A cross-border intelligent logistics cloud management system based on big data

Abstract

The present invention relates to the field of cross-border logistics data processing technology, and specifically discloses a cross-border intelligent logistics cloud management system based on big data, which includes: a transportation transit section division module, a transportation road condition impact assessment module, a transportation path optimization adjustment module and a cross-border logistics database. The present invention can help to more accurately control and manage the logistics transportation process by refining the transportation route into multiple transit sections, thereby improving the controllability and flexibility of transportation. Through real-time road condition and meteorological data collection and analysis, transportation strategies can be adjusted in time to cope with emergencies, thereby reducing transportation delays and ensuring that goods arrive on time. The present invention helps to optimize the allocation of transportation resources. By evaluating the degree of impact of the transportation feasibility of each section, a more appropriate mode of transportation can be selected, which not only improves the efficiency and reliability of transportation, but also enhances the flexibility and responsiveness of the entire logistics system.

Description

Technical Field

[0001] The present invention relates to the field of cross-border logistics data processing technology, and specifically to a cross-border intelligent logistics cloud management system based on big data. Background Art

[0002] With the rapid development of cross-border e-commerce, the status of foreign trade shopping platforms has gradually improved, and the demand for logistics systems has also increased. Cross-border e-commerce involves long-distance cargo transportation, and the logistics process is complex and changeable, which requires efficient and intelligent logistics systems to support it.

[0003] For example, the invention patent with announcement number CN112150069B is a cross-border logistics operation and maintenance optimization management system, including a cross-border e-commerce logistics transportation system and a management and control center, which is characterized in that the cross-border e-commerce logistics transportation system includes an order module, a transportation management system, a procurement module and a warehousing module; the transportation management system includes transportation equipment, and the transportation equipment includes a smart lock module, a monitoring module and a camera module; the smart lock module is used to lock the goods after the transportation equipment is loaded, and the monitoring module is used to monitor and collect the status of the smart lock. If the smart lock is activated, the switch status of the smart lock is judged, and the camera module is started according to the switch status, so that the camera module takes videos or photos and sends them to the management and control center; the transportation equipment is provided with a UWB module, a status module and an information module.

[0004] For example, the invention patent with announcement number CN115423416B is a cross-border logistics and transportation management system and its control method, including: a positioning module, used to obtain logistics cargo ship location data and cargo port location data; a port module, used to obtain cargo information data of each cargo port; a prediction module, which establishes a spatial index in GeoHash mode, and performs risk prediction based on the acquired cargo port location data; an evaluation module, which is used to evaluate the risk level of the risk prediction results according to the time node when the logistics cargo ship arrives at the cargo port; a cost module, which is used to predict the transportation cost fluctuation value according to the residence time of the logistics cargo ship and the cargo loading and unloading volume, as well as the risk level evaluation result; a management module, which adjusts the transportation price based on the transportation cost fluctuation value.

[0005] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application discovered that the above technology has at least the following technical problems: currently, the security monitoring of goods mainly focuses on the physical safety of goods, such as whether someone illegally opens the transportation equipment; risk prediction and management are singular and cannot adapt to other potential risks, thus limiting the system's emergency response mechanism from playing an effective role in actual transportation. Summary of the invention

[0006] In view of the shortcomings of the prior art, the present invention provides a cross-border intelligent logistics cloud management system based on big data, which can effectively solve the problems involved in the above-mentioned background technology.

[0007] To achieve the above objectives, the present invention is implemented through the following technical solutions: The first aspect of the present invention provides a cross-border intelligent logistics cloud management system based on big data, including: a transport transit section division module, which is used to extract transit nodes and pre-planned logistics transportation routes in the cross-border logistics transportation process from the cross-border logistics database, divide the logistics transportation routes into sections according to the transit nodes, and mark them as each transport transit section.

[0008] The transport road condition impact assessment module is used to collect transport road condition data, obtain the meteorological interference degree assessment value of each transport transit section at each future time point after processing, and obtain the pre-planned transportation mode of each transport transit section, and comprehensively analyze to obtain the logistics transportation feasibility impact degree index of each transport transit section. The pre-planned transportation mode includes water transportation mode, land transportation mode and air transportation mode.

[0009] The transport route optimization and adjustment module is used to adjust the transport route of each transport transit section according to the logistics transport feasibility impact index of each transport transit section.

[0010] As a further method, the transportation road condition data is collected, and the specific process is: obtaining the expected travel time period of each transportation transit section from the pre-planned logistics transportation route, and marking it as the travel time period of each section.

[0011] The meteorological data of each transport transfer section is monitored, and the traffic time period of each section is arranged at several future time points to obtain the meteorological forecast data and current meteorological forecast data of each transport transfer section at each future time point.

