200 results about "Multimodal transport" patented technology

The invention belongs to the technical field of logistics information, and discloses a multimodal transport trusted data space construction method based on privacy calculation and a block chain. The method comprises the following steps: deploying a modular intelligent data gateway at a core node of a multimodal transport line, unifying the format of multi-source heterogeneous original data, and chaining a hash value; after the multi-source heterogeneous data is subjected to customization processing, key metadata attributes of all the data are registered and linked in real time; constructing a core ontology model, and establishing a cross-domain semantic interoperation capability; establishing a privacy calculation and block chain collaborative dual-channel architecture, and cooperatively training an ETA prediction model in a local data isolation environment by adopting transverse federal learning to ensure that original trajectory data is not out of a domain; constructing a dispute arbitration and credible verification system, and realizing an arbitration process of judicial verification when a transportation dispute occurs; data minimization disclosure and compliance sharing are achieved through dynamic access control. According to the method, multimodal transport data can be available and invisible, and the whole operation process can be audited.

The invention relates to the technical field of logistics management, and discloses a multimodal transport cargo arrival time prediction method based on a graph neural network, and the method comprises the steps: receiving an arrival time prediction request for a target cargo; planning a candidate transport path according to the arrival time prediction request, and dynamically extracting a sub-graph from the global multimodal transport network; endowing each node and each directed edge with a feature vector, endowing each sub-graph with a global feature vector, and inputting the sub-graphs after feature initialization into a graph neural network model; the method comprises the following steps of: generating an initial state vector for each node through a graph neural network model; iteratively updating the state vector of the node through a multi-layer message transmission mechanism, and simulating the cascade propagation effect of the transportation event on the path network; and based on the final state vector of the destination node after iteration updating, regression is carried out to obtain a probability distribution parameter of the arrival time. According to the method, cascade propagation of transportation delay in the network can be simulated, probabilistic prediction containing uncertainty measurement is output, and the prediction precision is improved.

The invention relates to the technical field of multimodal transport, in particular to a multimodal transport one-box system intelligent credible collaboration method based on an artificial intelligence technology, comprising the following steps: S1, obtaining a standardized JSON data packet from transport data according to an agreed rule, performing triple encryption to obtain an encrypted data packet, and broadcasting the encrypted data packet to a block chain; s2, the verification node carries out consensus verification on the Internet of Things equipment data and the electronic waybill key field, and if the verification is passed, the verification is written into the block chain; s3, the digital twin system fuses the multi-source data and extracts calculation data, and artificial intelligence calculates an optimal loading and unloading sequence and a transportation path according to the calculation data; and S4, uploading the longitude and latitude data to the block chain node through the Beidou positioning terminal and the 5G / LoRa gateway along the way, and automatically identifying the abnormity in the transportation stage based on the geofence technology. According to the method, data rules are unified, efficient connection of cross-transportation modes is achieved, manual input errors or violation operation is prevented, the risk of goods damage is reduced through dynamic route planning, the transfer efficiency is improved, and logistics time and cost consumption are reduced.

The invention relates to a multimodal transport path selection method based on multi-agent reinforcement learning. The method comprises the following steps: constructing a multimodal transport network and a system environment; carrying out initial planning by adopting a self-adaptive large neighborhood search algorithm based on a multimodal transport network and a system environment; and when an accident occurs, acquiring accident information, optimizing the initial plan by adopting a multi-agent reinforcement learning algorithm based on the accident information in combination with a cost objective function to obtain an accident action decision, and executing a transportation task based on the accident action decision. By constructing a large neighborhood disturbance response mechanism fused with ALNS, when it is detected that node / path service time offset exceeds a threshold value, path reconstruction is intelligently triggered, a new alternative path is generated, based on an MADDPG multi-agent reinforcement learning framework, all carrier Agents can obtain global information in centralized training and make decisions independently in execution, and therefore the probability that the node / path service time offset exceeds the threshold value is lowered. And the utilization efficiency of transportation resources and the task allocation fairness are effectively improved.

