Logistics terminal data real-time processing method based on edge computing

Abstract

The present application belongs to the field of logistics information technology and edge computing technology, and discloses a logistics terminal data real-time processing method based on edge computing, which comprises the following steps: initializing a bottom hardware interface, mapping heterogeneous original signals into a unified structured data frame; activating a streaming correlation analysis engine, calculating the joint deviation intensity of multi-modal physical quantities and triggering cross-modal fusion; based on task characteristic fingerprints and system load prediction trajectories, hard binding the task to a specified processor core; realizing business logic atomic switching through double-buffer bytecode hot updating; starting a Markov chain probability state machine to maintain autonomous decision-making and perform incremental differentiated storage when the network is disconnected. The present application improves the real-time performance, reliability and business flexibility of logistics terminal data processing, and is suitable for various logistics terminals that require edge computing for real-time data processing. The present application solves the problems of control response delay and business rule updating difficulty of existing logistics terminals under network fluctuation and high concurrent load working conditions.

Description

Technical Field

[0001] This invention belongs to the field of logistics information technology and edge computing technology, specifically relating to a real-time data processing method for logistics terminals based on edge computing. Background Technology

[0002] As the global logistics industry undergoes a profound transformation towards intelligence and digitalization, devices such as smart parcel lockers, unmanned sorting robots, automated guided vehicles, and high-performance handheld data acquisition terminals deployed in warehousing, distribution, and last-mile delivery constitute the front-end network for interaction between the physical world and digital systems. During operation, these terminals continuously generate massive amounts of multi-dimensional business data, including image bitmap streams, motion posture frequencies, geographic coordinates, and environmental simulation signals.

[0003] Traditional centralized processing models based on cloud computing are prone to high latency due to backbone communication bandwidth bottlenecks and network fluctuations caused by long-distance transmission. This makes them unable to meet the stringent requirements of modern logistics operations for millisecond-level equipment response and real-time control decisions. Existing edge computing application solutions mainly issue tasks by pre-setting static type complexity matrices or calling pre-compiled dynamic link libraries at the underlying level. Such architectures based on hard coding and static rules are difficult to capture the deep semantic relationships between multi-source sensor data when dealing with highly dynamic and strongly spatiotemporally correlated logistics streaming data. This results in low efficiency of data fusion analysis, and the business execution logic is tightly coupled with the underlying operating environment. This means that frequent changes in routing strategies and iterations of sorting rules must rely on the recompilation and downtime deployment of the entire terminal system.

[0004] When the logistics terminal network environment exhibits significant fluctuations and uncertainties, the system lacks autonomous fault tolerance and adaptive takeover mechanisms at the edge, making it highly susceptible to node processing bottlenecks and data backlogs under weak network conditions, which can lead to partial paralysis of automated logistics production lines and large-scale business interruptions. Summary of the Invention

[0005] The purpose of this invention is to provide a real-time data processing method for logistics terminals based on edge computing, which solves the technical problems of existing logistics terminals lacking streaming semantic mining mechanisms and flexible deployment capabilities for business logic, resulting in sluggish system control response, difficulty in updating business rules, and low reliability of continuous operation under network fluctuations and high concurrency load conditions.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0007] A real-time data processing method for logistics terminals based on edge computing includes the following steps:

[0008] Step 1: Initialize the underlying hardware interface and independent data acquisition thread. Allocate the video buffer through memory mapping and call and configure the bus controller interface to map the heterogeneous raw signals from different sensors into a unified structured data frame containing device identifier and high-precision timestamp, and write it into the circular buffer.

[0009] Step 2: Activate the streaming correlation analysis engine, construct a sliding window on the structured data stream, monitor the relative change gradient of the instantaneous data arrival rate in real time to dynamically adjust the step size of the sliding window, perform mean normalization processing on the structured data frame within the sliding window, calculate the joint deviation strength of the multimodal physical quantities relative to the system normality characterized by the historical mean and standard deviation, and trigger the cross-modal feature fusion logic when the joint deviation strength exceeds the set anomaly threshold.

[0010] Step 3: Extract the binary code segment of the logistics business task to be processed, generate a three-dimensional feature vector fingerprint containing computational density and expected memory access pattern, and use the multi-core affinity scheduling module combined with the system load prediction trajectory output by the long short-term memory network model to hard bind the logistics business task to be processed to the specified processor core of the heterogeneous multi-core architecture in the edge control unit.

[0011] Step 4: Receive the bytecode file containing the new sorting strategy, divide the memory into a foreground execution area and an independent background loading area, complete the deserialization and sandbox self-check of the new logic in the independent background loading area, and use comparison and exchange of underlying instructions to overwrite the bus pointer of the business logic entry point from the foreground execution area to the background loading area.

[0012] Step 5: Periodically monitor the physical status of the network link through the probe thread. Under the condition of network disconnection, strip the end-cloud collaboration mode, start the probabilistic state machine model based on Markov chain to solve the maximum likelihood estimate of the next physical sorting action to drive the end execution mechanism, and perform incremental differentiated storage on the core business flow generated locally.

[0013] Furthermore, the process of writing to the circular buffer in step 1 specifically includes: for the visual acquisition thread, calling the preprocessing acceleration unit to calculate the Laplacian response value of the pixel grayscale matrix to extract the region of interest of dense texture; for the inertial state acquisition thread, extracting the difference of high-precision timestamps between adjacent data frames, and when the difference of high-precision timestamps is greater than the set tolerance upper limit, triggering the third-order spline interpolation compensation mechanism to reconstruct the lost physical timing.

[0014] Furthermore, the step of dynamically adjusting the step size of the sliding window in step 2 includes: calculating the relative change gradient of the instantaneous data arrival rate. The mathematical formula is:

[0015]

[0016] in, The relative change gradient, The instantaneous data arrival rate for the current sampling period. The instantaneous data arrival rate of the previous sampling period; when the relative change gradient When the preset burst threshold is exceeded, the step size of the sliding window is forcibly converged to the lower limit of the set step time.

[0017] Furthermore, the mathematical formula for calculating the combined deviation strength in step 2 is as follows:

[0018]

[0019] in, The combined deviation intensity, This represents the total number of dimensions of the sensor data streams currently active within the sliding window. For the first Dynamic weighting coefficients for dimensional sensor data. The first one in the current sliding window Real-time acquisition values ​​of dimensional sensor data, For the first The historical average of sensor data over a long-term monitoring period. For the first The standard deviation of the sensor data over the long-term monitoring period.

[0020] Furthermore, after triggering the cross-modal feature fusion logic in step 2, a spatial hysteresis filtering step based on a discrete global grid system is also included: obtaining the real-time spatial location of the logistics terminal. The current center coordinates of the grid and the center coordinates of adjacent grids When the judgment formula is met, it is confirmed that a physical grid crossing has occurred and the local service parameter set is retrieved:

[0021]

[0022] in, The Euclidean distance between the real-time spatial location and the coordinates of the center of the adjacent grid is given. The distance between the real-time spatial location and the coordinates of the center of the current grid is Euclidean distance. This is the preset hysteresis dead zone radius value.

