A disposable optical fiber composite anti-theft net cover and a logistics anti-damage alarm system

Abstract

The application discloses a one-time optical fiber composite anti-theft net cover and a logistics anti-damage alarm system, relates to the field of Internet of Things security and protection, and obtains an original optical power time sequence, extracts a dynamic disturbance index containing a jitter amplitude, a spectrum centroid and an energy attenuation slope, matches the cosine similarity with a pre-stored shear, impact and static pressure three types of mechanical load template, and outputs a shear damage judgment signal. Synchronous three-axis acceleration sensor vibration data acquisition extracts the spectrum centroid and energy entropy, a pre-trained random forest classifier identifies the transportation environment category, combines the environment interference compensation coefficient to dynamically calibrate the anti-jitter threshold, finally generates the AES encryption traceability data package containing the damage type, latitude and longitude and responsible subject, adapts to the whole transportation scene, and supports the accurate identification of the whole chain responsibility.

Description

Technical Field

[0001] This application relates to the field of Internet of Things (IoT) security, and in particular to a disposable fiber optic composite anti-theft mesh cover and a logistics anti-vandalism alarm system. Background Technology

[0002] Currently, anti-theft monitoring in the logistics and transportation of high-value goods mostly relies on electronic tags, video surveillance, or ordinary fiber optic perimeter alarm systems. Among them, fiber optic solutions are widely used due to their advantages such as resistance to electromagnetic interference and passive explosion-proof properties, but they still have significant drawbacks: existing technical solutions mostly use single-point decision logic based on optical power thresholds, triggering alarms only through optical signal attenuation or interruption. They cannot distinguish between human-caused shearing damage and non-destructive disturbances such as logistics bumps, cargo stacking, and loading and unloading collisions, resulting in a high false alarm rate. Existing technical solutions do not consider the differences in vibration characteristics of different transportation scenarios and use fixed anti-shake thresholds, making it difficult to adapt to the differentiated needs of high-vibration scenarios such as gravel roads, conventional railway lines, and aircraft take-off and landing sections, and low-vibration scenarios such as high-speed cruising and warehouse static storage. Summary of the Invention

[0003] The purpose of this application is to provide a disposable fiber optic composite anti-theft mesh cover and a logistics anti-vandalism alarm system to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, this application provides the following technical solution: a logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover, comprising: Data acquisition module: used to sample the analog voltage signal, calculate the mean optical power, standard deviation and spectral energy through a sliding window, and generate a dynamic disturbance index sequence; Feature matching module: used to extract the jitter amplitude mean, spectral centroid and energy decay slope of the dynamic disturbance index sequence to construct the feature vector to be tested, calculate the cosine similarity with each force template in the vector library, and output the shear failure judgment signal and real-time duration according to the discrimination rules; Environmental Analysis Module: Used to perform spectral analysis on vibration acceleration time series to obtain transportation environment category, judgment confidence level and first anti-shake time threshold; Backscattering detection module: used to analyze and generate optical path blockage status flag and alarm trigger flag based on shear failure judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence and vibration energy entropy; Traceability module: After an alarm is triggered, it is used to determine the link where the damage occurred and the responsible party based on the historical sequence of dynamic disturbance index and logistics trajectory database, and generate traceability data package.

[0005] In a preferred embodiment of this solution, the data acquisition module specifically includes: The analog voltage signal is sampled at a preset sampling frequency through the analog-to-digital converter built into the photoelectric conversion module, and an original optical power time series of length N is constructed. A Hanning window of length L is set as a sliding window, and the window is slid over the original optical power time series with a step size S. The mean optical power and standard deviation of optical power in each window are calculated to obtain the optical power jitter amplitude sequence. Perform a fast Fourier transform on the data segment within each window to obtain the corresponding frequency domain energy spectrum, and extract the spectral energy integral value within a preset frequency range as the spectral energy distribution feature. Based on the optical power jitter amplitude sequence and the spectral energy distribution characteristics, the dynamic disturbance index is calculated according to the preset dynamic disturbance index formula to establish a dynamic disturbance index sequence.

