Logistics transportation system based on big data analysis

Abstract

The invention discloses a logistics transportation system based on big data analysis, which comprises a data integration and analysis module, a real-time decision support module and a transfer station management and capacity optimization module, and is characterized in that the data integration and analysis module is used for integrating data of an air transportation network, cargo types and destination requirements; comprehensive logistics optimization analysis is provided; the real-time decision support module is used for adjusting a transfer path of goods in real time by using an intelligent algorithm on the basis of continuously changing conditions and new data so as to ensure that an optimal transportation scheme is selected; the transfer station management and capacity optimization module is used for comprehensively evaluating the load condition and the transportation flow of the transfer station through a deep analysis and big data analysis algorithm so as to realize intelligent management and capacity optimization of the transfer station; the problems of data fragmentation, inaccurate path selection and the like existing at present can be solved, and a more efficient and intelligent solution is provided for air transportation logistics.

Description

Technical Field

[0001] The present invention relates to the technical field of big data analysis, and in particular to a logistics and transportation system based on big data analysis. Background Art

[0002] In the field of modern logistics and transportation, the rapid development of air transportation has made optimizing logistics links particularly critical. Traditional logistics systems face numerous challenges. First, data fragmentation is a major issue. Data in existing systems is often scattered across different sources, resulting in fragmented information and making it difficult to form a comprehensive and accurate logistics picture. This makes it difficult to optimize and analyze the entire transportation network globally. Second, inaccurate route selection is a prominent issue. Current systems lack sufficient intelligence in route selection and fail to fully consider real-time transportation conditions, destination requirements, and transit station conditions. This leads to inadequate selection of optimal routes and reduced transportation efficiency. Furthermore, insufficient real-time decision support is another challenge facing the system. The system's limited response speed to changing conditions and new data, coupled with a lack of real-time decision support mechanisms, makes it difficult to make immediate optimization decisions during transportation, impacting the system's flexibility and responsiveness. With the rapid development of big data technology, the logistics industry has ushered in new opportunities. This paper focuses on leveraging technologies such as big data integration and analysis, real-time decision support, and deep learning to improve the efficiency of logistics systems. By integrating the functions of various modules, this patent aims to solve the problems existing in the existing technology, such as data fragmentation, inaccurate route selection, and unintelligent transfer station management, thereby providing a more efficient, intelligent and innovative solution for air transport logistics. Summary of the Invention

[0003] The purpose of the present invention is to provide a logistics transportation system based on big data analysis to solve the problems raised in the above background technology.

[0004] In order to solve the above technical problems, the present invention provides the following technical solutions: a logistics and transportation system based on big data analysis, including a data integration and analysis module, a real-time decision support module and a transfer station management and capacity optimization module, characterized in that: the data integration and analysis module is used to integrate air transportation networks, cargo types, and destination requirement data to provide comprehensive logistics optimization analysis; the real-time decision support module is used to use intelligent algorithms to adjust the transfer routes of cargo in real time based on changing situations and new data to ensure the selection of the optimal transportation plan; the transfer station management and capacity optimization module is used to comprehensively evaluate the transfer station load situation and transportation flow through in-depth analysis and big data analysis algorithms to achieve intelligent management and capacity optimization of transfer stations.

[0005] According to the above technical solution, the data integration and analysis module includes a data collection and integration module, a data classification and feature identification module, and a destination data collection and analysis module. Among them, the data collection and integration module is used to collect and integrate air transportation network information, including routes and airports; the data classification and feature identification module is used to establish a cargo classification module to accurately identify cargo characteristics and provide a better understanding of transportation; the destination data collection and analysis module is used to collect special requirements of the destination, such as cargo handling procedures and delivery time, to optimize routes and transit points.

[0006] According to the above technical solution, the real-time decision support module includes a data interaction and information acquisition module, a path adjustment module and a decision optimization module. Among them, the data interaction and information acquisition module is used to ensure that the module obtains the latest analysis results and data in real time to support instant decision-making; the path adjustment module is used to use complex algorithms and rule engines to adjust the cargo transit path in real-time interaction and select the optimal transportation plan; the decision optimization module is used to comprehensively consider uncertain factors, such as weather changes and airport conditions, and optimize decisions through real-time monitoring.