[0023] Furthermore, in step 2, the streaming correlation analysis engine is embedded with a logic deduction operator based on causal analysis, extracting the linear velocity and angular velocity of the streaming terminal, and performing discretized integral deduction of the driving trajectory in the future time domain in combination with the current high-precision timestamp, and solving the overlap probability matrix of the generated forward predicted trajectory curve set and the known obstacle feature points in the environment in the two-dimensional spatiotemporal domain.

[0024] Furthermore, before hard-binding to a designated processor core of the heterogeneous multi-core architecture within the edge control unit in step 3, a scheduling decision step based on a processing cost function is also included: extracting the current clock frequency of the edge control unit. Data transmission volume of pending business tasks and current Ethernet uplink bandwidth By substituting the corresponding parameters of the local processing path and the unloading cooperative path into the processing cost function, respectively... To obtain the local processing cost and the offloading coordination cost; by comparing the cost functions of the two scheduling paths. The scheduling strategy is to determine the path corresponding to the one with the least substitution value based on the size of the alternative.

[0025]

[0026] in, Let be the processing cost function of the path to be evaluated. The estimated total number of instruction cycles required to execute the pending business task. The preset time response weighting balance factor, This is a preset physical energy efficiency weighted balance factor.

[0027] 8. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that the system load prediction trajectory in step 3 is output by a long short-term memory network model, using the system load related time series within a preset time period in the past as input, and performing rolling prediction of the system load topology within a future time step. When the predicted trajectory value exceeds the preset warning level, an interruption mechanism is triggered to suspend non-critical background asynchronous processes.

[0028] Furthermore, step 4 also includes a version concurrent operation evaluation step: the structured data frame is synchronously pushed into the foreground execution area and the background loading area set as a restricted permission domain to build a virtual sandbox, the hardware control messages output by the background loading area are intercepted and redirected to the virtual black hole device, the business processing indicators output by different logic areas are compared in real time, and the virtual sandbox is released and atomic pointer switching is performed after the expected constraint function is met.

[0029] Furthermore, in step 5, the transition probability matrix of the probabilistic state machine model is generated in real time by training historical job data locally cached by the edge control unit within a preset time period, satisfying the state transition probability formula:

[0030]

[0031] in, For logistics operations, the objects are in discrete states Transition to discrete state The transition probability, Time node The discrete state of the system in which the logistics operation object is located. Time node The discrete state of the system in which the logistics operation object is located. and It belongs to a predefined set of discrete states in the lifecycle of a single logistics package.

[0032] Furthermore, the incremental differential storage of the locally generated core business flow in step 5 specifically involves: converting the core flow into a binary stream and writing it into the secure sector of the local non-volatile memory; when the physical link is detected to have recovered and maintained a stable state, the incremental synchronization mechanism is activated, a bidirectional breakpoint resume tunnel is established based on the causal timestamp sequence, and baseline alignment is performed on the local control decisions made based on the probabilistic state machine.

[0033] Furthermore, the edge control unit maintains a computing power topology routing table with multiple surrounding computing nodes through a local area network broadcast protocol. When the length of the local processor's waiting queue exceeds a safety threshold, it performs mathematical slicing on the non-hard real-time feature extraction computing task to generate an independent computing power request micro-thread. The independent computing power request micro-thread is then routed to the adjacent computing node with the largest available memory block capacity in the computing power topology routing table for collaborative computation using a consistent hashing algorithm.

[0034] Furthermore, a final confidence check must be performed on the driven end effector beforehand, and a confidence score must be calculated using a multimodal confidence scoring equation. :

[0035]

[0036] in, The confidence score is... This represents the grayscale texture contrast probability output by the image recognition operator. This is the real-time radial deviation value output by the high-precision positioning module. This is the maximum allowable error threshold. This represents the historical matching probability of the decision path in the probabilistic state machine model. , as well as These are the preset weight coefficient matrices corresponding to the above parameters; only when the confidence score is... When the set lower limit for releasing the physical interlock is reached, the physical interlock is released to trigger the execution instruction.

[0037] Furthermore, before routing the independent computing power request micro-thread to the adjacent computing node with the largest available memory block capacity in the computing power topology routing table for collaborative computation using the consistent hashing algorithm, the system establishes a spatiotemporal link stability assessment model based on the three-dimensional spatial coordinate sequence of the logistics terminal and the radio frequency signal strength. Specifically, this includes: extracting the current velocity vector of the local edge control unit. with three-dimensional spatial coordinates It also extracts the corresponding broadcast speed vector of the candidate computing node from the computing power topology routing table. with three-dimensional spatial coordinates The link maintenance coefficient of each candidate computing node is calculated using the spatiotemporal link stability assessment formula. :

[0038]

[0039] in, The link preservation coefficient is... This represents the Euclidean physical distance between the local edge control unit and the candidate computing nodes. Let the relative velocity modulus of the two be denoted as . As a reference communication attenuation distance constant, For the Doppler frequency shift tolerance velocity constant, To characterize the dynamic adjustment parameters of spatial topology and relative kinematic weights; the link-preserving coefficient is removed. After the candidate computing nodes are below the preset link threshold, the consistent hashing algorithm is used to route the independent computing power request micro-thread to the adjacent computing node with the largest available memory block capacity in the remaining node set.

[0040] Furthermore, before initiating the maximum likelihood estimation of the next physical sorting action using a Markov chain-based probabilistic state machine model, the edge control unit triggers a dynamic pruning mechanism for the discrete state space based on information entropy to reduce memory usage. Specifically, this involves: for the current system discrete state... Calculate all potential target states Local information entropy of the transition probability distribution :

[0041]

[0042] in, The current system discrete state The local information entropy of the transition probability distribution. The discrete state set is related to the current system discrete state. The total number of target states with transition paths. For logistics operations, the objects are in discrete states Transition to discrete state The transition probability; when the local information entropy When the uncertainty exceeds a set threshold, the transition probability is located and removed from the set of discrete states. For sparse state transition branches below the absolute truncation lower bound, the transition probability of the remaining path is proportionally amplified using a normalization operator to reconstruct the dimension-reduced transition probability matrix. The maximum likelihood estimate of the next physical sorting action is then solved based on the reconstructed transition probability matrix.