[0012] As a further method, the processing obtains the meteorological interference degree assessment value of each transport transit section at each future time point. The specific process is: comparing the current meteorological monitoring data with the current meteorological forecast data, marking the absolute value of the difference between the monitored wind speed and the predicted wind speed as the wind speed deviation value, and marking the absolute value of the difference between the monitored precipitation and the predicted precipitation as the precipitation deviation value.

[0013] The wind speed deviation value and the precipitation deviation value are respectively compared with the wind speed interference assessment influencing factors and precipitation interference assessment influencing factors corresponding to each wind speed deviation value interval and each precipitation deviation value interval stored in the cross-border logistics database to obtain the wind speed interference assessment influencing factors and precipitation interference assessment influencing factors based on the current time point.

[0014] The critical wind speed and critical precipitation are obtained from the cross-border logistics database. Based on the meteorological forecast data of each transport transit section at each future time point, a comprehensive analysis is performed to obtain the meteorological interference degree assessment value of each transport transit section at each future time point.

[0015] As a further method, the comprehensive analysis obtains the logistics transportation feasibility impact index of each transport transit section, including when the pre-planned transport mode is water transport, the specific analysis process is: monitor the channel depth and water flow speed of each transport transit section, and obtain the reference channel depth and reference water flow speed from the cross-border logistics database.

[0016] Based on the channel depth, water flow speed and the assessed values ​​of the degree of meteorological interference of each transport transit section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section.

[0017] As a further method, the comprehensive analysis obtains the logistics transportation feasibility impact index of each transport transit section, including when the pre-planned transport mode is air transport mode, the specific analysis process is: monitor the cloud base height of each transport transit section and obtain the critical cloud base height from the cross-border logistics database.

[0018] According to the cloud base height and the assessed values ​​of the meteorological interference degree of each transport transit section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section.

[0019] As a further method, the comprehensive analysis obtains the logistics transportation feasibility impact index of each transport transit section, including when the pre-planned transportation mode is land transportation. The specific analysis process is: monitor the traffic density of each transport transit section and obtain the critical traffic density from the cross-border logistics database.

[0020] According to the traffic density and the assessed values ​​of the meteorological interference degree of each transport transfer section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transfer section.

[0021] As a further method, the transportation route of each transport transit section is adjusted according to the logistics transport feasibility impact degree index of each transport transit section. The specific analysis process is: obtaining the pre-planned transportation mode of each transport transit section, obtaining the logistics transport feasibility impact degree index threshold value corresponding to each pre-planned transportation mode from the cross-border logistics database, and obtaining the logistics transport feasibility impact degree threshold value of each transport transit section.

[0022] The logistics transport feasibility impact index of each transport transit section is compared with the logistics transport feasibility impact index threshold. If the logistics transport feasibility impact index of a transport transit section is greater than or equal to the logistics transport feasibility impact index threshold, the transport route of the transport transit section is switched according to the preset alternative transport route.

[0023] Compared with the prior art, the embodiments of the present invention have at least the following advantages or beneficial effects:

[0024] (1) The present invention provides a cross-border intelligent logistics cloud management system based on big data. By refining the transportation route into multiple transit sections, it can help to more accurately control and manage the logistics transportation process, thereby improving the controllability and flexibility of transportation. By evaluating the degree of impact of the transportation feasibility of each section, a more appropriate transportation method can be selected, which not only improves the efficiency and reliability of transportation, but also enhances the flexibility and responsiveness of the entire logistics system.

[0025] (2) The present invention can timely detect and warn of extreme weather conditions by real-time monitoring of wind speed and precipitation, providing sufficient response time for logistics transportation and reducing losses. Based on the monitoring results of wind speed and precipitation, it is helpful to flexibly adjust the transportation route to avoid areas that are greatly affected by the weather, ensuring smooth and safe transportation.

[0026] (3) The present invention can provide protection for the entire transportation process in waterway logistics transportation by comprehensively analyzing the channel depth, water flow speed and meteorological data, effectively respond to the challenges brought by water flow changes, and improve navigation safety. The comprehensive analysis of the channel depth, water flow speed and meteorological data not only improves transportation efficiency, but also reduces navigation time and energy consumption by optimizing route planning and navigation strategies. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0028] Figure 1 It is a schematic diagram of system module connection of the present invention.

[0029] Figure 2 The figure is a schematic diagram of the functional relationship between the wind speed and the meteorological interference degree assessment value involved in the embodiment of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Reference Figure 1 As shown, the first aspect of the present invention provides a cross-border intelligent logistics cloud management system based on big data, including: a transport transit section division module, which is used to extract transit nodes and pre-planned logistics transportation routes in the cross-border logistics transportation process from a cross-border logistics database, divide the logistics transportation routes into sections according to the transit nodes, and mark them as various transport transit sections.

[0032] It should be understood that the pre-planned logistics transportation route in this embodiment refers to a theoretically optimal transportation path that is pre-planned and determined based on the starting point, destination and transportation method of the goods before the actual transportation of the goods.

