A logistics abnormality early warning method and system based on big data analysis

Abstract

The application relates to the technical field of logistics monitoring and data analysis, and discloses a logistics abnormality early warning method and system based on big data analysis. The method comprises the following steps: receiving bottom-layer heterogeneous data streams output by multiple sensors; performing state determination based on vehicle kinematics constraint conditions, extracting temperature difference values when static reference conditions are met, calculating and updating thermodynamic normalization parameters; performing time-domain feature extraction on vibration data and combining with motion displacement to perform spatial-domain alignment and aggregation, so that spatial slice feature data is generated; calculating a frequency domain and spatial gradient coupling degradation risk index in combination with the thermodynamic normalization parameters; calculating a degradation acceleration gradient, sending an interrupt instruction when a trigger setting value is reached, adjusting a communication bus clock frequency and a physical sampling rate and resetting a step length parameter; and based on a sliding window, performing accumulated integral calculation on the risk index, and outputting abnormal trace data packets when a warning threshold is reached. The application realizes effective spatial alignment of data, and takes into account power consumption and monitoring accuracy.

Description

Technical Field

[0001] This invention relates to the field of logistics monitoring and data analysis technology, specifically to a logistics anomaly early warning method and system based on big data analysis. Background Technology

[0002] In modern high-value-added and cold chain logistics transportation, continuous multi-dimensional monitoring of the thermodynamic state and mechanical vibration environment inside the vehicle is crucial to ensuring cargo quality. Existing logistics monitoring equipment typically has fixed environmental baseline parameters or alarm thresholds preset at the factory, lacking the ability for dynamic in-situ calibration during complex transportation processes. In actual operation, the scouring of external dynamic airflow and mechanical vibration of the chassis significantly alter the actual heat dissipation characteristics of the packaging system. If temperature data is directly collected for baseline judgment while the vehicle is in motion or experiencing severe micro-vibration, the initial thermodynamic parameters will have significant errors due to dynamic environmental interference, failing to obtain the true thermal resistance characteristics that accurately reflect the actual packaging material properties, thus leading to false alarms in subsequent early warning mechanisms.

[0003] Furthermore, conventional multi-sensor heterogeneous data fusion primarily relies on absolute time axes for recording and alignment. However, the speed of logistics vehicles during transportation is constantly changing, and the actual physical displacement and mechanical stress exposure corresponding to a fixed time span observation window vary significantly. Because the accumulation of mechanical vibration energy differs from the physical response period of thermodynamic temperature drift, simply relying on time axes for data recording makes it difficult to accurately aggregate heterogeneous data on the same physical spatial scale. This time drift phenomenon leads to spatial mapping biases in multi-dimensional features during fusion calculations, failing to accurately reconstruct the comprehensive physical damage suffered by goods within a specific transportation segment.

[0004] Meanwhile, existing logistics monitoring equipment typically uses fixed clock frequencies and physical sampling rates for data acquisition. In long-distance logistics scenarios, maintaining high-frequency sampling throughout the entire process will continuously generate massive amounts of redundant data, leading to a significant increase in overall system power consumption and failing to meet the equipment's long-term battery life requirements. On the other hand, if a conventional low-frequency sampling strategy is adopted to control operating power consumption, it is easy to miss high-frequency, high-risk anomaly features when the system encounters sudden severe road conditions or rapid temperature degradation caused by packaging damage, making it difficult to capture data with sufficient resolution to support subsequent anomaly detection and evidence tracing. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a logistics anomaly early warning method and system based on big data analysis. It aims to solve the problems in existing technologies, such as large errors in reference thermodynamic parameters due to the lack of dynamic in-situ calibration of logistics monitoring equipment, spatial mapping deviations caused by relying solely on absolute time axis recording, and the inability of fixed hardware sampling frequency to balance low-power system operation with high-frequency capture of sudden anomaly characteristics.

[0006] To achieve the above objectives, the present invention provides the following technical solution: The first aspect of the present invention provides a logistics anomaly early warning method based on big data analysis, comprising the following steps:

[0007] After the edge computing node is powered on, it loads the initial hardware parameters and receives the underlying heterogeneous data stream continuously output by the positioning module, high-frequency triaxial accelerometer, internal temperature sensor and external ambient temperature sensor.

[0008] Based on the vehicle kinematic constraints output by the positioning module, the state of the underlying heterogeneous data stream is determined. When the static reference conditions are met, the temperature difference between the internal temperature sensor and the external ambient temperature sensor in the underlying heterogeneous data stream is extracted, and the thermodynamic normalized parameters of the system are calculated and updated.

[0009] The vibration data output by the high-frequency triaxial accelerometer in the underlying heterogeneous data stream is extracted in the time domain, and spatial domain alignment and aggregation are performed in combination with the motion displacement calculated by the positioning module to generate spatial slice feature data.

[0010] Extract the spatial temperature gradient and vibration feature components from the spatial slice feature data, and combine them with thermodynamic normalization parameters to calculate the frequency domain and spatial gradient coupling degradation risk index within the current spatial slice;

[0011] Based on the frequency domain and spatial gradient coupling degradation risk index, the degradation acceleration gradient between adjacent spatial slices is calculated. When the degradation acceleration gradient reaches the trigger set value, an interrupt command is sent to the hardware interrupt controller to adjust the clock frequency of the communication bus and the physical sampling rate of each sensor, and reset the step length parameter of the spatial slice.

[0012] Based on a fixed-length spatial slice sliding window maintained in static random access memory, the frequency domain and spatial gradient coupling degradation risk index is accumulated and integrated. When the accumulated integral value reaches the warning threshold, an anomaly is determined, an anomaly traceability data packet is generated, and an output operation is performed.

[0013] The beneficial effects of this invention are as follows: It solves the feature alignment problem caused by traditional IoT devices simply recording data based on a time axis. By dividing continuous physical displacement into fixed-length spatial slices through a positioning system, it effectively aligns mechanical vibration energy and thermodynamic temperature drift on the same spatial scale. Simultaneously, the system introduces an adaptive modulation mechanism in the underlying hardware, directly mapping the degradation acceleration gradient calculated by the upper-layer software to the operating parameters of the underlying communication bus and analog-to-digital converter. This enables dynamic switching between low-power operation and high-frequency fine sampling under abnormal conditions, improving the accuracy of multidimensional stress coupling analysis and the effectiveness of data traceability.

[0014] Preferably, the state determination of the underlying heterogeneous data stream is based on the vehicle kinematic constraints output by the positioning module, specifically including:

[0015] The system calculates the kinematic velocity state within the time observation window, performs a first-level velocity constraint judgment, and determines whether the global peak velocity in the velocity vector sequence within the time observation window is continuously lower than the set static drift tolerance. It then calculates the mechanical vibration state within the time observation window, performs a second-level vibration constraint judgment, calculates the dynamic fluctuation characteristics of the triaxial composite acceleration to generate a triaxial acceleration variance, and compares this variance with a preset chassis background noise threshold. A logic AND gate mechanism is used to comprehensively judge the calculation results of the first-level velocity constraint judgment and the second-level vibration constraint judgment. When both judgments are simultaneously valid, it confirms that the logistics vehicle is currently in a purely static physical environment with no macroscopic displacement and no microscopic excitation, and controls the data processing pipeline to enter the static baseline operating condition. The logic AND gate mechanism includes software Boolean logic multiplication instructions executed within the microcontroller unit or hardware logic AND gate circuits physically integrated on the underlying printed circuit board.

[0016] In one specific embodiment, calculating and updating the thermodynamic normalized parameters of the system specifically includes:

[0017] By constructing a discretized formula for the time derivative of internal temperature, the derivative value of internal temperature with respect to time is calculated, and a baseline heat dissipation model without mechanical interference is established. Simultaneously, the external ambient temperature sequence within the time observation window is extracted, and a basic thermodynamic normalized model is constructed in conjunction with Newton's law of cooling. External ambient temperature data with a strict absolute time alignment relationship with the effective observation step size is extracted, and the average temperature difference between the inner and outer sides within the time span is calculated. The derivative value of internal temperature with respect to time is projected onto a unit temperature difference baseline to calculate the unfiltered transient heat transfer characteristic parameters. The transient heat transfer characteristic parameters are then subjected to state estimation and dynamic smoothing using a recursive filtering model. The computational resources of the microcontroller unit are called to execute the Kalman filter algorithm to dynamically remove high-frequency random disturbance signals that are unrelated to the actual thermal resistance of the packaging material, output the normalized heat transfer coefficient, and generate and update the thermodynamic normalized parameters.

[0018] Furthermore, time-domain feature extraction is performed on the vibration data output from the high-frequency triaxial accelerometer in the underlying heterogeneous data stream, specifically including:

[0019] A sliding time observation window is set under a constant high-frequency period. Windowing and fast Fourier transform are performed on the intercepted vibration acceleration time series. The original triaxial acceleration time series is preprocessed by time-domain dot product with the Hanning window function. A triaxial fast Fourier transform is then performed to map the time-domain discrete mechanical signal into a complex frequency domain sequence. Based on the complex frequency domain sequence, the spectral amplitude within the preset target frequency band is extracted to determine the upper and lower physical frequency boundaries of the damage frequency band. Based on the system sampling rate, the upper and lower physical frequency boundaries of the damage frequency band are mapped into discrete frequency index intervals. Frequency domain energy integration and synthesis operations are performed on the power spectral density in the three-dimensional orthogonal directions within the discrete frequency index intervals to calculate the vibration energy of the damage frequency band within a single time observation window.

[0020] Preferably, spatial domain alignment and aggregation are performed based on the motion displacement calculated by the positioning module to generate spatial slice feature data, specifically including:

[0021] The cumulative physical displacement of the logistics vehicle is calculated by performing discrete Riemann integration on the truncated velocity vector sequence. A hardware triggering mechanism based on displacement boundary conditions is established to perform a cyclic non-blocking comparison between the real-time updated cumulative physical displacement and the default spatial slice length maintained in the system memory. When the cumulative physical displacement value reaches the default spatial slice length, a truncation command is sent to forcibly terminate the current integration calculation window. Based on the extracted start and end timestamps of the spatial slice, an arithmetic mean dimensionality reduction operation is performed on the vibration energy of the damaging frequency band within the current slice range to calculate the average vibration feature scalar within the current fixed-length spatial slice. The thermodynamic state parameters at the boundary of the spatial slice are extracted and recorded simultaneously. The start and end temperature values ​​of the spatial slice are extracted and packaged together with the average vibration feature scalar into the current spatial slice feature data frame to generate spatial slice feature data.

[0022] In one specific embodiment, the spatial temperature gradient and vibrational feature components are extracted from the spatial slice feature data. Combined with thermodynamic normalized parameters, the frequency domain and spatial gradient coupling degradation risk index within the current spatial slice is calculated, specifically including:

[0023] Thermodynamic boundary conditions within the spatial slice feature data frame are extracted, and the starting and ending temperature values ​​of the packaged and overwritten spatial slice are extracted. Discrete division of the pure physical spatial temperature gradient is performed. The average ambient temperature difference between the inner and outer sides within the current slice period is extracted, and the theoretical tolerance of natural temperature drift is calculated by combining the normalized heat transfer coefficient. Heterogeneous data fusion is performed by combining the abnormal temperature drift and the mechanical damage energy under spatial axis mapping. Logarithmic function is used to perform pre-smoothing of mechanical stress characteristics, and hard boundary filter function is used to threshold and truncate thermodynamic variables. The single-time degradation risk index increment of the frequency domain and spatial gradient coupled degradation risk index is calculated.

[0024] Furthermore, based on the frequency domain and spatial gradient coupling degradation risk index, the degradation acceleration gradient between adjacent spatial slices is calculated. When the degradation acceleration gradient reaches the trigger set value, an interrupt command is sent to the hardware interrupt controller. Specifically, this includes: extracting the degradation increment of the current spatial slice and the degradation increment of the previous spatial slice, performing discrete difference differentiation based on physical spatial displacement to calculate the degradation acceleration gradient between adjacent spatial slices; retrieving the preset degradation trigger threshold from the read-only memory, importing the degradation acceleration gradient and the degradation trigger threshold into the numerical comparator register of the microcontroller unit to perform real-time comparison and judgment of the gradient magnitude, and using the hysteresis comparison algorithm integrated in the comparator logic to require the degradation acceleration gradient to remain above the preset degradation trigger threshold for two consecutive calculation cycles to be confirmed as a valid over-limit; when the degradation acceleration gradient is detected to exceed the preset degradation trigger threshold, a high-priority hardware interrupt signal is generated to lock the current program counter stack, and an interrupt command is sent to the hardware interrupt controller.