The invention relates to a multimodal transport cargo tracking method and device, equipment and a medium. According to the method, a standardized data set containing time-space attributes is generated by obtaining original circulation data of different transportation modes and hub nodes; on the basis of the timestamp continuity of the data set, constructing a cargo circulation association graph marked with transportation mode switching points; performing path feature clustering according to continuity identifiers in the map, and generating a standard path template library in combination with a geographic information library; matching and identifying a common path section through a real-time cargo track and a template library to form a cargo grouping tracking label; when the transportation duration in the group exceeds a threshold value, calculating an alternative path combination and outputting a track correction instruction; and generating a track synchronization instruction based on the instruction simulation resource topological relation, updating the association graph and outputting a dynamic track report. Continuous visual tracking of goods in the whole transportation process is achieved, the track path is automatically corrected under the abnormal working condition, and the timeliness and accuracy of logistics state feedback are improved.

The invention discloses an intelligent sorting and scheduling method and system for a multimodal transport scene, and relates to the technical field of warehousing, and the method comprises the steps: collecting the transportation mode identifiers of a plurality of transportation carriers, the physical attribute information of corresponding goods, and the operation state information of a three-dimensional sorting system in real time; analyzing the multimodal transport rigid rule information to generate a dynamic boundary condition of multimodal transport; importing the transportation mode identifier, the physical attribute information, the operation state information and the dynamic boundary condition into a deep reinforcement learning model; outputting a goods shelf distribution strategy for dynamically distributing storage levels according to the goods gravity center parameters and an equipment task sequence for controlling the AGV carrying robot to move; and a sorting execution instruction is automatically generated so as to correspondingly drive the three-dimensional sorting system to carry out goods sorting. According to the method, a'secure storage-time coupling-space cooperation 'technical closed loop is formed, and the comprehensive logistics cost of a multimodal transport full link is remarkably reduced.

The invention discloses a logistics full-process optimization method based on multimodal transport digital development, and belongs to the field of logistics, the logistics full-process optimization method based on multimodal transport digital development comprises the following steps: formulating a unified data exchange standard, and defining a standardized digital format for each type of documents; on the basis of a unified data exchange standard, a unique digital main document which runs through the whole transportation process is created for goods transported each time, and the digital main document serves as a unique digital certificate of the ownership of the goods and a transportation contract; the method has the beneficial effects that a trusted data closed loop capable of automatically circulating, checking and responding to exceptions is constructed by formulating a unified data exchange standard, creating a digital main document running through the whole process, constructing a cloud collaboration platform and embedding an intelligent rule engine; therefore, a traditional mode of multimodal transport depending on manual coordination is converted into an efficient, automatic and transparent digital operation mode.

The invention provides a dynamic optimization and visual decision-making system for port and navigation multimodal transport knowledge graph collaboration, and solves the problems of high transport mode conversion cost, difficult information sharing and the like. Aiming at the defects that a traditional static data warehouse (such as Hadoop) is insufficient in real-time performance, a general data fusion framework (such as Apache NiFi) is poor in field adaptability and the like, the system innovatively constructs a multi-source heterogeneous database, integrates port production, economic association, infrastructure and freight data, and realizes accurate path planning and personalized logistics scheme recommendation through an intelligent algorithm. The system adopts a React + ECharts + ShadcnUI technology stack to realize knowledge graph visualization, practice shows that the operation efficiency can be remarkably improved, the transportation cost is reduced, and technical support is provided for sustainable development of ports.

The invention discloses a carbon emission accounting method for a container multimodal transport network, and the method comprises the steps: firstly, determining a modeling boundary and a data set of the container multimodal transport network, and enabling the modeling boundary to comprise a space range, a time range and a modeling object; the data set comprises node attributes, connection edge attributes and node traffic volume data of data required for constructing the multimodal transport network model; then, establishing a data-driven multimodal transport network model, identifying a network topology structure through the model, and estimating network side point traffic; and finally, calculating the network carbon emission by combining the energy consumption characteristics of different transportation modes and different combined transportation transfer modes. The scheme not only can serve carbon emission evaluation of a single hub transportation network to realize hub enterprise freight network carbon emission footprint tracking and driving factor identification, but also can serve carbon emission evaluation of a regional multimodal transportation network to realize regional freight network carbon emission management level evaluation and driving factor identification, so that the economic benefit is improved. And a theoretical tool is provided for measuring the carbon emission reduction development level of the multimodal transport network with different dimensions.