[0033] It should be understood that the transit nodes in this embodiment refer to multiple transit stations or cities that the goods may need to pass through during the transportation process. These transit nodes are used to change transportation tools, redistribute goods, or perform necessary customs clearance, inspection and other procedures.

[0034] The pre-planned logistics transportation route includes multiple transfer nodes, and the section between two transfer nodes is the transportation transfer section.

[0035] The transport road condition impact assessment module is used to collect transport road condition data, obtain the meteorological interference degree assessment value of each transport transit section at each future time point after processing, and obtain the pre-planned transportation mode of each transport transit section, and comprehensively analyze to obtain the logistics transportation feasibility impact degree index of each transport transit section. The pre-planned transportation mode includes water transportation mode, land transportation mode and air transportation mode.

[0036] It should be understood that the transportation road condition data in this embodiment is different from the data collected under different transportation modes. Under the water transportation mode, the transportation road condition data includes the channel depth and water flow speed; under the land transportation mode, the transportation road condition data includes the traffic density; under the air transportation mode, the transportation road condition data includes the cloud base height.

[0037] The transport route optimization and adjustment module is used to adjust the transport route of each transport transit section according to the logistics transport feasibility impact index of each transport transit section.

[0038] Specifically, the transportation road condition data is collected, and the specific process is: obtain the expected travel time period of each transportation transit section from the pre-planned logistics transportation route, and mark it as the travel time period of each section.

[0039] It should be understood that the traffic time period of each road section in this embodiment refers to the future traffic time period of each transport transit road section.

[0040] The meteorological data of each transport transfer section is monitored, and the traffic time period of each section is arranged at several future time points to obtain the meteorological forecast data and current meteorological forecast data of each transport transfer section at each future time point.

[0041] It should be understood that the meteorological data in this embodiment includes wind speed and precipitation, which can be monitored by anemometers and weather radars. Meteorological forecast data refers to the forecast of current or future meteorological data by meteorological agencies, which can be obtained directly from meteorological stations or ground observation stations.

[0042] In a specific embodiment, wind speed and precipitation have a significant impact on cross-border logistics transportation. Strong winds may cause unstable navigation of vehicles such as aircraft and ships, increasing the risk of accidents. In land transportation, strong winds may also affect the driving safety of trucks, such as blowing over vehicles or causing cargo to fall. When the wind speed is too high, the aircraft may need to adjust the route or reduce the flight speed to ensure safety; in sea transportation, strong winds may cause ports to be closed, and ships cannot dock or leave the port, causing transportation delays. Extreme precipitation weather such as heavy rain and floods may cause waterlogging on roads, congested traffic, or even interruptions, causing transportation delays. In air transportation, heavy rain may cause flight cancellations or delays. At the same time, excessive precipitation may cause goods to become damp, moldy or damaged, especially for those goods that are sensitive to humidity, such as electronic products, food, etc.

[0043] In a specific embodiment, by real-time monitoring of wind speed and precipitation, extreme weather conditions can be discovered and warned in a timely manner, providing sufficient response time for logistics transportation and reducing losses. Based on the monitoring results of wind speed and precipitation, it is helpful to flexibly adjust the transportation route to avoid areas that are greatly affected by the weather, ensuring smooth and safe transportation. At the same time, based on the monitoring results of precipitation, it is possible to help take corresponding moisture-proof and waterproof measures to ensure the safety of goods during transportation.

[0044] Specifically, the meteorological interference degree assessment value of each transport transit section at each future time point is obtained by processing. The specific process is: comparing the current meteorological monitoring data with the current meteorological forecast data, marking the absolute value of the difference between the monitored wind speed and the predicted wind speed as the wind speed deviation value, and marking the absolute value of the difference between the monitored precipitation and the predicted precipitation as the precipitation deviation value.

[0045] The wind speed deviation value and the precipitation deviation value are respectively compared with the wind speed interference assessment influencing factors and precipitation interference assessment influencing factors corresponding to each wind speed deviation value interval and each precipitation deviation value interval stored in the cross-border logistics database to obtain the wind speed interference assessment influencing factors and precipitation interference assessment influencing factors based on the current time point.

[0046] It should be understood that in this embodiment, based on the relationship between the wind speed deviation value and the wind speed interference assessment impact factor in the historical data, a mapping set of wind speed deviation value intervals and wind speed interference assessment impact factors is constructed, and the corresponding wind speed interference assessment impact factor is obtained from the mapping set by determining the interval to which the wind speed deviation value belongs.

[0047] It should be understood that in this embodiment, based on the relationship between the precipitation deviation value in the historical data and the precipitation interference assessment influencing factor, a mapping set of precipitation deviation value intervals and precipitation interference assessment influencing factors is constructed, and the corresponding precipitation interference assessment influencing factors are obtained from the mapping set by determining the interval to which the precipitation deviation value belongs.

