An unmanned flow robot cargo identification management system

Abstract

The application provides an unmanned logistics robot cargo identification management system, relates to the cargo identification technical field, extracts a torque closed-loop error under a vibration of an actuator to construct an electromechanical excitation feature matrix, and matches the radio frequency space phase gradient covariance matrix, computer electrical radio frequency homologous modulation coherent tensor is calculated, bypass interference is filtered out through cross-physical field mapping, and absolute identity binding of digital identity and physical entity is realized; an implicit degradation verification mechanism is innovatively introduced into the system, electromagnetic observation is automatically cut off when the radio frequency channel fails, only single-mode electromechanical torque feedback is relied on, blind recognition deduction is carried out in combination with a hidden Markov model and global task probability. The application breaks the limitation of pure information verification, and improves the anti-counterfeiting error correction capability of the system and the business continuity under extreme working conditions.

Description

Technical Field

[0001] This invention relates to the field of cargo identification technology, specifically to a cargo identification and management system for unmanned logistics robots. Background Technology

[0002] With the explosive growth of e-commerce and the deepening of "smart logistics," traditional manual sorting and handling methods, hampered by inefficiency and high error rates, are no longer sufficient to meet the flexible demands of modern warehousing. Early automated guided vehicles (AGVs) relied solely on magnetic strips or QR codes for fixed-route transport, and their identification systems were limited to simple barcode scanning, lacking environmental awareness and complex processing capabilities. In recent years, breakthroughs in machine vision, deep learning, and multi-sensor fusion technologies have led to a profound evolution in cargo identification and management systems from "automation" to "intelligence." By introducing 3D vision and AI algorithms, modern unmanned logistics robots can not only navigate autonomously but also accurately identify and adaptively grasp goods that are stacked haphazardly or have damaged waybills, truly achieving a leap from "seeing" to "understanding," becoming a core technological support for improving the efficiency of the entire logistics chain.

[0003] The existing technology, with publication number CN105631617A, entitled "Automatic Goods Identification and Settlement Computer Management System," includes a central processing unit, RFID tags attached to each product, tag readers at entrances and exits, multiple automatic goods statistics and settlement terminals, an automatic replenishment module, and a goods status query module. This computer management system, based on Internet of Things (IoT) technology, can automatically identify goods or products entering and leaving supermarkets or warehouses, eliminating the need for customers to scan and identify goods at the exit. Furthermore, the system can automatically identify the inventory status of goods and issue replenishment requests based on that status. The entire process is fully automated, requiring no manual identification or statistics, thus improving production efficiency and business profitability.

[0004] Existing automatic cargo identification systems typically rely on fixed radio frequency reading or pure visual barcode scanning, which have significant technical limitations in highly dense mixed-temperature unmanned logistics sorting centers (such as pharmaceutical cold chain or fresh food storage); due to radio frequency multipath interference and microwave signal penetration caused by high-density stacking of goods, the system is very likely to capture "ghost tags" outside the depth of field, resulting in a disconnect between digital identity and the actual physical entity being grasped.

[0005] Meanwhile, the frosting under complex temperature control environments and the dynamic ambiguity caused by high-frequency picking lead to a sharp increase in the failure rate of passive tag reading. The aforementioned single-dimensional sensing solutions cannot establish a consistent binding between "digital RFID identity" and "physical inertial entity," easily leading to risks of mixed batches and swapping, and lacking adaptive continuous operation capabilities under boundary conditions of tag damage. This invention aims to overcome the limitations of pure information-layer verification by establishing an intelligent anti-counterfeiting and error correction system with strictly isomorphic active physical questioning and digital feature responses through cross-physical field homogeneity feature mapping. Summary of the Invention

[0006] The purpose of this invention is to provide an unmanned logistics robot cargo identification and management system to solve the problems mentioned in the background art.

[0007] To achieve the above objectives, the present invention provides the following technical solution:

[0008] An unmanned logistics robot cargo identification and management system, specifically including:

[0009] The instruction generation module is used to obtain the electromechanical excitation feature matrix generated by the underlying power feedback timing of the actuator when the actuator of the unmanned logistics robot contacts the target object;

[0010] The feature extraction module is used to obtain the radio frequency spatial phase gradient covariance matrix generated by polling and scanning the spatial region where the target object is located;

[0011] The fusion adjudication module is used to perform cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix using a preset electromechanical radio frequency covariance tensor operator, so as to calculate the covariance tensor.

[0012] The signal generation module is used to generate an indication signal based on the eigenvalues ​​of the co-source modulation coherence tensor to characterize the physical challenge-based identity and quality forced locking effect; wherein the indication signal includes a first indication signal characterizing successful authentication and a second indication signal characterizing channel anomaly.

[0013] The linkage execution module includes a function for receiving an indication signal and synchronously executing a radio frequency addressing filtering instruction for the space region and a physical attribute verification instruction for the target object based on the indication signal.

[0014] Furthermore, the instruction generation module acquires the electromechanical excitation feature matrix generated by the underlying dynamic feedback timing of the actuator, specifically including:

[0015] Read the excitation frequency parameters pre-stored in an external structured data carrier, and convert the excitation frequency parameters into a sequence of control commands for driving the actuator;

[0016] Based on the control command sequence, a drive signal containing a preset high-frequency perturbation amplitude is injected into the actuator to drive the actuator to generate mechanical vibration with a specific frequency domain pattern when it comes into contact with the target object;

[0017] The real-time torque closed-loop error of the actuator during the mechanical vibration cycle is extracted as a resistance feature; based on the time-series change feedback of the resistance feature, the electromechanical excitation feature matrix is ​​constructed.

[0018] Furthermore, the specific process by which the feature extraction module obtains the radio frequency spatial phase gradient covariance matrix includes:

[0019] The temporal physical displacement of the actuator over time is mapped to a virtual spatial sampling array to construct a synthetic aperture spatial reconstruction transformation mechanism; the in-phase and quadrature complex baseband sequences of radio frequency echoes received by polling the spatial region are extracted;

[0020] By combining the mileage state quantity of the actuator, spatiotemporal phase compensation processing is performed on the in-phase and orthogonal complex baseband sequences of the radio frequency echo to extract the continuous phase evolution characteristics of the complex radio frequency echo sequence on the displacement time sequence of the actuator; based on the continuous phase evolution characteristics, mathematical inversion reconstruction is performed to calculate and generate the radio frequency spatial phase gradient covariance matrix of the spatial region where the target object is located.

[0021] Furthermore, the fusion adjudication module performs cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix, including pre-alignment and denoising. The specific process includes:

[0022] The environmental background compensation baseline, time synchronization delay constant, and dynamic time warping tolerance parameters are read from the local independent structured data carrier. Based on the environmental background compensation baseline, the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix are subjected to spectral subtraction denoising processing using adaptive notch background cancellation logic to filter out system operating noise.

[0023] Based on the dynamic time warping tolerance parameter, the dynamic time warping algorithm is applied to the denoised electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix. By performing a smooth mapping of the time scale, the time domain misalignment caused by the sampling rate of heterogeneous sensors is eliminated.

[0024] Furthermore, the fusion adjudication module calculates the coherent tensor of the same source modulation through the following process:

[0025] Extract the alignment weight matrix representing spatial physical constraints from the external configuration carrier;

[0026] Frequency domain transformation tensor mapping is performed on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix after forward displacement compensation of the time synchronization delay constant, respectively, to obtain the frequency domain representation of the two under the corresponding time slices.

[0027] The frequency domain representation of the obtained electromechanical excitation feature matrix and the frequency domain representation of the radio frequency spatial phase gradient covariance matrix are subjected to Kronecker product operation; the output of the Kronecker product operation is multiplied by the alignment weight matrix, and the operation result is discretely accumulated and summed within a set time series window to generate an electromechanical radio frequency coherence tensor for characterizing the coherence in the same frequency time domain.

