Intelligent stowage service method for import and export multimodal transport based on hybrid planning algorithm

Abstract

The invention relates to the technical field of intelligent logistics and operation planning optimization, and discloses an import and export multimodal transport intelligent stowage service method based on a hybrid planning algorithm, which comprises the following steps: establishing an intermodal transport node operation impact database and an intermodal transport network diagram, and defining a node stacking operation state set; according to the force transmission attribute of the corner column of the container, the discrete stacking hierarchy is mapped into vertical pre-pressure, and the node working condition inherent frequency of the container-cargo combination is calculated through a pre-pressure and rigidity correlation model; in combination with node pulse dominant frequency data, constructing a response gain index including resonance interval intensity and parameter fluctuation sensitivity; and establishing a multimodal transport stowage optimization model, bringing the index into a target function, and carrying out collaborative solution on a cargo loading layout, a transport link and a node stacking level. Resonance risks caused by discrete stacking and lateral impact coupling can be effectively recognized and avoided, and a stowage scheme with high structural safety is output.

Description

Technical Field

[0001] This invention relates to the field of intelligent logistics and operations optimization technology, and more specifically, to an intelligent loading service method for import and export multimodal transport based on a hybrid planning algorithm. Background Technology

[0002] With the deepening of global trade, multimodal transport (including sea, rail, road and port transshipment) has become the mainstream form of logistics transportation. In the multimodal transport chain, containers need to be frequently transferred between different means of transport (ships, trains, trucks) and different hub nodes (seaports, railway stations, and storage yards).

[0003] Existing intelligent container loading technologies typically employ operations research-based packing algorithms (such as 3D-BPP for solving the three-dimensional packing problem). These methods primarily focus on the following dimensions: Geometric space utilization: How to fit goods of different sizes into containers to maximize fill rate; Static weight constraint: Ensure that the total weight of the goods does not exceed the weight limit and that the center of gravity is in static equilibrium. Basic route cost: Select the transportation method with the lowest cost based on transportation distance and rates.

[0004] In existing planning models, containers are typically treated as rigid bodies with constant properties, while transportation nodes (such as ports and yards) are considered merely static locations providing storage capacity. Existing loading systems, when generating plans, often assume that once cargo is loaded, its structural safety depends only on the way the cargo is secured, with a weak coupling relationship to the external circulation environment.

[0005] However, in actual import and export multimodal transport engineering practice, the aforementioned existing technologies have significant blind spots, leading to potential structural safety risks in certain operational stages of the loading scheme: First, existing technologies neglect the impact of stacking levels on container dynamics. Multimodal transport hubs (especially seaports) commonly employ multi-layer stacking (typically 4-5 layers). Containers transmit vertical loads through corner posts. When containers are stacked, the significant vertical preload from the upper layers significantly alters the equivalent lateral stiffness of the container frame through geometric stiffness and surface friction effects. This means that the natural frequencies of the same container during single-container transport are completely different from those when stacked at a port. Existing technologies fail to consider the number of stacking layers as a variable that alters structural properties, leading to a severe disconnect between the dynamic parameters in the model and actual operating conditions.

[0006] Second, existing technologies fail to identify the resonance risk caused by "parameter quantization jumps." Port nodes commonly experience strong directional lateral impact sources (such as container stacking operations using reach stackers). Because the number of stacking layers is a discrete integer (e.g., 1, 2, ..., 5 layers), the natural frequency of the container does not change continuously but rather in a step-like jump. When the natural frequency corresponding to a certain layer happens to fall into the dominant frequency band of the equipment's lateral impact, a random parametric resonance window is formed, causing the response amplitude to be magnified exponentially. Existing loading methods only consider static or quasi-static mechanical constraints and cannot predict this dynamic resonance risk caused by changes in the number of layers, often leading to accidental damage to cargo during high-level stacking operations.

[0007] Third, existing technologies lack robust control over structural parameter drift. Even with a fixed number of layers, parameters such as the clearance of container fasteners (e.g., twistlocks) and the securing tension can drift randomly. Existing technologies cannot transform these random disturbances into calculable optimization indicators during the planning stage; they can only rely on the experience of on-site workers for passive adjustments, and cannot generate loading and routing coordination schemes that can avoid such dynamic risks from the source. Summary of the Invention

[0008] This invention provides a method for intelligent loading and unloading services for import and export multimodal transport based on a hybrid planning algorithm, which solves the technical problems mentioned in the background art.

[0009] This invention provides a method for intelligent loading and unloading services for import and export multimodal transport based on a hybrid planning algorithm, comprising: Establish a database of intermodal node operation impacts containing node operation pulse frequency data, and construct an intermodal network diagram describing the transportation link and a node stacking operation status set defining the operation capabilities of each node; Based on the force transmission properties of the container corner columns, the set of node stacking operation states is converted into vertical preload of the corner columns, and the natural frequency of the node operation conditions of the container assembly is calculated through the load stiffness correlation model of preload and structural stiffness. By combining the inherent frequency of the node's operating condition and the main frequency of the node's operating pulse, a resonance response gain index is constructed that includes a resonance interval intensity factor and a structural parameter fluctuation sensitivity factor. A multimodal transport loading optimization model is constructed, and the resonance response gain index is used as the optimization objective to collaboratively solve the cargo loading layout scheme, the intermodal transport link scheme, and the node stacking hierarchy plan. The output includes intelligent loading and unloading schemes for import and export multimodal transport with recommended stacking levels for each node.

[0010] The beneficial effects of this invention are as follows: By constructing a process of node operation status - corner column preload - dual-mechanism stiffness - resonance response index, it overcomes the hidden dynamic resonance problem caused by discrete changes in stacking levels in import and export multimodal transport. This invention utilizes a load stiffness correlation model to transform unpredictable random parameter drift into an optimization objective, and combines it with a hybrid programming algorithm to collaboratively solve for cargo loading layout, transport link schemes, and node stacking level plans. This effectively avoids the structural resonance risk caused by lateral impacts at ports during the loading planning stage, significantly improving the structural safety and engineering feasibility of multimodal transport solutions in complex operating environments. Attached Figure Description

[0011] Figure 1 This is a flowchart of the intelligent loading service method for import and export multimodal transport based on a hybrid planning algorithm according to the present invention; Figure 2 This is a schematic diagram illustrating a specific implementation of the present invention. Detailed Implementation

[0012] The subject matter described herein will now be discussed with reference to exemplary embodiments. It should be understood that these embodiments are discussed only to enable those skilled in the art to better understand and implement the subject matter described herein, and changes may be made to the function and arrangement of the elements discussed without departing from the scope of this specification. Various processes or components may be omitted, substituted, or added as needed in the examples. Furthermore, features described in some examples may be combined in other examples.

