A green and low-carbon supply chain collaborative distribution system for power materials

Abstract

This application relates to the field of logistics and distribution technology, and discloses a green and low-carbon supply chain collaborative distribution system for power materials. The system includes modules for environmental perception, business analysis, load reconfiguration, global optimization, and scheduling execution. The system constructs a spatiotemporal road network model and obtains the road surface friction coefficient in conjunction with meteorological parameters; it analyzes the three-dimensional attributes of materials and establishes temporal loading and unloading constraints; under long downhill and slippery conditions, it adjusts the three-dimensional loading coordinates of the reverse-recovery materials and increases the normal load distribution rate of the rear drive axle to improve anti-skid safety load capacity; it uses the braking energy recovery power cutoff function to calculate carbon impedance for path optimization; and finally outputs control commands containing the driving trajectory and three-dimensional loading coordinates. This invention combines macroscopic path optimization with the microscopic physical and mechanical state of the vehicle compartment, improving anti-skid safety under complex terrain, avoiding loading and unloading interference, and achieving precise low-carbon scheduling.

Description

Technical Field

[0001] This invention relates to the field of logistics and distribution technology, specifically to a green and low-carbon supply chain collaborative distribution system for power materials. Background Technology

[0002] Current power supply distribution systems primarily prioritize shortest routes or time-based scheduling, but they have significant limitations when operating in complex terrain. Because they fail to incorporate road gradients and dynamic weather parameters to influence the loading status of materials inside the vehicle, traditional volumetric stacking methods neglect the overall vehicle weight distribution. On long downhill slopes and slippery surfaces, the rear drive axle often lacks sufficient normal load, making it prone to skidding due to insufficient tire grip.

[0003] Meanwhile, when multiple nodes are mixed to deliver new equipment in the forward direction and recycled materials in the reverse direction, the existing solutions usually do not combine the unloading sequence with the three-dimensional space constraints of the vehicle. The recycled materials loaded first can easily block the unloading channel of the subsequent materials, resulting in repeated handling.

[0004] Furthermore, existing systems lack prior mechanical verification of the forward inertial force of cargo during braking on long downhill slopes, posing a risk of cargo displacement and damage. At the low-carbon route planning level, existing algorithms mostly employ conventional energy conversion models, failing to consider the mechanical braking consumption of heavy vehicles exceeding the motor's energy recovery limit during long downhill journeys. The wear and tear caused by frequent mechanical braking and its hidden carbon emission costs are ignored, resulting in generated scheduling routes that fail to reflect the true comprehensive environmental costs and are ill-suited to real-world low-carbon operation requirements. Summary of the Invention

[0005] To address the shortcomings of existing technologies, this invention provides a green and low-carbon supply chain collaborative distribution system for power materials. It solves the problems of poor downhill anti-slip capability, interference in loading and unloading space at multiple nodes and risk of cargo displacement caused by unreasonable center of gravity distribution in complex terrain and weather conditions, as well as inaccurate low-carbon path planning due to the lack of consideration for mechanical braking penalties in existing distribution systems.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a green and low-carbon supply chain collaborative distribution system for power materials, comprising: The environmental perception module is used to construct a four-dimensional spatiotemporal road network model, extract road surface physical parameters and real-time meteorological parameters, so as to determine the actual longitudinal sliding friction coefficient of the road surface. The business analysis module is used to analyze the three-dimensional attributes of power materials and logistics task requirements; The load reconfiguration module is used to perform material loading coordinate and weight intervention based on terrain prediction, actual longitudinal sliding friction coefficient of road surface, and three-dimensional properties of power materials in a four-dimensional spatiotemporal road network model. The global optimization module is used to perform load-slope coupling and execute path optimization algorithms based on the results of material loading coordinates and weight intervention to calculate the theoretical optimal solution; The scheduling and execution module is used to output control commands for vehicles and warehousing equipment to recognize and execute in business scenarios based on the theoretical optimal solution.

[0007] Furthermore, the environmental perception module is specifically used to: obtain the directed edge driving distance and node elevation difference through the geographic information system interface to calculate the average slope angle as a physical parameter of the road surface; simultaneously access the meteorological service interface to obtain precipitation and temperature prediction data as real-time meteorological parameters, and determine the actual longitudinal sliding friction coefficient of the road surface by multi-dimensional interpolation mapping the meteorological-adhesion attenuation matrix.

[0008] Furthermore, the business analysis module is specifically used to analyze the three-dimensional attributes of power materials and logistics task requirements: extract the three-dimensional geometric dimensions, mass and density of materials from the warehouse management system, and define the logistics recycling task as reverse materials; establish a three-dimensional spatial envelope model for discrete materials in reverse materials; and reserve unloading channels in the longitudinal direction of the carriage according to the stopping order of delivery nodes, and establish time-series loading and unloading constraints.

[0009] Furthermore, the load reconfiguration module calculates the cumulative elevation difference of the downstream path using vector integration as terrain prediction. When there is a long downhill slope downstream and the actual longitudinal sliding friction coefficient of the road surface is lower than the preset safety threshold, the load reconfiguration module performs center of gravity reconfiguration calculation based on the reverse material three-dimensional properties to realize material loading coordinate and weight intervention. The load reconfiguration module changes the distance between the vehicle's center of gravity and the front axle by adjusting the three-dimensional loading coordinates of the material in the compartment, improves the normal load distribution rate of the drive axle, and reversely derives the ultimate anti-slip safe load.

[0010] Furthermore, when performing the center of gravity reconfiguration calculation, the load reconfiguration module generates the initial three-dimensional candidate loading coordinates of the reverse material and extracts the pre-defined unobstructed unloading clearance area from the temporal loading and unloading constraints; it calculates the geometric envelope model of the reverse material along the longitudinal direction of the carriage using a ray projection algorithm to generate a projection coverage area on the bottom plane of the carriage; it performs an intersection operation between the projection coverage area and the unloading clearance area, performs a last-in-first-out temporal spatial anti-interference check, and outputs the transition loading coordinates.

[0011] Furthermore, after locking the spatial position, the load reconfiguration module calculates the inertial forward force of the cargo under downhill braking conditions, and calculates the additional longitudinal binding force required to prevent the material from shifting, in conjunction with the static friction of the bottom plate; under the premise of meeting the limit of the longitudinal binding force, the load reconfiguration module locks the loading coordinates of the material.

[0012] Furthermore, when performing load-slope coupling, the global optimization module calculates the carbon impedance per unit road segment based on the departure load, road segment slope, and meteorological parameters. The global optimization module uses the braking energy recovery power cutoff function to handle the mechanical braking penalty after exceeding the physical recovery limit. By cutting off the recovery benefits and superimposing the physical wear penalty, the module quantifies the comprehensive carbon cost of the vehicle under extreme downhill conditions.

