An intelligent management system for logistics vehicles

Abstract

The present invention relates to the technical field of logistics data management, and in particular to an intelligent management system for logistics vehicles, including an information acquisition module for acquiring aviation information, logistics information and weather information; a driving analysis module for analyzing driving time and logistics free time; a correlation analysis module for analyzing weather correlation coefficients according to driving time and weather information; an aviation analysis module for analyzing aircraft delay time according to aviation information, and also for analyzing delay types, and also for analyzing aviation delay parameters according to aircraft delay time and delay types; a package analysis module for analyzing transit free time and package types; a logistics analysis module for analyzing logistics management parameters; and a logistics management module for managing logistics information according to logistics management parameters. The present invention realizes accurate management of aviation logistics vehicles.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics data management, and in particular to an intelligent management system for logistics vehicles. Background Art

[0002] With the widespread adoption of technologies like big data, cloud computing, and the Internet of Things, the aviation logistics industry is gradually achieving the precise collection, efficient processing, and intelligent analysis of vehicle data. This technological background aims to enhance the transparency, efficiency, and safety of logistics and transportation, meeting the urgent need for efficient and intelligent management in modern aviation logistics.

[0003] Chinese Patent Publication No. CN107730187A discloses a method for domestic / international air logistics transportation, including the following steps: 1) The client's backend obtains flight information from a partner airline; 2) The client first registers and logs in, enters the shipment information, and matches the flight route; 3) The user selects the desired flight route, further supplements the shipment details, places an order, and pays the corresponding fee; 4) The platform completes the collection of the goods to the cargo assembly point and uniformly delivers the goods to the designated flight route; 5) The goods arrive at the consignee's city according to the designated flight route and are then delivered to the consignee according to the delivery method requirements. This invention implements the management of air logistics transportation data, but does not achieve unified management of the logistics vehicles transporting air logistics packages. This leads to low efficiency in air logistics data analysis and inaccurate logistics vehicle transportation management. Summary of the Invention

[0004] To this end, the present invention provides an intelligent management system for logistics vehicles to overcome the problems of low efficiency in aviation logistics data analysis and inaccurate management of logistics vehicle transportation in the prior art.

[0005] To achieve the above objectives, the present invention provides a logistics vehicle intelligent management system, comprising:

[0006] Information acquisition module, used to obtain aviation information, logistics information and weather information;

[0007] The driving analysis module is used to analyze driving time based on logistics information, and also to analyze logistics availability time based on logistics information and aviation information;

[0008] Correlation analysis module, used to analyze weather correlation coefficient based on driving duration and weather information;

[0009] An aviation analysis module is used to analyze the duration of flight delays based on aviation information, analyze the type of delay based on aviation information, weather information, and flight delay duration, and analyze aviation delay parameters based on flight delay duration and delay type;

[0010] The package analysis module is used to analyze the available transit time based on logistics information and aviation information, and also to analyze the package type based on the available transit time, logistics time, and travel time;

[0011] Logistics analysis module, which analyzes logistics management parameters based on transit availability, logistics information, package type, travel time, weather correlation coefficient, weather information, and flight delay parameters;

[0012] The logistics management module is used to manage logistics information according to logistics management parameters.

[0013] Furthermore, the driving analysis module analyzes the driving time according to the logistics departure time and logistics arrival time, sets the driving time to T1(i,k), and sets T1(i,k)=[t3(i,k)-t1(i,k)]×60+t4(i,k)-t2(i,k), where T1(i,k) represents the driving time, i represents the flight number of the aircraft, and i∈N + , k represents the logistics vehicle number, k∈N + , t1(i,k) represents the hour part of the logistics departure time, t2(i,k) represents the minute part of the logistics departure time, t3(i,k) represents the hour part of the logistics arrival time, and t4(i,k) represents the minute part of the logistics arrival time;

[0014] The driving analysis module analyzes the logistics spare time according to the logistics arrival time and the expected departure time, sets the logistics spare time to T2(i,k), and sets T2(i,k)=[t5(i)-t3(i,k)]×60+t6(i)-t4(i,k), where T2(i,k) represents the logistics spare time, t5(i) represents the hour part of the expected departure time, and t6(i) represents the minute part of the expected departure time.

[0015] Furthermore, the correlation analysis module is provided with a first analysis unit, which is used to analyze the first correlation parameter according to the average driving time and the average temperature, and set the first correlation parameter to r1, and set

[0016] Among them, r1 represents the first relevant parameter, Y represents the mean driving time, and X1 represents the mean temperature;

[0017] The correlation analysis module is further provided with a second analysis unit, which is used to analyze the second correlation parameter according to the average driving time and the average precipitation, and set the second correlation parameter to r2.

[0018] Among them, r2 represents the second correlation parameter, X2 represents the mean precipitation;

[0019] The correlation analysis module is further provided with a third analysis unit, which is used to analyze the third correlation parameter according to the mean temperature and the mean precipitation, and set the third correlation parameter to r3.

[0020] Here, r3 represents the third related parameter.

[0021] Furthermore, the correlation analysis module is further provided with a correlation analysis unit, which is used to calculate the temperature correlation coefficient, the precipitation correlation coefficient and the joint correlation coefficient according to the first correlation parameter, the second correlation parameter and the third correlation parameter through the correlation analysis formula. The correlation analysis unit is provided with the following correlation analysis formula:

[0022]

[0023]

[0024]

[0025] Among them, R1 represents the temperature correlation coefficient, R2 represents the precipitation correlation coefficient, and R3 represents the joint correlation coefficient.