[0028] Furthermore, the fusion adjudication module performs homology quantization determination based on the generated electromechanical radio frequency homologous modulation coherence tensor, specifically including:

[0029] Perform a joint spatial mapping between the electromechanical-radio frequency coherent modulation tensor and the electromechanical dynamic mass distribution covariance matrix and topological permeability information entropy matrix characterizing the prior state of the system; extract the main diagonal eigenvalue sequence of the mapped electromechanical-radio frequency coherent modulation tensor.

[0030] A preset confidence baseline is retrieved, and the difference between the main diagonal eigenvalue sequence and the confidence baseline is continuously compared. When the main diagonal eigenvalue shows a step change and exceeds the confidence baseline, it is quantitatively determined that the external radio frequency spatial phase fluctuation and the internal electromechanical torque excitation belong to the same physical source, and based on this, it is confirmed that the target radio frequency tag is uniquely attached to the physical goods being excited by the current actuator.

[0031] Furthermore, the signal generation module generates an indication signal to characterize the physical challenge-based identity and quality-based forced lock-in effect, the specific process of which includes:

[0032] Extract the main diagonal eigenvalue sequence of the homologous modulation coherence tensor;

[0033] A preset system confidence baseline is retrieved from an external structured storage medium; the main diagonal eigenvalue sequence is dynamically and continuously compared with the system confidence baseline; when the main diagonal eigenvalue sequence is higher than the system confidence baseline, the abstract tensor space calculation result is binarized to generate a first indication signal for characterizing successful cross-modal authentication matching.

[0034] Furthermore, when generating the indication signal, the signal generation module also includes anomaly monitoring and failure signal generation logic: setting a preset time window monitoring threshold;

[0035] When the main diagonal eigenvalue sequence falls below the system confidence baseline and the duration of this state exceeds the preset time window monitoring threshold, it is determined that the current electromagnetic domain observation channel has generated an extreme anomaly, and a second indication signal characterizing the failure of the radio frequency channel is output.

[0036] Furthermore, upon receiving the first indication signal, the linkage execution module synchronously executes the radio frequency addressing filtering instruction and the physical attribute verification instruction, specifically including:

[0037] Based on the first indication signal triggering the frequency band locking mechanism, a bandpass filtering command is issued to actively cut off and discard all bypass interference radio frequency data streams that do not have the characteristics of the same modulated coherent tensor frequency band, so as to complete the radio frequency side identity purification in the space area.

[0038] The electromechanical excitation feature matrix associated with the synchronous locking of the coherent tensor of the same source modulation is then used to extract the high-fidelity electromechanical torque feedback parameters from the electromechanical excitation feature matrix and directly spatially map them into a physical quality evaluation matrix for the target object, thereby completing the forced association and verification closed loop of digital identity extraction and physical attribute measurement.

[0039] Furthermore, after receiving the second indication signal, the linkage execution module also includes executing an implicit degradation verification mechanism to maintain continuous system operation, specifically:

[0040] Based on the second indication signal, the unreliable electromagnetic domain observation channel is immediately cut off, and the sending of analytical requests for the radio frequency spatial phase gradient covariance matrix is ​​stopped; the single-mode mechanical feature extraction path is fully activated, and only the electromechanical excitation feature matrix of the target object is extracted as a local observation variable;

[0041] Read the task state machine flow record issued by the system schedule to obtain the preloaded global logic trajectory flow probability corresponding to the current spatiotemporal node; use the preset hidden Markov model to perform a fusion deduction based on the posterior probability tensor on the electromechanical excitation feature matrix as a local observation variable and the global logic trajectory flow probability, and output the blind recognition deduction result representing the passive logic identity of the target object.

[0042] Compared with the prior art, the beneficial effects of the present invention are:

[0043] This invention injects a drive signal containing a preset high-frequency perturbation amplitude into the actuator through a command generation module, extracts the real-time torque closed-loop error within the vibration cycle of the actuator as a resistance feature to construct an electromechanical excitation feature matrix; simultaneously, a feature extraction module performs spatiotemporal phase compensation processing and mathematical inversion reconstruction on the in-phase and orthogonal complex baseband sequences of the radio frequency echo to generate a radio frequency spatial phase gradient covariance matrix; then, a fusion adjudication module performs cross-correlation time series matching processing on the above two matrices, calculates the coherent tensor of the electromechanical radio frequency homo-modulation and extracts its main diagonal eigenvalue sequence; when it is confirmed that the sequence exhibits a step change and exceeds a preset system confidence baseline, a first indication signal is generated; finally, the linkage execution module triggers a frequency band locking mechanism based on the first indication signal, actively truncating and discarding bypass interference radio frequency data streams that do not possess the frequency band characteristics of the electromechanical radio frequency homo-modulation coherent tensor; this process establishes an identification system that is isomorphic between active physical challenge and digital feature response through cross-physical field mapping, fundamentally eliminating "ghost tag" interference and achieving absolute identity binding between digital radio frequency identity and physical entity;

[0044] This invention also introduces an implicit degradation verification mechanism. When the eigenvalue sequence of the main diagonal is detected to fall below the system confidence baseline and the duration exceeds the preset time window monitoring threshold, the signal generation module outputs a second indication signal representing the failure of the radio frequency channel. After receiving the second indication signal, the linkage execution module immediately cuts off the electromagnetic domain observation channel and fully enables the single-mode mechanical feature extraction path, extracting only the electromechanical excitation feature matrix of the target object as a local observation variable. Subsequently, the task state machine flow record is read to obtain the preloaded global logic trajectory flow probability, and the hidden Markov model is used to perform a fusion deduction based on the posterior probability tensor of the electromechanical excitation feature matrix and the preloaded global logic trajectory flow probability, outputting a blind recognition deduction result representing the passive logical identity of the target object. This mechanism ensures that the system can still achieve adaptive verification by relying on pure electromechanical torque feedback and macroscopic spatiotemporal probability under extreme conditions such as RFID tag detachment or shielding, avoiding hard error reporting that causes downtime, and improving the business continuity of the system at high frequency and the core survivability under extreme conditions. Attached Figure Description

[0045] Figure 1 This is a schematic diagram of the overall system flow of the present invention;

[0046] Figure 2 This is a flowchart illustrating the operational framework of the signal generation module and the linkage execution module in this invention. Detailed Implementation

[0047] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0048] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and those skilled in the art can make similar extensions without departing from the spirit of the invention. Therefore, the invention is not limited to the specific embodiments disclosed below.

[0049] Please see Figure 1 and Figure 2 This invention provides a technical solution: an unmanned logistics robot cargo identification and management system, specifically comprising:

[0050] The instruction generation module is used to obtain the electromechanical excitation feature matrix generated by the underlying power feedback timing of the actuator when the actuator of the unmanned logistics robot contacts the target object;

[0051] The feature extraction module is used to obtain the radio frequency spatial phase gradient covariance matrix generated by polling and scanning the spatial region where the target object is located;

[0052] The fusion adjudication module is used to perform cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix using a preset electromechanical radio frequency covariance tensor operator, so as to calculate the covariance tensor.

[0053] The signal generation module is used to generate an indication signal based on the eigenvalues ​​of the co-source modulation coherence tensor to characterize the physical challenge-based identity and quality forced locking effect; wherein the indication signal includes a first indication signal characterizing successful authentication and a second indication signal characterizing channel anomaly.

[0054] The linkage execution module includes a function for receiving the indication signal and synchronously executing, based on the indication signal, radio frequency addressing filtering instructions for the spatial region and physical attribute verification instructions for the target object.

[0055] The instruction generation module acquires the electromechanical excitation feature matrix generated by the underlying power feedback timing of the actuator, specifically including:

[0056] Read the excitation frequency parameters pre-stored in an external structured data carrier, and convert the excitation frequency parameters into a sequence of control commands for driving the actuator;

[0057] Based on the control command sequence, a drive signal containing a preset high-frequency perturbation amplitude is injected into the actuator to drive the actuator to generate mechanical vibration with a specific frequency domain pattern when it comes into contact with the target object;

[0058] The real-time torque closed-loop error of the actuator during the mechanical vibration cycle is extracted as a resistance feature; based on the time-series change feedback of the resistance feature, the electromechanical excitation feature matrix is ​​constructed.

