Multi-dimensional physical constraint loss prevention loading method for electric power materials based on industrial big data

Abstract

This invention relates to the field of intelligent loading technology for power materials, specifically disclosing a multi-dimensional physical constraint-based loss-prevention loading method for power materials based on industrial big data. The method includes: responding to a loading request by acquiring associated industrial big data, which at least includes power material data, transport vehicle data, and cross-regional road condition data; based on the industrial big data, constructing a material object model and a vehicle object model containing geometric and physical attributes, and generating an initial candidate loading queue. This invention achieves comprehensive control from geometric boundaries to dynamic mechanics by constructing a multi-dimensional physical constraint hierarchical verification mechanism. Specifically, it utilizes dynamic overturning compensation logic to intelligently identify the hidden instability risks of high-center-of-gravity materials without reducing the overall loading rate, and eliminates overturning hazards through virtual grouping and binding technology, effectively resolving the contradiction between dynamic safety and loading efficiency for high-center-of-gravity materials.

Description

Technical Field

[0001] This invention relates to the field of intelligent loading technology for power materials, and in particular to a multi-dimensional physical constraint-based anti-damage loading method for power materials based on industrial big data. Background Technology

[0002] Currently, in the logistics and transportation of power materials, due to the wide variety of materials, such as large transformers, insulator strings, and disc cables, their physical forms cover cubes, cylinders, and irregular shapes, and they often have characteristics such as large weight, high center of gravity, and strict load-bearing limitations.

[0003] Existing loading technologies mostly employ bin-packing problem algorithms based on static geometric boundaries, focusing on maximizing space utilization and verifying loading feasibility solely through simple volume calculations and bottom contact area. However, in long-distance cross-regional transportation, especially when dealing with complex road conditions such as mountainous areas, this static verification method has significant safety blind spots.

[0004] This leads to a significant conflict between the failure of safety constraints and low loading efficiency in practical applications:

[0005] On the one hand, some high-center-of-gravity or heterogeneous materials meet the support rate requirements under static testing, but under the action of lateral inertial force generated by vehicles passing through large-curvature curves, they are very prone to hidden instability and overturning. If the global support threshold is increased simply for safety, a large number of ordinary materials will be mistakenly killed, resulting in a significant decrease in vehicle loading rate.

[0006] On the other hand, existing multi-objective global optimization algorithms are computationally time-consuming. When a sudden situation occurs at the loading site, such as material damage or partial unavailability of the carriage, rerunning the global algorithm will cause serious production line stagnation. Furthermore, simple manual replacement of on-site workers can easily disrupt the overall vehicle's center of gravity balance, making it impossible to balance on-site response speed with global physical safety. Summary of the Invention

[0007] This invention aims to at least partially solve one of the technical problems in related technologies. Therefore, the purpose of this invention is to propose a multi-dimensional physical constraint-based loss-prevention loading method for power materials based on industrial big data, achieving efficient and loss-prevention scheduling of power materials under complex road conditions.

[0008] To achieve the above objectives, a first aspect of the present invention proposes a multi-dimensional physical constraint-based damage prevention loading method for power materials based on industrial big data, comprising the following steps:

[0009] In response to a material loading request, relevant industrial big data is obtained, which includes at least power material data, transport vehicle data, and cross-regional road condition data.

[0010] Based on the aforementioned industrial big data, a material object model and a vehicle object model containing geometric and physical attributes are constructed, and an initial candidate loading queue is generated.

[0011] Perform constraint-guided dynamic sorting on the initial candidate loading queue, and perform multi-dimensional physical constraint hierarchical verification on the sorted candidate loading schemes to determine the target loading scheme;

[0012] Initiate the output process for the target loading plan to generate loading instructions to guide the physical loading of materials;

[0013] The execution process of the multidimensional physical constraint hierarchical verification includes:

[0014] First-level verification: In response to the geometric boundary parameters of the candidate position, determine whether the material exceeds the boundary of the carriage and meets the rotation constraint rules;

[0015] Second-level verification: If the first-level verification passes, calculate the total weight of the car and the material support rate to determine whether the maximum load capacity constraint and stability constraint are met.

[0016] Third-level verification: If the second-level verification passes, then determine whether the load transfer constraint is met based on the remaining load matrix of the lower-level materials, and verify the cross-vehicle binding relationship;

[0017] If all three levels of verification results pass, the current candidate loading scheme is deemed valid.

[0018] To achieve the above objectives, a second aspect of the present invention proposes a multi-dimensional physical constraint-based damage prevention loading system for power materials based on industrial big data, applied in an intelligent scheduling platform for power material logistics, comprising:

[0019] The data acquisition and modeling module is used to respond to material loading requests, acquire industrial big data including power material data, transport vehicle data and cross-regional road condition data, and construct material object models and vehicle object models that include geometric and physical attributes.

[0020] The constraint-oriented calculation module is used to generate an initial candidate loading queue, perform constraint-oriented dynamic sorting based on material volume, emergency attributes and cross-vehicle binding identifiers, and perform multi-dimensional physical constraint hierarchical verification, including geometric boundary verification, load and stability verification, and load transfer verification, on the sorted candidate loading schemes.

[0021] The dynamic overturning compensation module is used to calculate the expected maximum lateral acceleration based on road curvature and vehicle speed during stability verification, identify the hidden instability state of high center of gravity materials, and trigger the search for complementary materials to build a combined rigid body model to eliminate the risk of overturning.

[0022] The local topology repair module is used to respond to the material failure signal during the loading execution phase, lock the target torque invariant of the repair void, and search for a substitute combination in the loading queue whose torque deviation meets the global safety tolerance to directly replace the failed material.

[0023] The instruction generation and output module is used to generate loading instructions that guide the physical loading of materials, based on the verified target loading scheme and the optimization results of the multi-objective balance function.

[0024] To achieve the above objectives, a third aspect of the present invention provides an electronic device, including a memory, a processor, and a computer program stored in the memory. When the computer program is executed by the processor, it implements the above-described method for preventing damage to power materials based on industrial big data through multi-dimensional physical constraints.

[0025] The multi-dimensional physical constraint anti-loss loading method for power materials based on industrial big data in this invention realizes all-round control from geometric boundaries to dynamic mechanics by constructing a multi-dimensional physical constraint hierarchical verification mechanism.

[0026] Specifically, it utilizes dynamic overturning compensation logic to intelligently identify the hidden instability risks of high-center-of-gravity materials without reducing the overall loading rate, and eliminates overturning hazards through virtual grouping and binding technology, effectively resolving the contradiction between dynamic safety and loading efficiency of high-center-of-gravity materials. At the same time, the solution introduces a local repair technology based on torque invariant locking. When an anomaly occurs at the loading site, there is no need to recalculate the complex global model. It only needs to search for equivalent torque replacement combinations in the local topology domain to complete the solution repair within milliseconds. This ensures both the continuity of on-site operations and the preservation of the vehicle's center of gravity and stability, achieving efficient, damage-preventable, and intelligent scheduling of power materials under complex road conditions. Attached Figure Description

[0027] Figure 1 This is a flowchart illustrating the multi-dimensional physical constraint-based damage prevention loading method for power materials based on industrial big data provided by the present invention.