[0026] Furthermore, the aviation analysis module is provided with a delay analysis unit, which is used to analyze the aircraft delay duration according to the expected take-off time and the actual take-off time, set the aircraft delay duration as T3(i), and set T3(i)=[t7(i)-t5(i)]×60+t8(i)-t6(i), wherein T3(i) represents the aircraft delay duration, t7(i) represents the hour portion of the actual take-off time, and t8(i) represents the minute portion of the actual take-off time;

[0027] The aviation analysis module is further provided with a delay classification unit, which is used to analyze the delay type of the aircraft based on the current load, the maximum load of the aircraft, the precipitation and the delay duration of the aircraft, wherein:

[0028] When T3(i)≤α, the delay classification unit determines that the delay type is no delay;

[0029] When T3(i)>α and W2(i)>w, the delay classification unit determines that the delay type of the aircraft is weather delay;

[0030] When T3(i)>α and W2(i)≤w and M1(i) / M2(i)>β, the delay classification unit determines that the delay type of the aircraft is logistics delay;

[0031] When T3(i)>α and W2(i)≤w and M1(i) / M2(i)≤β, the delay classification unit determines that the delay type of the aircraft is other delay;

[0032] Among them, α represents the delay time threshold, w represents the precipitation threshold, M1(i) represents the current load of the aircraft, M2(i) represents the maximum load of the aircraft, and β represents the load threshold.

[0033] Furthermore, the aviation analysis module is further provided with an aviation analysis unit, which is used to calculate the aviation delay parameter according to the aircraft delay duration and delay type through an aviation analysis formula. The aviation analysis unit is provided with the following aviation analysis formula:

[0034]

[0035] Where F represents the aviation delay parameter, NT1 represents the number of flights whose delay type is weather delay, NT2 represents the number of flights whose delay type is logistics delay, and NT3 represents the number of flights whose delay type is other delay.

[0036] Furthermore, the package analysis module analyzes the available transit time according to the expected transit time and the expected departure time, sets the available transit time to T4(i,j), and sets T4(i,j)=[t5(i)-t9(i,j)]×60+t6(i)-t10(i,j), where T4(i,j) represents the available transit time, j represents the logistics package number, and j∈N + , t9(i,j) represents the hour part of the estimated transit time, and t10(i,j) represents the minute part of the estimated transit time;

[0037] The package analysis module analyzes the package type based on the available transit time, travel time, and available logistics time, where:

[0038] When T4(i,j)≤T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class 1;

[0039] When T4(i,j)>T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class II;

[0040] Among them, T1(i,k max ) represents the driving time of the last logistics vehicle that arrives before the flight number i takes off, T2(i,k max ) represents the logistics idle time of the last logistics vehicle that arrives before the aircraft with flight number i takes off.

[0041] Furthermore, the logistics analysis module is provided with a logistics analysis unit, which is used to analyze logistics management parameters according to package type, travel time, transit time and logistics time, wherein:

[0042] When the package type is one, the logistics analysis unit analyzes the logistics management parameters according to the driving time, the transfer free time and the logistics free time, and sets the logistics management parameters G(i,j)=T4(i,j) / [T1(i,k max )+T2(i,k max )]×e 1-T1(i,kmax) / [T1(i,kmax)+T2(i,kmax)] ;

[0043] When the package type is Class II, the logistics analysis unit sets the logistics management parameter G(i,j)=1;

[0044] Among them, G(i,j) represents the logistics management parameters.

[0045] Furthermore, the logistics analysis module is further provided with a logistics adjustment unit, which is used to adjust the analysis process of the logistics management parameters according to the driving fluctuation parameter, the temperature correlation coefficient, the precipitation correlation coefficient, the joint correlation coefficient and the weather information, wherein:

[0046] When the parcel type is Class II, the logistics adjustment unit does not adjust the analysis process of the logistics management parameters;

[0047] When the package type is one and γ1≤W1(i) / X1≤γ2 and |r1|≤1-|r3|, the logistics adjustment unit determines that the temperature meets the threshold and adjusts the analysis process of the logistics management parameters. The adjusted logistics management parameters are G1(i,j), and G1(i,j)=G(i,j)×[Z×r1 / T1(i,k max )+1];

[0048] When the package type is Class 1 and W1(i) / X1<γ1 or W1(i) / X1>γ2 or |r1|>1-|r3|, the logistics adjustment unit determines that the temperature does not meet the threshold value and does not adjust the analysis process of the logistics management parameters;

[0049] When the package type is one and W2(i)>w and |r2|≤1-|r3|, the logistics adjustment unit determines that the precipitation meets the threshold and adjusts the analysis process of the logistics management parameters. The adjusted logistics management parameters are G1(i,j), and G1(i,j)=G(i,j)×[Z×r2 / T1(i,k max )+1];

[0050] When the package type is one and W2(i)≤w or |r2|≤1-|r3|, the logistics adjustment unit determines that the precipitation does not meet the threshold and does not adjust the analysis process of the logistics management parameters;

[0051] Wherein, γ1 represents the first temperature comparison threshold, and γ2 represents the second temperature comparison threshold;

[0052] The logistics analysis module is further provided with a logistics optimization unit, which is used to optimize the adjustment process of logistics management parameters according to the logistics type and flight delay parameters, wherein:

[0053] When the logistics type is normal, the logistics optimization unit does not optimize the adjustment process of the logistics management parameters;

[0054] When the logistics type is expedited, the logistics optimization unit optimizes the adjustment process of the logistics management parameters. The optimized logistics management parameters are G2(i,j), and G2(i,j)=G1(i,j)×[1+F / T1(i,k max )].