[0043] Compared with the prior art, the present invention has the following beneficial effects:

[0044] This invention addresses the challenges of sluggish response and insufficient business resilience in existing logistics terminals under complex operating conditions by constructing a multi-layered edge processing architecture encompassing underlying hardware abstraction, streaming semantic association, dynamic resource scheduling, and autonomous fault tolerance. The invention unifies heterogeneous sensor signals into structured data frames with high-precision timestamps, activates a streaming association analysis engine to calculate the joint deviation strength of multimodal physical quantities to trigger cross-modal fusion, and achieves hard binding of task processor cores through three-dimensional feature vector fingerprints and long short-term memory network load prediction. Simultaneously, it utilizes double-buffered bytecode hot updates and Markov chain probabilistic state machines to ensure business continuity and network outage self-healing capabilities. By reconstructing the entire link from data acquisition, analysis, scheduling, and updates to fault tolerance at the edge, the invention comprehensively improves the processing efficiency and real-time decision-making of logistics terminals in high-concurrency and weak network environments, reduces the dependence on system downtime for deployment due to business logic changes, enhances the completeness of data acquisition under complex electromagnetic and vibration conditions, and effectively avoids the risk of production line paralysis caused by single-point computing power bottlenecks.

[0045] This invention utilizes Laplacian region of interest extraction for visual data and third-order spline interpolation compensation for inertial data to reduce redundant data processing overhead and reconstruct lost physical time sequences, thereby improving data quality and system resource utilization efficiency. A sliding window dynamic adjustment mechanism based on relative gradient changes and joint deviation strength calculation using weighted standardized bias enable the system to have higher temporal resolution and anomaly detection sensitivity for bursty data streams. Based on a discrete global grid system and spatial hysteresis filtering logic, the high-frequency switching of service parameters caused by positioning drift near physical boundaries is eliminated, achieving complete decoupling of terminal control logic and spatial coordinates. Local and offloading collaborative scheduling decisions based on cost functions, combined with a spatiotemporal link stability assessment model, significantly improve the delivery success rate of computing power collaborative tasks. Attached Figure Description

[0046] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained from these drawings without creative effort.

[0047] Figure 1 This is a flowchart of the method described in this invention.

[0048] Figure 2 This is a flowchart of the cross-modal feature fusion and anomaly handling sub-process of the present invention.

[0049] Figure 3 This is a flowchart of the hot update sub-process of the business logic of the present invention.

[0050] Figure 4 This is a flowchart of the autonomous decision-making sub-process of the probabilistic state machine under network disconnection conditions according to the present invention. Detailed Implementation

[0051] In the following description, only certain exemplary embodiments are briefly described. As those skilled in the art will recognize, the described embodiments can be modified in various ways without departing from the spirit or scope of the embodiments of the invention. Therefore, the drawings and description are considered to be exemplary in nature and not restrictive.

[0052] The following is in conjunction with the appendix Figures 1-4 The embodiments of the present invention will be described in detail below.

[0053] Example 1: This example provides a real-time data processing method for logistics terminals based on edge computing. The execution carrier of the method mainly relies on the edge control unit deployed at the front end of the logistics operation. The edge control unit adopts an asymmetric multiprocessing (AMP) heterogeneous multi-core architecture design, which aims to naturally isolate deterministic real-time tasks and high-concurrency computing tasks at the physical hardware level.

[0054] In specific implementation, the edge control unit integrates a quad-core application processor (a Cortex-A series processor with out-of-order execution and superscalar pipeline capabilities, with a clock frequency of [missing information - likely a specific value]). ) and dual-core real-time microcontrollers (Cortex-M series with nested vector interrupt controllers, with a main frequency of 1000 MHz). ).

[0055] The specific method is as follows:

[0056] Step 1: Initialization of the underlying hardware interface and independent data acquisition thread. During the kernel operating system boot phase, the edge control unit performs pin multiplexing and retrieval and configures the bus controller by writing the underlying device tree. To ensure physical layer transmission reliability in complex industrial electromagnetic environments, a high-speed signal bus network with electrical isolation is constructed around the edge control unit. For sensor data with low speed and high anti-interference requirements, the system enables... The system features an optically isolated RS-485 bus controller interface. For high-frequency, high-throughput data, the system activates a Gigabit Ethernet physical layer chip (PHY) interface supporting the IEEE 802.3at standard (PoE+). To prevent bus contention caused by high-frequency interrupts, the edge control unit hard-binds hardware interrupt requests (IRQs) from different physical buses to designated cores via a programmable interrupt controller (GIC). After initialization, the system creates four independent data acquisition threads with different priority scheduling strategies in the user space of the quad-core application processor using the POSIX thread library (pthread).

[0057] Step 1.1: Zero-copy acquisition and hardware-accelerated preprocessing of visual data. For barcode image bitmap streams acquired by CMOS image sensors, traditional data copying methods consume a significant amount of CPU clock cycles and front-side bus bandwidth.

[0058] In this embodiment, the thread responsible for visual acquisition allocates a contiguous block of physical memory as a video buffer allocator (VideoDataBuffer) using memory mapping (mmap) technology. The CMOS sensor inputs the image differential signal to the edge control unit via the MIPI-CSI2 physical layer protocol, and directly uses the direct memory access (DMA) controller to write the pixel matrix of the image into the aforementioned video buffer, achieving zero-copy data transfer.

[0059] After the data enters main memory, in order to remove a large number of invalid background pixels, the system calls the hardware preprocessing acceleration unit (ISP) integrated within the edge control unit to execute the Region of Interest (ROI) extraction algorithm. This ROI extraction algorithm uses a lightweight second-order differential operator to perform spatial filtering on the image matrix; its core lies in calculating the Laplace response value of the pixel grayscale function. For the discrete two-dimensional grayscale matrix of the input image... The approximate formula for calculating the second derivative of a pixel is:

[0060]

[0061] in, coordinates Laplace response intensity at the location, This represents the grayscale value of the original pixel. and These represent the row and column coordinates of the image matrix, respectively.

[0062] Hardware ISP unit with Macroblocks are scanned in units The integral value is used when the variance of the response intensity within a certain macroblock region exceeds a preset texture threshold. If the region is identified as a high-density textured ROI region containing a barcode or label, the acquisition thread will ultimately only crop the extracted ROI bitmap and push it into the subsequent processing queue. This significantly reduces the bandwidth usage of internal DDR memory during image processing, effectively avoiding resource constraints caused by microsecond-level sensing interruptions in visual computing.

[0063] Step 1.2: Timing alignment and interpolation compensation of high-frequency inertial states; Motion attitude frequency data generated by the nine-axis inertial measurement unit (IMU) built into the logistics terminal, including three-axis acceleration, three-axis angular velocity, and three-axis geomagnetic vector, is transmitted via the SPI high-speed bus. The signal is sent to the edge control unit at a fixed frequency.

[0064] Because momentary bus congestion may occur in edge systems when processing network protocol stacks or file read / write operations, the low-frequency pull-down of individual SPI clock cycles may be delayed, leading to frame loss and time series breaks in IMU data frames. An interpolation compensation algorithm based on clock domain synchronization is configured in the acquisition thread. When reading from the circular buffer allocated in DDR memory, the acquisition thread checks the difference in millisecond-level high-precision timestamps between two adjacent frames of data. .