[0007] According to the above technical solution, the transfer station management and capacity optimization module includes a transfer station comprehensive evaluation module, a demand forecasting and scale adjustment module, and a cargo flow monitoring module. Among them, the transfer station comprehensive evaluation module is used to deeply evaluate the load situation and transportation flow of the transfer station through data analysis algorithms, providing a basis for optimization; the demand forecasting and scale adjustment module is used to use big data algorithms to predict future transfer station demand, adjust the scale in advance, and ensure efficient operation; the cargo flow monitoring module is used to monitor the cargo flow path in real time, adjust facilities according to actual conditions, and maximize the utilization of transfer station resources.

[0008] According to the above technical solution, the operation method of the logistics transportation system mainly includes the following steps:

[0009] Step S1: Realize comprehensive logistics optimization through data integration and analysis;

[0010] Step S2: Intelligently adjust the transit route of the goods through the real-time decision support module;

[0011] Step S3: Achieve precise management and capacity optimization of transfer stations through deep learning and intelligent adjustment.

[0012] According to the above technical solution, step S1 further includes the following steps:

[0013] Step S11: A comprehensive data set is established in the system, including the air transport network, airport information, cargo types, and destination requirements. The system obtains air transport network data through detailed information on each route, including departure points, destinations, and transfer points. It also collects and integrates airport information, including the capacity, facility conditions, and transportation capacity of each airport. For cargo types, the system establishes a classification module covering cargo size, weight, and special requirements. The system also collects special requirements for different destinations, including special cargo handling procedures and customized delivery times.

[0014] Step S12: When transporting goods, high-precision data on the location of the goods is obtained by using the global positioning system, and a sensor network is deployed on the transportation vehicle to monitor the status of the goods in real time, and then the data is transmitted to the system. In terms of transportation time prediction, the system combines deep learning and neural networks, uses a large amount of historical transportation data for training, and establishes a transportation time prediction model based on different routes, seasonal changes, and traffic conditions. In the estimation of transportation costs, the system adopts a cost calculation model and combines the airline's transportation costs, fuel price changes, and airport service fees for cost calculation. These factors are comprehensively analyzed through machine learning algorithms to develop an accurate cost estimation model. For the capacity planning of transit stations, the system uses data mining technology to analyze historical transportation flow, cargo types, and demand peak and valley information. Based on these data, the system predicts future transportation demand and uses optimization algorithms to determine the optimal transit station capacity configuration.

[0015] According to the above technical solution, step S2 further includes the following steps:

[0016] Step S21: The system establishes a real-time decision support module, which interacts with the big data analysis engine in real time. Through real-time interaction, the module can quickly obtain the latest analysis results and data. The real-time decision support module uses a complex rule engine and algorithm library, including a hybrid model based on conditional statements, logical rules, and machine learning algorithms;

[0017] Step S22: The machine learning module can automatically learn and adjust decision-making strategies and adapt to changes under different transportation conditions. The module can make real-time adjustments to cargo transit routes based on the results of big data analysis. During the adjustment process, the system adjusts the cargo transit route through intelligent algorithms based on the current status of the cargo in transportation, the real-time requirements of the target location, and new transportation conditions and factors to select the optimal transportation plan, including selecting transit stations that are faster, more economical, or more in line with the requirements of specific destinations. In the decision-making process, the system comprehensively considers the uncertainty factors in the actual transportation situation, such as sudden weather changes and airport emergencies, and timely perceives these changes by introducing real-time monitoring, and takes them into consideration in decision-making.

[0018] According to the above technical solution, step S3 further includes:

[0019] Step S31: The system uses a big data analysis engine to collect data related to transfer station management in real time, including the types of transported goods, the current capacity of the transfer station, and the historical flow trajectory of the goods. Taking into account different types of goods, the system uses machine learning algorithms to perform detailed classification and identify the characteristics of the goods. In the initial stage, the system collects a large amount of data related to the characteristics of the goods, such as the size, weight, shape, and sensitivity of fragile goods. Subsequently, the system performs feature extraction and feature selection to reduce redundant information. Finally, a convolutional neural network model is selected and trained using the prepared data set. The trained model can accurately classify and identify the characteristics of the goods. Based on the model's prediction results, the system can plan the facilities of the transfer station, set up a special processing area for fragile goods, and equip it with shockproof equipment or temperature and humidity control equipment;

