276 results about "Multimodal transport" patented technology

The invention relates to a multimodal transport end-to-end supply chain collaborative management method based on container logistics, and belongs to the technical field of supply chain management. The method comprises the following steps: virtually integrating scattered cargo owner demand, carrier transport capacity and transit point operation capability resources through a cloud computing platform to form a shared resource pool, and constructing a dynamic collaborative network; performing intelligent matching and scheduling on the shared resource pool through a contribution degree distribution mechanism to obtain a supply chain full-link state; dynamic routing optimization is carried out according to the full-link state of the supply chain, and optimal path selection is carried out on the transportation mode of container logistics based on the business process of multimodal transportation; a task is automatically decomposed through an intelligent contract and issued to a node, a carrier automatically uploads a voucher through an RFID gate after completing the node task, and the contract verifies the voucher and then triggers a next node task. Seamless connection and efficient collaboration among all nodes of the supply chain are achieved, and the transportation efficiency and reliability of container logistics are improved.

The invention discloses a multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method and system. The multimodal transport cargo receiving, unloading and transferring multi-machine cooperation method comprises the steps of obtaining freight basic elements, constraint conditions and real-time environment information, and assigning a matched number of unmanned transfer vehicles and unmanned aerial vehicle sets; generating an initial optimal path; and the local path of the initial optimal path is adjusted in real time, the unmanned transfer vehicle and the unmanned aerial vehicle set advance to the unloading destination according to the global optimal path, and multi-vehicle cooperative cargo unloading is completed according to the conflict-free motion trail. A multi-machine cooperation technology is adopted, advanced technologies such as unmanned driving, the Internet of Things and an artificial intelligence algorithm are fused, the unmanned transfer trolley, the unmanned aerial vehicle and the intelligent gantry crane can work efficiently and cooperatively, various operation scenes such as indoor and outdoor switching, place layout difference and extreme weather can be dealt with, cargoes can be rapidly and accurately received, unloaded and transferred, and the working efficiency is improved. And the efficiency of multimodal transport is greatly improved.

The invention discloses a parcel logistics multimodal transport cooperative scheduling method based on big data, which relates to the technical field of big data, and comprises the following steps: when an information monitoring terminal monitors an abnormal condition, triggering dynamic correction of a transport efficiency coefficient, a cost fluctuation coefficient and a service risk coefficient; and when the absolute value of the difference value between the updated evaluation index and the original evaluation index exceeds a preset threshold value, the scheduling decision terminal re-executes the scheme generation process to generate an emergency scheme. Through a built-in evaluation model, according to scheduling modes such as cost priority, timeliness priority and safety priority selected by clients, weights are dynamically allocated, evaluation indexes are output, a transportation mode combination is generated in combination with a genetic algorithm, time connection verification and equipment matching verification are carried out, multi-target collaborative optimization is realized, different client requirements can be flexibly adapted, and the method is suitable for large-scale popularization and application. And the transportation efficiency, the cost and the safety are balanced, and the multimodal transport resource allocation efficiency is improved.

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 provides a new class of transportation with a partially aerostatically supported ram air cushion ship (PASRACS) that can provide safe, fast, efficient global transport services with extraordinary comfort, luxury and amenities. The PASRACS employs inventive synergistic combinations of lift from aerostatic, aerodynamic, hydrostatic and hydrodynamic forces for different modes of operation ranging from a stationary floating configuration on a water surface, to flight in ground effect at high speed. A propulsion system can utilize hydrogen as an energy source driving fluid-dynamic thrusters, to enable zero carbon emissions operations. A transition method is provided for a PASRACS to transition from a floating mode to a takeoff mode to a flight in ground-effect mode in an inventive optimized manner. A transport method for multimodally transporting payload is provided with PASRACS vehicles and systems, with quick turn time operations enabled by the use of payload transfer transport modules and transfer vehicles.

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 a deep reinforcement learning-based multimodal transport path optimization method, which belongs to the field of logistics management, and comprises the following steps of S1, forming a plurality of feasible paths from a starting point to an end point as training samples, calculating transportation time, freight cost, carbon emission, accident rate and maximum load index of each sub-path as quality evaluation indexes of the corresponding feasible path; s2, constructing a deep reinforcement learning agent based on the state space, the action space and the reward function; s3, selecting a sample by the intelligent agent by adopting a Sum Tree priority sampling mechanism, and generating an optimal path; and S4, in the intermodal process, monitoring the quality evaluation index of the path, and dynamically generating an optimal path according to the real-time path state data. The method can adapt to dynamic factors such as real-time traffic changes, manual intervention is reduced, the emergency response efficiency is high, the occurrence probability of accidents can be reduced, and carbon emission and emission of other pollutants are reduced.