The invention discloses a logistics arrival time forecasting system and method based on multi-data fusion, relates to the technical field of logistics arrival time forecasting, and aims to solve the technical problem that the deviation between the forecasting result of an existing logistics arrival time forecasting system and the actual arrival time is large. The system comprises a data acquisition module, a path planning module, a time calculation module and an early warning and output module. The output end of the data acquisition module is connected with the path planning module and the time calculation module, and the data acquisition module is used for acquiring multi-source data. The multi-source data comprises historical logistics data, order and delivery information data, weather data, real-time road condition data, map data, transportation tool characteristic data, cargo special demand data, transportation tool connection hidden loss data and transportation tool collaborative delay association data; and the output end of the path planning module is connected with the time calculation module. The method has the advantage of improving the accuracy of the arrival time forecast in the multimodal transport scene.

The invention discloses an AI-based multimodal transport collaborative optimization method and system, and relates to the technical field of intelligent transportation, and the method comprises the steps: obtaining a temperature sampling sequence of cold-chain goods in a non-temperature-control region, building a self-adaptive baseline, and recognizing a temperature peak factor and a change trend; aiming at the temperature deviation cumulant with the rising risk, constructing a temperature deviation safety window through inertia correction and an internal loss dynamic model; identifying the intermodal transport risk based on the security window, and if the intermodal transport risk exceeds a threshold, constructing a matching function to perform intermodal transport capacity unit matching; monitoring transport capacity temperature deviation in real time after matching, and regulating and controlling a refrigeration strategy in a grading manner based on a corrected urgency coefficient; the system comprises a trend identification module, a window determination module, a combined transport matching module and a model optimization module. According to the method, dynamic tracking, transport capacity matching and model self-optimization of the cold chain transport temperature deviation risk are realized, and the multimodal transport temperature control efficiency and the goods damage prediction accuracy are improved.

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.

The invention discloses a multimodal transport cargo transportation state monitoring system based on a standardized intelligent tray group. The system comprises a modular tray group composed of a main control tray and at least one functional tray; the main control tray is integrated with an edge calculator, an RFID reader-writer, a temperature and humidity sensor and a power supply module; the functional tray comprises a strain sensor and an acceleration sensor; the tray groups are rigidly connected through electromagnetic lock catches, and a voltage sensor is arranged to monitor the connection state; the tray group transmits data through a mixed communication channel; LoRa wireless communication is adopted by default, and magnetic attraction wired connection is switched when the packet loss rate is greater than 1%; after being preprocessed by the edge calculator, the data are encrypted by the Beidou system and uploaded to the background server; the background server executes overload and unbalanced load analysis, vibration grade evaluation, environmental risk early warning and lock catch state monitoring; and triggering a user side to give an alarm when an exception is detected. The safety monitoring problem of goods transportation is effectively solved, the transportation safety is improved, and the accident risk is reduced.

The invention provides a railway cold chain combined transportation distribution center operation scheduling optimization method, and belongs to the technical field of railway cold chain scheduling. Comprising the following steps: S1, resource pre-allocation and time window constraint embedding; s2, dynamic and static priority collaborative decision making; s3, optimizing a spatial constraint guided path; s4, collaboratively executing the operation process; and S5, performing real-time monitoring and feedback. Through triple technical breakthrough of multi-agent collaborative decision-making, dynamic and static priority coupling and space-time resource pre-allocation, the industrial pain points of unbalanced resource allocation, frequent path conflicts and high goods damage rate of the railway cold chain collecting and distributing center are solved, collaborative optimization of efficiency, quality and cost is realized, and the method is suitable for popularization and application. And a core technical support is provided for intelligent upgrading of cold chain multimodal transport.

The invention belongs to the technical field of emergency material multimodal transport scheduling, and particularly relates to an emergency material multimodal transport scheduling optimization method considering risk sensitivity, which comprises the following steps of: A, constructing an emergency material multimodal transport network in which a trunk line and a tail end are coordinated, mapping trunk line transport and tail end transport into a multimodal transport network diagram, determining nodes, feasible line sets and parameters thereof; b, establishing an emergency material multimodal transport scheduling optimization model considering risk sensitivity, and establishing a risk-sensitive scheduling optimization model based on an emergency material multimodal transport network and the obtained parameters; and C, generating a model solving result and a scheduling scheme, converting the scheduling optimization model into a solvable form, generating an executable scheduling scheme, verifying the result, and forming a complete technical closed loop. The method has the advantages that the adaptive capacity and robustness of the emergency logistics system under extreme situations such as path blocking are remarkably improved, and the method is suitable for popularization and application. And important decision support is provided for scientifically coping with disaster rescue.