[0020] Step S32: The system uses deep analysis and big data analysis algorithms, combined with historical data and real-time monitoring, to conduct a comprehensive assessment of the transfer station load and transportation flow. First, the system monitors the flow of goods within the transfer station in real time through data interaction with the transfer station's sensor network and the airline logistics company, including the number of goods entering and leaving the transfer station, the type of goods, and the processing speed in each period. Historical data records past transportation conditions, including peak and trough transportation flows, and provides trends in transfer station load changes. With the support of big data analysis algorithms, the system conducts in-depth mining of historical data to identify the periodicity and trends of transportation demand, enabling the system to predict future demand conditions at the transfer station. For example, the system can identify a trend of a possible surge in transportation demand in a certain region in a certain quarter.

[0021] Step S33: When the system detects an impending surge in transport demand, it will use intelligent algorithms to adjust the size of the transfer station in the area in advance, including adding processing equipment, optimizing cargo storage layout, or adjusting staff arrangements. In addition, the system comprehensively considers the flow of goods and tracks the real-time flow path of goods throughout the entire logistics process through real-time monitoring and analysis technology. The system can adjust the cargo distribution of the transfer station based on the cargo flow in a certain period of time.

[0022] Compared with the existing technology, the beneficial effects achieved by the present invention are as follows: First, through data integration and analysis, the system constructs a multidimensional data set including air transportation network, airport information, cargo types and destination requirements, so as to fully understand the transportation environment; secondly, through the real-time decision support module, the system can intelligently adjust the transit path of the cargo, and use complex rule engines and machine learning algorithms to ensure immediate response to changing transportation conditions and select the optimal path and transit station; finally, through deep learning and intelligent adjustment, the system realizes the precise management and capacity optimization of the transit station, uses the big data analysis engine to collect data related to the transit station management in real time, and uses machine learning algorithms to classify and identify the cargo in detail, and then plans the facilities of the transit station to improve the intelligence and efficiency of the overall logistics system. This system comprehensively considers multiple aspects of air transportation logistics, including path planning, real-time decision-making and transit station management, and provides an innovative and efficient solution for optimizing the entire logistics chain. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0024] Figure 1 It is a schematic diagram of the platform module composition of the present invention. DETAILED DESCRIPTION

[0025] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0026] See also Figure 1 The present invention provides a technical solution: a logistics and transportation system based on big data analysis, including a data integration and analysis module, a real-time decision support module, and a transfer station management and capacity optimization module, characterized in that: the data integration and analysis module is used to integrate air transportation network, cargo type, and destination requirement data to provide comprehensive logistics optimization analysis; the real-time decision support module is used to use intelligent algorithms to adjust the transfer path of cargo in real time based on changing conditions and new data to ensure the selection of the optimal transportation plan; the transfer station management and capacity optimization module is used to comprehensively evaluate the transfer station load and transportation flow through in-depth analysis and big data analysis algorithms to achieve intelligent management and capacity optimization of transfer stations;

[0027] The data integration and analysis module includes a data collection and integration module, a data classification and feature identification module, and a destination data collection and analysis module. The data collection and integration module is used to collect and integrate air transportation network information, including routes and airports; the data classification and feature identification module is used to establish a cargo classification module to accurately identify cargo characteristics and provide a better understanding of transportation; the destination data collection and analysis module is used to collect special requirements of the destination, such as cargo handling procedures and delivery time, to optimize routes and transit points;

[0028] The real-time decision support module includes a data interaction and information acquisition module, a route adjustment module, and a decision optimization module. The data interaction and information acquisition module is used to ensure that the module obtains the latest analysis results and data in real time to support immediate decision-making; the route adjustment module is used to use complex algorithms and rule engines to adjust cargo transit routes in real-time interaction and select the optimal transportation solution; the decision optimization module is used to comprehensively consider uncertain factors such as weather changes and airport conditions and optimize decisions through real-time monitoring;

[0029] The transfer station management and capacity optimization module includes a transfer station comprehensive evaluation module, a demand forecasting and scale adjustment module, and a cargo flow monitoring module. Among them, the transfer station comprehensive evaluation module is used to deeply evaluate the load situation and transportation flow of the transfer station through data analysis algorithms, providing a basis for optimization; the demand forecasting and scale adjustment module is used to use big data algorithms to predict future transfer station demand, adjust the scale in advance, and ensure efficient operation; the cargo flow monitoring module is used to monitor the cargo flow path in real time, adjust facilities according to actual conditions, and maximize the utilization of transfer station resources.