[0013] Furthermore, the global optimization module uses a large-scale neighborhood search algorithm to generate the driving route and material pickup combination scheme with the minimum total carbon resistance in the four-dimensional spatiotemporal topology map as the theoretical optimal solution. When the algorithm generates a new route in each iteration, the system internally calls the physical space and mechanical balance verification logic. When no feasible solution is found in multiple consecutive iterations, the global optimization module automatically splits the reverse load task or relaxes the time window constraint through an adaptive dimensionality reduction fault tolerance mechanism.

[0014] Furthermore, when outputting control commands, the scheduling and execution module sends driving trajectory and departure time commands to the execution end, and simultaneously sends packing commands with loading coordinates and binding requirements to the warehousing system.

[0015] Furthermore, when the scheduling and execution module issues a packing instruction, it: summarizes the loading data of scrap materials that have undergone temporal and spatial anti-interference verification and extreme anti-slip safety load verification in the early stage, and constructs a three-dimensional loading coordinate matrix; it issues the three-dimensional loading coordinate matrix and fastening specification instructions to the corresponding node's warehouse management system and forklift operation terminal; if an abnormal blocking signal is triggered on site due to equipment size deviation or damage to available binding points, the system marks the node task as suspended and re-triggers local load recalculation.

[0016] This invention provides a green and low-carbon collaborative distribution system for the power supply chain. It has the following beneficial effects: 1. This invention acquires road slope and meteorological data through an environmental perception module. When a long downhill slope with a low road surface sliding friction coefficient is identified, the load reconstruction module adjusts the three-dimensional loading coordinates of the reverse-recovered materials in the vehicle compartment and concentrates the heavier materials above the rear drive axle of the vehicle. This directly increases the normal load distribution rate of the drive axle, thereby improving the vehicle tire's ultimate anti-skid safety load capacity at the physical level and reducing the risk of the vehicle skidding on wet downhill sections.

[0017] 2. This invention uses a business analysis module to reserve a longitudinal unloading channel for the carriage based on the node stopping order, and uses a ray projection algorithm to perform last-in-first-out temporal spatial anti-interference verification, ensuring that the loaded reverse materials will not block the unloading path of the subsequent forward materials, reducing repeated handling operations in the multi-node delivery process. At the same time, the system further calculates the inertial forward force of the goods and the required binding force under long downhill braking conditions to prevent the goods from shifting during the journey, ensuring the reliability of the physical loading inside the carriage.

[0018] 3. This invention uses a global optimization module to perform load-slope coupling and introduces a braking energy recovery power cutoff function to calculate the carbon impedance of a unit road segment. For the energy that exceeds the physical recovery limit of the electric drive system during a long downhill process, the system converts it into mechanical braking penalty and adds it to the comprehensive carbon cost. This reflects the actual energy consumption and mechanical wear emissions of the vehicle under extreme terrain, so that the output path and load combination scheme can accurately meet the low-carbon scheduling requirements. Attached Figure Description

[0019] Figure 1 This is a schematic diagram of the system architecture of the present invention; Figure 2 This is a schematic diagram of the method flow of the present invention; Figure 3 This is a graph showing the iterative convergence curve of the spatiotemporal dynamic carbon impedance of the present invention; Figure 4 This is a comparison diagram of the dynamic distribution of normal loads on the front and rear axles under severe downhill conditions according to the present invention. Figure 5 This is a histogram comparing the total system energy consumption and hidden carbon penalty distribution of the present invention. Figure 6 This is a visualization diagram of the three-dimensional loading coordinates and anti-interference envelope inside the carriage according to the present invention.

[0020] Among them, 110 is the environmental perception module; 120 is the business parsing module; 130 is the load reconstruction module; 140 is the global optimization module; and 150 is the scheduling execution module. Detailed Implementation

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] See attached document Figure 1 This invention provides a green and low-carbon supply chain collaborative distribution system for power materials, the system comprising: The environmental perception module 110 is used to construct a four-dimensional spatiotemporal road network model and extract road surface physical parameters and real-time meteorological parameters. Business analysis module 120 is used to analyze the three-dimensional attributes of power materials and logistics task requirements; The load reconfiguration module 130 is used to perform material loading coordinate and weight intervention based on terrain prediction. The global optimization module 140 is used to perform load-slope coupling and execute path optimization algorithms. The scheduling and execution module 150 is used to output control commands that can be recognized and executed by vehicles and warehousing equipment in business scenarios.

[0023] The environmental perception module 110 obtains the directed edge travel distance and node elevation difference through the geographic information system interface. The environmental perception module 110 calculates the average slope angle. The environmental perception module 110 simultaneously connects to the meteorological service interface to obtain precipitation and temperature forecast data, and determines the actual longitudinal sliding friction coefficient of the road surface through the meteorological-adhesion attenuation matrix.

[0024] The business analysis module 120 extracts the three-dimensional geometric dimensions, mass, and density of materials from the warehouse management system. The business analysis module 120 establishes a three-dimensional spatial envelope model for discrete materials. Based on the stopping sequence of delivery nodes, the business analysis module 120 reserves unloading channels along the longitudinal axis of the vehicle and establishes temporal loading and unloading constraints.

[0025] The load reconfiguration module 130 calculates the cumulative elevation difference along the downstream path. When there is a long downhill slope downstream and the road surface friction coefficient is lower than a preset safety threshold, the load reconfiguration module 130 performs a center of gravity redistribution calculation based on the three-dimensional properties of the reverse material. The load reconfiguration module 130 changes the distance between the vehicle's center of gravity and the front axle by adjusting the three-dimensional loading coordinates of the material within the cargo compartment. The load reconfiguration module 130 increases the normal load distribution rate of the drive axle and reverse-derives the ultimate anti-skid safe load.

[0026] The global optimization module 140 calculates the carbon impedance per unit road segment based on departure load, road slope, and meteorological parameters. It utilizes a braking energy recovery power cutoff function to handle mechanical braking penalties exceeding the physical recovery limit. Finally, the global optimization module 140 employs a large-scale neighborhood search algorithm to generate the driving route and cargo pickup combination scheme with the minimum total carbon impedance in the four-dimensional spatiotemporal topology graph.

[0027] The scheduling execution module 150 transforms the theoretical optimal solution calculated by the global optimization module 140 into control instructions that can be recognized and executed by vehicles and warehousing equipment in the business scenario.

[0028] See attached document Figure 2 This invention provides a method for collaborative distribution of green and low-carbon power supply chains, the method comprising the following steps: Step S10: The environmental perception module 110 scans the entire delivery area map, establishes a three-dimensional terrain distribution model, and generates a four-dimensional spatiotemporal topology map by associating it with the meteorological forecast sequence.