[0065] like (based on Ideal cycle If the set tolerance upper limit is reached, then the third-order spline interpolation compensation mechanism is triggered. Assume the missing time step is... Given the set of valid data points before and after the missing point. , , and Missing data items The reconstruction formula is:

[0066]

[0067] in, This refers to the specific physical quantities after reconstruction (such as X-axis acceleration). For the precise timestamp of the lost time point, For a known reference point timestamp; polynomial coefficients , , , The interpolation technique is obtained by solving a system of equations consisting of four known data points before and after the data points using Gaussian elimination. This technique can smoothly restore the periodic high-frequency peaks of mechanical vibrations, preventing misjudgments in subsequent situational awareness models due to data glitches.

[0068] Step 1.3: Adaptive rate of change resampling mechanism for low-frequency state quantities; relative to the high-frequency characteristics of the IMU, the temperature and humidity analog signals input by the environmental sensor array through the ADC channel belong to low dynamic response data.

[0069] Using a fixed high-frequency sampling method would generate a large number of redundant and invalid values. Therefore, an adaptive rate-of-change resampling state machine is embedded in the environmental data acquisition thread. The state machine sets a dynamically sliding reference register, which only resampling when the current ADC channel's sampled value... Compared with the previous frame transmission value stored in the reference register The absolute difference between them exceeds the physical preset threshold. Only when the time is right will a complete data encapsulation and reporting command be activated.

[0070] For example, regarding the working environment temperature, Set as Meanwhile, to prevent the cloud from misjudging sensor disconnection due to prolonged periods of constant temperature, the state machine also incorporates a heartbeat fallback time. This dual-threshold mechanism allows the load on the edge bus to be precisely limited to the moment when a change in physical properties actually occurs.

[0071] Step 1.4: Memory-level protocol decapsulation and structured mapping of heterogeneous data frames; the image ROI streams, compensated IMU time series, high-precision geographic coordinates, and temperature and humidity signals from the four independent threads mentioned above, in their original physical form, range from floating-point numbers converted from analog levels to high-density matrix arrays. The heterogeneous data then enters the multi-dimensional heterogeneous data acquisition and normalization layer constructed within the edge control unit. This normalization layer allocates a protected shared memory pool in Linux user space. The normalization layer uses customized C structures to perform memory-level pointer casts and shift operations on various types of data, stripping away the underlying transmission headers of various physical buses and extracting the pure physical quantity payload.

[0072] Heterogeneous data is mapped into compact binary structured data frames of a unified format. The beginning of each frame structure... The first bit stores the device's unique identifier (UUID), followed by... The first bit is written with a millisecond-level absolute timestamp, synchronized by the edge controller's internal real-time clock (RTC) and the IEEE 1588 Precision Time Protocol (PTP). Subsequent bit segments contain data type tag enumeration values ​​and specific physical quantity payload segments. At the end of each frame, the system quickly appends a CRC-32 cyclic redundancy check code using a lookup table to detect memory bit flip errors that may occur during internal inter-process communication (IPC) transmission.

[0073] After the heterogeneous data frames are fully written to the circular buffer and the underlying protocol is decapsulated, a continuous, structured data stream with high-precision absolute timestamps is formed in the physical memory of the edge control unit. This data stream not only includes the motion state of the logistics terminal in three-dimensional physical space, but also encompasses visual perception and environmental parameters. At this point, the system needs to use a streaming architecture to perform causal logic binding on these discrete physical signals.

[0074] Step 2: Time window aggregation and high-dimensional semantic space projection calculation of streaming data; the streaming correlation analysis engine deployed in the user-mode space of the edge control unit is awakened by the operating system's memory write completion interruption. Considering the extremely sudden arrival rate of logistics business data, this engine abandons the traditional static time window mechanism and adopts a dynamic sliding window aggregation model based on Shannon information entropy.

[0075] Step 2.1: Dynamic sliding window step size convergence mechanism. The streaming correlation analysis engine monitors the step size per unit time in real time. Total number of heterogeneous data frames written to the internal buffer The system sets the step size reference range for the sliding window to be... to To achieve adaptive adjustment of the window step size, the engine embeds a lightweight rate differential estimator. The instantaneous data arrival rate for the current sampling period. Given the rate of the previous cycle, the system calculates the gradient of the relative change in rate. .when Exceeding a preset burst threshold (e.g., can be set to...) When this occurs, it indicates that the terminal is in a high-frequency interaction state (such as dense continuous scanning or rapid turning and obstacle avoidance), and the engine will force the sliding window step size to converge to... The lower limit is used to obtain extremely high temporal resolution; conversely, when When the value approaches zero and the system is in a stable cruise state, the window step size expands flexibly to... This reduces the context switching overhead of CPU processing slices at the physical level.

[0076] Step 2.2: High-dimensional semantic space projection based on weighted standardized bias; within a defined time window, various heterogeneous data exhibit characteristics of different dimensions and varying physical meanings. The streaming correlation analysis engine executes dimensionality reduction and projection algorithms to map observations with different physical properties to a dimensionless high-dimensional semantic space. The engine first processes the extracted real-time data of each dimension... Zero-mean standardization is performed. The core of this process is calculating the joint deviation strength between the data items. The combined deviation strength The calculation follows the formula:

[0077]

[0078] in, The total number of dimensions of the sensor data streams active within the current time window; For the first The preset weighting coefficients for the dimensional data are not static but dynamically allocated based on the real-time signal-to-noise ratio of the sensor, following the formula... ,in For the first The current real-time signal-to-noise ratio of the dimensional sensor. This is the health decay coefficient calculated based on historical failure rates.

[0079] For example, the health decay coefficient , For the first time in the statistical period The percentage of times a sensor outputs unreliable data; The first in the current time window Real-time acquisition values ​​of sensor data; For this dimension of data, over a long-term monitoring period (e.g.) The historical average within ) , where represents the corresponding standard deviation. This mathematical model essentially assesses the degree of joint deviation of current multimodal physical quantities from the system's normal state, as characterized by historical mean and standard deviation.

[0080] For example, in high-speed sorting operations, to ensure absolute synchronization between visual recognition and physical actions, the system uses kernel parameters to assign weights to the barcode image recognition results. Locked to The weights of the attitude data Set as Once the joint deviation strength is obtained... Exceeding the preset abnormal threshold (For example, Can be set to If the physical terminal indicates a potential risk of deviating from the predetermined process, the system will immediately bypass the conventional pipeline and directly trigger the cross-modal fusion intervention logic.

[0081] Step 2.3: Dynamic Geofencing and Spatial Hysteresis Filtering; When handling spatial location-level correlations, this implementation completely abandons the traditional hard-coded logic based on absolute latitude and longitude comparisons, and instead introduces a dynamic geofencing algorithm based on a discrete global grid system. The algorithm mathematically and geometrically divides a wide-area logistics operation area (such as a smart sorting center of tens of thousands of square meters) into continuous hexagonal grid cells, with the physical side length of each cell precisely constrained within a certain range. to Within the range.