[0025] Preferably, adjusting the clock frequency of the communication bus and the physical sampling rate of each sensor, and resetting the step length parameter of the spatial slice, specifically includes:

[0026] The system executes dynamic scaling instructions for spatial slice stepping parameters, shortens the next hardware truncation trigger distance, calculates and generates the length of the new spatial slice, and resets the stepping length parameters. It modifies the peripheral clock division coefficient and the sensor's internal control register via the internal communication bus, writes a new clock division word into the control register to increase the bus baud rate to adjust the clock frequency of the communication bus, and switches the operating mode of the sensor's internal analog-to-digital converter from low-power, low-frequency standby to high-performance, high-frequency continuous output mode to synchronously improve the physical sampling rate of each sensor.

[0027] In one specific embodiment, based on a fixed-length spatial slice sliding window maintained in static random access memory, the frequency domain and spatial gradient coupling degradation risk index is accumulated and integrated. When the accumulated integral value reaches the warning threshold, an anomaly determination is performed, an anomaly tracing data packet is generated, and an output operation is executed. Specifically, this includes:

[0028] The degradation features of all valid spatial slices within the current fixed-length spatial slice sliding window are extracted. Discrete summation based on physical space and integral accumulation of multidimensional fatigue features are performed to calculate the cumulative degradation risk value. This cumulative degradation risk value is compared with a preset warning threshold in memory. A condition is determined if the cumulative degradation risk value exceeds the warning threshold for three consecutive calculation cycles, and the absolute temperature difference between the internal and external environments at the current moment is consistently greater than the set lower limit protection threshold. Based on the spatial slice timestamp that triggered the warning, the time-domain characteristic energy spectrum, thermodynamic boundary data frame, and underlying spatial coordinate sequence within the preset physical span preceding the warning time are extracted. A lossless compression algorithm is used to generate a traceability evidence packet containing the device's unified media access control address identifier and the absolute timestamp of the warning, and the output operation is performed. The communication link for this output operation includes, but is not limited to, mobile cellular networks, low-power wide-area networks, short-range wireless communication protocols, or directly interactive wired interfaces.

[0029] A second aspect of the present invention provides a logistics anomaly early warning system based on big data analysis, comprising:

[0030] The edge computing node comprises a positioning module, a high-frequency triaxial accelerometer, an internal temperature sensor, and an external ambient temperature sensor. These components are electrically connected to the edge computing node via a communication bus. The edge computing node integrates static random access memory and a hardware interrupt controller, and is equipped with multiple logic processing modules, specifically including:

[0031] The bus concurrent acquisition module is used to load initial hardware parameters after the edge computing node is powered on, and to receive the underlying heterogeneous data stream continuously output by the positioning module, high-frequency triaxial accelerometer, internal temperature sensor and external ambient temperature sensor.

[0032] The static reference in-situ calibration module is used to determine the state of the underlying heterogeneous data stream based on the vehicle kinematic constraints output by the positioning module. When the static reference conditions are met, the temperature difference between the internal temperature sensor and the external ambient temperature sensor in the underlying heterogeneous data stream is extracted, and the thermodynamic normalized parameters of the system are calculated and updated.

[0033] The spatiotemporal dual-track mapping module is used to extract time-domain features from vibration data output by high-frequency triaxial accelerometers in the underlying heterogeneous data stream, and combine it with the motion displacement calculated by the positioning module to perform spatial domain alignment and aggregation, generating spatial slice feature data.

[0034] The multidimensional parameter coupling calculation module is used to extract the spatial temperature gradient and vibration feature components from the spatial slice feature data, and combine them with thermodynamic normalized parameters to calculate the frequency domain and spatial gradient coupling degradation risk index within the current spatial slice.

[0035] The underlying hardware adaptive modulation module is used to calculate the degradation acceleration gradient between adjacent spatial slices based on the degradation risk index coupled with the frequency domain and spatial gradient. When the degradation acceleration gradient reaches the trigger set value, it sends an interrupt command to the hardware interrupt controller to adjust the clock frequency of the communication bus and the physical sampling rate of each sensor, and reset the step length parameter of the spatial slice.

[0036] The multi-level joint early warning module is used to accumulate and integrate the frequency domain and spatial gradient coupling degradation risk index based on a fixed-length spatial slice sliding window maintained in static random access memory. When the accumulated integral value reaches the early warning threshold, an anomaly is determined, an anomaly traceability data packet is generated, and an output operation is performed.

[0037] It should be noted that the multiple logic processing modules described in the system architecture of this embodiment of the invention, in terms of specific physical hardware implementation, include software forms consisting of computer program code stored in memory executed by a microprocessor, digital signal processor or microcontroller, as well as hardware circuit entities consisting of application-specific integrated circuits or field-programmable gate arrays, or a combination of the above software and underlying hardware circuits.

[0038] This invention provides a method and system for early warning of logistics anomalies based on big data analysis. It has the following beneficial effects:

[0039] 1. This invention divides the continuous physical trajectory into fixed-length spatial slices by calculating the motion displacement through the positioning module, and performs time-domain feature extraction on the vibration data output by the high-frequency triaxial accelerometer and spatial-domain alignment and aggregation with the motion displacement. This changes the limitation of conventional monitoring equipment that only relies on the absolute time axis to record data, and enables the mechanical vibration parameters and thermodynamic temperature state to achieve benchmark alignment of heterogeneous data on the same physical spatial scale. This solves the problem of multi-dimensional feature fusion deviation caused by time drift in traditional methods.

[0040] 2. This invention utilizes the calculated degradation acceleration gradient between adjacent spatial slices as a physical trigger condition. When the degradation acceleration gradient exceeds a set value, a command is sent to the hardware interrupt controller to adjust the clock frequency of the communication bus and the physical sampling rate of the sensor, simultaneously resetting the step length parameter of the spatial slice. This hardware-software collaborative mechanism enables the system to maintain low-frequency sampling under normal transportation conditions to control the overall power consumption of the equipment, and to autonomously increase the data acquisition resolution by the underlying hardware when the risk of degradation intensifies, thus balancing the system's power consumption limitations with the lossless capture of high-risk anomalies.

[0041] 3. Based on the vehicle kinematic constraints output by the positioning module, this invention performs physical state determination on the underlying heterogeneous data stream. When the system meets the pure static baseline conditions, the difference between the internal and external temperature sensors is extracted to calculate and update the thermodynamic normalized parameters. By introducing dual constraints of velocity and vibration, the interference of external dynamic airflow and the heat dissipation error caused by mechanical excitation of logistics vehicles are eliminated. The baseline thermal resistance characteristic parameters that conform to the actual packaging material characteristics are obtained, providing an accurate data basis for the subsequent calculation of the risk index and effectively reducing the false alarm rate of the system. Attached Figure Description

[0042] Figure 1 This is a system architecture diagram of the present invention;

[0043] Figure 2 This is a flowchart of the method of the present invention;

[0044] Figure 3 This is a three-dimensional schematic diagram of the travel trajectory and dynamic spatial slice distribution of a logistics vehicle according to an embodiment of the present invention;

[0045] Figure 4 A graph showing the comparison of cloud feature data intake between the traditional time domain and the spatial domain of the present invention, according to an embodiment of the present invention.

[0046] Figure 5 This is a diagram showing the correlation between the cumulative degradation risk evolution of the sliding window and the step response of the sampling frequency in an embodiment of the present invention. Sub-figure A is the measured curve of the nonlinear degradation risk evolution, and sub-figure B is the step response curve. Detailed Implementation

[0047] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0048] See attached document Figure 1 This invention provides a logistics anomaly early warning system based on big data analysis, including: an edge computing node, a positioning module, a high-frequency triaxial accelerometer, an internal temperature sensor, and an external ambient temperature sensor.

[0049] The positioning module, high-frequency triaxial accelerometer, internal temperature sensor, and external ambient temperature sensor are electrically connected to the edge computing node via a communication bus. The communication bus adopts either an integrated circuit built-in bus or a serial peripheral interface bus standard. The edge computing node integrates static random access memory and a hardware interrupt controller.

[0050] The edge computing node internally comprises multiple logical processing modules, specifically including a bus concurrent acquisition module, a static reference in-situ calibration module, a spatiotemporal dual-track mapping module, a multi-dimensional parameter coupled calculation module, a low-level hardware adaptive modulation module, and a multi-level joint early warning module. The system utilizes static random access memory to divide storage blocks for data caching and status parameter interaction among the aforementioned logical processing modules.

[0051] The various logic processing modules work together to form a data flow and command feedback channel. The underlying physical signals collected by the positioning module and sensors are input to the edge computing node, where they are processed by the various logic processing modules, and finally output hardware control commands and early warning data.

[0052] It should be noted that the multiple logic processing modules (including the bus concurrent acquisition module, the static reference in-situ calibration module, etc.) in the system architecture of this invention can be implemented in various ways. In practice, they can be implemented by a microprocessor, digital signal processor, or microcontroller executing computer program code stored in memory (i.e., software implementation); or by hardware circuit entities such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or complex programmable logic devices (CMLPs) (i.e., hardware implementation); or a combination of the above software and hardware implementations. This invention does not limit the uniqueness of this underlying implementation.

[0053] See attached document Figure 2 This invention provides a logistics anomaly early warning method based on big data analysis, comprising the following steps:

[0054] S100 executes the bus concurrent acquisition module. After the edge computing node is powered on, it loads the initial hardware parameters and receives the low-level heterogeneous data stream continuously output by the positioning module, high-frequency triaxial accelerometer, internal temperature sensor and external ambient temperature sensor.

[0055] S200 executes the static reference in-situ calibration module, performs state determination based on the vehicle kinematic constraints output by the positioning module, and extracts the temperature difference between the internal temperature sensor and the external ambient temperature sensor when the static reference conditions are met, and calculates and updates the thermodynamic normalized parameters of the system.

[0056] S300 executes the spatiotemporal dual-track mapping module to extract time-domain features from the vibration data output by the high-frequency triaxial accelerometer, and combines it with the motion displacement calculated by the positioning module to perform spatial domain alignment and aggregation, generating spatial slice feature data;

[0057] S400 executes the multi-dimensional parameter coupling calculation module to extract the spatial temperature gradient and vibration feature components from the current spatial slice feature data, and calculates the frequency domain and spatial gradient coupling degradation risk index in the current spatial slice by combining thermodynamic normalization parameters.

[0058] S500 executes the underlying hardware adaptive modulation module to calculate the degradation acceleration gradient between adjacent spatial slices. When the degradation acceleration gradient reaches the trigger set value, it sends an interrupt command to the hardware interrupt controller to adjust the clock frequency of the communication bus and the physical sampling rate of each sensor, and resets the step length parameter of the spatial slice.

[0059] S600 executes a multi-level joint early warning module, which calculates the degradation risk index by accumulating and integrating the fixed-length spatial slice sliding window maintained in the static random access memory. When the accumulated integral value reaches the early warning threshold, an anomaly is determined, an anomaly traceability data packet is generated, and an output operation is performed.

[0060] The above steps will be described in detail below with reference to specific embodiments and accompanying drawings.

[0061] Regarding step S100 above, the edge computing node completes the physical mapping of underlying hardware resources and the acquisition of heterogeneous data streams through the concurrent acquisition module of the execution bus. To achieve accurate early warning of special logistics anomalies based on big data analysis, the system must establish a unified spatiotemporal reference to fuse sensor signals with different physical attributes. This step specifically includes the following sub-steps.