[0059] Furthermore, the instruction generation module pre-reads the excitation frequency parameters stored in an external structured data carrier (i.e., a cloud database or a local configuration server), preferably setting them to a specific value within the range of 50Hz-200Hz (100Hz is typically used in this embodiment) to match the mechanical response characteristics of the unmanned logistics robot actuator under high-frequency picking operations, avoiding damage or excessive vibration to the goods; the module converts the excitation frequency parameters into a control instruction sequence, and injects a drive signal containing a preset high-frequency perturbation amplitude into the actuator based on the sequence, wherein the perturbation amplitude is preferably controlled within the range of 1%-5% of the actuator's rated torque (2%-3% relative amplitude is typically used in this embodiment, corresponding to an absolute value of approximately 0.05-0.2Nm, which is adaptively adjusted according to the different load goods weights of 0.5-50kg); the drive signal drives the actuator to generate mechanical vibrations with specific frequency domain regularity when contacting the target object (mainly with a 100Hz fundamental frequency, accompanied by a small number of harmonics), and simultaneously extracts the torque closed-loop error within the vibration cycle in real time as a resistance feature, and constructs an electromechanical excitation feature matrix based on its temporal changes;

[0060] The specific process by which the feature extraction module obtains the radio frequency spatial phase gradient covariance matrix includes:

[0061] The temporal physical displacement of the actuator over time is mapped to a virtual spatial sampling array to construct a synthetic aperture spatial reconstruction transformation mechanism; the in-phase and quadrature complex baseband sequences of radio frequency echoes received by polling the spatial region are extracted;

[0062] By combining the mileage state quantity of the actuator, spatiotemporal phase compensation processing is performed on the in-phase and orthogonal complex baseband sequences of the radio frequency echo to extract the continuous phase evolution characteristics of the complex radio frequency echo sequence on the displacement time sequence of the actuator; based on the continuous phase evolution characteristics, mathematical inversion reconstruction is performed to calculate and generate the radio frequency spatial phase gradient covariance matrix of the spatial region where the target object is located.

[0063] Furthermore, the virtual space sampling array is constructed by discretizing the continuous physical displacement d(t) of the actuator on the movement trajectory according to a preset time step Δt, and defining the spacing between virtual array elements. This maps temporal displacement into a synthetic aperture radar array with spatial resolution; where v is the actuator's moving speed, representing the real-time physical speed of the robot actuator (i.e., the end effector or chassis) during movement, and Δs is the virtual element spacing: referring to the physical distance between two adjacent virtual sampling points in the synthetic aperture radar mechanism, and Δs is preferably set to the wavelength λ of the radio frequency signal. to (In this embodiment, Δs is set to 8cm to 16cm at a frequency of 920MHz.) Δt is the sampling interval, which represents the time interval between a single frequency sweep or pulse transmission by the radio frequency sensor. By adjusting the sampling frequency, a stable spatial sampling density can be obtained at different speeds.

[0064] During the spatiotemporal phase compensation process, the system extracts the displacement increment Δd using the odometer state variables and derives the phase compensation angle based on the Doppler phase evolution law. ;in, Δd is the phase compensation angle, representing the phase offset that needs to be removed from the original signal, in radians; Δd is the displacement increment, measured by an odometer or inertial navigation system, representing the precise physical distance the actuator moves between two sampling points; λ is the radio frequency signal wavelength, representing the wavelength of the radio frequency signal in the medium, in this embodiment 920MHz corresponds to approximately 0.326m.

[0065] The specific calculation rule is as follows: the original RF echo complex baseband sequence Sraw is combined with the complex rotation factor. Performing complex multiplication, i.e. This eliminates the non-stationary phase shift caused by the robot's macroscopic motion; where Scom is the distorted complex signal, and the phase of the signal has been corrected to the reference coordinate system. For complex number rotation operators, i.e. Sraw is the original IQ signal of the radio frequency echo, which is expressed as a complex number of I+jQ.

[0066] The specific calculation rules for the mathematical inversion and reconstruction employ a spatial energy focusing algorithm based on inverse Fast Fourier Transform. The system maps the distortion-free phase evolution feature sequence to a angular-range coordinate system and calculates the autocorrelation matrix of the sequence. The feature vectors characterizing the correlation of spatial discrete points are extracted, and then the radio frequency spatial phase gradient covariance matrix is ​​generated by inversion; where R represents the covariance matrix, which is the final generated radio frequency spatial phase gradient covariance matrix, and its eigenvalues ​​reflect the spatial energy distribution; S represents the multi-dimensional signal vector after compensation. Let S be the conjugate transpose of S; It represents the expected value in mathematics, which is achieved in practical engineering by taking a moving average over multiple observation periods;

[0067] Furthermore, when performing spatiotemporal phase compensation processing on the in-phase and quadrature complex baseband RF echo sequences, two parameters are calculated, including the normalized transient torque wave impedance and the spatial reconstruction phase compensation factor.

[0068] The normalized transient torque wave impedance is obtained by extracting the torque closed-loop error of the servo drive in real time and comparing it with the angular velocity feedback error to obtain the absolute mechanical impedance. The value range is limited to 0 to 1 by using the preset no-load baseline impedance and the maximum stall limit impedance for linear scaling and extreme value clamping. This is used to characterize the transient dynamic impedance characteristics of the target to high-frequency mechanical waves.

[0069] The spatial reconstruction phase compensation factor integrates the macroscopic phase offset obtained by multiplying the macroscopic temporal displacement of the actuator chassis by the radio frequency spatial wavenumber, and the microscopic phase offset obtained by multiplying the aforementioned normalized transient torque wave impedance by the adaptive impedance-displacement compliance conversion coefficient by the wavenumber. The two are added together and normalized to the 0 to 1 range through nonlinear cosine mapping. This is used to accurately eliminate the radio frequency echo phase distortion caused by the macroscopic and microscopic motion of the system.

[0070] Furthermore, the preset no-load baseline impedance refers to the inherent mechanical impedance of the actuator when it is not in contact with any object, and its reference range is preferably 0.2 to 0.5 Nm / (rad / s); while the maximum stall limit impedance refers to the protection threshold impedance when the actuator is completely restricted and the servo motor outputs peak current, and its reference range is preferably 5.0 to 15.0 Nm / (rad / s), which is set to 10.0 Nm / (rad / s) in this embodiment;

[0071] The fusion formula for the spatial reconstruction phase compensation factor is defined as follows: Where Φtotal is the total compensated phase, representing the total phase weight ultimately used to correct the RF signal; Φmacro represents the macroscopic phase offset, determined by the odometry displacement, used to solve the problem of how far the robot travels, and the formula is... (k is the wave number) Φmicro is the phase change per unit length; Φmicro is the microscopic phase shift, expressed by the formula: Where η is the adaptive impedance displacement compliance transformation coefficient, and Znorm is the normalized transient torque wave impedance; after linearly adding Φmacro and Φmicro, the result is subjected to nonlinear cosine mapping. It is mapped to the [0,1] interval to accurately synchronize the microscopic disturbances of physical layer vibration and radio frequency phase, and to eliminate micro-nano-level phase distortion.

