Logistics transportation scheme optimization method and system based on internet of things

Abstract

The present application relates to a logistics transportation scheme optimization method and system based on the Internet of Things, belonging to the cross technical field of intelligent logistics and the Internet of Things, obtaining inherent vibration frequency characteristics of goods to be transported and historical operation data of a road network; performing fast Fourier transform on historical sensing data to construct a dynamic excitation frequency prediction model of a road network section at different vehicle speeds; constructing a resonance avoidance constraint condition according to the inherent frequency characteristics and the prediction model; generating a scheme containing a target route and a corresponding dynamic driving speed control interval and issuing the scheme with the minimum comprehensive cost parameter and the satisfaction of resonance avoidance as the target. When the actual vehicle speed deviates from the preset interval, the edge module is activated to reconstruct the safe vehicle speed or trigger the micro-start-stop instruction to block the resonance accumulation; in the extreme vehicle speed unadjustable sliding condition, the system solves the chassis offset frequency variable and reconstructs the main stiffness of the active suspension to realize the physical frequency domain offset of the chassis, and realizes the anti-seismic protection of the precision goods in the frequency domain.

Description

Technical Field

[0001] This invention belongs to the field of interdisciplinary technology of smart logistics and Internet of Things, specifically, it relates to a method and system for optimizing logistics transportation solutions based on the Internet of Things. Background Technology

[0002] In modern logistics systems, the demand for transporting high-value, precision goods such as core components of medical magnetic resonance imaging systems, aspherical optical lens assemblies, and sensitive biochemical reagents is increasing. These goods are extremely sensitive to mechanical vibrations, and conventional physical packaging often fails to isolate the underlying destructive energy in specific frequency bands. Existing logistics transportation scheduling systems generally adopt a logic of spatial route detours based on macroscopic road surface smoothness when mitigating the risk of cargo damage.

[0003] Taking the "Logistics Transportation Route Planning Method, Apparatus, Computer Equipment, and Storage Medium" disclosed in Chinese Patent Document CN120235536A as an example, this prior art proposes to acquire bumpy road segment information from driving road condition information, use this road condition information as a basis to calculate the predicted object scattering degree of non-stop stations, and then perform spatial logistics route planning based on the scattering degree to correct the loading detection results. Analyzing the underlying operating logic of this type of prior art, its loss prevention mechanism is entirely based on the dimension of "macro-spatial avoidance". When the system detects road bumps, it determines that the road segment has a high risk of damage, and then instructs logistics vehicles to physically detour and avoid the bumps by replanning their routes.

[0004] This existing technology, which relies on macroscopic turbulence for route avoidance, faces insurmountable physical and logical bottlenecks in practical engineering applications. Summary of the Invention

[0005] The purpose of this invention is to provide a method and system for optimizing logistics transportation based on the Internet of Things, which solves the technical problem in the prior art that high-value and precision goods suffer irreversible physical damage during logistics transportation due to the mechanical resonance induced by the coupling between the specific driving speed of the vehicle and the undulations of the road surface.

[0006] The objective of this invention can be achieved through the following technical solutions:

[0007] An optimization method for logistics transportation solutions based on the Internet of Things (IoT) includes:

[0008] Obtain cargo attribute data of the goods to be transported and historical operation data of the road network;

[0009] An initial logistics transportation route is generated based on the cargo attribute data and the historical operation data.

[0010] The method of obtaining cargo attribute data of the goods to be transported includes obtaining the inherent vibration frequency characteristic data of the goods to be transported; the method further includes:

[0011] The historical operation data of the road network is extracted by frequency domain conversion using a big data processing module to construct a dynamic excitation frequency prediction model for each road segment under different driving speeds.

[0012] Resonance avoidance constraints are constructed based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model.

[0013] The objective function is determined by minimizing the comprehensive transportation cost parameter and the resonance avoidance constraint, and the target logistics transportation scheme is obtained based on the objective function. The target logistics transportation scheme includes the target spatial driving route and the dynamic driving speed control interval corresponding to each segment of the target spatial driving route.

[0014] The target logistics transportation plan is sent to the vehicle's onboard terminal system.

[0015] Furthermore, during the period when the vehicle travels according to the target logistics transportation plan, the real-time maximum allowable speed parameter of the current road segment and the forward road waveform detection data are obtained.

[0016] Determine whether the real-time maximum allowable vehicle speed parameter is within the dynamic driving speed control range;

[0017] If the real-time maximum allowable vehicle speed parameter deviates from the dynamic driving speed control range, the edge computing module of the vehicle terminal system is activated.

[0018] Furthermore, the edge computing module extracts the road surface vibration frequency characteristic parameters from the forward road surface waveform detection data;

[0019] Combining the vehicle's current dynamic load data and the inherent vibration frequency characteristic data, a new safe speed control range is calculated within a range not exceeding the real-time maximum allowable vehicle speed parameter.

[0020] The new safe speed control range is sent to the vehicle's underlying speed execution unit.

[0021] Furthermore, if the new safe speed control range does not exist within a value range not exceeding the real-time maximum allowable speed parameter, a micro-start-stop control command sequence is generated.

[0022] The micro-start-stop control command sequence includes drive acceleration commands and braking deceleration commands that are issued alternately according to a preset time period;

[0023] The micro-start-stop control command sequence is sent to the vehicle's underlying speed execution unit.

[0024] Furthermore, it also includes:

[0025] Determine whether the vehicle's current real-time driving speed is within the dynamic driving speed control range;

[0026] If the real-time feedback vehicle speed deviates from the dynamic vehicle speed control range and a non-adjustable vehicle speed control signal is received, an active suspension intervention command is generated.

[0027] The real-time feedback vehicle speed is obtained by acquiring wheel speed sensor monitoring data and global positioning system coordinate data collected by the vehicle-mounted sensing terminal, and performing Kalman filter fusion processing to calculate and output the result.

[0028] The non-adjustable vehicle speed control signal is generated by receiving an activation flag triggered by the vehicle's anti-lock braking system or the autonomous driving domain controller.

[0029] Furthermore, the generation of active suspension intervention commands includes:

[0030] The chassis offset frequency variable is calculated by combining the real-time feedback vehicle speed and the inherent vibration frequency characteristic data;

[0031] Based on the chassis offset frequency variable, the target suspension stiffness setting value and the target shock absorber damping setting value are obtained by searching the preset vehicle dynamics mapping database.

[0032] The active suspension intervention command is generated based on the target suspension stiffness setting value and the target shock absorber damping setting value, and the command is sent to the vehicle's active suspension controller.

[0033] Furthermore, the step of using a big data processing module to perform frequency domain transformation and extraction on the historical operating data of the road network to construct a dynamic excitation frequency prediction model for each road segment under different driving speeds includes:

[0034] Extract the time-series vibration acceleration sensor data and the corresponding historical matching vehicle speed data contained in the historical operation data;

[0035] Perform a fast Fourier transform on the time-series vibration acceleration sensing data to obtain the road surface excitation frequency distribution matrix under the corresponding historical matching vehicle speed;

[0036] The dynamic excitation frequency prediction model is generated by fitting the historical vehicle speed data and the road surface excitation frequency distribution matrix.

[0037] Furthermore, the step of determining the objective function by minimizing the comprehensive transportation cost parameter and the resonance avoidance constraint, and obtaining the target logistics transportation scheme based on the objective function, includes:

[0038] Assign first preset weights to the transportation time parameter and the energy consumption parameter and construct a basic cost calculation function;

[0039] Extract the fatigue accumulation benchmark threshold corresponding to the inherent vibration frequency feature data;

[0040] The cumulative predicted amount of frequency domain fatigue damage for each road segment at candidate vehicle speeds is calculated based on the dynamic excitation frequency prediction model.