[0029] Step S20: The business analysis module 120 analyzes the geometric and mechanical characteristics of the power materials, extracts the three-dimensional envelope dimensions of the materials, and reserves unloading channel space in the carriage according to the node stopping sequence.

[0030] In step S30, the load reconstruction module 130 performs elevation vector integration on the nodes ahead in the driving direction to determine whether there is a gravitational potential energy recovery window and to identify the slippery road surface conditions.

[0031] Step S40: Load reconstruction module 130 performs load reconfiguration. Load reconstruction module 130 performs last-in-first-out temporal spatial anti-interference verification using a ray projection algorithm. Load reconstruction module 130 calculates the inertial forward thrust of the cargo under downhill braking conditions. Under the premise of satisfying the longitudinal binding force limit, load reconstruction module 130 locks the material loading coordinates.

[0032] In step S50, the global optimization module 140 performs dynamic carbon impedance calculation. The system calculates energy consumption based on the real-time load and slope relationship. The global optimization module 140 iteratively searches the topology graph for the driving scheme with the minimum total carbon impedance using a large-scale neighborhood search algorithm.

[0033] Step S60: The system issues collaborative execution instructions. The dispatch center sends the driving trajectory and departure time to the execution terminal, and simultaneously sends a packing instruction with loading coordinates and binding requirements to the warehousing system to optimize the physical center of gravity of the vehicle.

[0034] To further clarify the implementation of each technical aspect of the present invention, the following will provide a detailed description of the implementation of each functional module involved above and its internal processing flow.

[0035] In this embodiment, step S10 is executed by the environment perception module 110. This module is used to perform physical boundary calibration, and by fusing external multi-dimensional environmental data, establishes a mapping relationship between the real world and the internal computing space of the system, thereby providing basic environmental parameters for the reverse derivation of the anti-slip load. Step S10 may include the following sub-steps: S101 and the environmental perception module 110 obtain the underlying coordinate data of the delivery road network through the geographic information system interface and construct a directed graph topology model of the road network. The system extracts the directed edges between any two related nodes in the directed graph. And obtain the vehicle along the directed edge. Driving space and driving distance As a preferred method, spatial driving distance This includes not only the horizontal projection distance but also the actual surface length touching the ground due to road undulations. The environmental perception module 110 simultaneously acquires the starting node of this directed edge. and the end node The elevation data are subtracted to obtain the node elevation difference. The system is based on spatial travel distance. and node elevation difference Calculate directed edges Average slope angle of the corresponding road section The average slope angle The calculation formula is: ; In the formula, For nodes To the node The average slope angle of the road section For nodes With nodes The difference in altitude, in meters. The actual distance traveled along the road surface is expressed in meters. From a general mechanical perspective, the slope angle directly determines the magnitude of the component of gravity acting downwards along the slope when a vehicle is going downhill; this component is the primary source of kinetic energy that causes the vehicle to accelerate downhill. For conventional methods of acquiring data for geographic information systems and constructing directed graph topology models, those skilled in the art can use existing commercial map application programming interfaces and graph theory algorithms. Node traversal and connectivity determination are well-known techniques in this field and will not be elaborated upon here.

[0036] S102. After determining the terrain undulation pattern, it is also necessary to further understand the dynamic changes in the road surface's skid resistance. The environmental perception module 110 connects to the meteorological service application interface to obtain the future planning time window of the delivery road network area. The system contains predicted precipitation and ambient temperature data. The system's internal database pre-stores meteorological-adhesion attenuation matrices. In this embodiment, the weather-adhesion attenuation matrix... The matrix is ​​constructed using a combination of offline calibration and online querying. The input dimensions of the matrix are pre-divided into multiple discrete intervals; for example, rainfall intensity can be divided into light rain, moderate rain, and heavy rain levels, temperature can be divided into above-freezing and below-freezing intervals, and road surface material categories cover common paving materials such as asphalt and cement. The environmental perception module 110 inputs the received precipitation and temperature data into this meteorological-adhesion attenuation matrix. Perform multidimensional interpolation mapping and output directed edges. within a specific time window The corresponding actual longitudinal sliding friction coefficient In a physical sense, this coefficient represents the ratio of the maximum tangential resistance to which the tire can grip the road surface to the normal load, typically ranging from 0.1 to 1.0. This is the actual longitudinal sliding friction coefficient. This reflects the degree to which meteorological factors such as precipitation weaken the anti-skid performance of the road surface. To ensure the integrity of the algorithm under extreme conditions such as disconnection of the meteorological interface, the environmental perception module 110 is equipped with a fault-tolerant mechanism. When forecasted meteorological data is missing, the system will default to calling the measured data from the previous time window or using the preset minimum friction coefficient value for rain and snow.

[0037] S103. To establish a complete force equilibrium model, in addition to external environmental input, the environmental perception module 110 also needs to extract and initialize the basic physical constraint parameters of the heavy vehicles performing this delivery task from the vehicle management database. The basic operating boundary conditions set by the system include the vehicle's unloaded weight. Physical wheelbase between the front and rear axles of the vehicle The permissible regenerative braking power limit of the vehicle drive system And the limit of longitudinal binding force that the fasteners for cargo inside the carriage can provide. Braking energy recovery power limit The setting can be adjusted based on the maximum allowable charging power of the vehicle's power battery pack and the bottleneck of the motor's reverse drag power. Longitudinal binding force limit. This typically depends on the rated safe tensile force threshold of the vehicle's securing devices, such as high-strength ratchet straps. The aforementioned initialized vehicle-level physical constraint parameters define the boundaries of the vehicle's physical response under full load and long downhill conditions.

[0038] In this embodiment, step S20 is executed by the business parsing module 120. The business parsing module 120 is mainly used to convert macroscopic logistics orders into three-dimensional spatial boundary and mass constraint parameters required for vehicle loading calculation. Step S20 may include the following sub-steps: S201, the business analysis module 120 establishes data communication with the warehouse management system to extract supply and recovery tasks within the preset delivery cycle. Typically, logistics operations have forward and reverse directions. Therefore, the business analysis module 120 defines the task of transporting new equipment (such as new transformers, insulators, etc.) from the originating warehouse to each substation node as a rigid forward load. Rigid positive loads are characterized by being indivisible and requiring full unloading at designated nodes, manifesting as fixed space occupancy and mass consumption in the algorithm logic. Meanwhile, the task of transporting scrap materials from each substation node back to the originating warehouse is defined as a flexible reverse load. This type of load typically allows the algorithm to dynamically adjust based on the vehicle's remaining load capacity and real-time road conditions, possessing business attributes such as the ability to split, partially pick up, or delay to the next scheduling cycle. The business parsing module iterates through each delivery node 120 times, statistically analyzing each node. The corresponding rigid positive load demand and flexible reverse load demand establish a basic benchmark for subsequent calculation of the instantaneous load state of the whole vehicle.