[0072] In the specific calculation process, the system reads the excitation frequency and boundary parameters from the external carrier, continuously acquires mileage state quantities, and transmits radio frequency scanning signals. The command generation module converts the excitation frequency into a control level sequence, injects a high-frequency perturbation drive signal into the actuator, and forces the contact target to generate a specific frequency domain mechanical wave. Within this cycle, the system calculates the normalized transient torque wave impedance cycle by cycle based on the real-time extracted torque and speed closed-loop errors, and constructs an electromechanical excitation feature matrix as a digital mirror of the target's local physical properties in a time sequence. Simultaneously, when the feature extraction module determines that the actuator is in a physical contact state, it extracts the original radio frequency in-phase / orthogonal complex baseband sequence of the target spatial region, uses the spatial reconstruction phase compensation factor calculated above to perform complex multiplication rotation compensation on the transient phase angle of the complex sequence, and outputs a distortion-free continuous phase evolution feature sequence. The distortion-free feature sequence is subjected to inverse fast Fourier transform along the azimuth dimension of the virtual space sampling array to focus spatial energy, and matrix multiplication is performed with its conjugate transpose to extract the correlation of spatial discrete points. Finally, the radio frequency spatial phase gradient covariance matrix of the spatial region where the target object is located is generated.

[0073] By directly using the electromechanical perturbation torque fluctuations in the physical domain as a priori terms for the RF phase compensation weights in the electromagnetic domain, the RF focusing process, which is susceptible to multipath interference, is forcibly guided and converged. The impedance displacement compliance conversion coefficient is set using closed-loop dynamic evaluation logic. The system adaptively adjusts the weight of this coefficient by comparing the variance-to-signal ratio (SNR) of the actual load impedance sequence with that of the reference no-load sequence (i.e., increasing the physical prior dominance at high SNR and decreasing the weight at low SNR to prevent overcompensation). This cross-modal collaborative design not only completely eliminates phase ambiguity caused by mechanical vibration but also transforms the micro-vibrations generated by specific physical contact into a target-specific RF dynamic watermark. This completely suppresses out-of-bounds "ghost tags" that are not subject to specific physical excitation because they cannot pass this coupled phase compensation verification, ultimately achieving a synergistic gain effect of "physical touch giving RF vision absolute focal length" and extremely high environmental robustness.

[0074] Furthermore, the "specific frequency domain mechanical wave" refers to the second and third harmonic components (i.e., specific frequency domain responses of 200Hz and 300Hz) excited by the nonlinear physical structure of the target cargo (i.e., internal liquid sloshing or solid collision) under the dominance of the 100Hz excitation fundamental frequency. Such characteristics are injected into the electromechanical excitation feature matrix as the "physical acoustic fingerprint" of the cargo.

[0075] In the closed-loop dynamic evaluation logic, the logic for adaptively adjusting the weight of this coefficient (impedance displacement compliance conversion coefficient) is incrementally adjusted with a step size μ = 0.05 to 0.1, and its gain coefficient is preferably 1.2 to 1.5, with the total weight constraint range set between [0.2, 0.8]. When the actual measured signal-to-noise ratio is lower than the preset threshold (set to 15dB in this embodiment), the system automatically reduces the correction step size of the physical compensation weight to prevent false discrimination caused by overcompensation in environments with severe multipath interference.

[0076] The fusion adjudication module performs cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix, including pre-alignment and denoising. The specific process includes:

[0077] The environmental background compensation baseline, time synchronization delay constant, and dynamic time warping tolerance parameters are read from the local independent structured data carrier. Based on the environmental background compensation baseline, the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix are subjected to spectral subtraction denoising processing using adaptive notch background cancellation logic to filter out system operating noise.

[0078] Based on the dynamic time warping tolerance parameter, the dynamic time warping algorithm is applied to the denoised electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix. By performing a smooth mapping of the time scale, the time domain misalignment caused by the sampling rate of heterogeneous sensors is eliminated.

[0079] Furthermore, the environmental background compensation baseline refers to the combined vector of the actuator's mechanical self-excited vibration noise power spectrum (approximately -40dBm to -60dBm) and the radio frequency environmental thermal noise floor, measured in advance under no-load and no-load conditions; the time synchronization delay constant is a fixed compensation value set based on the difference between the radio frequency front-end processing delay (approximately 2ms to 5ms) and the motor driver control loop response delay (approximately 1ms), with a preferred value range of 3ms to 10ms; the dynamic time warping tolerance parameter is set to 10% to 15% of the sliding sampling window length (if the sampling line segment length is 100 points, then the tolerance w=10), used to limit the optimization path from deviating too far from the main diagonal;

[0080] Furthermore, the "adaptive" feature is manifested in the system continuously tracking the non-characteristic harmonic peaks in the actuator feedback signal through real-time fast Fourier transform, automatically locking the notch filter center frequency at the motor commutation frequency or environmental power frequency interference (such as 50Hz or its harmonics); the spectrum subtraction threshold Thsub is set to 1.2 times the root mean square value of the environmental background compensation baseline; when the real-time signal power spectrum P(f) exceeds this threshold for 3 consecutive cycles, it is determined to be non-homogeneous interference noise, and a subtraction operation is performed to remove it, thereby ensuring that only the excited response generated by the excitation of the target object enters the subsequent stages.

[0081] When applying the dynamic time warping algorithm, to address the resampling differences between heterogeneous sensors (RF frequency at MHz and motor feedback at kHz), this embodiment uses the squared Euclidean distance as the local distance metric and introduces a Sakoe-Chiba constraint band to limit the search window. The window width is set to... , where L is the sequence length, ensuring that when eliminating phase misalignment caused by sampling rate mismatch, false alignment will not occur due to excessive stretching of the sequence.

[0082] The fusion adjudication module calculates the coherent tensor of the same source modulation through the following process:

[0083] Extract the alignment weight matrix representing spatial physical constraints from the external configuration carrier;

[0084] Frequency domain transformation tensor mapping is performed on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix after forward displacement compensation of the time synchronization delay constant, respectively, to obtain the frequency domain representation of the two under the corresponding time slices.

[0085] The frequency domain representation of the obtained electromechanical excitation feature matrix and the frequency domain representation of the radio frequency spatial phase gradient covariance matrix are subjected to Kronecker product operation; the output of the Kronecker product operation is multiplied by the alignment weight matrix, and the operation result is discretely accumulated and summed within a set time series window to generate an electromechanical radio frequency coherence tensor for characterizing the coherence in the same frequency time domain.

[0086] The "frequency domain transformation tensor mapping" is not a purely mathematical operation, but a hard-wired processing performed by the digital signal processor (DSP) or field-programmable gate array (FPGA) in the robot's main control board for the hardware acquisition buffer. The specific process is as follows: the current / torque timing signal (physical mechanical domain) fed back by the motor servo driver and the I / Q complex sequence (physical electromagnetic domain) output by the RF demodulator are loaded into the dual-port RAM respectively, and point-to-point windowed FFT processing is performed under a unified clock trigger, thereby mapping the physical "mechanical vibration" and "waveform distortion" into the corresponding frequency domain energy distribution tensor.

[0087] The "dot product" and "discrete accumulation" operations are performed within a set sliding time window (Twin), with the window size preferably set to 20ms to 100ms (covering 1 to 5 complete perturbation cycles); the summation formula is defined as:

[0088]

[0089] Wherein, the step size n slides with the robot's sampling clock, N is the total number of sample points in the window (e.g., N=512), Walign is the alignment weight matrix, representing a preset spatial mask matrix, which forcibly filters out random electromagnetic clutter in the environment by assigning high weights to certain frequency bands and low weights to certain interference frequency bands; M(t,f) is the electromechanical frequency domain characterization, representing the torque feedback characteristic value of the robotic arm at time t and frequency f; R(t,f) is the radio frequency domain characterization, representing the phase fluctuation characteristic value of the RFID tag at time t and frequency f; the criterion for window selection is that it must satisfy the Nyquist sampling theorem and be able to cover at least one complete mechanical response hysteresis period to ensure the statistical reproducibility of the calculation results under dynamic working conditions.

[0090] The fusion adjudication module performs homology quantization determination based on the generated electromechanical radio frequency homo-modulated coherent tensor, specifically including:

[0091] Perform a joint spatial mapping between the electromechanical-radio frequency coherent modulation tensor and the electromechanical dynamic mass distribution covariance matrix and topological permeability information entropy matrix characterizing the prior state of the system; extract the main diagonal eigenvalue sequence of the mapped electromechanical-radio frequency coherent modulation tensor.