[0030] The operation method of the logistics transportation system mainly includes the following steps:

[0031] Step S1: Realize comprehensive logistics optimization through data integration and analysis;

[0032] Step S2: Intelligently adjust the transit route of the goods through the real-time decision support module;

[0033] Step S3: Accurately manage transfer stations and optimize capacity through deep learning and intelligent adjustments

[0034] The step S1 further comprises the following steps:

[0035] Step S11: A comprehensive data set is established in the system, including the air transport network, airport information, cargo types, and destination requirements. The system obtains air transport network data through detailed information on each route, including departure points, destinations, and transfer points. It also collects and integrates airport information, including the capacity, facility conditions, and transportation capacity of each airport. For cargo types, the system establishes a classification module covering cargo size, weight, and special requirements. The system also collects special requirements for different destinations, including special cargo handling procedures and customized delivery times.

[0036] Step S12: When transporting goods, high-precision data on the location of the goods is obtained by using the global positioning system, and a sensor network is deployed on the transportation vehicle to monitor the status of the goods in real time, and then the data is transmitted to the system. In terms of transportation time prediction, the system combines deep learning and neural networks, uses a large amount of historical transportation data for training, and establishes a transportation time prediction model based on different routes, seasonal changes, and traffic conditions. In the estimation of transportation costs, the system adopts a cost calculation model and combines the airline's transportation costs, fuel price changes, and airport service fees for cost calculation. These factors are comprehensively analyzed through machine learning algorithms to develop an accurate cost estimation model. For the capacity planning of transit stations, the system uses data mining technology to analyze historical transportation flow, cargo types, and demand peak and valley information. Based on these data, the system predicts future transportation demand and uses optimization algorithms to determine the optimal transit station capacity configuration.

[0037] The step S2 further comprises the following steps:

[0038] Step S21: The system establishes a real-time decision support module, which interacts with the big data analysis engine in real time. Through real-time interaction, the module can quickly obtain the latest analysis results and data. The real-time decision support module uses a complex rule engine and algorithm library, including a hybrid model based on conditional statements, logical rules, and machine learning algorithms;

[0039] Step S22: The machine learning module can automatically learn and adjust decision-making strategies and adapt to changes under different transportation conditions. The module can make real-time adjustments to cargo transit routes based on the results of big data analysis. During the adjustment process, the system adjusts the cargo transit route through intelligent algorithms based on the current status of the cargo in transportation, the real-time requirements of the target location, and new transportation conditions and factors to select the optimal transportation plan, including selecting transit stations that are faster, more economical, or more in line with the requirements of specific destinations. In the decision-making process, the system comprehensively considers the uncertainty factors in the actual transportation situation, such as sudden weather changes and airport emergencies, and timely perceives these changes by introducing real-time monitoring, and takes them into consideration in decision-making.

[0040] The step S3 further comprises:

[0041] Step S31: The system uses a big data analysis engine to collect data related to transfer station management in real time, including the types of transported goods, the current capacity of the transfer station, and the historical flow trajectory of the goods. Taking into account different types of goods, the system uses machine learning algorithms to perform detailed classification and identify the characteristics of the goods. In the initial stage, the system collects a large amount of data related to the characteristics of the goods, such as the size, weight, shape, and sensitivity of fragile goods. Subsequently, the system performs feature extraction and feature selection to reduce redundant information. Finally, a convolutional neural network model is selected and trained using the prepared data set. The trained model can accurately classify and identify the characteristics of the goods. Based on the model's prediction results, the system can plan the facilities of the transfer station, set up a special processing area for fragile goods, and equip it with shockproof equipment or temperature and humidity control equipment;

[0042] Step S32: The system uses deep analysis and big data analysis algorithms, combined with historical data and real-time monitoring, to conduct a comprehensive assessment of the transfer station load and transportation flow. First, the system monitors the flow of goods within the transfer station in real time through data interaction with the transfer station's sensor network and the airline logistics company, including the number of goods entering and leaving the transfer station, the type of goods, and the processing speed in each period. Historical data records past transportation conditions, including peak and trough transportation flows, and provides trends in transfer station load changes. With the support of big data analysis algorithms, the system conducts in-depth mining of historical data to identify the periodicity and trends of transportation demand, enabling the system to predict future demand conditions at the transfer station. For example, the system can identify a trend of a possible surge in transportation demand in a certain region in a certain quarter.

