Unmanned aerial vehicle leasing service identity verification system of low-altitude shared economic platform

Abstract

The invention discloses an unmanned aerial vehicle leasing service identity verification system for a low-altitude shared economic platform, and the system comprises the following modules: a public key disturbance module which is used for obtaining a user public key, and constructing a signature fragment set; the activation signature module is used for generating an original identity activation signature; the behavior signature module is used for generating a behavior time sequence spectrum and combining the behavior time sequence spectrum with the original identity activation signature to generate a relay biological behavior signature; the challenge signature module is used for generating a latent challenge area instruction based on the relay biological behavior signature and embedding the latent challenge area instruction into a control channel to generate a time domain latent signature; the spatial signature module is used for generating a behavior field domain kinking signature; the signature integration module is used for generating an autophagy closed-loop signature structure; and the chain type evidence storage module is used for carrying out sealing processing on the autophagy closed-loop signature structure and writing the autophagy closed-loop signature structure into a chain type signature data structure. According to the method, the multi-level behavior signature and the chain type packaging method are fused, and strong binding, high safety and whole-chain traceability of unmanned aerial vehicle leasing identity authentication are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of identity security authentication, and particularly relates to a UAV rental service identity verification system of a low-altitude sharing economy platform. BACKGROUND

[0002] With the gradual opening of urban low-altitude airspace and the continuous rise of UAV rental demand, a low-altitude intelligent equipment scheduling platform based on a sharing economy model has become an important infrastructure for future smart traffic and urban air logistics. However, the existing UAV rental service identity authentication mechanism generally has the following problems:

[0003] The identity verification means of the rental user is single, and mostly relies on static passwords, fingerprints or face recognition and other primary verification means, which is difficult to effectively prevent high-risk operations such as fake rental requests and unauthorized use of device control rights, and there is a large blind area for identity fraud and flight responsibility tracing; the existing identity signature method cannot realize multi-stage, context-aware authentication in the whole rental process, resulting in a disconnection between dynamic control behavior in flight and identity authorization before rental, and it is difficult to accurately reflect the consistency between the real identity of the operator and the operation behavior; most platforms lack a deep binding mechanism for the fingerprint of the UAV entity device, and the signature is only constructed based on user-side information, ignoring the enhancement of the authentication credibility by the UAV's own sensing behavior and environmental disturbance factors in the flight process; the traditional chain signature method does not fully integrate behavior characteristics and biological dynamic indicators, lacks a dynamic signature mechanism for operation intention, flight mode and physical field twist behavior, and cannot realize identity persistence verification and behavior chain sealing for the whole flight process. At the same time, the authentication result records of most current platforms still remain in centralized databases or single log systems, which cannot meet the needs of verifiability, non-tamperability and multi-party traceability of identity verification data.

[0004] Therefore, how to provide a UAV rental service identity verification system of a low-altitude sharing economy platform is a problem that those skilled in the art need to solve. SUMMARY

[0005] One object of the present application is to provide a UAV rental service identity verification system of a low-altitude sharing economy platform, which integrates multi-modal biological behavior feature extraction, hardware fingerprint binding and chain signature structure packaging methods to realize multi-dimensional dynamic authentication of the identity of the UAV rental user. Through progressive signature nesting of biological behavior time series spectrum, flight behavior characteristics and device information, the security, uniqueness and tamper resistance of the authentication are effectively improved, while supporting whole-process identity tracing and responsibility confirmation, which is suitable for high-frequency rental and high-risk operation scenarios in a low-altitude sharing platform, and has the advantages of strong real-time performance, good privacy protection and high system compatibility.

[0006] The unmanned aerial vehicle leasing service identity verification system of the low-altitude sharing economy platform according to the embodiment of the application comprises the following modules: A public key disturbance module is configured to obtain a user public key, decompose the user public key into multiple elliptic curve point segments, introduce offset disturbances generated by user iris image information into the multiple elliptic curve point segments respectively, and construct a signature fragment set. An activation signature module is configured to combine the signature fragment set, a unique hardware identifier of the unmanned aerial vehicle, and a timestamp to generate an original identity activation signature. A behavior signature module is configured to generate a behavior time sequence spectrum, combine the behavior time sequence spectrum with the original identity activation signature, and generate a relay biological behavior signature containing a delay unlocking structure. A challenge signature module is configured to generate a latent challenge area instruction based on the relay biological behavior signature, embed the latent challenge area instruction into a control channel, generate a time-domain latent signature by completing a challenge response at a random time point by the user, and generate a time-domain latent signature. A space signature module is configured to generate a space behavior graph, construct a field twist matrix, combine the field twist matrix with the time-domain latent signature, and generate a behavior field twist signature. A signature integration module is configured to combine the original identity activation signature, the relay biological behavior signature, the time-domain latent signature, and the behavior field twist signature to generate an autophagy closed-loop signature structure. A chain storage module is configured to perform closed processing on the autophagy closed-loop signature structure and write the autophagy closed-loop signature structure into a chain signature data structure for identity verification and responsibility tracing.

[0007] The unmanned aerial vehicle leasing service identity verification method of the low-altitude sharing economy platform according to the embodiment of the application comprises the following steps: Step one: obtain a user public key, decompose the user public key into multiple elliptic curve point segments, introduce offset disturbances generated by user iris image information into the multiple elliptic curve point segments respectively, and construct a signature fragment set. Step two: combine the signature fragment set, a unique hardware identifier of the unmanned aerial vehicle, and a timestamp to generate an original identity activation signature. Step three: collect user facial micro-expression actions, fingertip touch rhythms, and thermal imaging breathing frequencies, generate a behavior time sequence spectrum, combine the behavior time sequence spectrum with the original identity activation signature, and generate a relay biological behavior signature containing a delay unlocking structure. Step four: generate a latent challenge area instruction based on the relay biological behavior signature, embed the latent challenge area instruction into a control channel, generate a time-domain latent signature by completing a challenge response at a random time point by the user, and generate a time-domain latent signature. Step five: collect the inertial measurement unit data, global positioning system data, magnetometer data and wind speed sensor data during the flight of the unmanned aerial vehicle, generate a spatial behavior graph, construct a field domain twist matrix, and combine the field domain twist matrix with the time domain latent signature to generate a behavior field domain twist signature; Step six: after the flight is completed, combine the original identity activation signature, relay biological behavior signature, time domain latent signature and behavior field domain twist signature to generate an autophagy closed loop signature structure; Step seven: perform structure closure processing on the autophagy closed loop signature structure, and store the autophagy closed loop signature structure in a chain signature data structure for realizing unmanned aerial vehicle rental service identity verification.