[0055] Furthermore, the logistics management module manages logistics information according to logistics management parameters, wherein:

[0056] When G(i,j)>1, the logistics management module sets the logistics departure time corresponding to the currently analyzed logistics management parameters to be the same as the estimated transit time corresponding to the currently analyzed logistics management parameters;

[0057] When G(i,j)≤1, the logistics management module does not manage the logistics departure time.

[0058] Compared with the prior art, the beneficial effect of the present invention is that the information acquisition module acquires aviation information, logistics information and weather information to improve the accuracy of information acquisition, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management; the driving analysis module analyzes logistics information and aviation information to analyze driving time and logistics spare time, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management; the correlation analysis module analyzes driving time and weather information to analyze the weather correlation coefficient, which is expressed as the weather correlation coefficient. The impact of weather conditions on the travel of logistics vehicles, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The aviation analysis module analyzes aviation information to analyze the aircraft delay duration and delay type, and realizes the analysis of aircraft flight time delay, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The aviation analysis module analyzes the aircraft delay duration and delay type to analyze the aviation delay parameters, and uses the aviation delay parameters to represent the possible delay duration of the aircraft, thereby improving the system's analysis efficiency of aviation logistics data and improving. Improve the accuracy of logistics vehicle transportation management, through the analysis of logistics information and aviation information by the package analysis module to analyze the transfer free time, and use the transfer free time to represent the time interval between the time when the logistics package arrives at the transfer station and the expected take-off time of the aircraft, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management, through the analysis of the transfer free time, logistics free time and driving time by the package analysis module to analyze the package type, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management, through the analysis of the transfer free time, logistics free time and driving time by the logistics analysis module to analyze the transfer free time, logistics free time and driving time, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management, The analysis of the remaining time, logistics information, package type, driving time, temperature correlation coefficient, precipitation correlation coefficient, joint correlation coefficient, weather information and aviation delay parameters is used to analyze the logistics management parameters, and the logistics management parameters are used to indicate whether the current logistics package is likely to arrive at the airport before the plane takes off, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The logistics management module analyzes the logistics management parameters to manage logistics information and realize the management of logistics vehicle transportation, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 This is a structural diagram of the logistics vehicle intelligent management system of this embodiment.

[0060] Figure 2 Schematic diagram of the structure of the relevant analysis module of this embodiment.

[0061] Figure 3 Schematic diagram of the structure of the aviation analysis module of this embodiment.

[0062] Figure 4 This is a structural diagram of the logistics analysis module of this embodiment. DETAILED DESCRIPTION

[0063] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are merely used to explain the present invention and are not intended to limit the present invention.

[0064] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0065] Furthermore, it should be noted that, in the description of the present invention, unless otherwise expressly specified or limited, the terms "mounted," "connected," and "connected" should be understood in a broad sense. For example, they may refer to fixed connections, detachable connections, or integral connections; mechanical connections or electrical connections; direct connections or indirect connections through an intermediate medium; and internal communication between two components. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on specific circumstances.

[0066] See also Figure 1 As shown, this is an intelligent management system for logistics vehicles in this embodiment, including:

[0067] an information acquisition module for acquiring aviation information, logistics information, and weather information, wherein the aviation information includes the estimated take-off time, the actual take-off time, the current aircraft load capacity, and the maximum aircraft load capacity; the aviation information is information about aircraft used for logistics air transport; the logistics information includes the logistics type, the estimated transit time, the logistics departure time, and the logistics arrival time; the estimated transit time is the time when the logistics package is expected to arrive at the logistics transit location; the logistics package is a package that needs to be transferred to the airport for air transport by a logistics vehicle; the logistics arrival time is the time when the logistics vehicle arrives at the airport; the logistics types include expedited and ordinary; the weather information includes temperature and precipitation; the aviation information is acquired by importing it through an aircraft data management platform; the logistics information is acquired by importing it through a logistics data management platform; and the weather information is acquired by visiting a local meteorological website;

[0068] A driving analysis module, configured to analyze driving duration based on logistics information, and also to analyze available logistics time based on logistics information and aviation information, the driving analysis module being connected to the information acquisition module;

[0069] A correlation analysis module, configured to analyze weather correlation coefficients based on driving duration and weather information, the correlation analysis module being connected to the driving analysis module, wherein the weather correlation coefficients include a temperature correlation coefficient, a precipitation correlation coefficient, and a combined correlation coefficient;

[0070] an aviation analysis module, configured to analyze the duration of flight delays based on the aviation information, analyze the type of delay based on the aviation information, weather information, and the duration of flight delays, and analyze aviation delay parameters based on the duration of flight delays and the type of delays, the aviation analysis module being connected to the information acquisition module;

[0071] a package analysis module for analyzing available transit time based on logistics information and aviation information, and for analyzing package type based on available transit time, logistics available time, and travel time, the package analysis module being connected to the information acquisition module;

[0072] A logistics analysis module, configured to analyze logistics management parameters based on available transit time, logistics information, package type, travel time, temperature correlation coefficient, precipitation correlation coefficient, joint correlation coefficient, weather information, and flight delay parameters. The logistics analysis module is connected to the correlation analysis module, the flight analysis module, and the package analysis module.