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 belongs to the field of intelligent transportation system planning and industrial artificial intelligence, and particularly relates to a one-order system coal multimodal transport interpretable customer portrait construction method, which comprises the following steps: A, acquiring a customer coal multimodal transport mode selection questionnaire and historical one-order system coal multimodal transport waybill data, and carrying out data processing and data fusion; b, using a causal discovery FCI algorithm to construct a causal graph, screening out feature variables having a causal relationship with customer intermodal mode selection, and removing pseudo-correlation features at the same time; c, performing clustering by using a k-prototype hybrid clustering method to obtain customer categories; and D, training a LightGBM classification model to predict the category of the customer, checking the portrait precision, describing the customer portrait in combination with an SHAP interpretable analysis method, and providing decision support with scientificity and business landing for coal multimodal transport customer accurate classification and single system service strategy optimization.

The invention provides a multimodal transport scheme generation system and method based on multiple agents. Comprising a task analysis module used for generating a task context; the network modeling module is used for acquiring data related to a transportation network and an operation state and constructing environment situation representation; the candidate path generation module is used for generating a candidate path set meeting constraints on the multimodal transport network; the resource checking module is used for carrying out resource availability and time sequence connection checking on the candidate path set; the decision module is used for generating an evaluation vector for the checked candidate path set and generating a structured description; and the output module is used for generating a scheme document and a machine readable instruction and providing the scheme document and the machine readable instruction to an external execution system. According to the method, environment situation representation unification of multi-source dynamic data, constraint-based candidate path set automatic generation and resource time sequence checking, target scheme selection and structured description generation under intelligent agent collaboration, event-triggered increment re-planning and machine readable instruction output can be realized.

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 disclosure relates to a method for streamlining baggage screening across modes of transportation. The method includes scanning a baggage item at an airport and uploading scanned baggage imagery of the contents and license plate identification to a secure cloud storage system. A screening manifest is generated which lists an additional mode of transportation requiring different screening rules. The contents of the stored baggage imagery are reanalyzed according to these different rules and regulations, to determine compliance, and creates a baggage authorization record. Based on the compliance result, represented by an indicium affixed to the baggage, a trusted custody handler can directly deliver the baggage to the next mode of transportation without additional screening, or direct it for further security screening if non-compliant. This system improves efficiency and security in multi-modal transportation networks.

The invention discloses an emergency management method and system based on multimodal transport, and relates to the technical field of emergency management, and the method comprises the following steps: S1, obtaining the dynamic geographic information of a disaster area in real time through a satellite remote sensing technology; according to the method, a dynamic cross-modal cooperative scheduling mechanism is constructed by fusing satellite remote sensing data, real-time mastering of geographic information of a disaster area is achieved, the problem that scheduling lacks real-time geographic information support is solved, connection nodes and transfer processes of different transportation modes are automatically adjusted through a model according to real-time road conditions, and the scheduling efficiency is improved. The conditions of transportation mode adjustment lagging and too long transfer waiting time are avoided, the timeliness of emergency material transportation is improved, information sharing and collaborative cooperation among the transportation modes are promoted by establishing cross-modal collaborative scheduling, an efficient transportation chain is formed, the emergency demand of emergency management for material transportation is met, and the emergency management efficiency is improved. Dynamic adaptation and efficient scheduling of multimodal transport of emergency materials are realized, and timeliness and effectiveness of emergency response are guaranteed.

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 present disclosure relates to systems, non-transitory computer-readable media, and methods for determining different routes along which provider vehicles and rental vehicles can transport a requestor to a destination. For instance, in some cases, a multi-modal-transportation system receives a transportation request that includes a pickup location and a destination location. The multi-modal-transportation system analyzes the transportation request to generate (i) a provider transportation route by which a provider vehicle transports the requestor to the destination location and (ii) a rental route by which one or more intermediate vehicles and a rental vehicle transport the requestor along rental-route segments to the destination location. The multi-modal-transportation system further transmits data for both routes to a requestor device to display within a multi-modal graphical user interface.