[0008] Optionally, the step one is specifically: Receiving a user identity authentication request through a user terminal, and calling an elliptic curve encryption public key bound to the user identity from an authentication server as the user public key; Performing nonlinear increasing position mapping on the user public key in the elliptic curve domain, and performing variable pitch division on the user public key data in the elliptic curve definition domain according to a high-order ratio function to obtain a plurality of elliptic curve point segments of different lengths; Collecting a user iris image, extracting an iris texture feature map using Gabor filtering, and generating an iris feature vector set using a joint of a histogram of oriented gradients and a local binary pattern; Grouping and hashing the iris feature vector set to generate a perturbation mask matrix, the perturbation mask matrix being a group of perturbation control parameters corresponding to the elliptic curve point segments, the perturbation control parameters including a perturbation rotation angle, a perturbation direction identifier and a perturbation weight coefficient; According to the perturbation control parameters in the perturbation mask matrix, performing a point rotation perturbation operation on the corresponding elliptic curve point segment, the point rotation perturbation operation taking the curve parameter center of each elliptic curve point segment as a rotation reference, performing affine rotation transformation in the elliptic curve definition domain according to the perturbation rotation angle, determining the perturbation axis direction according to the perturbation direction identifier, and adjusting the perturbation amplitude under the action of the perturbation weight coefficient to generate a perturbed point segment sequence; Recombining the perturbed point segment sequence according to the index order of the perturbation mask matrix to construct a signature fragment set, and inputting the signature fragment set as a root-level key.

[0009] Optionally, the unique hardware identifier of the unmanned aerial vehicle includes a unique fixed serial number of an unmanned aerial vehicle master control chip and an initial factory bias parameter set of a gyroscope and an accelerometer in an inertial measurement unit; The unique fixed serial number is a hardware fingerprint string burned in the read-only storage area of the unmanned aerial vehicle master control chip, which has uniqueness and tamper resistance; The initial factory bias parameter set is constructed by collecting a plurality of groups of original sensor output data in a stationary state of the unmanned aerial vehicle, calculating a steady-state mean vector, and constructing the initial factory bias parameter set; The unique solidification serial number is subjected to a splice hash operation with the initial factory bias parameter set to obtain a fingerprint summary of the unmanned aerial vehicle device, and the signature fragment set and the time stamp are combined to generate an original identity activation signature.

[0010] Optionally, the user's facial micro-expression action, fingertip touch rhythm, and thermal imaging breathing frequency are collected to generate a behavior timing spectrum, and the original identity activation signature is combined to generate a relay biological behavior signature, specifically: The user's facial micro-expression action is collected, and the instantaneous changes of the eyelid, mouth corner, and nasal alar muscle group are image sequence labeled to extract micro-expression action interframe relative displacement, angle change rate, and duration parameters; The user's fingertip touch rhythm is collected, and the rhythm frequency, time interval, contact pressure change curve, and multi-finger trajectory direction of multi-point contact on the electrostatic capacitive touch screen are measured; The user's thermal imaging breathing frequency is collected, and the periodic temperature change waveform of the nasal alar and upper lip area is captured using an infrared thermal imaging sensor to calculate the heat flux change rate and breathing rhythm stability; The user's facial micro-expression action parameters, user's fingertip touch rhythm parameters, and thermal imaging waveform parameters are respectively preliminarily synchronized according to the sampling time stamp, a reference time axis is constructed using a time window sliding mechanism, and linear interpolation and phase normalization processing are performed on the feature flow of each channel to generate a time feature matrix in a unified time scale, wherein each row corresponds to a type of behavior channel, and each column corresponds to a standardized time segment; In the time feature matrix, the similarity measurement result between behavior channel pairs is obtained, and the average cross-correlation coefficient is extracted as a cross-channel timing correlation index; In each channel, a sliding window is used to calculate a local change rate curve, and a transient mutation section is identified based on a Z-score anomaly detection algorithm, and the abnormal peak density is counted as a mutation point detection value; The time point overlap degree of the wave peaks and wave troughs in the time window is counted for each channel, and the proportion of the overlapping events in the total period is calculated as a waveform cooperative change factor; A stability score function is constructed based on the cross-channel timing correlation index, abnormal peak density, and waveform cooperative change factor to calculate a stability score; The stability score corresponding to each time slice is calculated, the continuous time section with a stability score greater than a set score threshold is selected as a high stability period, and the channel feature vector sequence of the high stability period is extracted and connected to form a behavior timing spectrum; The behavior timing spectrum is feature compressed and a fusion vector is extracted as a disturbance factor; a hash digest is performed on the original identity activation signature to obtain a signature digest value; the fusion vector and the signature digest value are weighted according to channel stability scores, a hash mask insertion operation is performed to generate a disturbance mixed result; the disturbance mixed result is signed by using a user private key to obtain a relay biological behavior signature.

[0011] Optionally, the delay unlocking structure includes a delay timer, an activation threshold condition set and a unlocking signal buffer; The delay timer is set to delay at least N seconds from the signature generation time before allowing triggering verification; The activation threshold condition set includes a similarity threshold of the behavior timing spectrum and the historical behavior model, a behavior feature vector change amplitude stability threshold and a consistency score lower threshold of behavior channel intercoordination fluctuation; The unlocking signal buffer is used to buffer the signature request and release the relay biological behavior signature result after meeting the delay timing and activation threshold conditions.

[0012] Optionally, the step four is specifically: The relay biological behavior signature is hash mapped, a challenge seed value is generated in combination with a preset challenge factor table, the challenge seed value is input into a pseudo-random number generator to generate a latent challenge area instruction, and the latent challenge area instruction includes time trigger points, channel disturbance types and response condition parameters; The latent challenge area instruction is encoded and embedded in the control channel of the flight control system in the form of a disturbance frame to form a latent behavior challenge that is not perceived by the user; In the flight control process, a challenge event is initiated at a pseudo-random set target time point, and whether the user completes a behavior response according to a preset manner is detected through an unmanned aerial vehicle end sensor, and the behavior response includes a sudden attitude control, a button rhythm input and a mobile device micro-inertial gesture matching; If an operation behavior matching the response condition parameter is detected within the challenge window, it is considered that the challenge response is successful, a challenge response result is recorded, and the challenge response result includes a challenge response time, a behavior feature code and a challenge number; The challenge response result is bound with the relay biological behavior signature, a hash mixing is performed and a user key is called to complete signature generation to obtain a time domain latent signature.