[0062] S101, the edge computing node powers on and resets, executes the system bus initialization process, and completes the communication clock configuration. The edge computing node calls the microcontroller's low-level driver to establish a concurrent acquisition channel. As a preferred approach, considering the significant differences in sampling rates among multiple data sources, the system independently sets the communication clock frequency division for the serial peripheral interface bus, the integrated circuit built-in bus, and the universal asynchronous transceiver bus. For the high-frequency triaxial accelerometer with high data throughput, the system allocates a serial peripheral interface channel and writes it to the working mode register to ensure lossless transmission of high-frequency vibration characteristics. For the internal temperature sensor and the external ambient temperature sensor, the system allocates an integrated circuit built-in bus to reduce the bus polling overhead of the microcontroller and avoid consuming excessive system-level computing resources. The specific pin multiplexing mapping and bus baud rate calculation method for the microcontroller can be conventionally set by those skilled in the art based on the selected integrated circuit chip datasheet; the related timing control configuration is well-known in the field and will not be elaborated here.

[0063] S102, the bus concurrent acquisition module divides storage blocks in the static random access memory (SRAM) of the edge computing node and loads the core control parameter set allocated in system memory. The system allocates a contiguous memory address space in SRAM, configured in a circular overwrite mode, to establish a first-in-first-out (FIFO) cache queue for spatial slice data during subsequent operation, thereby physically preventing memory overflow anomalies caused by continuous influx of large sample data. The system reads preset values ​​from internal flash memory and writes them to the working area of ​​SRAM as initial state definition variables. The loaded core control parameter set specifically covers the system's default spatial slice length, sliding window length, warning threshold for triggering alarm judgment, and the initial heat transfer reference coefficient required for thermodynamic calculations.

[0064] In this embodiment, the range of values ​​for the aforementioned default spatial slice length and its determination basis are controlled by the average low-frequency vibration wavelength of a typical logistics vehicle traveling on a regular road surface; the sliding window length is set based on the minimum fatigue accumulation period required for irreversible micro-deformation of the packaging material. The preset of the above parameters establishes physical dimensions and judgment criteria for subsequent avoidance of false alarms from single extreme values ​​and for conducting multi-dimensional stress coupling calculations.

[0065] S103 configures a hardware timer to trigger continuous concurrent sampling from multiple physical sensors, establishing a heterogeneous data time series variable based on an absolute time reference. Based on the inherent physical causal mapping relationship between the different physical quantities, the bus concurrent acquisition module sends data addressing and reading commands to each sensor node via the communication bus according to the system's set high-frequency period. The system receives digital signals returned by the positioning module, the high-frequency triaxial accelerometer, the internal temperature sensor, and the external ambient temperature sensor. To address the time misalignment issue caused by inconsistent physical output rates of the sensors, the system parses the data frames and uniformly appends the current system's global absolute timestamp, generating a heterogeneous data time set formula:

[0066] ;

[0067] in, It is a heterogeneous data set in the time domain, representing the combined mechanical and thermodynamic physical environment state of the internal and external parts of the logistics vehicle at a specific time node; This is a sequence of velocity vectors, representing the kinematic characteristics of the logistics vehicle, which is used for subsequent effective integration of spatial domain displacement on the underlying time series. It is a sequence of vibration acceleration vectors, reflecting the intensity of external road mechanical excitation currently experienced by the logistics vehicle, and serves as an input parameter for assessing material fatigue; The internal temperature sequence reflects the thermodynamic state of the core storage environment inside the logistics packaging box; The external ambient temperature sequence serves as a key reference parameter for dynamically subtracting heat exchange interference from the external natural environment. It serves as a global absolute timestamp variable to ensure that heterogeneous data have a strict and unique baseline time alignment relationship before entering spatial feature aggregation.

[0068] After executing the bus concurrent acquisition module, the system establishes the physical source of the underlying data, converting the continuous physical parameters of the external environment into discretized digital sequence variables that can be directly accessed by the microcontroller unit. The multi-source heterogeneous data is initially cached in the continuous memory defined in the aforementioned sub-step S102, forming the underlying data support system for subsequent spatial domain alignment analysis and static benchmark calibration calculations.

[0069] Based on the physical addressing and memory caching of multi-source heterogeneous data completed in step S100, the system establishes a unified spatiotemporal data benchmark. For step S200, the edge computing nodes execute a static benchmark in-situ calibration module to utilize the stopping intervals of logistics vehicles to eliminate non-natural heat loss caused by external dynamic wind resistance and road mechanical stress, thereby obtaining the true thermal resistance background characteristics of the logistics packaging box. The specific implementation of this stage includes the following sub-steps:

[0070] S201, the static reference in-situ calibration module allocates an independent buffer addressing space in the static random access memory and sets a time observation window for state determination. Based on the global absolute timestamp, the system synchronously extracts the velocity vector sequence and vibration acceleration vector sequence within this time observation window from the first-in-first-out buffer queue established by the aforementioned bus concurrent acquisition module. Given the business scenario where logistics vehicles frequently stop at distribution centers or service areas during actual transportation, the system needs to strictly isolate the coupling interference of external mechanical excitation on the thermal conductivity of the packaging box, and thus introduces dual physical constraints for rigorous identification of static operating conditions.

[0071] S202, the system calculates the kinematic velocity state within the time observation window and performs the first-level velocity constraint judgment. Considering the inherent multipath effect and ephemeris drift error of the positioning module in a stationary state, the system abandons the absolute zero velocity judgment logic and introduces static drift tolerance as a comparison benchmark. The system traverses the velocity vector sequence within the time observation window, extracts the instantaneous scalar velocity point by point, and determines whether the global peak velocity in the sequence is continuously lower than the set static drift tolerance. If this condition is strictly met within the time observation window, the system determines that the first-level physical constraint is met. The specific value of the aforementioned static drift tolerance is determined based on the hardware cold start static error parameters of the positioning module. As a preferred method, this tolerance is usually set within the empirical range of 0.5 to 1.5 meters per second to avoid false triggering caused by signal drift and ensure the robustness of the state judgment.

[0072] S203, the system calculates the mechanical vibration state within the time observation window and performs a second vibration constraint judgment. For situations where the chassis may still experience localized excitation interference due to engine idling or cargo loading / unloading operations even when the vehicle is stationary without displacement, the system introduces a high-frequency energy assessment mechanism. After ensuring the total number of valid sampling points within the mandatory verification time observation window is non-zero to avoid the anomaly of the denominator approaching zero in division, the system extracts the vibration acceleration vector sequence, calculates the dynamic fluctuation characteristics of the triaxial composite acceleration, and generates the triaxial acceleration variance formula:

[0073] ;

[0074] in, The variance of the three-axis acceleration within the time observation window represents the total energy of the current mechanical background noise of the logistics vehicle chassis. The total number of valid sampling points of the high-frequency triaxial accelerometer within the observation window is used as the basic divisor for obtaining statistical characteristics of the discrete data sequence. For system-wide absolute timestamps High-frequency triaxial vibration acceleration vector obtained by sampling; For sampling point variables; For the first The second norm of the vibration acceleration vector at each sampling point represents the absolute excitation amplitude in three-dimensional space at that moment. It is the arithmetic mean of the magnitudes of all vibration acceleration vectors within the time observation window, representing the static DC component caused by gravitational acceleration and the constant installation tilt angle of the sensor.

[0075] The system compares the calculated triaxial acceleration variance with a preset chassis noise threshold in memory. When the triaxial acceleration variance is strictly less than the chassis noise threshold, the second physical constraint is deemed valid. The specific calibration process for this chassis noise threshold can be described by those skilled in the art. Under the absolutely static condition of the logistics vehicle being powered off and personnel off the vehicle, high-frequency vibration data can be collected over a long period using a microcontroller unit, and the upper bound of the variance envelope can be determined. The calibration and threshold writing operations are well-known techniques in the field and will not be elaborated upon here.

[0076] S204, the static benchmark in-situ calibration module uses a logic AND gate mechanism to comprehensively determine the aforementioned calculation results. When the first velocity constraint determination and the second vibration constraint determination are both met simultaneously, the system confirms that the logistics vehicle is currently in a purely static physical environment with no macroscopic displacement and no microscopic excitation. The edge computing node then flips the flag bits of the multi-level state machine inside the system, and the control data processing pipeline officially enters the static benchmark working condition. The accurate positioning and triggering of this working condition establishes a pure computing environment for subsequent in-situ extraction of thermodynamic features under conditions without mechanical fatigue interference.

[0077] The logic AND gate mechanism mentioned in this embodiment can be either a software Boolean logic multiplication instruction executed inside the microcontroller unit, or a hardware logic AND gate circuit physically integrated on the underlying printed circuit board. The system can flexibly choose the implementation mechanism of hardware triggering or software polling according to different requirements for interrupt response speed.

[0078] In S205, as a preferred approach, after the system formally enters the static benchmark operating condition via the logical determination of the aforementioned sub-step S204, the static benchmark in-situ calibration module immediately initiates the in-situ extraction procedure for thermodynamic characteristics. Based on the global absolute timestamp established during the concurrent acquisition phase of multi-source heterogeneous data, the edge computing node accurately extracts the internal temperature sequence strictly corresponding to the current clean computing environment from the cache queue in the static random access memory. The static benchmark in-situ calibration module extracts the internal temperature sequence within the time observation window and performs digital smoothing preprocessing to eliminate high-frequency thermal fluctuations. Considering the inevitable local airflow disturbances and background thermal noise of the internal temperature sensor in the actual physical environment of the logistics vehicle, the system filters the extracted internal discrete temperature sampling points to prevent local numerical jumps from interfering with subsequent slope calculations. The system calls the digital signal processing instruction set of the microcontroller unit to apply a moving average low-pass filter to the original internal temperature sequence to remove high-frequency glitches and obtain a smooth temperature sequence that can truly characterize the core heat distribution trend inside the packaging box. For setting the cutoff frequency and selecting the window function of the low-pass filter, those skilled in the art can perform conventional configuration based on the heat capacity of the air inside the packaging box and the thermal response time constant of the temperature sensor. The filtering algorithm implementation is a well-known technology in this field and will not be elaborated here. The preprocessed smooth temperature sequence provides high-confidence thermodynamic fundamental data support for multi-dimensional stress coupling calculations based on big data analysis.

[0079] S206, the system uses the acquired smoothed temperature sequence to calculate the derivative characteristics of the internal temperature with respect to absolute time, establishing a baseline heat dissipation model under no mechanical interference. Based on the premise of entering the static baseline working condition, the current logistics vehicle is in a physical state without macroscopic displacement and microscopic excitation. At this time, the heat overflow or accumulation inside the packaging box is determined only by the inherent thermal insulation properties of the material itself. To quantitatively evaluate the rate of heat change in this purely static physical environment, the static baseline in-situ calibration module sets an effective observation step size within the time observation window and extracts the smoothed temperature values ​​of the starting and ending points corresponding to this step size for differential calculation. To avoid the divisor zero anomaly caused by the overlap of adjacent timestamps or extremely short time intervals due to sampling jitter of the underlying sensors, the system's underlying logic forcibly verifies the time difference and, after confirming that the divisor is compliant, constructs a discretized calculation formula for the derivative of the internal temperature with respect to time:

[0080] ;

[0081] in, The value of the derivative of internal temperature with respect to time represents the rate of heat change in the internal space of the logistics packaging box under static reference conditions without mechanical stress damage. The smoothed internal temperature value at the end of the effective observation step within the time observation window; The smoothed internal temperature value at the start time of the effective observation step of the time observation window; To effectively observe the system's global absolute timestamp corresponding to the step size endpoint; To effectively observe the system's global absolute timestamp corresponding to the starting point of the observation step; To effectively observe the time span, the system forcibly limits the span value to be strictly greater than a preset minimum physical time threshold, thereby ensuring the completeness of the discrete calculus operation process and the stability of the mathematical boundary.

[0082] After executing the above calculation logic of the static benchmark in-situ calibration module, the system successfully reduced the dimensionality of the continuously distributed time and temperature multidimensional data into an independent physical parameter that can reflect the current health status of the vehicle's insulation box. This parameter completely eliminates the interference of non-natural heat loss caused by dynamic wind resistance and road vibration, and establishes an accurate reference coordinate system for the subsequent construction of a normalized model of internal and external temperature difference and for capturing the fatigue deterioration trend of materials.