[0082] Each hexagon is assigned a globally unique H3 spatial index value. The positioning and parsing thread in the edge control unit calculates the terminal's current high-precision latitude and longitude coordinates in real time. The grid number to which it belongs. To prevent frequent "ping-pong" switching of index numbers due to slight positioning drift at the physical boundary between two adjacent hexagonal grids, the system injects spatial hysteresis filter logic at this point.

[0083] Set the center coordinates of the current grid as The coordinates of the center of the adjacent grid are The terminal's real-time location is The positioning and parsing thread pre-transforms the terminal's latitude and longitude coordinates and the latitude and longitude coordinates of each grid center to a local Cartesian coordinate system (e.g., a universal transverse Mercator projection), obtaining the corresponding two-dimensional coordinates. Subsequent Euclidean distance calculations are based on these Cartesian coordinates. Only when the Euclidean distance satisfies... Time (of which) The value is set to the hysteresis dead zone radius. Only after the engine confirms that a grid crossing has occurred. The moment the crossing event is confirmed, the correlation analysis engine automatically retrieves a set of local business parameters strongly associated with the new grid (including a specific sorting grid number matrix and regional delivery instruction priority restrictions) from non-volatile memory.

[0084] Step 2.4: Forward Causal Prediction Based on Kinematic Model; In highly dynamic terminal scenarios such as automated guided vehicles (AGVs), relying solely on current perception data is insufficient to meet obstacle avoidance requirements under extreme conditions. The streaming correlation analysis engine embeds a logical inference operator based on causal analysis. The logical inference operator extracts the terminal's current kinematic parameters: linear velocity. and angular velocity Combined with current high-precision timestamps For the future time domain The driving trajectory within the AGV is discretized and integrally derived. The state vector of the AGV is defined as follows: ,in Let be the heading angle. The difference update equation for its forward trajectory is:

[0085]

[0086]

[0087]

[0088] The engine calculates the overlap probability matrix by combining the calculated set of forward predicted trajectory curves with the known obstacle feature points in the environment identified by the LiDAR and vision module in a two-dimensional spatiotemporal domain. When the integral value of the overlap probability exceeds the safety limit, the system can detect when a physical collision occurs. Generate and issue chassis braking or steering intervention commands.

[0089] Step 3: Edge Node State Awareness and Dynamic Resource Extreme Scheduling. After semantic-level correlation analysis of multimodal data is completed, a large number of concurrent tasks (such as policy calculation, database persistence, network packet processing, etc.) will suddenly occur. If strict hardware-level resource management is not applied to these tasks, the cache hit rate of the edge computing core will plummet and bus deadlock will occur. Therefore, the system activates a multi-core affinity scheduler based on load prediction.

[0090] Step 3.1: Task Monitoring Module and Feature Fingerprint Extraction. The kernel-mode task monitoring module, located in the operating system, continuously detects the instruction execution density of each processing core at a microsecond-level granularity by reading the hardware registers of the ARM processor's Performance Monitoring Unit (PMU).

[0091] Each business task extracted from the analytics engine must be physically characterized by a feature fingerprint generator before entering the scheduling queue.

[0092] Static analysis and dynamic sampling are performed on the binary code segment of this task to generate a feature vector fingerprint containing three-dimensional parameters. ,in To calculate density, which characterizes the percentage of floating-point and logical operation instructions in the total instruction cycles, it is specifically obtained by statically analyzing the basic blocks of the binary code segment and statistically analyzing the percentage of cycles for floating-point arithmetic instructions and logical operation instructions. To ensure data relevance, a task data dependency graph is constructed, and the average out-degree of each node in the graph is calculated and normalized. The interval is obtained; To determine the expected memory access span pattern, the step size distribution of the task memory access address sequence is analyzed by dynamic sampling, and the step size variance is mapped to a preset pattern category index.

[0093] Based on this feature fingerprint, the scheduling algorithm performs hard binding matching in heterogeneous multi-core processor architectures (such as performance cores with large-capacity L2 caches and computing cores with high-frequency FPUs).

[0094] For example, large-scale barcode parsing tasks that require a huge dictionary are forced to be bound to processor cores with larger L2 caches to avoid frequent cache misses; while computationally intensive instruction streams involving SM4 symmetric encryption are precisely routed to cores equipped with high-performance floating-point units (FPUs).

[0095] Step 3.2: Process the construction of the cost function and multipath comparison.

[0096] During peak periods of logistics operations, the computing power of a single edge node can easily be overwhelmed. Instead of relying on simple CPU utilization thresholds to determine whether to offload tasks to the cloud, a more stringent physical processing cost function is introduced. When a node receives a routine service request, the system immediately extracts the current edge terminal's main frequency. Data transmission volume and the uplink bandwidth of current Ethernet or 5G networks. Processing cost function Expressed as:

[0097]

[0098] in, This represents an estimated total number of instruction cycles required to execute the task under the current microarchitecture. The preset time response weighting balance factor, The two are preset physical energy efficiency weighted balance factors, which are weights set by the system based on the current energy efficiency ratio target and real-time constraints.

[0099] The scheduler calculates the cost of each local processing path. Value of collaborative processing paths that are offloaded to neighboring nodes or the cloud By comparing the two values, the path corresponding to the one with the smallest replacement value is selected as the scheduling strategy. Due to the discharge curve characteristics of physical batteries, when the terminal has sufficient power, It is assigned a higher weight to pursue the ultimate local processing speed; when entering power-saving mode, the weight allocation is dynamically reconfigured. For example, under normal power supply conditions, it can be set... , In power-saving mode, settings can be configured. , .

[0100] Step 3.3: Millisecond-level resource consumption advance prediction based on LSTM. Passive cost calculation still carries the risk of scheduling lag. A miniaturized Long Short-Term Memory (LSTM) prediction model resides in a very small memory area within the background monitoring thread. The LSTM prediction model does not process complex business data, but rather relies on past system data... Using time series data of CPU, memory utilization, and I / O throughput as input, the system predicts future performance using a rolling time step. The system load topology within the system. Its core forget gate operation mechanism ensures that only timing characteristics that are indicative of burst loads are retained.

[0101] Once the system prediction load trajectory output by the LSTM neuron penetrates the preset path... The system will trigger the interrupt protection mechanism before any physical obstruction occurs, setting a warning water level. The scheduler will forcibly suspend all non-critical background asynchronous processes (such as system log compression and packaging, and batch disk analysis of historical data) and release all bus bandwidth to prioritize hard real-time control links involving physical motion safety (such as emergency stop and braking logic).

[0102] Step 4: Dual-buffered smooth hot update and sandbox isolation mechanism based on an independent bytecode interpreter. Complex routing rules and sorting strategies in logistics scenarios exhibit high-frequency iterative physical characteristics. This embodiment constructs a lightweight business logic execution environment independent of the underlying Linux kernel within the user-space space of the edge control unit. The core of the execution environment is a bytecode interpreter employing a hybrid architecture of Just-In-Time (JIT) compilation and Automatic Over-the-Time (AOT) compilation.