[0028] Figure 2 This is a schematic diagram illustrating the implementation of the multi-dimensional physical constraint anti-damage loading system for power materials based on industrial big data provided by the present invention.

[0029] Figure 3 This is a schematic diagram of the structure of the electronic device provided by the present invention. Detailed Implementation

[0030] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0031] The following description, with reference to the accompanying drawings, describes a method, system, and electronic device for preventing damage to power materials based on industrial big data using multi-dimensional physical constraints.

[0032] Example 1:

[0033] This embodiment proposes a multi-dimensional physical constraint-based loss-prevention loading method for power materials based on industrial big data. This method is applied to an intelligent scheduling platform for power material logistics, aiming to solve the technical problems in existing technologies where high-center-of-gravity power materials are prone to overturning in complex road conditions, resulting in low loading efficiency and weak on-site response to emergencies due to insufficient static geometric verification. This embodiment achieves comprehensive control from geometric boundaries to dynamic mechanics by constructing a multi-dimensional physical constraint hierarchical verification mechanism combined with industrial big data.

[0034] like Figure 1 As shown, the method in this embodiment mainly includes the following steps:

[0035] Step S1: In response to the material loading request, obtain the relevant industrial big data and build an object model.

[0036] Specifically, the system first responds to a material loading request. This request, typically initiated by a warehouse management system (WMS) or logistics dispatch center, includes the manifest ID of the materials to be transported and their destination information. In response to this request, the system retrieves relevant industrial big data from a cloud database or edge computing node via a data interface. It is important to note that this industrial big data includes at least power material data, transport vehicle data, and cross-regional road condition data.

[0037] For example, the power equipment data includes not only the basic names of the equipment, such as transformers, instrument transformers, and insulators, but also detailed geometric dimensions (length, width, and height), mass data, centroid coordinates, packaging material type, and maximum permissible pressure. The transport vehicle data includes the internal dimensions of the cargo box (length, width, and height), the rated load capacity of the floor, the stiffness coefficient of the suspension system, the number of axles, and the vehicle's dynamic response characteristics under different loads. The cross-regional road condition data is road information along the transport route obtained based on a GIS geographic information system, including but not limited to road grade, road surface roughness, turning radius, slope angle, and historical accident-prone road section markings.

[0038] Based on the aforementioned industrial big data, the system further constructs material object models and vehicle object models that include geometric and physical attributes. During the construction process, in order to accurately describe the physical characteristics of the materials, this embodiment adopts an object-oriented data structure.

[0039] Specifically, the construction of the material object model and vehicle object model containing geometric and physical attributes includes: defining the shape identifier of the material object model and setting a rotation constraint Boolean value, which allows six degrees of freedom rotation when the Boolean value is true.

[0040] In practical modeling, the material object model includes a shape identifier to distinguish whether the material is a regular cube, cylinder, or irregular irregular shape. For each type of material, the system sets a rotation constraint Boolean value. For example, for a box containing a precision oil-immersed transformer, it must be kept vertical and is strictly prohibited from being upside down or lying on its side. Therefore, its rotation Boolean values ​​around the X and Y axes are set to False, allowing only rotation around the Z axis (vertical axis) (which is safe if space permits, i.e., set to True). For a reel cable or a small hardware box, the Boolean value being True allows six degrees of freedom of rotation, meaning that the algorithm can attempt to flip the material by 90 degrees, 180 degrees, or any angle to fit the remaining space during subsequent space searches.

[0041] Simultaneously, for the vehicle object model, this embodiment also defines a road condition adaptation score for the vehicle object model. This score is used to associate the road type and slope parameter in the cross-regional road condition data. The system uses a weighted algorithm to comprehensively calculate the road condition adaptation score by combining the road type (e.g., highway with a weight of 1.0, mountain dirt road with a weight of 0.6) and the slope parameter (e.g., the score drops sharply if the slope is greater than 15%) from the acquired cross-regional road condition data. This score will serve as an important reference factor for setting the stability threshold in subsequent verification stages.

[0042] Step S2: Generate an initial candidate loading queue and perform constraint-guided dynamic sorting.

[0043] After completing object modeling, the system initially generates an initial candidate loading queue. To improve the convergence speed of loading calculations and prioritize the transportation of critical materials, this embodiment performs constraint-guided dynamic sorting on the initial candidate loading queue.

[0044] The execution of the constraint-guided dynamic sorting of the initial candidate loading queue includes the nested execution of multiple sorting logics.

[0045] First, the system calculates the volume parameters of the materials and generates a basic sequence by sorting them in descending order of volume. Specifically, the system extracts the bounding box volume of each material object model and uses a quicksort algorithm to place large-volume materials, such as the main transformer body, at the front of the queue and small-volume materials (such as parts boxes) at the back. This loading heuristic helps improve the overall space utilization rate.

[0046] Secondly, in response to the emergency attribute identifiers of materials, the system elevates high-priority materials to the top of the basic sequence. In emergency scenarios such as power restoration, certain materials, such as circuit breakers for repair, have emergency attribute identifiers. Regardless of their size, once this identifier is identified, the system uses a priority queue algorithm to forcibly move them to the top of the sequence, ensuring that they are loaded first and located in a position in the carriage that facilitates rapid unloading, such as the rear of the carriage.

[0047] Secondly, in response to cross-vehicle binding identifiers, the system forces adjacent locking of non-cross-vehicle-binding material groups within the basic sequence. In power engineering, some equipment consists of multiple components, such as multiple chambers of a GIS switchgear, which must be transported on the same vehicle for on-site assembly. The system identifies the cross-vehicle binding identifiers (Group_ID) of these materials, packages and locks materials with the same Group_ID in the sequence, treating them as a logical macro-material for sorting, preventing them from being dispersed across different vehicles.

[0048] Finally, the system introduces a dynamic feedback mechanism, which calculates the pass rate of materials in the hierarchical verification in real time. If the pass rate is lower than a preset first probability threshold, the ranking priority of that material in the basic sequence is increased. During the algorithm iteration process, if materials with unusual shapes or numerous constraints (i.e., difficult-to-load materials) fail verification in multiple trials, i.e., the pass rate is < The system will identify this as a loading bottleneck. To address this issue, the system automatically prioritizes the resource, allowing it to be loaded in the early stages when space is plentiful, thus preventing it from being unable to be loaded later due to space fragmentation.

[0049] Step S3: Perform multidimensional physical constraint hierarchical verification on the sorted candidate loading schemes.

