Intelligent power grid material loading method

Abstract

The invention provides an intelligent power grid material loading method, and aims to solve the problems of low efficiency, insufficient space utilization rate, weak safety and the like in a traditional loading mode. According to the method, geometric dimensions, weight and priority information of materials are obtained through data acquisition and preprocessing, and a loading planning model is constructed in combination with the loading capacity of a transportation tool and cargo space constraints. Based on an optimization algorithm, a material loading scheme is generated, and it is ensured that the space utilization rate is maximized, gravity center shift meets the safety requirement, and priority scheduling is reasonable. In addition, a dynamic adjustment mechanism is introduced, a loading scheme can be optimized in real time according to temporary requirements, and the method adapts to a complex power grid repair scene. According to the method, the transportation efficiency is remarkably improved, the logistics cost is reduced, rapid dispatching and safe transportation of the materials are guaranteed in an emergency scene, and the method has wide application value and economic benefits.

Description

Technical Field

[0001] This invention belongs to the field of logistics, and more specifically relates to a method for loading materials for power grids. Background Technology

[0002] With the continuous expansion of power system scale and the increasing complexity of power grid operation, the demand for transporting power grid materials is growing. Especially in emergency repairs, equipment maintenance, and large-scale engineering construction, the efficient and precise transportation of power grid materials has become a crucial link in ensuring the stable operation of the power system. Power grid materials are diverse, including cables, coils, transformers, and insulation materials. These materials vary significantly in geometry, weight distribution, center of gravity location, and loading priorities, posing a significant challenge to loading planning during transportation. Furthermore, the diversity of transportation vehicles (such as trucks and aircraft) and the complexity of their cargo hold structures (such as curved roofs, segmented compartments, and pressure restrictions) further increase the difficulty of designing loading schemes.

[0003] Traditional methods of loading power grid materials rely heavily on manual experience and simple loading rules, making it difficult to fully utilize the space and load-bearing capacity of transport vehicles. This often leads to wasted cargo space, low transportation efficiency, and safety hazards caused by improper loading. For example, if the center of gravity of the materials does not match the ideal center of gravity envelope of the transport vehicle, it may cause the vehicle to become unbalanced; if the loading sequence of materials does not adequately consider unloading route planning, it may increase unloading time costs; and if cargo space is not fully utilized, it may result in a low load rate of the transport vehicle, increasing logistics costs. Furthermore, in emergency repair scenarios, the loading priority of materials may not be effectively guaranteed, potentially delaying the repair progress and affecting the rapid restoration of the power system.

[0004] In recent years, with the development of technologies such as the Internet of Things (IoT), digital twins, and artificial intelligence (AI), intelligent loading methods have gradually become a research hotspot for solving power grid material transportation problems. Through IoT sensors, 3D modeling software, and other technologies, precise data collection and modeling of materials and transportation vehicles can be achieved; through optimization algorithms, loading schemes that meet multiple constraints can be generated; and through dynamic adjustment mechanisms, changing demands in complex transportation scenarios can be addressed. However, current research still has the following shortcomings: First, data integration capabilities are limited, making it difficult to systematically process the multidimensional attributes of materials and transportation vehicles; second, the intelligence level of loading planning algorithms is insufficient, making it difficult to simultaneously meet the comprehensive requirements of safety, efficiency, and priority; and third, a unified optimization framework for the selection of transportation vehicles and the design of intermodal transport schemes has not yet been formed, making it difficult to cope with complex scenarios of large-scale material scheduling. Summary of the Invention

[0005] This invention proposes a method for loading power grid materials, aiming to systematically solve the multi-objective and multi-constraint problems in power grid material loading through demand analysis and data collection, data preprocessing, loading planning model construction, and transportation tool selection. This invention fully utilizes IoT, digital twin, and optimization algorithm technologies to achieve intelligent design and dynamic adjustment of loading schemes, significantly improving the transportation efficiency, safety, and priority guarantee capabilities of power grid materials, thus meeting the actual needs of power grid operation and maintenance.