[0039] S202. In order to realize the microscopic interior space layout of the carriage, the business analysis module 120 extracts the bill of materials attribute matrix from the flexible reverse load. Based on this, the system categorizes materials using digital tags. For continuous bulk materials such as scrap cables that can be cut and loaded at will, the system primarily extracts and records their total mass parameters. For non-disassembled discrete sets of scrap equipment such as scrap transformers or large switchgear, the business analysis module 120 extracts their individual mass. A three-dimensional spatial geometric envelope model based on length, width, and height dimensions is established. As a preferred approach, the system uses discrete individual materials... Using a specific feature vertex (e.g., the lower left front corner near the floor of the vehicle compartment) as the origin, extract its length in the Cartesian coordinate system inside the vehicle compartment. ,Width ,high These envelope dimensions serve as spatial envelope dimensions. In a three-dimensional Cartesian coordinate system, these envelope dimensions define the minimum bounding volume of the circumscribed cuboid of the material, objectively reflecting the physical space required for loading. The conventional process of retrieving 3D modeling data from a database and generating the envelope matrix can be achieved by those skilled in the art using existing computer-aided design analysis tools. The methods for extracting envelope parameters are well-known in the field and will not be elaborated upon here.

[0040] S203. After obtaining the spatial envelope characteristics of the materials, in order to reduce the risk of goods obstructing each other when vehicles stop for loading and unloading at multiple nodes, the business analysis module 120 establishes a time-series loading and unloading channel constraint within the cargo compartment. Specifically, the system obtains the node stopping sequence of the delivery task and, based on the last-in-first-out logistics loading and unloading principle, sets the longitudinal axis direction of the cargo compartment (i.e., parallel to the vehicle's driving direction, pointing towards the unloading port at the rear door of the cargo compartment) as the main axis direction for unloading. When the vehicle completes the rigid positive load unloading at the current node and is ready to load the discrete waste equipment of that node, the cargo compartment usually still contains positive materials that need to be unloaded at downstream nodes. The business analysis module 120 generates a two-dimensional vertical projection profile on the plane where the cargo compartment floor is located based on the loading coordinates and spatial envelope dimensions of these future unloaded positive materials. Subsequently, the system sweeps and extends this projection profile along the longitudinal axis of the cargo compartment towards the rear door of the cargo compartment, forcibly defining the swept area as a clear area. This allows for the reservation of an unobstructed unloading channel for forklifts to directly reach the tailgate for subsequent goods that need to be unloaded.

[0041] In this embodiment, step S30 is executed by the load reconfiguration module 130. The load reconfiguration module 130 primarily performs a forward-looking quantitative analysis of the terrain and weather conditions of the path ahead to assess the existence of physical conditions for gravitational potential energy recovery and potential driving safety risks, thereby providing a decision-making basis for subsequent active load allocation intervention. Therefore, step S30 may include the following sub-steps: S301. During the virtual forward simulation of the global optimization algorithm, when the pre-planned vehicle intends to leave the current node within a specific time window, the load reconstruction module 130 needs to perform a potential energy environment assessment on subsequent candidate paths. Considering that short-distance undulations of a single road segment may mask the true macroscopic terrain trend, the system sets a look-ahead step size in the directed graph topology model. Extract continuous downstream candidate path sequences The elevation change data for each road segment. The load reconstruction module 130 calculates the cumulative elevation difference within the look-ahead interval using vector integration. The calculation formula is as follows: ; In the formula, The cumulative elevation difference of downstream candidate paths reflects the potential well depth of the terrain on a macro scale, in meters. The number of forward-looking road sections; For the first on the forward-looking path The elevation difference between nodes of adjacent road segments. As a preferred method, the lookahead step size... The value of the elevation vector is not only determined based on the path node density, but also dynamically adjusted by converting the warning distance from the thermal volume limit of the vehicle braking system. The value typically ranges from 3 to 10. Through the aforementioned elevation vector integration, the system can smooth out localized minor undulations, effectively filter out high-frequency terrain noise, and extract macroscopic long-distance terrain change trends.

[0042] S302, Obtaining Cumulative Elevation Difference Subsequently, the load reconfiguration module 130 determines whether the road ahead possesses the physical conditions for gravitational potential energy recovery based on terrain feature trigger thresholds. Long downhill driving conditions typically mean that, under the influence of the vehicle's own weight as it descends the slope, there is a physical window for continuously converting gravitational potential energy into electrical energy. Therefore, the system internally presets a macroscopic downhill threshold. The threshold is determined based on the equivalent elevation of the minimum gravitational component required for a vehicle to overcome mechanical friction and rolling resistance at a specific speed, and is typically defined as a positive threshold. When the cumulative elevation difference... The negative value less than the macroscopic downslope threshold (i.e. When the load reconfiguration module 130 identifies a long downhill section of the road ahead, the system immediately marks this section as a high potential energy recovery zone, providing a status indicator for the subsequent energy consumption optimization model. If this condition is not met, the system will process the road according to the normal flat road or uphill logic to avoid frequently triggering unnecessary load recalculation.

[0043] While long downhill sections like S303 offer opportunities for energy recovery, they also introduce physical risks such as brake fade or sideslip, especially when road surface adhesion decreases. To balance energy gains with underlying physical safety, the load reconfiguration module 130, based on the identification of long downhill terrain, further integrates environmental meteorological parameters to perform a high-risk condition assessment combining severe weather and terrain. The system retrieves the actual longitudinal sliding friction coefficient of the road segment within the corresponding time window, output by the environmental perception module 110. And compare it with the preset road surface adhesion safety threshold. Compare the road surface adhesion safety thresholds. The value is set based on the minimum coefficient of friction required for heavy-duty trucks to meet regulatory braking distance requirements under full load, and is typically between 0.3 and 0.4. When the system determines that both conditions are met... and At this point, it indicates that the vehicle is about to enter a slippery, long downhill section. Under normal conditions, the normal load distribution may not be sufficient to offset the risk of runaway due to gravitational potential energy conversion. Based on this coupling condition, the load reconfiguration module 130 generates an active load adjustment trigger signal. This signal instructs the system to break away from the conventional random packing mode by volume and actively utilize heavier scrap materials as physical counterweights. At the current node, a physical load adjustment program is initiated to increase the normal pressure on the drive axle by changing the normal load distribution of the vehicle's front and rear axles, thereby improving the actual grip of the tires from a physical perspective.