[0043] Step S33: When the system detects an impending surge in transport demand, it will use intelligent algorithms to adjust the size of the transfer station in the area in advance, including adding processing equipment, optimizing cargo storage layout, or adjusting staff arrangements. In addition, the system comprehensively considers the flow of goods and tracks the real-time flow path of goods throughout the entire logistics process through real-time monitoring and analysis technology. The system can adjust the cargo distribution of the transfer station based on the cargo flow in a certain period of time.

[0044] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0045] Finally, it should be noted that the above descriptions are merely preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art will be able to modify the technical solutions described in the aforementioned embodiments or substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

Claims

1. A logistics and transportation system based on big data analysis, including a data integration and analysis module, a real-time decision support module, and a transfer station management and capacity optimization module, characterized by: The data integration and analysis module is used to integrate data on air transport networks, cargo types, and destination requirements to provide comprehensive logistics optimization analysis; the real-time decision support module is used to use intelligent algorithms to adjust the transit routes of cargo in real time based on changing conditions and new data to ensure the selection of the optimal transportation plan; the transit station management and capacity optimization module is used to comprehensively evaluate the transit station load and transportation flow through in-depth analysis and big data analysis algorithms to achieve intelligent management and capacity optimization of transit stations.

2. The logistics and transportation system based on big data analysis according to claim 1, characterized in that: The data integration and analysis module includes a data collection and integration module, a data classification and feature identification module, and a destination data collection and analysis module. The data collection and integration module is used to collect and integrate air transportation network information, including routes and airports; the data classification and feature identification module is used to establish a cargo classification module to accurately identify cargo characteristics and provide a better understanding of transportation; the destination data collection and analysis module is used to collect special requirements of the destination, such as cargo handling procedures and delivery time, to optimize routes and transit points.

3. The logistics and transportation system based on big data analysis according to claim 2, characterized in that: The real-time decision support module includes a data interaction and information acquisition module, a route adjustment module, and a decision optimization module. The data interaction and information acquisition module is used to ensure that the module obtains the latest analysis results and data in real time to support immediate decision-making; the route adjustment module is used to use complex algorithms and rule engines to adjust the cargo transit route in real-time interaction and select the optimal transportation plan; the decision optimization module is used to comprehensively consider uncertain factors such as weather changes and airport conditions, and optimize decisions through real-time monitoring.

4. The logistics and transportation system based on big data analysis according to claim 3, characterized in that: The transfer station management and capacity optimization module includes a transfer station comprehensive evaluation module, a demand forecasting and scale adjustment module, and a cargo flow monitoring module. Among them, the transfer station comprehensive evaluation module is used to deeply evaluate the load situation and transportation flow of the transfer station through data analysis algorithms, providing a basis for optimization; the demand forecasting and scale adjustment module is used to use big data algorithms to predict future transfer station demand, adjust the scale in advance, and ensure efficient operation; the cargo flow monitoring module is used to monitor the cargo flow path in real time, adjust facilities according to actual conditions, and maximize the utilization of transfer station resources.

5. The logistics and transportation system based on big data analysis according to claim 4, characterized in that: The operation method of the logistics transportation system mainly includes the following steps: Step S1: Realize comprehensive logistics optimization through data integration and analysis; Step S2: Intelligently adjust the transit route of the goods through the real-time decision support module; Step S3: Achieve precise management and capacity optimization of transfer stations through deep learning and intelligent adjustment.