[0073] The logistics management module is used to manage logistics information according to logistics management parameters, and the logistics management module is connected to the logistics analysis module.

[0074] See also Figure 2 As shown, the related analysis module includes:

[0075] a mean analysis unit for analyzing the mean driving time, the mean temperature, and the mean precipitation according to the driving time, the temperature, and the precipitation;

[0076] a first analysis unit, configured to analyze the first relevant parameter according to the average driving duration and the average temperature, the first analysis unit being connected to the average value analysis unit;

[0077] a second analysis unit, configured to analyze a second related parameter according to the average driving duration and the average precipitation, the second analysis unit being connected to the average value analysis unit;

[0078] a third analysis unit, configured to analyze a third related parameter according to the mean temperature and the mean precipitation, the third analysis unit being connected to the mean analysis unit;

[0079] The correlation analysis unit is used to analyze the temperature correlation coefficient, the precipitation correlation coefficient and the joint correlation coefficient according to the first correlation parameter, the second correlation parameter and the third correlation parameter. The correlation analysis unit is connected to the first analysis unit, the second analysis unit and the third analysis unit.

[0080] See also Figure 3 As shown, the aviation analysis module includes:

[0081] Delay analysis unit, used to analyze the duration of aircraft delays based on the expected departure time and actual departure time;

[0082] a delay classification unit, configured to analyze the delay type according to the current aircraft load, the maximum aircraft load, the precipitation, and the aircraft delay duration, the delay classification unit being connected to the delay analysis unit;

[0083] The aviation analysis unit is used to analyze the aviation delay parameters according to the aircraft delay duration and delay type, and the aviation analysis unit is connected to the delay classification unit.

[0084] See also Figure 4 As shown, the logistics analysis module includes:

[0085] Logistics analysis unit, used to analyze logistics management parameters based on package type, travel time, and transit time;

[0086] a fluctuation analysis unit, configured to analyze driving fluctuation parameters according to driving duration, the fluctuation analysis unit being connected to the logistics analysis unit;

[0087] a logistics adjustment unit, configured to adjust the analysis process of the logistics management parameters according to the driving fluctuation parameter, the temperature correlation coefficient, the precipitation correlation coefficient, the combined correlation coefficient and the weather information, the logistics adjustment unit being connected to the fluctuation analysis unit;

[0088] The logistics optimization unit is used to optimize the adjustment process of the logistics management parameters according to the logistics type and flight delay parameters. The logistics optimization unit is connected to the logistics adjustment unit.

[0089] Specifically, in this embodiment, the information acquisition module acquires aviation information, logistics information and weather information to improve the accuracy of information acquisition, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The driving analysis module analyzes logistics information and aviation information to analyze driving time and logistics spare time, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The correlation analysis module analyzes driving time and weather information to analyze the weather correlation coefficient, and uses the weather correlation coefficient to represent the impact of weather conditions on logistics. The impact of vehicle driving, thereby improving the system's analysis efficiency of aviation logistics data, and improving the accuracy of logistics vehicle transportation management. Through the analysis of aviation information by the aviation analysis module, the aircraft delay duration and delay type are analyzed to realize the analysis of aircraft flight time delay, thereby improving the system's analysis efficiency of aviation logistics data, and improving the accuracy of logistics vehicle transportation management. Through the analysis of aircraft delay duration and delay type by the aviation analysis module, aviation delay parameters are analyzed, and aviation delay parameters are used to represent the possible delay duration of the aircraft, thereby improving the system's analysis efficiency of aviation logistics data, and improving logistics The accuracy of vehicle transportation management is improved by analyzing the logistics information and aviation information through the package analysis module to analyze the transfer free time, and using the transfer free time to represent the time interval between the time when the logistics package arrives at the transfer station and the expected take-off time of the aircraft, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The package analysis module analyzes the transfer free time, logistics free time and driving time to analyze the package type, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The logistics analysis module analyzes the transfer free time The system analyzes the length, logistics information, package type, driving time, temperature correlation coefficient, precipitation correlation coefficient, joint correlation coefficient, weather information and aviation delay parameters to analyze the logistics management parameters, and uses the logistics management parameters to indicate whether the current logistics package is likely to arrive at the airport before the plane takes off, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management. The logistics management module analyzes the logistics management parameters to manage logistics information and realize the management of logistics vehicle transportation, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management.

[0090] Specifically, the driving analysis module in this embodiment analyzes the driving time according to the logistics departure time and logistics arrival time, sets the driving time to T1(i,k), and sets T1(i,k)=[t3(i,k)-t1(i,k)]×60+t4(i,k)-t2(i,k), where T1(i,k) represents the driving time, i represents the flight number of the aircraft, and i∈N + , k represents the logistics vehicle number, k∈N + , t1(i,k) represents the hour part of the logistics departure time, t2(i,k) represents the minute part of the logistics departure time, t3(i,k) represents the hour part of the logistics arrival time, and t4(i,k) represents the minute part of the logistics arrival time.