[0083] S207. Based on the internal temperature derivative with respect to time obtained from the aforementioned sub-steps, the system needs to further isolate the impact of changes in the absolute temperature difference of the external climate environment on local data to extract the objective physical properties of the logistics packaging material itself. The static benchmark in-situ calibration module synchronously extracts the external environmental temperature sequence within the time observation window and constructs a basic thermodynamic normalized model based on Newton's law of cooling. Based on the inherent physical properties of the system, the heat dissipation rate inside the packaging box is directly proportional to the absolute temperature gradient between the inner and outer spaces of the box. The edge computing node extracts external environmental temperature data with a strict absolute time alignment relationship with the aforementioned effective observation step from the cache queue of the static random access memory and calculates the average temperature difference between the inner and outer sides within the time span. To prevent the temperature difference from approaching zero when the inside and outside of the logistics vehicle are in thermal equilibrium, thus triggering division calculation anomalies and underlying kernel crashes, the system's underlying logic sets a lower limit protection threshold for the temperature difference. When the measured absolute temperature difference is lower than the set lower limit protection threshold, the system will suspend the calculation process of the current calibration cycle until the environmental temperature difference data meets the compliance conditions again. After confirming that the temperature difference denominator data is compliant, the system projects the derivative of the internal temperature with respect to time onto the unit temperature difference benchmark and calculates the transient heat transfer characteristic parameters without filtering and smoothing.

[0084] S208 utilizes a recursive filtering model to perform state estimation and dynamic smoothing on transient heat transfer characteristic parameters, generating and updating thermodynamic normalized parameters. Considering the random abrupt changes in external air convection intensity and the high-frequency thermal noise embedded in the sensor's analog-to-digital conversion circuit, unprocessed transient heat transfer characteristic parameters exhibit high-frequency numerical oscillations. Direct adoption would lead to a drastic drift in subsequent anomaly warning judgment criteria. The system calls upon the microcontroller's computing resources to execute the Kalman filter algorithm. The system uses the transient heat transfer characteristic parameters as observation input variables and loads them into the preset state-space model. Based on the minimum mean square error criterion, the system performs cross-iteration of the prediction and update equations, dynamically removing high-frequency random disturbance signals unrelated to the actual thermal resistance of the packaging material, ultimately converging and outputting the normalized heat transfer coefficient formula:

[0085] ;

[0086] in, The normalized heat transfer coefficient is a physical quantity representing the inherent and highly stable background heat conduction of a logistics packaging box under static reference conditions without mechanical fatigue damage. This is the state estimation function for the Kalman filter, used to filter out high-frequency thermal noise components in the discrete observation input signal sequence through recursive operations; The value of the derivative of internal temperature with respect to time is the key parameter extracted in the aforementioned sub-step that reflects the rate of change of pure heat in the internal space of the logistics packaging box. To effectively observe the arithmetic mean of the external ambient temperature within the step length, and to characterize the thermodynamic environment parameters of the external macroscopic physical space where the logistics vehicle is currently located; To effectively observe the arithmetic mean of internal temperatures within a step size, a benchmark thermodynamic parameter characterizing the core storage space of a logistics packaging box is established. The average temperature difference between the internal and external environments serves as the physical boundary condition for heat transfer in the system driven by Newton's law of cooling, and also acts as the mathematical denominator in the normalization operation.

[0087] For the specific initialization configuration and parameter optimization process of the state transition matrix, observation matrix and process noise covariance matrix in the Kalman filter algorithm, those skilled in the art can make conventional settings based on the nominal signal-to-noise ratio of the selected temperature sensor and the floating-point computing resources of the system processor. The core state recursive equation is a well-known technology in this field and will not be elaborated here.

[0088] After executing the complete logic of the aforementioned static benchmark in-situ calibration module, the system successfully achieved the dimensionality reduction transformation from high-frequency dynamically sensed physical quantities to steady-state thermodynamic fingerprint features. The newly calculated normalized heat transfer coefficient is then overwritten into a specific address area of ​​the static random access memory of the edge computing node, replacing the initial heat transfer benchmark coefficient loaded in step S100. This parameter provides a highly adaptive and robust benchmark scale that excludes climate interference for the system's subsequent execution of spatial slice feature aggregation and multi-dimensional stress coupling calculations.

[0089] After completing the static in-situ calibration of step S200, the system obtains a pure thermodynamic reference frame free from external interference. However, when the logistics vehicle is in dynamic driving conditions, complex road mechanical excitation will cause internal stress fatigue in the packaging material, thus irreversibly damaging its thermal insulation performance. To accurately quantify this deterioration driving force caused by micro-vibrations, the time series data must be transformed to a specific spatial dimension for joint calculation. For step S300, the edge computing node initiates high-frequency feature extraction at the pure time axis layer by executing the spatiotemporal dual-track mapping module. The specific implementation of this stage includes the following sub-steps:

[0090] S301, the edge computing node executes a spatiotemporal dual-track mapping module, extracting a high-frequency triaxial vibration acceleration vector sequence with absolute timestamp continuity from the cache queue of static random access memory. Based on actual logistics and transportation scenarios, not all broadband vibrations in all frequency bands cause equivalent damage to packaging materials. High-frequency vibrations are usually absorbed and dissipated by the vehicle's mechanical suspension system, while mechanical excitations in specific low-to-mid-frequency bands can easily resonate with the packaging box and cushioning materials, inducing microscopic fractures and delamination of the insulation layer within the material. Therefore, the system needs to use a frequency domain transformation mechanism to convert the original broadband time-domain signal into an energy distribution spectrum that can intuitively reflect the fatigue damage intensity of the material, thereby removing invalid data from non-damaging frequency bands.

[0091] S302, the spatiotemporal dual-track mapping module sets a sliding time observation window under a constant high-frequency period to perform windowing truncation and Fast Fourier Transform on the truncated vibration acceleration time series. As a preferred method, to strictly meet the underlying hardware addressing and memory flow logic of the Fast Fourier Transform radix-2 algorithm, the system forcibly sets the total number of effective sampling points of the sliding time observation window to an integer power of 2 (e.g., 1024 or 2048 points, to adapt to the cache depth of the microcontroller's internal direct memory access controller). To suppress the spectral leakage effect caused by the non-periodic truncation in the time domain and avoid high-energy broadband noise from adjacent frequency bands leaking into the target frequency band and causing misjudgment in fatigue assessment, the system preprocesses the original triaxial acceleration time series with a Hanning window function by performing a time-domain dot product before performing the frequency domain transformation. The characteristic of the window function gradually decaying to zero at both ends smooths the signal edges. Subsequently, the system calls the hardware floating-point arithmetic instruction set of the microcontroller to independently execute the triaxial Fast Fourier Transform, mapping the time-domain discrete mechanical signal into a complex frequency domain sequence. For the specific butterfly operation flow graph and window function constant generation method of Fast Fourier Transform, those skilled in the art can make conventional configurations based on the digital signal processing standard library of the microcontroller unit. The basic operation implementation is a well-known technology in this field and will not be described in detail here.

[0092] S303, based on the acquired complex frequency domain sequence, the system extracts the spectral amplitude within the preset target frequency band, calculates and outputs the vibration energy of the damaging frequency band within a single time observation window. The system determines the upper and lower physical frequency boundaries of the damaging frequency band based on a priori empirical model of the packaging material's resonance frequency pre-loaded into memory, and maps these physical boundaries to a discrete frequency index interval based on the system sampling rate. In this embodiment, for common foamed polyurethane cold chain packaging boxes, the upper and lower physical frequency boundaries of this damaging frequency band are typically set to the low-to-mid frequency band of 5Hz to 50Hz, which is a typical excitation range that induces high-amplitude strain in the packaging structure. Within this discrete frequency interval, the system performs frequency domain energy integral synthesis calculations on the power spectral density in the three-dimensional orthogonal directions to establish the vibration energy formula for the damaging frequency band:

[0093] ;

[0094] in, The damage-causing frequency band vibration energy within a single time observation window, physically meaning the total mechanical excitation work applied by the logistics vehicle to the packaging box within that specific time period that is sufficient to induce structural resonance damage; The discrete frequency index value variable corresponding to the lossy frequency band; The discrete frequency index value corresponding to the lower limit frequency of the lossy frequency band serves as the starting mathematical boundary for the energy accumulation and summation algorithm. The discrete frequency index value corresponding to the upper limit frequency of the lossy frequency band serves as the termination mathematical boundary for the energy accumulation and summation algorithm. The acceleration in the X-axis direction within the time observation window, after being processed by the Fast Fourier Transform, is at the 1st... Complex amplitude values ​​at each frequency index; The acceleration in the Y-axis direction within the time observation window, after being processed by the Fast Fourier Transform, is at the 1st... Complex amplitude values ​​at each frequency index; The acceleration in the Z-axis direction within the time observation window, after being processed by the Fast Fourier Transform, is at the 1st... Complex amplitude values ​​at each frequency index; The square of the modulus of the complex amplitude with respect to each spatial axis is used to obtain the energy spectral density dimension which is proportional to the mechanical power distribution at that frequency. The frequency resolution of the Fast Fourier Transform is equal to the ratio of the underlying physical sampling rate to the total number of sampling points in the sliding time observation window. As the frequency domain step size parameter of the Discrete Riemann Integral, the underlying system forces this resolution to be greater than zero to avoid anomalies in subsequent division and multiplication operations.

[0095] After performing the aforementioned feature extraction operations at the timeline level, the system successfully reduced the dimensionality of massive high-frequency time-domain acceleration sequences, which are difficult to directly assess for physical destructive force, into highly condensed scalar features of damaging energy. The newly generated vibration energy parameters of the damaging frequency band are re-bound to the absolute timestamp of the center of their corresponding time observation window and stored in a buffer stack, providing standardized time-track input data for subsequent spatial domain alignment and aggregation in spatiotemporal dual-track mapping to prevent memory overflow.

[0096] In step S304, the spatiotemporal dual-track mapping module initiates the spatial axis mapping process based on the extracted velocity vector sequence, setting the physical boundary conditions for the displacement integral. The internal microscopic fractures of the packaging material and the peeling of the insulation layer essentially depend on the accumulated physical distance and impact work of the vehicle's actual travel on the rough road surface, rather than simply the running time. To eliminate temporal feature redundancy caused by vehicle stagnation, the system retrieves a velocity vector sequence strictly aligned with the current calculation time sequence from the aforementioned heterogeneous data time set. The edge computing node calls the default spatial slice length parameter pre-loaded into the static random access memory in step S100. This default spatial slice length directly reflects the spatial mapping relationship between the typical road surface low-frequency vibration wavelength and the vehicle suspension system's natural frequency, constituting the rigid physical boundary of this spatial resampling aggregation algorithm. As a preferred approach, this default spatial slice length is typically preset to 100 to 500 meters. The value is chosen to ensure that the slice interval contains a sufficient number of effective mechanical excitation cycles to truly reflect the gradual process of material fatigue degradation, while also considering the refined requirements of spatial early warning positioning.

[0097] S305 performs discrete Riemann integration on the captured velocity vector sequence to dynamically calculate the cumulative physical displacement of the logistics vehicle. The edge computing node calls the microcontroller's timer register to extract the absolute time difference between adjacent velocity sampling frames. The system's underlying logic enforces a check on the monotonically increasing property of this time difference. If a timestamp rollback or a zero difference is detected, the system determines that there is a timing error on the current communication bus and automatically discards the invalid sampling frame to ensure the stability and mathematical rigor of the discrete integration boundary. After confirming that the time difference is compliant and greater than a preset minimum value, the system sums the rectangular areas of the instantaneous scalar velocity point by point to establish the cumulative displacement integral formula:

[0098] ;

[0099] in, It represents the cumulative physical displacement of the logistics vehicle within the current monitoring period. Its physical meaning is the actual running distance of the vehicle under mechanical interaction and external excitation on the real road surface. For effective discrete velocity sampling point variables; The total number of valid discrete velocity sampling points participating in this spatial mapping cycle is used as the upper limit of the mathematical iteration for the integral summation operation; For system-wide absolute timestamps The underlying velocity vector obtained through concurrent sampling at any time; The absolute scalar velocity modulus at the corresponding time point represents the instantaneous speed of the logistics vehicle. This is the global absolute timestamp corresponding to the current valid sampling point; The global absolute timestamp corresponding to the preceding adjacent valid sampling point; The absolute time span between two adjacent frames of data serves as the bottom parameter of the infinitesimal element used to perform discrete Riemann integration. Subject to pre-verification logic constraints, this parameter is strictly ensured to be positive to prevent backtracking or numerical singularities in the displacement integral.