[0013] like Figure 1 As shown, the intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm includes: Establish a database of intermodal node operation impacts containing node operation pulse frequency data, and construct an intermodal network diagram describing the transportation link and a node stacking operation status set defining the operation capabilities of each node; Based on the force transmission properties of the container corner columns, the set of node stacking operation states is converted into vertical preload of the corner columns, and the natural frequency of the node operation conditions of the container assembly is calculated through the load stiffness correlation model of preload and structural stiffness. By combining the inherent frequency of the node's operating condition and the main frequency of the node's operating pulse, a resonance response gain index is constructed that includes a resonance interval intensity factor and a structural parameter fluctuation sensitivity factor. A multimodal transport loading optimization model is constructed, and the resonance response gain index is used as the optimization objective to collaboratively solve the cargo loading layout scheme, the intermodal transport link scheme, and the node stacking hierarchy plan. The output includes intelligent loading and unloading schemes for import and export multimodal transport with recommended stacking levels for each node.

[0014] In a preferred embodiment, an intermodal node operation impact database containing node operation pulse frequency data is established, including: Construct a database of intermodal transport node operation impacts that includes a set of basic physical parameters of goods and a set of operational parameters of node equipment; For each node in the intermodal transport node operation impact database The equivalent half-sine collision pulse duration is calibrated based on the device type of the node. ; The node operation pulse frequency data is calculated using the following formula. : in, Pi For nodes The equivalent half-sine collision pulse duration, For nodes The node operation pulse frequency data.

[0015] Preferably, the impact load during node operations typically exhibits short duration and high amplitude characteristics. A half-sine pulse is a typical equivalent fitting form for this type of collision impact in engineering, and its waveform can well match the force-time curve when the actual loading / unloading equipment contacts the container. Therefore, this form is chosen to characterize the impact characteristics of node operations. To determine the core frequency characteristics of the impact signal (for subsequent resonance response analysis of the container assembly), the dominant frequency of the pulse needs to be derived: the time domain expression of the half-sine pulse is as follows: (The range of values ​​for t is) (where A is the pulse amplitude). Performing a Fourier transform on this time-domain form yields the spectrum, the transform formula is: Calculating this integral yields ,when When the spectrum shows a main peak, it corresponds to the main frequency of the signal, from which the following can be derived: Specifically, when constructing the intermodal transport node operation impact database, the basic parameters related to the cargo that need to be included include, but are not limited to, the cargo's mass, dimensions, and center of gravity coordinates. The operational parameters related to the node equipment include, but are not limited to, the model, rated operating capacity, and operating stroke of the loading and unloading equipment configured at the node. These parameters are determined subsequently. The basic input; for each node v, determine The process needs to be combined with the type of loading and unloading equipment actually configured at the node: for example, when node v is configured with a quayside container crane, it is necessary to collect time series data of the crane's spreader contacting and separating from the container under rated operating conditions, perform time-domain analysis on the force signal in the time series, extract the impact duration period, and then fit the actual force waveform within the duration period with a half-sine waveform, aiming to minimize the fitting error, to obtain the corresponding force waveform for node v. Node operation pulse frequency data The optimal value for π, calculated using the derived formula above, is 3.1416 (this value is a commonly used precision value in engineering calculations, balancing computational complexity with result accuracy). The duration of the equivalent half-sine collision pulse corresponding to node v. The main frequency characteristics of the impact signal at node v are characterized. This parameter will be used for subsequent matching analysis of the natural frequency and impact frequency of the container cargo assembly.

[0016] In a preferred embodiment, constructing an intermodal network diagram describing the transportation links and a node stacking operation state set defining the operational capabilities of each node includes: Constructing an intermodal transport network diagram ,in For a set of nodes, For transport edge set; For each node in the intermodal transport network diagram Define the node stack job state set : in, For nodes The maximum stacking capacity; Define container At the node Equivalent stacking layers The value is taken from the node stack job state set. Variables.

[0017] Preferably, the multimodal transport process involves the connection of multiple transit hubs and different modes of transport. Using a graph structure from graph theory, the topological relationships between each hub and transport segment can be systematically represented, facilitating the traversal and selection of transport routes during subsequent loading processes. Therefore, constructing an intermodal network graph is essential. The container stacking operation at a node needs to match the node's actual carrying capacity, and the number of stacking layers in actual operation can only be a non-negative integer. Therefore, a discrete set needs to be defined to limit the feasible number of stacking layers, i.e., the node stacking operation state set. Specifically, in the intermodal network diagram... It covers various hubs in the transportation process, including port loading and unloading areas, railway freight marshalling yards, and highway logistics distribution centers; For any two nodes, the available transportation links are associated with each transportation edge, along with the corresponding transportation mode (e.g., sea or rail transport) and the basic operational parameters of that link. For each node... Node stack job state set The design concept is as follows: 0 corresponds to the container not being stacked at the node and being directly transferred, while positive integer values ​​correspond to the actual number of layers of the container being stacked at the node. The optimal value needs to be determined through the infrastructure parameters of the node. Specifically, it needs to be combined with the load-bearing capacity per unit area of ​​the node's yard, the maximum operating height of the stacking equipment, and the standard height of the containers. The maximum number of stackable integer layers is calculated under the constraint that the ground load-bearing capacity meets the total weight requirement of the corresponding number of containers and the stacking height does not exceed the maximum operating height of the stacking equipment. At the node Equivalent stacking layers To take the value of The variable whose value must match the node. The stacking capacity limitations and container transfer requirements must be considered to ensure that stacking operations are within the node's operational capacity range.

[0018] In a preferred embodiment, based on the force transmission properties of the container corner posts, the set of node stacking operation states is converted into vertical preload on the corner posts, including: The vertical preload of the corner column is calculated using the following formula. : in, It is the acceleration due to gravity. The total mass of the container load. The value is the equivalent stacking layer number derived from the node stacking job state set. For nodes The equivalent average weight of each box above is given, with the denominator 4 representing the load shared by the four corner prisms.

[0019] Preferably, the vertical load-bearing components of the container are its four corner posts. During stacking operations, the load above needs to be transferred through the corner posts. Therefore, it is necessary to quantify the vertical preload borne by a single corner post. Specifically: the total weight of a single container is determined by the product of its total mass after loading and the acceleration due to gravity. In the stacked state, the weight of each layer above needs to be added to the corner posts of the current container. The total load is the sum of its own weight and the total weight of all layers above, and then evenly distributed among the four corner posts. Based on this, the calculation formula for the vertical preload of the corner posts is obtained: in, The preferred value is 9.8 m / s 2 This value is the standard engineering calculation value of the gravitational acceleration on the Earth's surface, which can balance the ease of calculation with the adaptability to actual working conditions. The total mass of the container after loading of goods needs to be obtained by collecting actual data from the weighing equipment after the container is loaded. The value range corresponds to the node stacking operation state set, which is the equivalent stacking layer of the container at node v; It is necessary to collect the mass data of containers at each layer in the historical stacking operations of node v, and calculate the average value at that node. This simplifies the calculation of the weight of the upper layers and conforms to the actual operation of the node. The denominator of the formula corresponds to the four corner posts of the container, which is the standard structural configuration of the container. The vertical load is evenly distributed among the four corner posts. Therefore, the total load needs to be divided by 4 to obtain the vertical preload of a single corner post. .