[0048] In a specific embodiment, by calculating the deviation value, the difference between the actual meteorological conditions and the predicted results can be quantified, so as to more intuitively understand the accuracy of the prediction. According to the size of the deviation value, the risk level of the current meteorological conditions to logistics transportation can be evaluated. For example, when the wind speed deviation value is larger, it may mean that the actual wind speed changes more, which may pose a threat to transportation safety.

[0049] The critical wind speed and critical precipitation are obtained from the cross-border logistics database. Based on the meteorological forecast data of each transport transit section at each future time point, a comprehensive analysis is performed to obtain the meteorological interference degree assessment value of each transport transit section at each future time point.

[0050] In a specific embodiment, a comprehensive analysis is performed to obtain the meteorological interference level evaluation value of each transport transfer section at each future time point, and the specific numerical expression is:

[0051] ;

[0052] In the formula, represents the meteorological interference assessment value of the i-th transport transfer section at the t-th future time point, represents the wind speed of the i-th transport transfer section at the t-th future time point, represents the precipitation of the i-th transport transit section at the t-th future time point, represents the critical wind speed, represents the critical precipitation, represents the wind speed interference assessment impact factor, represents the impact factor of precipitation interference assessment, i represents the number of each transport transfer section, i=1,2,3,...,n, n represents the total number of transport transfer sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points.

[0053] like Figure 2 As shown, in a specific embodiment, =20m / s, =10 mm / h, assuming that in the current assessment scenario, the effects of wind speed and precipitation are roughly equal, = =1. When the precipitation is 5mm / h, the functional relationship between wind speed and meteorological interference degree assessment value is shown in curve a; when the precipitation is 10mm / h, the functional relationship between wind speed and meteorological interference degree assessment value is shown in curve b; when the precipitation is 15mm / h, the functional relationship between wind speed and meteorological interference degree assessment value is shown in curve c. The meteorological interference degree assessment value is used to quantitatively assess the degree of interference of meteorological data on logistics and transportation. The greater the wind speed and precipitation, the greater the corresponding meteorological interference degree assessment value, indicating that the greater the interference of meteorological data on logistics and transportation.

[0054] It should be understood that there is a certain interaction between wind speed and precipitation in this embodiment. Precipitation is formed by the cooling and condensation of rising airflow, and this process is often accompanied by changes in wind. For example, the movement of the frontal system will cause changes in wind direction and wind speed. The heat released during the precipitation process can change the structure of the atmosphere and thus affect the local wind field. At the same time, wind plays a role in transporting moist air and increasing the probability of precipitation. By comprehensively analyzing wind speed and precipitation, the accuracy of the prediction of the degree of meteorological interference can be improved.

[0055] Specifically, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section, including when the pre-planned transport mode is water transport. The specific analysis process is: monitoring the channel depth and water flow speed of each transport transit section, and obtaining the reference channel depth and reference water flow speed from the cross-border logistics database.

[0056] It should be understood that in this embodiment, the channel depth is monitored by satellite remote sensing technology, and the channel depth directly determines whether the ship can pass safely. The greater the water depth, the lower the risk of the ship running aground during navigation, thereby improving the safety of navigation. On the other hand, deep-water channels also provide more draft space for large ships, allowing these ships to carry more cargo and improve transportation efficiency. However, too deep waters may also bring unfavorable factors such as increased difficulty in rescue, decreased stability when encountering bad weather, weakened satellite navigation signals, and possible encounters with magnetic anomaly areas. The reference channel depth in this embodiment is the most suitable channel depth for transportation obtained based on historical data.

[0057] It should be understood that in this embodiment, the water velocity is monitored by a radar current meter, and the speed of the water velocity directly affects the navigation speed and stability of the ship. When sailing on a waterway with a larger flow rate, the ship needs to consume more power to overcome the water flow resistance, thereby increasing energy consumption and transportation costs. At the same time, fast water flow may also cause the ship's maneuverability to deteriorate, increasing the risk of navigation. On the other hand, when the water flow direction is opposite or the same as the route direction, there will be different effects. When the directions are the same, energy consumption and transportation costs will be reduced, and mechanical wear can be reduced. Therefore, when the water flow direction is opposite or the same as the route direction, there are different suitable flow rates.

[0058] It should be understood that in this embodiment, the channel depth is the minimum value monitored in the transport transit section, and the water flow velocity is the maximum value monitored in the transport transit section.

[0059] Based on the channel depth, water flow speed and the assessed values ​​of the degree of meteorological interference of each transport transit section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section.