[0041] The frequency domain fatigue damage cumulative prediction is assigned a second preset weight and a penalty function is constructed.

[0042] The basic cost calculation function and the penalty function are combined to execute a heuristic optimization algorithm to output the target logistics transportation plan.

[0043] Furthermore, the combination of the vehicle's current dynamic load data and the inherent vibration frequency characteristic data includes:

[0044] Get interactive data on unloading lists from logistics stations along the route the vehicle passes through;

[0045] The vehicle's overall mass reduction and physical center of gravity shift are calculated based on the unloading list interaction data.

[0046] The vehicle vibration transfer function model is updated based on the vehicle mass attenuation and the physical center of gravity offset.

[0047] The updated vehicle vibration transfer function model is used as an input parameter in the calculation of the new safe speed control range.

[0048] An IoT-based logistics transportation optimization system includes:

[0049] The initial route generation module is used to acquire cargo attribute data and historical road network operation data of the goods to be transported and generate an initial logistics transportation route. The cargo attribute data includes inherent vibration frequency characteristic data.

[0050] The model building module is used to extract the frequency domain conversion of historical road network operation data using the big data processing module, and to build a dynamic excitation frequency prediction model for each road segment under different driving speeds.

[0051] The constraint and optimization module is used to construct resonance avoidance constraints based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model, determine the objective function by minimizing the comprehensive transportation cost parameter, and obtain the target logistics transportation scheme based on the objective function.

[0052] The terminal interaction module is used to send the target logistics transportation plan to the vehicle's on-board terminal system, so that the on-board terminal system can perform edge computing optimization or active suspension intervention when the vehicle speed deviates from the dynamic driving speed control range.

[0053] The beneficial effects of this invention are:

[0054] 1. This invention maps road condition fluctuations into a dynamic excitation frequency prediction model at different vehicle speeds using fast Fourier transform. The system guides vehicles to travel within a specific speed range in unavoidable bumpy road sections, actively crossing the inherent resonance frequency band of the cargo in a frequency domain misalignment manner, thus cutting off the resonance damage chain while significantly reducing detour distance and time costs.

[0055] 2. In extreme conditions where vehicle speed is limited due to congestion and cloud-based replanning fails, this invention reconstructs the dynamic transfer function by relying on the onboard edge computing module and the changes in vehicle mass and center of gravity caused by unloading at multiple sites. When encountering obstacles in the pure speed range, it innovatively triggers micro-start-stop control commands, forcibly breaking down the concentrated resonant frequency spectrum peaks into a broadband white noise energy spectrum through continuous variable acceleration actions, thus preventing the continuous physical accumulation of resonant energy in the time dimension under low-speed creep conditions.

[0056] 3. In situations where the vehicle speed is completely unadjustable, such as long downhill slopes on ice or snow, or during emergency braking, this invention relies on the real vehicle speed output by Kalman filtering to derive the safe frequency offset. It then directly reconstructs the main stiffness of the suspension by changing the volume and pressure of the air spring chambers. This instantly transforms the chassis system into a physical band-stop filter, using impedance mismatch to reflect and dissipate destructive energy at the spring base, creating an impenetrable physical barrier for delicate cargo. Attached Figure Description

[0057] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0058] Figure 1 This is a flowchart of the global anti-resonance control logic of the present invention;

[0059] Figure 2 This is a simulation diagram of the anti-resonance speed optimization for highway sections according to the present invention;

[0060] Figure 3 This is a simulation diagram of the anti-resonance speed optimization for urban road sections according to the present invention;

[0061] Figure 4This is a simulation diagram of the speed optimization for preventing resonance on bumpy road sections during the construction of this invention;

[0062] Figure 5 This is a diagram showing the module connections of the IoT-based logistics transportation solution optimization system of the present invention. Detailed Implementation

[0063] The technical solutions of the present invention will be clearly and completely described below with reference to the embodiments of the present invention. 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 of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0064] Figure 1 This diagram illustrates the hierarchical decision-making logic of the system, from macro-level planning to micro-level intervention. The process begins with matching the inherent frequency of goods with the road network excitation model in the cloud, establishing an initial "path + speed" corridor through a heuristic algorithm. The diagram details two key logical branch points: first, when external traffic conditions restrict vehicle speed, the logic flows to on-board edge computing for model reconstruction; second, when the physical path for speed adjustment is interrupted, the logic switches to underlying active suspension intervention. This diagram defines the algorithmic jump rules of the system under different stress states.

[0065] Figure 2 This reflects the vehicle's frequency domain performance on roads with high smoothness. Simulation curves show that the excitation energy generated at high speeds is mainly concentrated in the low-frequency range (e.g., 1.5Hz to 2.5Hz). Through dynamic speed optimization, the main peak of the predicted excitation spectrum is precisely shifted outside the envelope of the cargo's 10Hz resonance band. The predicted cumulative damage D value shown in the figure is at an extremely low level (e.g., 0.0042), demonstrating that under high-speed conditions, excellent physical health can be achieved by fine-tuning the vehicle speed.

[0066] Figure 3 The optimization results are presented under moderate speed-limited conditions. Compared to highway sections, the excitation spectrum of urban roads exhibits a multi-peak distribution with increased amplitude. The simulation interface shows that the system can still calculate a safe speed corridor avoiding the 10Hz resonance band even within a relatively low permissible speed range (e.g., around 50 km / h). This figure demonstrates the robustness of the algorithm in extracting the cargo immune frequency band and maintaining physical isolation under complex frequency interference backgrounds.

[0067] Figure 4This simulation depicts a strategic game under extreme road surface excitation conditions. Due to the severe road surface undulations, the predicted energy amplitude of the excitation spectrum increases significantly. The red shaded area in the figure clearly marks the resonance dead zone of the cargo, where the distance between the excitation peak and the dead zone boundary narrows. This simulation illustrates the intervention effect of the penalty function at this point: the system forcibly lowers the excitation frequency by sacrificing some transportation time (reducing the vehicle speed to below 30 km / h), ensuring that the cargo can still escape the resonance amplification effect under harsh physical conditions.

[0068] Figure 5 The diagram reveals the physical links and information topology of the various hardware units within the system. Within the cloud processor cluster, the initial path vector, excitation prediction model, and target scheme control matrix constitute a vertically cascaded data chain. The onboard edge computing platform, as a local core, receives downlink commands from the cloud via a mobile communication interface and collects raw signals from the sensing hardware in real time. The diagram clearly shows the parameter injection path from the "load update submodule" to the "edge computing module." The underlying controller and execution hardware are located at the link terminals. The longitudinal dynamic commands issued by the edge module drive the motor and braking system, while the physical impedance adjustment commands generated by the active intervention module directly act on the active suspension controller, achieving a closed loop from digital commands to transient reconstruction of mechanical characteristics.