[0092] A preset confidence baseline is retrieved, and the difference between the main diagonal eigenvalue sequence and the confidence baseline is continuously compared. When the main diagonal eigenvalue shows a step change and exceeds the confidence baseline, it is quantitatively determined that the external radio frequency spatial phase fluctuation and the internal electromechanical torque excitation belong to the same physical source, and based on this, it is confirmed that the target radio frequency tag is uniquely attached to the physical goods being excited by the current actuator.

[0093] The joint space mapping achieves dimensionality-upgrading fusion of heterogeneous matrices through the Kronecker product, and then constructs a projection operator using the electromechanical dynamic mass distribution covariance matrix Cm and the topological permeability information entropy matrix Er. ;in, Er is the inverse of the information entropy matrix, where Er represents the degree of disorder in the radio frequency environment (the higher the entropy, the more disordered). Taking its inverse matrix means that the more disordered the environment, the lower the weight of this term; the clearer the environment, the higher the weight of this term.

[0094] The dimension alignment logic is as follows: if the coherence tensor has n×n dimensions, the projection operator compresses it into an n×k (k<n) principal component feature space through singular value decomposition; the final mapping result is obtained through the following formula: , where Tfinal is the eigenvalue sequence / resonance vector, representing the final quantization vector, whose magnitude directly determines the confidence level of "whether they are the same object"; diag() is the diagonal operator, which extracts the main diagonal elements from the mapped matrix. In matrix theory, diagonal elements represent the eigenenergy of each dimension, eliminating cross-interference between dimensions. The conjugate transpose represents the adjoint matrix of the projection matrix, used to perform a basis transformation of the spatial coordinate system, projecting the original signal onto the physically constrained subspace; Tcoh is the original coherence tensor, i.e., Tsync calculated by the aforementioned formula, which is the original cross-modal data without physical constraint correction; the mapping process is explained as follows: the formula, through matrix operations (quadratic transformation), forcibly projects the disordered RF signal Tcoh onto the physical trajectory determined by the mechanical rigidity PP. If the RF signal is not caused by this mechanical action, its value will approach zero after projection.

[0095] This process projects the high-dimensional electromagnetic interference manifold into a low-dimensional physically rigid constraint subspace, forcing non-homogeneous interferences (since they cannot form an effective projection in the physical subspace defined by P) to zero, retaining only the eigenvalues ​​on the main diagonal that characterize "physical resonance".

[0096] Furthermore, by obtaining the minimum cumulative path distance of dynamic time warping of the denoised electromechanical and radio frequency matrices, dividing it by the dynamic time warping tolerance parameter that characterizes the maximum cache period of the hardware, the time domain offset ratio factor is obtained. Then, the factor is subtracted from the constant to obtain the cross-modal time scale alignment (Dal), thereby standardizing the result to the range of 0 to 1, which is used to characterize the alignment closeness of heterogeneous sensors after eliminating time domain misalignment.

[0097] The maximum intrinsic value of the main diagonal of the coherent tensor after joint spatial mapping is extracted, and the baseline of the environmental confidence in the passive silent state is subtracted to obtain the net intrinsic mutation value. Then, it is divided by the effective dynamic range of the system composed of the difference between the hardware maximum response extreme value limit parameter and the baseline to obtain the intrinsic resonance confidence score (Sco). The value range is limited to the interval between 0 and 1, which is used to quantitatively determine whether cross-modal homologous physical resonance has occurred.

[0098] The fusion adjudication module reads the electromechanical excitation feature matrix, the radio frequency spatial phase gradient covariance matrix, and the environmental background compensation baseline from the local carrier. It performs spectral subtraction denoising processing using adaptive notch background cancellation logic to remove environmental noise energy. Based on the preset time synchronization delay constant, it performs forward displacement compensation. After determining that the minimum cumulative path distance is within the tolerance range, it performs smooth interpolation mapping of the time scale according to the cross-modal time scale alignment and the optimal regular path, and outputs a feature matrix pair that is completely time-domain aligned.

[0099] Tensor mapping of the above aligned matrix pairs is performed using fast Fourier transform, multidimensional frequency domain representation tensors are extracted and Kronecker product operation is performed to generate unconstrained cross-coherence matrices; alignment weight matrices representing spatial physical constraints are extracted, and Hadamard multiplication is performed with the unconstrained cross-coherence matrix, and discrete accumulation and summation are performed within a set continuous time series window, finally outputting electromechanical radio frequency homo-source modulation coherence tensors representing the coherence of the same frequency in the time domain;

[0100] To completely eliminate crosstalk caused by dense tag stacking, the covariance matrix of electromechanical dynamic quality distribution and the topological permeability information entropy matrix of the preceding cache are extracted as prior state inputs and subjected to joint spatial multi-matrix multiplication dimension reduction projection with the generated electromechanical RF homo-modulation coherence tensor. During this process, the internal coefficients of the alignment weight matrix are dynamically calibrated inversely proportionally by collecting the topological permeability information entropy of the spatial sector. That is, when the environmental RF scattering is chaotic and the information entropy is extremely high, the system will force the sidelobe weight coefficients to be reduced and the passband narrowed. Through this adaptive manifold projection adjustment, the RF response is forced to achieve absolute phase engagement with the electromechanical torque within an extremely narrow frequency domain slice. Any high-energy interference signal from bypass refraction is forced to zero by the mathematical projection law because it is orthogonal to the electromechanical constraint subspace.

[0101] The intrinsic resonance confidence score (Sco) exceeding the preset confidence change threshold is used as the sole exclusive trigger condition for determining the occurrence of same-frequency physical resonance. Under this condition, the system completes the binding of the RFID tag with the physical source of the currently excited physical goods, thereby intercepting non-similar counterfeit attacks at the physical feature level.

[0102] The signal generation module generates an indication signal to characterize the physical challenge-based identity and quality-forced lock-in effect, the specific process of which includes:

[0103] Extract the main diagonal eigenvalue sequence of the homologous modulation coherence tensor;

[0104] A preset system confidence baseline is retrieved from an external structured storage medium; the main diagonal eigenvalue sequence is dynamically and continuously compared with the system confidence baseline; when the main diagonal eigenvalue sequence is higher than the system confidence baseline, the abstract tensor space calculation result is binarized to generate a first indication signal for characterizing successful cross-modal authentication matching.

[0105] When generating the indication signal, the signal generation module also includes anomaly monitoring and failure signal generation logic: setting a preset time window monitoring threshold;

[0106] When the main diagonal eigenvalue sequence falls below the system confidence baseline and the duration of this state exceeds the preset time window monitoring threshold, it is determined that the current electromagnetic domain observation channel has generated an extreme anomaly, and a second indication signal characterizing the failure of the radio frequency channel is output.

[0107] Upon receiving the first indication signal, the linkage execution module synchronously executes the radio frequency addressing filtering instruction and the physical attribute verification instruction, specifically including:

[0108] Based on the first indication signal triggering the frequency band locking mechanism, a bandpass filtering command is issued to actively cut off and discard all bypass interference radio frequency data streams that do not have the characteristics of the same modulated coherent tensor frequency band, so as to complete the radio frequency side identity purification in the space area.

[0109] The electromechanical excitation feature matrix associated with the synchronous locking of the coherent tensor of the same source modulation is then used to extract the high-fidelity electromechanical torque feedback parameters from the electromechanical excitation feature matrix and directly spatially map them into a physical quality evaluation matrix for the target object, thereby completing the forced association and verification closed loop of digital identity extraction and physical attribute measurement.