[0020] In a preferred embodiment, the nodal operating natural frequencies of the container assembly are calculated using a load-stiffness correlation model of preload and structural stiffness, including: Construct the load-stiffness correlation model to calculate the equivalent lateral stiffness. : in, For structural geometric stiffness components, For contact locking stiffness components; The calculation formulas for the structural geometric stiffness component and the contact locking stiffness component are as follows: in, Based on the fundamental geometric stiffness, This is the equivalent critical preload scale. This is the geometric stiffness attenuation coefficient; Based on contact stiffness, This is the contact stiffness enhancement factor; The natural frequency of the node's operating condition is calculated using the following formula. : in, The equivalent mass of a container.

[0021] Preferably, the container-cargo assembly will be subjected to lateral impact loads during node operations. Its dynamic response characteristics are determined by its natural frequencies, and lateral stiffness is the core parameter affecting the natural frequencies. Simultaneously, preload will alter the geometric deformation characteristics and contact locking effect of the structure. Therefore, it is necessary to quantify and superimpose the stiffness components corresponding to these two effects to obtain the equivalent lateral stiffness. Then, based on the natural frequency relationship of a single-degree-of-freedom vibration system, the natural frequencies under node operation conditions are derived. First, a load-stiffness correlation model is constructed to calculate the equivalent lateral stiffness. Its expression is The formula for calculating the structural geometric stiffness components is as follows: , The optimal value is obtained through a lateral stiffness test in an empty container state. Specifically, a lateral excitation is applied to an empty container and the deformation is measured. The initial stiffness is obtained from the ratio of force to deformation. The optimal value is determined through container pressure test, that is, the preload value corresponding to the gradual increase of preload until the geometric stiffness approaches zero; The optimal values ​​are obtained by fitting multiple sets of experimental data on preload and geometric stiffness, with the goal of minimizing the error between the experimental values ​​and the calculated values. The formula for calculating the contact locking stiffness component is as follows: , The optimal value is obtained through a contact stiffness test of the contact surface between the empty container and the storage yard. Specifically, a normal force is applied to the empty container and the contact deformation is measured. The initial contact stiffness is obtained from the ratio of force to deformation. The optimal value was obtained by fitting multiple sets of experimental data on preload and contact stiffness, reflecting the rate of increase in contact stiffness as preload increases. Then, it was determined using the formula... Calculate the natural frequency of the node's operating condition, where The value corresponds to the total mass of the container. That is, the total mass of the container and cargo assembly that participates in vibration.

[0022] In a preferred embodiment, by combining the inherent frequency of the node's operating condition and the dominant frequency data of the node's operating pulse, a resonance response gain index is constructed, comprising a resonance interval intensity factor and a structural parameter fluctuation sensitivity factor, including: The intensity factor of the resonance interval is calculated using the following formula. : in, This refers to the main frequency data of the node operation pulse. This refers to the inherent frequency of node operation conditions. The container damping ratio; The resonance response gain index is constructed using the following formula. This includes the structural parameter fluctuation sensitivity factor: in, Let be the partial derivative of the intensity factor in the resonance interval with respect to the vertical preload of the diagonal column. The standard deviation of the prestress disturbance. This is the magnification factor. This refers to the structural parameter fluctuation sensitivity factor.

[0023] Preferably, the container truck assembly experiences base impact excitation during node operations. Its response amplitude is determined by the matching relationship between the excitation frequency and its natural frequency. The base excitation transmissibility formula for a single-degree-of-freedom linear vibration system can quantify the response intensity under this matching state, which is the basis for deriving the resonance interval intensity factor. Simultaneously, in actual operations, the vertical preload of the corner columns fluctuates. This fluctuation affects the natural frequency by changing the equivalent lateral stiffness, thereby altering the response intensity. Therefore, it is necessary to introduce the product of the response factor's sensitivity to preload and the degree of fluctuation to quantify the additional response risk caused by parameter fluctuations. Superimposing both yields a gain index that comprehensively reflects the resonance response risk. First, the resonance interval intensity factor is calculated. Its formula is: in The optimal value is obtained through damping tests on the container-cargo combination. Specifically, a free vibration excitation is applied to the loaded container, its amplitude decay curve is measured, and the damping ratio is calculated using the logarithmic decay rate. This value is typically selected within the range of 0.01 to 0.05 to adapt to the combined damping characteristics of the container and cargo. Then, a resonance response gain index is constructed. Its formula is: in It needs to be obtained through chain rule differentiation, The expression for When taking the derivative, it is necessary to combine the relationship formula between the equivalent lateral stiffness and the preload to gradually derive the partial derivative result. The preferred value is obtained by calculating the standard deviation of the vertical preload of multiple sets of corner columns at node v, based on the actual measured data. This value reflects the actual fluctuation of the preload at that node. The optimal value is determined based on engineering experience, and is usually between 1 and 3. This value is used to amplify the impact of parameter fluctuations on the resonant response, in order to cover the risks under extreme fluctuation conditions. This is the structural parameter fluctuation sensitivity factor.

[0024] In a preferred embodiment, a multimodal transport loading optimization model is constructed, using the resonance response gain index as the optimization objective, to collaboratively solve for the cargo loading layout scheme, intermodal transport link scheme, and node stacking hierarchy plan, including: Define the decision variables for cargo loading layout schemes Decision variables for intermodal transport link schemes and node stacking hierarchy planning decision variables ; Construct the following stacking hierarchy association constraints: in, This represents the upper limit of node stacking capacity. For the reason Exported node access state variables; Construct the following minimized objective function for solution: in, For transportation costs, For resonance risk weighting coefficient, The resonant response gain index is given.

[0025] Preferably, the three decision dimensions of multimodal transport allocation—cargo loading, transport links, and node stacking—are strongly correlated. Optimizing any one dimension alone can lead to conflicts in actual operation. Therefore, it is necessary to define decision variables covering all three dimensions simultaneously, and then constrain the access and stacking operations of related nodes. Finally, by integrating transportation costs and resonance risks, an objective function is constructed to achieve multi-dimensional collaborative optimization. The cargo loading layout decision variable corresponds to the allocation relationship between cargo and containers, the intermodal transport link decision variable corresponds to the selection relationship between containers and transport edges, and the node stacking hierarchy plan decision variable corresponds to the number of stacking layers of containers at nodes. The construction logic of the stacking hierarchy correlation constraint is that the access state variable of node v is only changed when the transport link selected by container c includes a transport edge passing through node v. It will only take 1 if it is true, otherwise it will take 0. Therefore, when When it is 0, The value should be 0, meaning no stacking is allowed; when When it is 1, The value of does not exceed the stacking capacity limit of node v. This ensures the matching between stacking operations and node access states, and the constraint formula is as follows: Then, a minimum objective function is constructed and solved. The expression of the objective function is: The first half of the objective function The total cost of selecting the transport side for all containers. The optimal value needs to be obtained by summarizing the actual operating expenditure data such as the basic freight, surcharges along the way, and resource occupancy fees corresponding to the transportation mode of transportation edge e; the latter half The comprehensive risk value of resonance for all containers at each node. The optimal value needs to be determined based on the actual business priorities. If the business is more concerned with transportation costs, A value of 0.1 to 0.3 is acceptable. If there is a greater concern about resonance safety risks, A value between 0.7 and 0.9 is acceptable; if a balance between the two is required, The value can be between 0.4 and 0.6. This weighting coefficient can be used to flexibly adjust the proportion of cost and risk in the optimization objective. Finally, by solving the objective function, a loading scheme that simultaneously meets the requirements of cost and safety can be obtained.