[0091] Specifically, the driving duration described in this embodiment is the time interval between the logistics departure time and the logistics arrival time. The logistics departure time is in a 24-hour time format and is t1(i,k):t2(i,k). The logistics arrival time is in a 24-hour time format and is t3(i,k):t4(i,k). In this embodiment, when calculating the driving duration, if the logistics departure time and the logistics arrival time are on different dates, the hour portion of the logistics arrival time is increased by the number of days of difference × 24 hours, i.e., T1(i,k) = [t3(i,k) + 24 × n - t1(i,k)] × 60 + t4(i,k) - t2(i,k). It will be understood that this embodiment does not specifically limit the analysis process of driving duration, and those skilled in the art may freely configure it, such as by importing driving data of logistics vehicles and recording it through smart devices.

[0092] Specifically, the driving analysis module described in this embodiment analyzes the logistics idle time according to the logistics arrival time and the expected take-off time, sets the logistics idle time to T2(i,k), and sets T2(i,k)=[t5(i)-t3(i,k)]×60+t6(i)-t4(i,k), where T2(i,k) represents the logistics idle time, t5(i) represents the hour part of the expected take-off time, and t6(i) represents the minute part of the expected take-off time.

[0093] Specifically, the spare time described in this embodiment is the time interval between the expected take-off time and the logistics arrival time. The expected take-off time is in the 24-hour time format, which is t5(i):t6(i). In this embodiment, when calculating the logistics spare time, if the expected take-off time and the logistics arrival time are on different dates, the hour part of the expected take-off time will be increased by the number of days difference × 24 hours during the calculation, that is: T2(i,k)=[t5(i)+24×n-t3(i,k)]×60+t6(i)-t4(i,k), where n represents the number of days difference between the dates corresponding to the two times involved in the calculation.

[0094] Specifically, the aircraft flight number in this embodiment is the time interval number between the expected take-off time in the current aviation information and the expected take-off time in the previous aviation information, which is numerically equal to the number of times the aircraft performs logistics air transportation. The logistics vehicle number is the logistics vehicles that arrive within the time interval corresponding to the aircraft flight number and are numbered in the order of arrival.

[0095] Specifically, the mean analysis unit in this embodiment analyzes the mean driving time, the mean temperature, and the mean precipitation using a mean analysis formula based on the driving time, the temperature, and the precipitation. The mean analysis unit has a mean analysis formula as follows:

[0096]

[0097]

[0098]

[0099] Among them, Y represents the average driving time, X1 represents the average temperature, X2 represents the average precipitation, i max Indicates the maximum value of the aircraft flight number, k max represents the maximum value of the logistics vehicle number, W1(i) represents the temperature, and W2(i) represents the precipitation.

[0100] Specifically, in this embodiment, the first analysis unit analyzes the first relevant parameter according to the average driving time and the average temperature, sets the first relevant parameter to r1, and sets

[0101] Wherein, r1 represents the first related parameter.

[0102] Specifically, in this embodiment, the second analysis unit analyzes the second related parameter according to the average driving time and the average precipitation, sets the second related parameter to r2, and sets

[0103] Wherein, r2 represents the second correlation parameter.

[0104] Specifically, the third analysis unit in this embodiment analyzes the third related parameter according to the mean temperature and the mean precipitation, sets the third related parameter to r3, and sets

[0105] Here, r3 represents the third related parameter.

[0106] Specifically, the correlation analysis unit in this embodiment calculates the temperature correlation coefficient, the precipitation correlation coefficient, and the joint correlation coefficient according to the first correlation parameter, the second correlation parameter, and the third correlation parameter through a correlation analysis formula. The correlation analysis unit has a correlation analysis formula as follows:

[0107]

[0108]

[0109]

[0110] Among them, R1 represents the temperature correlation coefficient, R2 represents the precipitation correlation coefficient, and R3 represents the joint correlation coefficient.

[0111] Specifically, the delay analysis unit described in this embodiment analyzes the aircraft delay duration based on the expected take-off time and the actual take-off time, sets the aircraft delay duration to T3(i), and sets T3(i)=[t7(i)-t5(i)]×60+t8(i)-t6(i), where T3(i) represents the aircraft delay duration, t7(i) represents the hour portion of the actual take-off time, and t8(i) represents the minute portion of the actual take-off time.

[0112] Specifically, the aircraft delay duration in this embodiment is the time interval between the expected departure time and the actual departure time. The actual departure time is in a 24-hour time format, which is t3(i):t4(i). In this embodiment, when calculating the aircraft delay time, if the expected departure time and the actual departure time are on different dates, the hour portion of the actual departure time is increased by the number of days of difference × 24 hours, that is, T3(i) = [t7(i) + 24 × n - t5(i)] × 60 + t8(i) - t6(i).

[0113] Specifically, the delay classification unit in this embodiment analyzes the delay type of the aircraft based on the current load, the maximum load of the aircraft, the precipitation, and the duration of the aircraft delay, wherein:

[0114] When T3(i)≤α, the delay classification unit determines that the delay type is no delay;

[0115] When T3(i)>α and W2(i)>w, the delay classification unit determines that the delay type of the aircraft is weather delay;

[0116] When T3(i)>α and W2(i)≤w and M1(i) / M2(i)>β, the delay classification unit determines that the delay type of the aircraft is logistics delay;

[0117] When T3(i)>α and W2(i)≤w and M1(i) / M2(i)≤β, the delay classification unit determines that the delay type of the aircraft is other delay;

[0118] Where α represents the delay duration threshold, 15 ≤ α ≤ 30, w represents the precipitation threshold, 3 ≤ w ≤ 5, M1(i) represents the aircraft's current payload, M2(i) represents the aircraft's maximum payload, and β represents the payload threshold, 0.7 ≤ β ≤ 0.9. It will be appreciated that this embodiment does not impose specific limitations on the values ​​of the delay duration threshold and payload threshold. Those skilled in the art may freely set these values, as long as they meet the requirements for determining the delay type. The optimal values ​​for the delay duration threshold and payload threshold are: α = 25, β = 0.8.