[0110] After receiving the second indication signal, the linkage execution module further includes executing an implicit degradation verification mechanism to maintain continuous system operation, specifically:

[0111] Based on the second indication signal, the unreliable electromagnetic domain observation channel is immediately cut off, and the sending of analytical requests for the radio frequency spatial phase gradient covariance matrix is ​​stopped; the single-mode mechanical feature extraction path is fully activated, and only the electromechanical excitation feature matrix of the target object is extracted as a local observation variable;

[0112] Read the task state machine flow record issued by the system schedule to obtain the preloaded global logic trajectory flow probability corresponding to the current spatiotemporal node; use the preset hidden Markov model to perform a fusion deduction based on the posterior probability tensor on the electromechanical excitation feature matrix as a local observation variable and the global logic trajectory flow probability, and output the blind recognition deduction result representing the passive logic identity of the target object.

[0113] Furthermore, the high-fidelity electromechanical torque feedback parameter is a high-dimensional physical expression of the resistance characteristics in the command generation module. Specifically, when the actuator (i.e., the robotic arm) contacts and excites the cargo, the physical properties of the cargo (mass, friction force, and moment of inertia) constitute the external "resistance characteristics." These resistance characteristics directly act on the servo motor, causing small fluctuations in the current loop and speed loop. The "electromechanical torque feedback parameter" extracted by the signal generation module is a digital sequence representing the rate of change of the aforementioned resistance characteristics after being denoised by the high-performance filter inside the servo driver.

[0114] During implicit degradation verification, the system maps the abstract HMM model elements to the physical operation process one by one as follows:

[0115] Implicit state: The "true logical identity" of the current cargo (e.g., cargo A, cargo B, or empty), which is a target that the system cannot directly perceive through a failed radio frequency channel.

[0116] Observation status: corresponds to the current "electromechanical excitation feature matrix". The system infers the physical properties of the grasped object by sensing the torque fluctuation trajectory at the end of the robotic arm.

[0117] Transition probability: Corresponds to "global logical trajectory transition probability". Based on system scheduling, if the previous action was "retrieving goods", the probability of the current state transitioning to "holding goods" is given in advance by the scheduling algorithm.

[0118] Launch probability: corresponds to the "physical fingerprint database" pre-stored in the system. That is, the probability distribution of specific torque fluctuation characteristics generated when goods of a certain identity are excited by the actuator.

[0119] Furthermore, to generate an indication signal for characterizing the physical challenge-based identity and quality-forced lock-in effect, the signal generation module extracts the main diagonal eigenvalue sequence of the coherent modulation tensor and maps its maximum value to the 0-1 interval through a normalization function, using it as the current coherent feature value; simultaneously, it retrieves a preset system confidence baseline from an external structured storage medium; to overcome the shortcomings of conventional static thresholds in adapting to complex warehouse electromagnetic topology changes, this embodiment creatively adopts a dynamic system confidence baseline Bco. The calculation logic of the dynamic system confidence baseline Bco is as follows: obtain the radio frequency noise floor energy time series of the environmental monitoring radio frequency antenna array within a preset sampling period, and extract its signal dispersion extreme value; divide the extreme value by the radio frequency antenna saturation energy constant preset by the hardware factory to obtain the basic noise floor rate; read the static safety margin constant from the external structured storage, and use the constant one minus the static safety margin constant to obtain the remaining dynamic space ratio; wherein, the "static safety margin constant" (marked as safe) is an engineering empirical constant set based on the inherent thermal noise floor of the system hardware circuit and the minimum detectable signal ratio. Its value is determined by ensuring that the dynamic system confidence baseline Bco does not collapse to 0 in extremely quiet and low-temperature environments, thereby preventing false triggering due to excessive sensitivity. The preferred range for this constant is 0.15 to 0.25, and it is read into the firmware register from external storage during system initialization.

[0120] By multiplying the baseline noise rate by the proportion of the remaining dynamic space, and then adding the static safety margin constant to the product, the dynamic system confidence baseline Bco, strictly constrained within the range of 0 to 1, is derived. This adaptive adjustment based on information entropy ensures that the more chaotic the environmental noise, the higher the threshold requirement for system verification.

[0121] After establishing a comparison benchmark, the signal generation module dynamically and continuously compares the current coherent feature value with the dynamic system confidence baseline Bco, and incorporates anomaly monitoring and failure signal generation logic within this process. The system pre-sets a preset time window monitoring threshold to filter out transient multipath fading. When the current coherent feature value is significantly higher than the dynamic system confidence baseline Bco, the system timer resets to zero, and the signal generation module binarizes the abstract tensor space calculation result and assigns a high-level signal, outputting a first indication signal characterizing successful cross-modal authentication matching. Conversely, if the current coherent feature value drops below or equal to the dynamic system confidence baseline Bco, the system immediately triggers a failure timer; if and only if the duration of this feature value drop significantly exceeds the preset time window monitoring threshold, the signal generation module determines that the current electromagnetic domain observation channel has experienced an extreme anomaly (such as encountering a radio frequency black hole or tag physical damage), and outputs a second indication signal characterizing radio frequency channel failure accordingly.

[0122] Upon receiving the first indication signal, the linkage execution module determines that the system is in a multimodal coherent strong latch-up state and then synchronously executes the radio frequency addressing filtering command and the physical attribute verification command. Specifically, the linkage execution module triggers a frequency band locking mechanism based on the first indication signal, reads the characteristic frequency band corresponding to the coherent modulation tensor as the passband parameter of the bandpass filter, issues a bandpass filtering command, and actively truncates and discards all bypass interference radio frequency data streams that do not possess the frequency band characteristics of the coherent modulation tensor at the hardware level, so as to complete the absolute purification of the radio frequency side identity in the spatial area; simultaneously, the linkage execution module locks the electromechanical excitation feature matrix associated with the generation of the coherent modulation tensor, extracts the high-fidelity electromechanical torque feedback parameter in the electromechanical excitation feature matrix, and uses the kinematic model of the unmanned logistics robot to directly spatially map it into a physical quality evaluation matrix for the target object, thereby completing the forced association and verification closed loop of digital identity extraction and physical attribute measurement.

[0123] Upon receiving the second indication signal, the linkage execution module no longer triggers traditional crash reports. Instead, it executes an implicit degradation verification mechanism to maintain continuous system operation. Based on this second indication signal, the linkage execution module immediately cuts off the power supply to the unreliable electromagnetic domain observation channel and physically stops sending any analytical requests for the radio frequency spatial phase gradient covariance matrix. At this time, the system fully utilizes the single-mode mechanical feature extraction path, extracting only the electromechanical excitation feature matrix of the target object as the sole local observation variable. Simultaneously, it reads the task state machine flow record issued by the system schedule to obtain the preloaded global logic trajectory flow probability corresponding to the current spatiotemporal node.

[0124] To output the blind recognition inference result representing the passive logical identity of the target object, the system uses a pre-set Hidden Markov Model to perform a fusion inference based on the posterior probability tensor of local observation variables and global logical trajectory flow probability. In this embodiment, the posterior blind recognition confidence is output in this fusion, and its parameter symbol is Cbl. The specific calculation process is as follows: extract the current absolute average fluctuation amplitude from the electromechanical excitation feature matrix, subtract it from the expected standard torque fluctuation amplitude of the target cargo issued by the system scheduling, and take the absolute value to obtain the absolute value of torque shape deviation; divide the absolute value by the expected standard torque fluctuation amplitude of the target cargo to obtain the local mechanical deviation rate (if the value is greater than a constant, it is forcibly assigned a constant); use the constant to subtract the local mechanical deviation rate to generate the single-modal mechanical matching degree. To overcome the shortcomings of conventional scalar multiplication, which is susceptible to common-mode noise pollution such as road bumps, this system adopts a covariance cross-fusion method based on Riemannian manifold distance: the characteristic covariance matrix associated with local observation variables and the prior covariance matrix associated with the global logical trajectory are mapped to a non-Euclidean Riemannian space; the time derivative of the end-effector acceleration (i.e., mechanical impact rate) is obtained, and a constant is divided by this impact rate for normalization, which is then used as the fusion weight of the mechanical characteristic matrix; based on the normalized fusion weight, the geometric centroids of the two covariance matrices on the geodesic line of the Riemannian manifold are calculated, and the fused output is finally constrained to the interval between 0 and 1 for the posterior blind recognition confidence level Cbl; the posterior blind recognition confidence level Cbl is compared with the set minimum blind recognition tolerance baseline constant. If it is greater than the baseline constant, the blind recognition deduction is successful, and the system continues to operate, thus achieving robust confirmation of the target's physical identity under extreme hardware failure conditions.