[0060] In a specific embodiment, the water transport feasibility influencing factors corresponding to the preset channel depth, water flow speed and meteorological interference degree assessment values ​​are extracted from the cross-border logistics database, and the preset allowable deviation channel depth and allowable deviation water flow speed are obtained from the cross-border logistics database. The logistics transport feasibility influencing degree index of each transport transit section is obtained through comprehensive analysis. The specific numerical expression is:

[0061] ;

[0062] In the formula, It represents the impact index of logistics transport feasibility of the i-th transport transit section, represents the channel depth of the i-th transport transfer section, represents the water flow velocity of the i-th transport transfer section, represents the reference channel depth, represents the reference water velocity, Indicates the preset allowable deviation channel depth. Indicates the preset allowable deviation water velocity, represents the meteorological interference assessment value of the i-th transport transfer section at the t-th future time point, Indicates the influencing factor of waterway transportation feasibility corresponding to the preset channel depth, Indicates the influencing factor of water transport feasibility corresponding to the preset water flow velocity, It represents the influencing factor of water transport feasibility corresponding to the preset meteorological interference degree assessment value, i represents the number of each transport transit section, i=1,2,3,...,n, n represents the total number of transport transit sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points.

[0063] It should be understood that the value range of the water transport feasibility influencing factors corresponding to the preset channel depth, water flow velocity and meteorological interference degree assessment values ​​in this embodiment is between 0 and 1. Through the relationship between the channel depth, water flow velocity and meteorological interference degree assessment values ​​and the logistics transportation feasibility impact index in historical data, a mapping set of channel depth, water flow velocity and meteorological interference degree assessment values ​​and corresponding water transport feasibility influencing factors is constructed, and the channel depth, water flow velocity and meteorological interference degree assessment value are input to obtain the water transport feasibility influencing factors corresponding to the channel depth, the water transport feasibility influencing factors corresponding to the water flow velocity and the water transport feasibility influencing factors corresponding to the meteorological interference degree assessment value from the mapping set.

[0064] Table 1 Example of data on the impact index of logistics transport feasibility

[0065]

[0066] As shown in Table 1, in a specific embodiment, let g = 1. When the flow direction of the water is consistent with the sailing direction of the transport ship, =12m, =3m / s, =2m, =0.5m / s, =0.5, =0.3, =0.2. The logistics transport feasibility impact index in this embodiment is a quantitative index obtained by comprehensively analyzing the channel depth, water flow velocity and meteorological interference degree assessment value, which is used to quantitatively assess the degree to which the logistics transport feasibility is affected by external factors when the pre-planned transportation mode is water transportation. The smaller the channel depth, the greater the water flow velocity and meteorological interference degree assessment value, and the corresponding logistics transport feasibility impact index is greater, indicating that the logistics transport feasibility is lower.

[0067] It should be understood that in this embodiment, there is an interactive relationship between the channel depth, water flow velocity and meteorological interference degree evaluation value. When the meteorological interference degree evaluation value is larger, the corresponding wind speed and precipitation are larger, and the water flow velocity of the channel is larger under the influence of wind speed and precipitation. Water flow velocity is one of the important factors affecting the depth of the channel. When the water flow velocity is faster, the kinetic energy it carries is larger, which can wash away the silt and sediment on the riverbed, thereby deepening the channel. When the water flow velocity is slower, the kinetic energy it carries is reduced, and silt and sediment are easily deposited in the river channel, causing the channel to become shallower.

[0068] In a specific embodiment, by conducting a comprehensive analysis of the channel depth, water flow speed and meteorological data, the entire transportation process can be guaranteed in water logistics transportation, effectively responding to the challenges brought by water flow changes and improving the safety of navigation. This comprehensive analysis not only improves transportation efficiency, but also reduces navigation time and energy consumption by optimizing route planning and navigation strategies, and further reduces transportation costs. Ships can adjust their loading rates based on real-time data to achieve optimal resource allocation while reducing waiting time and inventory costs. The comprehensive analysis of channel depth, water flow speed and meteorological data provides strong decision-making support for water logistics transportation, ensuring the safety, efficiency and sustainability of the transportation process.

[0069] Specifically, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section, including when the pre-planned transport mode is air transport. The specific analysis process is as follows: the cloud base height of each transport transit section is monitored, and the critical cloud base height is obtained from the cross-border logistics database.

[0070] It should be understood that the cloud base height in this embodiment refers to the vertical distance from the bottom of the cloud layer to the ground surface, usually in meters, and can be measured by a laser cloud meter. The cloud base height will have an impact on aircraft transportation. When the cloud base height is too low, the visibility will be greatly reduced when the aircraft lands through these clouds. This may cause the pilot to still be unable to visually see the runway at the decision altitude, increasing the difficulty and risk of landing. Low clouds are often accompanied by meteorological factors such as cumulonimbus clouds and ice accumulation. There are strong rising or sinking air currents in cumulonimbus clouds, which may be accompanied by lightning, turbulence and ice accumulation, which seriously hinder the normal flight of the aircraft. Ice accumulation may change the aerodynamic performance of the aircraft and affect flight stability.

[0071] It should be understood that the cloud base height in this embodiment is the minimum value monitored in the transport transit section.

[0072] According to the cloud base height and the assessed values ​​of the meteorological interference degree of each transport transit section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section.