[0006] In a preferred embodiment of this solution, the feature matching module specifically includes: Retrieve the pre-stored reference microbending loss feature vector library. The feature vector library stores the dynamic disturbance index time-frequency feature vector templates corresponding to the application of different shear forces, impact forces and continuous static pressures to the mesh under laboratory calibration environment. The dynamic disturbance index time-frequency feature vector templates include the jitter amplitude mean, the spectral centroid and the energy attenuation slope. Frame buffering is performed on a continuous M-frame dynamic perturbation index sequence. The jitter amplitude mean, spectral centroid and energy attenuation slope of the M-frame dynamic perturbation index sequence are extracted to construct a real-time feature vector to be measured. Calculate the cosine similarity between the feature vector to be measured and the time-frequency feature vector templates of the dynamic disturbance index corresponding to different shear forces, impact forces and continuous static pressures, and obtain the similarity coefficients between the feature vector to be measured and the time-frequency feature vector templates of the dynamic disturbance index corresponding to different shear forces, impact forces and continuous static pressures. The maximum value of the time-frequency feature vector template of the dynamic disturbance index corresponding to the shear force, impact force and continuous static pressure is selected and judged according to the preset discrimination rule. If it meets the discrimination rule, a high-level shear failure judgment signal Scut=1 is output to indicate a high-level effective state. If it does not meet the discrimination rule, a low-level shear failure judgment signal Scut=0 is output. Start the built-in hardware timer and increment the count of Scut=1 in 1ms increments to obtain the real-time duration.

[0007] In a preferred embodiment of this solution, the environmental analysis module specifically includes: The built-in triaxial accelerometer collects the vibration acceleration time series under the current transportation environment at a preset acquisition frequency, and performs a fast Fourier transform on the vibration acceleration time series to obtain the frequency domain energy distribution. The dominant frequency component in the preset frequency band is extracted and marked as the core excitation source frequency. Calculate the mean deviation and standard deviation of vibration acceleration corresponding to the time series; The frequency probability density is obtained by normalizing the frequency domain energy distribution, and the vibration energy entropy is calculated according to the formula. The frequency domain energy distribution is calculated to obtain the reference spectral centroid of the current transportation environment. The pre-stored reference spectral centroid is retrieved, and the spectral centroid offset is calculated according to the formula. By classifying and matching the mean acceleration difference, standard acceleration deviation, spectral centroid offset, and vibration energy entropy corresponding to the current transportation environment with the preset standard parameter templates corresponding to different categories of transportation environments, the transportation environment category and judgment confidence level corresponding to the current transportation environment are obtained. Obtain typical vibration waveform templates corresponding to different transportation environment categories collected in advance under simulated transportation environment. The typical vibration waveform templates store the expected width of a single impact pulse, the expected interval between adjacent pulses, and the pre-calibrated environmental interference compensation coefficients under the transportation environment category. The safety factor is dynamically selected based on the confidence level, and the initial stabilization threshold is calculated according to the preset calculation formula. The initial stabilization threshold is then used to calculate the first stabilization time threshold according to the preset formula.

[0008] In a preferred embodiment of this solution, the backscattering detection module specifically includes: Retrieve the latest data on the output shear failure judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence level, and vibration energy entropy; Obtain a predefined set of high vibration intensity scenarios and a set of low vibration intensity scenarios. If the transportation environment category belongs to the high vibration intensity scenario set, then the first anti-shake time threshold is adjusted according to the corresponding high vibration intensity scenario correction coefficient. This is recorded as the current judgment threshold. If the transportation environment category belongs to the low vibration intensity scene set, the corrected current judgment threshold is calculated according to the preset formula. Based on the preset discrimination conditions, it is determined whether the conditions of Scut=1 and real-time duration > current judgment threshold are met. If not, the latest value corresponding to all data is read and the current judgment threshold is corrected. All data includes shear damage judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence and vibration energy entropy. If the preset discrimination conditions are met, the value of the judgment confidence level is retrieved. After verification, the judgment confidence level = judgment confidence level × auxiliary verification parameter. If the judgment confidence level after verification is greater than or equal to the preset judgment confidence level threshold, a first-level activation signal is generated. If the judgment confidence level after verification is less than the preset judgment confidence level threshold, a second-level confirmation process is performed. The second-level confirmation process controls the microprocessor to continuously perform three backscattered light signal detections at preset intervals. Each detection yields a breakpoint detection result, where 1 indicates that a physical fiber breakpoint is detected and 0 indicates that a physical fiber breakpoint is not detected. If all three detections are 1, a second-level activation signal is generated. If any detection is 0, the latest value corresponding to all data is read back to read and the current judgment threshold is read again. The first-level activation signal or the second-level activation signal is used as input to generate a PWM drive signal; The PWM drive signal is sent to the miniature heating wire drive circuit of the stress shearing mechanism and matched with the corresponding execution process. After the miniature heating wire drive circuit completes the corresponding execution process, it outputs the optical path blocking status flag and the alarm trigger flag.