[0050] The system performs multi-dimensional physical constraint hierarchical verification on the ranked candidate loading schemes to determine the target loading scheme. This verification mechanism abandons the traditional simple judgment based solely on geometric volume, and instead adopts a three-layer progressive filtering funnel model of geometry-static force-transfer.

[0051] The execution process of the multidimensional physical constraint hierarchical verification specifically includes the following three levels:

[0052] First level of verification: Geometric boundary and rotation constraint verification.

[0053] Specifically, the system responds to the geometric boundary parameters of the candidate location to determine whether the material exceeds the car boundary and meets the rotation constraint rules. When the algorithm attempts to place a material at a coordinate point (x, y, z) within the car, it first calculates the 3D bounding box (AABB) of the material at that position and in its current pose. The system then checks whether this bounding box is completely contained within the effective loading space of the car. ≥0, ≤ , ≥0, ≤ , ≥0, ≤ Simultaneously, the system utilizes the Separation Axis Theorem (SAT) to detect whether the material overlaps spatially with other loaded materials (collision detection). Furthermore, the system re-verifies the previously set rotation constraint Boolean values ​​to ensure the current posture is permitted. If the material exceeds the boundary, collides, or violates rotation prohibitions, the first-level verification fails, and the candidate solution is immediately discarded without further complex physical calculations.

[0054] Second level of verification: load and stability verification.

[0055] If the first-level verification passes, the system proceeds to the physical-level verification. The system calculates the total weight of the carriage and the material support rate to determine whether the maximum load capacity constraint and stability constraint are met.

[0056] In terms of load capacity, the system adds up the mass of currently loaded materials to the mass of new materials to ensure that the total mass is less than the rated load capacity of the vehicle floor and that the axle load distribution meets the requirements of road transport regulations.

[0057] In terms of stability, this is crucial for damage-prevention loading. The second-level verification determines whether stability constraints are met, including differentiated calculations for materials of different forms:

[0058] First, the system obtains the contact bottom surface morphology type of the material.

[0059] For cubic materials, such as control cabinet packaging boxes, the system calculates the ratio of the contact area to the base area as the support ratio. For example, when a material straddles two other materials, there may be a suspended area in between. The system calculates the actual contact base area. With respect to the bottom area of ​​the material itself ratio Simultaneously, the system performs extremely rigorous edge detection, checking whether the height of the bottom vertex is less than a preset first distance threshold. This is to prevent the corners of the goods from being suspended and causing concentrated stress that could damage the packaging. If any of the four corners is suspended and the suspension range exceeds the threshold, such as 5cm, a tipping hazard is identified.

[0060] For cylindrical materials, such as cable reels or insulator strings, stability assessment is more complex. The system calculates the ratio of contact length to axial length as the support ratio. For example, for a horizontally placed cylinder, its support depends on the busbar contact. The system calculates the effective contact segment length. Total length of cylinder The ratio. In addition, the system must determine whether the contact line direction is parallel to the main axis of the carriage. In order to prevent the cylinder from rolling when the vehicle accelerates or brakes, this embodiment requires that the main axis of the cylinder must be parallel to the carriage's travel direction (longitudinal) (or within the allowable angular error range), or must be equipped with a thrust wedge, which is reflected as a constraint condition in the model.

[0061] Finally, the system will calculate the support rate. A preset stability threshold mapped to the current road condition level A comparison is performed. The preset stability threshold is dynamically changing and is related to the road condition adaptation score calculated in step S1. For example, on a flat highway, the support rate threshold may be set to 60%; while on a rugged mountain road, the threshold will automatically increase to 90% or even 100%. If the calculated support rate is greater than the preset stability threshold, the stability constraint is deemed satisfied, and the second-level verification passes.

[0062] Third-level verification: load transfer and cross-vehicle binding verification.

[0063] If the second-level verification passes, it means the material can be placed inside and is stable, but it doesn't guarantee that the material below can withstand the load. The system then proceeds to the third level, determining whether the load transfer constraints are met based on the remaining load-bearing matrix of the material below, and verifying the cross-vehicle binding relationship.

[0064] The third-level verification determines whether the load transfer constraint is met based on the remaining load-bearing matrix of the lower-level materials, including refined mesh mechanical analysis:

[0065] Specifically, the system divides the top plane of the loaded goods into a standard grid and records the remaining load-bearing capacity of each grid. This embodiment constructs a two-dimensional matrix grid corresponding to the dimensions of the carriage floor, for example, using 10cm × 10cm as a unit. For each grid unit... The system maintains a value This indicates the additional weight that the location can currently withstand. Initially, the load-bearing capacity of the carriage floor is extremely high; after the first layer of supplies is placed, the remaining load-bearing value of this grid is updated to the maximum allowable pressure value of the top cover of the first layer of supplies minus the weight already borne.

[0066] When attempting to place a new resource, the system responds to the loading position of the new resource by calculating the percentage of overlap between the bottom grid of the new resource and the grid below it. Assume the new resource covers the entire grid set. For each grid in the set, calculate its coverage ratio. .

[0067] Next, based on the overlap area ratio, the system distributes the weight load of the new material to the corresponding grid in the lower layer. The total weight of the new material... The load is discretized and distributed, with each grid sharing the load. .

[0068] The system then determines whether the remaining load-bearing capacity of any stressed grid is less than the assigned weight load. That is, it checks whether this condition is met for all involved grids. If so, meaning any grid is overloaded, it indicates that the local stress on the lower-level material exceeds the pressure limit of its packaging box, which could easily cause the lower-level material to collapse. In this case, the system triggers parameter correction for the loading posture of the new material, such as fine-tuning the position to find the load-bearing beam or directly determining that the load transfer constraint verification has failed.

[0069] In addition, at the third level, the system will reconfirm the cross-vehicle binding relationship to ensure that the supplies that were forcibly locked to be adjacent during the sorting phase were indeed arranged in adjacent positions within the same vehicle and were not separated by the algorithm during spatial search.

[0070] If the above three levels of verification (geometry, stability, and load-bearing capacity) all pass, the current candidate loading scheme is deemed valid and is taken as a feasible intermediate or final solution.

[0071] Step S4: Coordinate alignment correction.

[0072] After determining a preliminary feasible target loading scheme, a coordinate alignment correction step was performed in order to further improve the compactness of the loading and simulate the fitting habits of manual loading.

[0073] Specifically, in response to the gap data between materials, the system performs a projection set intersection operation to calculate the Euclidean distance between each surface of the current material and other loaded materials or the walls of the carriage.

[0074] If the material is a cube and the gap data is less than a preset second distance threshold, such as 5cm, the system determines that this is an unnecessary virtual space gap and corrects the material's coordinate parameters to a zero-gap state. This means that the algorithm will move the material along the coordinate axis to make it adhere tightly to the previous material or the carriage wall, thereby eliminating invalid gaps and preventing sliding and impact during transportation.