[0103] Step 4.1: Logical Abstraction Interface and Digital Signature Verification. When the cloud management platform distributes a bytecode file containing the new sorting strategy, the file enters the memory pool of the edge control unit through a secure channel based on the TLS 1.3 protocol. Loading is not performed immediately; instead, the cryptographic hardware acceleration engine integrated within the ARMTrustZone SecureWorld is invoked. The hardware engine extracts the header hash digest of the bytecode file and performs asymmetric signature verification using a public key pre-installed in an immutable one-time programmable (OTP) memory. After verification confirms the payload is legitimate, the bytecode is mapped to the interpreter's protected memory space. The interpreter communicates with the underlying hardware driver through a standardized logical abstraction interface (API), cutting off the path for business code to directly manipulate physical addresses.

[0104] Step 4.2: Double-buffered memory architecture and atomic logic switching. To achieve smooth transitions without interrupting business operations, the bytecode interpreter allocates independent "foreground execution area" and "background loading area" in the heap memory.

[0105] In normal operation mode, the foreground thread continuously parses the old business logic based on the current memory pointer. After the update instruction is triggered, the background daemon thread begins deserializing the new bytecode and linking dependent libraries in the "background loading area," constructing a completely new execution context and symbol table. After the new logic is initialized, the system forces it to perform an idle input test to verify that no segmentation fault has occurred in the instruction stream. The moment the test passes, the edge control unit uses the Compare-And-Swap (CAS) atomic low-level instruction supported by the multi-core processor to directly overwrite the bus pointer of the business logic entry point from the "foreground execution area" to the "background loading area." The process time is usually compressed to within... Within this timeframe, a seamless, millisecond-level transition from the old state to the new state is achieved, and the package data stream being processed in the foreground will not be aware of any instruction pause.

[0106] Step 4.3: A / B testing evaluation for concurrent version execution. For major logic updates involving global routing changes, the edge control unit supports activating sandbox testing mode. Using memory copying technology, the same standardized heterogeneous data frame output by the streaming correlation engine is simultaneously pushed into the virtual sandboxes containing both the old and new logic versions.

[0107] Because the new logic operates in a restricted access domain, its output hardware control messages are forcibly intercepted and redirected to the virtual black hole device ( / dev / null), preventing the driving of physical execution mechanisms. Internal monitoring probes capture and compare the sorting hit rate, instruction computation cycle, and peak memory consumption of both under the same physical input conditions in real time. The verification cycle is set to... If all indicators of the new logic meet the expected constraint function for continuous data frames, the sandbox environment is removed, and a formal atomic pointer switch is executed.

[0108] Step 5: Local Probabilistic State Machine and Incremental Backtracking Compensation under Network Disconnection Conditions. Logistics operations face complex metal obstructions and electromagnetic interference, making instantaneous packet loss or disconnection of the uplink backbone unavoidable. The system abandons the traditional deadlock waiting mechanism and introduces a localized closed-loop processing architecture based on Markov chains.

[0109] Step 5.1: Adaptive Network Awareness and Pattern Degradation. Probe threads deployed at the bottom layer of the network protocol stack... The system periodically sends ICMP probe packets with high-precision timestamps to the cloud gateway. It also calculates the link's round-trip time (RTT) and smoothed packet loss rate parameters in real time. If the RTT value exceeds the set physical tolerance limit for three consecutive periods... (For example, Can be set to When a socket physical layer disconnection occurs, the system status register is hard-written, triggering an interrupt and forcibly stripping the terminal service flow from the "end-cloud collaboration mode" to the "edge autonomous guarantee mode".

[0110] Step 5.2: Probabilistic deduction based on Markov state machine. In autonomous guarantee mode, the edge control unit cannot obtain the latest global routing database. At this time, the built-in probabilistic state machine model is invoked. The complete lifecycle of a single package processing at the logistics terminal is defined as a discrete set of states. ,in This represents the package's current physical state, such as "scanned successfully," "weight verified," or "direction determined." The transition probability matrix between these states... It is not static, but rather generated by the edge control unit in the past. Historical job data cached locally is used for real-time training and generation. Specifically, the edge control unit traverses historical job records and statistically analyzes discrete states. Transfer to frequency Then the transition probability This generates the transition probability matrix. This matrix satisfies the formula:

[0111]

[0112] Faced with an uncertain sorting destination, the system extracts some visible attributes of the current package (such as volume and barcode prefix) and combines them with the state matrix. The maximum likelihood estimate of the next physical action is calculated. Based on this probability extreme value, the terminal drives the physical actuator to guide the package to the sorting slot or temporary storage buffer with the highest probability in the site's history.

[0113] Step 5.3: Differentiated Incremental Storage and Baseline Alignment Synchronization. During this physical degradation operation, the system stops all non-essential log generation. Critical core business flows, such as terminal physical action sequences, package IDs, and probabilistic inference results, are input into a customized differential compression algorithm.

[0114] The algorithm strips away redundant structure headers, transforms the data into a high-density binary stream, and sequentially writes it page-by-page to secure sectors of local non-volatile RAM. When the probe thread detects that the physical link has recovered and remains stable... Subsequently, the system activates the incremental synchronization mechanism. Instead of full concurrent upload, the edge control unit establishes a bidirectional breakpoint resume tunnel based on the causal timestamp sequence of the data. After receiving the differential data, the cloud performs base-line alignment on the local decisions made based on the probabilistic state machine. For the very few misclassified components caused by probabilistic deduction, the system instantly generates a reverse correction command stream, driving the end device to perform a secondary physical callback.

[0115] Step 6: Device-level peer-to-peer collaboration and distributed hash power offloading. The physical computing power of a single edge node has a rigid upper limit. To overcome this physical limit, this implementation constructs a P2P collaborative scheduling network based on physical layer multicast among multiple edge control units located within the same local area network (such as a single logistics station).

[0116] Each node maintains a dynamic computing power topology routing table within the local area network via UDP broadcast protocol. This table is refreshed with second-level granularity, showing the idle CPU clock cycle quota and available memory block capacity of each neighboring node. If a node experiences a sudden surge in high-density barcode scanning (such as unloading a truckload of goods), causing the local processor's wait queue length to exceed a safe threshold... The node immediately becomes the scheduler initiator. The system mathematically slices non-hard real-time feature extraction tasks (such as morphological restoration algorithms for damaged barcodes) that enter memory, generating independent micro-threads for requesting computing power. Through a consistent hashing algorithm, these micro-threads are precisely routed to the idle neighboring nodes with the largest computing power reserve in the topology table. The neighboring nodes absorb the computing tasks during their background idle periods and inject the final feature extraction matrix back into the shared memory pool of the initiating node. This spontaneously formed computing power tidal scheduling within the physical space completely avoids congestion and paralysis of single-point physical devices.