[0009] In a preferred embodiment of this scheme, the auxiliary verification parameters obtained in the backscattering detection module specifically include: Obtain the shear failure determination signal Scut, the maximum cosine similarity, the transportation environment category, and the determination confidence level; If the maximum cosine similarity is greater than the preset maximum cosine similarity threshold and the transportation environment category belongs to the high vibration intensity scene set, then output a high-confidence damage flag V1. If the maximum cosine similarity is less than or equal to the preset maximum cosine similarity threshold and the transportation environment category belongs to the high vibration intensity scene set, then output a high-confidence damage flag V2. If the maximum cosine similarity is greater than the preset maximum cosine similarity threshold and the transportation environment category belongs to the low vibration intensity scene set, then output a high-confidence damage flag V3. If the maximum cosine similarity is less than or equal to the preset maximum cosine similarity threshold and the transportation environment category belongs to the low vibration intensity scene set, then output a high-confidence damage flag V4. By traversing the preset high-confidence destruction identifier lookup table, auxiliary verification parameters corresponding to the high-confidence destruction identifiers V1, V2, V3, and V4 are obtained.

[0010] In a preferred embodiment of this solution, the tracing module specifically includes: Obtain the alarm trigger flag bit, parse the alarm trigger flag bit, and obtain the damage type field, alarm timestamp, and dynamic disturbance index historical sequence corresponding to the alarm trigger flag bit; Retrieve the pre-stored logistics trajectory database, extract the cargo location coordinates corresponding to the alarm timestamp, and match the logistics node attributes to which the location coordinates belong. The logistics node attributes include transportation sections, storage sites, and loading and unloading areas. The damage is determined based on the changing trend of the historical sequence of the dynamic disturbance index. If the historical sequence of the dynamic disturbance index shows a monotonically increasing trend within 10 seconds before the alarm, it is marked as damage during transportation. If the historical sequence of the dynamic disturbance index suddenly changes to above the preset dynamic disturbance index threshold within 1 second before the alarm, it is marked as damage during loading and unloading. Based on the attributes of logistics nodes and the stage at which damage occurs, the corresponding responsible entity identifier ID is extracted from the pre-stored responsible entity mapping table; Generate a traceability data package containing optical path blockage status flags, alarm trigger flags, responsible entity IDs, damage occurrence tags, and logistics node attributes.

[0011] To achieve the above objectives, this application also provides the following technical solution: a disposable fiber optic composite anti-theft mesh cover, comprising a memory and a processor, wherein the memory stores computer instructions that can run on the processor, characterized in that the computer instructions running on the processor are modules that execute a logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover.

[0012] This application extracts the three-dimensional time-frequency features of the dynamic disturbance index—jitter amplitude, spectral centroid, and energy attenuation slope—and performs cosine similarity matching with pre-stored shear, impact, and static pressure templates to achieve accurate characterization of fiber deformation, distinguishing between man-made shearing damage and various non-destructive interferences at the signal level. Simultaneously, based on multi-feature fusion classification of vibration acceleration, it identifies the current transportation environment, dynamically calibrates the anti-shake threshold for suitable scenarios, and combines confidence-based hierarchical decision-making with an OTDR secondary confirmation mechanism. This effectively addresses the pain points of poor adaptability of fixed thresholds and low accuracy of single-feature recognition, significantly reducing the system's false alarm rate.

[0013] This application blocks the optical path through an irreversible physical self-destruct mechanism to prevent attackers from restoring the mesh. At the same time, it generates an encrypted traceability data packet containing information such as the type of damage, the time of occurrence, latitude and longitude coordinates, and the responsible party. Through multi-mode redundant communication, it ensures that alarm messages are uploaded to the cloud with zero loss. It can directly support the determination of responsibility throughout the entire logistics process and is suitable for all types of transportation scenarios such as road, rail, and air. It has high reliability and applicability. Attached Figure Description

[0014] The present application will be further illustrated with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present application. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0015] Figure 1 This is a schematic diagram of module connections in an embodiment of this application. Detailed Implementation

[0016] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0017] Please see Figure 1 This application provides a disposable fiber optic composite anti-theft mesh cover and a logistics anti-vandalism alarm system. The system includes a data acquisition module, a feature matching module, an environmental analysis module, a backscatter detection module, and a traceability module, wherein: The data acquisition module is used to sample the analog voltage signal, calculate the mean optical power, standard deviation and spectral energy through a sliding window, and generate a dynamic disturbance index sequence. Furthermore, when the data acquisition module samples the analog voltage signal output by the photoelectric conversion module to obtain the dynamic disturbance exponential sequence, it is specifically used for: The analog voltage signal is sampled point by point by an analog-to-digital converter at a preset sampling frequency fs, and the continuous analog voltage is converted into discrete digital quantized values. The original optical power time series P(n) with a length of N=1024 is constructed according to the sampling order, where n=1,2,…,N. The physical meaning of P(n) is the optical power quantized value corresponding to the nth sampling point, and the quantization accuracy is 12 bits.