[0073] In a specific embodiment, the air transport feasibility influencing factors corresponding to the preset cloud base height and meteorological interference degree assessment values ​​are extracted from the cross-border logistics database, and the logistics transport feasibility influencing degree index of each transport transit section is obtained through comprehensive analysis. The specific numerical expression is:

[0074] ;

[0075] In the formula, It represents the impact index of logistics transport feasibility of the i-th transport transit section, represents the cloud base height of the i-th transport transfer section, represents the critical cloud base height, represents the meteorological interference assessment value of the i-th transport transfer section at the t-th future time point, Indicates the air transport feasibility factor corresponding to the preset cloud base height, It represents the air transport feasibility influencing factor corresponding to the preset meteorological interference degree assessment value, i represents the number of each transport transit section, i=1,2,3,...,n, n represents the total number of transport transit sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points.

[0076] It should be understood that the value range of the air transport feasibility influencing factors corresponding to the preset cloud base height and meteorological interference degree assessment values ​​in this embodiment is between 0 and 1. Through the relationship between the cloud base height and meteorological interference degree assessment values ​​and the logistics transportation feasibility impact index in historical data, a mapping set of cloud base height and meteorological interference degree assessment values ​​and corresponding air transport feasibility influencing factors is constructed. The cloud base height and meteorological interference degree assessment value are input, and the air transport feasibility influencing factors corresponding to the cloud base height and the air transport feasibility influencing factors corresponding to the meteorological interference degree assessment value are obtained from the mapping set.

[0077] It should be understood that the logistics transport feasibility impact index in this embodiment is used to quantitatively evaluate the degree to which logistics transport feasibility is affected by external factors when the pre-planned transport mode is air transport. The smaller the cloud base height, the greater the meteorological interference degree assessment value, and the corresponding logistics transport feasibility impact index is greater, indicating that the logistics transport feasibility is lower.

[0078] It should be understood that in this embodiment, the meteorological interference level assessment value and the cloud base height interact with each other. Generally speaking, the lower the cloud base height, the greater the probability of precipitation. Wind speed can affect the change of cloud base height, and thus affect the distribution of precipitation. By accurately predicting and evaluating the cloud base height, wind speed and precipitation on the flight route, it is possible to avoid potential dangerous weather areas, flexibly adjust the route, and choose a safer and more efficient flight path, thereby ensuring flight safety.

[0079] Specifically, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section, including when the pre-planned transport mode is land transport. The specific analysis process is as follows: the traffic density of each transport transit section is monitored, and the critical traffic density is obtained from the cross-border logistics database.

[0080] It should be understood that in this embodiment, the traffic density refers to the total number of vehicles running on the road at a certain moment within a unit length (usually 1km), which can reflect the congestion of the road. The traffic density can be expressed as the ratio of the total number of vehicles running in the observed road area to the length of the observed road section, with the unit being vehicles per kilometer. The traffic density will affect the timeliness of land transportation. When the traffic density is small, vehicles can travel freely, the average speed is high, and the traffic flow does not reach the maximum value; as the traffic density increases, the phenomenon of following between vehicles becomes increasingly obvious, the speed is restricted, and the traffic flow gradually increases; when the traffic density approaches or reaches the optimal traffic density, the traffic flow reaches the maximum value; and when the traffic density continues to increase and exceeds the optimal traffic density, the traffic will be in a congested state, the speed and traffic flow will both decrease, and even traffic jams will occur.

[0081] It should be understood that the traffic flow in this embodiment refers to the number of vehicles passing through the transport transfer section within a unit time (such as 1 hour, 1 minute), and the critical traffic density is the optimal traffic density, at which the traffic flow reaches the maximum value.

[0082] According to the traffic density and the assessed values ​​of the meteorological interference degree of each transport transfer section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transfer section.

[0083] In a specific embodiment, the land transport feasibility influencing factors corresponding to the preset vehicle flow density and meteorological interference degree evaluation values ​​are extracted from the cross-border logistics database, and the logistics transport feasibility influencing degree index of each transport transit section is obtained through comprehensive analysis. The specific numerical expression is:

[0084] ;

[0085] In the formula, It represents the impact index of logistics transport feasibility of the i-th transport transit section, represents the traffic density of the i-th transport transfer section, represents the critical traffic density, represents the meteorological interference assessment value of the i-th transport transit section at the t-th future time point, Indicates the air transport feasibility influencing factor corresponding to the preset traffic density, It represents the land transport feasibility influencing factor corresponding to the preset meteorological interference degree assessment value, i represents the number of each transport transit section, i=1,2,3,...,n, n represents the total number of transport transit sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points.

[0086] It should be understood that the Softplus function in this embodiment is a common activation function in the field of neural networks and deep learning, and can be regarded as a smoothed version of the ReLU (Rectified Linear Unit) function, whose formula is: f(x)=ln(1+exp(x)). The Softplus function is close to zero in the negative region and close to the linear function in the positive region, so it provides a smooth transition and helps alleviate the gradient vanishing problem.