The invention relates to the technical field of comprehensive traffic planning and logistics optimization, in particular to a multimodal transportation junction transportation path collaborative optimization system and a method thereof.The multimodal transportation junction transportation path collaborative optimization system comprises a data layer, a model layer, an application layer and a feedback layer, the data layer comprises a data acquisition module and a data processing module, the data acquisition module mainly acquires node basic data, network topology data, freight demand data and cost aging data, and the processing content of the data processing module comprises standardization processing, space-time alignment, abnormal value cleaning and data storage processing; and the model layer is used for multi-objective optimization model and intelligent algorithm processing. According to the method, the multi-objective optimization model is constructed, so that the scientific site selection of the transit hub, the intelligent matching of the transportation mode and the path and the collaborative optimization of the whole link cost and the time efficiency are realized, meanwhile, various resource constraint conditions are met, and the overall operation efficiency of the multimodal transportation network is improved.

The invention relates to the technical field of intelligent logistics, in particular to an intelligent logistics collaborative scheduling method based on multimodal transport dynamic optimization, which comprises the following steps of: acquiring high-speed and railway transport data through the Internet of Things, generating an all-road-condition data set, evaluating and eliminating an overload path, matching resources according to task timeliness and cargo attributes, sequencing, continuously tracking and dynamically adjusting, and performing collaborative scheduling. And finally, optimizing and generating an optimal scheduling scheme. Traffic monitoring equipment and a sensor are used for collecting flow, speed, load and cargo information of a path, a transportation data set is generated, path load is evaluated according to the flow, bearing capacity and speed, available paths are screened, resources are distributed according to task priority and path load, task progress is tracked in real time, and the priority and resources are adjusted. And path selection is optimized according to the traffic state, so that task timeliness and resource utilization maximization are ensured.

The invention is suitable for the technical field of intelligent logistics scheduling, and provides a cross-border multimodal transport path dynamic planning method, device and equipment and a storage medium. Multi-source real-time data is acquired and fused, policy information in the multi-source real-time data is analyzed and quantified, and the real-time data is obtained; according to the method, an unstructured policy text is converted into a dynamic constraint condition which can be understood by a multi-objective optimization model, so that dynamic optimization of path planning is realized, changes of an external policy environment can be responded in real time, a generated path scheme achieves a better balance in multiple objectives such as compliance, cost and timeliness, and the path planning efficiency is improved. And the accuracy, timeliness and anti-risk capability of the planning result are improved.

The invention relates to a multimodal transport highway-railway path dynamic planning method, which comprises the following steps of: acquiring multisource dynamic data of highways, railways and connection points in real time, predicting data in a specified time period in the future, and preprocessing the data to generate a dynamic database; a space-time coupling objective function with the shortest total transportation time, the lowest total transportation cost and the highest highway-railway connection efficiency as the cooperation objective is constructed; introducing a fitness dynamic calibration mechanism to correct a target function value; setting a decision window by taking the current moment as a starting point, solving a local optimal path in a rolling manner, and generating an initial scheme including a road real-time navigation path, railway dynamic shift matching and a connection point resource reservation time slice; setting a three-level early warning threshold based on the predicted value, and triggering a hierarchical adjustment mechanism to generate an adjustment scheme; and evaluating the time / cost change rate and the execution feasibility of the adjustment scheme, and executing after reaching the standard. The prospective performance, the dynamic adaptability and the landing reliability of path planning are effectively improved, the transportation cost and delay are effectively reduced, and the highway-railway combined transportation efficiency is remarkably improved.

The invention belongs to the technical field of port multimodal transport scheduling, and particularly relates to a port multimodal transport collaborative scheduling system and method based on real-time data. According to the invention, through the data access and standardization module, the emergency judgment module, the scheduling adjustment measure generation module and the visual presentation and interaction module, automatic judgment and strategy matching of four types of emergencies such as ship delay and storage yard congestion are realized, and an overall scheme of multimodal transport cooperative scheduling is determined. Cooperative scheduling of ship-port-railway-road multimodal transport is facilitated, manual intervention is reduced, response time is shortened, and emergency processing efficiency and safety are improved. Dynamic visualization of full-chain data is achieved through the collaborative dispatching board, differentiated permissions are configured for port dispatchers, railway dispatchers and cargo owners, data safety is guaranteed, the efficient collaborative effect of multiple subjects is achieved, collaborative barriers caused by information asymmetry are solved, and information sharing and collaboration are promoted.