[0006] To achieve the above objectives, the present invention employs the following technical solution:

[0007] Demand Analysis and Data Collection: Collect information on power grid materials to be loaded, including the quantity, type, geometric dimensions, weight, center of gravity, and loading and unloading priorities of the materials; collect information on transportation vehicles, including load capacity, cargo hold dimensions, center of gravity location, and pressure limits; integrate key attribute data of materials and transportation vehicles; and build a complete database for loading decision-making.

[0008] Data preprocessing: Establish the geometric mapping relationship between materials and transportation vehicles, optimize the placement posture of materials, generate a rotation feasibility report, analyze the loading compatibility of materials, and comprehensively evaluate the loading priority of materials.

[0009] Loading planning model construction: Based on the cargo hold space parameters, pressure limits, and center of gravity envelope of the transport vehicle, an optimization algorithm is used to generate a loading plan and determine the placement and loading sequence of the materials;

[0010] Transportation vehicle selection: Construct a multi-dimensional matching model for transportation vehicles, select the optimal transportation vehicle based on the loading requirements of the material set, and generate a batch transportation plan when a single vehicle cannot meet the requirements to ensure the priority of materials and transportation safety.

[0011] In one embodiment, in step S1, the minimum outer envelope cuboid dimensions of the irregular material are obtained by a 3D laser scanner, the weight of the material is measured by a high-precision weighbridge, and the theoretical center of gravity coordinates of the material are calculated using mechanical simulation software to ensure the accuracy of the material data.

[0012] In one embodiment, step S1 involves collecting information about the transport vehicle by acquiring the pressure threshold distribution of each compartment through a network of pressure sensors on the cargo hold floor, and extracting cargo hold space parameters, including the height curve of the top arc structure and the door opening restrictions, through reverse modeling using digital twin technology.

[0013] In one scheme, in step S2, the loading priority assessment of materials is combined with the scheduling urgency parameters of the emergency command system and the unloading path planning results of the logistics management system to generate priority labels to indicate the loading scheme of the power grid materials according to claim 1. In step S3, the loading planning model generates loading schemes through optimization algorithms, wherein the front cargo hold uses a non-dominated sorting genetic algorithm for complex scenario optimization, and the rear cargo hold uses a greedy algorithm to quickly generate loading schemes that meet the constraints.

[0014] In one scheme, in step S3, the generation of the loading scheme must ensure that the center of gravity of the cargo hold conforms to the ideal center of gravity envelope of the transport vehicle, wherein the longitudinal allowable deviation does not exceed ±0.5 meters and the lateral allowable deviation does not exceed ±0.2 meters.

[0015] In one scheme, during step S4, when a single means of transport cannot meet the loading requirements, multi-tool intermodal transport optimization is triggered to generate a batch transport scheme, wherein each batch of material subsets must meet strict constraints on loading priority and unloading priority.

[0016] In one approach, step S4 involves selecting the transportation vehicle based on a fuzzy comprehensive evaluation method. This method comprehensively considers the vehicle's load-bearing capacity, cargo hold volume, geometric constraints, and real-time available load to ensure a precise match between the transportation vehicle and the cargo set.

[0017] Beneficial effects of this invention:

[0018] This invention addresses the problems of low efficiency, insufficient space utilization, and weak safety assurance in traditional power grid material loading methods by employing precise data collection and integration, loading priority optimization, improved space utilization, intelligent transportation tool selection, and a dynamic adjustment mechanism. Through the construction of a loading planning model and optimization algorithm, this invention achieves scientific and intelligent material loading, significantly improving transportation efficiency, reducing logistics costs, and ensuring priority scheduling of materials in emergency repair scenarios. Simultaneously, the dynamic adjustment mechanism enables the loading scheme to adapt to temporary changes in demand, meeting the complex needs of power grid operation and maintenance, and demonstrating significant economic benefits and application value. Attached Figure Description

[0019] Figure 1 This is a flowchart of the method of the present invention;

[0020] Figure 2 This is a flowchart of the loading model construction process for the present invention. Detailed Implementation

[0021] To facilitate understanding of the present invention, a more complete description will be given below with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.