[0018] A Hanning window of length L=128 is set as the sliding window, and the sliding is performed on the original optical power time series P(n) with a step size S=64 sampling points. Each time, a data segment of length L, xj(i)=P((j-1)×S+i)×w(i), i=1,2,…,L, is extracted, where w(i)=0.5×[1-cos(2πi / (L-1))] is the weighting coefficient of the Hanning window function, which is used to suppress the spectrum. Leakage; calculate the mean optical power μj and standard deviation of optical power σj for each data segment, and use σj as the optical power jitter amplitude ΔPj for that window; perform a 128-point fast Fourier transform on each data segment xj(i) to obtain the corresponding frequency domain energy spectrum Xj(k), extract the frequency index range k1=⌈5×L / fs⌉ to k2=⌊50×L / fs⌋ within the preset 5-50Hz frequency band, and calculate the spectral energy integral value within this frequency band. As a characteristic of spectral energy distribution.

[0019] Based on the optical power jitter amplitude ΔPj and the spectral energy distribution characteristic Esumj, the dynamic disturbance index of the j-th window is calculated according to the preset formula DPIj=0.6×(ΔPj / μj)+0.4×log(Esumj). The DPIj of all windows are spliced ​​together in time order to establish the dynamic disturbance index sequence DPIseq. In the formula, 0.6 and 0.4 are pre-calibrated weighting coefficients, which are obtained by laboratory testing of DPI characteristics under different damage types and are used to balance the contribution of time-domain jitter and frequency-domain energy.

[0020] Feature matching module: used to extract the jitter amplitude mean, spectral centroid and energy decay slope of the dynamic disturbance index sequence to construct the feature vector to be tested, calculate the cosine similarity with each force template in the vector library, and output the shear failure judgment signal and real-time duration according to the discrimination rules.

[0021] Furthermore, when performing pattern matching on the dynamic perturbation index sequence to obtain the shear failure determination signal and its real-time duration, the shear failure determination module is specifically used for: The reference microbending loss feature vector library, which is pre-stored in the non-volatile flash memory of the microprocessor, is retrieved. The feature vector library stores dynamic disturbance index sequences collected under laboratory calibration conditions when shear force, impact force, and continuous static pressure are applied to the mesh cover. After feature extraction, three types of time-frequency feature vector templates are obtained: shear force template vector Vcut, impact force template vector Vimp, and static pressure template vector Vst. Each type of template consists of three dimensions: the average jitter amplitude, the spectral centroid, and the energy attenuation slope. The calibration process is as follows: three loads of known magnitude are applied to the mesh cover on a controllable mechanical testing machine, optical power data is collected simultaneously, and the DPI sequence is calculated. The three-dimensional features of the stable segment are extracted as template vectors.

[0022] Frame buffering is performed on a continuous M=16 frame dynamic perturbation exponent sequence, and the average jitter amplitude of the 16 frame sequence is extracted. The 5-50Hz frequency band spectral centroid Fc and energy attenuation slope Ke (obtained by fitting the linear trend of a 16-frame DPI sequence) are used to construct the real-time measured feature vector Vreal=[ ,Fc,Ke].

[0023] The cosine similarity between the feature vector Vreal to be tested and the three types of template vectors is calculated using the formula ρ=Vreal·Vtemp / ∣Vreal∣×∣Vtemp∣, where Vtemp is the template vector, Vreal·Vtemp is the dot product of the two vectors, and ∣V∣ is the magnitude of the vector. The corresponding similarity coefficients ρcut (corresponding to Vcut), ρimp (corresponding to Vimp), and ρsta (corresponding to Vsta) are then calculated.

[0024] The maximum value ρmax among the three similarity coefficients is selected, and a preset discrimination rule is executed: if ρmax corresponds to the shear force template vector Vcut and ρmax>0.85 (0.85 is the laboratory-calibrated threshold for distinguishing between shear and non-shear behavior, obtained through ROC curve analysis), then a high-level shear failure judgment signal Scut=1 is output (the Scut pin is set to 3.3V high level); if ρmax does not correspond to Vcut or ρmax≤0.85, then a low-level shear failure judgment signal Scut=0 is output (the Scut pin is set to 0V low level).

[0025] The microprocessor's built-in hardware timer performs timing operations: when Scut=1, the duration of the high level is accumulated with a timing step of 1ms to obtain the real-time duration τ; when Scut=0, the hardware timer is cleared, τ is set to 0, Scut is latched into the microprocessor's logic register, and τ is written into the threshold comparison unit's register.

[0026] Environmental analysis module: used to perform spectral analysis on vibration acceleration time series to obtain transportation environment category, judgment confidence level and first anti-shake time threshold; Furthermore, when the environmental analysis module calculates the first anti-shake time threshold based on vibration environment data, it is specifically used for: The vibration acceleration time series a(t) under the current transportation environment is collected by the built-in triaxial accelerometer at a preset acquisition frequency of 8kHz. a(t) is the magnitude of the triaxial acceleration vector at time t, in g. A fast Fourier transform is performed on a(t) to obtain the frequency domain energy distribution A(f). The frequency point with the largest energy in the 0-200Hz frequency band is extracted as the dominant frequency component fdom and marked as the core excitation source frequency.

