatial fidelity of the antinode into the frame deformation constraint field and adjust its confidence weight in real time based on the riding posture. The handlebar torque during steering generates a spatial verification tolerance coefficient, and the seat displacement due to bumpy roads generates a time domain compensation factor. The resonance confidence coefficient is generated through field coupling calculation of the spatial fidelity of the antinode, the spatial verification tolerance coefficient, and the time domain compensation factor. Energy convergence determination subcomponent, used to associate the anti-node time domain convergence factor with the riding energy dissipation base state, the attenuation baseline of the wind speed gradient modulation convergence factor, and the tire ground noise generation environmental disturbance compensation amount; The energy convergence factor is output through dynamic calibration of the inverse node time domain convergence factor, reduced baseline and environmental disturbance compensation amount; A dual-factor joint trigger subcomponent is used to perform a tensor product operation on the resonance confidence coefficient and the energy convergence factor in the motion constraint space. The spatial distribution of the confidence coefficient is mapped to the geometric constraint space, and the time domain variation of the convergence factor is converted into an energy dissipation constraint. When the intersection volume of the geometric constraint space and the energy dissipation constraint exceeds a critical threshold, a stress standing wave topology locking signal is generated to determine that the energy node distribution is valid.

4. The voiceprint unlocking smart car lock system according to claim 3, characterized in that: The dual-factor combined trigger subcomponent includes: A spatial discretization module is used to input the resonance confidence coefficient into the frame topology constraint field. Based on the spatial grid distribution of the frame's tubular structure, the coefficient value is mapped to a discrete confidence density in the handlebar stem area. The density gradient extends along the frame's top tube to form a confidence propagation vector. The discrete confidence density and confidence propagation vector are converted into a geometric constraint space through vector field integration. The geometric constraint space is the set of physical frame regions where the confidence exceeds a critical value. The time-domain discretization module is used to couple the energy convergence factor with the cycling motion time axis, extract the attenuation reference point of the energy convergence factor on the acceleration time-varying curve, and generate the energy dissipation time window based on the tire ground contact period; An energy dissipation constraint is generated by time-domain convolution of the attenuation reference point and the energy dissipation time window. The energy dissipation constraint represents the compliant attenuation path of the resonance energy in the time domain. The four-dimensional constraint volume construction module is used to expand the geometric constraint space along the time dimension. The frame deformation rate determines the time expansion coefficient of the spatial voxel, and the wind resistance gradient modulates the time-varying curvature of the spatial boundary. The dynamic geometric constraint four-dimensional volume is constructed through the geometric constraint space, time expansion coefficient and time-varying curvature. The dissipation path embedding module is used to embed energy dissipation constraints into the four-dimensional body. The dissipation path is aligned with the time axis of the four-dimensional body to generate a reference dissipation channel. The actual resonant energy attenuation trajectory is compared with the reference dissipation channel to generate a path offset. Update the effective constraint volume of the four-dimensional body through the coupling operation of the reference dissipative channel and the path offset; A topology locking trigger module is used to calculate the spatiotemporal intersection of the dynamic geometric constraint four-dimensional volume and the effective constraint volume, extract the proportion of stable standing waves in the intersection volume, and measure the energy leakage rate at the volume boundary; When the proportion of stable standing waves is greater than the energy leakage rate and the safety factor, a stress standing wave topology locking signal is generated.

5. The voiceprint unlocking smart car lock system according to claim 4, characterized in that: Topology lock trigger module, including: The locking signal spatial decoding submodule is used to input the stress standing wave topology locking signal into the frame structure dynamic field. The analytical signal includes the antinode density certification vector of the handlebar stem area and the anti-node attenuation certification scalar of the seat support point. The energy node spatial anchor point is generated through the antinode density certification vector and the anti-node attenuation certification scalar. The motion entropy field parameter binding submodule is used to couple the energy node spatial anchor point with the riding soundprint entropy field output by the motion entropy field verification body subsystem. The anchor point position is mapped to the node migration trajectory of the entropy field, and the anchor point strength is associated with the node distortion rate of the entropy field. The entropy field constraint energy node is generated through the differential homeomorphism transformation of the node migration trajectory and the node distortion rate. The human-vehicle coupling domain verification submodule is used to map the entropy field constraint energy node to the preset human-vehicle coupling constraint domain. The node generates biomechanical fit within the constraint domain, and the distortion rate forms a dynamic tolerance gradient with the constraint domain boundary. When the biomechanical fit > dynamic tolerance gradient × safety factor, the energy node biometric authentication is output. Unlock the decision fusion submodule to perform four-dimensional correlation between energy node biometric authentication and the effective constraint volume of the dual-factor joint trigger subcomponent: the authentication time covers the energy dissipation time window, and the biometric characteristics run through the geometric constraint space; when the effective action time range of energy dissipation and the spatiotemporal distribution volume of the biometric characteristic parameters in the geometric constraint space jointly meet the critical value conditions, an effective authentication of the energy node distribution is generated.