[0022] Unless otherwise defined, all technical and scientific terms used in this invention have the same meaning as understood by one of ordinary skill in the art to which this invention pertains. The terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. To facilitate understanding, the invention will now be described more fully with reference to the accompanying drawings. Typical embodiments of the invention are shown in the drawings. However, the invention can be implemented in many different forms and is not limited to the embodiments described herein. Rather, these embodiments are provided to make the disclosure of the invention more thorough and complete. Figure 1 As shown, a method for loading power grid materials includes:

[0023] S1. Requirements Analysis and Data Collection:

[0024] Collect and analyze information on power grid materials to be loaded, including the quantity, type, dimensions, weight, center of gravity, and loading and unloading priorities of the materials.

[0025] Collect information on means of transport (such as vehicles or aircraft), including load capacity, cargo hold dimensions, center of gravity location, and pressure limits.

[0026] During the demand analysis and data collection phase, it is necessary to systematically integrate key attribute data of power grid materials and transportation vehicles to construct a complete database for loading decisions. For power grid materials, physical characteristic data for each item needs to be collected through a combination of IoT sensors, a materials management system, and manual data entry: the minimum outer envelope cuboid dimensions (length × width × height) of irregular materials such as cables and coils are obtained using a 3D laser scanner; the actual weight is measured using a high-precision weighbridge; and the theoretical center of gravity coordinates are calculated using mechanical simulation software combined with the material distribution. Regarding loading priorities, it is necessary to connect with the emergency command system to obtain urgency parameters for material dispatch (e.g., priority 1 for emergency repair materials, 0 for regular materials), and simultaneously generate unloading priority labels based on the unloading route planning of the logistics management system. Data acquisition for transport vehicles requires a focus on addressing the multidimensional constraints of the cargo hold structure. A digital twin model of the transport aircraft's cargo hold is reconstructed using 3D modeling software to extract the three-dimensional spatial parameters of the forward and aft cargo holds (including segment lengths, door opening limits, and the height curve of the top arc structure). Pressure threshold distribution maps for each zone are obtained through a network of pressure sensors on the cargo hold floor, and the ideal center of gravity envelope range (longitudinal allowable deviation ±0.5 meters, lateral ±0.2 meters) is obtained by calling the trim database of the flight control system. The data preprocessing stage requires establishing a geometric mapping relationship between materials and the cargo hold. A matching degree analysis is performed between the cylindrical diameter of the cable reel and the radius of curvature of the cargo hold's dome, and a rotation feasibility report is automatically generated for oversized materials. All data is ultimately integrated into a structured loading knowledge graph, including material nodes (attributes: ID / type / size / weight / priority), transport vehicle nodes (attributes: section / pressure / geometric constraints), and their association matrix, providing multidimensional input for subsequent intelligent loading algorithms.

[0027] S2, Pretreatment:

[0028] Pre-processing operations such as rotating and merging of materials are performed to improve space utilization and loading efficiency.

[0029] Determine the loading and unloading priorities of materials so that they can be optimized in subsequent steps.

[0030] In the preprocessing stage, the original material data needs to be spatially reconstructed through geometric transformations and combinatorial optimization. For rotation operations, a rotation transformation model of the material bounding box in a three-dimensional coordinate system is established: for any material i, its original dimension vector is D. i =(l i ,w i ,h i The set of rotational angles allowed around the vertical axis is Θ = {0°, 90°, 180°, 270°}, and the effective size after rotation is defined as... in By solving constrained optimization problems l c ,w c (For cargo hold cross-sectional dimensions), determine the optimal rotational attitude of each material, at which point the space filling rate can be increased by 12%-35%.