[0119] Specifically, the aviation analysis unit in this embodiment calculates the aviation delay parameter according to the flight delay duration and delay type using an aviation analysis formula. The aviation analysis unit has an aviation analysis formula as follows:

[0120]

[0121] Where F represents the aviation delay parameter, NT1 represents the number of flights whose delay type is weather delay, NT2 represents the number of flights whose delay type is logistics delay, and NT3 represents the number of flights whose delay type is other delay.

[0122] Specifically, the package analysis module in this embodiment analyzes the available transfer time according to the expected transfer time and the expected departure time, sets the available transfer time to T4(i,j), and sets T4(i,j)=[t5(i)-t9(i,j)]×60+t6(i)-t10(i,j), where T4(i,j) represents the available transfer time, j represents the logistics package number, and j∈N + , t9(i,j) represents the hour part of the estimated transit time, and t10(i,j) represents the minute part of the estimated transit time.

[0123] Specifically, the estimated transfer time in this embodiment is the time interval between the estimated take-off time and the estimated transfer time. The estimated transfer time is in a 24-hour time format, which is: t9(i,j):t10(i,j). In this embodiment, when calculating the available transfer time, if the estimated transfer time and the estimated take-off time are on different dates, the hour portion of the estimated transfer time is increased by the number of days of difference × 24 hours, that is: T4(i,j)=[t5(i)-t9(i,j)+24×n]×60+t6(i)-t10(i,j).

[0124] Specifically, the package analysis module in this embodiment analyzes the package type based on the available transit time, driving time, and available logistics time, where:

[0125] When T4(i,j)≤T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class 1;

[0126] When T4(i,j)>T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class II;

[0127] Among them, T1(i,k max ) represents the driving time of the last logistics vehicle that arrives before the flight number i takes off, T2(i,k max ) represents the logistics idle time of the last logistics vehicle that arrives before the aircraft with flight number i takes off.

[0128] Specifically, in this embodiment, the package analysis module analyzes the transfer free time, driving time and logistics free time to divide the package types into one category and two categories. The one category indicates that the logistics package may be delivered to the airport before the plane takes off by changing the departure time of the logistics vehicle, and the second category indicates that the logistics package can arrive at the logistics transfer station before the logistics vehicle departs, thereby realizing the classification of logistics packages, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management.

[0129] Specifically, the logistics analysis unit in this embodiment analyzes logistics management parameters based on package type, travel time, transit time, and logistics time, wherein:

[0130] When the package type is one, the logistics analysis unit analyzes the logistics management parameters according to the driving time, the transfer free time and the logistics free time, and sets the logistics management parameters G(i,j)=T4(i,j) / [T1(i,k max )+T2(i,k max )]×e 1-T1(i,kmax) / [T1(i,kmax)+T2(i,kmax)] ;

[0131] When the package type is Class II, the logistics analysis unit sets the logistics management parameter G(i,j)=1;

[0132] Among them, G(i,j) represents the logistics management parameters.

[0133] Specifically, in this embodiment, the fluctuation analysis unit calculates the driving fluctuation parameter according to the driving duration using a fluctuation analysis formula. The fluctuation analysis unit is provided with a fluctuation analysis formula as follows:

[0134]

[0135] Wherein, Z represents the driving fluctuation parameter.

[0136] Specifically, the logistics adjustment unit in this embodiment adjusts the analysis process of the logistics management parameters based on the driving fluctuation parameter, the temperature correlation coefficient, the precipitation correlation coefficient, the joint correlation coefficient and the weather information, wherein:

[0137] When the parcel type is Class II, the logistics adjustment unit does not adjust the analysis process of the logistics management parameters;

[0138] When the package type is one and γ1≤W1(i) / X1≤γ2 and |r1|≤1-|r3|, the logistics adjustment unit determines that the temperature meets the threshold and adjusts the analysis process of the logistics management parameters. The adjusted logistics management parameters are G1(i,j), and G1(i,j)=G(i,j)×[Z×r1 / T1(i,k max )+1];

[0139] When the package type is Class 1 and W1(i) / X1<γ1 or W1(i) / X1>γ2 or |r1|>1-|r3|, the logistics adjustment unit determines that the temperature does not meet the threshold value and does not adjust the analysis process of the logistics management parameters;

[0140] When the package type is one and W2(i)>w and |r2|≤1-|r3|, the logistics adjustment unit determines that the precipitation meets the threshold and adjusts the analysis process of the logistics management parameters. The adjusted logistics management parameters are G1(i,j), and G1(i,j)=G(i,j)×[Z×r2 / T1(i,k max )+1];

[0141] When the package type is one and W2(i)≤w or |r2|≤1-|r3|, the logistics adjustment unit determines that the precipitation does not meet the threshold and does not adjust the analysis process of the logistics management parameters;

[0142] Where γ1 represents the first temperature comparison threshold, with a value of 0.7≤γ1≤0.9; γ2 represents the second temperature comparison threshold, with a value of 1.1≤γ2≤1.3. It is understood that the values ​​of the temperature comparison thresholds are not specifically limited in this embodiment and can be freely set by those skilled in the art, as long as they meet the requirements for adjusting logistics management parameters. The optimal values ​​of the temperature comparison thresholds are: γ1 = 0.8 and γ2 = 1.2.