[0013] Optionally, the step five is specifically: In the process of unmanned aerial vehicle flight, the angular velocity and acceleration data output by the inertial measurement unit, the position information provided by the global positioning system, the three-axis geomagnetic data output by the magnetometer, and the external disturbance wind field data recorded by the wind speed sensor are collected, time stamped and synchronized, and a continuous time sequence feature set is constructed, based on the trajectory curvature, attitude rotation rate, magnetic vector offset and wind speed disturbance value, a corresponding spatial behavior atlas is generated, which reflects the motion trend and environmental interaction characteristics of the unmanned aerial vehicle in the physical space; The trajectory curvature, attitude rotation rate, magnetic vector offset and wind speed disturbance value in the spatial behavior atlas are encoded in time sequence to construct a multi-dimensional time series data set, and a tensor encoding method is used to establish the interaction between behavior variables, forming a field twist matrix with spinor relationship representation; The field twist matrix and the time domain latent signature are subjected to hash fusion processing, and a user private key is called for signature operation to generate a behavior field twist signature.

[0014] Optionally, the step six is specifically: After the flight task is completed, the original identity activation signature, the relay biological behavior signature, the time domain latent signature and the behavior field twist signature are structurally integrated in time sequence, and a nested chain encapsulation method is used to recursively bind the signature content of each level with the digest of the previous layer as part of the input of the next layer, generating a self-lysing closed loop signature structure; In the self-lysing closed loop signature structure, each signature node forms a chain data structure through hash digest reference, and the final signature node contains a hash aggregation digest of all signature digests, which is used to ensure integrity verification; The self-lysing closed loop signature structure has structural non-decomposability, and any missing, tampering or breaking of a layer of signature will cause the whole signature chain to fail the check, which is configured for non-repudiation verification and chain responsibility binding traceability.

[0015] Optionally, the step seven is specifically: A chain seal identification bit is added to the last level signature node of the self-lysing closed loop signature structure to lock the reference path; at the same time, a closed hash digest of the whole self-lysing closed loop signature structure is calculated and marked as read-only state; The self-lysing closed loop signature structure after the closed processing is stored in a chain signature data structure, which is constructed by using a multi-node distributed ledger technology, supporting sequential verification and traceability check of the signature node; The chain signature data structure is bound to the identity management system of the low-altitude sharing economy platform, used for multi-level signature consistency comparison of the lease user, actual operation behavior and flight record, realizing identity verification and responsibility traceability based on the signature chain.

[0016] The beneficial effects of the present application are: The present application constructs a multi-layer nested signature structure based on user biological feature disturbance, combined with unmanned aerial vehicle device hardware fingerprint and flight behavior timing, aiming at the problems existing in the current low-altitude sharing platform, such as single identity authentication means, coarse authentication granularity, and non-sustainable binding of signature, etc. A composite authentication system based on disturbance weighted signature and chain-like spatiotemporal behavior encapsulation is proposed. In the lease start-up stage, the present application realizes the unique dynamic construction of the identity root key through the elliptic curve point segment decomposition of the user public key and the iris disturbance reconstruction mechanism; in the device activation stage, the present application introduces the hardware fingerprint of the unmanned aerial vehicle master chip solidified serial number and inertial initial bias parameter to construct the original identity activation signature, which guarantees the device binding of the authentication chain; in the control process, the present application fuses the user facial micro-expression, fingertip rhythm and thermal imaging breathing frequency to construct a multi-channel behavior timing spectrum, and extracts the high-confidence period through the stability scoring mechanism to generate a relay biological behavior signature; in the middle of the flight, the present application pseudo-randomly implants the control channel through the challenge area instruction generated by the relay signature, and verifies it in real time combined with the micro-behavior response to output the time-domain latent signature; in the late flight, the present application collects the spatial behavior variables in the flight process to construct a spin tensor field and generate a coupled twist matrix, and jointly generates a behavior field domain twist signature with the time-domain signature. Finally, the present application encapsulates the multi-layer signature nodes in time sequence to form an indissoluble autophagy closed loop signature structure, and completes the chain signature evidence. Through the above technical scheme, the present application realizes the dynamic fusion and structured binding among the identity signature, flight behavior and device entity, effectively improves the real-time performance, anti-falsity and traceability of identity authentication in the leasing process, and enhances the flight behavior responsibility closed loop verification ability of the platform. BRIEF DESCRIPTION OF DRAWINGS

[0017] The accompanying drawings are included to provide a further understanding of the present application, and constitute a part of the specification, illustrate the present application together with the embodiments thereof, and explain the present application, but do not limit the present application. In the drawings: Figure 1 The structure schematic diagram of the unmanned aerial vehicle leasing service identity verification system of the low-altitude sharing economic platform proposed by the present application; Figure 2 The overall flowchart of the unmanned aerial vehicle leasing service identity verification method of the low-altitude sharing economic platform proposed by the present application. DETAILED DESCRIPTION

[0018] The present application will now be described in further detail with reference to the drawings. These drawings are simplified schematic diagrams, and only illustrate the basic structure of the present application in a schematic manner, and therefore only show the components related to the present application.

[0019] Reference Figure 1 The unmanned aerial vehicle leasing service identity verification system of the low-altitude sharing economic platform includes the following modules: A public key perturbation module is configured to obtain a user public key, decompose the user public key into multiple elliptic curve point segments, introduce offset perturbations generated by user iris image information respectively, and construct a signature fragment set; An activation signature module is configured to combine the signature fragment set with a unique hardware identifier of the UAV and a timestamp to generate an original identity activation signature; A behavior signature module is configured to generate a behavior timing spectrum, combine the behavior timing spectrum with the original identity activation signature, and generate a relay biological behavior signature containing a delay unlocking structure; A challenge signature module is configured to generate a latent challenge area instruction based on the relay biological behavior signature, embed the latent challenge area instruction in a control channel, generate a time-domain latent signature by completing a challenge response at a random time point by the user, and generate a time-domain latent signature; A space signature module is configured to generate a space behavior graph, construct a field domain twist matrix, and combine the field domain twist matrix with the time-domain latent signature to generate a behavior field domain twist signature; A signature integration module is configured to combine the original identity activation signature, the relay biological behavior signature, the time-domain latent signature, and the behavior field domain twist signature to generate a self-lysosomal closed-loop signature structure; A chain storage module is configured to perform closed processing on the self-lysosomal closed-loop signature structure and write the self-lysosomal closed-loop signature structure into a chain signature data structure for identity verification and responsibility tracing.