[0031] For cylindrical materials such as cable reels, the Chebyshev bounding box reduction method is used to transform the cylinder diameter d and height h into the minimum bounding cuboid dimensions (d, d, h), allowing arbitrary rotation around the central axis. The merging operation is based on the maximum clique algorithm in graph theory, constructing a material compatibility graph G = (V, E), where vertex set V represents materials and edges e... ij ∈E if and only if materials i and j satisfy ||CG i -CG j ||2≤Δ safe And the total weight after the merger By solving the maximum weight clique problem Select the mergeable subset with the highest density, where the virtual resource size is updated to l after merging. merge =max(l i ,l j ),w merge =max(w i ,w j ),h merge =h i +h j +δ inter (δ inter (for safety clearance).

[0032] The loading priority comprehensive decision adopts the fuzzy TOPSIS method, and constructs the evaluation matrix A = [a ij ] n×4 Including urgency level α i Uninstallation time t i Material value v i Environmental sensitivity i Four indicators. The weighted normalized matrix R = [r] is calculated using the weight vector W = (0.4, 0.3, 0.2, 0.1). ij ],in Determine the ideal solution

[0033] R + =(maxr i1 ,minr i2 ,maxr i3 ,minr i4 )

[0034] With negative ideal solution

[0035] R - =(minr i1 ,maxri2 ,minr i3 ,maxr i4 The final priority score is determined by... Confirmed, among which This process prioritizes loading high-value, fragile materials while ensuring that emergency repair materials can be unloaded quickly.

[0036] S3. Loading planning model construction:

[0037] A mathematical model is constructed based on multiple objectives and constraints, clarifying the objective function (such as maximizing the load weight or prioritizing the quantity of materials) and the constraints (such as load limits or size limits).

[0038] Power grid materials mainly consist of irregularly shaped goods such as cables and coils. Based on the conceptual model of loading planning, a multi-objective, multi-constraint mathematical model is constructed. First, the power grid material loading problem is explained as follows:

[0039] 1) Goods are placed longitudinally or laterally along the vehicle;

[0040] 2) Different structural constraints exist in different parts of the vehicle, requiring separate analysis;

[0041] For the materials to be loaded {C1,C2,…,C…} N} and transport aircraft P j It has the following definition:

[0042] 1) Material definition: The weight of each material is {α1, α2, ..., α} N}; The geometric dimensions of the i-th item are Center of gravity Material loading priority is defined as {u1, u2, ..., u N}, u i For materials ∈{0,1}, the priority loading value is 1, otherwise it is 0; the material unloading priority is defined as {η1,η2,…,η N},η i For ∈{0,1}, the value is 1 for priority unloading and 0 otherwise.

[0043] 2) Definition of transport aircraft: The maximum payload of a transport aircraft is W. j The ideal center of gravity position for a transport aircraft is The center of gravity envelope is The geometric dimensions of the forward cargo hold of the transport aircraft are The dimensions of the rear cargo hold are Cargo hold pressure limit is K represents the number of cargo compartment sections.

[0044] 3) Post-loading definition: Cargo hold front section allocation array This indicates that material i is loaded at the front of the vehicle, with 0 indicating no loading; the allocation array for the rear of the vehicle... This indicates that material i is loaded at the rear of the vehicle; a value of 0 indicates that it is not loaded. R = [r1, r2, ..., r L ],r i ∈{1,2…,Q},r i =1 indicates that material i is placed in the first position. The actual center of gravity of the transport aircraft after loading is CGj, and the total dimensions of the materials in the front section of the cargo hold are CG. j The total dimensions of the cargo in the forward section of the cargo hold are The overall dimensions of the cargo at the rear of the cargo hold are The pressure value of each zone in the cargo hold is

[0045] The objective function is then:

[0046]

[0047] The constraints are satisfied as follows:

[0048]

[0049] like Figure 2 As shown, Step 1: Vehicle geometry restrictions, depot pressure restrictions; Information on materials to be loaded (quantity, type, weight, center of gravity, geometry, loading priority, unloading priority).

[0050] Step 2: Perform pre-processing such as rotating and merging on the materials to be transferred to improve the utilization rate of vehicle width.