[0075] If the material is cylindrical, to prevent slight lateral displacement due to vibration during transportation, the system corrects the coordinate parameters of the bottom center to the center of the support surface or the midpoint of the contact line. For example, when a cylindrical oil drum is placed on a pallet, the algorithm forces its center to align with the geometric center of the pallet. Simultaneously, the system corrects the radial clearance to less than a preset third distance threshold, ensuring that the cylinder's sides maintain close contact with other objects or a sufficient safe distance, depending on whether lateral contact is permitted.

[0076] Step S5: Multi-objective balancing optimization and loading instruction output.

[0077] After the above verification and correction, the system may obtain multiple feasible loading schemes. At this point, it is necessary to initiate the output process for the target loading scheme to generate loading instructions to guide the physical loading of materials. To select the best among the best, the output process for the target loading scheme is initiated, including a multi-objective balancing optimization process.

[0078] Specifically, the system constructs a multi-objective equilibrium function. The function includes at least a space utilization factor. Formation calculation time factor Formation center of gravity offset factor and stability risk factor The function can be expressed as:

[0079] ;

[0080] in, These are the weighting coefficients. To adapt to different business needs, the system dynamically adjusts the weighting coefficients of the above factors based on whether the current business scenario is an emergency scenario or a heavy materials scenario, using the Analytic Hierarchy Process (AHP).

[0081] For example, in emergency scenarios such as typhoon repairs, time is of the essence. The system will utilize the AHP algorithm to significantly improve the formation calculation time factor. The weight of the formation's center of gravity offset factor is prioritized, even at the cost of sacrificing some space utilization; in heavy material scenarios, such as transporting large transformers, safety is paramount, and the system will significantly increase the center of gravity offset factor. and stability risk factor The weight is adjusted to ensure that the vehicle's center of gravity is extremely low and centered, preventing rollovers.

[0082] The system calculates the optimal solution to the multi-objective balance function, selects the scheme with the highest comprehensive score, and outputs a loading instruction that includes the optimal loading location and delivery plan. This instruction can be converted into a 3D visualization for loading and unloading workers to view, or into machine-readable code to be sent to an automated palletizing robot.

[0083] Step S6: Dynamic adjustment and feedback for abnormalities.

[0084] During actual loading and transportation, the environment is dynamically changing. Therefore, this embodiment also includes an anomaly dynamic adjustment step:

[0085] The system monitors the status data during loading and delivery in real time. This data comes from vehicle-to-everything (V2X) sensors, such as onboard weighing sensors, gyroscopes, and accelerometers.

[0086] The system responds to abnormal signals detected continuously for a preset duration by triggering a coordinated adjustment process. For example, if the sensor detects that the vehicle is continuously in a high-frequency bumpy state for more than 10 seconds.

[0087] If the abnormal signal indicates a sudden change in road conditions, for example, a previously expected flat road becomes a temporary bumpy section due to construction, the system automatically increases the preset threshold standards for the center of gravity offset constraint and support ratio constraint in the multi-dimensional physical constraint layered verification and re-executes the verification. This means that the system will dynamically notify subsequent vehicles that have not yet departed or the current vehicle that it must adopt a more conservative and stable loading scheme when loading at the next station, such as increasing the support ratio requirement, to adapt to the deteriorating road conditions.

[0088] In summary, this embodiment, driven by industrial big data and combined with refined multi-level physical verification and dynamic optimization logic, not only solves the geometric arrangement problem in the loading of power materials, but also fundamentally solves the physical safety problem in complex transportation environments, achieving a dual improvement in loss prevention and efficiency.

[0089] Example 2:

[0090] This embodiment, based on Embodiment 1, further optimizes and expands the stability constraint determination step in the multi-dimensional physical constraint hierarchical verification. Specifically, this embodiment focuses on the dynamic overturning compensation logic based on industrial big data, aiming to solve the hidden instability risk that still exists in high-center-of-gravity power materials during transportation under complex road conditions, even though static support requirements are met.

[0091] As shown in the figure, the method in this embodiment includes the following dynamic overturning compensation logic steps when performing stability constraint determination:

[0092] Step S201: Obtain road condition and vehicle operation data, and calculate the expected maximum lateral acceleration.

[0093] Specifically, before performing stability verification, the system first needs to quantitatively predict the dynamic characteristics of the transportation environment. In response to the transportation route determined in the material loading request, the system extracts cross-regional road condition data from the associated industrial big data. This cross-regional road condition data not only includes static road grade information, but more importantly, it includes the geometric parameters of each curve on the route. The system traverses the entire transportation route, identifies all curve nodes, and extracts the radius of curvature of each curve node. The system then selects the one with the smallest value, defining it as the minimum road radius of curvature, denoted as . .

[0094] For example, in a mountainous area where power supplies are transported, there may be a series of hairpin bends. The system uses high-precision map data from a GIS geographic information system to determine the minimum road curvature radius of this section. It is 50 meters.

[0095] Simultaneously, the system combines transport vehicle data and road speed limits to determine the preset travel speed of vehicles passing through the section with the minimum radius of curvature, denoted as... The speed value is usually set by taking into account the vehicle's dynamic limits, the type of cargo, and traffic regulations, for example, it may be set to 30 km / h.

[0096] Based on the minimum road curvature radius and preset vehicle speed in the cross-regional road condition data, the system calculates the expected maximum lateral acceleration at the current loading position. This calculation is based on the centripetal acceleration formula for circular motion. Let the expected maximum lateral acceleration be... The calculation formula is as follows:

[0097] ;

[0098] in, This is a road condition correction factor used to compensate for the effects of road surface slope (superelevation) and road friction coefficient on lateral forces. If the outer side of the road surface is higher than the inner side, it helps to counteract centrifugal force. The value can be less than 1; otherwise, it is greater than 1. This formula accurately quantifies the maximum lateral inertial force that materials may experience during transportation, providing a dynamic benchmark for subsequent stability assessments.

[0099] Step S202: Construct a physical model of the materials and calculate the critical overturning acceleration.

[0100] After quantifying the external disturbance force, the system then assesses the material's own anti-overturning capability. Specifically, based on the material object model, the system extracts the physical geometric attributes of the material to be loaded. For each material to be verified, the system obtains the vertical height of its center of mass relative to the bottom surface, defined as the material's center of gravity height, denoted as . Simultaneously, the system analyzes the contact area between the bottom surface of the materials and the materials in the carriage or lower layer to determine the support range of the materials in the potential overturning direction. The system calculates the shortest horizontal distance from the projection point of the center of mass on the ground to the edge of the support surface, defined as the geometric distance of the bottom support edge, denoted as . .