[0027] Calculate the mean difference of acceleration μa and the standard deviation of acceleration σa of the vibration acceleration time series; The frequency domain energy distribution A(f) is normalized to obtain the frequency probability density p(f) = A(f) / ∑A(f). The vibration energy entropy H = -∑p(f)log2p(f) is calculated according to the formula to characterize the randomness of the vibration signal. Calculate the spectral centroid fc=∑f⋅A(f) / ∑A(f) of the current transportation environment, retrieve the reference spectral centroid fc0 corresponding to the current transportation mode (the reference value is the average spectral centroid of the stable cruise phase under the same transportation mode) stored in the flash memory, and calculate the spectral centroid offset Δfc=|fc-fc0| according to the formula, which is used to characterize the degree of fluctuation during the operation phase; The current transportation environment's μa, σa, Δfc, and H are input into a pre-trained random forest classifier (composed of 100 decision trees, trained based on millions of historical logistics vibration data, with classification rules embedded in the microprocessor's ROM). The output is the current transportation environment category (composed of a three-dimensional combination of transportation mode category, medium of travel subcategory, and operational stage) and the decision confidence level γ (ranging from 0 to 1, representing the reliability of the classification result). Retrieve typical vibration waveform templates corresponding to the transportation environment category from the pre-existing vibration spectrum library of logistics scenarios. The templates are statistically obtained based on measured vibration data of the same type of environment at the level of 100,000. They store the expected width of a single impact pulse E[Tpulse], the expected interval between adjacent pulses E[Tinterval], and the pre-calibrated environmental interference compensation coefficient β (within the range of 0.8-1.5, the higher the vibration intensity, the larger the value, which is obtained by calibrating through vibration tests simulating different road conditions in the laboratory). The safety factor k is dynamically selected based on the confidence level γ: k=1.2 when γ≥0.95, k=1.5 when 0.85≤γ<0.95, and k=2.0 when γ<0.85. The initial anti-shake threshold is calculated according to the formula Tth0=k×(E[Tpulse]+E[Tinterval]), and the first anti-shake time threshold is calculated according to the formula Tth=β×Tth0. Tth, transportation environment category, γ, and H are written into the threshold register.

[0028] Backscattering detection module: used to analyze and generate optical path blockage status flag and alarm trigger flag based on shear failure judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence and vibration energy entropy; Furthermore, the backscattering detection module is specifically used for: Retrieve the latest cached Scut, τ, Tth, transport environment category, γ, and H from the register of the threshold comparison unit; The system retrieves a predefined set of high-vibration-intensity scenarios (including highways with gravel surfaces, railways with conventional tracks, and aviation with takeoff / landing phases) and a set of low-vibration-intensity scenarios (including highways with paved surfaces, railways with high-speed tracks, and aviation with cruising phases). If the transportation environment belongs to the high-vibration-intensity scenario set, Tth is directly used as the current decision threshold Tth′. If the transportation environment belongs to the low-vibration-intensity scenario set, the corrected current decision threshold is calculated according to the formula Tth′=Tth×(1-0.1×eH), where e is a natural constant. This formula is obtained through testing and calibration in low-vibration scenarios and is used to adapt to the sensitivity requirements in a stable environment. Execute the preset discrimination conditions: determine whether Scut=1 and τ>Tth′ are satisfied; if not, return to retrieve the latest values ​​of all parameters and repeat the threshold correction and discrimination process; If the discrimination condition is met, the value of γ is retrieved, and γ is multiplied by the auxiliary verification parameter (output by the auxiliary verification parameter generation module of weight 6) to obtain the confidence level after verification. If the confidence level after verification is ≥0.85, a first-level activation signal is generated. If the confidence level after verification is <0.85, a second-level confirmation process is initiated: the OTDR algorithm module built into the control microprocessor continuously performs three backscattered light signal detections at 100ms intervals. During each detection, a 1550nm narrow pulse probe light with a pulse width of 10ns and a peak power of 20mW is emitted. The backscattering curve is collected and the slope abrupt change point is calculated. If the abrupt change point exists, it is determined that a physical fiber break has been detected, and the break point detection result di=1 is output; otherwise, di=0 is output. If di is 1 for all three detections, a second-level activation signal is generated. If di=0 exists for any detection, the latest values ​​of all parameters are retrieved again, and the threshold correction and discrimination process is repeated. Using the first-level activation signal or the second-level activation signal as input, a PWM drive signal with a duty cycle of 50% and a frequency of 20kHz is generated. This parameter matches the drive characteristics of the miniature heating wire (resistance 10Ω, power supply voltage 5V, heating to 180℃ requires 500ms). The PWM drive signal is output to the micro heating wire drive circuit of the stress shearing mechanism. The drive circuit controls the heating wire to be energized and heated to 180°C and maintained for 500ms, causing the polymer support structure covering the optical path redirection prism to shrink by 0.5mm through thermal melting. Simultaneously, the prestressed spring with a pre-compression amount of 2mm is driven to release mechanical energy, which drives the tungsten steel blade with a cutting edge angle of 30° to move downward by 1.5mm to cut the incident optical fiber. At the same time, the optical path redirection prism is pushed to rotate 15° around the axis, causing the reflecting surface to deviate from the optical axis, and the optical path loss is increased to more than 30dB. After the action is completed, the action completion feedback signal of the stress shearing mechanism is set, and the optical path blocking status flag bit R=1 (written into the microprocessor status register) and the alarm trigger flag bit A=1 (output to the trigger pin of the wireless communication module) are output.