[0051] Step 3: Determine the set of planned materials to be loaded {C1, C2, ..., C} N}

[0052] Step 4: The set of available transport aircraft is {P1, P2, ..., P}. M Based on factors such as the transport aircraft's status and the geometric and weight characteristics of the materials to be loaded, the system automatically selects transport aircraft P. j This is the transport aircraft currently awaiting loading.

[0053] Step 5: Distribute materials in the forward section of the cargo hold of the transport aircraft.

[0054] Step 5.1: Initialize the population Individual code is The length of the yard is consistent with the quantity of supplies.

[0055] Step 5.2: The objective function for this stage is:

[0056]

[0057] The constraints are:

[0058]

[0059] There are two objective functions: Equation (1) represents maximizing the loaded weight, and Equation (2) represents maximizing the quantity of priority loaded materials. The constraints consist of four expressions: Equations (3) and (4) indicate that the size of a single material and the total material should not exceed the maximum load capacity of the transport aircraft, and Equations (5) and (6) indicate that the size of a single material and the total material should not exceed the corresponding space in the cargo hold.

[0060] Step 5.3: Perform non-dominated sorting on the population to obtain... After calculating the crowding degree, selection, crossover, and mutation operations are performed to generate a new generation of population.

[0061] Step 5.4: Merge the new population with the parent generation, perform Pareto ranking and calculate crowding, implement an elite retention strategy on the merged population, and generate a new population.

[0062] Step 5.5: If the new population satisfies the iteration termination condition, output the optimal solution and go to step 6; otherwise, go to step 5.3.

[0063] Step 6: Allocate supplies to the rear section of the transport aircraft's cargo hold. Due to the short length of the rear hold, the types and quantities of supplies that can be loaded are limited. Therefore, a greedy algorithm is suitable for solving the allocation array. Define the remaining maximum payload of the transport aircraft Add the materials to be loaded one by one, satisfying the objective function as follows:

[0064]

[0065] And it meets the relevant weight and size constraints.

[0066] Step 7: Optimize the vehicle placement layout of the allocated resources. This can be regarded as a single-objective optimization process, which is solved using a genetic algorithm.

[0067] Step 7.1: Initialize the population; the resource set allocated to the front of the vehicle is as follows: The materials allocated in the later stage are as follows The hybrid encoding method shown in the figure is adopted.

[0068]

[0069] Hybrid encoding method

[0070] here Indicates supplies The placement of items in the front section of the cargo hold, dis fore This indicates the distance between the first item and the front of the vehicle; similarly, the subsequent items are represented in a similar manner.

[0071] Step 7.2: Calculate the fitness function for each individual. The objective function is:

[0072]

[0073] The constraints are satisfied as follows:

[0074]

[0075]

[0076] Step 7.3: Select, crossover, and mutation operations were performed on the population, resulting in population improvement.

[0077] Step 7.4: Determine if the termination condition has been met. If it is met, output the optimal individual; otherwise, go to step 7.2.

[0078] Step 8: Remove the planned materials from the set of materials to be loaded, forming a new set of materials to be loaded {C1, C2, ..., C...} N′} Determine if the set is empty. If it is empty, output the loading scheme; otherwise, go to step 4.

[0079] S4. Transportation vehicle selection:

[0080] Select the appropriate means of transport for loading based on the condition of the transport vehicle and the geometric and weight characteristics of the goods.

[0081] During the transportation selection phase, a multi-dimensional matching model needs to be constructed to achieve accurate mapping between the material set and the transportation vehicles. First, a candidate vehicle set K = {k|state} is established. k =available,k∈TransportDB}, where the attributes of each tool k include maximum load capacity. Cargo hold volume V k Geometric constraint function Γ k (D) (Returns a Boolean value to determine if size D can accommodate the object), Center of gravity tolerance range and real-time available load For the set of goods to be shipped, I = {i|Priority} i ≥τ}, the requirement is to solve a multi-objective optimization problem:

[0082]

[0083] Where δ ki η represents the loading compatibility between material i and tool k (determined by the association matrix generated in the preprocessing stage). k ∈(0.6,0.9] is the volume utilization correction factor for tool k (considering actual loading clearance), ρ safeThis is the safe fill rate threshold (typically set to 0.85). Risk item. k Calculated using the entropy weight method:

[0084]

[0085] The final selection decision is made through the fuzzy comprehensive evaluation function.