[0020] Reference Figure 2 The UAV rental service identity verification method of the low-altitude sharing economy platform includes the following steps: Step one: obtain a user public key, decompose the user public key into multiple elliptic curve point segments, introduce offset perturbations generated by user iris image information respectively, and construct a signature fragment set; Step two: combine the signature fragment set with a unique hardware identifier of the UAV and a timestamp to generate an original identity activation signature; Step three: collect user facial micro-expression actions, fingertip touch rhythms, and thermal imaging breathing frequencies to generate a behavior timing spectrum, combine the behavior timing spectrum with the original identity activation signature, and generate a relay biological behavior signature containing a delay unlocking structure; Step four: generate a latent challenge area instruction based on the relay biological behavior signature, embed the latent challenge area instruction in a control channel, generate a time-domain latent signature by completing a challenge response at a random time point by the user, and generate a time-domain latent signature; Step five: collect inertial measurement unit data, global positioning system data, magnetometer data, and wind speed sensor data during the flight of the UAV to generate a space behavior graph, construct a field domain twist matrix, and combine the field domain twist matrix with the time-domain latent signature to generate a behavior field domain twist signature; Step six: after the flight, the original identity activation signature, relay biological behavior signature, time domain latent signature and behavior field domain twist signature are combined to generate a self-cannibalistic closed loop signature structure; Step seven: performing structure closure processing on the self-cannibalistic closed loop signature structure, and storing the self-cannibalistic closed loop signature structure in a chain signature data structure, for realizing unmanned aerial vehicle leasing service identity verification.

[0021] In the embodiment, the step one is specifically: Receiving a user identity authentication request through a user terminal, and calling an elliptic curve encryption public key bound to the user identity from an authentication server as the user public key; Performing nonlinear increasing position mapping on the user public key in an elliptic curve domain, and performing variable pitch division on the user public key data in an elliptic curve definition domain according to a high-order ratio function to obtain a plurality of elliptic curve point segments of different lengths; Collecting a user iris image, extracting an iris texture feature map using Gabor filtering, and generating an iris feature vector set using a histogram of oriented gradients and a local binary pattern; Performing grouping hash operation on the iris feature vector set to generate a perturbation mask matrix, the perturbation mask matrix being a group of perturbation control parameters corresponding to the elliptic curve point segments, the perturbation control parameters including a perturbation rotation angle, a perturbation direction identifier and a perturbation weight coefficient; In the embodiment, in order to improve the individual uniqueness and irreversibility of the elliptic curve point segment perturbation process, a technical path of performing grouping hash operation on the iris feature vector set to generate the perturbation mask matrix is adopted, and the specific steps are as follows: The iris feature vector set after Gabor filtering and joint encoding of the histogram of oriented gradients and the local binary pattern (LBP) is divided into a plurality of iris sub-vector groups according to a fixed length Each iris sub-vector group is input into a hash function as a perturbation seed, and a hash digest value is generated by using a SHA-3 encryption hash algorithm to control the perturbation behavior.

[0022] Each hash digest value is structurally parsed, and different bit segments are intercepted as generation sources of different perturbation parameters: The perturbation rotation angle is intercepted from the high bits of the hash digest value, and is proportionally mapped to a preset angle range ), which is used to control the rotation amplitude of the elliptic curve point segment around its parameter center; The perturbation direction identifier is extracted from the lowest two bits of the middle segment, and takes values respectively mapped to the perturbation directions of the four quadrants of the XY axis; The perturbation weight coefficient is intercepted from the lowest 10 bits of the hash digest value, and is normalized to The range is used to adjust the perturbation intensity, ensuring that the perturbation amplitude strikes a balance between individual identification accuracy and security.

[0023] Finally, the three sets of perturbation parameters are combined to form a complete perturbation control parameter triplet, and the same operation is performed on all iris sub-vector groups to form a perturbation mask matrix.

[0024] The perturbation mask matrix corresponds one-to-one with the set of elliptic curve point segments. It is called item by item during the elliptic curve point rotation perturbation stage, ensuring that the perturbation operation of each point segment has randomness and non-reconstructability driven by unique biometric features, which greatly enhances the anti-forgery capability and identity binding accuracy of the signature fragment set.

[0025] According to the perturbation control parameters in the perturbation mask matrix, a point rotation perturbation operation is performed on the corresponding elliptic curve point segments. The point rotation perturbation operation takes the center of the curve parameter of each elliptic curve point segment as the rotation reference, performs an affine rotation transformation in the domain of the elliptic curve according to the perturbation rotation angle, determines the perturbation axis according to the perturbation direction identifier, and adjusts the perturbation amplitude under the action of the perturbation weight coefficient to generate a perturbation point segment sequence. The perturbated point segment sequence is recombined according to the index order of the perturbation mask matrix to construct a signature fragment set, and the signature fragment set is used as the root key input.

[0026] In this embodiment, the unique hardware identifier of the UAV includes the unique fixed serial number of the UAV main control chip, and the initial factory bias parameter set of the gyroscope and accelerometer in the inertial measurement unit. The unique, fixed serial number is a hardware fingerprint string burned into the read-only storage area of ​​the UAV main control chip, and it is unique and tamper-proof; The initial factory bias parameter set is constructed by collecting multiple sets of raw sensor output data continuously while the UAV is stationary and calculating the steady-state mean vector. The unique fixed serial number is concatenated with the initial factory bias parameter set and hashed to obtain the UAV device fingerprint digest, which is then combined with the signature fragment set and timestamp to generate the original identity activation signature.

[0027] In this embodiment, the process of collecting user facial micro-expression movements, fingertip touch rhythm, and thermal imaging breathing frequency to generate a behavioral temporal spectrum, and combining it with the original identity activation signature to generate a relay biometric behavioral signature, specifically involves: The system collects users' facial micro-expression movements, annotates the instantaneous changes of the eyelids, corners of the mouth, and nasal wing muscles with image sequences, and extracts the parameters of relative displacement, angle change rate, and duration between micro-expression movement frames. Collecting user fingertip touch rhythm, measuring multi-point contact rhythm frequency, time interval, contact pressure change curve and multi-finger trajectory direction on the electrostatic capacitive touch screen; Collecting user thermal imaging breathing frequency, using infrared thermal imaging sensor to capture the periodic temperature change waveform of the nasal ala and upper lip area, calculating the heat flux change rate and breathing rhythm stability; Synchronizing the user facial micro-expression action parameters, user fingertip touch rhythm parameters and thermal imaging waveform parameters according to the sampling time stamp respectively, using time window sliding mechanism to construct the reference time axis, and performing linear interpolation and phase normalization processing on the feature flow of each channel, generating a time feature matrix under the unified time scale, where each row corresponds to a type of behavior channel, and each column corresponds to a standardized time segment; In the time feature matrix, the similarity measurement result between the behavior channel pairs is obtained, and the average cross-correlation coefficient is extracted as the cross-channel time sequence correlation index; In the time feature matrix, first, the time series of any two behavior channels are paired, and the dynamic time warping method is used to nonlinearly align the time series of the two behavior channels, obtaining a group of shortest path matching sequences. The matching process allows local compression or expansion of the time axis, so that the two channel behavior change trends can establish a time mapping relationship under the premise of not complete synchronization.