[0101] For example, for a vertical high-voltage switchgear, its center of gravity is relatively high, assuming... It is 1.5 meters long, but its base is relatively narrow, and its lateral overturning direction... It is only 0.4 meters long.

[0102] Based on the geometric distance between the material's center of gravity and the edge of its bottom support, the system calculates the critical overturning acceleration. Critical overturning acceleration refers to the lateral acceleration threshold that brings the material to a state of just-overturning equilibrium, i.e., when the gravitational torque and the inertial torque are equal. Let the critical overturning acceleration be... The acceleration due to gravity is The calculation formula is as follows:

[0103] ;

[0104] This formula reflects the geometric stability characteristics of the material itself, when The larger, The smaller, The larger the size, the less likely the goods are to tip over.

[0105] Step S203: Identify latent instability states.

[0106] It's also important to note that traditional loading algorithms often only focus on static support ratios, assuming safety as long as the bottom surface of the material has sufficient support area. However, this is far from adequate for dynamic transportation. This embodiment accurately identifies risks by comparing external disturbance forces with internal resistance forces.

[0107] Specifically, the system first checks whether the current material meets the preset stability threshold. This usually refers to the static support rate verification mentioned in Example 1, such as whether the bottom surface contact area ratio exceeds 80%. If it does not meet the threshold, the verification is directly judged as a failure.

[0108] If the current material meets the preset stability threshold, the system will further compare the two acceleration values ​​calculated above. If the calculated critical overturning acceleration... Less than the expected maximum lateral acceleration ,Right now This means that when the vehicle turns, the centrifugal torque generated will be greater than the restoring torque provided by gravity, causing the supplies to tip over. At this point, the system determines that the supplies are in a state of latent instability.

[0109] This concealment lies in the fact that when the vehicle is stationary or moving in a straight line, the material appears completely stable, and the risk is only exposed under specific dynamic conditions.

[0110] Step S204: Initiate the association and binding process and search for complementary resources.

[0111] Once a latent instability state is identified, the system does not simply refuse to load the material, but instead attempts to mitigate the risk through intelligent combination strategies, thereby ensuring loading efficiency. In response to the latent instability state, the system initiates an association and binding process.

[0112] Specifically, the system performs a global search within the initial candidate loading queue. The search objective is to find another material (referred to as a complementary material) that can be physically combined with the current high-center-of-gravity material (referred to as a risk material). Search constraints include geometric matching and physical proximity. The system calculates the spatial distances between other materials in the queue and the current risk material in the pre-simulated loading scheme, and searches the initial candidate loading queue for complementary materials that can form a contact surface distance with the current material that is less than a contact threshold.

[0113] For example, suppose the hazardous material is a tall, thin control cabinet; the system might detect a short, wide transformer parts box, or another identical control cabinet. The contact threshold is typically set to a very small value, such as 0 or a few centimeters, meaning the two can be placed very close together.

[0114] Step S205: Construct a combined rigid body model and perform virtual group binding.

[0115] Once a suitable complementary resource is found, the system attempts to virtually bind the two together. Specifically, the system constructs a combined rigid body model of the current resource (risky resource) and the complementary resource. Mathematically, this means treating two independent objects as a new, unified object.

[0116] The system needs to recalculate the physical properties of this new object. First, the new centroid location. Let the mass of the hazardous material be... The height of the center of mass is The quality of complementary materials is The height of the center of mass is The equivalent center of gravity height of the composite rigid body model The calculation is as follows:

[0117] ;

[0118] Typically, by introducing complementary materials with a lower center of gravity, the overall center of gravity height can be significantly reduced.

[0119] Secondly, the system calculates the geometric characteristics of the bottom support after combination. If the two materials are placed side by side and close together, the geometric distance between their bottom support edges will increase significantly. Let half of the effective support width after combination be... .

[0120] Next, the system recalculates the equivalent bracing ratio and equivalent critical overturning acceleration of the combined rigid body model. (Equivalent critical overturning acceleration) Updated to:

[0121] ;

[0122] because The increase and The reduction, usually It will be significantly larger than the original risk materials. .

[0123] Step S206: Generate atomic loading unit instructions. Finally, the system performs a second verification using the updated parameters. If the combined rigid body model satisfies the constraints, that is, it satisfies... Furthermore, the equivalent support rate meets the static requirements, indicating that the risk of overturning was successfully eliminated by the method of sticking together for warmth.

[0124] Specifically, after the system confirms the feasibility of the combination scheme, it will modify the originally independent loading instructions to generate a loading instruction that binds the two materials into an atomic loading unit. An atomic loading unit means that in subsequent loading operations, the two materials are treated as an inseparable whole. The instruction will explicitly state: material A and material B should be physically bound together before loading, or tightly arranged side-by-side in the wagon and secured using uniform fasteners.

[0125] Optionally, if no single complementary material that meets the requirements can be found in the initial queue, the system can iteratively try to search for two or more materials to combine until the constructed multi-material combination rigid body model meets the dynamic stability requirements.

[0126] Through the aforementioned dynamic overturning compensation logic, this embodiment not only effectively identifies safety hazards that are difficult to detect from a static perspective, but also avoids the inefficiency of having to reduce the overall vehicle loading rate or change vehicle models due to the insufficient stability of individual materials through intelligent grouping strategies, thus achieving the dual optimization of safety and efficiency.

[0127] Example 3:

[0128] Building upon the overall architecture of Embodiments 1 and 2, this embodiment further elaborates on the local topology repair step based on torque invariant locking, which is included in the output process for initiating the target loading scheme, in response to potential anomalies during the loading execution phase. The core of this embodiment lies in solving the technical challenge of how to quickly repair the loading scheme and maintain the physical stability of the entire vehicle in a dynamic scenario where material damage or temporary shortage occurs, without restarting the time-consuming global optimization algorithm, solely through local mathematical equivalent replacement.

[0129] Once the system enters the output and execution phase of the target loading scheme, the method described in this embodiment is not a one-way command issuance, but rather includes a closed-loop dynamic repair mechanism. Specifically, the local topology repair step based on torque invariant locking includes the following detailed process:

[0130] Step S301: Respond to material failure signals and identify affected areas.

[0131] Specifically, during actual operations at the loading site, various unforeseen circumstances are inevitable. For example, a transformer packaging box of a specific model may be accidentally dropped and damaged during handling, or an inventory error in the warehousing system may result in a shortage of a certain item on the list. When such situations occur, on-site personnel will immediately send a signal via handheld terminals or the on-site intelligent visual monitoring system. In response to the material failure signal received during the loading execution phase, the system will immediately interrupt the current command flow and lock the material ID that caused the anomaly.