[0026] In a preferred embodiment, the output includes an intelligent loading scheme for import and export multimodal transport that comprises recommended stacking levels for each node, including: Decision variables based on cargo loading layout scheme Generate packing list and box layout : in, Assign a cargo number, For cargo coordinates, Orientation of the goods; Decision variables based on intermodal transport link scheme Generate intermodal transport route sequence : in, For the first Section of transport side, Total number of segments; Based on node stacking hierarchy, plan decision variables Generate a node stacking hierarchy plan : in, Indicates the intermodal transport route sequence The nodes included This is the recommended equivalent stacking layer number for this node; The output includes the , and The aforementioned intelligent loading and unloading solution for multimodal transport of import and export.

[0027] Preferably, the decision variables obtained from the multimodal transport loading optimization model are abstract, identifiable, or numerical variables. However, actual operations require concrete operational guidance information. Therefore, based on the values ​​of each decision variable, they need to be mapped to corresponding practical content so that the optimization results can directly guide on-site operations. First, a packing list and internal layout are generated based on the cargo loading layout plan decision variables. The expression is: Where i is a unique identifier that distinguishes different goods. Let be the three-dimensional spatial coordinates of cargo i within container c. Its values ​​must be determined by combining the internal length, width, and height dimensions of container c with the dimensions of cargo i itself. Under the premise of maximizing the utilization of the internal space of the container, specific coordinate values ​​are allocated; The orientation of cargo i within container c must be determined by matching the packaging form of cargo i with the container's loading space limitations to avoid wasted space due to improper orientation. Subsequently, an intermodal transport route sequence is generated based on the intermodal transport link scheme decision variables. Its expression is: in Let T be the t-th transport edge, and T be the total number of edges. The value of T is determined by the value of the edge corresponding to box c. The number of transport edges with a value of 1 is determined, and the order of the transport edges must follow the logical sequence of the transport process, i.e., from the originating node through each transit node to the destination node. Finally, a node stacking hierarchy plan is generated based on the node stacking hierarchy plan decision variables. Its expression is: in Indicates the sequence of intermodal transport routes The nodes included are the actual operational nodes that container C passes through during the transportation process. This represents the recommended equivalent stacking layer number for this node, and its value directly corresponds to the number obtained from solving the optimization model. The numerical results, the final output includes , and The loading scheme provides complete operational guidance for on-site operations.

[0028] It should be noted that the cargo basic parameter set is a summary of basic cargo parameters adapted to import and export multimodal transport loading scenarios. It includes data such as cargo mass, dimensions, and center of gravity coordinates, and is one of the inputs for building the intermodal transport node operation impact database. For example, for a batch of import and export precision instruments, this set would include information such as the net weight of each instrument (2.5kg), the length × width × height of the outer packaging (40cm × 30cm × 20cm), and the three-dimensional coordinates of the center of gravity within the packaging (20cm, 15cm, 10cm).

[0029] It should be noted that the node equipment operation parameter set is a collection of parameters summarizing the equipment characteristics of each multimodal transport operation node. It includes information such as the model, rated operating capacity, and operating stroke of the loading and unloading equipment configured at the node, and serves as input for constructing the multimodal transport node operation impact database. For example, this set for a port container loading and unloading node would include parameters such as the model of the quay crane (e.g., STS50t-45m), rated lifting capacity of 50 tons, lifting operating height of 38 meters, and trolley travel distance of 45 meters.

[0030] It should be noted that node v is a general term for various operational hubs in a multimodal transport network, encompassing nodes with different functions such as port loading and unloading areas, railway freight marshalling yards, and highway logistics distribution centers. It is used to uniformly represent the operational status of different hubs in the loading and unloading process. For example, in a China-Europe freight train multimodal transport scenario, node v1 can refer to the container loading and unloading node at Shenzhen Port, and node v2 can refer to the transshipment node at Chongqing Railway Freight Marshalling Yard.

[0031] It should be noted that the duration of the equivalent half-sine collision pulse... This parameter quantifies the temporal characteristics of the impact during node operations. It is obtained by equivalently fitting the impact load waveform during actual node operation to a half-sine pulse, and then calculating the duration of that pulse. For example, when the node is configured with a gantry crane, the time series of force signals from the contact between the spreader and the container to their separation under rated operating conditions needs to be collected. The actual force waveform within this time period is then fitted to a half-sine waveform, and the duration obtained with the minimum fitting error as the objective is the duration corresponding to that node. .

[0032] It should be noted that the node operation pulse frequency data It is a parameter used to characterize the core frequency of the impact signal during node operation, and is determined by the duration of the equivalent half-sine collision pulse. Through formula This parameter is derived. It can reflect the dominant frequency characteristics of node operation impacts, such as the frequency of a certain node's... When it is 0.3 seconds, the corresponding This allows us to determine the frequency of operational impacts at that node.

[0033] It should be noted that the intermodal transport network diagram This is the topology model constructed in this scheme to describe the relationships between multimodal transport links and nodes, where... Represents a set of nodes. This represents the set of transport edges. For example, in a multimodal transport scenario along an import / export land-sea corridor, this model... It will include the Qinzhou Port loading and unloading node, the Guiyang Railway marshalling node, and the Kunming highway distribution node, etc. It will include the railway transportation side from Qinzhou Port to Guiyang marshalling yard, the highway transportation side from Guiyang marshalling yard to Kunming distribution node, etc. Through this model, the node connections and link distribution of the transportation process can be systematically presented.

[0034] It should be noted that the node set It is an intermodal transport network diagram. A multimodal transport system is a collection of all operational hubs in a multimodal transport process, encompassing nodes with different functions such as port loading and unloading areas, railway freight marshalling yards, and road logistics distribution centers. For example, in a multimodal transport process from Shanghai to Europe... It will include all operational points involved in the transportation process, such as the Shanghai Port loading and unloading node, the Zhengzhou Railway marshalling node, and the Hamburg Port distribution node, to clarify the operational nodes in the transportation network.