[0117] Step 7: Confidence Assessment Feedback and Physical Hardware Safety Constraints. The physical control of automated logistics must ensure absolute accuracy. Before translating any digital decision into electrical signals to drive relays, the system must undergo a final confidence check. The end effector is in a hardware-level physical interlock state by default; only control commands that pass the confidence check can be unlocked and triggered.

[0118] Step 7.1: Multimodal Confidence Scoring Equation. Each control decision output by the association analysis engine is accompanied by a quantified confidence score. The calculation of this score takes into account both the signal-to-noise ratio of the underlying physical sensors and the convergence of the algorithm. The model is as follows:

[0119]

[0120] in, This represents the grayscale texture contrast probability output by the image recognition operator. This refers to the real-time radial deviation value for high-precision positioning. This is the maximum allowable error threshold. This represents the historical matching probability of the decision path in the Markov model. This is the coefficient matrix corresponding to the weights. The system forcibly sets a safety threshold (e.g., ...). Only Only when the edge control unit releases the physical interlock will it trigger the sorting solenoid valve or motor driver. If the judgment score is below the lower limit, the package will be physically guided to the system's preset manual abnormality handling area, thus preventing malfunctions of the physical mechanism.

[0121] Step 7.2: Dynamic Adaptive Physical Energy Efficiency. The edge control unit has an onboard dedicated power management chip (PMIC). When the terminal detects that it is in lithium battery powered mode and its physical voltage is lower than the set discharge curve... When the threshold is reached, the power manager intervenes. It bypasses the operating system and directly adjusts the processor's internal phase-locked loop (PLL) and power rails, performing dynamic voltage and frequency regulation (DVFS). The system rigidly reduces the clock frequency of the non-real-time computing cores, doubles the sampling wake-up cycle of the temperature and humidity environmental sensors, and shuts down the power amplifiers (PAs) of the RF front-end during bus idle periods. This extremely tight physical power consumption ensures that the core business sorting logic can sustain until the next shift changeover.

[0122] Example 2: In highly dynamic and intensive logistics operation scenarios, such as matrix-style unmanned sorting centers with large-scale automated guided vehicles (AGV) clusters, the relative physical locations between nodes and the radio frequency communication environment are constantly changing drastically. This highly dynamic characteristic exposes traditional distributed computing power offloading methods based on pure memory capacity to a serious risk of collaborative interruption.

[0123] This embodiment, based on the aforementioned Embodiment 1, creatively extends the mechanism for independent computing power request micro-thread routing within the local area network by incorporating a deep physical kinematic dimension. When parsing the computing power topology routing table, the system no longer relies solely on memory quota as a single routing metric, but instead introduces a spatiotemporal link stability assessment model based on a three-dimensional spatial coordinate sequence and radio frequency signal strength.

[0124] Each moving logistics terminal, while announcing its available resources via UDP broadcast protocol, also transmits its current velocity vector obtained by the high-precision positioning module. with three-dimensional spatial coordinates It is pushed into the broadcast message payload.

[0125] After intercepting these broadcast messages, the scheduling kernel of the local edge control unit will reconstruct the physical kinematic topology of all adjacent computing nodes in the local area network in real time in memory.

[0126] Before generating independent computing power request microthreads and preparing to address and distribute them, the scheduling kernel extracts its own current velocity vector. with three-dimensional spatial coordinates Substitute the values ​​into the preset spatiotemporal link stability evaluation formula to calculate the link maintenance coefficient for each candidate device. .

[0127] In this exponential decay assessment model, the baseline communication attenuation distance is constant. The lower limit of the reliable transmission radius of a typical industrial Wi-Fi 6 network under unobstructed line-of-sight conditions is defined as a value. Doppler shift tolerance velocity constant The speed limit of the automated guided vehicle (AGV) at the logistics site is strictly constrained and set to [specific speed limit]. Dynamically adjust parameters It is generated by the real-time received signal strength indication (RSSI) feedback loop at the radio frequency layer.

[0128] For example, the current received signal strength can be... (Unit: dBm) mapped to ,in Set as , Set as When the environment has severe metal shelving obstructing the signal, causing the physical distance and signal attenuation to be nonlinear, The value of will be adaptively suppressed, thus assigning higher weights to relative kinematic features. The system forcibly removes the link-preserving coefficient. Below the preset link threshold All alternative nodes are selected to ensure that only high-computation-density microthreads are offloaded to peer devices where the physical distance will not drastically increase or the relative speed will be low within the estimated future computation period. This eliminates the failure of distributed hash mapping and the loss of microthread execution results caused by the "enclave" effect.

[0129] To address extreme degradation scenarios where network connectivity is severely disrupted and local hardware resources are heavily consumed, a dynamic pruning engine for discrete state space based on information entropy is integrated into the probabilistic state machine model based on Markov chains.

[0130] After completely stripping away the edge-cloud collaboration model, the L1 instruction cache mounted inside the edge control unit is extremely vulnerable to being overwhelmed by an excessively large and sparse state transition probability matrix. Before each round of physical sorting simulation, the pruning engine will analyze the current discrete state of the system. Iterate through all potential target states it points to. The transition probability is a discrete mathematical distribution, and the local information entropy is calculated. This local information entropy, from a thermodynamic perspective, characterizes the degree of disorder and unpredictability of logistics destinations in the current state. If... The integral value exceeded the uncertainty threshold (usually set to 0.5%) that was pre-programmed into the boot sector. (Neppe)) The system determines that the current decision tree is in an over-divergent state, which is very likely to trigger frequent page replacement operations in the local non-volatile memory, resulting in inference timeout.

[0131] After confirming excessive divergence, the pruning engine directly addresses the memory pool at microsecond speeds, forcibly pruning those components in the transition probability matrix that are below the absolute truncation lower bound (e.g., ...). The sparse state transition branches of the maximum likelihood estimation. The pruned branches usually represent anomalous backtracking routes that have occurred very rarely in history or low-frequency edge blind zone mappings. They contribute very little to the final result of the maximum likelihood estimation but occupy a large number of computation cycles.

[0132] After the pruning process is completed, the normalization operator inside the engine immediately performs proportional numerical inflation compensation on the retained high-probability backbone transition paths, ensuring that the dimensionality-reduced transition probability matrix reconvers to the rigid mathematical constraint that the sum of probabilities is 1 in its row vector space. With this dynamic dimensionality reduction mechanism executed just before the derivation, the system reduces the effective dimensions involved in matrix multiplication and addition operations by nearly 70%.

[0133] In practical implementation, it includes 120 automated guided vehicles. A high-density sorting facility of square meters was used for a period of time The system underwent rigorous stress simulation testing for several hours. During the test, high-frequency white noise was injected into the local area network to simulate a sudden network outage caused by the metal shielding enclosure. The entire cluster was then commanded to perform high-speed, unordered interleaving movements, creating a condition that pushed computing and communication resources to their limits. The relevant core performance metrics were summarized and recorded in the table below.

[0134] The table compares the performance of collaborative scheduling and state reduction strategies under 72-hour dynamic extreme pressure conditions.