[0132] It's also important to note that in stacking and loading, there are complex physical support dependencies between materials, with lower-level materials often supporting one or more upper-level materials. Therefore, the failure of a single material can often trigger a chain reaction. To accurately define the scope of the impact, the system calls upon the generated loading scheme data structure and, based on the support transmission relationships between materials, identifies the failed material and all upper-level materials that are physically supported by it. The system employs a graph theory traversal algorithm, using the failed material as the root node and searching upwards along the support tree until all leaf node materials that depend on that root node for direct or indirect support are found.

[0133] For example, suppose material A located at coordinates (1,1,0) at the bottom of the car is damaged, while materials B and C are partially or completely stacked on top of material A. Even if B and C are intact, because their underlying physical support A has failed, B and C must be removed from the current loading sequence for replanning. The system refers to the aforementioned failed material (A) and all the upper-level materials (B, C) physically supported by it as the removal set, and defines the set of space occupied by these materials as the repair void region. This repair void region is an irregular polyhedral space defined by a series of geometric coordinate points in a three-dimensional Cartesian coordinate system. It represents the physical space suddenly released for refilling in the currently loaded semi-finished car due to the removal of the failed component.

[0134] Step S302: Calculate and lock the target torque invariant.

[0135] After identifying the missing regions to be repaired, the traditional approach might be to rerun the global loading algorithm. However, global algorithms typically involve tens of thousands of iterative calculations, which can take minutes or even tens of minutes, causing severe stalls in the loading pipeline. To achieve millisecond-level rapid response, this embodiment introduces the core physical concept of torque invariance. The basic principle is that as long as the resultant torque generated by the newly added material combination is consistent with or very close to the resultant torque generated by the originally planned material combination, the center of gravity position, axle load distribution, and roll stability of the entire vehicle will not change, thus eliminating the need to re-verify the materials in other unaffected areas within the vehicle.

[0136] Specifically, the system extracts all material data and centroid coordinate data located within the repaired gap region from the original target loading plan. Let the set of materials located within this region in the original plan include... Individual, of which the first The quality of each material is recorded as The position vector of its centroid in the car coordinate system is denoted as . .

[0137] Based on the principles of rigid body mechanics, the system calculates the resultant static moment vector relative to the origin of the carriage. This vector... It is a three-dimensional vector containing lateral moment, longitudinal moment, and vertical moment components, and its calculation formula is as follows:

[0138] ;

[0139] In the formula, This represents the multiplication operation between a scalar and a vector. The resulting static moment vector accurately describes the contribution of this portion of the materials to the overall vehicle balance as originally planned.

[0140] Subsequently, the system locks this vector as the target torque invariant. Locking means that during subsequent repair searches, this value is set as a rigid physical constraint target that must be approximated. Regardless of the shape or weight of the materials subsequently used for filling, the resulting torque characteristics must revert to this invariant, thus ensuring that small local changes do not affect the overall global equilibrium.

[0141] Step S303: Perform a local combinatorial search in the remaining queue.

[0142] After locking onto the target, the system shifts its focus to the unloaded supply pool. Specifically, the system performs a local combined search within the remaining unloaded supply queue. This remaining unloaded supply queue refers to supplies not only originally planned for allocation to the current vehicle, but also supplies from the reserve supply area, temporary dispatch area, or supplies from subsequent batches of the original vehicle that have not yet been loaded.

[0143] The goal of the search is to select candidate replacement combinations that can fill the missing domain and satisfy the geometric boundary constraints. The system employs a heuristic combinatorial optimization algorithm to attempt to select a set of resources from the remaining queue, denoted as a candidate combination. The primary screening criterion is geometric compatibility, i.e., the number of candidate combinations. The total volume and outer contour of all materials after being tightly arranged must be able to be contained within the aforementioned repair gap area, and must not collide with other loaded materials outside the boundary of that area.

[0144] For example, if the missing domain to be repaired is a cube space with dimensions of 1 meter, 1 meter, and 1 meter, the system might attempt to search for a large 1x1x1 box, or a matrix combination of eight small 0.5x0.5x0.5 boxes. This process reuses the first-level geometric boundary verification logic mentioned in Example 1.

[0145] Step S304: Calculate the moment deviation of the candidate combinations. For each candidate alternative combination that passes the geometric screening, the system needs to further verify its mechanical equivalence. Specifically, the system calculates the resultant static moment vector of the candidate alternative combination. Assume the candidate combination contains K new materials, and the mass of the j-th new material is... Its predetermined position vector within the repaired void region is The resultant static moment vector of the candidate combinations. The calculation is as follows:

[0146] ;

[0147] Next, the system calculates the magnitude of the vector difference between itself and the target torque invariant. This magnitude value... This represents the degree of disturbance to the overall vehicle balance caused by replacing the old solution with the new one. The calculation formula is:

[0148] ;

[0149] This involves calculating the Euclidean norm of the difference between two three-dimensional vectors. The smaller this value, the closer the new combination is to the original combination in terms of mass distribution characteristics, and the higher the safety of the replacement.

[0150] Step S305: Determine and generate correction instructions.

[0151] Finally, the system makes a decision based on the calculated torque deviation. Specifically, the system presets a very small physical quantity threshold, called the global safety tolerance, denoted as . This threshold is set based on the tolerance of the vehicle's suspension system and relevant transportation safety standards.

[0152] If the magnitude of the vector difference is less than the preset global safety tolerance, that is This means that the torque disturbance generated by the new combination can be ignored, and the stability of the entire vehicle remains within a controllable range. At this point, the system determines that the repair plan is effective, directly uses the candidate substitute combination to replace the corresponding materials in the original plan, and generates a revised loading command.

[0153] The generated correction instruction will then explicitly instruct on-site personnel or automated equipment to cancel the original loading tasks for materials A, B, and C, and instead load new materials X, Y, and Z in the original locations of A, B, and C. Since this process only involves comparing and calculating local data, the computational complexity is extremely low, typically completed within milliseconds. This ensures the continuity of on-site loading operations, preventing the entire production line from halting and requiring a global recalculation due to minor material damage.

[0154] Optionally, if no suitable result is found in a single search... With the combination of these factors, the system will gradually relax geometric constraints, such as allowing fine-tuning to repair loose resources around the missing domain, or issuing an early warning to the dispatch center to request manual intervention or call for backup vehicles. However, in most conventional scenarios, the rich resource pool based on big data is sufficient to support this automated local topology repair.

[0155] Through the above steps, this embodiment constructs a loading control logic with high self-healing capability, ensuring that the multi-dimensional physical constraint anti-loss loading method for power materials based on industrial big data is not only perfect at the theoretical planning level, but also has strong robustness and adaptability at the execution level full of uncertainties.

[0156] Example 4:

[0157] like Figure 2As shown, this embodiment proposes a multi-dimensional physical constraint-based loss-prevention loading system for power materials based on industrial big data. This system is deployed and applied in an intelligent scheduling platform for power material logistics. It aims to solve the technical problems in existing technologies, such as the susceptibility of power materials to hidden instability during transportation under complex road conditions and the low efficiency of handling emergencies at the loading site, caused by relying solely on static geometric loading algorithms. The system described in this embodiment corresponds one-to-one with the methods described in Embodiments 1 to 3, serving as the physical carrier and functional architecture for executing the aforementioned methods.