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 relates to the technical field of data processing, in particular to a method for port multimodal transport path planning by using an AI big language model. Comprising the following steps: acquiring and preprocessing multi-modal data and historical multi-modal data to obtain preprocessed multi-modal data and preprocessed historical multi-modal data; obtaining historical comprehensive data based on the preprocessed historical multi-modal data, and constructing a path planning model to obtain a path planning result; obtaining real-time comprehensive data based on the preprocessed multi-modal data, and processing the real-time comprehensive data through a path planning model to obtain a preliminary path plan; and introducing multi-dimensional influence factors, and optimizing the preliminary path planning by using a multi-objective optimization algorithm to obtain an optimized path planning. The technical problems that port multimodal transport path planning is poor in self-adaption and low in efficiency and safety are solved.

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 utility model relates to the technical field of cargo multimodal transport, and discloses a cargo multimodal transport unloading platform. Comprising a platform body and further comprises a mounting frame arranged at the top of the platform body, the bottom of the mounting frame is fixedly connected with a mounting plate, and the bottom of the mounting plate is fixedly connected with the top of the platform body; the buffering mechanism is arranged at the bottom of the platform body and facilitates buffering and unloading of the platform during cargo transportation, the platform body can be conveniently buffered during cargo transportation through the buffering mechanism, vibration and impact of cargoes in the unloading process can be reduced through buffering measures, the cargoes are effectively prevented from being damaged, and the service life of the platform is prolonged. By reducing impact on the platform during unloading, abrasion to unloading equipment and the platform can be reduced, so that the service life of the equipment is prolonged, goods slipping or equipment failures caused by impact are reduced, and the safety of operators and the working environment is improved.

The utility model discloses a multimodal transport loading and unloading machine. The multimodal transport loading and unloading machine comprises a vehicle body, a lifting mechanism, a working accessory and a rotation driving mechanism, and the lifting mechanism is provided with a lifting seat capable of vertically lifting. The working accessory comprises a first frame body capable of being vertically and rotatably connected to the hoisting base, a turnover frame capable of being connected into the first frame body in a turnover mode and a lifting frame, the lifting frame can be connected to the turnover frame in a lifting and sliding mode, the lifting frame is provided with a lower frame body connected with the container, and the lower frame body is provided with a spin lock structure. The rotation driving mechanism is connected with the first frame body. The device can be directly matched with an F-TR lock (spin lock) on the flatcar to assemble and lock a hoisted container on the flatcar, or unlock and hoist out the container on the flatcar; the vehicle body can also transport the container to a designated position or transport the container to a flatcar from other positions, and the cross-country transportation effect is achieved; bulk cargoes in a container can be dumped out by matching with the overturning frame and the lifting frame, multimodal transport of a railway is achieved, and engineering equipment does not need to be replaced.

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 field of logistics transportation, in particular to a multimodal transportation digital logistics management method based on an AI algorithm and big data analysis. The method comprises the steps of performing feature extraction on historical data of different transportation modes, constructing a transportation task feature data set, and calculating similarity and distribution density between data points; and based on the similarity and the distribution density between the data points, updating the positions of the data points of the transportation task, comprehensively evaluating the path, and selecting an optimal path. The problems that resource allocation is unreasonable and path selection is inaccurate in the transportation process due to the fact that a traditional transportation management method cannot comprehensively coordinate and optimize multiple transportation modes are solved; decision delay and information lagging exist, emergency or sudden transportation task changes are difficult to deal with effectively, multi-dimensional comprehensive analysis means are lacked, and the actual benefits of a path cannot be evaluated accurately. And the problem that the global optimization of path selection and resource scheduling cannot be realized due to the lack of path optimization capability based on big data and AI technologies is solved.

The invention provides a logistics cargo boxing management method and system based on multimodal transport, and the method comprises the steps: detecting an entity image corresponding to a target article in real time through a preset camera array when the target article is detected to need to be warehoused in real time, target article information corresponding to the target article is determined in real time according to the entity image; generating a target RFID tag corresponding to the target article in real time according to the target article information based on a preset rule, and matching a target carrier loader matched with the target article in real time in a preset database according to the target RFID tag; and the target article is correspondingly transferred into the target carrying vehicle, corresponding loading progress information is generated in real time, and the target RFID tag has uniqueness. According to the invention, classification and transfer of each article can be completed objectively and accurately, the logistics progress can be generated at the same time, and the working efficiency is correspondingly improved.