[0125] Furthermore, since the covariance matrix space is a non-Euclidean symmetric positive definite matrix manifold, this embodiment employs a log-Euclidean metric to perform the fusion calculation to ensure the physical validity of the results. The fusion formula is as follows:

[0126]

[0127] Where Cmech represents the local observation covariance matrix generated by the electromechanical feature matrix; Cprio represents the prior state covariance matrix associated with the global trajectory; log() and exp() are matrix logarithm and matrix exponent operations with the natural constant e as the base, used to map the SPD matrix to the tangent space for linear weighting. At the same time, the matrix logarithm operation log() refers to an operator with the natural constant e as the base, which, together with the matrix exponent operation exp(), constitutes a log-Euclidean mapping pair for symmetric positive definite matrix manifolds, used to achieve linear fusion of non-Euclidean space features; w1 and w2 represent the normalized fusion weights calculated based on the mechanical impact rate, where w1 is the mechanical feature weight and w2 is the prior feature weight. In this embodiment, w1+w2=1 is set. This formula ensures that the geometric centroid after fusion always remains within the Riemannian manifold and will not have negative values ​​or non-physical distortions.

[0128] Furthermore, the aforementioned mechanism for maintaining continuous system operation is a temporary fault-tolerant mechanism. The system does not remain in a degraded state until it is scrapped after a failure; its execution logic is as follows:

[0129] Task-level retention: Degradation mode is maintained only for the current cargo's complete "grab-identify-place" cycle to prevent operational interruption;

[0130] Node reset detection: When the current goods are processed and enter the next task node (i.e., empty return), the linkage execution module will forcibly trigger the radio frequency channel self-test logic;

[0131] Status recovery: If the self-test results show that the electromagnetic environment has returned to normal or the sensor transient fault has disappeared, the system will reset the failure timer and automatically switch from single-mode blind recognition mode back to cross-mode multi-mode coherent verification mode; if the fault persists, a maintenance warning will be reported after the current schedule ends.

[0132] To verify the effectiveness of the linkage control and implicit degradation verification mechanism described in this invention, this embodiment constructs a discrete event simulation environment for "cross-modal dynamic interaction of unmanned logistics robots" to reproduce real warehouse operating conditions including dynamic multipath fading, tag damage and disconnection, and electromechanical servo feedback hysteresis.

[0133] Table 1: Comparison of parameter flow and execution results under different operating conditions.

[0134]

[0135] Scenario 1 and Scenario 2 represent standard extraction without interference; Scenario 3 and Scenario 4 represent transient electromagnetic interference; Scenario 5 and Scenario 6 represent complete label destruction and failure; Control Group A and Control Group B are comparative operating conditions of existing technologies. Control Group A is a case of direct error judgment without tolerance, corresponding to the transient interference environment of Scenario 3 and Scenario 4, but it runs a traditional logistics system algorithm that lacks a time smoothing mechanism. Due to the lack of protection from the "time window monitoring threshold," once a few milliseconds of metal obstruction causes the signal to drop, the system will immediately determine "goods lost or identity mismatch," directly triggering an error and causing the production line to stop, resulting in an accuracy rate of 0%; Control Group B is a case of direct shutdown without degradation, corresponding to the environment of complete label destruction in Scenario 5 and Scenario 6, running a traditional algorithm that lacks a "hidden Markov model blind inference" mechanism; when the system confirms that the radio frequency signal is completely lost, due to the lack of a backup channel for single-mode (mechanical) degradation verification, the system can only completely fall into a state of crash or forced shutdown (downtime), requiring manual intervention for investigation, causing a hard break in the supply chain at this point;

[0136] Mea represents the mean of the eigenvalue sequence along the main diagonal, Sys represents the system confidence baseline, Sta represents the state duration (ms), Tim represents the time window monitoring threshold (ms), Tra represents the trajectory transition probability, Istab represents the cross-modal coherence stability index, and ST represents the system response mode and target recognition accuracy.

[0137] Furthermore, the cross-modal coherence stability index Istab is obtained by extracting the integral value of the "main diagonal eigenvalue sequence" within a preset time window, subtracting the integral value of the "system confidence baseline" within the same time window, and dividing the difference by the "maximum saturation upper limit integral value of eigenvalues" defined by the system hardware. If the difference is less than zero, it is forcibly set to zero.

[0138] Technical meaning: This parameter quantitatively characterizes the robustness of the physical perturbation and radio frequency response of a target entity under excited vibration conditions over a time scale. The higher the value, the more stable the physical-digital binding relationship against sudden environmental noise.

[0139] The simulation data directly demonstrates the synergistic gain effect between the signal generation module and the linkage execution module.

[0140] Analysis of the "transient electromagnetic interference" data sets for scenarios three and four: Although the eigenvalue sequences of the main diagonal (28.5 and 19.2) fell below the system confidence baseline (45.0), the system did not trigger the second indication signal because the duration of the state (45ms and 82ms) was strictly controlled within the time window monitoring threshold (100ms). Instead, it successfully crossed the transient fading region due to the time delay, ultimately recovering the first indication signal and achieving 100% recognition accuracy. Compared to control group A (existing technology directly triggers shutdown without a time tolerance mechanism, resulting in 0% accuracy and disrupting the logistics cycle), the anti-false alarm interception capability of this invention represents a qualitative leap.

[0141] Secondly, regarding the data analysis of "complete label destruction and failure" in scenarios five and six: when the duration (105ms and 250ms) exceeds the time window monitoring threshold (100ms), the system accurately outputs a second indication signal. In the case of direct system failure in control group B (existing technology), the linkage execution module of this invention successfully utilized a Hidden Markov Model for blind inference. Relying on trajectory flow probabilities as high as 0.96 and 0.85, the system achieved accuracies of 98.5% and 94.2%, respectively. Calculations show that even under the extreme boundary condition of 100% failure of the RF sensor, this invention still maintained a correct execution rate better than 94% (compared to the baseline of 0% absolute shutdown, the system availability improved by more than 94% in absolute terms). This data irrefutably proves that the "graceful degradation verification mechanism" is not only effective but also directly determines the core survivability of the unmanned logistics system under harsh conditions.

[0142] It should be noted that all calculation formulas in this application employ regression analysis, including but not limited to machine learning algorithms, to deeply analyze the collected parameters and identify their natural trends and interrelationships. Specialized software, such as Python's Scikit-learn library or the R language, is used to automatically generate mathematical models that match the data. Then, cross-validation and other methods are used to objectively evaluate the model performance, and continuous feedback and optimization are combined to ensure that the created formulas truly reflect the inherent laws of the data, thereby guaranteeing their effectiveness and accuracy. In all calculation formulas in this application, the parameters in each formula undergo dimensionless processing within a consistent range to ensure that different physical quantities are compared on the same scale; dimensionless processing techniques include, but are not limited to, min-max-normalization and Z-score standardization.

[0143] The technical solution of this invention, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random-access memory (RAM), flash memory, hard disk, or optical disk, etc., including several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods of various embodiments of this invention.

[0144] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (such as a computer-based system, a processor-including system, or other system that can fetch and execute instructions from, an instruction execution system, apparatus, or device). For the purposes of this specification, "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transmit programs for use by, or in conjunction with, an instruction execution system, apparatus, or device.

[0145] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention, and all such modifications or substitutions should be covered within the scope of the claims of the present invention.