6. The logistics and transportation system based on big data analysis according to claim 5, characterized in that: The step S1 further comprises the following steps: Step S11: A comprehensive data set is established in the system, including the air transport network, airport information, cargo types, and destination requirements. The system obtains air transport network data through detailed information on each route, including departure points, destinations, and transfer points. It also collects and integrates airport information, including the capacity, facility conditions, and transportation capacity of each airport. For cargo types, the system establishes a classification module covering cargo size, weight, and special requirements. The system also collects special requirements for different destinations, including special cargo handling procedures and customized delivery times. Step S12: When transporting goods, high-precision data on the location of the goods is obtained by using the global positioning system, and a sensor network is deployed on the transportation vehicle to monitor the status of the goods in real time, and then the data is transmitted to the system. In terms of transportation time prediction, the system combines deep learning and neural networks, uses a large amount of historical transportation data for training, and establishes a transportation time prediction model based on different routes, seasonal changes, and traffic conditions. In the estimation of transportation costs, the system adopts a cost calculation model and combines the airline's transportation costs, fuel price changes, and airport service fees for cost calculation. These factors are comprehensively analyzed through machine learning algorithms to develop an accurate cost estimation model. For the capacity planning of transit stations, the system uses data mining technology to analyze historical transportation flow, cargo types, and demand peak and valley information. Based on these data, the system predicts future transportation demand and uses optimization algorithms to determine the optimal transit station capacity configuration.

7. The logistics and transportation system based on big data analysis according to claim 6, characterized in that: The step S2 further comprises the following steps: Step S21: The system establishes a real-time decision support module, which interacts with the big data analysis engine in real time. Through real-time interaction, the module can quickly obtain the latest analysis results and data. The real-time decision support module uses a complex rule engine and algorithm library, including a hybrid model based on conditional statements, logical rules, and machine learning algorithms; Step S22: The machine learning module can automatically learn and adjust decision-making strategies and adapt to changes under different transportation conditions. The module can make real-time adjustments to cargo transit routes based on the results of big data analysis. During the adjustment process, the system adjusts the cargo transit route through intelligent algorithms based on the current status of the cargo in transportation, the real-time requirements of the target location, and new transportation conditions and factors to select the optimal transportation plan, including selecting transit stations that are faster, more economical, or more in line with the requirements of specific destinations. In the decision-making process, the system comprehensively considers the uncertainty factors in the actual transportation situation, such as sudden weather changes and airport emergencies, and timely perceives these changes by introducing real-time monitoring, and takes them into consideration in decision-making.

8. The logistics and transportation system based on big data analysis according to claim 7, characterized in that: The step S3 further comprises: Step S31: The system uses a big data analysis engine to collect data related to transfer station management in real time, including the types of transported goods, the current capacity of the transfer station, and the historical flow trajectory of the goods. Taking into account different types of goods, the system uses machine learning algorithms to perform detailed classification and identify the characteristics of the goods. In the initial stage, the system collects a large amount of data related to the characteristics of the goods, such as the size, weight, shape, and sensitivity of fragile goods. Subsequently, the system performs feature extraction and feature selection to reduce redundant information. Finally, a convolutional neural network model is selected and trained using the prepared data set. The trained model can accurately classify and identify the characteristics of the goods. Based on the model's prediction results, the system can plan the facilities of the transfer station, set up a special processing area for fragile goods, and equip it with shockproof equipment or temperature and humidity control equipment; Step S32: The system uses deep analysis and big data analysis algorithms, combined with historical data and real-time monitoring, to conduct a comprehensive assessment of the transfer station load and transportation flow. First, the system monitors the flow of goods within the transfer station in real time through data interaction with the transfer station's sensor network and the airline logistics company, including the number of goods entering and leaving the transfer station, the type of goods, and the processing speed in each period. Historical data records past transportation conditions, including peak and trough transportation flows, and provides trends in transfer station load changes. With the support of big data analysis algorithms, the system conducts in-depth mining of historical data to identify the periodicity and trends of transportation demand, enabling the system to predict future demand conditions at the transfer station. For example, the system can identify a trend of a possible surge in transportation demand in a certain region in a certain quarter. Step S33: When the system detects an impending surge in transport demand, it will use intelligent algorithms to adjust the size of the transfer station in the area in advance, including adding processing equipment, optimizing cargo storage layout, or adjusting staff arrangements. In addition, the system comprehensively considers the flow of goods and tracks the real-time flow path of goods throughout the entire logistics process through real-time monitoring and analysis technology. The system can adjust the cargo distribution of the transfer station based on the cargo flow in a certain period of time.

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