[0035] It should be noted that the transport edge collection It is an intermodal transport network diagram. The components are a summary set of available transportation links between any two nodes, and each transportation edge is associated with a corresponding transportation mode and related operational parameters. For example, in a certain import and export multimodal transport scenario, It will include the road transport edge from Shanghai Port to Zhengzhou marshalling yard (related to the unit cost and transport time of road transport) and the rail transport edge from Zhengzhou marshalling yard to Hamburg Port (related to the schedule and transport cost of China-Europe freight trains), which are used to characterize the transport connection relationship between different nodes.

[0036] It should be noted that the node stacking job state set It is a set that limits the scope of node stacking operations, and its form is: Where 0 represents that the container is not stacked at this node and is directly transferred, and the positive integer value represents the actual number of layers of the container being stacked at the node. This is the maximum stacking capacity of the node. For example, the stacking capacity of a node in a port yard. If the value is 5, then the node's The value {0,1,2,3,4,5} indicates that containers can be stacked up to 5 layers at this node.

[0037] It should be noted that there is an upper limit to the node stacking capability. These are parameters that standardize the stacking operation of storage nodes. They are jointly determined by the load-bearing capacity per unit area of ​​the storage yard, the maximum operating height of the stacking equipment, and the standard height of the containers. It is essential to ensure that the total weight after stacking does not exceed the ground load-bearing capacity and the total height does not exceed the operating capacity of the stacking equipment. For example, if the load-bearing capacity per unit area of ​​the storage yard is 25 tons, the maximum operating height of the stacking equipment is 10 meters, and the standard height of the containers is 2 meters, then the calculated... This represents the maximum number of integer layers that the node can be stacked, to prevent stacking operations from exceeding the node's capacity and the device's capabilities.

[0038] It should be noted that containers It is a general term for all containers in the multimodal transport loading process, used to associate information such as container and cargo allocation, transport link selection, and node stacking status. For example, in a multimodal transport scenario for import and export e-commerce goods, containers... It can refer to a standard 40-foot shipping container used for loading digital products. It can refer to a 20-foot container loaded with clothing and other goods. This universal identifier can be used to manage loading information for different containers in a unified manner.

[0039] It should be noted that the equivalent stacking layer number It is a parameter characterizing the stacking state of containers at a node, and its value range corresponds to the node stacking operation state set. For example, shipping containers. At the node of Taking 4 represents the container at node It is located at the position of being stacked on the 4th layer.

[0040] It should be noted that the vertical preload of the corner column These are the mechanical parameters that quantify the vertical load borne by the corner posts of a container, derived from the structural properties of the force transmission of the corner posts. The formula is obtained by evenly distributing the sum of the container's own weight load and the total weight load superimposed above the nodes across the four corner posts. This parameter reflects the stress on the corner column when it is stacked. It is a key input for subsequent calculations of the equivalent stiffness and natural frequency of the container assembly. For example, when a container is stacked three layers deep at a port yard node, this parameter can accurately determine the magnitude of the vertical preload borne by a single corner column.

[0041] It should be noted that the total mass of the container load... This is a cargo mass parameter adapted to multimodal transport loading scenarios. It specifically refers to the total mass of the container after all cargo has been loaded. This data must be obtained through actual weighing equipment, not a theoretical estimate. This parameter is the core basis for calculating the container's own gravity load; for example, for a container loaded with a batch of machinery, its... The total weight of the equipment body, packaging materials, and fixing accessories must be included to ensure the accuracy of the gravity load calculation.

[0042] It should be noted that nodes The equivalent average box weight per layer above These are statistical parameters that simplify the calculation of the weight of multiple layers of containers above, and are summarized by nodes. The average weight per container is calculated from the actual weight data of all containers in historical stacking operations. For example, if the average weight of containers stacked at a certain port yard node over the past three months is 28 tons, then the average weight per container at that node is... The value is set at 28 tons, which allows for a quick estimation of the total superimposed gravity load without the need to measure the actual mass of each container on each layer above, while also aligning with the mass distribution pattern of actual operations at the node.

[0043] It should be noted that the equivalent lateral stiffness It is a comprehensive mechanical parameter characterizing the lateral deformation resistance of a container cargo assembly, obtained by superimposing the structural geometric stiffness component and the contact locking stiffness component, as shown in the formula: This parameter integrates the dual influence of preload on structural geometry and contact state, and is a core prerequisite for calculating the natural frequency of nodal operating conditions. For example, the same container at different stacking layers (corresponding to different...) Below, its The dynamic response characteristics will change due to variations in the two stiffness components.

[0044] It should be noted that the structural geometric stiffness components This is the stiffness component that quantifies the effect of preload on the geometry of the container frame. Its characteristic is that it decreases as the vertical preload on the corner posts increases. The calculation formula is: This component reflects the weakening effect of axial preload on the lateral bending capacity of the frame, for example, when the number of container stacking layers increases. When it increases, This will decrease accordingly, causing a geometric reduction in the lateral deformation resistance of the container assembly.

[0045] It should be noted that the contact locking stiffness component This is the stiffness component that quantifies the effect of preload on the contact state. Its characteristic is that it increases with the increase of the vertical preload on the corner column. The calculation formula is: This component reflects the increased contact tightness and higher friction threshold effect brought about by pre-stress, such as when containers are stacked in a yard. The increased size allows for tighter contact between the corner posts and locking devices, and between the container and the yard floor, increasing frictional resistance and thus enhancing lateral anti-slip capability, which manifests as... The increase.

[0046] Basic geometric stiffness These are the baseline parameters for constructing the structural geometric stiffness component model, obtained through lateral stiffness tests in an empty container. The test procedure involves applying a constant amplitude lateral excitation to an empty container without cargo, measuring the corresponding lateral deformation, and calculating the initial geometric stiffness from the force-deformation ratio. For example, if a lateral force of 100kN is applied to a 20-foot empty container and the measured lateral deformation is 5mm, then the container's... It is 20 kN / mm.

[0047] It should be noted that the geometric stiffness attenuation coefficient It is a fitting parameter that quantifies the rate of decrease in geometric stiffness with preload, derived from multiple sets of experimental data on geometric stiffness under different preloads. The experiments need to cover the common preload range of nodes, collect measured values ​​of geometric stiffness under corresponding preloads, and then perform linear fitting with the goal of minimizing the error between the experimental values ​​and the calculated values ​​to determine the optimal parameters. The specific values ​​are determined by fitting the measured geometric stiffness values ​​of a container under five different preload conditions. This value determines Follow The rate of change.

[0048] It should be noted that the equivalent critical preloading scale This is the limiting parameter that defines the boundary of geometric stiffness attenuation, determined through container compression tests. The test process involves gradually increasing the vertical preload on the corner columns, monitoring the change in structural geometric stiffness in real time. The preload value corresponding to when the geometric stiffness approaches zero is the limit parameter. This parameter is an important reference for preventing the container frame from becoming unstable due to excessive preload. For example, the preload of a certain container... Therefore, in actual operation, it is necessary to control It should be much smaller than this value to prevent excessive attenuation of geometric stiffness from causing structural risks.