[0100] In S306, the system establishes a hardware triggering mechanism based on displacement boundary conditions to dynamically divide and truncate fixed-length spatial slices. The spatiotemporal dual-track mapping module performs a cyclic, non-blocking comparison between the real-time updated cumulative physical displacement and the default spatial slice length maintained in the system memory. When the cumulative physical displacement value reaches or exceeds the set default spatial slice length, the edge computing node immediately sends a truncation command to the internal multi-level state machine, forcibly terminating the current integration calculation window. The system encapsulates the start and end absolute timestamps of the segment that meets the physical displacement boundary conditions, the corresponding geospatial coordinates, and environmental state parameters to generate basic feature data of the spatial slice with independent temporal and location identifiers. Subsequently, the microcontroller clears the cumulative physical displacement calculation register and restarts the next round of Riemann integration and spatial feature mapping, starting from the current system time node. This underlying triggering mechanism completely blocks the influx of invalid data under low-speed or idling conditions, achieving adaptive throttling protection for the limited memory resources of the edge computing node.

[0101] S307, the spatiotemporal dual-track mapping module performs an arithmetic mean dimensionality reduction operation on the vibration energy of the damaging frequency band within the current slice range based on the extracted start and end timestamps of the spatial slice. Based on the physical causal mapping relationship, the degree of mechanical damage to the packaging material depends not only on the instantaneous impact peak value but also on the total effective vibration work accumulated within a unit mileage. The system retrieves the basic feature data of the spatial slice encapsulated in the aforementioned sub-step S306 and extracts its strictly corresponding absolute start and end times. Edge computing nodes use this time span as the retrieval boundary and recall all compliant time-observation window vibration energy parameters within this interval from the buffer stack. To avoid network congestion or sensor resets causing the number of valid samples in the interval to be zero, thus triggering memory anomalies in division operations, the system's underlying logic pre-verifies the total number of valid vibration energy samples. When the total number of valid samples is detected to be zero, the system triggers the default exception handling mechanism, directly writing the preset background energy safety value to the spatial slice; after confirming the denominator's compliance, it calculates and outputs the formula for the average vibration characteristic of a single spatial slice:

[0102] ;

[0103] in, It is the average vibration characteristic scalar within the current fixed-length spatial slice. Its physical meaning is the standardized mechanical fatigue driving force applied to the packaging box by the logistics vehicle during the period when it travels a specific fixed physical distance. The total number of valid time observation windows contained within the start and end time span of the current spatial slice is used as the basic mathematical denominator for the arithmetic mean of discrete data. The system-level error-proofing design ensures that its value is always greater than zero. The first one calculated in the aforementioned sub-step S303 Vibration energy in the damaging frequency band within a time observation window; It serves as an effective traversal index for discrete-time observation windows, and its iteration range is strictly limited by the physical start and end boundaries of the current spatial slice.

[0104] S308, after completing the spatial domain aggregation of mechanical vibration characteristics, the system synchronously extracts and records the thermodynamic state parameters at the boundary of the spatial slice, completing the feature space alignment of multi-source heterogeneous data. As a preferred approach, to provide an accurate physical boundary benchmark for subsequent steps to evaluate abnormal heat dissipation caused by mechanical damage, the system must capture the transient temperature profiles at the start and end points of the fixed physical displacement. The spatiotemporal dual-track mapping module uses the start and end absolute timestamps of the spatial slice to perform internal bus pointer addressing in the internal temperature sequence cache queue maintained by the static random access memory. Considering the possible millisecond-level asynchronous deviation between the physical sampling frequency of the temperature bus and the underlying slice truncation timer, the system adopts the nearest neighbor interpolation algorithm logic to automatically match and extract the compliant temperature sampling point with the smallest absolute error from the slice boundary timestamp as the boundary replacement value, thereby compensating for the time non-rigid alignment error during the concurrent sampling process of multiple sensors. For the specific address offset calculation and timing fault-tolerant compensation mechanism of the nearest neighbor interpolation algorithm, those skilled in the art can perform conventional code deployment based on the main clock frequency and bus timing of the microcontroller unit. Its time sequence synchronous interpolation principle is a well-known technology in the field and will not be elaborated here.

[0105] In step S309, the system extracts the starting and ending temperature values ​​of the acquired spatial slice, along with the previously calculated average vibration characteristic scalar, and packages them together, overwriting them into the designated reserved register address of the current spatial slice feature data frame. Through this data structure reorganization operation, the system completely breaks down the data silos caused by different physical sensing dimensions during the concurrent acquisition phase of the original heterogeneous data. At this point, each spatial slice independently and completely carries the highly concentrated mechanical damage energy within a unit physical distance, as well as the accurate thermodynamic boundary conditions at both ends of that physical displacement. The execution of this spatial-temporal dual-track heterogeneous data mapping and resampling algorithm fundamentally prevents the risk of invalid data memory overflow under complex road conditions such as low-speed congestion. This provides a high-confidence, purified, and time-series-aligned spatiotemporal feature base database for the subsequent multi-dimensional stress coupling degradation calculation model that includes spatial temperature gradients and vibration characteristic components.

[0106] In this embodiment, based on the high-confidence, purified, and time-series-aligned spatiotemporal feature base database constructed in step S300, the system needs to further establish a causal quantitative analysis model between mechanical vibration physical quantities and thermodynamic physical quantities using pure mathematical methods. Edge computing nodes, by executing a multi-dimensional parameter coupling calculation module, quantitatively evaluate the evolution of thermal resistance degradation in packaging materials due to mechanical fatigue, specifically including the following sub-steps:

[0107] In step S401, the multi-dimensional parameter coupling calculation module extracts the thermodynamic boundary conditions within the current spatial slice feature data frame and performs a discrete division operation on the pure physical spatial temperature gradient. Based on the thermodynamic diffusion distribution law, when abnormal structural damage or insulation layer peeling occurs inside the logistics packaging box, it will cause a significant abrupt change in the heat loss rate per unit physical operating space. Based on strict spatiotemporal causal mapping, the system retrieves the starting and ending temperature values ​​of the spatial slice overwritten in the aforementioned sub-step S309 and performs low-level timing verification to confirm that the sampling timestamps of these two sets of temperature data are strictly aligned with the absolute time boundaries of the start and end of the current spatial integral slice, thereby avoiding gradient distortion caused by misalignment of multi-source data. To ensure the dimensional consistency of the spatial gradient and the absolute stability of the mathematical boundary, the system's low-level logic forcibly verifies the default spatial slice length parameter allocated in the aforementioned system memory, ensuring that its value is strictly greater than zero, thereby avoiding processor core anomalies caused by the denominator approaching zero during the division operation from the source. After confirming the denominator compliance, the system constructs the pure physical spatial temperature gradient formula:

[0108] ;

[0109] in, The temperature gradient is a purely physical spatial gradient. Its physical meaning is the absolute temperature drift of the core storage environment inside the packaging box when the logistics vehicle travels a unit physical distance forward. It is used to intuitively characterize the intensity of heat dissipation along the spatial trajectory. The transient internal temperature value corresponding to the endpoint of the current fixed-length spatial slice extracted in the aforementioned steps; The transient internal temperature value corresponding to the starting point of the current fixed-length spatial slice extracted in the aforementioned steps; The default spatial slice length is preset in the system memory, representing the rigid physical distance scale of this spatial resampling aggregation algorithm, and serving as the basic mathematical denominator for discrete difference operations.

[0110] In step S402, after obtaining the pure physical space temperature gradient, the system introduces fundamental thermodynamic prior variables to establish a hard boundary filtering mechanism for natural heat exchange disturbances. As a preferred approach, considering that even if the packaging structure is intact during actual operation, the absolute temperature difference between the internal and external environments will still drive basic heat conduction according to Newton's law of cooling, resulting in normal ambient temperature drift. To accurately isolate this natural background noise and avoid relying on a single extreme temperature difference to trigger false alarms, the system must strictly decouple the heat dissipation caused by natural climate from the abnormal heat dissipation caused by mechanical damage using mathematical decoupling. The multidimensional parameter coupling calculation module extracts the average ambient temperature difference between the internal and external environments within the current slice period and, combined with the dynamically updated normalized heat transfer coefficient in the aforementioned sub-step S208, calculates the reasonable theoretical tolerance for natural temperature drift under the current operating conditions.

[0111] S403, the system integrates abnormal temperature drift with the mechanical damage energy under spatial axis mapping, performs heterogeneous data fusion calculations, and generates a nonlinear product coupling formula for the incremental single-time degradation risk index. Based on multi-dimensional stress coupling logic, the thermal fatigue degradation of packaging materials is not caused by a single physical factor, but rather by the cumulative work of underlying mechanical stress damaging the original thermal insulation microstructure, thus exacerbating a complex causal mapping process of macroscopic heat loss. To scientifically and quantitatively map this cross-physical domain failure process, the system uses a logarithmic function to pre-smooth the mechanical stress characteristics and a hard boundary filter function to threshold-truncate the thermodynamic variables, thereby constructing the calculation formula:

[0112] ;

[0113] in, The increment of the single degradation risk index represents the quantitative score of the packaging material's additional irreversible thermal resistance damage due to mechanical vibration within the current spatial slice's travel displacement. This multidimensional weighted output effectively avoids the one-sided judgment that relies solely on the extreme values ​​of temperature or vibration. It is a natural logarithmic function used to perform nonlinear compression and smoothing of the extreme values ​​of high-frequency mechanical impact at the mathematical level. Its technical purpose is to prevent sporadic, single, severe shocks from being linearly amplified, and to ensure that the cumulative fatigue assessment process conforms to the real physical law of gradual changes in material stress. This is the weighting scaling factor for mechanical excitation energy. Its value is empirically calibrated based on the elastic modulus and damping characteristics of the selected packaging foam material. It is usually between 0.01 and 0.1, aiming to map the seismic performance of different materials to a unified mathematical scale. The average vibration characteristic scalar within the current fixed-length spatial slice calculated and obtained in the aforementioned sub-step S307; For hard-boundary filtering functions, their mathematical logic is that when the input variable... If the value is greater than zero, the function outputs itself; otherwise, it forces the output to be zero. This function, combined with the normalized heat transfer coefficient, is used to dynamically subtract the temperature drift caused by natural heat exchange, ensuring that the subsequent abnormal degradation calculation loop is activated only when the actual temperature gradient exceeds the theoretical natural heat dissipation tolerance. It represents the absolute value of the pure physical space temperature gradient within the current space slice, reflecting the intensity of the actual total heat fluctuation. The heat transfer compensation gain coefficient is used to fine-tune the basic heat transfer fluctuations caused by unmodeled external factors such as environmental wind resistance and solar radiation. Its value is usually dynamically adaptive between 1.0 and 1.2. The specific value is positively correlated with the estimated air velocity of the external environment to compensate for the additional heat loss under severe convective weather. The normalized heat transfer coefficient output by Kalman filtering in the aforementioned sub-step S208 serves as a solid benchmark for evaluating the inherent thermal insulation background physical quantity. To effectively observe the arithmetic mean of the external ambient temperature within the step size; To effectively observe the arithmetic mean of internal temperatures within a step size; It is the absolute value of the average temperature difference between the internal and external environments corresponding to the current space slice, and serves as the boundary multiplier characterizing the thermodynamic driving potential energy of the external climate environment.

[0114] After executing the aforementioned frequency domain and spatial gradient coupled degradation risk calculation model, the system successfully integrated discrete kinematic road condition characteristics and thermodynamic gradient parameters into a dimensionless risk increment pointing to a single target (i.e., material thermal fatigue failure) through purely mathematical mapping. This increment index effectively suppresses the unilateral interference of transient extreme impacts on the road surface and sudden drops in natural temperature, providing a high-confidence single criterion source for subsequent multidimensional cumulative summation and anomaly early warning judgment at the global system level.