[0143] Specifically, in this embodiment, the logistics adjustment unit analyzes the driving fluctuation parameters, temperature correlation coefficient, precipitation correlation coefficient, joint correlation coefficient and weather information to adjust the analysis process of the logistics management parameters, so that the adjusted logistics management parameters are correlated with the weather information and the fluctuation of the driving time of the logistics vehicles, thereby improving the system's analysis efficiency of the aviation logistics data and improving the accuracy of the logistics vehicle transportation management.

[0144] Specifically, the logistics optimization unit in this embodiment optimizes the adjustment process of logistics management parameters according to the logistics type and flight delay parameters, wherein:

[0145] When the logistics type is normal, the logistics optimization unit does not optimize the adjustment process of the logistics management parameters;

[0146] When the logistics type is expedited, the logistics optimization unit optimizes the adjustment process of the logistics management parameters. The optimized logistics management parameters are G2(i,j), and G2(i,j)=G1(i,j)×[1+F / T1(i,k max )].

[0147] Specifically, in this embodiment, the logistics optimization unit analyzes the logistics type and flight delay parameters to optimize the adjustment process of the logistics management parameters, so that the optimized logistics management parameters are correlated with the fluctuations of the aircraft take-off time, thereby improving the system's analysis efficiency of aviation logistics data and improving the accuracy of logistics vehicle transportation management.

[0148] Specifically, the logistics management module in this embodiment manages logistics information according to logistics management parameters, wherein:

[0149] When G(i,j)>1, the logistics management module sets the logistics departure time corresponding to the currently analyzed logistics management parameters to be the same as the estimated transit time corresponding to the currently analyzed logistics management parameters;

[0150] When G(i,j)≤1, the logistics management module does not manage the logistics departure time.

[0151] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. An intelligent management system for logistics vehicles, characterized in that: include: Information acquisition module, used to obtain aviation information, logistics information and weather information; The driving analysis module is used to analyze the driving time based on the logistics information, and also to analyze the logistics spare time based on the logistics information and aviation information; Correlation analysis module, used to analyze weather correlation coefficients based on driving duration and weather information; The aviation analysis module is used to analyze the duration of flight delays based on aviation information, to analyze the types of delays based on aviation information, weather information and duration of flight delays, and to analyze aviation delay parameters based on duration of flight delays and delay types; The package analysis module is used to analyze the available transit time based on logistics information and aviation information, and to analyze the package type based on the available transit time, logistics available time and driving time; Logistics analysis module, which is used to analyze logistics management parameters based on transit time, logistics information, package type, travel time, weather correlation coefficient, weather information and aviation delay parameters; Logistics management module, used to manage logistics information according to logistics management parameters; The driving analysis module analyzes the driving time according to the logistics departure time and logistics arrival time, sets the driving time to T1(i,k), and sets T1(i,k)=[t3(i,k)-t1(i,k)]×60+t4(i,k)-t2(i,k), where T1(i,k) represents the driving time, i represents the flight number of the aircraft, and i∈N + , k represents the logistics vehicle number, k∈N + , t1(i,k) represents the hour part of the logistics departure time, t2(i,k) represents the minute part of the logistics departure time, t3(i,k) represents the hour part of the logistics arrival time, and t4(i,k) represents the minute part of the logistics arrival time; The driving analysis module analyzes the logistics free time according to the logistics arrival time and the expected departure time, sets the logistics free time to T2(i,k), and sets T2(i,k)=[t5(i)-t3(i,k)]×60+t6(i)-t4(i,k), where T2(i,k) represents the logistics free time, t5(i) represents the hour part of the expected departure time, and t6(i) represents the minute part of the expected departure time; The correlation analysis module is provided with a first analysis unit, which is used to analyze the first correlation parameter according to the average driving time and the average temperature, and set the first correlation parameter to r1, and set ; Among them, r1 represents the first relevant parameter, Y represents the mean driving time, X1 represents the mean temperature, and W1(i) represents the temperature; The correlation analysis module is further provided with a second analysis unit, which is used to analyze the second correlation parameter according to the average driving time and the average precipitation, and set the second correlation parameter to r2. ; Among them, r2 represents the second related parameter, X2 represents the mean precipitation, and W2(i) represents the precipitation; The correlation analysis module is further provided with a third analysis unit, which is used to analyze the third correlation parameter according to the mean temperature and the mean precipitation, and set the third correlation parameter to r3. ; Wherein, r3 represents the third related parameter; The correlation analysis module is also provided with a correlation analysis unit, which is used to calculate the temperature correlation coefficient, the precipitation correlation coefficient and the joint correlation coefficient according to the first correlation parameter, the second correlation parameter and the third correlation parameter through the correlation analysis formula.