[0069] Example 1

[0070] This embodiment provides an IoT-based logistics transportation scheme optimization method, including: acquiring cargo attribute data of goods to be transported and historical operation data of the road network; generating an initial logistics transportation route based on the cargo attribute data and the historical operation data; acquiring cargo attribute data of goods to be transported includes acquiring inherent vibration frequency characteristic data of the goods to be transported; the method further includes: using a big data processing module to perform frequency domain conversion on the historical operation data of the road network to extract and construct a dynamic excitation frequency prediction model for each road segment under different driving speeds; constructing resonance avoidance constraints based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model; determining an objective function by minimizing the comprehensive transportation cost parameter and the resonance avoidance constraints, and obtaining a target logistics transportation scheme based on the objective function, the target logistics transportation scheme including a target spatial driving route and dynamic driving speed control intervals corresponding to each road segment in the target spatial driving route; and sending the target logistics transportation scheme to the vehicle's on-board terminal system. The method of using a big data processing module to extract frequency domain conversion data from historical operating data of the road network and constructing a dynamic excitation frequency prediction model for each road segment under different driving speeds includes: extracting time-series vibration acceleration sensor data and corresponding historical matching vehicle speed data contained in the historical operating data; performing fast Fourier transform processing on the time-series vibration acceleration sensor data to obtain the road surface excitation frequency distribution matrix under the corresponding historical matching vehicle speed; and fitting the dynamic excitation frequency prediction model based on each historical matching vehicle speed data and the road surface excitation frequency distribution matrix. The specific construction process of the dynamic excitation frequency prediction model is as follows: using a support vector machine regression algorithm to map the spatial features of road segments and vehicle dynamic speeds to frequency domain main peak values. The input candidate vehicle speed is set to... The feature vector of road surface excitation frequency distribution extracted based on historical fast Fourier transform is: The corresponding historical true frequency domain peak value is The nonlinear regression solution formula for this dynamic excitation frequency prediction model is defined as follows:

[0071] In the formula, Represents the given candidate vehicle speed Below, the model predicts the main peak value of the road excitation frequency that the cargo box floor is expected to face; The total number of samples representing support vectors; and The non-negative penalized Lagrange multipliers of the support vector machine obtained by solving the Lagrange multiplier method are represented. Represents the radial basis kernel function, which is used to map low-dimensional velocity inputs to a high-dimensional feature space to handle the step characteristics caused by the nonlinear damping of the shock absorber. This represents the regression bias parameter. Through this model, the system can transform continuous physical vehicle speed variables into accurate frequency-domain excitation prediction parameters.

[0072] The process of minimizing the comprehensive transportation cost parameter and determining the objective function based on the resonance avoidance constraint, and obtaining the target logistics transportation scheme based on the objective function, includes: assigning a first preset weight to the transportation time parameter and energy consumption parameter and constructing a basic cost calculation function; extracting the fatigue accumulation benchmark threshold corresponding to the inherent vibration frequency characteristic data; calculating the frequency domain fatigue damage accumulation prediction amount for each road segment at the candidate vehicle speed according to the dynamic excitation frequency prediction model; assigning a second preset weight to the frequency domain fatigue damage accumulation prediction amount and constructing a penalty function; and performing a heuristic optimization algorithm by fusing the basic cost calculation function and the penalty function to output the target logistics transportation scheme. When logistics vehicles cross road surface geometric undulations, the road surface deformation input is converted into high-frequency temporal vibration through suspension mechanical coupling. Conventional solutions often use air suspension to passively adjust stiffness for buffering. The airbag inflation and deflation response delay of existing air suspensions is usually in the range of 0.5 seconds to 1.5 seconds. When the vehicle speed is greater than 40 km / h, the physical action of stiffness adjustment is seriously delayed compared to the road surface excitation input, and it is completely unable to isolate the low-frequency excitation energy in the 1Hz to 15Hz band. This absolute lag at the physical level requires that control actions be performed in advance; the system must actively change the vehicle's spatial trajectory or speed before the vehicle reaches a bumpy section of road.

[0073] The system hardware architecture for performing the above operations employs a cloud-based central processing unit (CPU) cluster deployed in the regional dispatch center. This cluster integrates 64 to 128 core parallel computing nodes to handle matrix throughput and floating-point operations across provincial road network maps. The cloud-based CPU cluster communicates with the vehicle's onboard terminal system via a mobile communication network for full-duplex data streaming.

[0074] During the model building phase, the big data processing module needs to quantify the road surface vibration state at different speeds. The module extracts historical operational data from a cloud-based non-relational database, including time-series vibration acceleration sensor data and corresponding historical matched vehicle speed data. These massive samples are fed back by lead trucks that previously traversed the characteristic road sections, and the data originates from triaxial MEMS accelerometers with a sampling frequency of 1000Hz and a range of ±10g, installed at the chassis stress points. The time-domain waveform exhibits broadband oscillations in the coordinate system. An attempt was made to directly extract characteristic peaks using a thresholding method on the time axis, but the periodic geometric excitation waveform of the road surface was completely masked by high-frequency random noise generated by fine gravel or tire noise, causing the peak-finding logic to fall into a local dead loop or output spurious peaks; this approach was rejected.

[0075] The cloud-based central processing unit cluster performs Fast Fourier Transform (FFT) processing on the time-series vibration acceleration sensing data. The mathematical-physical transformation relationship of the FFT is defined by the discrete complex integral series formula:

[0076] In the physical mapping relationship of this formula, The discrete-time vibration acceleration scalar points, uniformly sampled at equal intervals along the time axis, represent the transient force impact amplitude state of the vehicle chassis in the vertical direction of three-dimensional space; parameters Represents the total number of valid time-domain data sampling points within a single processing time window, with a set value range of 1024 to 4096; parameter Represents the frequency index step size number on the discrete frequency domain coordinate axis after transformation, corresponding to a physical frequency range limited to 0.1Hz to 500Hz; exponential factor term. The rotated complex exponential basis mapping function serves as the energy analysis function in the frequency domain; the solution obtained from the left side of the equation is... It represents the extreme values ​​of the complex absolute amplitude and phase distribution of mechanical vibration energy at various discrete frequency scale points.

[0077] The transformation operation decomposes and reassembles the chaotic acceleration jumps in the time domain into a road surface excitation frequency distribution matrix corresponding to the historical matching vehicle speed. The matrix characterizes the frequency band where the main destructive vibration energy transmitted from the road segment to the cargo box accumulates at a specific vehicle speed. After obtaining the matrix library, the cloud-based central processing unit cluster introduces a support vector machine regression algorithm to perform high-dimensional spatial surface fitting. The damping coefficient of the hydraulic oil in the vehicle's shock absorber exhibits nonlinear step characteristics with the piston's movement speed. Conventional polynomial fitting cannot recreate this step inflection point; the support vector machine, through kernel function mapping, better fits this nonlinear surface. The optimized fitted surface outputs the main peak value of the excitation frequency that the vehicle is expected to face and its energy attenuation bandwidth, establishing and generating the dynamic excitation frequency prediction model within the cloud server. By inputting the candidate planned road segment identifier and the simulated initial driving speed into the model, the model calculates the oscillation wave impact frequency experienced by the loading platform.

[0078] The cloud-based central processing unit cluster constructs resonance avoidance constraints based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model. Precision goods possess specific inherent vibration frequency characteristic data; let its resonance sensitive center frequency be... The coincidence of the mechanical excitation frequency ripple applied externally with the natural frequency will generate an exponential energy resonance amplification. This resonance avoidance constraint is manifested in the system's underlying code as a Boolean logic exclusion boundary with absolute penalty, and its rigorous mathematical inequality constraint is defined as follows:

[0079] In the formula, The dynamic excitation frequency prediction model is used at candidate vehicle speeds The output estimate is as follows; This represents the critical value of the safety isolation bandwidth set by the system. This isolation bandwidth is used to prevent secondary interference from higher harmonics. The value of is strictly limited to When a specific vehicle speed is within the candidate planned road segment This causes the predicted excitation frequency to fall into When the open interval is in a dead zone, the constraint judgment logic outputs a Boolean value of false (0). In the global optimization algorithm, this "path-velocity" combination is given an infinite penalty cost, thereby forcibly requiring the main peak of the road surface excitation frequency output by the dynamic prediction model to maintain a minimum distance from the natural frequency range of the cargo. Physical isolation ensures that the possibility of resonance is blocked from the digital simulation stage.