[0115] In this embodiment, after the multi-dimensional parameter coupling calculation in step S400, the system has discretized the incremental risk of thermal resistance degradation of the packaging material of the logistics vehicle within a single spatial slice. However, material fatigue failure in the physical world often exhibits nonlinear acceleration characteristics, meaning that once a microcrack initiates, its propagation rate under the same mechanical excitation will increase exponentially. Based on the technical objective of capturing the nonlinear failure trend of materials, the system cannot rely solely on the current static cumulative degradation value but must further explore the dynamic trend of degradation evolution. For step S500, the edge computing node constructs a cross-slice spatial differentiation and trend prediction mechanism by executing the underlying hardware adaptive modulation module. The specific implementation of this stage includes the following sub-steps:

[0116] The S501, with its underlying hardware adaptive modulation module, retrieves feature data frames of historical spatial slices from the static random access memory of the edge computing nodes, performing temporal rigid alignment and validity verification of adjacent slice features. The system requires evaluation data in at least two consecutive physical spatial dimensions. The microcontroller reads the index of the latest spatial slice that has been written and uses a decrementing offset pointer to backtrack through the historical buffer stack, extracting the feature payload of the previous adjacent spatial slice. To avoid slice sequence breaks caused by system cold starts, temporary communication bus resets, or cross-regional routing reconnections, the system's underlying logic rigorously verifies the continuity of the current slice and the previous adjacent slice in absolute timestamps and compares their spatial coordinate offsets to match the preset default spatial slice length. When insufficient historical buffer stack depth or failure to verify the physical continuity of adjacent slices is detected, the system triggers a data smoothing compensation mechanism, temporarily suspending gradient calculations for the current cycle until consecutive slice pairs satisfying boundary conditions are collected, thus ensuring the mathematical causal rigor of subsequent calculus operations.

[0117] S502, after confirming the compliance of adjacent spatial slice data, the system extracts the degradation risk index increment corresponding to the current slice and the previous adjacent slice, performs discrete difference differentiation based on physical spatial displacement, and establishes a degradation acceleration gradient formula. As a preferred method, in order to map the purely abstract risk index to an evolution slope with definite physical dimensions, the system treats the two selected continuous spatial slices as discrete differential elements. The system subtracts the degradation increment of the current spatial slice from the degradation increment of the previous spatial slice and performs a division operation using a fixed physical displacement slice length as the normalization scale. To prevent the division denominator from approaching zero and causing memory overflow in the arithmetic logic unit of the microcontroller, the system places a hardware-level check lock before the division instruction to force verification that the physical displacement scale is always a preset positive integer; if the check finds an abnormal denominator or missing data, the system will directly output a zero-value gradient and suspend the current calculation cycle to avoid the underlying kernel crash. After the microcontroller performs the arithmetic operation, it outputs the single degradation acceleration gradient formula:

[0118] ;

[0119] in, The deterioration acceleration gradient is physically defined as the instantaneous spatial derivative of the rate of thermal resistance failure of the packaging material when the logistics vehicle passes through the current spatial displacement node, which intuitively represents the deterioration slope of fatigue degradation. The current number extracted in the aforementioned steps The increment of the single degradation risk index corresponding to each spatial slice reflects the mechanical and thermodynamic coupling damage within the latest physical mileage. The previous adjacent number extracted in the aforementioned steps The increment of the single degradation risk index corresponding to each spatial slice is used as the historical comparison benchmark for differential operations. The default spatial slice length is preset in the system memory, representing the exact physical distance scalar between two adjacent discrete evaluation nodes, and serves as the differential base and basic mathematical denominator of the spatial differentiation algorithm.

[0120] Based on the aforementioned calculation logic of the degradation acceleration gradient, the system successfully achieved a dimensionality reduction mapping from static risk assessment to dynamic trend prediction. This degradation acceleration gradient not only filters out the constant high-level risk noise generated by logistics vehicles under uniform road conditions, but also accurately separates the intrinsic degradation acceleration impulse caused by the depletion of the material's fatigue resistance threshold. This trend characteristic variable, which includes both direction and amplitude attributes, provides a forward-looking mathematical benchmark for the system to dynamically allocate the sensor sampling frequency of the underlying concurrent acquisition module and achieve adaptive control that trades edge computing resources for early warning time margins.

[0121] S503, the underlying hardware adaptive modulation module retrieves a preset degradation trigger threshold from the read-only memory and performs real-time comparison and judgment of the gradient amplitude. This threshold is determined based on the stress and strain critical derivative characteristics of the packaging material when the microstructure yields. The system imports the aforementioned calculated degradation acceleration gradient and this threshold into the numerical comparator register of the microcontroller unit.

[0122] As a preferred approach, to prevent the gradient spikes caused by a single, occasional road surface pothole from falsely triggering the system alarm, and to avoid the one-sided judgment based solely on a single extreme value, the comparator logic integrates a hysteresis comparison algorithm. This algorithm requires that the acceleration gradient must remain above the threshold for two consecutive calculation cycles before it can be confirmed as a valid over-limit. This weighted state confirmation mechanism effectively filters out non-continuous high-frequency oscillation interference at the hardware level.

[0123] S504: When the degradation acceleration gradient is detected to have severely exceeded the preset degradation trigger threshold, the underlying hardware adaptive modulation module immediately generates a high-priority hardware interrupt signal, forcibly suspending the current main program loop. The system uses a direct jump from the interrupt vector table to ensure a response to the emergency within microseconds. Once the comparator's logic output flips to a high level, the system interrupt controller immediately captures the rising edge signal, locks the current program counter stack, and unconditionally transfers system control to the preset crisis handling interrupt service routine. The system defines the Boolean logic formula for triggering this hardware interrupt as follows:

[0124] ;

[0125] in, This is the hardware interrupt trigger flag. A value of 1 indicates that the system hardware interrupt controller is activated and generates a non-maskable interrupt request in the underlying logic circuit. A value of 0 indicates that the system maintains the current monitoring state. The degradation acceleration gradient calculated in the aforementioned sub-step S502 is the acceleration vector that characterizes the deterioration of the thermal resistance performance of the packaging material within the current space slice. The degradation trigger threshold is a critical constant preset in the system firmware based on the material properties of the packaging box. It characterizes the inflection point slope of the material from the linear fatigue accumulation stage to the nonlinear accelerated failure stage. For typical polyurethane foam materials, this threshold is usually calibrated in the empirical range of 0.05 to 0.15. This is the safety redundancy factor for the hysteresis comparator, typically ranging from 0.1 to 0.2. It is used to construct an asymmetric trigger and reset window to prevent frequent oscillations of the interrupt signal caused by small fluctuations in the gradient value near the threshold critical point.

[0126] After executing the above hardware comparison and interrupt logic, the system successfully established a direct channel between physical layer data and underlying control logic. Once... The setting of the bit indicates that the packaging box is in a rapid deterioration process. The system no longer passively waits for the next regular sampling cycle, but instead intervenes directly through a hardware interrupt, providing fundamental control for the subsequent real-time adjustment of the sensor array's operating mode. For the specific interrupt priority configuration and stack protection logic, those skilled in the art can configure the conventional registers according to the microprocessor's technical manual; the underlying implementation is well-known in the field and will not be elaborated upon here.

[0127] The S505, with its underlying hardware adaptive modulation module, executes dynamic scaling instructions for spatial slice step parameters to significantly improve spatial observation resolution under deteriorating conditions. Based on the underlying logic of spatiotemporal dual-track mapping, the physical length of the spatial slice directly determines the observation granularity of degradation feature aggregation calculation. When material damage exhibits an accelerating trend, maintaining the existing long slice span can lead to the local fatigue-induced damage energy being diluted and masked by global spatial averaging calculations, thus causing system warning delays. The system shortens the hardware truncation trigger distance for the next Riemann integral by directly addressing and overwriting the spatial slice boundary parameters maintained in the static random access memory. As a preferred approach, the system introduces a negative exponential smoothing mapping mechanism to dynamically calculate the new physical boundary based on the current degradation acceleration gradient and output the spatial observation resolution modulation formula:

[0128] ;

[0129] in, The length of the newly generated spatial slice after hardware interrupt modulation represents the shorter physical distance threshold set by the system to improve the finer granularity of observation after capturing the accelerated trend of material degradation. This is the minimum physical space slice limit allowed by the system hardware architecture. Limited by the lower limit of the spatial calculation accuracy of the underlying global positioning system, this value is forcibly set to prevent memory block overflow and crash due to the slice length approaching zero. The default space slice length preset in the aforementioned system memory serves as the absolute reference for this dimensionality reduction and scaling. It is an exponential function with the natural constant as its base, used to mathematically smoothly map a linearly increasing degradation acceleration gradient into a non-linear decay scaling factor. The spatial resolution approximation coefficient is used to finely adjust the dynamic sensitivity of the slice length reduction with the degradation gradient. It is usually taken between 0.5 and 2.0 and is pre-calibrated based on the dynamic response sensitivity of the vehicle suspension system. The degradation acceleration gradient calculated in the aforementioned sub-step S502 is used to intuitively represent the instantaneous physical slope of the deterioration of the internal thermal resistance performance of the current logistics packaging box.

[0130] S506: The system directly modifies the peripheral clock division factor and the sensor's internal control register via the internal communication bus to synchronously increase the physical concurrent sampling frequency. After performing the aforementioned space slice shortening operation, the number of time observation windows per unit physical travel distance will be sharply reduced. To prevent the effective vibration and temperature sampling points within a single newly generated space slice from failing to satisfy the Nyquist sampling theorem, the system must synchronously increase the generation density of the underlying data. The underlying hardware adaptive modulation module calls the microcontroller's direct memory access controller to write a new clock division word into the control register of the serial peripheral interface or internal integrated circuit bus to forcibly increase the bus baud rate. The system further sends hexadecimal instructions to the configuration registers of the high-frequency triaxial accelerometer and the internal temperature sensor to switch the operating mode of their internal analog-to-digital converter from low-power, low-frequency standby to high-performance, high-frequency continuous output mode. The specific hardware high-frequency modulation formula is as follows:

[0131] ;

[0132] in, The new physical concurrent sampling frequency after the underlying registers are overwritten is the physical meaning of the total number of discrete data frames that the multi-source heterogeneous sensor array concurrently acquires and outputs to the system bus per unit absolute time. The maximum physical sampling frequency that can be jointly supported by the microcontroller's main frequency and the analog-to-digital conversion architecture of the peripheral sensor hardware serves as the hard upper limit boundary of the system's anti-crash protection. This refers to the default physical sampling frequency configured for the system under the aforementioned static baseline conditions. The high-frequency excitation capture gain coefficient characterizes the aggressiveness with which the system allocates computing resources to the sensing layer in response to high-frequency mechanical abrupt signals. It is usually set between 1.0 and 3.0, depending on the bus bandwidth margin of the microcontroller unit. This is the degradation acceleration gradient calculated above; The aforementioned degradation trigger threshold is preset in the system read-only memory firmware; This is a dimensionless degradation exceedance ratio parameter, used to accurately quantify the relative severity of the current degradation slope exceeding the safety threshold on a mathematical scale. It is limited by the division pre-check in the system's underlying logic, and the denominator... A constant that is strictly guaranteed to be greater than zero.

[0133] For the specific derivation logic of the underlying bus frequency division coefficient, the timing setup and hold time settings, and the specific address pointer overwrite operation of the multi-axis sensor configuration register, those skilled in the art can perform conventional driver firmware development based on the microprocessor's register mapping table and the official datasheet of the peripheral chip. The underlying electrical timing control and bus communication protocol implementation are well-known technologies in this field and will not be elaborated here.

[0134] After executing the aforementioned low-level control commands, the edge computing node successfully established a closed-loop reverse feedback channel from the data analysis layer to the physical sensing layer. When the system mathematically verified signs of irreversible accelerated damage to the packaging material, it autonomously blocked the conventional polling monitoring mode and instead used hardware interrupts to force a focus on the entire failure evolution process with extremely high spatiotemporal resolution. This deep software and hardware collaborative adaptive modulation logic adheres to the technical approach of increasing edge computing resources to gain sufficient early warning time margin, providing a solid physical foundation for comprehensively supporting the dynamic linkage characteristics of the underlying hardware.