[0044] In this embodiment, step S40 is executed by the load reconfiguration module 130. Upon receiving a high-risk operating condition trigger signal, the load reconfiguration module 130 initiates microscopic-level physical space and mechanical coupling calculations. This module actively intervenes in the load distribution within the vehicle compartment to alter the overall vehicle's underlying mechanical response, aiming to improve downhill braking safety in harsh environments and maximize the amount of reverse-flow waste materials that can be recovered. Step S40 may include the following sub-steps: S401, Load Reconfiguration Module 130 retrieves the independent mass of discrete complete sets of scrap equipment in flexible reverse load. The system uses three-dimensional spatial envelope parameters. To increase the ground pressure of the drive wheels under conditions of road adhesion decay, the system arranges them spatially as physical counterweights. As a preferred method, the load reconstruction module 130 establishes a three-dimensional Cartesian coordinate system inside the cargo compartment, setting the origin at the geometric center of the intersection of the front baffle and the floor of the cargo compartment. The system uses the area directly above the rear drive axle of the cargo compartment as the target load-bearing surface to generate the initial three-dimensional candidate loading coordinates of the material. .in, Let be the length coordinate along the longitudinal axis of the carriage. The horizontal axis represents the width coordinate. For vertical height coordinates. The system will initially... The value is set to the longitudinal position closest to the center line of the rear drive shaft, so that the gravity vector of the waste material can act directly on the vehicle drive shaft to the maximum extent, thereby obtaining a higher rear axle normal load distribution.

[0045] S402. Generating initial candidate coordinates is only theoretically optimal in terms of mechanics; its practical feasibility depends on compatibility with the loading and unloading sequence of logistics. Therefore, the load reconstruction module 130 performs a last-in-first-out (LIFO) temporal spatial anti-interference check. The system extracts the temporal loading and unloading channel constraints established in step S203, i.e., the pre-defined unobstructed unloading clearance area. The system calculates the geometric envelope model of the waste material along the longitudinal direction of the carriage using a ray projection algorithm, generating its projection coverage area on the bottom plane of the carriage. The load reconstruction module 130 performs an intersection operation on this projection coverage area and the unloading clearance area to determine whether geometric interference occurs. If there is area overlap, it indicates that the current candidate coordinates will block the downstream forward material removal. At this time, the load reconstruction module 130 iteratively shifts the coordinates along the horizontal y-axis or vertical x-axis by a preset spatial translation step size until the material envelope avoids the unloading channel, thus outputting interference-free transition loading coordinates. If interference still exists after traversing all translatable spaces, the system determines that the material cannot be loaded at present and triggers a task delay instruction to avoid the algorithm falling into an infinite loop.

[0046] S403. After locking the spatial position, the system needs to further calculate the mechanical boundary of that coordinate during braking on a long downhill slope. The load reconfiguration module 130 calculates the maximum safe braking deceleration of the vehicle under full load and long downhill conditions. The forward thrust generated by the combination of materials during braking. This deceleration. The value is usually determined with reference to the safety limits of commercial vehicle braking regulations, ranging from 0.3g to 0.5g. Due to the slope angle... The existence of total forward momentum of materials This is the superposition of the braking inertial force and the component of gravity acting downwards along the slope. The mathematical model of this force is expressed by the following formula: ; In the formula, Total forward momentum, measured in Newtons; For the independent quality of materials; For maximum safe braking deceleration; Let be the gravitational acceleration constant, taken as 9.8 m / s². 2 ; This represents the average slope angle of the road section. Simultaneously, static friction is generated between the bottom of the supplies and the floor of the truck bed. The formula is expressed as: ; In the formula, The static friction force of the base plate; The static friction coefficient of the car floor material is pre-calibrated.

[0047] The system calculates the additional longitudinal binding force required to prevent the materials from shifting. The load reconfiguration module 130 then determines the required value. Does it exceed the preset vehicle longitudinal binding force limit? If the limit is exceeded, for materials that are allowed to be disassembled, the system will trigger load reduction logic to decrease their quality. For non-disassembled complete sets of equipment, the system outputs a physical execution command to increase the number of high-strength straps. If the number of available strapping points is insufficient to compensate for the mechanical difference, the equipment is abandoned. For the basic mechanical calculations of inertial forces and frictional forces, those skilled in the art can solve them using theoretical mechanical equations. The force analysis is a well-known technique in this field and will not be elaborated upon here.

[0048] S404. After dual verification of anti-interference and binding force, the load reconstruction module 130 is based on the finally locked safe loading coordinates. Update the horizontal distance between the vehicle's center of gravity and the front axle. Based on this new center of gravity position, the system calculates the normal load distribution rate of the drive shaft. Its calculation method is as follows ,in This refers to the vehicle's physical wheelbase. It is combined with the actual longitudinal sliding friction coefficient of the current road section. and slope angle The system reverse-engineers the maximum permissible anti-skid safe load of the vehicle under conditions where wheel lock-up and sideslip do not occur. The calculation formula is as follows: ; In the formula, For extreme anti-slip safety load capacity; This represents the actual longitudinal sliding friction coefficient of the road surface. Rear axle normal load distribution rate; This is the maximum rated braking resistance constant of the vehicle braking system. This constant is calibrated through bench testing and reflects the maximum mechanical braking capacity of the brake at a specific temperature. The slope angle. By shifting the originally concentrated weight to the rear axle, the normal load distribution rate... This improvement increases the numerical value of the numerator in the formula at the mathematical and physical levels, which is equivalent to increasing the vehicle's overall grip reserve. The load reconfiguration module 130 confirms that the current planned load does not exceed the expanded limit for safe anti-slip load. Ultimately, the actual amount of the reverse-flow waste material collected and the coordinates of the load inside the carriage were determined.

[0049] In this embodiment, step S50 is executed by the global optimization module 140. The global optimization module 140 primarily transforms the previously calculated physical stress state and spatial geometric boundaries into quantified energy consumption and carbon emission indicators, thereby constructing a comprehensive evaluation function. Based on this, the module searches for the vehicle route and load allocation combination scheme that minimizes overall carbon emissions within the delivery network, which includes both time and spatial dimensions. Step S50 may include the following sub-steps: S501. When a vehicle is driving in complex terrain, its energy consumption performance is closely related to its real-time load and the gradient of the road section. When the vehicle is on an uphill or flat road (i.e., at a gradient angle...), the energy consumption will be significantly affected. When the vehicle enters a directed edge, the global optimization module 140 calculates the electrical energy required for the vehicle drive system to overcome gravity and rolling resistance, and maps it equivalently to carbon impedance. Specifically, the system obtains the electrical energy required for the vehicle to overcome gravity and rolling resistance. Real-time weight The real-time weight By the vehicle's unloaded weight The result is obtained by summing the current forward load mass and the reverse load mass. This is based on real-time weight. and the average slope angle of the road section The system constructs the carbon impedance equation under the driven work state, which is expressed as follows: ; In the formula, The equivalent carbon emissions of a vehicle under driving conditions on this road section, expressed in kilograms; The conversion factor for grid electricity to carbon emissions is usually determined by looking up a table based on the local grid's energy structure (such as the proportion of thermal power and wind power), and the range is generally between 0.5 and 0.7 kg CO2 / kWh. It is the acceleration due to gravity; This is the rolling resistance coefficient between the tire and the road surface. This coefficient is obtained by looking up tables based on road material and meteorological data. The value range for normal paved roads is approximately 0.006 to 0.015. For directed edges Space driving distance; The overall mechanical transmission efficiency of a vehicle's electric drive system is typically calibrated in the range of 0.85 to 0.95. This equation converts driving resistance into energy consumption using a physical work formula, and then uses a conversion factor to quantify it into environmental carbon cost.