[0086]

[0087] Quantitative evaluation, selecting argmin k Φ k As the optimal mode of transportation, the model also incorporates a dynamic adjustment mechanism that triggers multi-modal transport optimization when K is empty, generating a result that satisfies... The phased transportation plan, in which each batch of materials is a subset I s Must meet and Strict priority constraints.

[0088] Example:

[0089] The implementation process of the method of the present invention will be described in detail below with reference to a specific embodiment:

[0090] During a power grid emergency repair operation, a batch of materials needed to be transported from the warehouse to the fault site. The materials included a transformer (1.5m × 1.2m × 1.2m, 800kg), cables (10 rolls, 0.8m × 0.8m × 0.8m each, 120kg each), toolboxes (5 boxes, 0.5m × 0.4m × 0.3m, 25kg each), and several protective devices (6 pieces, 0.6m × 0.5m × 0.5m, 50kg each). The transport vehicle was a 5-ton truck with a cargo hold measuring 6m × 2.5m × 2m.

[0091] Step 1: Data Collection and Preprocessing

[0092] First, the geometric dimensions of all materials were acquired using 3D laser scanning technology, and their weights were measured using a weighbridge to obtain complete material attribute data. A dataset was then established by combining the cargo hold dimensions, load capacity, and center of gravity offset limits (longitudinal deviation no more than ±0.5m, lateral deviation no more than ±0.2m) of the transport vehicle. Through preprocessing, redundant data was removed, and the materials were grouped to generate a material attribute matrix. The material attribute matrix is ​​as follows:

[0093] Transformers: 1.5, 1.2, 1.2, 800

[0094] Cable: 0.8, 0.8, 0.8, 120×10

[0095] Toolbox: 0.5, 0.4, 0.3, 25×5

[0096] Protective equipment: 0.6, 0.5, 0.5, 50×6

[0097] Step 2: Loading Priority Assessment

[0098] Based on the urgency of the repair task, the transformer was given the highest priority, followed by cables, protective equipment, and toolboxes. The priority labels were: transformer (1), cable (2), protective equipment (3), and toolbox (4). Based on the unloading route planning results at the repair site, it was determined that the transformer needed to be placed near the door of the cargo hold for quick unloading.

[0099] Step 3: Loading Planning Model Construction

[0100] A loading planning model was constructed using an optimization algorithm, with constraints set as maximizing cargo hold space utilization, ensuring the center of gravity offset remains within safe limits, and prioritizing safety measures. The resulting loading plan is as follows:

[0101] The transformer is placed near the cargo hold door, centered horizontally and 0.5m away from the cargo hold door vertically.

[0102] Ten rolls of cable were stacked in two layers behind the transformer, arranged horizontally and 1 meter away from the transformer.

[0103] Six protective devices were placed behind the cable, arranged horizontally, with a longitudinal distance of 0.5m from the cable.

[0104] Five toolboxes were placed behind the protective equipment, arranged horizontally, with a longitudinal distance of 0.5m from the protective equipment.

[0105] Step 4: Selection and Dynamic Adjustment of Transportation Vehicles

[0106] Based on the total weight of the goods (800kg + 120kg × 10 + 25kg × 5 + 50kg × 6 = 2700kg) and the cargo hold space requirements (occupying approximately 80% of the cargo hold volume), it is confirmed that the truck meets the transportation needs and no additional tools are required for intermodal transport. During transportation, if two reels of cable (weighing 240kg) are needed due to temporary requirements, the system will automatically readjust the loading plan, moving the toolbox to the top of the protective equipment to make room for the additional cables, while ensuring that the cargo hold's center of gravity offset range meets safety requirements.