[0135] In this embodiment, after establishing the underlying hardware adaptive modulation logic and dynamically capturing high-frequency anomaly features in step S500, the system needs to perform a global state determination on the accumulated damage within a macroscopic physical path. For step S600, the edge computing node executes a multi-level joint early warning module to complete the continuous integral calculation of spatial domain data and solidify the evidence chain. The specific implementation of this stage includes the following sub-steps:

[0136] The S601 multi-level joint early warning module establishes a fixed-depth circular buffer in the static random access memory to construct a first-in-first-out queue for spatial feature data. Based on the technical objective of dynamically evaluating the progressive fatigue of material structures, the system cannot rely solely on a single high-frequency impact to make a final assertion; instead, it must establish a continuous macroscopic observation field. Based on time-series timestamp alignment logic, whenever the underlying layer completes the encapsulation and overwriting of a new spatial slice feature data frame, the system triggers a pointer forward command. The newly arrived data frame is pushed to the tail of the circular buffer queue, while the system forcibly pops and destroys the oldest data frame at the head of the queue, thus maintaining a sliding observation window with a fixed physical distance.

[0137] As a preferred approach, the depth of this sliding observation window is directly dependent on the typical total delivery mileage of the logistics vehicle and the default spatial slice length, typically set to accommodate 50 to 200 slice units. This configuration aims to ensure that the early warning calculations can cover a sufficiently long period of mechanical fatigue accumulation, obtaining a smooth and high-confidence state assessment spatial basis while keeping the microcontroller's memory consumption under control.

[0138] In S602, the system performs integral accumulation of multidimensional fatigue features within the sliding observation window. To avoid a single local extremum dominating the global judgment, the multi-level joint early warning module extracts the degradation features of all valid spatial slices within the current sliding window and performs discrete summation based on physical space. Before the calculation, the system's underlying logic forcibly verifies whether the total number of slices currently actually stored in the queue reaches the preset minimum effective calculation base, in order to avoid the risk of missed reports due to insufficient accumulated samples caused by system cold start or data gaps. After confirming data completeness and compliance, the system performs weighted correction on the time series of multi-source heterogeneous data and establishes a sliding window cumulative degradation evaluation formula:

[0139] ;

[0140] in, To accumulate the total risk of deterioration, the physical meaning is the sum of irreversible thermal resistance damage suffered by the packaging material during the macroscopic physical distance corresponding to the entire sliding window of the logistics vehicle, which serves as the absolute criterion for ultimately triggering the system's early warning. The total number of valid spatial slices currently contained in the sliding observation window serves as the upper bound of the mathematical iteration of the discrete summation algorithm; For the extracted first The increment of the single degradation risk index corresponding to each spatial slice; The spatial slice traversal index within the current sliding window is used, and incremental iteration is performed based on the absolute timestamp order. This is a spatial attenuation weighting coefficient, introduced because in physical causality, the more recent the mechanical excitation, the greater its contribution to the current crack propagation. The coefficient value is dynamically assigned based on the spatial distance of the slice from the current moment, typically exhibiting a linear or exponentially decreasing distribution between 0.5 and 1.0, to give a higher damaging weight to recent mechanical impacts and avoid excessive dilution of current state judgment by long-term historical data.

[0141] The S603 multi-level joint early warning module compares the calculated cumulative degradation risk value with the preset early warning threshold in memory. Based on multi-dimensional state confirmation logic, simple numerical exceedances are easily affected by sensor temperature drift or occasional high-intensity electromagnetic interference. The system sets composite trigger conditions, requiring the cumulative degradation risk value to cross the early warning threshold for three consecutive calculation cycles, and the absolute temperature difference between the internal and external environments at the current moment to be continuously greater than the lower limit protection threshold set by the system. Only when the above multi-dimensional spatial and thermodynamic conditions are simultaneously met does the system formally determine that the packaging material has suffered unacceptable substantial thermal resistance failure. The multi-level joint early warning module then pulls down the level of the early warning pin and sends an alarm interrupt to the next-level central processing unit. The specific value of this early warning threshold needs to be pre-calibrated according to the compression and rebound test standards of different packaging materials, which falls within the scope of conventional hardware matching before implementation in this field.

[0142] S604: Once the warning conditions are met and triggered, the system immediately initiates the process of solidifying and uploading the traceability evidence chain. To provide remote cloud control platforms or maintenance engineers with a complete root cause analysis of failures, simply outputting Boolean logic alarm signals is insufficient to support the subsequent quality traceability system. The multi-level joint warning module initiates precise backtracking addressing to the historical buffer stack based on the spatial slice timestamp that triggered the warning. The system automatically captures and extracts the time-domain characteristic energy spectrum within the pre-defined physical span preceding the warning time, the thermodynamic boundary data frames with strict spatiotemporal alignment, and the corresponding underlying spatial coordinate sequence. The system uses a lossless compression algorithm to package the above-mentioned multi-source heterogeneous features into a data structure, generating a traceability evidence package containing the device's unified media access control address identifier and the absolute timestamp of the warning. Subsequently, the edge computing node wakes up the wireless communication RF front-end in a dormant polling state, pushes the traceability evidence package into the direct memory access transmission queue, and sends it to the cloud server via mobile cellular network or low-power wide area network. For the establishment of physical layer connections in wireless communication links, the handshake and retransmission mechanism of transmission control protocols, and the cyclic redundancy check encapsulation of data packets, those skilled in the art can call the standard IoT communication protocol stack firmware matched by the microcontroller unit. Its network routing and data transmission processes are well-known technologies in the field and will not be described in detail here.

[0143] In this embodiment, in order to further disclose and verify the overall engineering feasibility of the present invention, the system selected cross-provincial cold chain transportation of biopharmaceuticals as a specific verification scenario. The aforementioned edge computing node for performing hardware modulation was deployed on the outside of the polyurethane foam cold chain packaging box carrying the highly sensitive vaccine, in order to provide a purified and dimensionally reduced spatiotemporal slice evidence package for the big data center.

[0144] In actual long-distance delivery missions, logistics vehicles face highly complex dynamic operating conditions. When a logistics vehicle encounters low-speed congestion lasting for several hours, if a traditional pure time-domain equal-frequency polling architecture is used, the sensor array will continuously feed massive amounts of invalid idling vibration and gradual temperature drift data into the big data analysis cloud platform. For this situation, edge computing nodes execute the aforementioned method steps.

[0145] At the edge, a spatiotemporal dual-track mapping module is first used to take over the underlying physical boundary. By calculating the discrete Riemann integral, the cumulative displacement is dynamically determined, preventing invalid data truncation during idling. Based on this edge-side preprocessing mechanism, the system achieves strict "overflow prevention" throttling protection at the physical level, avoiding congestion in the cloud's big data receiving queue.

[0146] When logistics vehicles enter sections of road subjected to high-intensity, continuous mechanical vibration (such as unpaved, potholed surfaces), the risk of tearing of the polyurethane insulation layer at the microscopic level surges. The system's underlying layer captures the energy in this damaging frequency band, and a multi-dimensional parameter-coupled calculation module outputs the incremental degradation risk index for each instance. As a preferred approach, once the underlying hardware adaptive modulation module detects that the degradation acceleration gradient continuously exceeds its limit, the system immediately triggers a non-maskable hardware interrupt, dynamically reducing the spatial observation resolution from 100 meters to 25 meters and increasing the physical concurrent sampling frequency from 1 Hz to 10 Hz.

[0147] like Figure 3 As shown, in the absolute driving trajectory presented in the three-dimensional coordinate system, when in a smooth or congested road section, the system maintains the default long-span spatial slice, that is, the normal spatial slice under smooth operating conditions. Figure 3 The relatively sparse circular markers effectively suppress the generation of redundant data.

[0148] When entering a high-frequency vibration section where deterioration accelerates, the slicing step parameters are forcibly reduced by hardware-level commands, and the slice length is reduced to 25 meters. Figure 3 The dense triangular markers (in the middle) visually and completely demonstrate the spatial reconstruction process where the underlying observation resolution adaptively improves as road conditions deteriorate. Through this spatiotemporal reconstruction, edge nodes generate high-resolution traceability evidence packages and push them to the big data center before macroscopic thermal runaway occurs in the material. After receiving the feature package, the cloud analysis cluster can invoke the pre-set global degradation risk knowledge graph to execute multi-node joint early warning issuance.

[0149] To quantitatively evaluate the effectiveness of the edge-cloud collaborative early warning architecture proposed in this invention, a big data replay simulation test was conducted based on a real road survey dataset provided by a logistics company (containing vibration and temperature time series of 50 test vehicles and 500 kilometers of mixed road conditions per vehicle). Two control groups were set up in the experiment:

[0150] Traditional Big Data Analysis Group (Group A): Employs a conventional time-domain fixed-frequency monitoring and full-data cloud aggregation solution. The sampling frequency at the edge is fixed at 1Hz, and all raw data is directly transmitted back to the cloud via the cellular network. The cloud uses a conventional 5-minute sliding time window for energy arithmetic averaging and over-temperature determination.

[0151] The joint early warning group of this invention (Group B) deploys the aforementioned complete steps, enables hardware interrupt and spatiotemporal dual-track adaptive modulation logic on the terminal side, and only receives spatiotemporal slice traceability evidence packets after discrete differentiation and hardware-level filtering on the cloud side, and performs sliding window accumulation judgment.

[0152] The same road condition playback dataset was input into two test environments, and the statistical results of the system's core performance indicators are shown in Table 1.

[0153] Table 1. Comparison of Core Performance of Big Data Early Warning Systems

[0154] Performance evaluation metrics Traditional Big Data Analytics Group (Group A) This invention relates to a joint early warning group (Group B). End-side memory overflow exception reset rate 14.2% (mostly occurring in sections of road with prolonged congestion) 0% Cloud daily invalid data intake Approximately 45.8GB (including significant idle redundancy) Approximately 1.2GB (precise feature slices) High-frequency microcrack underreporting rate 68.5% 1.2% Average warning lead time for thermal resistance failure 0 minutes (overheating alarm will sound afterward) 47.5 minutes (Trend Forecast and Early Warning)

[0155] Combined with Table 1 Figure 4 The curve evolution trend shows that as the accumulated mileage increases, the traditional group A (traditional time domain), lacking filtering based on physical causal dimensions, exhibits a steep pseudo-linear surge in cloud data reception due to its full-volume blind backhaul mode (e.g., Figure 4 As shown by the solid line with a square marker), massive amounts of disordered time-series data severely strain cloud-based concurrent computing resources; conversely, the precise feature slicing back transmission mode adopted by Group B (dual-track domain) of this invention (such as...) Figure 4 As shown by the dashed line with a triangle marker, the cumulative data volume curve remained at an extremely low level in the 0-300 km range, with only a slight increase in feature packet data appearing after the 300 km mark on the difficult road sections. Furthermore, the overall data reception capacity was far lower than that of traditional methods. This data comparison demonstrates the substantial technical contribution of edge-side spatiotemporal aggregation logic in freeing up cloud computing bandwidth and reducing data intake by approximately 97%.

[0156] Furthermore, combined Figure 5 The measured curve of nonlinear degradation risk evolution in neutron graph A shows that when the total accumulated degradation risk exceeds the system's preset degradation trigger threshold, the system of this invention accurately anchors the nonlinear inflection point of fatigue evolution based on degradation acceleration gradient calculation (e.g., ...). Figure 5(As indicated by the pentagram early warning point in sub-diagram A). Compared to traditional reactive alarms for cold air loss, this mechanism waits until the cold air is completely lost and the absolute temperature exceeds the limit before issuing a delayed alarm (such as...). Figure 5 (The post-overheating scrap point marked with a cross at the end of sub-figure A) This invention outputs a high-confidence alarm in the early stage of insulation layer yielding, successfully gaining a warning time margin of up to 47.5 minutes.

[0157] At the same time, such as Figure 5 As shown in the step response curve of neutron diagram B, at the same absolute time coordinate of the trigger inflection point, the physical concurrent sampling frequency of the underlying communication bus jumps instantaneously from a low-power polling state of 1Hz, exhibiting a standard square wave step, and then stably locks to a high-frequency concurrent tracking state of 10Hz (e.g., Figure 5 (As indicated by the circular transition edge marker in sub-figure B). This physical-level square wave abrupt change rigorously confirms that the underlying adaptive modulation logic of hardware and software collaboration has been executed precisely. The above multi-dimensional experimental cross-validation fully demonstrates the engineering practicality and technological advancement of coupling the dynamic linkage of underlying hardware with big data feature extraction.