[0080] Actual cross-provincial routes involve vibration losses in secondary frequency bands. Low-amplitude oscillations that do not reach the resonance threshold do not reach the critical value for stress damage in a single bump. However, as the vehicle's mileage accumulates significantly, high-frequency alternating stress waves can generate microcracks in the cargo's microstructure, leading to irreversible fatigue damage. The peak transient limitation method only intercepts single large impacts and cannot reflect the propagation of such microcracks. The system extracts the fatigue accumulation benchmark threshold corresponding to the inherent vibration frequency characteristic data. Based on the linear fatigue cumulative damage rule, the frequency domain fatigue damage accumulation prediction of each road segment under candidate vehicle speed combinations is calculated iteratively. The core logical equation is defined as follows:

[0081] Parameter When a vehicle travels a long distance at a specific planned speed, the predicted position of the cargo is... Number of alternating mechanical oscillation cycles within a stress amplitude range; parameter This indicates that the material of the goods is subject to the first The total number of standard cyclic impacts that the material can withstand under alternating stress amplitude levels until microfracture occurs; parameters The total number of discrete stress amplitude levels is set to a range of 8 to 16 based on the characteristics of the SN fatigue life curve of the metal or polymer material; parameter This is a scalar value for the cumulative predicted quantity of fatigue damage in the frequency domain. In engineering applications, original manufacturing defects and deviations in the material are taken into account. The baseline threshold constant is set in the range of 0.7 to 0.85. When the predictor variable... As the value approaches the threshold constant, the system assigns a second preset weight to the acquired cumulative fatigue damage prediction in the frequency domain, using this as the core independent variable to construct a nonlinear dynamic penalty function within the model. As the cumulative fatigue damage value increases, the fitness score of the relevant spatial path and speed combination in the evaluation system is mathematically penalized, triggering the algorithm to actively discard high-loss routes. The cloud-based central processing unit cluster initiates a macroscopic path and speed joint optimization calculation process. The transportation time parameter and energy consumption parameter are assigned a first preset weight to construct a basic cost calculation evaluation function. In this scenario, the weight of a single edge in the logistics network is strongly coupled with the initial vehicle velocity and acceleration, exhibiting a dynamic function parameter. Traditional Dijkstra's algorithm or A* pathfinding algorithm, when dealing with such dynamic edge weights, experiences an exponential explosion in node traversal time, failing to output usable results within the scheduling time window. The system integrates the basic cost calculation function and penalty function within the algorithm framework to construct a comprehensive fitness evaluation objective function, employing an ant colony optimization algorithm with a polymorphic pheromone adaptive update mechanism.

[0082] A virtual agent representing a route plan explores a virtual road network topology. The agent's movement between intersections involves selecting road segments on a two-dimensional planar map, simultaneously matching a specific safe driving speed scalar within the legally mandated speed limit. When a spatial and speed combination triggers a resonance avoidance constraint boundary, its fitness value is assigned a positive infinity value, resulting in direct elimination during population evolution. For routes avoiding absolute resonance dead zones, the algorithm evaluates the trade-off between the increased basic costs of detours and speed limits and the increased fatigue accumulation penalty. When the optimal solution space converges to a numerical equilibrium state, the cloud processor outputs a target logistics transportation plan that minimizes the sum of the comprehensive fitness objective function. The target plan is composed of a multi-dimensional state control matrix interwoven with geospatial location node identifiers and corresponding time node vehicle absolute speed scalars, subdividing road segment feature points and strictly mapping independently calculated dynamic driving speed control intervals. This speed interval constitutes an anti-resonance safe speed time corridor, forcibly constraining tire rotation frequency and chassis impact frequency.

[0083] The cloud platform encapsulates the final execution instructions of the multi-dimensional state matrix information using high-strength data encryption and sends them to the vehicle's onboard intelligent sensing terminal system via a wide-area wireless communication network. The onboard terminal system unpacks the data and synchronously loads it onto the main control display screen in the truck's cab and the memory of the underlying dynamics domain controller. Under traditional driving conditions, the onboard terminal system presents the dynamic driving speed control range to the driver within the feedforward time window when the vehicle enters the corresponding road segment. If the speed exceeds the limit, a warning sound is triggered to guide adjustment of the accelerator pedal or brake pressure. For vehicles equipped with adaptive cruise control systems, the terminal system directly uses the unpacked speed control range array as a strong constraint boundary for the vehicle's longitudinal dynamic trajectory planning. The system sends dynamic torque limiting commands to the engine control unit and transmission controller via the onboard controller's local area network bus, adjusting the caliper clamping intervention parameters of the electromechanical braking system. When the vehicle traverses undulating road surfaces, the physical vibration frequency is controlled within the safe, immune frequency band of the cargo.

[0084] Example 2

[0085] This embodiment provides an IoT-based logistics transportation scheme optimization method. During the cycle of a vehicle traveling according to the target logistics transportation scheme, the method acquires the real-time maximum permissible speed parameter and the forward road surface waveform detection data of the current road segment. It then determines whether the real-time maximum permissible speed parameter is within the dynamic driving speed control range. If the real-time maximum permissible speed parameter deviates from the dynamic driving speed control range, the edge computing module of the vehicle terminal system is activated. The edge computing module extracts the road surface excitation frequency characteristic parameter from the forward road surface waveform detection data. Combining the vehicle's current dynamic load data and the inherent vibration frequency characteristic data, a new safe speed control range is calculated within a value range not exceeding the real-time maximum permissible speed parameter. This new safe speed control range is then sent to the vehicle's underlying speed execution unit. If the new safe speed control range does not exist within a value range not exceeding the real-time maximum permissible speed parameter, a micro-start-stop control command sequence is generated. This micro-start-stop control command sequence includes drive acceleration commands and braking deceleration commands issued alternately according to a preset time period. The micro-start-stop control command sequence is then sent to the vehicle's underlying speed execution unit. The process of combining the vehicle's current dynamic load data and its inherent vibration frequency characteristic data includes: acquiring unloading manifest interaction data from logistics stations along the vehicle's route; calculating the vehicle's overall mass attenuation and physical center of gravity shift based on the unloading manifest interaction data; updating the vehicle's overall vibration transfer function model based on the overall mass attenuation and physical center of gravity shift; and using the updated overall vibration transfer function model as input parameters to calculate the new safe speed control range. When the vehicle leaves the logistics distribution center and is actually driving in the physical road network, if it encounters road congestion, traffic accident-related traffic restrictions, or traffic control, the traffic signs or navigation traffic flow received by the vehicle will output a hard upper speed limit threshold (the set constraint range is usually between 10km / h and 30km / h). The onboard perception terminal responsible for coordinating the vehicle's information flow continuously identifies and acquires the real-time maximum allowable speed parameter of the current road segment through the vehicle network interface or the forward-facing camera. The onboard computing platform performs a real-time Boolean comparison operation between this real-time maximum allowable speed parameter and the cloud-preset dynamic driving speed control range. When the real-time maximum permissible speed parameter deviates from the dynamic driving speed control range, the vehicle loses the physical conditions to maintain the original anti-resonance speed. The system relies on a cloud-based central processing unit to replan, but this is limited by network latency (typically >500ms) in cellular base station coverage blind spots and the lack of physical bypasses for the current road segment, creating a deadlock. The system immediately activates the edge computing module integrated within the vehicle terminal system. This edge computing module, built using tensor processing units deployed within the vehicle's cockpit control domain, possesses offline local matrix floating-point arithmetic capabilities and directly takes over the anti-resonance control logic for the current road segment.The primary computational task of the edge computing module after taking over control is to address the distortion of the vehicle's underlying physical model caused by multi-site unloading. The commercial vehicle chassis control logic calibrates the curb weight before departure, treating the chassis as a rigid body system with constant mass throughout the entire transport voyage. In real multi-node delivery scenarios, as vehicles unload goods at logistics stations along the route, the static load on the chassis suspension system experiences a step-like decrease (the amount of goods unloaded in a single operation may range from 2000kg to 5000kg). In conventional load monitoring solutions, dynamic strain gauges are typically installed at each axle suspension node to acquire real-time force changes. However, the harsh environment of commercial vehicle chassis, including mud and water splashes, high-frequency and severe vibrations, and alternating temperatures, leads to extremely high zero-point calibration drift rates for strain gauges. Hardware failures and subsequent maintenance and replacements significantly increase the operating costs per vehicle. This solution employs software-defined physics logic, acquiring unloading manifest data from logistics stations along the route via the vehicle's internal local area network interface. This data stream includes the weight scalar of the unloaded goods and its geometric projection position on the two-dimensional coordinate system of the cargo box floor. The edge computing module calculates the current vehicle mass decay and the physical center of gravity offset in three-dimensional space through the torque balance equation. The specific physical quantity calculation logic is as follows: the curb weight of the vehicle at the initial departure time is set to . The initial three-dimensional physical centroid coordinates of the vehicle are Analyze the unloading manifest interaction data of logistics stations along the route to extract the unloading data of each station. A separate cargo unit, assuming the first The mass scale of each cargo unit is Its centroid projection position in the three-dimensional reference coordinate system of the cargo compartment is The aforementioned vehicle mass reduction The calculation formula is:

[0086] Based on the principle of rigid body moment equilibrium, the unloading action disrupts the original static force distribution of the chassis, resulting in an updated offset of the vehicle's physical center of gravity (in the new center of gravity coordinates). The formula for calculating the characterization is:

[0087]

[0088] ;

[0089]

[0090] The above parameters establish the dynamic static load distribution ratio of each support node of the chassis suspension system and the geometric displacement of the force lever arm under the unloading conditions along the route, providing boundary conditions for subsequent dynamic reconstruction.

[0091] Changes in the mass boundary conditions cause alterations in the system's inherent dynamic properties. These two dynamic variables are introduced into the chassis dynamics reconstruction algorithm to update the vehicle's overall vibration transfer function model. The mathematical formula for the dynamic transfer function describing the vibration response from road surface excitation to the cargo box floor in the Laplace complex frequency domain is defined as follows:

[0092] In this mathematical formula for the transfer relation in the complex frequency domain, Represents the Laplace operator transformation variable; This represents the initial baseline value for the fully loaded curb weight of a vehicle, with a set range of 18,000 kg to 31,000 kg. This represents the amount of vehicle weight reduction calculated by accumulating data from the unloading manifest interaction, with a set value range of 0 to... ; The equivalent linear damping feedback coefficient representing the hydraulic circuit of the vehicle chassis shock absorber has a calibration range of 30,000 N·s / m to 80,000 N·s / m. This represents the principal stiffness coefficient of the axle air springs, with a calibrated value range of 400 kN / m to 1200 kN / m. As goods are unloaded along the route... The value increases in a stepped manner, while the effective dynamic load-bearing mass in the denominator decreases sharply. (Suspension principal stiffness) Maintaining a constant state, according to the inherent frequency equation, the resonant frequency of the vehicle chassis structure will inevitably deteriorate and shift towards higher frequency bands. Using the static transfer function model from the initial launch to guide edge computing, the calculated new vehicle speed range will cause the excitation frequency to coincide with the shifted actual chassis frequency, inducing physical resonance loss in the vehicle. The edge computing module uses the updated vehicle vibration transfer function model as the core underlying input parameter in safety strategy derivation, ensuring a rigorous mapping between the mathematical model and actual stress conditions; after establishing the updated vehicle vibration transfer function model... Then, the edge computing module executes a traversal optimization algorithm with hard inequality constraints to calculate the new safe speed control interval. The specific optimization process is as follows: First, using the transfer function... Extract the true natural resonance frequency of the chassis suspension system after unloading. Its undamped approximate calculation formula is:

[0093]

[0094] Simultaneously, the edge computing module analyzes the forward road surface waveform detection data and extracts the main undulation spatial wavelength parameter of the forward road surface ripples through spatial discrete Fourier transform. When the vehicle is at a certain planned dynamic speed The dynamic vibration frequency transmitted from the road surface to the chassis during driving. The mapping relationship is defined as To achieve absolute resonance avoidance, the system establishes a mechanism regarding the independent variable, vehicle speed. The system of hard-constrained inequality equations. Let the natural vibration frequency characteristic data of the precision cargo to be transported be the dominant frequency. The system's set security isolation bandwidth constant is Candidate safe speed The physical boundary constraints must be met simultaneously to prevent resonance with the precision cargo itself and to prevent resonance with the chassis after unloading frequency deviation:

[0095] Furthermore, the real-time maximum permissible speed parameter for the current road segment is introduced. As the boundary of the closed-loop solution, i.e. The edge computing module performs intersection operations within the feasible solution space bounded by the above system of inequalities. If a continuous set of real velocity solutions is obtained... If the condition is met, it is established as the new safe speed control range and sent to the underlying execution unit; if it is determined that the set of inequalities has no solution in the extremely low speed limit area, it proves that the pure speed regulation path has failed, and the system sequentially triggers the generation of micro start-stop control command sequence to forcibly disperse the frequency energy spectrum.

[0096] After calibrating its own dynamic model, the edge computing module synchronously drives a binocular vision camera and a 128-line LiDAR array deployed behind the vehicle's front bumper or windshield. The sensing hardware scans the road surface within 50 meters ahead using a 20Hz beam capture, acquiring 3D point cloud data containing physical undulation geometry, and generating forward road surface waveform detection data. The edge computing module's built-in algorithm performs a spatial-domain to frequency-domain conversion on this detection data, extracting the road surface excitation frequency characteristic parameters. The system faces stringent physical boundary conditions; the algorithm must, within a value range not exceeding the real-time maximum allowable vehicle speed parameter (e.g., limited to below 20 km / h due to the distance to the vehicle ahead), combine the updated vehicle vibration transfer function model and the inherent vibration frequency characteristic data of the precision cargo to inversely solve for a new safe vehicle speed control range. The edge computing module executes an ergonomic optimization algorithm with hard inequality constraints to find a set of available vehicle speeds that allow the excitation energy spectrum transmitted to the cargo compartment to avoid the cargo's inherent frequency (e.g., calculating that the vehicle speed needs to be controlled between 12 km / h and 14 km / h). The calculated new safe speed control range is sent to the engine control unit or drive motor controller via the vehicle's controller area network bus. The underlying speed execution unit takes over the logic response of the accelerator pedal and forces the vehicle to drive within the suboptimal safe speed range replanned by the local computing power by limiting the fuel injection quantity or inverter torque output.