6. The voiceprint unlocking smart car lock system according to claim 5, characterized in that: Unlock the decision fusion submodule, including: The time coverage quantification unit is used to input the time interval of the energy node biometric authentication into the cycling time axis. The authentication start and end points are compared with the boundary of the energy dissipation time window to generate the coverage depth. The authentication pulse density within the time window is used to derive the time intensity factor. The time coverage authentication quantity is generated by multiplying the coverage depth and the time intensity factor. The spatial penetration quantification unit is used to map the biometric parameters of the energy node biometric authentication to the geometric constraint space. The distribution of biomechanical characteristics in the spatial grid forms the biopenetration flux, and the gradient change of the flux along the tubular structure of the frame generates the spatial permeability. The spatial penetration authentication quantity is generated by the field integral of the biopenetration flux and the spatial permeability. A four-dimensional reference volume construction unit is used to use the effective constraint volume of the dual-factor joint triggering subassembly as a reference, the time-varying strain field of the frame modulates the spatiotemporal expansion coefficient of the volume, and the cycling biorhythm generates a phase calibration vector; a dynamic four-dimensional reference volume is constructed using the effective constraint volume, spatiotemporal expansion coefficient, and phase calibration vector; The authentication quantity fusion unit is used to couple the time coverage authentication quantity and the space penetration authentication quantity in the reference body. The time quantity is expanded along the time axis of the reference body into the authentication time manifold, and the spatial quantity is projected onto the spatial grid of the reference body to form the authentication space skeleton. Generate a fused authentication four-dimensional body through the spatiotemporal interweaving of the authentication time manifold and the authentication space skeleton; The critical volume determination unit is used to calculate the intersection of the fusion authentication four-dimensional body and the dynamic four-dimensional reference body, extract the spatiotemporal volume density of the intersection area, and generate the effective authentication volume through density integration; when the effective authentication volume is not less than the critical volume threshold, the energy node distribution effective authentication is generated.

7. The voiceprint unlocking smart car lock system according to claim 6, characterized in that: Critical volume determination unit, including: The spatiotemporal reference alignment subunit is used to synchronize the time axis of the dynamic four-dimensional reference volume with the motion time field of the fused authentication four-dimensional volume. The frame strain rate generates the time axis expansion factor, and the riding acceleration gradient derives the spatial grid calibration coefficient. The spatiotemporal reference normalization of the dual four-dimensional volume is performed using the time axis expansion factor and the spatial grid calibration coefficient. The stress wave interference detection subunit is used to excite virtual stress waves in the normalized time and space domain, emit reference constraint wavefronts at the reference body boundary, and generate authentication response ripples on the authentication body surface; The initial boundary of the intersection domain is extracted through the interference fringes of the reference constraint wavefront and the certification response ripple; The tubular structure constraint reinforcement subunit is used to input the tubular topology field of the frame at the initial boundary of the intersection domain. The spatial anchoring stress field is generated at the vertices of the frame triangle, and the boundary constraint strength is modulated by the tube wall thickness distribution. The intersecting boundary is structurally strengthened and deformed through the spatial anchoring stress field and the boundary constraint strength to form a stable boundary that conforms to the frame mechanics. The energy density field generation subunit is used to construct a space-time energy grid within the enhanced boundary. The reference volume grid points are assigned with constraint energy levels, and the certification volume grid points carry certification energy flows. The space-time volume density field is generated by coupling the constraint energy levels with the certification energy flows. The biorhythm integration sub-unit is used to integrate the space-time volume density field along the cycling biorhythm, the pedaling cycle drives the density longitudinal integration, and the steering frequency modulates the lateral density aggregation; the effective certification volume is output through the rhythmic synchronous integration of the longitudinal density integration and the lateral density aggregation.

8. The voiceprint unlocking smart car lock system according to claim 1, characterized in that: in, The entropy field of riding soundprint includes: the migration trajectory of energy nodes in the frame space, the distortion rate of node strength with acceleration, and the phase difference between wind shear sound and structural resonance.

9. The voiceprint unlocking smart car lock system according to claim 1, characterized in that: Also includes: The elastic boundary waveguide modeling subsystem is used to collect the tubular conduction ripples of the sound waves within the e-bike frame structure when the e-bike rider makes a sound. By analyzing the multiphase damped oscillations formed by the sound waves at the contact points between the tube walls of the frame structure and the rider, a dynamic boundary waveguide mechanism is constructed. The dynamic boundary waveguide mechanism transforms the frame structure into an acoustic transmission line with an elastic structural fingerprint. Its waveguide characteristics integrate the frame structure geometry with the rider's biomechanical characteristics.

10. A method for unlocking a smart car lock with a voiceprint according to any one of claims 1 to 9, characterized in that: The following steps are involved: When an e-bike rider makes a sound, the system collects the tubular conduction ripples of the sound waves within the e-bike's frame structure. By analyzing the multiphase damped oscillations formed by the sound waves at the contact points between the frame's tubular walls and the rider, a dynamic boundary waveguide mechanism is constructed. This dynamic boundary waveguide mechanism transforms the frame structure into an acoustic transmission line with an elastic structural fingerprint. Its waveguide characteristics integrate the frame's structural geometry with the rider's biomechanical characteristics. Based on the output elastic structure fingerprint, the intrinsic vibration spectrum of the frame structure is generated. The intrinsic vibration spectrum of the frame structure is modulated by the resonant mode of the frame structure material and the rider's contact stiffness. The structural carrier resonance is only excited when the sound frequency matches a specific mode. The rider needs to resonate at the sound frequency of the structural carrier resonance to generate energy node distribution at the handlebars or seat cushion. The generated energy node distribution is input into the motion entropy processor, and combined with the riding status to generate the riding soundprint entropy field; by mapping the parameters of the riding soundprint entropy field to the deformation tolerance of the preset human-vehicle coupling constraint domain, unlocking is triggered when the tolerance interval converges to the threshold.

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