[0028] Let the channel and the channel The aligned mapping time sequence is and After alignment, based on the alignment index, the following normalized cross-correlation coefficient is calculated : ; Where, , respectively represent the mean of each channel time series, , represent the corresponding standard deviation, represent the number of aligned time segments in the behavior time sequence.

[0029] For all channel pairing combinations Repeat the above cross-correlation coefficient calculation, and average all channel pair cross-correlation coefficients to obtain the cross-channel time sequence correlation value index : ; Where, represents the number of behavior channels.

[0030] In each channel, a sliding window is used to calculate the local change rate curve, and the instantaneous mutation segment is identified based on the Z-score anomaly detection algorithm, and the abnormal peak density is counted as the mutation point detection value; The time point overlap degree of the wave peak and the wave trough in each channel in the time window is counted, and the proportion of the overlapping event in the total period is calculated as the waveform coordinated change factor; Based on the cross-channel time sequence correlation index, the abnormal peak density and the waveform coordinated change factor, a stability score function is constructed, and a stability score is calculated; ; Among them, The stability score is represented by S, The cross-channel time sequence correlation index is represented by R, The abnormal peak density is represented by D, The waveform coordinated change factor is represented by F, And The preset weight coefficient is represented by w, and satisfies ; The stability score of each time slice is calculated, the continuous time segment with a stability score greater than a set score threshold is selected as a high stability period, and the channel feature vector sequence of the high stability period is extracted and connected to form a behavior time sequence spectrum; The behavior time sequence spectrum is compressed and a fusion vector is extracted as a disturbance factor; a hash digest of the original identity activation signature is obtained; the fusion vector and the signature digest value are weighted according to the channel stability score, and a hash mask insertion operation is performed to generate a disturbance mixing result; the disturbance mixing result is signed by using a user private key to obtain a relay biological behavior signature.

[0031] In the embodiment, the delay unlocking structure includes a delay timer, a set of activation threshold conditions and an unlocking signal buffer; The delay timer is set to delay at least N seconds from the signature generation time before allowing triggering verification; The set of activation threshold conditions includes a similarity threshold of the behavior time sequence spectrum and the historical behavior model, a behavior feature vector change amplitude stability threshold, and a consistency score lower threshold of the coordinated fluctuation between behavior channels; By setting the similarity threshold of the behavior time sequence spectrum and the historical behavior model, the behavior feature vector change amplitude stability threshold, and the consistency score lower threshold of the coordinated fluctuation between behavior channels, the effectiveness of the relay biological behavior signature is dynamically verified and controlled as a condition triggering criterion in the delay unlocking structure. When the user generates the relay biological behavior signature, the system does not immediately allow the signature to enter the next step of verification or chain encapsulation, but enters a preset delay determination stage.

[0032] In this phase, the system compares the current behavior time series with the user historical behavior model, calculates the similarity index, evaluates whether the current behavior feature vector changes within the preset stable range, and analyzes whether the coordinated fluctuations between the multi-channel behavior curves meet the lower limit of the consistency score. Only when all the above indicators meet the corresponding set threshold, the unlocking signal is triggered, the relay biological behavior signature is considered valid after the delay period ends, and the next authentication chain level is entered.

[0033] The unlocking signal buffer is used to cache the signature request and release the relay biological behavior signature result after meeting the delay timing and activation threshold conditions.

[0034] In this embodiment, step four is specifically: Hash mapping is performed on the relay biological behavior signature, a challenge seed value is generated in combination with a preset challenge factor table, the challenge seed value is input into a pseudo-random number generator, a latent challenge area instruction is generated, and the latent challenge area instruction includes time trigger points, channel disturbance types, and response condition parameters. The latent challenge area instruction is encoded and embedded in the control channel of the flight control system in the form of a disturbance frame, forming a latent behavior challenge that is not perceived by the user. During flight control, challenge events are initiated at pseudo-random target points, and sensors on the unmanned aerial vehicle detect whether the user has completed behavior responses according to the preset manner, including sudden attitude control, button rhythm input, and mobile device micro-inertial gesture matching. If an operation behavior matching the response condition parameter is detected within the challenge window, it is considered that the challenge response is successful, the challenge response result is recorded, and the challenge response result includes the challenge response time, behavior feature code, and challenge number. The challenge response result is bound to the relay biological behavior signature, hash mixing is performed, and the user key is called to complete signature generation, obtaining a time-domain latent signature.

[0035] In this embodiment, step five is specifically: During the flight of the unmanned aerial vehicle, angular velocity and acceleration data output by the inertial measurement unit, position information provided by the global positioning system, three-axis geomagnetic data output by the magnetometer, and external disturbance wind field data recorded by the wind speed sensor are collected, time stamp alignment and synchronous interpolation are performed, a continuous time series feature set is constructed, a corresponding spatial behavior graph is generated based on trajectory curvature, attitude rotation rate, magnetic vector offset, and wind speed disturbance value, and the spatial behavior graph reflects the motion trend and environmental interaction characteristics of the unmanned aerial vehicle in the physical space. The trajectory curvature, attitude rotation rate, magnetic force vector offset and wind speed disturbance value in the space behavior graph are sequentially encoded to construct a multi-dimensional time series data set, and a tensor encoding method is used to establish the interaction between behavior variables to form a field twist matrix with a representation of a rotation coupling. Specifically, the trajectory curvature, attitude rotation rate, magnetic force vector offset and wind speed disturbance value in the space behavior graph are respectively taken as characteristic variables of four types of physical dimensions, sequentially encoded in sampling order under a unified time axis, and a multi-dimensional time series data set is formed.

[0036] The trajectory curvature is calculated from the direction change rate of the flight path in unit time, the attitude rotation rate is derived from the differential value of the Euler angle provided by the inertial measurement unit, the magnetic force vector offset is obtained by the difference between the current geomagnetic field and the preset reference geomagnetic field, and the wind speed disturbance value is derived from the three-axis wind force change of the wind speed sensor.

[0037] The above dimension data is combined to construct a high-order tensor structure, a tensor encoding method is used to model the time correlation between different behavior dimensions, the main correlation components are extracted by tensor decomposition, the key disturbance area is further extracted, the rotation coupling interval is identified, the continuous nonlinear change trend of multi-dimensional behavior in the spatial domain is represented, and then the field twist matrix containing the rotation coupling feature is formed as the core representation of the spatial behavior structure.

[0038] The field twist matrix and the time domain latent signature are subjected to hash fusion processing, a user private key is called for signature operation, and a behavior field twist signature is generated.