Claims

1. A logistics anomaly early warning method based on big data analysis, characterized in that, Includes the following steps: Receives the underlying heterogeneous data streams from the positioning module, the high-frequency triaxial accelerometer, the internal temperature sensor, and the external ambient temperature sensor; Based on the vehicle kinematic constraints output by the positioning module, the state of the underlying heterogeneous data stream is determined. When the static reference conditions are met, the temperature difference between the internal temperature sensor and the external ambient temperature sensor in the underlying heterogeneous data stream is extracted, and the thermodynamic normalized parameters are calculated and updated. The temporal domain features of vibration data in the underlying heterogeneous data stream are extracted and combined with the motion displacement calculated by the positioning module for spatial domain alignment and aggregation to generate spatial slice feature data. Extract the spatial temperature gradient and vibration feature components from the spatial slice feature data, and combine them with the thermodynamic normalization parameters to calculate the frequency domain and spatial gradient coupling degradation risk index within the current spatial slice; Calculate the degradation acceleration gradient between adjacent spatial slices. When the degradation acceleration gradient reaches the trigger set value, send an interrupt command to the hardware interrupt controller, adjust the sampling frequency of the communication bus, and reset the step length parameter. The frequency domain and spatial gradient coupling degradation risk index is accumulated and integrated based on a sliding window. When the accumulated integral value reaches the warning threshold, an abnormal traceability data packet is generated and an output operation is performed.

2. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The process of determining the state of the underlying heterogeneous data stream based on the vehicle kinematic constraints output by the positioning module specifically includes: Calculate the kinematic velocity state within the time observation window, perform the first velocity constraint judgment, and determine whether the global peak velocity in the velocity vector sequence within the time observation window is continuously lower than the set static drift tolerance; The mechanical vibration state within the time observation window is calculated, the second vibration constraint judgment is performed, the dynamic fluctuation characteristics of the triaxial composite acceleration are calculated to generate the triaxial acceleration variance, and the triaxial acceleration variance is compared with the preset chassis background noise threshold. The calculation results of the first-level velocity constraint determination and the second-level vibration constraint determination are comprehensively determined by using the logical AND gate mechanism. When the first-level velocity constraint determination and the second-level vibration constraint determination are both true, it is confirmed that the logistics vehicle is currently in a pure static physical environment with no macroscopic displacement and no microscopic excitation, and the control data processing pipeline enters the static reference working condition.

3. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The calculation and updating of the system's thermodynamic normalized parameters specifically includes: By constructing a discretized calculation formula for the derivative of internal temperature with respect to time, the derivative value of internal temperature with respect to time is calculated, and a benchmark heat dissipation model without mechanical disturbance is established. Simultaneously extract the external ambient temperature sequence within the time observation window, construct a basic thermodynamic normalization model in conjunction with Newton's law of cooling, extract the external ambient temperature data that has a strict absolute time alignment relationship with the effective observation step size, calculate the average temperature difference between the inner and outer sides within the time span, project the derivative of the internal temperature with respect to time onto the unit temperature difference reference, and calculate the transient heat transfer characteristic parameters without filtering and smoothing. The recursive filtering model is used to perform state estimation and dynamic smoothing of transient heat transfer characteristic parameters. The computational resources of the microcontroller unit are called to execute the Kalman filter algorithm to dynamically remove high-frequency random disturbance signals that are unrelated to the actual thermal resistance of the packaging material, output the normalized heat transfer coefficient, and generate and update the thermodynamic normalized parameters.

4. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The extraction of time-domain features from the vibration data output by the high-frequency triaxial accelerometer in the underlying heterogeneous data stream specifically includes: Under a constant high-frequency period, a sliding time observation window is set, and windowing and fast Fourier transform are performed on the intercepted vibration acceleration time series. The original triaxial acceleration time series is preprocessed by time-domain dot product with the Hanning window function, and the triaxial fast Fourier transform is performed to map the time-domain mechanical discrete signal into a complex frequency domain sequence. Based on the complex frequency domain sequence, the spectral amplitude within the preset target frequency band is extracted, the upper and lower physical frequency boundaries of the damaged frequency band are determined, and the upper and lower physical frequency boundaries of the damaged frequency band are mapped to discrete frequency index intervals based on the system sampling rate. Frequency domain energy integration and synthesis operations are performed on the power spectral density in the three-dimensional orthogonal directions within the discrete frequency index intervals. By establishing the vibration energy formula of the damaged frequency band, the vibration energy of the damaged frequency band within a single time observation window is calculated.

5. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The step of combining the motion displacement calculated by the positioning module with spatial domain alignment and aggregation to generate spatial slice feature data specifically includes: The discrete Riemann integral operation is performed on the intercepted velocity vector sequence, and the cumulative physical displacement of the logistics vehicle is calculated by establishing a cumulative displacement integral formula; Establish a hardware triggering mechanism based on displacement boundary conditions, and perform a non-blocking comparison between the real-time updated cumulative physical displacement and the default space slice length maintained in the system memory. When the cumulative physical displacement value reaches the default space slice length, send a truncation command to forcibly terminate the current integral calculation window. Based on the extracted start and end timestamps of the spatial slice, an arithmetic mean dimensionality reduction operation is performed on the vibration energy of the loss-causing frequency band within the current slice range, and the average vibration characteristic scalar within the current fixed-length spatial slice is calculated using the single spatial slice average vibration characteristic formula. The thermodynamic state parameters at the boundary of the space slice are extracted and recorded simultaneously. The starting temperature value and ending temperature value of the space slice are extracted and packaged together with the average vibration feature scalar and overwritten into the current space slice feature data frame to generate space slice feature data.

6. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The step of extracting the spatial temperature gradient and vibrational feature components from the spatial slice feature data, and combining them with the thermodynamic normalization parameters, to calculate the frequency domain and spatial gradient coupling degradation risk index within the current spatial slice, specifically includes: Thermodynamic boundary conditions within the feature data frame of the spatial slice are extracted, and the starting and ending temperature values ​​of the packaged and overwritten spatial slice are extracted. Discrete division operation of the pure physical spatial temperature gradient is performed by constructing a pure physical spatial temperature gradient formula. Extract the average ambient temperature difference between the inner and outer sides within the current slicing cycle, and combine it with the thermodynamic normalized parameters to calculate the theoretical tolerance of natural temperature drift. By combining the abnormal temperature drift and the mechanical damage energy under spatial axis mapping, heterogeneous data fusion calculation is performed. Logarithmic function is used to perform pre-smoothing of mechanical stress characteristics, and hard boundary filter function is used to threshold truncate thermodynamic variables. The single-time degradation risk index increment is calculated by generating a nonlinear product coupling formula for the single-time degradation risk index increment, and the single-time degradation risk index coupled with the spatial gradient is output.

7. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The step of calculating the degradation acceleration gradient between adjacent spatial slices based on the frequency domain and spatial gradient coupling degradation risk index, and sending an interrupt command to the hardware interrupt controller when the degradation acceleration gradient reaches a trigger set value, specifically includes: Extract the degradation increment of the current spatial slice and the degradation increment of the previous spatial slice, perform discrete difference differentiation operation based on physical spatial displacement, and calculate the degradation acceleration gradient between adjacent spatial slices by establishing a degradation acceleration gradient formula. The preset degradation trigger threshold is retrieved from the read-only memory. The degradation acceleration gradient and the degradation trigger threshold are imported into the numerical comparator register of the microcontroller unit to perform real-time comparison and judgment of the gradient magnitude. The comparator logic integrates a hysteresis comparison algorithm, which requires the degradation acceleration gradient to be maintained above the preset degradation trigger threshold for two consecutive calculation cycles to be confirmed as a valid limit exceedance. When the degradation acceleration gradient is detected to exceed the preset degradation trigger threshold, a high-priority hardware interrupt signal is generated. The current program counter stack is locked by defining a Boolean logic formula for hardware interrupt triggering, and an interrupt instruction is sent to the hardware interrupt controller.

8. The logistics anomaly early warning method based on big data analysis according to claim 5, characterized in that, The adjustment of the communication bus clock frequency and the physical sampling rate of each sensor, and the resetting of the step length parameter of the spatial slice, specifically include: The dynamic reduction instruction of the spatial slice stepping parameters is executed to shorten the hardware truncation trigger distance of the next Riemann integral. The length of the new spatial slice is calculated and generated by outputting the spatial observation resolution modulation formula, and the stepping length parameter is reset. By modifying the peripheral clock division coefficient and the sensor's internal control register via the internal communication bus, a new clock division word is written into the control register to increase the bus baud rate in order to adjust the clock frequency of the communication bus. The operating mode of the sensor's internal analog-to-digital converter is switched from low-power, low-frequency standby to high-performance, high-frequency continuous output mode through a hardware high-frequency modulation formula in order to synchronously improve the physical sampling rate of each sensor.

9. The logistics anomaly early warning method based on big data analysis according to claim 1, characterized in that, The fixed-length spatial slice sliding window maintained in the static random access memory is used to accumulate and integrate the frequency domain and spatial gradient coupling degradation risk index. When the accumulated integral value reaches the warning threshold, an anomaly is determined, an anomaly traceability data packet is generated, and an output operation is performed. Specifically, this includes: Extract the degradation features of all valid spatial slices within the current fixed-length spatial slice sliding window, and calculate the total cumulative degradation risk by performing discrete summation based on physical space and integral accumulation of multidimensional fatigue features through the establishment of a sliding window cumulative degradation evaluation formula. The cumulative total risk of degradation is compared with the preset warning threshold in memory. The cumulative total risk of degradation must cross the warning threshold for three consecutive calculation cycles, and the absolute temperature difference between the internal and external environment at the current moment must be greater than the set lower limit protection threshold. In this case, the packaging material is judged to have suffered substantial thermal resistance failure. Based on the spatial slice timestamp that triggered the early warning, the time-domain characteristic energy spectrum, thermodynamic boundary data frame, and underlying spatial coordinate sequence within the pre-set physical span before the early warning time are extracted. A lossless compression algorithm is used to generate a traceability evidence package containing the device's unified media access control address identifier and the absolute timestamp of the early warning, and then the output operation is performed.

10. A logistics anomaly early warning system based on big data analysis, characterized in that, The logistics anomaly early warning method based on big data analysis, as described in any one of claims 1-9, includes: an edge computing node, a positioning module, a high-frequency triaxial accelerometer, an internal temperature sensor, and an external ambient temperature sensor; The positioning module, high-frequency triaxial accelerometer, internal temperature sensor, and external ambient temperature sensor are electrically connected to the edge computing node via a communication bus. The edge computing node integrates a static random access memory and a hardware interrupt controller, and is equipped with multiple logic processing modules, which specifically include: The bus concurrent acquisition module is used to load initial hardware parameters after the edge computing node is powered on, and to receive the underlying heterogeneous data stream continuously output by the positioning module, the high-frequency triaxial accelerometer, the internal temperature sensor and the external ambient temperature sensor. The static reference in-situ calibration module is used to determine the state of the underlying heterogeneous data stream based on the vehicle kinematic constraints output by the positioning module. When the static reference conditions are met, the temperature difference between the internal temperature sensor and the external ambient temperature sensor in the underlying heterogeneous data stream is extracted, and the thermodynamic normalized parameters of the system are calculated and updated. The spatiotemporal dual-track mapping module is used to extract time-domain features from the vibration data output by the high-frequency triaxial accelerometer in the underlying heterogeneous data stream, and combine it with the motion displacement calculated by the positioning module to perform spatial domain alignment and aggregation to generate spatial slice feature data. The multidimensional parameter coupling calculation module is used to extract the spatial temperature gradient and vibration feature components from the spatial slice feature data, and combine them with the thermodynamic normalization parameters to calculate the frequency domain and spatial gradient coupling degradation risk index in the current spatial slice. The underlying hardware adaptive modulation module is used to calculate the degradation acceleration gradient between adjacent spatial slices based on the frequency domain and spatial gradient coupling degradation risk index. When the degradation acceleration gradient reaches the trigger set value, it sends an interrupt command to the hardware interrupt controller to adjust the clock frequency of the communication bus and the physical sampling rate of each sensor, and reset the step length parameter of the spatial slice. The multi-level joint early warning module is used to perform cumulative integral calculation on the frequency domain and spatial gradient coupling degradation risk index based on the fixed-length spatial slice sliding window maintained in the static random access memory. When the cumulative integral value reaches the early warning threshold condition, an anomaly is determined, an anomaly traceability data packet is generated, and an output operation is performed.

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