[0049] It should be noted that the basic contact stiffness These are the baseline parameters for constructing the contact locking stiffness component model, obtained through contact stiffness tests on the contact surface between an empty container and the yard. The test procedure involves applying vertical normal forces of different amplitudes to an empty container, measuring the corresponding contact deformation, and calculating the initial contact stiffness from the force-deformation ratio. For example, if a vertical force of 50kN is applied to an empty container and the measured contact deformation is 2mm, then the container's... .

[0050] It should be noted that the contact stiffness enhancement coefficient It is a fitting parameter that quantifies the contact stiffness as a function of the preload increase rate, derived from multiple sets of contact stiffness test data under different preloads. The tests need to cover the common preload ranges of the nodes, collect measured contact stiffness values ​​under corresponding preloads, and then determine the parameter through linear fitting. The value of is taken. For example, the measured contact stiffness values ​​of a container under 6 different preloads are obtained through fitting. This value determines Follow The rate of growth.

[0051] It should be noted that the inherent frequency of node operation conditions This is a dynamic parameter characterizing the dynamic response of a container-cargo assembly. It is calculated by taking the square root of the ratio of the equivalent lateral stiffness to the equivalent mass of the container, as shown in the formula: This parameter is crucial for subsequent analysis of the impact of node operations and the resonance risk of the container-cargo assembly. For example, if the main frequency of the operational pulse at a certain node is 10Hz, and the container-cargo assembly... If the frequency approaches 10Hz, be wary of resonance phenomena that could amplify the response amplitude.

[0052] It should be noted that the equivalent mass of a container This is a mass parameter adapted to multimodal dynamics analysis scenarios, specifically referring to the total mass of the container-cargo assembly involved in lateral vibration. Its value is related to the total mass of the container loading. Consistent. This parameter is a standardized representation of mass in dynamic analysis, such as the mass of a container. tons, then its To ensure consistency in the mass calculation of the vibration system, and to avoid deviations in the calculation of dynamic parameters due to differences in mass definitions.

[0053] It should be noted that the intensity factor in the resonance region These are dynamic parameters representing the amplitude of the impact response of the quantified container cargo assembly to node operations. They are derived from the base excitation transmissibility formula of a single-degree-of-freedom linear vibration system, and the expression is: This parameter reflects the degree of matching between the main frequency of the node operation pulse and the inherent frequency of the container cargo assembly. When the two are close, This will increase significantly, indicating a higher risk of resonance. For example, if the main frequency of the operating pulse at a certain node is 8Hz and the natural frequency of the container cargo assembly is 7.8Hz, The value will be significantly greater than the value when the natural frequency is 5Hz, reflecting the response intensity in the resonance range.

[0054] It should be noted that the resonant response gain index It is a core optimization index for comprehensively assessing the impact of resonance risk and parameter fluctuations. It is composed of the resonance interval intensity factor and the structural parameter fluctuation sensitivity factor, and the formula is as follows: This indicator encompasses both the fundamental resonance risk arising from the matching of the natural frequency and the excitation frequency, and the additional risks caused by pre-stress fluctuations, such as the risk of a container at a certain node. The sensitivity factor caused by pre-pressure fluctuations is 0.5. This comprehensively reflects the overall resonance response risk during the operation of this node.

[0055] It should be noted that the standard deviation of the preload disturbance These are statistical parameters that quantify the degree of pre-stress fluctuation at nodes, obtained by collecting data from nodes. The standard deviation of the vertical preload of multiple sets of corner columns was calculated using actual measurement data. This parameter reflects the stability of the preload during node operations. For example, the standard deviation of the preload at a port yard node was calculated using 100 sets of measured data. This indicates that the prestress at this node fluctuates relatively little around the average value, while if This indicates that the pre-pressure fluctuations are severe, and its impact on the resonance response needs to be carefully considered.

[0056] It should be noted that the magnification factor This is an adjustment coefficient highlighting the risk of parameter fluctuations. Based on engineering experience, its value is set within the range of 1-3 to amplify the impact of prestressing disturbances on the resonant response. The value of this coefficient can be adjusted according to the business's risk tolerance; for example, for containers carrying precision instruments or fragile items, the value can be adjusted accordingly. Choosing 3 strengthens the weight of parameter fluctuation risk in the gain index, preventing a sudden increase in resonance response caused by pre-stress fluctuations; for general cargo containers, Option 1 is preferable, balancing the optimization priorities of risk and cost.

[0057] It should be noted that the structural parameter fluctuation sensitivity factor This parameter quantifies the impact of pre-pressure fluctuations on the resonant response. It is composed of the amplification factor, the absolute value of the partial derivative of the vertical pre-pressure of the diagonal column with respect to the resonance interval intensity factor, and the standard deviation of the pre-pressure disturbance. This factor directly reflects the risk of resonant response changes caused by pre-pressure fluctuations, such as the risk at a certain node. , , The sensitivity factor is 0.32, indicating that pre-pressure fluctuations may lead to an additional gain of 0.32 in the resonant response.

[0058] It should be noted that the decision variables for cargo loading layout schemes It is a binary decision variable that determines the allocation relationship between goods and containers. A value of 1 indicates that the goods... Load into containers A value of 0 indicates that the cargo will not be loaded. This variable is the core of cargo loading optimization, and it is determined by constraints. Ensure that each piece of cargo is allocated to only one container. For example, in a loading task containing 10 pieces of cargo in 3 containers, This indicates that the first item was loaded into the second container. This indicates that the third item will not be loaded into the first container, defining the loading and allocation logic of the goods.

[0059] It should be noted that the decision variables for intermodal transport link schemes It is a binary decision variable for determining the shipping route of containers; a value of 1 represents the container's... Select transport side A value of 0 indicates no selection. This variable is closely related to the intermodal network diagram, ensuring that each container corresponds to a unique intermodal link through path flow conservation constraints. For example, in an intermodal network of port A - railway station B - destination port C, and Characterizing containers Choose the transportation link of port A → railway station B → destination port C to clarify the entire transportation route of the container.

[0060] It should be noted that the node access state variable It is a binary variable relating the transport link and node stacking operations, determined by the intermodal transport link decision variables. Linear derivation, a value of 1 represents a container. Passing through the node A value of 0 indicates that the container does not pass through the stack. This variable is crucial for constructing stacking constraints; for example, if the containers... The transport link includes nodes The transport side, then ,allow Take the value from the node stack job status set; if Then the constraint This ensures that stacking operations only occur at the nodes that the containers actually pass through.