[0158] The system consists of five core functional modules, which interact with each other through a high-bandwidth data bus to collaboratively complete the entire process control from data access to command output.

[0159] First, the system includes a data acquisition and modeling module. This module is the perception center of the entire system, and its main function is to respond to material loading requests. When the logistics dispatch center issues a task instruction, this module immediately starts and acquires relevant industrial big data through a standardized API interface. To overcome the limitations of existing technologies that only consider the geometric dimensions of materials, the scope of industrial big data acquired by this module has been significantly expanded to include at least power material data, transport vehicle data, and cross-regional road condition data. Specifically, it not only extracts the geometric information of materials such as length, width, and height, but also physical attributes such as the center of mass position and packaging pressure limit; it not only extracts the dimensions of the vehicle's cargo box, but also the vehicle's dynamic parameters; more importantly, it introduces cross-regional road condition data including road curvature and slope. Based on the aforementioned industrial big data, this module uses object-oriented technology to construct material object models and vehicle object models containing geometric and physical attributes in memory. This process provides an accurate digital twin foundation for subsequent simulation calculations, ensuring that the system can perceive the rotational constraints and morphological identification of materials, as well as the road condition adaptation score of vehicles.

[0160] Secondly, the system includes a constraint-guided calculation module. This module is the logical core of the system and is responsible for performing complex nesting calculations. It first generates an initial candidate loading queue. To balance space utilization and business urgency, this module performs constraint-guided dynamic sorting based on material volume, emergency attributes, and cross-vehicle binding identifiers. This means the system automatically prioritizes large-volume materials, materials marked for emergency repair, and components requiring transport in the same vehicle. Subsequently, this module performs multi-dimensional physical constraint layer-by-layer verification on the sorted candidate loading schemes. This verification mechanism corresponds to the three-layer funnel model in Example 1, specifically including multi-dimensional physical constraint layer-by-layer verification of geometric boundaries, load and stability, and load transfer. Through a parallel computing architecture, this module quickly filters out unsafe schemes that, while meeting the geometric placement conditions, would lead to overloading of the carriage, instability of the center of gravity, or crushing of the underlying materials. This achieves a leap from simple geometric jigsaw puzzles to complex physical and mechanical control at the algorithmic level.

[0161] To address the critical issue of high-center-of-gravity materials being prone to overturning on mountain curves in the background technology, this system integrates a dynamic overturning compensation module. This module works closely with the constraint-guided calculation module, specifically for in-depth dynamic analysis during stability verification. It calculates the expected maximum lateral acceleration based on road curvature and vehicle speed, a calculation that is linked in real-time to the most challenging curves in the actual transport route. The module compares the calculated lateral inertial force with the material's inherent anti-overturning capability, thereby accurately identifying the latent instability state of high-center-of-gravity materials. Once materials that meet static support requirements but fail dynamic road testing are identified, the module does not simply discard them; instead, it triggers a search for complementary materials to construct a combined rigid body model. By finding geometrically complementary materials in the loading queue that can lower the overall center of gravity, the module binds the two together in virtual space and re-verifies the stability of the combined model, thus eliminating the overturning risk without reducing the loading rate. This function corresponds to the dynamic compensation logic in Embodiment 2, significantly improving transport safety.

[0162] Furthermore, to address the issue of production line stagnation caused by damaged or out-of-stock materials at the loading site, the system introduces a local topology repair module. This module is in real-time standby mode, responding to material failure signals during the loading execution phase. When an anomaly is reported from the site, the module quickly identifies the affected area, i.e., the repair void region, and calculates the resultant static moment of the original solution in that region, locking it as the target moment invariant for repairing the void region. Subsequently, the module performs a rapid search in the loading queue to find a substitute solution that can fill the void and has a similar mass distribution. Specifically, it searches the loading queue for substitute combinations whose moment deviations satisfy the global safety tolerance. Once a combination that meets the conditions is found, the module generates a correction instruction by directly replacing the failed material, without having to roll back to the constraint-guided calculation module to rerun the time-consuming global algorithm. This mechanism corresponds to the local repair logic in Implementation Example 3, greatly improving the system's on-site adaptability and response speed.

[0163] Finally, the system includes an instruction generation and output module. After the aforementioned verification and repair processes are completed, this module generates loading instructions to guide the physical loading of materials, based on the verified target loading scheme and the optimization results of the multi-objective balance function. This module comprehensively considers factors such as space utilization, center of gravity offset, and operation time to output the optimal solution. The loading instructions not only include the spatial coordinates of the materials but also the binding operation requirements generated by the dynamic overturning compensation module and the temporary change notification generated by the local topology repair module. Ultimately, these are presented to the user or automated equipment in the form of a 3D visualization view or machine code.

[0164] In summary, the system in this embodiment, through the organic combination of its various modules, achieves closed-loop control from data perception, physical verification, dynamic compensation to anomaly repair, effectively resolving the contradiction between safety and efficiency in the loading of power materials.

[0165] Example 5:

[0166] Corresponding to the above embodiments, the present invention also proposes an electronic device.

[0167] like Figure 3 The diagram shows a structural schematic of an electronic device according to the present invention. The electronic device 100 includes a processor 101 and a memory 103. The processor 101 and the memory 103 are connected, for example, via a bus 102. Optionally, the electronic device 100 may further include a transceiver 104. It should be noted that in practical applications, the transceiver 104 is not limited to one unit, and the structure of this electronic device 100 does not constitute a limitation on the embodiments of the present invention.

[0168] Processor 101 may be a CPU, a general-purpose processor, a DSP, an ASIC, an FPGA, or other programmable logic device, transistor logic device, hardware component, or any combination thereof. It may implement or execute the various exemplary logic blocks, modules, and circuits described in connection with this disclosure. Processor 101 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0169] Bus 102 may include a pathway for transmitting information between the aforementioned components. Bus 102 may be a PCI bus or an EISA bus, etc. Bus 102 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 3 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0170] The memory 103 stores a computer program corresponding to the multi-dimensional physical constraint loss prevention loading method for power materials based on industrial big data according to the above embodiments of the present invention. This computer program is controlled and executed by the processor 101. The processor 101 executes the computer program stored in the memory 103 to implement the content shown in the aforementioned method embodiments.