[0087] It should be understood that the value range of the land transport feasibility influencing factors corresponding to the preset traffic density and meteorological interference degree assessment values ​​in this embodiment is between 0 and 1. Through the relationship between the traffic density and meteorological interference degree assessment values ​​and the logistics transportation feasibility impact index in historical data, a mapping set of traffic density and meteorological interference degree assessment values ​​and corresponding land transport feasibility influencing factors is constructed, and the traffic density and meteorological interference degree assessment values ​​are input to obtain the land transport feasibility influencing factors corresponding to the traffic density and the land transport feasibility influencing factors corresponding to the meteorological interference degree assessment values ​​from the mapping set.

[0088] It should be understood that the logistics transport feasibility impact index in this embodiment is used to quantitatively evaluate the degree to which logistics transport feasibility is affected by external factors when the pre-planned transportation mode is land transportation. The greater the traffic density and the greater the meteorological interference assessment value, the greater the corresponding logistics transport feasibility impact index, indicating that the logistics transport feasibility is lower.

[0089] It should be understood that in this embodiment, the meteorological interference degree evaluation value and the traffic density have an interactive relationship. The larger the meteorological interference degree evaluation value, the worse the weather conditions, the greater the probability of traffic congestion, and the corresponding greater the traffic density. By comprehensively analyzing the traffic density and meteorological data, it is possible to predict in advance the traffic congestion that may be caused by bad weather, which helps to take preventive measures to avoid or alleviate congestion. Combining real-time traffic density data and meteorological data can improve the accuracy of future traffic flow predictions, help adjust cargo distribution plans, select the best routes and time windows, and ensure that goods are delivered on time.

[0090] Specifically, the transportation route of each transport transit section is adjusted according to the logistics transport feasibility impact index of each transport transit section. The specific analysis process is: obtain the pre-planned transportation mode of each transport transit section, obtain the logistics transport feasibility impact index threshold corresponding to each pre-planned transportation mode from the cross-border logistics database, and obtain the logistics transport feasibility impact index threshold of each transport transit section.

[0091] The logistics transport feasibility impact index of each transport transit section is compared with the logistics transport feasibility impact index threshold. If the logistics transport feasibility impact index of a transport transit section is greater than or equal to the logistics transport feasibility impact index threshold, the transport route of the transport transit section is switched according to the preset alternative transport route.

[0092] In a specific embodiment, the process of determining the alternative transportation routes is as follows: collecting all possible transportation routes for each transportation transit section, counting the path lengths and transportation costs of all possible transportation routes, extracting the critical transportation cost from the cross-border logistics database, marking the transportation routes with transportation costs less than or equal to the critical transportation cost as feasible transportation routes, and sorting the alternative transportation routes in ascending order according to the path length. It is understandable that the feasible transportation route ranked first is recorded as the pre-planned transportation route, and the subsequent feasible transportation routes are recorded as alternative transportation routes.

[0093] When the logistics transport feasibility impact index corresponding to the pre-planned transport route is low, the alternative transport routes are switched in order of path length.

[0094] The cross-border logistics database is used to store cross-border logistics transportation-related data, including indicators such as critical wind speed, critical precipitation, and reference channel depth. The data in the cross-border logistics database can be obtained through a variety of channels, including obtaining waybill information from customs and airlines, using global positioning systems and sensor technology to monitor the status of goods, and obtaining meteorological and waterway data from meteorological stations and waterway management agencies. The integration and analysis of these data help optimize logistics processes and improve transportation efficiency and safety.

[0095] In a specific embodiment, the present invention provides a cross-border intelligent logistics cloud management system based on big data, which can help to more accurately control and manage the logistics transportation process by refining the transportation route into multiple transit sections, thereby improving the controllability and flexibility of transportation. By evaluating the degree of impact of the transportation feasibility of each section, a more appropriate mode of transportation can be selected, which not only improves the efficiency and reliability of transportation, but also enhances the flexibility and responsiveness of the entire logistics system.

[0096] The above contents are merely examples and explanations of the structure of the present invention. The technicians in this technical field may make various modifications or additions to the specific embodiments described or replace them in a similar manner. As long as they do not deviate from the structure of the invention or exceed the scope defined by the present invention, they should all fall within the protection scope of the present invention.