[0061] It should be noted that the resonance risk weighting coefficient This is a weighting parameter that balances transportation costs and resonance risk optimization objectives, with a value ranging from 0.1 to 0.9, set according to business priorities. This parameter adjusts the proportion of the resonance response gain index in the overall objective function. For example, when the business requirement is to ensure the safety of cargo transportation (such as precision equipment or dangerous goods), the weighting parameter can be adjusted. Setting it to 0.9 prioritizes reducing resonance risk in the optimization model; when the business focuses more on transportation cost control (such as for general bulk commodities), it can... Taking 0.1 prioritizes reducing transportation costs; when a balance between safety and cost needs to be struck, A value of 0.5 can be used to achieve synergistic optimization between the two.

[0062] It should be noted that the goods is a general identifier for goods suitable for multimodal transport stowage scenarios, used to distinguish goods of different attributes, and is the core associated object of the decision variables for the goods loading layout. For example, in a stowage scenario of an import and export e-commerce, the goods can refer to a laptop computer weighing 50 kg and with dimensions of 60 cm × 50 cm × 40 cm, and the goods can refer to a batch of clothing packages weighing 200 kg and with dimensions of 120 cm × 80 cm × 60 cm. Through this identifier, the parameters of the goods can be associated with the loading decision.

[0063] It should be noted that the transport edge is a general identifier for the transport link in the intermodal network diagram, corresponding to a transport journey between any two nodes, and associating specific transport modes, transport costs, transport timings and other parameters. For example, in a multimodal transport scenario of the China-Europe freight train, the transport edge can refer to the railway transport section from Tianjin Port to Alashankou Station, associating a transport cost of 1,200 yuan / box and a transport timing of 5 days; the transport edge can refer to the railway transport section from Alashankou Station to Hamburg Port, associating a transport cost of 2,800 yuan / box and a transport timing of 12 days, and is the basic unit of the transport link decision.

[0064] It should be noted that the transport edge cost is a parameter suitable for the multimodal transport cost accounting scenario, specifically referring to the total operating expenditure corresponding to the transport edge , summarizing expense items such as basic freight, additional fees along the way (such as port fees, transit fees), resource occupancy fees (such as container type occupancy fees), etc. This parameter is the core composition of the cost optimization goal. For example, for a certain sea transport edge with a basic freight of 3,000 yuan / box, a port operation fee of 500 yuan / box, and a transit fee of 800 yuan / box, then the yuan / box of this transport edge directly affects the cost ranking and optimization selection of the transport link.

[0065] It should be noted that the packing list and the layout inside the container are output parameters that concretize the goods loading decision, generated based on the decision variable of the goods loading layout plan , and the expression is , including all the goods loaded into the container and their three-dimensional coordinates and placement orientations inside the container. This parameter is the basis for on-site container loading operations. For example, for the of the container , it means that the goods is placed inside the container at the coordinate (20 cm, 30 cm, 10 cm) with a 0° orientation, and the goods Place the box at a 90° angle at the coordinates (100cm, 30cm, 10cm) to ensure that the packing operation is standardized and orderly.

[0066] It should be noted that the cargo coordinates These are parameters for accurately locating the position of goods inside the container, referring to the goods... In containers Three-dimensional spatial coordinates within ( The coordinates are allocated based on the internal dimensions of the container, the dimensions of the cargo itself, and the principle of maximizing space utilization. The coordinates must be set to avoid cargo overlap and fully utilize the internal space of the container. For example, if a 40-foot container is 12m long, 2.4m wide, and 2.6m high, and is loaded with a piece of cargo that is 1m long, 0.8m wide, and 1.2m high, the coordinates should be allocated as follows: This ensures that the edges of goods maintain a reasonable distance from the inner walls of the container and other goods, while improving space utilization.

[0067] It should be noted that the cargo is oriented... These are parameters that optimize the utilization of space within the container and the stability of the cargo, referring to the cargo... In containers The placement angle within the container (such as 0°, 90°, 180°) must match the packaging shape, center of gravity distribution, and container loading space limitations. For example, rectangular furniture can be placed along the length of the container (0° orientation) to reduce space waste; equipment with a high center of gravity can be placed perpendicular to the length of the container (90° orientation) to improve stability and prevent tipping during transportation.

[0068] It should be noted that the intermodal transport route sequence These are the output parameters of a concrete container transport link, based on the decision variables of intermodal transport link schemes. Generate, the expression is (in This parameter, consisting of multiple transport segments arranged sequentially according to the transport process, specifies the entire transport route of the container. For example, the container... of ,in For the sea route from Shanghai Port to Ningbo Port, Located beside the railway line from Ningbo Port to Changsha Station. Along the highway from Changsha Station to Guiyang Distribution Center, the entire multimodal transport chain of sea freight, rail freight, and road freight is clearly presented.

[0069] It should be noted that the node stacking hierarchy plan These are the output parameters for standardizing node stacking operations, and are decision variables for planning based on the node stacking hierarchy. Generate, the expression is , including containers The nodes along the transportation route and their corresponding recommended equivalent stacking layers. This parameter is a core guideline for node operations, such as for containers. of This indicates that the container is at the node. (Port yard) It is recommended to stack 2 layers, at the node (Railway marshalling yard) No stacking (direct transfer), at the node (Destination port yard) It is recommended to stack 3 layers to ensure that the stacking operation meets the requirements of node capacity and resonance risk control.

[0070] It should be noted that the first Section transport edge It is a series of intermodal transport routes. The segmentation identifiers of the transport edges are used to clearly define the sequence of transport links. The value ranges from 1 to the total number of segments. For example, intermodal transport route sequences. middle, This is the first transport edge (from the originating node to the first transit node). This is the second transport edge (from the first transit node to the second transit node). For the third transport edge (from the second transit node to the destination node), define the segmented execution logic of the transport link.

[0071] It should be noted that the total number of segments It is a series of intermodal transport routes. The total number of transport edges included is determined by the intermodal transport link decision variables. The number of transport edges with a value of 1 is determined by the number of transport segments in multimodal transport. For example, if a container's transport link only includes one sea transport edge from the port of origin to the port of destination, then... (Direct transport); if it includes two sea transport segments: port of origin → transshipment port and transshipment port → destination port, then (Transit transport); if it includes the port roadside, port to transit port seaside, or transit port to destination railway side, then (Three-stage multimodal transport) adapts to the expression needs of different transportation scenarios.

[0072] It should be noted that the intermodal transport route includes the following nodes. This is a description adapted to scenarios involving the association of multimodal transport route nodes, specifically referring to a sequence of intermodal transport routes. The origin and destination nodes corresponding to all transport edges, i.e., containers The actual operational nodes traversed. For example, intermodal transport route sequences. ,in The starting point is The endpoint is , The starting point is The endpoint is ,but The corresponding node is , , This clarifies all the stages that require operations (such as loading, unloading, and stacking) during container transportation.