Claims

1. A cargo identification and management system for unmanned logistics robots, characterized in that, Specifically, it includes: The instruction generation module is used to obtain the electromechanical excitation feature matrix generated by the underlying power feedback timing of the actuator when the actuator of the unmanned logistics robot contacts the target object; The feature extraction module is used to obtain the radio frequency spatial phase gradient covariance matrix generated by polling and scanning the spatial region where the target object is located; The fusion adjudication module is used to perform cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix using a preset electromechanical radio frequency covariance tensor operator, so as to calculate the covariance tensor. The signal generation module is used to generate an indication signal based on the eigenvalues ​​of the co-source modulation coherence tensor to characterize the physical challenge-based identity and quality forced locking effect; wherein the indication signal includes a first indication signal characterizing successful authentication and a second indication signal characterizing channel anomaly. The linkage execution module includes a function for receiving an indication signal and synchronously executing a radio frequency addressing filtering instruction for the space region and a physical attribute verification instruction for the target object based on the indication signal.

2. The unmanned logistics robot cargo identification and management system according to claim 1, characterized in that: The instruction generation module acquires the electromechanical excitation feature matrix generated by the underlying power feedback timing of the actuator, specifically including: Read the excitation frequency parameters pre-stored in an external structured data carrier, and convert the excitation frequency parameters into a sequence of control commands for driving the actuator; Based on the control command sequence, a drive signal containing a preset high-frequency perturbation amplitude is injected into the actuator to drive the actuator to generate mechanical vibration with a specific frequency domain pattern when it comes into contact with the target object; The real-time torque closed-loop error of the actuator during the mechanical vibration cycle is extracted as a resistance feature; based on the time-series change feedback of the resistance feature, the electromechanical excitation feature matrix is ​​constructed.

3. The unmanned logistics robot cargo identification and management system according to claim 2, characterized in that: The specific process by which the feature extraction module obtains the radio frequency spatial phase gradient covariance matrix includes: The temporal physical displacement of the actuator over time is mapped to a virtual spatial sampling array to construct a synthetic aperture spatial reconstruction transformation mechanism; the in-phase and quadrature complex baseband sequences of radio frequency echoes received by polling the spatial region are extracted; By combining the mileage state quantity of the actuator, spatiotemporal phase compensation processing is performed on the in-phase and orthogonal complex baseband sequences of the radio frequency echo to extract the continuous phase evolution characteristics of the complex radio frequency echo sequence on the displacement time sequence of the actuator; based on the continuous phase evolution characteristics, mathematical inversion reconstruction is performed to calculate and generate the radio frequency spatial phase gradient covariance matrix of the spatial region where the target object is located.

4. The unmanned logistics robot cargo identification and management system according to claim 3, characterized in that: The fusion adjudication module performs cross-correlation time series matching processing on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix, including pre-alignment and denoising. The specific process includes: The environmental background compensation baseline, time synchronization delay constant, and dynamic time warping tolerance parameters are read from the local independent structured data carrier. Based on the environmental background compensation baseline, the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix are subjected to spectral subtraction denoising processing using adaptive notch background cancellation logic to filter out system operating noise. Based on the dynamic time warping tolerance parameter, the dynamic time warping algorithm is applied to the denoised electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix. By performing a smooth mapping of the time scale, the time domain misalignment caused by the sampling rate of heterogeneous sensors is eliminated.

5. The unmanned logistics robot cargo identification and management system according to claim 4, characterized in that: The fusion adjudication module calculates the coherent tensor of the same source modulation through the following process: Extract the alignment weight matrix representing spatial physical constraints from the external configuration carrier; Frequency domain transformation tensor mapping is performed on the electromechanical excitation feature matrix and the radio frequency spatial phase gradient covariance matrix after forward displacement compensation of the time synchronization delay constant, respectively, to obtain the frequency domain representation of the two under the corresponding time slices. The frequency domain representation of the obtained electromechanical excitation feature matrix and the frequency domain representation of the radio frequency spatial phase gradient covariance matrix are subjected to Kronecker product operation; the output of the Kronecker product operation is multiplied by the alignment weight matrix, and the operation result is discretely accumulated and summed within a set time series window to generate an electromechanical radio frequency coherence tensor for characterizing the coherence in the same frequency time domain.

6. The unmanned logistics robot cargo identification and management system according to claim 5, characterized in that: The fusion adjudication module performs homology quantization determination based on the generated electromechanical radio frequency homo-modulated coherent tensor, specifically including: Perform a joint spatial mapping between the electromechanical-radio frequency coherent modulation tensor and the electromechanical dynamic mass distribution covariance matrix and topological permeability information entropy matrix characterizing the prior state of the system; extract the main diagonal eigenvalue sequence of the mapped electromechanical-radio frequency coherent modulation tensor. A preset confidence baseline is retrieved, and the difference between the main diagonal eigenvalue sequence and the confidence baseline is continuously compared. When the main diagonal eigenvalue shows a step change and exceeds the confidence baseline, it is quantitatively determined that the external radio frequency spatial phase fluctuation and the internal electromechanical torque excitation belong to the same physical source, and based on this, it is confirmed that the target radio frequency tag is uniquely attached to the physical goods being excited by the current actuator.

7. The unmanned logistics robot cargo identification and management system according to claim 6, characterized in that: The signal generation module generates an indication signal to characterize the physical challenge-based identity and quality-forced lock-in effect, the specific process of which includes: Extract the main diagonal eigenvalue sequence of the homologous modulation coherence tensor; A preset system confidence baseline is retrieved from an external structured storage medium; the main diagonal eigenvalue sequence is dynamically and continuously compared with the system confidence baseline; when the main diagonal eigenvalue sequence is higher than the system confidence baseline, the abstract tensor space calculation result is binarized to generate a first indication signal for characterizing successful cross-modal authentication matching.

8. The unmanned logistics robot cargo identification and management system according to claim 7, characterized in that: When generating the indication signal, the signal generation module also includes anomaly monitoring and failure signal generation logic: setting a preset time window monitoring threshold; When the main diagonal eigenvalue sequence falls below the system confidence baseline and the duration of this state exceeds the preset time window monitoring threshold, it is determined that the current electromagnetic domain observation channel has generated an extreme anomaly, and a second indication signal characterizing the failure of the radio frequency channel is output.

9. The unmanned logistics robot cargo identification and management system according to claim 8, characterized in that: Upon receiving the first indication signal, the linkage execution module synchronously executes the radio frequency addressing filtering instruction and the physical attribute verification instruction, specifically including: Based on the first indication signal triggering the frequency band locking mechanism, a bandpass filtering command is issued to actively cut off and discard all bypass interference radio frequency data streams that do not have the characteristics of the same modulated coherent tensor frequency band, so as to complete the radio frequency side identity purification in the space area. The electromechanical excitation feature matrix associated with the synchronous locking of the coherent tensor of the same source modulation is then used to extract the high-fidelity electromechanical torque feedback parameters from the electromechanical excitation feature matrix and directly spatially map them into a physical quality evaluation matrix for the target object, thereby completing the forced association and verification closed loop of digital identity extraction and physical attribute measurement.

10. The unmanned logistics robot cargo identification and management system according to claim 9, characterized in that: After receiving the second indication signal, the linkage execution module further includes executing an implicit degradation verification mechanism to maintain continuous system operation, specifically: Based on the second indication signal, the unreliable electromagnetic domain observation channel is immediately cut off, and the sending of analytical requests for the radio frequency spatial phase gradient covariance matrix is ​​stopped; the single-mode mechanical feature extraction path is fully activated, and only the electromechanical excitation feature matrix of the target object is extracted as a local observation variable; Read the task state machine flow record issued by the system schedule to obtain the preloaded global logic trajectory flow probability corresponding to the current spatiotemporal node; use the preset hidden Markov model to perform a fusion deduction based on the posterior probability tensor on the electromechanical excitation feature matrix as a local observation variable and the global logic trajectory flow probability, and output the blind recognition deduction result representing the passive logic identity of the target object.

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