Claims

1. A cross-border intelligent logistics cloud management system based on big data, It is characterized in that include: The transport transit section division module is used to extract the transit nodes and pre-planned logistics transportation routes in the cross-border logistics transportation process from the cross-border logistics database, divide the logistics transportation routes into sections according to the transit nodes, and mark them as various transport transit sections; The transport road condition impact assessment module is used to collect transport road condition data, obtain the meteorological interference degree assessment value of each transport transit section at each future time point after processing, obtain the pre-planned transport mode of each transport transit section, and comprehensively analyze to obtain the logistics transport feasibility impact degree index of each transport transit section, wherein the pre-planned transport mode includes water transport mode, land transport mode and air transport mode; The transport route optimization and adjustment module is used to adjust the transport route of each transport transit section according to the logistics transport feasibility impact index of each transport transit section; The specific process of collecting the transportation road condition data is as follows: obtaining the estimated time period of each transportation transit section from the pre-planned logistics transportation route, and marking it as the time period of each section; Monitor the meteorological data of each transport transfer section, arrange the traffic time period of each section at several future time points, and obtain the meteorological forecast data and current meteorological forecast data of each transport transfer section at each future time point; When the pre-planned mode of transport is water transport, the specific numerical expression of the impact index of the feasibility of logistics transport of each transport transit section is: ； In the formula, It represents the impact index of logistics transport feasibility of the i-th transport transit section, represents the channel depth of the i-th transport transfer section, represents the water flow velocity of the i-th transport transfer section, represents the reference channel depth, represents the reference water velocity, Indicates the preset allowable deviation channel depth. Indicates the preset allowable deviation water velocity, represents the meteorological interference assessment value of the i-th transport transit section at the t-th future time point, Indicates the influencing factor of waterway transportation feasibility corresponding to the preset channel depth, Indicates the influencing factor of water transport feasibility corresponding to the preset water flow velocity, represents the water transport feasibility influencing factor corresponding to the preset meteorological interference degree assessment value, i represents the number of each transport transit section, i=1,2,3,...,n, n represents the total number of transport transit sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points; The specific numerical expression of the meteorological interference degree evaluation value of each transport transfer section at each future time point is: ； In the formula, represents the meteorological interference assessment value of the i-th transport transit section at the t-th future time point, represents the wind speed of the i-th transport transfer section at the t-th future time point, represents the precipitation of the i-th transport transit section at the t-th future time point, represents the critical wind speed, represents the critical precipitation, Indicates the wind speed interference assessment impact factor at the current time point, represents the impact factor of precipitation interference assessment at the current time point, i represents the number of each transport transfer section, i=1,2,3,...,n, n represents the total number of transport transfer sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points; The wind speed deviation value and the precipitation deviation value are respectively compared with the wind speed interference assessment influencing factors and precipitation interference assessment influencing factors corresponding to each wind speed deviation value interval and each precipitation deviation value interval stored in the cross-border logistics database to obtain the wind speed interference assessment influencing factors at the current time point and the precipitation interference assessment influencing factors at the current time point.

2. According to the cross-border intelligent logistics cloud management system based on big data as claimed in claim 1, Features: When the pre-planned transport mode is air transport, the specific analysis process of the logistics transport feasibility impact index of each transport transit section is as follows: Monitor the cloud base height of each transport transit section and obtain the critical cloud base height from the cross-border logistics database; According to the cloud base height and the assessed values ​​of the meteorological interference degree of each transport transit section at each future time point, a comprehensive analysis is conducted to obtain the logistics transport feasibility impact index of each transport transit section.

3. According to the cross-border intelligent logistics cloud management system based on big data as claimed in claim 1, Features: When the pre-planned mode of transport is land transport, the specific numerical expression of the logistics transport feasibility impact index of each transport transit section is: ； In the formula, It represents the impact index of logistics transport feasibility of the i-th transport transit section, represents the traffic density of the i-th transport transfer section, represents the critical traffic density, represents the meteorological interference assessment value of the i-th transport transit section at the t-th future time point, Indicates the air transport feasibility influencing factor corresponding to the preset traffic density, It represents the land transport feasibility influencing factor corresponding to the preset meteorological interference degree assessment value, i represents the number of each transport transit section, i=1,2,3,...,n, n represents the total number of transport transit sections, t represents the number of each future time point, t=1,2,3,...,g, g represents the total number of future time points.

4. According to the cross-border intelligent logistics cloud management system based on big data as claimed in claim 3, Features: The transport route of each transport transit section is adjusted according to the logistics transport feasibility impact index of each transport transit section. The specific analysis process is as follows: Obtain the pre-planned transport mode for each transport transit section, obtain the threshold value of the impact degree index of the logistics transport feasibility corresponding to each pre-planned transport mode from the cross-border logistics database, and obtain the threshold value of the impact degree index of the logistics transport feasibility for each transport transit section; The logistics transport feasibility impact index of each transport transit section is compared with the logistics transport feasibility impact index threshold. If the logistics transport feasibility impact index of a transport transit section is greater than or equal to the logistics transport feasibility impact index threshold, the transport route of the transport transit section is switched according to the preset alternative transport route.

5. According to the cross-border intelligent logistics cloud management system based on big data as claimed in claim 1, Features: The meteorological interference degree assessment value is a quantitative index obtained by analyzing wind speed and precipitation, and is used to quantitatively assess the degree of interference of meteorological data on logistics and transportation.

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