[0097] In physical traffic scenarios, simple speed range reconstruction algorithms can become mathematically unsolvable. When encountering low-speed congestion and creep, the real-time maximum permissible speed parameter is compressed to an extremely low value below 10 km / h. Regardless of the speed value within this low-speed range, after mapping using the forward-looking road surface waveform and transfer function, it all falls precisely within the inherent resonance dead zone of high-value goods (e.g., the sensitive frequency band of 1.5Hz to 3.5Hz). The algorithm determines that there is no new safe speed control range within a value not exceeding the real-time maximum permissible speed parameter. The system must not allow vehicles to continuously roll over undulating road surfaces at a constant speed. For such extreme frequency domain overlap conditions, an independent active full-hydraulic suspension system was previously evaluated for forced impedance decoupling. This hardware solution requires a high-pressure accumulator with a volume greater than 100L and a high-power independent servo hydraulic pump station, increasing the chassis weight by more than 300kg and incurring excessively high pipeline modification costs. This approach lacks commercial applicability for conventional logistics heavy trucks, where load capacity is a core indicator. Based on the existing underlying architecture, the edge computing module triggers a control command intervention mechanism to generate a micro-start and stop control command sequence.

[0098] This sequence alters the underlying logic of conventional driving, which prioritizes constant speed to reduce fuel consumption. Mechanical resonance, in its physical essence, is not a single instance of high-gravity acceleration impact damage, but rather a long-term energy accumulation and amplification process driven by a continuous, periodic excitation source highly consistent with the natural frequency. External excitation periodically injects energy into the mechanical system, with the injection time span exceeding the material's own energy dissipation period, resulting in a divergent, destructive trend in vibration amplitude. The micro-start-stop control command sequence breaks the periodicity of the excitation source through human intervention. The command sequence includes alternating drive acceleration and braking deceleration commands issued according to a preset time period. After the micro-start-stop control command sequence is sent to the underlying speed execution unit, the drive axle motor and the drive-by-wire hydraulic braking system alternately take over the vehicle's longitudinal dynamics. The mathematical formula for the energy accumulation of this physical force state is defined as:

[0099] In this energy assessment formula, The transient temporal physical speed parameter of the vehicle chassis, which is modulated by the micro-start-stop command sequence, is forcibly constrained to vary within the creep range of 0 to 10 km / h. This represents the time-varying road surface excitation input force generated by the road surface waveform under dynamic vehicle speed mapping. This represents the time-domain physical oscillation response speed of the cargo box floor under the excitation force; upper limit of integration. The threshold limit for the alternation time of micro-start and stop commands is strictly limited to a range of 0.5 seconds to 2.5 seconds. The critical value of the ultimate energy that the cargo's microstructure can withstand when it undergoes irreversible fatigue fracture or detachment is set in the range of 50J to 3000J, and is called synchronously with the inherent property constants of the cargo being transported. This represents the total harmful mechanical vibration energy transmitted and accumulated on the cargo body during a single start-stop cycle. Micro-start-stop strategies require a mandatory time threshold. The time required for resonant energy to accumulate to the critical state of destruction must be less than the physical incubation time. At the microscopic scale, the vehicle exhibits a non-linear, alternating pattern of stepped acceleration and short braking. The concentrated, single resonant frequency spectrum peak generated by uniform speed travel is forcibly broadened and dispersed into a uniformly distributed broadband white noise energy spectrum by the vehicle's continuous longitudinal acceleration and deceleration. The excitation energy cannot form a continuous, time-dimensional accumulation at a single frequency point, thus breaking the physical resonance chain. This distributed control architecture, which integrates macroscopic speed limiting with microscopic high-frequency chassis kinematic control, relies on autonomous command interactions at the vehicle end to achieve active vibration protection.

[0100] Example 3

[0101] This embodiment consists of the following steps: acquiring the vehicle's current real-time feedback driving speed; determining whether the real-time feedback driving speed is within the dynamic driving speed control range; and generating an active suspension intervention command if the real-time feedback driving speed deviates from the dynamic driving speed control range and a non-adjustable speed control signal is received. Generating the active suspension intervention command includes: calculating a chassis offset frequency variable by combining the real-time feedback driving speed and the inherent vibration frequency characteristic data; searching a preset vehicle dynamics mapping database based on the chassis offset frequency variable to obtain a target suspension stiffness setting value and a target shock absorber damping setting value; generating the active suspension intervention command based on the target suspension stiffness setting value and the target shock absorber damping setting value, and sending the command to the vehicle's active suspension controller. The process of obtaining the vehicle's current real-time feedback driving speed includes: acquiring wheel speed sensor monitoring data and global positioning system coordinate data collected by the vehicle-mounted sensing terminal; performing Kalman filter fusion processing on the wheel speed sensor monitoring data and the global positioning system coordinate data to calculate and output the real-time feedback driving speed; and receiving the vehicle's anti-lock braking system or autonomous driving domain controller trigger activation flag to generate the vehicle speed non-adjustable control signal.

[0102] When heavy-duty logistics vehicles encounter long downhill slopes covered with ice and snow with a gradient greater than 8%, or when radar detects a stationary obstacle 30 meters ahead and triggers emergency braking, the tire slip ratio exceeds the 15% adhesion limit. Obtaining the true longitudinal speed is a prerequisite for implementing frequency-based anti-vibration control. The single-sensor architecture completely fails at this point. The wheel speed sensor outputs zero km / h when the wheels lock up and 120 km / h when slipping, while the actual physical vehicle speed often remains around 40 km / h. The GPS coordinate data update frequency is between 1 Hz and 10 Hz, and there are physical signal breakpoints in tunnels or deep mountain canyons up to 2 kilometers long. An evaluation of introducing a six-axis inertial measurement unit for dead reckoning showed that, during 300 seconds of continuous driving without absolute position calibration, the zero bias error of the accelerometer's nominal 2 milligravimetric acceleration, after double integration, resulted in a velocity divergence error greater than 8 km / h. This magnitude of speed measurement error can cause a 1.5 Hz misalignment in the road surface excitation frequency calculated by the algorithm, directly crossing the sensitive resonance band of 2.0 Hz to 3.0 Hz in medical MRI equipment, leading to serious misjudgment. The solution employs a high-performance microprocessor within the vehicle-mounted sensing terminal to perform Kalman filtering fusion processing on the wheel speed sensor monitoring data and GPS coordinate data.

[0103] Kalman filtering performs optimal recursive estimation with a period of ten milliseconds, comprising two sets of equations: state prediction and observation update. The state prediction equation is defined as follows: Parameter The prior prediction vectors represent the vehicle's longitudinal velocity and acceleration at the current moment. The velocity scalar ranges from 0 to 33 meters per second, and the acceleration scalar ranges from -10 meters per second squared to 5 meters per second squared. Represents the state transition matrix; This represents the posterior optimal state estimate result from the previous time step. Represents the control input matrix; A scalar representing the torque command of an electric motor or hydraulic braking system, with a value range from -30,000 Nm to +30,000 Nm; The prior estimate error covariance matrix represents the current time step. This is the error covariance matrix of the previous period; The representative process excitation noise covariance matrix, calibrated from 0.001 to 0.1, is used to encompass unmodeled disturbances caused by abrupt changes in road slope from two to five degrees. After importing the observation data, the observation update equation is executed:

[0104] Parameter Represents the Kalman gain matrix; Represents the observation matrix; The measurement noise covariance matrix represents the internal variance elements, whose dynamic values ​​fluctuate between 0.1 and 1,000. The observation vector represents the combination of wheel speed sensors and the Global Positioning System; The output represents the real-time feedback of the vehicle speed. This represents the posterior estimation error covariance matrix. When icy or snowy road surfaces cause the wheel speed sensor slip rate to exceed 20%, the observed vector... The wheel speed residual increases sharply. Algorithm intervention will measure the noise matrix. The corresponding variance element was forcibly increased from 0.5 to 500. This numerical suppression forced the Kalman gain to... The corresponding feedback weight is close to zero. The system directly rejects distorted wheel speed signals and relies on state prediction values ​​and low-frequency positioning coordinates to maintain a continuous output of the true vehicle speed of 45 kilometers per hour.