[0171] Among them, electronic devices 100 include, but are not limited to: mobile terminals such as laptops and PADs (tablet computers) and fixed terminals such as desktop computers. Figure 3 The electronic device 100 shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0172] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A multi-dimensional physical constraint-based damage prevention loading method for power materials based on industrial big data, characterized in that, include: In response to a material loading request, relevant industrial big data is obtained, which includes at least power material data, transport vehicle data, and cross-regional road condition data. Based on the aforementioned industrial big data, a material object model and a vehicle object model containing geometric and physical attributes are constructed, and an initial candidate loading queue is generated. Perform constraint-guided dynamic sorting on the initial candidate loading queue, and perform multi-dimensional physical constraint hierarchical verification on the sorted candidate loading schemes to determine the target loading scheme; Initiate the output process for the target loading plan to generate loading instructions to guide the physical loading of materials; The execution process of the multidimensional physical constraint hierarchical verification includes: First-level verification: In response to the geometric boundary parameters of the candidate position, determine whether the material exceeds the boundary of the carriage and meets the rotation constraint rules; Second-level verification: If the first-level verification passes, calculate the total weight of the car and the material support rate to determine whether the maximum load capacity constraint and stability constraint are met. Third-level verification: If the second-level verification passes, then determine whether the load transfer constraint is met based on the remaining load matrix of the lower-level materials, and verify the cross-vehicle binding relationship; If all three levels of verification results pass, the current candidate loading scheme is deemed valid.

2. The method according to claim 1, characterized in that, The execution of constraint-guided dynamic sorting of the initial candidate loading queue includes: Calculate the volume parameters of the materials and generate a basic sequence by sorting them in descending order of volume; in response to the emergency attribute identifier of the materials, promote high-priority materials to the top of the basic sequence; In response to the cross-vehicle binding identifier, the material groups that cannot be bound across vehicles are forcibly locked to be adjacent in the basic sequence; The pass rate of materials in the hierarchical verification is calculated in real time. If the pass rate is lower than a preset first probability threshold, the sorting priority of the material in the basic sequence is increased.

3. The method according to claim 1, characterized in that, The second-level verification determines whether the stability constraints are met, including: The type of contact surface shape for acquiring materials; If the material is a cube, calculate the ratio of the contact area to the bottom area as the support ratio, and check whether the height of the bottom vertex is less than the preset first distance threshold. If the material is cylindrical, calculate the ratio of the contact length to the axial length as the support ratio, and determine whether the direction of the contact line is parallel to the direction of the main shaft of the carriage. The calculated support ratio is compared with a preset stability threshold mapped based on the current road condition level. If it is greater than the preset stability threshold, the stability constraint is determined to be satisfied.

4. The method according to claim 1, characterized in that, The third-level verification process, which determines whether the load transfer constraint is satisfied based on the remaining load-bearing matrix of the lower-level materials, includes: Divide the top plane of the loaded materials into a standard grid and record the remaining load-bearing value of each grid; In response to the loading location of the new material, calculate the percentage of overlap between the bottom grid of the new material and the grid below it; Based on the overlapping area ratio, the weight load of the new material is allocated to the corresponding grid in the lower layer; Determine whether the remaining load-bearing value of any stressed grid is less than the assigned weight load. If so, trigger parameter correction for the loading posture of the new material or determine that the load transfer constraint verification has failed.

5. The method according to claim 1, characterized in that, After determining the target loading scheme, a coordinate alignment correction step is also included, specifically: In response to the gap data between materials, perform the intersection operation of the projected set; If the material is a cube and the gap data is less than the preset second distance threshold, the material coordinate parameters will be corrected to a zero gap state. If the material is a cylinder, correct the coordinate parameters of the bottom center to the center of the support surface or the midpoint of the contact line, and correct the radial gap to be less than the preset third distance threshold.

6. The method according to claim 1, characterized in that, The construction of the material object model and vehicle object model, which include geometric and physical attributes, includes: Define the shape identifier of the material object model and set the rotation constraint Boolean value, which allows six degrees of freedom rotation when the Boolean value is true; Define a road condition adaptation score for the vehicle object model, which is used to associate the road type and slope parameter in the cross-regional road condition data.

7. The method according to claim 1, characterized in that, The initiation of the output process for the target loading scheme includes a multi-objective balancing optimization process: Construct a multi-objective equilibrium function, which includes at least a space utilization factor, a formation calculation time factor, a formation center of gravity offset factor, and a stability risk value factor. Depending on whether the current business scenario is an emergency scenario or a heavy materials scenario, the weight coefficients of the above factors are dynamically adjusted using the analytic hierarchy process. Calculate the optimal solution of the multi-objective balance function and output a loading instruction that includes the optimal loading location and delivery plan.

8. The method according to claim 1, characterized in that, It also includes abnormal dynamic adjustment steps, specifically: Real-time monitoring of status data during loading and delivery; If the detected abnormal signal continuously meets the preset duration, the collaborative adjustment process is triggered; If the abnormal signal indicates a sudden change in road conditions, the preset threshold standards for the center of gravity offset constraint and support ratio constraint in the multidimensional physical constraint layer verification will be automatically increased, and the verification will be re-executed.

9. The method according to claim 1, characterized in that, The multi-dimensional physical constraint hierarchical verification also includes dynamic overturning compensation logic when performing stability constraint determination, specifically: Based on the minimum road curvature radius and the preset vehicle speed in the cross-regional road condition data, the expected maximum lateral acceleration at the current loading position is calculated, and based on the geometric distance between the center of gravity height of the material and the bottom support edge, the critical overturning acceleration of the material is calculated. If the current material meets the preset stability threshold, but its calculated critical overturning acceleration is less than the expected maximum lateral acceleration, then the material is determined to be in a state of latent instability. In response to the latent instability state, the association binding process is initiated to search for complementary materials in the initial candidate loading queue that can form a contact surface distance with the current material that is less than the contact threshold. The current material and the complementary material are constructed into a combined rigid body model. The equivalent support ratio and equivalent critical overturning acceleration of the combined rigid body model are recalculated. If the combined rigid body model satisfies the constraints, a loading command is generated to bind the two together as atomic loading units.

10. The method according to claim 7, characterized in that, The process of initiating the output of the target loading scheme also includes a local topology repair step based on torque invariant locking, specifically: In response to the material failure signal received during the loading execution phase, based on the support and transmission relationship between materials, the failed material and all upper-level materials physically supported by it are identified, and the set of spaces occupied by the above materials is defined as the repair void domain. Extract the mass data and centroid coordinate data of all materials located in the repaired void region in the original target loading plan, calculate their composite static moment vector relative to the origin of the carriage, and lock this vector as the target moment invariant. Perform a local combination search in the remaining unloaded material queue to filter out candidate replacement combinations that can fill the repair void and satisfy the geometric boundary constraints; Calculate the combined static moment vector of the candidate substitute combination, and calculate the magnitude of the vector difference between it and the target moment invariant; If the magnitude of the vector difference is less than the preset global safety tolerance, the corresponding materials in the original scheme are directly replaced by the candidate substitute combination, and a corrected loading instruction is generated.

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