[0039] In the embodiment, the step six is specifically: After the flight task is completed, the original identity activation signature, the relay biological behavior signature, the time domain latent signature and the behavior field twist signature are structurally integrated in time sequence, and each level of signature content is recursively bound with the signature digest of the previous layer as part of the input of the next layer signature by using a nested chain encapsulation method to generate a self-lysing closed loop signature structure; In the self-lysing closed loop signature structure, each level of signature node forms a chain data structure through a hash digest reference method, and the final signature node contains a hash aggregation digest of all signature digests, which is used to ensure the integrity verification; The self-lysing closed loop signature structure has structural indecomposability, and any missing, tampering or breaking of a layer of signature will cause the overall signature chain to fail the check, which is configured for non-repudiation verification and on-chain responsibility binding traceability.

[0040] In the embodiment, the step seven is specifically: An identification bit of chain seal is added to the last signature node of the autophagy closed loop signature structure, and the reference path is locked; meanwhile, a closed hash digest of the autophagy closed loop signature structure as a whole is calculated, and is marked as a read-only state; The autophagy closed loop signature structure after the closed processing is stored in a chain signature data structure, the chain signature data structure is constructed by using a multi-node distributed ledger technology, and supports sequential verification and traceability check of the signature nodes; The chain signature data structure is bound to an identity management system of a low-altitude sharing economy platform, is used for multi-level signature consistency comparison of a leasing user, an actual operation behavior and a flight record, and realizes identity verification and responsibility tracing based on the signature chain.

[0041] Embodiment 1 In order to verify the feasibility of the application in implementation, the application is applied to a simulation operation environment built by a certain shared unmanned plane leasing platform, and the scene covers typical application modes such as short-distance logistics distribution, emergency view inspection, high-frequency round-trip transportation in a park and the like. The system simulates a plurality of leasing behavior processes, including registration, activation, flight control, challenge interaction and flight closed loop confirmation, and comprehensively investigates the performance of the layered signature structure proposed in the application in user identity right confirmation, behavior consistency check and flight responsibility binding.

[0042] In the test system, a user initiates a leasing request through an identity authentication terminal, a platform calls an encrypted public key bound to the user to generate a signature fragment set through nonlinear disturbance, and generates an original identity activation signature in combination with a unique hardware fingerprint of a leasing device and a current timestamp. Before starting a flight task, the system guides the user to complete a behavior collection process based on facial micro-expression, fingertip rhythm and thermal imaging breathing frequency, and constructs a behavior time sequence spectrum. The system extracts dynamic coordination relationship among channels, calculates average cross-correlation and waveform mutation degree, and selects high-faith time period features through a stability scoring function, and generates a relay biological behavior signature in combination with the original signature. The signature contains a delay unlocking structure, and can dynamically determine behavior consistency.

[0043] In the process of unmanned plane flight, the system embeds a behavior signature generated latent challenge instruction into a flight control channel according to the behavior signature, and randomly triggers a behavior response task at a non-predictive time point, detects whether the operator completes a required attitude control, input rhythm or micro-inertial device response, and the successful challenge result is bound to generate a time domain latent signature with the behavior signature. In the process of flight, the device synchronously collects trajectory, attitude, magnetic field and wind field disturbance data, generates a space behavior spectrum and constructs a field torsion matrix, and further fuses the behavior field torsion matrix with the latent signature to generate a behavior field torsion signature. Finally, the four-level signature is uniformly packaged into an autophagy closed loop signature structure, and is written into a multi-node chain ledger.

[0044] The platform is evaluated by 100 groups of randomly generated flight tasks, and the multi-dimensional performance of the system of the application and the control system of the traditional account + face authentication mechanism is compared.

[0045] Table 1 Chain signature traceability performance evaluation table Sample No. System Type Challenge-response Average Delay (ms) Success Response Rate Non-response Rate Signature Chain Closed Integrity Rate User Behavior Consistency Index A01-A25 Inventive System 386 95.2% 2.1% 100% 0.916 A26-A50 Inventive System 371 96.5% 1.7% 100% 0.928 A51-A75 Control System No Challenge Mechanism No Data No Data Not Applicable 0.701 A76-A100 Control System No Challenge Mechanism No Data No Data Not Applicable 0.682 From the data analysis of Table 1, it can be seen that the system of the application is superior to the control system in response ability, integrity guarantee and user behavior consistency of chain signature. First, the average delay of challenge response of sample A01-A25 and A26-A50 of the system of the application is 386 milliseconds and 371 milliseconds respectively, which is controlled within 400 milliseconds, showing excellent real-time performance. The control system lacks challenge mechanism and cannot provide delay data, indicating that it lacks chain behavior verification ability.

[0046] In terms of response quality, the success response rate of the system of the application is 95.2% and 96.5% respectively, and the non-response rate is only 2.1% and 1.7% respectively, indicating that the user has good reaction stability and system compatibility under random challenge conditions. The control system has no relevant data for evaluation under the condition of no challenge mechanism, reflecting its lack of real-time confirmation ability of operator identity.

[0047] In the signature chain closure integrity rate index, the system of the application achieves 100% in both sample groups, indicating that its nested signature structure can effectively complete the integration of all stages of signature, without data discontinuity or verification failure. The control system cannot complete the closed-loop structure due to the lack of challenge process and chain encapsulation mechanism.

[0048] User behavior consistency index is a core index for measuring the stability of user behavior mode across stages. The system of the application reaches 0.916 and 0.928 respectively, which is significantly higher than 0.701 and 0.682 of the control system, indicating that the system has higher precision and discriminability in biological behavior modeling and challenge response coordination.

[0049] Table 2 Chain signature traceability performance evaluation table Flight No. Total Number of Signature Nodes Tampered Simulated Location Verification Time (ms) Backtracking Positioning Accuracy Rate Tampering Blocking Effect Data Integrity Score (Full Score 1) T01 4 Middle Behavior Node 112 100% Success 1.00 T02 4 Time Domain Latent Node 109 100% Success 0.99 T03 4 Last Level Encapsulation Node 121 100% Success 1.00 T04 4 Original Identity Node 116 100% Success 0.98

[0050] As can be seen from Table 2, the chain signature system constructed by the application shows strong traceability accuracy, response speed and data recovery ability when facing node-level data tampering, verifying its practicability and robustness in identity verification and responsibility tracing.

[0051] Four groups of flight tasks from flight numbers T01 to T04 are all configured with complete four signature nodes, and simulation tampering operations are set at the middle behavior node, the time domain latency node, the last level encapsulation node and the original identity node. These nodes cover different key links of the signature chain and are representative. For each tampered node, the system achieves 100% backtracking positioning accuracy without relying on external intervention, which shows that the nested signature structure and chain index mechanism in the application can accurately locate the position of the anomaly, and ensure that the system has the ability to check as soon as it is wrong.