[0050] S502, When the vehicle is in a long downhill driving condition (i.e. When gravitational potential energy is recovered and converted into electrical energy, it generates a negative benefit from carbon impedance. Considering that this benefit is limited by the physical bottleneck of the underlying electrical hardware, the global optimization module 140 calculates the theoretical gravitational potential energy conversion power during the downhill process. The calculation formula is as follows: ; In the formula, This represents the vehicle's expected average speed on this road segment. The system will convert this into power. Braking regeneration power limit initialized with environmental perception module 110 Perform a comparison. When At that time, gravitational potential energy can be recovered by reverse dragging of the motor within the maximum allowable limit of the system.

[0051] when When there is excess power, it cannot be converted into electrical energy and must be dissipated through traditional mechanical friction braking. Frequent mechanical braking accelerates brake pad wear, resulting in additional environmental pollution and maintenance costs. Therefore, the system introduces a wear-equivalent carbon penalty coefficient. Construct the braking potential energy recovery cutoff and mechanical penalty function, expressed by the formula: ; In the formula, The equivalent carbon impedance under downhill braking conditions; For braking energy recovery efficiency; This represents the theoretical travel time of the vehicle on this downhill section. The penalty factor for mechanical brake wear is determined by the lifecycle carbon footprint of the brake material, reflecting the hidden carbon emission cost per kilowatt-hour of mechanical energy dissipated. This formula quantifies the overall carbon cost of a vehicle under extreme downhill conditions by cutting off recycling benefits and adding physical wear penalties.

[0052] S503. After completing the carbon impedance modeling for each road segment and load condition, the global optimization module 140 initiates a large-scale neighborhood search algorithm in the four-dimensional spatiotemporal topology with the optimization objective of minimizing the total global carbon impedance of the fleet. As a preferred approach, the system constructs a four-dimensional spatiotemporal state extension map including longitude, latitude, elevation, and time windows. In each iteration of the algorithm, the global optimization module 140 generates new candidate routes through destructive operators (such as randomly removing some delivery nodes) and repair operators (such as greedily inserting nodes). Each time a new route is generated, the system internally calls the physical space and mechanical balance verification logic in step S40. If the load distribution in the candidate route causes interference in the carriage or exceeds the limit of the binding force, the system applies a very large penalty value (such as setting a positive constant much larger than the conventional carbon emission). To eliminate infeasible solutions, an adaptive dimensionality reduction fault-tolerance mechanism is configured to prevent algorithm deadlock caused by all candidate routes being deemed infeasible under extreme conditions. When no feasible solution is found after multiple consecutive iterations, the global optimization module 140 will automatically split the reverse load task or relax the time window constraint, thereby expanding the search space for solutions. For the core iterative process, operator design, and temperature decay update mechanism of the large-scale neighborhood search algorithm, those skilled in the art can use standard heuristic optimization frameworks to implement them; the neighborhood search mechanism is a well-known technology in the field and will not be elaborated here. The system terminates the search after the preset number of iterations is reached, outputting the globally optimal route and node picking sequence with the lowest total carbon impedance.

[0053] In this embodiment, step S60 is executed by the scheduling execution module 150. The scheduling execution module 150 is primarily responsible for converting the theoretically optimal solution calculated by the global optimization module 140 into control commands that can be recognized and executed by vehicles and warehousing equipment in the business scenario. By connecting macroscopic path planning with microscopic loading actions, it aims to achieve closed-loop management from algorithmic decision-making to actual business operation. Step S60 may include the following sub-steps: S601, the scheduling execution module 150 receives the globally optimal route and node picking sequence, and generates macroscopic spatiotemporal driving routes and time window instructions accordingly. The system converts the spatial driving trajectory between each delivery node into a geographic coordinate sequence composed of latitude and longitude. Combining the vehicle's expected average driving speed on the route and the terrain conditions of the road segment, the system calculates the expected timestamps for arrival and departure from each node. The scheduling execution module 150 pushes the regular navigation instruction data stream, containing the departure timestamp and the optimal geographic driving trajectory, to the vehicle's onboard terminal via the wireless communication network, providing macroscopic driving guidance for the driver or vehicle control system. Considering the possible signal blind spots in the actual communication environment, the system presets instruction retransmission and offline caching mechanisms. When the onboard terminal fails to return a confirmation response packet within the preset time window, the scheduling execution module 150 will trigger breakpoint resume logic to ensure reliable delivery of route instructions. For the data encapsulation processing of vehicle navigation path planning and time window allocation, those skilled in the art can use the standard vehicle network communication message format, and its underlying data transmission and parsing mechanism is a well-known technology in the field, and will not be described in detail here.

[0054] S602. To implement the rear-axle booster-based loading strategy during loading and unloading operations, the scheduling execution module 150 also outputs the microscopic three-dimensional loading coordinates and binding specifications of the cargo compartment for each node's loading and unloading process. Specifically, the system aggregates the loading data of the scrap materials that has undergone temporal spatial anti-interference verification and extreme anti-slip safety load verification, and constructs a three-dimensional loading coordinate matrix. This matrix records the spatial position of the discrete sets of scrap equipment to be recycled in the Cartesian coordinate system inside the cargo compartment. Independent quality of materials In addition to the geometric envelope dimensions, the data structure of the three-dimensional loading coordinate matrix, as a preferred approach, not only includes the aforementioned static and spatial parameters, but also integrates the unique identification code of the material, the node number to which it belongs, and the loading sequence number, so as to facilitate the analysis and comparison by downstream storage equipment.