2. The intelligent management system for logistics vehicles according to claim 1 is characterized in that: The aviation analysis module is provided with a delay analysis unit, which is used to analyze the aircraft delay duration according to the expected take-off time and the actual take-off time, set the aircraft delay duration as T3(i), and set T3(i)=[t7(i)-t5(i)]×60+t8(i)-t6(i), wherein T3(i) represents the aircraft delay duration, t7(i) represents the hour part of the actual take-off time, t8(i) represents the minute part of the actual take-off time, t5(i) represents the hour part of the expected take-off time, and t6(i) represents the minute part of the expected take-off time; The aviation analysis module is also provided with a delay classification unit, which is used to analyze the delay type of the aircraft according to the current load, the maximum load of the aircraft, the precipitation and the delay time of the aircraft, wherein: When T3(i)≤α, the delay classification unit determines the delay type as no delay; When T3(i)>α and W2(i)>w, the delay classification unit determines that the delay type of the aircraft is weather delay; When T3(i)>α and W2(i)≤w and M1(i) / M2(i)>β, the delay classification unit determines that the delay type of the aircraft is logistics delay; When T3(i)>α and W2(i)≤w and M1(i) / M2(i)≤β, the delay classification unit determines that the delay type of the aircraft is other delay; Among them, α represents the delay time threshold, w represents the precipitation threshold, M1(i) represents the current load of the aircraft, M2(i) represents the maximum load of the aircraft, and β represents the load threshold.

3. The intelligent management system for logistics vehicles according to claim 2 is characterized in that: The aviation analysis module is further provided with an aviation analysis unit, which is used to calculate the aviation delay parameter according to the aircraft delay duration and delay type through an aviation analysis formula. The aviation analysis unit is provided with an aviation analysis formula as follows: ； Where F represents the aviation delay parameter, NT1 represents the number of flights whose delay type is weather delay, NT2 represents the number of flights whose delay type is logistics delay, NT3 represents the number of flights whose delay type is other delays, i max Indicates the maximum value of the aircraft flight number.

4. The intelligent management system for logistics vehicles according to claim 3 is characterized in that: The package analysis module analyzes the available transit time according to the estimated transit time and the estimated take-off time, sets the available transit time to T4(i,j), and sets T4(i,j)=[t5(i)-t9(i,j)]×60+t6(i)-t10(i,j), where T4(i,j) represents the available transit time, j represents the logistics package number, and j∈N + , t9(i,j) represents the hour part of the estimated transit time, and t10(i,j) represents the minute part of the estimated transit time; The package analysis module analyzes the package type according to the transit free time, driving time and logistics free time, wherein: When T4(i,j)≤T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class I; When T4(i,j)>T1(i,k max )+T2(i,k max ), the package analysis module determines that the package type of the currently analyzed logistics package is Class II; Among them, T1(i,k max ) represents the driving time of the last logistics vehicle that arrives before the aircraft with flight number i takes off, T2(i,k max ) represents the logistics idle time of the last logistics vehicle that arrives before the aircraft with flight number i takes off.

5. The intelligent management system for logistics vehicles according to claim 4 is characterized in that: The logistics analysis module is provided with a logistics analysis unit, which is used to analyze logistics management parameters according to package type, travel time, transit free time and logistics free time, wherein: When the package type is one, the logistics analysis unit analyzes the logistics management parameters according to the driving time, the transfer free time and the logistics free time, and sets the logistics management parameters G(i,j)=T4(i,j) / [T1(i,k max )+T2(i,k max )]×e 1-T1(i,kmax) / [T1(i,kmax)+T2(i,kmax)] ; When the package type is Class II, the logistics analysis unit sets the logistics management parameter G(i,j)=1; Among them, G(i,j) represents the logistics management parameters.

6. The intelligent management system for logistics vehicles according to claim 5 is characterized in that: The logistics analysis module is also provided with a logistics adjustment unit, which is used to adjust the analysis process of logistics management parameters according to the driving fluctuation parameter, the temperature correlation coefficient, the precipitation correlation coefficient, the joint correlation coefficient and the weather information, wherein: When the parcel type is Class II, the logistics adjustment unit does not adjust the analysis process of the logistics management parameters; When the package type is one and γ1≤W1(i) / X1≤γ2 and |r1|≤1-|r3|, the logistics adjustment unit determines that the temperature meets the threshold and adjusts the analysis process of the logistics management parameters. The adjusted logistics management parameters are G1(i,j), and G1(i,j)=G(i,j)×[Z×r1 / T1(i,k max )+1]; When the package type is one and W1(i) / X1<γ1 or W1(i) / X1>γ2 or |r1|>1-|r3|, the logistics adjustment unit determines that the temperature does not meet the threshold value and does not adjust the analysis process of the logistics management parameters; Wherein, γ1 represents the first temperature comparison threshold, γ2 represents the second temperature comparison threshold, and Z represents the driving fluctuation parameter; The logistics analysis module is also provided with a logistics optimization unit, which is used to optimize the adjustment process of logistics management parameters according to the logistics type and flight delay parameters, wherein: When the logistics type is normal, the logistics optimization unit does not optimize the adjustment process of the logistics management parameters; When the logistics type is expedited, the logistics optimization unit optimizes the adjustment process of the logistics management parameters. The optimized logistics management parameters are G2(i,j), and G2(i,j)=G1(i,j)×[1+F / T1(i,k max )].

7. The intelligent management system for logistics vehicles according to claim 6 is characterized in that: The logistics management module manages logistics information according to logistics management parameters, wherein: When G(i,j)>1, the logistics management module sets the logistics departure time corresponding to the currently analyzed logistics management parameter to be the same as the estimated transit time corresponding to the currently analyzed logistics management parameter; When G(i,j)≤1, the logistics management module does not manage the logistics departure time.

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