[0105] The system determines whether the real-time feedback vehicle speed of 45 km / h is within the dynamic driving speed control range of 20 km / h to 30 km / h. The speed deviates. Conventional anti-resonance logic would issue a deceleration command of three times the gravitational acceleration (-0.3), a correction action that absolutely conflicts with the current physical limits of the chassis. The vehicle's anti-lock braking system detects an excessive slip ratio and performs cyclic braking at a frequency of 15 Hz to release pressure; or the autonomous driving domain controller determines that the distance ahead is less than the physical braking limit and triggers full braking with the highest safety priority. The microprocessor receives the activation flag from the anti-lock braking system or the autonomous driving domain controller and then generates a non-adjustable speed control signal. The logistics vehicle is in a state of passive coasting or full braking at 45 km / h. This speed causes it to crush a 5-meter-spaced cement road joint, generating a continuous 2.5 Hz mechanical excitation frequency. This frequency falls within the inherent resonance dead zone of 2.4 Hz to 2.6 Hz for precision cargo. The adjustment path in the speed dimension is completely blocked.

[0106] The physical impedance decoupling logic takes over the highest control, and its core action is to generate active suspension intervention commands. Combining real-time feedback of vehicle speed at 45 km / h and the characteristic data of the natural vibration frequency at 2.5 Hz, the microprocessor calculates the target chassis offset frequency variable as +1.5 Hz, intending to push the chassis's natural transmission frequency to 4.0 Hz to avoid the cargo resonance zone. Simply changing the damper damping cannot meet this frequency offset requirement. Semi-active magnetorheological dampers were used for anti-vibration protection, increasing the control current from one ampere to five amperes to harden the compression stroke. This approach only reduced the absolute value of the resonance peak amplitude, without changing the position of the resonant main frequency in the coordinate system. The upper limit of the magnetorheological fluid's operating temperature is 90 degrees Celsius. When the vehicle absorbs huge excitation energy on a five-kilometer continuous washboard road, the internal fluid temperature exceeds 130 degrees Celsius within 45 seconds, causing the excitation coil insulation layer to melt and the damping to be completely lost. The hardware architecture is switched to an active suspension system with air springs and continuously variable damping valves, which reconstructs the principal stiffness characteristics by changing the air chamber pressure and volume:

[0107] Parameter This represents the target suspension stiffness setting value, with a physical adjustment range of 300 kN / m to 1500 kN / m; It represents the multivariate thermodynamic index constant of air, and takes a value of 1.4 under high-frequency adiabatic compression conditions. The absolute physical pressure value represents the target, and the control valve adjustment range is from 0.5 MPa to 1.2 MPa. This represents the effective load-bearing area of ​​the air spring, which varies from 0.03 square meters to 0.08 square meters depending on the chassis height. This represents the total physical volume scalar, switching between a 15-liter state with the main air chamber open and a 25-liter state with the main and auxiliary air chambers connected. Based on a chassis offset frequency variable of +1.5 Hz, the microprocessor queries the interpolation matrix in a preset vehicle dynamics mapping database and outputs... The setpoint is 850 kN / m, synchronously matching the target shock absorber damping setpoint. The microprocessor generates active suspension intervention commands and sends them to the active suspension controller at a baud rate of 500 kilobits per second via the controller area network bus. The controller drives a 24-volt solenoid valve assembly to cut off the connection circuit of the 10-liter auxiliary air chamber within 30 milliseconds. (The denominator parameter...) A step drop occurs. The high-voltage accumulator pipeline injects compressed air at 0.2 MPa into the main gas chamber, raising the numerator parameter. Numerical values. The rotating stepper motor inside the shock absorber compresses the cross-sectional area of ​​the hydraulic fluid, hardening the damping ratio to 0.6. The chassis's mechanical stiffness undergoes a transient overlap variation, forcibly shifting the vehicle's inherent transmission frequency from 2.5 Hz to 4.2 Hz. The out-of-control speed of 45 km / h continuously impacting road potholes generates 2.5 Hz excitation energy, which, when transmitted upwards, encounters a physical resistance structure with a frequency of 4.2 Hz. This severe mismatch in stiffness causes over 80% of the destructive energy to be directly reflected and dissipated at the spring base, cutting off the physical channel for resonant energy to flow into the cargo box.

[0108] Example 4

[0109] The logistics optimization system provided in this embodiment constructs a frequency safety protection architecture covering the entire transportation cycle through the collaborative coupling of the cloud center and the vehicle terminal. The system deeply integrates macro-level path planning with micro-level chassis physical response, utilizing computing resources to cut off the transmission chain of vibration energy in different dimensions.

[0110] The collaboration logic of the various components within the system is as follows:

[0111] The core system architecture and modules work together, with a highly parallel computing processor array configured within the cloud server cluster of the regional dispatch center. The attribute and path acquisition module utilizes the network streaming interface of this computing node to acquire the inherent vibration frequency data of precision cargo. During path planning, the processor establishes the initial physical trajectory of transportation by executing grid-based topology optimization code, converting static geographical coordinates into digital vectors available for subsequent computation.

[0112] The model building module, acting as the system's data analysis hub, invokes a dedicated tensor processing unit within the server. This unit reads sensor sampling data stored in the historical database and performs discrete complex integral transformations on these time-series signals. Through frequency feature mapping logic, it mathematically correlates the geometric undulations of the road segment with vehicle speed. The processor's computation results ultimately produce a dynamic excitation frequency prediction model, which defines the physical distribution of road segment excitation energy at different speed scales.

[0113] The constraint and optimization module, deployed on high-performance computing nodes in the cloud, is responsible for solving the control variables under nonlinear constraints. This module obtains the excitation distribution matrix produced by the model building module and introduces the cargo frequency immunity interval as an absolute exclusion condition. The processor searches for the optimal balance between transportation costs and physical losses within the solution space, generating a target solution composed of geographical location identifiers and corresponding vehicle speed limit matrices. This solution not only covers spatial trajectories but, more importantly, defines dynamic speed corridors to prevent resonance for each road segment.

[0114] The terminal interaction module relies on the communication microprocessor within the vehicle-mounted sensing terminal to achieve real-time synchronization between the cloud solution and the local execution unit. This microprocessor uses a 5 Mbps wireless communication link to receive encrypted control packets and parses them into speed limits that the underlying controller can recognize.

[0115] The system utilizes in-vehicle edge computing for execution support. It not only executes global strategies in the cloud but also leverages the edge computing module within the in-vehicle terminal to provide on-site decision-making capabilities. When the vehicle encounters external traffic flow restrictions while driving, and the real-time maximum speed limit restricts the preset control zone, the computing chip integrated within the in-vehicle terminal will take over control.

[0116] This module calls the vehicle's local area network interface to obtain unloading manifest data and uses a computing chip to calculate the vehicle's mass decay and physical center of gravity shift in real time. By reconstructing the vehicle's vibration transfer function, the edge computing module reverse-engineers a new safe speed range within a limited vehicle speed range and directly drives the drive motor controller or brake pressure regulating valve to forcibly adjust the vehicle's longitudinal dynamics.