[0107] Through the above specific implementation, the method of the present invention realizes the scientific loading and efficient transportation of power grid materials, ensuring the smooth completion of emergency repair tasks. At the same time, the dynamic adjustment mechanism effectively responds to temporary changes in demand, demonstrating the superiority and practicality of the present invention.

[0108] Those skilled in the art will understand that all or part of the processes in the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. The storage medium can be a magnetic disk, optical disk, read-only memory (ROM), or random access memory (RAM), etc.

[0109] It should be understood that the above detailed description of the technical solutions of the present invention with reference to preferred embodiments is illustrative and not restrictive. Those skilled in the art can modify the technical solutions described in the embodiments or make equivalent substitutions for some of the technical features based on reading this specification; however, these modifications or substitutions do not cause the essence of the corresponding technical solutions to depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for loading power grid materials, characterized in that: Includes the following steps: S1. Demand Analysis and Data Collection: Collect information on the power grid materials to be loaded, including the quantity, type, geometric dimensions, weight, center of gravity, and loading and unloading priorities of the materials; collect information on the transportation vehicles, including load capacity, cargo hold dimensions, center of gravity position, and pressure limits; integrate the key attribute data of the materials and transportation vehicles; and build a complete database for loading decision-making. S2. Data Preprocessing: Establish the geometric mapping relationship between materials and transportation vehicles, optimize the placement posture of materials, generate a rotation feasibility report, analyze the loading compatibility of materials, and comprehensively evaluate the loading priority of materials. S3. Loading planning model construction: Based on the cargo hold space parameters, pressure limits and center of gravity envelope of the transport vehicle, the loading plan is generated using optimization algorithms to determine the placement and loading sequence of the materials; S4. Transportation vehicle selection: Construct a multi-dimensional matching model for transportation vehicles, select the optimal transportation vehicle based on the loading requirements of the material set, and generate a batch transportation plan when a single vehicle cannot meet the requirements to ensure the priority of materials and transportation safety.

2. The method for loading power grid materials according to claim 1, characterized in that, In step S1, the minimum outer envelope cuboid size of the irregular material is obtained by a 3D laser scanner, the weight of the material is measured by a high-precision weighbridge, and the theoretical center of gravity coordinates of the material are calculated by mechanical simulation software to ensure the accuracy of the material data.

3. The method for loading power grid materials according to claim 1, characterized in that, In step S1, the information collection of the transport vehicle includes obtaining the pressure threshold distribution of each zone through a cargo hold floor pressure sensor network, and extracting cargo hold space parameters through reverse modeling using digital twin technology, including the height curve of the top arc structure and the door opening restrictions.

4. The method for loading power grid materials according to claim 1, characterized in that, In step S2, the loading priority assessment of materials combines the urgency parameters of the emergency command system with the unloading route planning results of the logistics management system to generate priority labels.

5. The method for loading power grid materials according to claim 1, characterized in that, In step S3, the loading planning model generates loading schemes through optimization algorithms. The front cargo hold uses a non-dominated sorting genetic algorithm for complex scenario optimization, while the rear cargo hold uses a greedy algorithm to quickly generate loading schemes that meet the constraints.

6. The method for loading power grid materials according to claim 1, characterized in that, In step S3, the loading scheme must be generated to ensure that the center of gravity of the cargo hold is within the ideal center of gravity envelope of the transport vehicle, wherein the longitudinal allowable deviation is no more than ±0.5 meters and the lateral allowable deviation is no more than ±0.2 meters.

7. The method for loading power grid materials according to claim 1, characterized in that, In step S4, when a single means of transport cannot meet the loading requirements, multi-tool intermodal transport optimization is triggered to generate a batch transport plan, wherein each batch of material subsets must meet strict constraints on loading priority and unloading priority.

8. The method for loading power grid materials according to claim 1, characterized in that, In step S4, the selection of transportation vehicles is based on the fuzzy comprehensive evaluation method, which comprehensively considers the load-bearing capacity, cargo hold volume, geometric constraints and real-time available load of the transportation vehicles to ensure accurate matching between the transportation vehicles and the material collection.

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