[0073] like Figure 2 As shown, Figure 2 The actual operation process of the intelligent loading service for import and export multimodal transport based on the hybrid planning algorithm is demonstrated: The intelligent loading service platform first integrates the impact parameters of the intermodal node operation impact database with the transportation link information of the intermodal network diagram to generate a node stacking level plan that includes recommended stacking levels. This plan will simultaneously guide the operation of each multimodal transport node. That is, the container is stacked according to the recommended stacking level in the port yard. When the containers are transferred to railway stations, inland hubs and other nodes by truck, rail and other transportation methods, the stacking operation is also carried out according to the corresponding recommended stacking level. Finally, intelligent loading and stacking collaborative operation of containers is realized among multiple nodes such as ports, railways, and inland hubs, and under multiple transportation modes such as sea, road and rail.

[0074] It is important to note that all input data described in this solution is acquired in real-time through legal and compliant hardware interfaces with the user's full knowledge, explicit consent, and active cooperation. The preset parameters, prior constants, and statistical means are all derived from publicly available scientific literature data, de-identified general research datasets, or calibration data from laboratory environments, and do not contain any unauthorized sensitive third-party information. The system's data processing is limited to local or volatile memory computation transmitted via encrypted channels. There is no illegal collection, theft, or retention of user biometric data or infringement of user privacy without the user's knowledge. All parameter calls and generation comply with the principles of data minimization, legality, legitimacy, and necessity.

[0075] The embodiments of this example have been described above. However, this example is not limited to the specific implementation methods described above. The specific implementation methods described above are merely illustrative and not restrictive. Those skilled in the art can make many other forms based on the guidance of this example, and all of them are within the protection scope of this example.

Claims

1. A method for intelligent loading and unloading services in import and export multimodal transport based on a hybrid programming algorithm, characterized in that, include: Establish a database of intermodal node operation impacts containing node operation pulse frequency data, and construct an intermodal network diagram describing the transportation link and a node stacking operation status set defining the operation capabilities of each node; Based on the force transmission properties of the container corner columns, the set of node stacking operation states is converted into vertical preload of the corner columns, and the natural frequency of the node operation conditions of the container assembly is calculated through the load stiffness correlation model of preload and structural stiffness. By combining the inherent frequency of the node's operating condition and the main frequency of the node's operating pulse, a resonance response gain index is constructed that includes a resonance interval intensity factor and a structural parameter fluctuation sensitivity factor. A multimodal transport loading optimization model is constructed, and the resonance response gain index is used as the optimization objective to collaboratively solve the cargo loading layout scheme, the intermodal transport link scheme, and the node stacking hierarchy plan. The output includes intelligent loading and unloading schemes for import and export multimodal transport with recommended stacking levels for each node.

2. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, Establish a database of intermodal node operation impacts that includes node operation pulse frequency data, including: Construct a database of intermodal transport node operation impacts that includes a set of basic physical parameters of goods and a set of operational parameters of node equipment; For each node in the intermodal transport node operation impact database, the equivalent half-sine collision pulse duration is calibrated according to the equipment type of that node; The ratio of pi to the duration of the equivalent half-sine collision pulse is used as the main frequency data of the node operation pulse.

3. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, Construct an intermodal network diagram describing the transportation links and a set of stacked node operation states defining the operational capabilities of each node, including: Construct an intermodal transport network graph consisting of a set of nodes and a set of transport edges, where each transport edge represents a transport journey and its destination node; For each node in the intermodal network diagram, a node stacking operation state set is defined, consisting of an integer sequence from zero to the node's stacking capacity limit. The equivalent stacking number of containers when they are stationary at the node is used as a structural state variable that takes values ​​from the stacking operation state set of the node.

4. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, Based on the force transmission properties of the container corner posts, the set of node stacking operation states is converted into vertical preload of the corner posts, including: The product of the total mass of the container and the acceleration due to gravity is obtained as its own gravity load; Based on the values ​​in the node stacking operation state set, calculate the total gravity load superimposed on the node corresponding to the value; The vertical preload of the corner column is obtained by adding its own gravity load to the total gravity load superimposed above the node and dividing by four.

5. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 4, characterized in that, The natural frequencies of the container cargo assembly under nodal operating conditions are calculated using a load-stiffness correlation model of preload and structural stiffness, including: A load stiffness correlation model is constructed, which is composed of the superposition of structural geometric stiffness components and contact locking stiffness components; the structural geometric stiffness components decrease with the increase of the vertical preload of the corner column, and the contact locking stiffness components increase with the increase of the vertical preload of the corner column. The sum of the structural geometric stiffness component and the contact locking stiffness component is taken as the equivalent lateral stiffness. Calculate the ratio of the equivalent lateral stiffness to the equivalent mass of the container, and perform a square root operation on this ratio to obtain the natural frequency of the node operation condition.

6. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, By combining the natural frequency of the node's operating condition and the dominant frequency of the node's operating pulse, a resonance response gain index is constructed, which includes a resonance interval intensity factor and a structural parameter fluctuation sensitivity factor, including: Calculate the ratio of the node operation pulse main frequency data to the node operation condition natural frequency, and combine it with the damping ratio parameter to construct the resonance interval intensity factor describing the amplitude of the base excitation response; Calculate the absolute value of the partial derivative of the vertical preload of the diagonal column of the intensity factor in the resonance interval, and multiply it by the standard deviation of the preload disturbance and the amplification factor to obtain the structural parameter fluctuation sensitivity factor. The resonance interval intensity factor is added to the structural parameter fluctuation sensitivity factor to obtain the resonance response gain index.

7. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, A multimodal transport loading optimization model is constructed, using the resonance response gain index as the optimization objective. This model is then used to collaboratively solve for cargo loading layout schemes, intermodal transport link schemes, and node stacking hierarchy plans, including: Define the decision variables for cargo loading layout scheme, intermodal transport link scheme, and node stacking hierarchy planning; The constraints are constructed to limit the range of values ​​of the node stacking level planning decision variables to the upper limit of the node stacking capacity, and to take effect only when the intermodal transport link scheme decision variable indicates that the node passes through the node. Construct a minimum objective function that includes a transportation cost term and a weighted resonance response gain term, and solve the above decision variables using mixed integer programming.

8. The intelligent loading service method for import and export multimodal transport based on hybrid planning algorithm according to claim 1, characterized in that, The output includes intelligent loading solutions for import and export multimodal transport with recommended stacking levels for each node, including: Based on the cargo loading layout decision variables determined by the solution, a packing list and internal layout containing cargo coordinates and orientation are generated. Based on the decision variables of the intermodal transport link scheme determined by the solution, a sequence of intermodal transport paths composed of multiple transport edges is generated; Extract the nodes traversed by the intermodal transport route sequence, and generate a node stacking hierarchy plan containing the recommended equivalent stacking layer number corresponding to the nodes based on the node stacking hierarchy plan decision variables determined by the solution. The packing list, container layout, intermodal transport route sequence, and node stacking hierarchy plan are combined to output the intelligent loading and unloading scheme for import and export multimodal transport.

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