[0135]

[0136] The above test data clearly demonstrates the anti-distortion capability of this invention under complex and variable constraints. The spatiotemporal evaluation model successfully improved the distributed computing power delivery success rate of high-speed motion terminals from near paralysis. The system was pushed to the reliability limit of the physical bus, effectively suppressing the power consumption of meaningless retransmission storms.

[0137] Meanwhile, the information entropy-based pruning mechanism significantly reduces the memory overhead and execution cycle of offline simulations to the microsecond range, which can be easily handled by low-power microcontrollers. This ensures that logistics production lines can maintain millisecond-level deterministic mechanical responses even under the dual pressures of severe cyberspace fluctuations and hardware computing power bottlenecks.

[0138] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0139] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for real-time processing of logistics terminal data based on edge computing, characterized in that, Includes the following steps: Step 1: Initialize the underlying hardware interface and independent data acquisition thread. Allocate the video buffer through memory mapping and call and configure the bus controller interface to map the heterogeneous raw signals from different sensors into a unified structured data frame containing device identifier and high-precision timestamp, and write it into the circular buffer. Step 2: Activate the streaming correlation analysis engine, construct a sliding window on the structured data stream, monitor the relative change gradient of the instantaneous data arrival rate in real time to dynamically adjust the step size of the sliding window, perform mean normalization processing on the structured data frame within the sliding window, calculate the joint deviation strength of the multimodal physical quantities relative to the system normality characterized by the historical mean and standard deviation, and trigger the cross-modal feature fusion logic when the joint deviation strength exceeds the set anomaly threshold. Step 3: Extract the binary code segment of the logistics business task to be processed, generate a three-dimensional feature vector fingerprint containing computational density and expected memory access pattern, and use the multi-core affinity scheduling module combined with the system load prediction trajectory output by the long short-term memory network model to hard bind the logistics business task to be processed to the specified processor core of the heterogeneous multi-core architecture in the edge control unit. Step 4: Receive the bytecode file containing the new sorting strategy, divide the memory into a foreground execution area and an independent background loading area, complete the deserialization and sandbox self-check of the new logic in the independent background loading area, and use comparison and exchange of underlying instructions to overwrite the bus pointer of the business logic entry point from the foreground execution area to the background loading area. Step 5: Periodically monitor the physical status of the network link through the probe thread. Under the condition of network disconnection, strip the end-cloud collaboration mode, start the probabilistic state machine model based on Markov chain to solve the maximum likelihood estimate of the next physical sorting action to drive the end execution mechanism, and perform incremental differentiated storage on the core business flow generated locally.

2. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, The process of writing to the circular buffer in step 1 specifically includes: for the visual acquisition thread, calling the preprocessing acceleration unit to calculate the Laplacian response value of the pixel grayscale matrix to extract the region of interest of dense texture; for the inertial state acquisition thread, extracting the difference of high-precision timestamps between adjacent data frames, and when the difference of high-precision timestamps is greater than the set tolerance limit, triggering the third-order spline interpolation compensation mechanism to reconstruct the lost physical timing.

3. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, The step of dynamically adjusting the step size of the sliding window in step 2 includes: calculating the relative change gradient of the instantaneous data arrival rate. The mathematical formula is: in, The relative change gradient, The instantaneous data arrival rate for the current sampling period. The instantaneous data arrival rate of the previous sampling period; when the relative change gradient When the preset burst threshold is exceeded, the step size of the sliding window is forcibly converged to the lower limit of the set step time.

4. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, The mathematical formula for calculating the combined deviation strength in step 2 is as follows: in, The combined deviation intensity, This represents the total number of dimensions of the sensor data streams currently active within the sliding window. For the first Dynamic weighting coefficients for dimensional sensor data. The first one in the current sliding window Real-time acquisition values ​​of dimensional sensor data, For the first The historical average of sensor data over a long-term monitoring period. For the first The standard deviation of the sensor data over the long-term monitoring period.

5. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, After triggering the cross-modal feature fusion logic in step 2, the process also includes a spatial hysteresis filtering step based on a discrete global grid system: obtaining the real-time spatial location of the logistics terminal. The current center coordinates of the grid and the center coordinates of adjacent grids When the judgment formula is met, it is confirmed that a physical grid crossing has occurred and the local service parameter set is retrieved: in, The Euclidean distance between the real-time spatial location and the coordinates of the center of the adjacent grid is given. The distance between the real-time spatial location and the coordinates of the center of the current grid is Euclidean distance. This is the preset hysteresis dead zone radius value.

6. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, In step 2, the streaming correlation analysis engine has an embedded logical deduction operator based on causal analysis. It extracts the linear velocity and angular velocity of the streaming terminal, and performs discretized integral deduction of the driving trajectory in the future time domain in combination with the current high-precision timestamp. The generated set of forward predicted trajectory curves and the known obstacle feature points in the environment are then used to solve the overlap probability matrix in the two-dimensional spatiotemporal domain.

7. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, Before hard-binding to a designated processor core of the heterogeneous multi-core architecture within the edge control unit in step 3, a scheduling decision step based on a processing cost function is also included: extracting the current clock frequency of the edge control unit. Data transmission volume of pending business tasks and current Ethernet uplink bandwidth By substituting the corresponding parameters of the local processing path and the unloading cooperative path into the processing cost function, respectively... To obtain the local processing cost and the unloading coordination cost; By comparing the cost functions of the two scheduling paths The scheduling strategy is to determine the path corresponding to the one with the least substitution value based on the size of the alternative. in, Let be the processing cost function of the path to be evaluated. The estimated total number of instruction cycles required to execute the pending business task. The preset time response weighting balance factor, This is a preset physical energy efficiency weighted balance factor.

8. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, The system load prediction trajectory in step 3 is output by the Long Short-Term Memory Network model. It takes the system load related time series in the past preset time period as input and performs rolling prediction of the system load topology in the future time step. When the predicted trajectory value exceeds the preset warning level, the interruption mechanism is triggered to suspend non-critical background asynchronous processes.

9. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, Step 4 also includes a version concurrent operation evaluation step: the structured data frame is synchronously pushed into the foreground execution area and the background loading area set as a restricted permission domain to build a virtual sandbox, the hardware control messages output by the background loading area are intercepted and redirected to the virtual black hole device, the business processing indicators output by different logic areas are compared in real time, and the virtual sandbox is released and atomic pointer switching is performed after the expected constraint function is met.

10. The real-time data processing method for logistics terminals based on edge computing according to claim 1, characterized in that, In step 5, the transition probability matrix of the probabilistic state machine model is generated in real time by training the historical job data locally cached by the edge control unit within a preset time period, satisfying the state transition probability formula: in, For logistics operations, the objects are in a discrete state Transition to discrete state The transition probability, Time node The discrete state of the system in which the logistics operation object is located. Time node The discrete state of the system in which the logistics operation object is located. and It belongs to a predefined set of discrete states in the lifecycle of a single logistics package.

Images