[0117] In extreme situations where speed adjustment is completely impossible, the active intervention module sends commands to the onboard active suspension controller. Upon receiving a signal indicating that the vehicle speed is unadjustable, the controller's microprocessor drives the solenoid valve assembly to adjust the internal air pressure and volume of the air springs. By altering the chassis's physical stiffness and damping characteristics, the system achieves impedance transformation at the hardware level. At this point, the chassis mechanical structure transforms into a band-stop filter for a specific excitation frequency, utilizing transient variations in physical characteristics to provide a final line of defense for the cargo compartment.

[0118] The entire system achieves a deep reconstruction of the precision cargo transportation environment through a hierarchical layout of cloud-based global optimization and vehicle-mounted edge autonomy. From macro-frequency prediction of the cloud cluster to micro-load correction of the vehicle-mounted chip, and then to physical impedance adjustment of the underlying controller, the system ensures at multiple levels that mechanical vibration energy cannot harmfully couple with the inherent frequency of the cargo, thereby establishing a stable and reliable safety isolation zone between the physical road network environment and high-value assets.

[0119] In the description of this specification, the references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0120] The above description is merely an example and illustration of the concept of the present invention. Those skilled in the art can make various modifications or additions to the specific embodiments described or use similar methods to replace them, as long as they do not deviate from the concept of the invention or exceed the scope defined in the claims, they should all fall within the protection scope of the present invention.

Claims

1. A method for optimizing logistics and transportation solutions based on the Internet of Things, characterized in that, include: Obtain cargo attribute data of the goods to be transported and historical operation data of the road network; An initial logistics transportation route is generated based on the cargo attribute data and the historical operation data. The method of obtaining cargo attribute data of the goods to be transported includes obtaining the inherent vibration frequency characteristic data of the goods to be transported; the method further includes: The historical operation data of the road network is extracted by frequency domain conversion using a big data processing module to construct a dynamic excitation frequency prediction model for each road segment under different driving speeds. Resonance avoidance constraints are constructed based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model. The objective function is determined by minimizing the comprehensive transportation cost parameter and the resonance avoidance constraint, and the target logistics transportation scheme is obtained based on the objective function. The target logistics transportation scheme includes the target spatial driving route and the dynamic driving speed control interval corresponding to each segment of the target spatial driving route. The target logistics transportation plan is sent to the vehicle's onboard terminal system.

2. The method according to claim 1, characterized in that, During the period when the vehicle travels according to the target logistics transportation plan, the real-time maximum allowable speed parameter of the current road segment and the forward road waveform detection data are obtained. Determine whether the real-time maximum allowable vehicle speed parameter is within the dynamic driving speed control range; If the real-time maximum allowable vehicle speed parameter deviates from the dynamic driving speed control range, the edge computing module of the vehicle terminal system is activated.

3. The method according to claim 2, characterized in that, The edge computing module extracts the road surface vibration frequency characteristic parameters of the forward road surface waveform detection data. Combining the vehicle's current dynamic load data and the inherent vibration frequency characteristic data, a new safe speed control range is calculated within a range not exceeding the real-time maximum allowable vehicle speed parameter. The new safe speed control range is sent to the vehicle's underlying speed execution unit.

4. The method according to claim 3, characterized in that, If the new safe speed control range does not exist within a value range not exceeding the real-time maximum allowable speed parameter, a micro-start-stop control command sequence is generated. The micro-start-stop control command sequence includes drive acceleration commands and braking deceleration commands that are issued alternately according to a preset time period; The micro-start-stop control command sequence is sent to the vehicle's underlying speed execution unit.

5. The method according to claim 1, characterized in that, Also includes: Determine whether the vehicle's current real-time driving speed is within the dynamic driving speed control range; If the real-time feedback vehicle speed deviates from the dynamic vehicle speed control range and a non-adjustable vehicle speed control signal is received, an active suspension intervention command is generated. The real-time feedback vehicle speed is obtained by acquiring wheel speed sensor monitoring data and global positioning system coordinate data collected by the vehicle-mounted sensing terminal, and performing Kalman filter fusion processing to calculate and output the result. The non-adjustable vehicle speed control signal is generated by receiving an activation flag triggered by the vehicle's anti-lock braking system or the autonomous driving domain controller.

6. The method according to claim 5, characterized in that, The generation of active suspension intervention commands includes: The chassis offset frequency variable is calculated by combining the real-time feedback vehicle speed and the inherent vibration frequency characteristic data; Based on the chassis offset frequency variable, the target suspension stiffness setting value and the target shock absorber damping setting value are obtained by searching the preset vehicle dynamics mapping database. The active suspension intervention command is generated based on the target suspension stiffness setting value and the target shock absorber damping setting value, and the command is sent to the vehicle's active suspension controller.

7. The method according to claim 1, characterized in that, The process of using a big data processing module to extract frequency domain data from historical road network operation data and constructing a dynamic excitation frequency prediction model for each road segment at different driving speeds includes: Extract the time-series vibration acceleration sensor data and the corresponding historical matched vehicle speed data contained in the historical operation data; Perform a fast Fourier transform on the time-series vibration acceleration sensing data to obtain the road surface excitation frequency distribution matrix under the corresponding historical matching vehicle speed; The dynamic excitation frequency prediction model is generated by fitting the historical vehicle speed data and the road surface excitation frequency distribution matrix.

8. The method according to claim 1, characterized in that, The process of determining the objective function by minimizing the comprehensive transportation cost parameter and the resonance avoidance constraint, and obtaining the target logistics transportation scheme based on the objective function, includes: Assign first preset weights to the transportation time parameter and the energy consumption parameter, and construct a basic cost calculation function; Extract the fatigue accumulation benchmark threshold corresponding to the inherent vibration frequency feature data; The cumulative predicted amount of frequency domain fatigue damage for each road segment at candidate vehicle speeds is calculated based on the dynamic excitation frequency prediction model. The frequency domain fatigue damage cumulative prediction is assigned a second preset weight and a penalty function is constructed. The basic cost calculation function and the penalty function are combined to execute a heuristic optimization algorithm to output the target logistics transportation plan.

9. The method according to claim 3, characterized in that, The combination of the vehicle's current dynamic load data and the natural vibration frequency characteristic data includes: Get interactive data on unloading lists from logistics stations along the route the vehicle passes through; The vehicle's overall mass reduction and physical center of gravity shift are calculated based on the unloading list interaction data. The vehicle vibration transfer function model is updated based on the vehicle mass attenuation and the physical center of gravity offset. The updated vehicle vibration transfer function model is used as an input parameter in the calculation of the new safe speed control range.

10. A logistics transportation solution optimization system based on the Internet of Things, characterized in that, include: The initial route generation module is used to acquire cargo attribute data and historical road network operation data of the goods to be transported and generate an initial logistics transportation route. The cargo attribute data includes inherent vibration frequency characteristic data. The model building module is used to extract the frequency domain conversion of historical road network operation data using the big data processing module, and to build a dynamic excitation frequency prediction model for each road segment under different driving speeds. The constraint and optimization module is used to construct resonance avoidance constraints based on the inherent vibration frequency characteristic data and the dynamic excitation frequency prediction model, determine the objective function by minimizing the comprehensive transportation cost parameter, and obtain the target logistics transportation scheme based on the objective function. The terminal interaction module is used to send the target logistics transportation plan to the vehicle's on-board terminal system, so that the on-board terminal system can perform edge computing optimization or active suspension intervention when the vehicle speed deviates from the dynamic driving speed control range.

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