[0055] For special working conditions such as long downhill slopes and reduced road surface adhesion, the scheduling execution module 150, based on the longitudinal binding force requirement calculated in step S403, simultaneously adds the required fastener strength specifications to the loading command. These specifications are typically quantified as the required rated tensile strength of the binding tape and recommended binding points. The system, spanning the vehicle scheduling domain, directly sends this three-dimensional loading coordinate matrix and fastening specification commands to the corresponding node's warehouse management system and forklift operation terminal. Upon receiving the command, the on-site forklift operator or automated guided vehicle, guided by the three-dimensional loading coordinate matrix, places the specified weight of scrap materials in the target coordinate area directly above the rear drive axle of the truck bed, and uses standard fasteners for auxiliary locking.

[0056] Furthermore, to avoid safety hazards caused by discrepancies between the actual material properties on-site and the system's calculated data, the warehousing terminal must send an actual loading confirmation message back to the scheduling execution module 150 after loading is completed. If the task cannot be executed according to the instructions on-site due to equipment size deviations or damage to available binding points, the terminal will trigger an abnormal blocking signal, and the system will then mark the task at that node as suspended and re-trigger local load recalculation. Through the above-mentioned cross-system data distribution and execution linkage, the system transforms the micro-level center of gravity adjustment algorithm into actual logistics loading and unloading actions, thereby optimizing the normal load distribution of the entire vehicle and the tire grip margin at the underlying mechanical boundary, effectively supporting the closed-loop control of the overall solution.

[0057] To enable those skilled in the art to more clearly understand the purpose, technical solution, and advantages of this invention, the present invention will be further described in detail below with reference to specific application embodiments, real experimental test data, and corresponding drawings. It should be noted that the embodiments described herein are only for explaining the present invention and are not intended to limit the scope of protection of the present invention.

[0058] I. Specific Application Examples 1. Scene and parameter settings: A power grid dispatch center in a mountainous region in Southwest China plans to carry out a loop delivery and recycling task from the central warehouse to distribution station A, distribution station B, distribution station C, and back to the central warehouse.

[0059] Vehicle parameters (step S103): Unloaded weight of heavy-duty electric truck kg, physical wheelbase m, Efficiency of integrated mechanical transmission in electric drive Braking energy recovery power limit kW, static friction coefficient of the car body floor Longitudinal binding force limit N.

[0060] Logistics tasks (steps S201-S202): The central warehouse shipped a batch of rigid positive loads (insulators and cables) to nodes A and C respectively. Node B has a discrete, complete set of scrap transformers (flexible reverse load) that needs to be recycled, with independent mass. kg, with a spatial envelope size of 1.5m × 1.2m × 1.6m.

[0061] Environmental characteristics (steps S101-S102): Substation B along the route Substation C is located on a typical long downhill mountain road with limited driving distance. m, node elevation difference m. The meteorological interface forecasts moderate rain within this time window. Through meteorological-adhesion attenuation matrix mapping, it is determined that the actual longitudinal sliding friction coefficient of this road section decreases to [value missing]. .

[0062] 2. Core Logic Triggering and Execution Deduction: Potential energy determination and high-risk warning (steps S302-S303): calculated by load reconfiguration module 130. The cumulative elevation difference of the section is less than the macroscopic downhill threshold (meeting the long downhill condition), and the predicted friction coefficient of 0.35 is less than the preset safety threshold of 0.4. The system determines that the section is a high-risk condition with high potential energy recovery potential and accompanied by the risk of slippery sideslip, and immediately triggers an active load control intervention signal.

[0063] Physical counterweight and interference verification (steps S401-S402): The system will use the 3000kg scrap transformer recovered at node B as a physical counterweight, with the goal of placing it as close as possible to the top of the rear drive axle (assuming the corresponding coordinates inside the vehicle). m). However, data from the system call to the business parsing module 120 revealed that the positive load on node C had not yet been unloaded. Through anti-interference verification using the ray projection algorithm, if the transformer is placed... At this location, the last-in-first-out unloading channel for goods at node C will be blocked. The system will automatically shift the transformer coordinates to one side. Output interference-free transition load coordinates.

[0064] Mechanical verification and load reconstruction (steps S403-S404): The system uses The emergency braking deceleration was verified, and the difference between the transformer's downhill impact force and the static friction force of the base plate was calculated as the required value of the longitudinal binding force. N, not exceeding the limit of 25000 N. After determining the safe coordinates, the system updates the vehicle's center of gravity. Since the 3000kg mass is highly concentrated near the rear axle, the drive axle normal load distribution rate... The pressure increased from 45% under no-load conditions to 62%. Reverse calculations proved that even with a road surface friction coefficient of only 0.35, the boosted drive shaft could provide sufficient ultimate anti-skid safety load, preventing wheel lock-up and sideslip.

[0065] Global optimization and distribution of carbon impedance (steps S50-S60): The global optimization module 140 substitutes the above state into the carbon impedance equation. On the downhill section from B to C, due to the increased load, the theoretical potential energy conversion power is higher. When the predicted power exceeds the braking regeneration limit of 120kW, the system accurately calculates the equivalent carbon penalty for mechanical brake wear. After large-scale neighborhood search algorithm iterations, the system finally locks in a pickup time window and route with the lowest overall carbon impedance, and the scheduling execution module 150 issues specific x, y, z coordinate loading instructions and 12500N fastening strap binding instructions to the warehouse forklift at node B.

[0066] II. Experimental Verification and Effect Comparison To further demonstrate the progressiveness of the present invention, this embodiment uses a digital simulation platform to conduct 30 multi-source random operating condition experiments on the above scenario.

[0067] 1. Setting up experimental and control groups Control group (traditional method): Employs the traditional shortest path planning algorithm combined with a volume-based greedy 3D bin packing algorithm. Loading does not consider the dynamic distribution of the vehicle's physical axle load, and downhill loading relies solely on the vehicle's anti-lock braking system (ABS), without considering the mechanical wear carbon penalty caused by excessive potential energy.

[0068] Experimental group (method of the present invention): The collaborative delivery method based on load reconstruction and carbon impedance global optimization large-scale neighborhood search algorithm (ALNS) according to the embodiments of the present invention is adopted.

[0069] 2. Test Result Analysis The simulation results show that the experimental group and the control group differed in three core key indicators, as shown in Table 1. Table 1: according to Figure 3 and Figure 5 It is evident that this invention has significant effects in reducing carbon emissions. Figure 3 The spatiotemporal dynamic carbon impedance iterative convergence curves show that the large-scale neighborhood search algorithm used in the experimental group, after nesting the mechanical penalty operator, can quickly escape local optima and converge to the minimum, thus reducing the comprehensive carbon emissions of a single loop by 12.1%. Figure 5 The energy consumption distribution comparison histogram further reveals that the experimental group accurately calculated and cut off the equivalent carbon penalty of mechanical braking, effectively avoiding the ineffective heavy-load steep slope emergency braking condition, maximizing the potential energy recovery rate, and significantly compressing the block area representing mechanical wear penalty.