Aiming at the technical difficulties of resource splitting, scheduling stiffness, insufficient path planning, imperfect conflict processing, limited expansibility and the like in the current low-altitude logistics and multimodal transport process, the embodiment of the invention provides a low-altitude logistics global coordination and dynamic scheduling method for multimodal transport, which is applied to the technical field of low-altitude logistics. Comprising the steps 1-6, global collaboration, real-time conflict resolution and efficient resource allocation in a multimodal transport environment can be realized by combining a recommendation algorithm and multi-constraint scheduling optimization on the basis of transport node unified modeling, time fragmentation, a cross-node dynamic resource pool and other mechanisms; and the flexibility, adaptability and execution efficiency of the low-altitude logistics transportation process are effectively improved.

The invention relates to the technical field of intelligent logistics and transportation management, and particularly discloses a multimodal transport goods source allocation system based on data analysis. The system comprises a data access and fusion module, a data quality governance and enhancement module, a time-space association and flow calculation module, a critical path and hub identification module and a dynamic transport capacity allocation decision module, and multi-source data integration, quality governance, time sequence association verification, critical path identification and dynamic transport capacity allocation are carried out. The accurate description of the flow direction of the goods source and the dynamic adaptation of the transport capacity resources are realized, and the operation efficiency and reliability of the multimodal transport system are improved.

A facility for receiving and storing transport containers, in particular containers (4), in seaports and inland ports or intermodal terminals, from external or internal means of transport (5) and for issuing the transport containers, in particular containers (4), to external or internal means of transport (5), comprising a transport container high-bay warehouse (1) for receiving, storing and issuing transport containers, in particular containers (4), wherein the transport container high-bay warehouse (1) has a plurality of transport container storage locations (7) arranged in blocks next to and above one another and storage aisles (8) for storage and retrieval machines (9) running between the block-arranged transport container storage locations (7), characterized in that the facility comprises at least one traffic route (3) for external or internal means of transport (5), which is arranged parallel to the storage aisles (8),and at least one stationary crane track system (2) arranged in the plant, which is arranged perpendicular to the at least one traffic route (3) and the storage aisles (8) and bridges the at least one traffic route (3) and the storage aisles (8) or an area (11) of the plant arranged in alignment with the storage aisles (8).

The application provides a low-carbon multimodal transport network design method considering consignor path selection, comprising: obtaining freight demand data, multimodal transport network topology, parameters of each transport mode, network node transfer time and cost; obtaining consignor preference data covering all origin-destination points in the planning period, brand effect, utility coefficient of transport time and transport cost, low-carbon subsidy data; based on the obtained data, constructing freight flow batch transport constraints, low-carbon subsidy constraints, consignor path selection constraints and multimodal transport network constraints respectively; establishing an objective function for the purpose of pursuing lower carbon emission cost on the premise of not paying a large amount of low-carbon subsidies; under the constraints, using a particle swarm optimization algorithm embedded with CPLEX to solve the objective function to obtain a low-carbon multimodal transport network design scheme. The method can accurately depict the consignor path selection behavior and realize the dual improvement of transport efficiency and environmental benefit.

This invention discloses a supply chain management method and system based on adaptive optimization using traceable containers. The method collects multi-dimensional supply chain data in real time using standardized traceable containers, including inbound volume, outbound volume, inventory, transportation location, environmental parameters, and carbon emission estimates; calculates data volatility and reliability to determine the importance of data collection time; integrates target smoothing parameters and uses reinforcement learning to correct predicted demand; generates multimodal transport schemes using K-D tree matching and the dragonfly algorithm; optimizes production and transportation scheduling based on simulated annealing algorithm, embedding carbon footprint constraints to minimize total green costs; and forms an adaptive closed loop through container feedback to improve system robustness. This method achieves real-time tracking, adaptive optimization, and sustainable development of the supply chain, and is suitable for complex and ever-changing supply chain environments.