[0052] In terms of verification response time, the tampering verification time of each node is between 109ms and 121ms, and the average is only 114.5ms, that is, the integrity check and abnormal node identification of the chain structure can be completed, which embodies the real-time feature of the system and is suitable for the rapid delivery and immediate audit demand in the dynamic leasing scene.

[0053] At the same time, from the tampering blocking effect, all nodes are marked as successful, which shows that the signature chain has a tampering chain breaking mechanism, which can prevent the subsequent chain from continuing to take effect when the signature is inconsistent or the content is replaced, thereby preventing data speculation or post-forgery.

[0054] In terms of data integrity score, the middle behavior node and the last level encapsulation node reach full score 1.00, which shows that the signature node after chain encapsulation remains consistent in data state even under attack; due to the more sensitive fluctuation of the associated behavior data dimension, the scores of the time domain latency node and the original identity node decrease slightly (0.99 and 0.98 respectively), but still remain at a high integrity level, which does not affect the overall performance of the system.

[0055] The embodiment realizes the dynamic perception of user identity, behavior consistency discrimination and whole-process traceable verification in the process of unmanned vehicle leasing by constructing a multi-dimensional, multi-source heterogeneous data fusion chain signature structure, which significantly improves the robustness and security of identity verification. Compared with the traditional single authentication mechanism, the system of the application can effectively prevent risks such as fake control instructions, illegal operation behavior and post-data tampering, and ensure that the identity authentication result has tamper-proof and behavior traceability, providing a high-reliability and high-credibility identity security protection mechanism for the low-altitude sharing economy platform.

[0056] The above is only the preferred specific embodiment of the application, but the protection scope of the application is not limited thereto, and any person skilled in the art can make equivalent replacement or change according to the technical solution and inventive concept of the application within the technical range disclosed by the application, which should be covered within the protection scope of the application.

Claims

1. An identity verification system for drone rental services on a low-altitude shared economy platform, characterized in that: Includes the following modules: The public key perturbation module is used to obtain the user's public key, decompose the user's public key into multiple elliptic curve point segments, introduce offset perturbations generated by the user's iris image information, and construct a signature fragment set. The activation signature module is used to combine the signature fragment set with the drone's unique hardware identifier and timestamp to generate the original identity activation signature; The behavior signature module is used to generate a behavior time sequence spectrum and combine it with the original identity activation signature to generate a relay bio behavior signature, wherein the relay bio behavior signature includes a delayed unlocking structure. The challenge signature module is used to generate a latent challenge area instruction based on the relay biological behavior signature and embed it in the control channel. The user completes the challenge response at a random time point to generate a time-domain latent signature. The spatial signature module is used to generate a spatial behavior map, construct a field knot matrix, and combine the field knot matrix with the temporal latent signature to generate a behavior field knot signature. The signature integration module is used to combine the original identity activation signature, relay biological behavior signature, temporal latency signature and behavioral field knot signature to generate an autophagy closed-loop signature structure. The chain-based evidence storage module is used to enclose the self-eating closed-loop signature structure and write it into the chain-based signature data structure for identity verification and accountability.

2. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 1, characterized in that, The modules are connected in the following way: Step 1: Obtain the user's public key, decompose the user's public key into multiple elliptic curve point segments, introduce offset perturbations generated by the user's iris image information into each segment, and construct a signature fragment set; Step 2: Combine the set of signature fragments with the drone's unique hardware identifier and timestamp to generate the original identity activation signature; Step 3: Collect the user's facial micro-expression movements, fingertip touch rhythm, and thermal imaging breathing frequency to generate a behavioral temporal spectrum, and combine it with the original identity activation signature to generate a relay biological behavioral signature, which contains a delayed unlocking structure; Step 4: Generate a latent challenge region instruction based on the relay biological behavior signature, and embed the latent challenge region instruction into the control channel. The user completes the challenge response at a random time point to generate a time-domain latent signature. Step 5: Collect inertial measurement unit data, global positioning system data, magnetometer data and wind speed sensor data during the UAV flight process, generate a spatial behavior map, construct a field kink matrix, and combine the field kink matrix with the time-domain latent signature to generate a behavioral field kink signature; Step Six: After the flight is completed, combine the original identity activation signature, relay biological behavior signature, temporal latency signature, and behavioral field knot signature to generate an autophagy closed-loop signature structure. Step 7: Perform structural closure processing on the autophagy closed-loop signature structure and store the autophagy closed-loop signature structure in a chained signature data structure for identity verification of drone rental services.

3. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, Step one specifically involves: The system receives user authentication requests through the user terminal and retrieves the elliptic curve cryptography public key bound to the user's identity from the authentication server, which serves as the user's public key. The user public key is mapped nonlinearly in an increasing manner on the elliptic curve domain, and the user public key data is divided into variable distances on the elliptic curve domain according to a higher-order ratio function to obtain several elliptic curve point segments of unequal lengths. The system collects user iris images, extracts iris texture feature maps using Gabor filtering, and generates iris feature vector sets by combining principal direction gradient histogram with local binary patterns. The iris feature vector set is subjected to group hash operation to generate a perturbation mask matrix. The perturbation mask matrix is ​​a set of perturbation control parameters corresponding to the elliptic curve point segment. The perturbation control parameters include perturbation rotation angle, perturbation direction identifier and perturbation weight coefficient. According to the perturbation control parameters in the perturbation mask matrix, a point rotation perturbation operation is performed on the corresponding elliptic curve point segments. The point rotation perturbation operation takes the center of the curve parameter of each elliptic curve point segment as the rotation reference, performs an affine rotation transformation in the domain of the elliptic curve according to the perturbation rotation angle, determines the perturbation axis according to the perturbation direction identifier, and adjusts the perturbation amplitude under the action of the perturbation weight coefficient to generate a perturbation point segment sequence. The perturbated point segment sequence is recombined according to the index order of the perturbation mask matrix to construct a signature fragment set, and the signature fragment set is used as the root key input.

4. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, The unique hardware identifier of the UAV includes the unique fixed serial number of the UAV main control chip, and the initial factory bias parameter set of the gyroscope and accelerometer in the inertial measurement unit; The unique, fixed serial number is a hardware fingerprint string burned into the read-only storage area of ​​the UAV main control chip, and it is unique and tamper-proof; The initial factory bias parameter set is constructed by collecting multiple sets of raw sensor output data continuously while the UAV is stationary and calculating the steady-state mean vector. The unique fixed serial number is concatenated with the initial factory bias parameter set and hashed to obtain the UAV device fingerprint digest, which is then combined with the signature fragment set and timestamp to generate the original identity activation signature.

5. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, The process involves collecting user facial micro-expressions, fingertip touch rhythms, and thermal imaging breathing frequencies to generate a behavioral temporal spectrum. This spectrum is then combined with the original identity activation signature to generate a relay biometric behavioral signature, specifically: The system collects users' facial micro-expression movements, annotates the instantaneous changes of the eyelids, corners of the mouth, and nasal wing muscles with image sequences, and extracts the parameters of relative displacement, angle change rate, and duration between micro-expression movement frames. The system collects the user's fingertip touch rhythm and measures the rhythm frequency, time interval, contact pressure change curve, and multi-finger trajectory direction of multi-point contact on an electrostatic capacitive touch screen. The system collects the user's thermal imaging respiratory rate, uses an infrared thermal imaging sensor to capture the periodic temperature change waveforms in the nasal ala and upper lip regions, and calculates the rate of change of heat flux and the stability of respiratory rhythm. The user's facial micro-expression parameters, the user's fingertip touch rhythm parameters, and the thermal imaging waveform parameters are initially synchronized according to the sampling timestamp. A reference time axis is constructed using a time window sliding mechanism, and the feature stream of each channel is linearly interpolated and phase normalized to generate a time feature matrix under a unified time scale, where each row corresponds to a type of behavior channel and each column corresponds to a standardized time segment. In the time feature matrix, the similarity measurement results between behavioral channel pairs are obtained, and the average cross-correlation coefficient is extracted as a cross-channel temporal correlation index. Within each channel, a sliding window is used to calculate the local rate of change curve, and the instantaneous abrupt change segment is identified based on the Z-score anomaly detection algorithm. The density of abnormal peaks is counted as the abrupt change point detection value. The overlap of peaks and troughs in each channel within the time window is statistically analyzed, and the proportion of overlapping events in the total period is calculated as a waveform co-change factor. A stability scoring function is constructed based on the cross-channel time-series correlation index, abnormal peak density, and waveform co-variation factor to calculate the stability score. For each time slice, calculate the corresponding stability score, select the continuous time segment with the stability score greater than the set score threshold as the high stability period, extract the channel feature vector sequence of the high stability period and connect them to form a behavioral time series spectrum; The behavioral time series spectrum is feature-compressed and a fusion vector is extracted as a perturbation factor; a hash digest is performed on the original identity activation signature to obtain a signature digest value; the fusion vector and the signature digest value are weighted according to the channel stability score, and a hash mask insertion operation is performed to generate a perturbation mixing result; the perturbation mixing result is signed using the user's private key to obtain the relay biometric behavior signature.

6. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, The delayed unlocking structure includes a delay timer, an activation threshold condition set, and an unlocking signal buffer; The delay timer is set to delay verification by at least N seconds from the moment the signature is generated. The set of activation threshold conditions includes the similarity threshold between the behavioral time series spectrum and the historical behavioral model, the stability threshold of the change amplitude of the behavioral feature vector, and the lower limit threshold of the consistency score of the cooperative fluctuation between behavioral channels. The unlock signal buffer is used to cache signature requests and release relay biological behavior signature results after the delay timer and activation threshold conditions are met.

7. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, Step four specifically involves: The relay biological behavior signature is hashed and mapped, and a challenge seed value is generated by combining it with a preset challenge factor table. The challenge seed value is then input into a pseudo-random number generator to generate a latent challenge area instruction. The latent challenge area instruction includes a time trigger point, a channel disturbance type, and response condition parameters. The latent challenge area command is encoded and embedded into the control channel of the flight control system in the form of a disturbance frame, forming a latent behavior challenge that is imperceptible to the user. During flight control, a challenge event is initiated at a pseudo-randomly set target time point. The drone's sensors detect whether the user completes the behavioral response according to the preset method. The behavioral response includes sudden attitude control, button rhythm input, and micro-inertial gesture matching of the mobile device. If an operation matching the response condition parameters is detected within the challenge window, the challenge response is considered successful, and the challenge response result is recorded. The challenge response result includes the challenge response time, behavior feature code, and challenge number. The challenge response result is bound to the relay biological behavior signature, hash mixing is performed, and the user key is called to complete the signature generation, resulting in a time-domain latent signature.

8. The drone rental service identity verification system of the low-altitude sharing economy platform according to claim 2, characterized in that, Step five specifically involves: During the flight of the UAV, angular velocity and acceleration data output by the inertial measurement unit, position information provided by the global positioning system, three-axis geomagnetic data output by the magnetometer, and external disturbance wind field data recorded by the wind speed sensor are collected. Timestamp alignment and synchronous interpolation are performed to construct a continuous temporal feature set. Based on trajectory curvature, attitude rotation rate, magnetic vector offset and wind speed disturbance value, a corresponding spatial behavior map is generated. The spatial behavior map reflects the motion trend and environmental interaction characteristics of the UAV in physical space. The trajectory curvature, attitude rotation rate, magnetic vector offset, and wind speed disturbance value in the spatial behavior map are encoded in time sequence to construct a multidimensional time series dataset. Tensor coding method is used to establish the interaction relationship between behavior variables to form a field kink matrix with spinor relationship representation. The field knot matrix and the time-domain latent signature are hashed and fused together, and the user's private key is used to perform the signature operation to generate the behavioral field knot signature.

9. The drone rental service identity verification system of the low-altitude sharing economy platform according to claim 2, characterized in that, Step six specifically involves: After the flight mission is completed, the original identity activation signature, relay biological behavior signature, temporal latent signature and behavior field knot signature are structurally integrated in chronological order. The nested chain encapsulation method is used to recursively bind the signature content of each level as part of the signature input of the next level, and generate an autophagy closed loop signature structure. In the self-eating closed-loop signature structure, signature nodes at each level form a chain data structure through hash digest references, and the final signature node contains a hash aggregate digest of all signature digests. The self-devouring closed-loop signature structure is structurally indivisible. Any missing, altered, or broken signature layer will cause the overall signature chain verification to fail. It is configured for non-repudiation verification and on-chain responsibility binding and tracing.

10. The drone rental service identity verification system of the low-altitude shared economy platform according to claim 2, characterized in that, Step seven specifically involves: Add a chain seal flag to the last-level signature node of the self-eating closed-loop signature structure to lock the reference path; at the same time, calculate the closed hash digest of the entire self-eating closed-loop signature structure and mark it as read-only. The closed-loop signature structure after the encapsulation process is stored in a chain signature data structure. The chain signature data structure is constructed using multi-node distributed ledger technology and supports sequential verification and traceability verification of signature nodes. The chain-signature data structure is interface-bound with the identity management system of the low-altitude shared economy platform to perform multi-level signature consistency comparison of rental users, actual operation behavior and flight records, so as to realize identity verification and responsibility traceability based on the signature chain.

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