[0029] Furthermore, when the auxiliary verification parameter generation module generates auxiliary verification parameters based on optical signal characteristics and vibration environment characteristics, it is specifically used for: Retrieve the shear failure determination signal Scut, the maximum cosine similarity ρmax, the transportation environment category, and the determination confidence γ; execute four-class classification logic: if ρmax > 0.85 and the transportation environment category belongs to the high vibration intensity scene set, output a high-confidence failure identifier V1; .... If ρmax is ≤0.85 and the transportation environment category belongs to the high vibration intensity scene set, then output a high confidence damage flag V2; if ρmax>0.85 and the transportation environment category belongs to the low vibration intensity scene set, then output a high confidence damage flag V3; if ρmax≤0.85 and the transportation environment category belongs to the low vibration intensity scene set, then output a high confidence damage flag V4. The system iterates through a pre-stored high-confidence destruction identifier lookup table (stored in flash memory, calibrated based on the false positive rate under different laboratory test scenarios) to obtain auxiliary verification parameters for each identifier: V1 corresponds to 1.1, V2 corresponds to 0.9, V3 corresponds to 1.05, and V4 corresponds to 0.95. The function of the auxiliary verification parameters is to amplify the confidence level when the optical signal characteristics are consistent with the vibration environment characteristics, and to reduce the confidence level when they are inconsistent, thereby improving the robustness of the decision.

[0030] Traceability module: After an alarm is triggered, it is used to determine the link where the damage occurred and the responsible party based on the historical sequence of dynamic disturbance index and logistics trajectory database, and generate traceability data package.

[0031] Furthermore, when the source tracing data packet generation module generates source tracing data packets based on alarm trigger status, it is specifically used for: Obtain the alarm trigger flag A1, parse A1, and read the corresponding damage type field, alarm timestamp talarm (read from the RTC real-time clock with an accuracy of 1ms) and dynamic disturbance index historical sequence from the microprocessor status register. Retrieve the logistics trajectory database pre-stored on the cloud server, extract the latitude and longitude coordinates of the goods output by the GPS module at the time of the talarm, and match the logistics node attributes to which the coordinates belong (predefined as three categories: transportation section, storage site, and loading and unloading operation area). The damage occurred at the point of origin based on the changing trend of the historical sequence of the dynamic disturbance index: if the DPI sequence shows a monotonically increasing trend within 10 seconds before the alarm, it is marked as damage during transportation; if the DPI sequence suddenly rises above the threshold within 1 second before the alarm, it is marked as damage during loading and unloading; the trend judgment is achieved through sliding window linear regression, and if the regression slope is greater than the preset threshold, it is judged as monotonically increasing. Based on the matching results between logistics node attributes and the damage occurrence stage, the corresponding responsible entity identifier ID is extracted from the pre-stored responsible entity mapping table (stored in flash memory, associating logistics nodes with responsible party IDs: transportation segment corresponds to carrier ID, warehousing site corresponds to warehouse ID, and loading and unloading area corresponds to loading and unloading party ID). Generate a traceability data packet containing an optical path blocking status flag R1, an alarm trigger flag A1, a responsible entity flag ID, a damage occurrence tag, and logistics node attributes. After AES-128 encryption and CRC16 verification, the data packet is sent by the subsequent communication module.

[0032] To achieve the above objectives, this application also provides the following technical solution: a disposable fiber optic composite anti-theft mesh cover, comprising a memory and a processor, wherein the memory stores computer instructions that can run on the processor, characterized in that the computer instructions running on the processor are modules that execute a logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover.