The invention relates to the technical field of information management, and discloses a multimodal transport information management method and system, and the method comprises the steps: obtaining transport data and a transport chain, and carrying out the analysis, and obtaining a basic feature set; identifying data structure features of different transportation modes, and when the data structure features accord with the feature structures, establishing a corresponding feature tag system to obtain differentiated feature identifiers; segmenting the transport chain and constructing a mapping relation to obtain a functional block sequence; optimizing the sequence to obtain a coordination weight matrix by blocks; performing evaluation adjustment according to the coordination weight matrix to obtain an evaluation result; constructing a data set according to an evaluation result to obtain a structured data set; management instructions of different functional blocks are generated according to the structured data set, and a precise management scheme is obtained; and performing information management according to the precise management scheme, and continuously monitoring and optimizing to complete continuously optimized multimodal transport information management. The method can solve the problem of low information processing efficiency.

This invention relates to the field of multimodal transport route planning technology and discloses a route planning method for multimodal freight transport. The method includes constructing an initial decision tree based on dynamic historical trajectories, where branches are sequenced path nodes, nodes are tool call chains, and leaf nodes are resource consumption profiles. By matching real-time constraints and cost objectives, iterative pruning and branch injection operations are performed on the decision tree to generate an optimized decision tree. After obtaining a set of candidate solutions through deduction, multi-dimensional robustness stress tests are conducted on them to simulate path stability and resource fluctuations under preset disturbances, selecting a subset of feasible solutions. Finally, the optimal solution is dynamically selected based on the real-time status and converted into an operation command. This method can adapt to changes in dynamic constraints and proactively ensure the robustness of solution execution.

The application discloses a kind of intelligent sorting scheduling method and system for multimodal transport scene, it is related to warehousing technical field, the method includes: the transport mode identifier of real-time acquisition multiple transport vehicles, the physical attribute information of corresponding goods and the running state information of stereoscopic sorting system;Multi-modal transport rigid rule information is parsed, and the dynamic boundary condition of multi-modal transport is generated;Transport mode identifier, physical attribute information, running state information and dynamic boundary condition are imported into deep reinforcement learning model;Output according to the goods gravity center parameter dynamic allocation warehousing level goods shelf allocation strategy, and the equipment task sequence of control AGV handling robot motion;Automatic generation sorting execution instruction, to correspond driving stereoscopic sorting system is sorted goods.The application forms "safe storage-time limit coupling-space coordination" technical closed loop, significantly reduces the comprehensive logistics cost of multimodal transport whole link.

The invention relates to the technical field of shipping service management, in particular to a container shipping service data management platform based on big data and artificial intelligence according to the application of supply chain data such as centralized shipping cabin booking, import and export documents, shipping fee auditing, global ports, air routes and multimodal transportation. The system comprises a data cleaning module, a data mapping module, a risk assessment module, a tool integration module and an authority control module. According to the method, a unified verification mechanism is established through field correction, multi-source data matching is realized through cross-table comparison, a dynamic risk level is generated by combining ship docking information and a risk list and introducing a adaptability factor, flow tracking and responsibility mapping are carried out by utilizing responsibility identification and a knowledge graph, and through role comparison and snapshot restoration, a dynamic risk level is generated. Sensitive data security and abnormal access recovery are guaranteed, and data accuracy, risk assessment refinement, process transparency and access security are improved.

The present invention discloses a multimodal transport inventory resonance management method, medium and equipment based on dynamic rules. The method includes: classifying and collecting raw data and preprocessing it into standardized intermodal transport data; constructing a transport time prediction model based on the preprocessed second transport state parameter to extract dynamic transport characteristics, and performing spectral analysis on the second inventory level parameter to obtain inventory cycle fluctuation characteristics; extracting the resonance coupling characteristics of the second transport state parameter and the second inventory level parameter through time-frequency correlation analysis and establishing a feature coding library; constructing a mapping relationship network between transport mode switching rules and inventory adjustment strategies based on the above characteristics; establishing a resonance control parameter conversion relationship between a baseline transport scenario and a target transport scenario; and finally constructing a multidimensional resonance management knowledge base. The present invention realizes the coordinated control of transport delays and inventory fluctuations under multimodal transport, effectively suppresses the supply chain resonance effect, and improves the system's responsiveness and stability.