[0070] according to Figure 4 It is evident that this invention possesses extremely high safety at the underlying physical level. The comparative bar chart of the dynamic distribution of normal loads on the front and rear axles under severe downhill conditions clearly shows that the experimental group successfully transferred the concentrated weight to the rear axle by reconstructing the center of gravity through reverse physical counterweighting. Compared to the control group, the experimental group experienced a moderate reduction in front axle load, while the normal load on the rear drive axle significantly increased. This change in physical state drastically reduced the maximum slippage risk coefficient of the drive axle from 0.88 (critical danger) to 0.65 (safe range), effectively mitigating the risk of wheel lock-up and sideslip on slippery surfaces.

[0071] according to Figure 6 It is evident that this invention significantly improves the recycling efficiency of reverse-flow waste materials. The visualization of the three-dimensional loading coordinates and anti-interference envelope inside the vehicle intuitively demonstrates the microscopic spatial scheduling capabilities: the gray solid cube representing the physical weight of the waste materials not only perfectly fits the area directly above the rear axle under system commands, but also achieves zero interference with the semi-transparent area representing the unloading channel through translational avoidance. This precise extreme anti-slip safety load calculation and three-dimensional anti-interference arrangement eliminates the need for the system to adopt an extremely conservative rejection strategy due to concerns about side slippage on downhill slopes, thereby increasing the reverse-flow waste material retrieval satisfaction rate from 76.5% to 94.2%.

[0072] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A green and low-carbon supply chain collaborative distribution system for power materials, characterized in that, include: The environmental perception module is used to construct a four-dimensional spatiotemporal road network model, extract road surface physical parameters and real-time meteorological parameters, so as to determine the actual longitudinal sliding friction coefficient of the road surface. The business analysis module is used to analyze the three-dimensional attributes of power materials and logistics task requirements; The load reconfiguration module is used to perform material loading coordinate and weight intervention based on the terrain prediction in the four-dimensional spatiotemporal road network model, the actual longitudinal sliding friction coefficient of the road surface, and the three-dimensional attributes of the power materials. The global optimization module is used to perform load-slope coupling and execute path optimization algorithms based on the results of the material loading coordinates and weight intervention, so as to calculate the theoretical optimal solution. The scheduling and execution module is used to output control commands that can be recognized and executed by vehicles and warehousing equipment in the business scenario based on the theoretical optimal solution.

2. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 1, characterized in that, The environmental perception module specifically includes: The directed edge travel distance and node elevation difference are obtained through the geographic information system interface in order to calculate the average slope angle, which is used as the physical parameter of the road surface. The system synchronously accesses the meteorological service interface to obtain precipitation and temperature forecast data as the real-time meteorological parameters, and determines the actual longitudinal sliding friction coefficient of the road surface by mapping the meteorological-adhesion attenuation matrix through multidimensional interpolation.

3. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 1, characterized in that, The business analysis module is specifically used for analyzing the three-dimensional attributes of the power materials and logistics task requirements: The three-dimensional geometric dimensions, mass, and density of materials are extracted from the warehouse management system, and the logistics recycling task is defined as reverse material; A three-dimensional spatial envelope model is established for the discrete materials in the reverse materials; Based on the stopping order of delivery nodes, unloading channels are reserved in the longitudinal direction of the carriage to establish time-sequence loading and unloading constraints.

4. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 3, characterized in that, The load reconstruction module calculates the cumulative elevation difference of the downstream path as the terrain prediction by vector integration; When there is a long downhill terrain downstream and the actual longitudinal sliding friction coefficient of the road surface is lower than the preset safety threshold, the load reconstruction module performs a center of gravity reconfiguration calculation based on the three-dimensional properties of the reverse material to realize the intervention of the loading coordinates and weight of the material. The load reconfiguration module changes the distance between the vehicle's center of gravity and the front axle by adjusting the three-dimensional loading coordinates of the materials inside the carriage, thereby improving the normal load distribution rate of the drive axle and inversely deriving the ultimate anti-slip safe load.

5. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 4, characterized in that, When the load reconfiguration module performs the center of gravity reconfiguration calculation: Generate the initial three-dimensional candidate loading coordinates of the reverse material, and extract the pre-defined unobstructed unloading clearance area from the time-series loading and unloading constraints; The geometric envelope model of the reverse material is calculated along the longitudinal direction of the carriage using a ray projection algorithm to generate a projection coverage area on the bottom plane of the carriage. The intersection of the projected coverage area and the unloading clearance area is calculated, and a last-in-first-out temporal spatial anti-interference check is performed to output the transition loading coordinates.

6. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 5, characterized in that, After locking the spatial position, the load reconfiguration module calculates the inertial forward force of the cargo under downhill braking conditions, and calculates the additional longitudinal binding force required to prevent the material from shifting, in conjunction with the static friction of the bottom plate; under the premise of meeting the longitudinal binding force limit, the load reconfiguration module locks the material loading coordinates.

7. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 1, characterized in that, When performing the load-slope coupling, the global optimization module calculates the carbon impedance per unit road segment based on the departure load, road segment slope, and meteorological parameters. The global optimization module uses a braking energy recovery power cutoff function to handle mechanical braking penalties after exceeding the physical recovery limit. By cutting off recovery benefits and superimposing physical wear penalties, it quantifies the comprehensive carbon cost of the vehicle under extreme downhill conditions.

8. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 7, characterized in that, The global optimization module uses a large-scale neighborhood search algorithm to generate the driving route and material pickup combination scheme with the minimum total carbon impedance in the four-dimensional spatiotemporal topology map, which is the theoretical optimal solution. In each iteration of the algorithm, when a new route is generated, the system internally calls the logic for verifying physical space and mechanical balance. When no feasible solution is found in multiple consecutive iterations, the global optimization module automatically splits the reverse load task or relaxes the time window constraint through an adaptive dimensionality reduction and fault tolerance mechanism.

9. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 1, characterized in that, When the scheduling execution module outputs the control command: The system sends driving trajectory and departure time instructions to the execution terminal, and simultaneously sends packing instructions with loading coordinates and binding requirements to the warehousing system.

10. The green and low-carbon supply chain collaborative distribution system for power materials according to claim 9, characterized in that, When the scheduling execution module issues the packing instruction: Summarize the loading data of waste materials that have undergone temporal and spatial anti-interference verification and extreme anti-slip safety load verification in the early stage, and construct a three-dimensional loading coordinate matrix; The three-dimensional loading coordinate matrix and fastening specification instructions are sent to the corresponding node's warehouse management system and forklift operation terminal; If an abnormal blocking signal is triggered on-site due to equipment size deviation or damage to available binding points, the system will mark the node task as suspended and re-trigger the local load recalculation.

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