[0033] The above are all preferred embodiments of this application, and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover, characterized in that: include: The data acquisition module is used to sample analog voltage signals, calculate the mean, standard deviation and spectral energy of optical power through a sliding window, and generate a dynamic disturbance index sequence. The feature matching module is used to extract the jitter amplitude mean, spectral centroid and energy decay slope of the dynamic disturbance index sequence to construct the feature vector to be tested, calculate the cosine similarity with each force template in the vector library, and output the shear failure judgment signal and real-time duration according to the discrimination rule. The environmental analysis module is used to perform spectral analysis on the vibration acceleration time series to obtain the transportation environment category, the judgment confidence level, and the first anti-shake time threshold. The backscattering detection module is used to analyze and generate optical path blockage status flags and alarm trigger flags based on shear failure judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence level and vibration energy entropy. The traceability module is used to determine the link where the damage occurred and the responsible party based on the historical sequence of dynamic disturbance index and logistics trajectory database after an alarm is triggered, and to generate a traceability data package.

2. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 1, characterized in that, The data acquisition module specifically includes: The analog voltage signal is sampled at a preset sampling frequency through the analog-to-digital converter built into the photoelectric conversion module, and an original optical power time series of length N is constructed. A Hanning window of length L is set as a sliding window, and the window is slid over the original optical power time series with a step size S. The mean optical power and standard deviation of optical power in each window are calculated to obtain the optical power jitter amplitude sequence. Perform a fast Fourier transform on the data segment within each window to obtain the corresponding frequency domain energy spectrum, and extract the spectral energy integral value within a preset frequency range as the spectral energy distribution feature. Based on the optical power jitter amplitude sequence and the spectral energy distribution characteristics, the dynamic disturbance index is calculated according to the preset dynamic disturbance index formula to establish a dynamic disturbance index sequence.

3. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 2, characterized in that, The feature matching module specifically includes: Retrieve the pre-stored reference microbending loss feature vector library, which stores the dynamic disturbance index time-frequency feature vector templates corresponding to the application of different shear forces, impact forces and continuous static pressures to the mesh cover under laboratory calibration environment. The dynamic disturbance index time-frequency feature vector templates include the jitter amplitude mean, the spectral centroid and the energy attenuation slope. Frame buffering is performed on a continuous M-frame dynamic perturbation index sequence. The jitter amplitude mean, spectral centroid and energy attenuation slope of the M-frame dynamic perturbation index sequence are extracted to construct a real-time feature vector to be measured. Calculate the cosine similarity between the feature vector to be measured and the time-frequency feature vector templates of the dynamic disturbance index corresponding to different shear forces, impact forces and continuous static pressures, and obtain the similarity coefficients between the feature vector to be measured and the time-frequency feature vector templates of the dynamic disturbance index corresponding to different shear forces, impact forces and continuous static pressures. The maximum value of the time-frequency feature vector template of the dynamic disturbance index corresponding to the feature vector to be tested and the shear force, impact force and continuous static pressure is selected respectively, and the discrimination is performed according to the preset discrimination rule. If the discrimination rule is met, a high-level shear failure judgment signal Scut=1 is output, and Scut=1 indicates a high-level effective state. If the discrimination rule is not met, a low-level shear failure judgment signal Scut=0 is output. Start the built-in hardware timer and increment the count of Scut=1 in 1ms increments to obtain the real-time duration.

4. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 1, characterized in that, The environmental analysis module specifically includes: The built-in triaxial accelerometer collects the vibration acceleration time series under the current transportation environment at a preset acquisition frequency, and performs a fast Fourier transform on the vibration acceleration time series to obtain the frequency domain energy distribution. The dominant frequency component in the preset frequency band is extracted and marked as the core excitation source frequency. Calculate the mean deviation and standard deviation of vibration acceleration corresponding to the time series; The frequency probability density is obtained by normalizing the frequency domain energy distribution, and the vibration energy entropy is calculated according to the formula. The frequency domain energy distribution is calculated to obtain the reference spectral centroid of the current transportation environment. The pre-stored reference spectral centroid is retrieved, and the spectral centroid offset is calculated according to the formula. By classifying and matching the mean acceleration difference, standard acceleration deviation, spectral centroid offset, and vibration energy entropy corresponding to the current transportation environment with the preset standard parameter templates corresponding to different categories of transportation environments, the transportation environment category and judgment confidence level corresponding to the current transportation environment are obtained. Obtain typical vibration waveform templates corresponding to different transportation environment categories collected in advance under simulated transportation environment. The typical vibration waveform templates store the expected width of a single impact pulse, the expected interval between adjacent pulses, and the pre-calibrated environmental interference compensation coefficients under the transportation environment category. The safety factor is dynamically selected based on the confidence level, and the initial stabilization threshold is calculated according to the preset calculation formula. The initial stabilization threshold is then used to calculate the first stabilization time threshold according to the preset formula.

5. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 1, characterized in that, The backscattering detection module specifically includes: Retrieve the latest data on the output shear failure judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence level, and vibration energy entropy; Obtain a predefined set of high vibration intensity scenarios and a set of low vibration intensity scenarios. If the transportation environment category belongs to the set of high vibration intensity scenarios, then the first anti-shake time threshold is corrected according to the corresponding high vibration intensity scenario correction coefficient, and recorded as the current decision threshold. If the transportation environment category belongs to the low vibration intensity scenario set, the corrected current judgment threshold is calculated according to the preset formula. Based on the preset discrimination conditions, it is determined whether the conditions of Scut=1 and real-time duration > current judgment threshold are met. If not, the latest value corresponding to all data is read and the current judgment threshold is corrected. All data includes shear damage judgment signal, real-time duration, first anti-shake time threshold, transportation environment category, judgment confidence and vibration energy entropy. If the preset discrimination conditions are met, retrieve the value of the judgment confidence level. After verification, the judgment confidence level = judgment confidence level × auxiliary verification parameter. If the judgment confidence level after verification is greater than or equal to the preset judgment confidence level threshold, then generate a first-level activation signal. If the confidence level after verification is less than the preset confidence level threshold, a secondary confirmation process is performed. The secondary confirmation process controls the microprocessor to continuously perform three backscattered light signal detections at preset intervals. Each detection yields a breakpoint detection result, where 1 indicates that a physical fiber breakpoint has been detected and 0 indicates that a physical fiber breakpoint has not been detected. If all three detections are 1, a secondary activation signal is generated. If any detection results in 0, then return the latest value corresponding to all data and readjust the current decision threshold. The first-level activation signal or the second-level activation signal is used as input to generate a PWM drive signal; The PWM drive signal is output to the miniature heating wire drive circuit of the stress shearing mechanism and matched with the corresponding execution process. After the miniature heating wire drive circuit completes the corresponding execution process, the optical path blocking status flag and the alarm trigger flag are output.

6. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 5, characterized in that, Obtaining the auxiliary verification parameters specifically includes: Obtain the shear failure determination signal, the maximum cosine similarity, the transportation environment category, and the determination confidence level; If the maximum cosine similarity is greater than the preset maximum cosine similarity threshold and the transportation environment category belongs to the high vibration intensity scene set, then output a high confidence damage flag V1; If the maximum cosine similarity is less than or equal to the preset maximum cosine similarity threshold and the transportation environment category belongs to the high vibration intensity scene set, then output a high confidence damage flag V2. If the maximum cosine similarity is greater than the preset maximum cosine similarity threshold and the transportation environment category belongs to the low vibration intensity scene set, then output a high-confidence damage flag V3. If the maximum cosine similarity is less than or equal to the preset maximum cosine similarity threshold and the transportation environment category belongs to the low vibration intensity scene set, then output a high confidence damage flag V4. By traversing the preset high-confidence destruction identifier lookup table, auxiliary verification parameters corresponding to the high-confidence destruction identifiers V1, V2, V3, and V4 are obtained.

7. The logistics anti-vandalism alarm system for a disposable fiber optic composite anti-theft mesh cover as described in claim 1, characterized in that, The traceability module specifically includes: Obtain the alarm trigger flag bit, parse the alarm trigger flag bit, and obtain the damage type field, alarm timestamp, and dynamic disturbance index historical sequence corresponding to the alarm trigger flag bit; Retrieve the pre-stored logistics trajectory database, extract the cargo location coordinates corresponding to the alarm timestamp, and match the logistics node attributes to which the location coordinates belong. The logistics node attributes include transportation sections, storage sites, and loading and unloading areas. The damage is determined based on the changing trend of the historical sequence of the dynamic disturbance index. If the historical sequence of the dynamic disturbance index shows a monotonically increasing trend within 10 seconds before the alarm, it is marked as damage during transportation. If the historical sequence of the dynamic disturbance index suddenly changes to above the preset dynamic disturbance index threshold within 1 second before the alarm, it is marked as damage during loading and unloading. Based on the attributes of logistics nodes and the stage at which damage occurs, the corresponding responsible entity identifier ID is extracted from the pre-stored responsible entity mapping table; Generate a traceability data package containing optical path blockage status flags, alarm trigger flags, responsible entity ID, damage occurrence stage tags, and logistics node attributes.

8. A disposable fiber optic composite anti-theft mesh cover, comprising a memory and a processor, wherein the memory stores computer instructions capable of running on the processor, characterized in that, When the computer instructions running on the processor are executed, the logistics anti-vandalism alarm system of any one of the disposable fiber optic composite anti-theft mesh covers according to claims 1-7 is performed.

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