An intelligent logistics transportation system and method based on big data fusion

Abstract

The present invention discloses an intelligent logistics transportation system and method based on big data fusion, which belongs to the field of logistics transportation technology, and specifically comprises the following steps: obtaining and pre-processing the planned transshipment cargo information of delayed flights and various data of all flights at the current airport; setting a future time window, screening out flights that take off normally within the time window and whose flight destination is the same as the delayed flight, and calculating the estimated loading time of the screened flights, screening three times according to the estimated loading time, and obtaining the remaining optional flights; grouping the cargo according to the cargo order and calculating the weight of each group of cargo; calculating the sum of the available load and available loading volume of the flight, and comparing it with the total weight and total volume of the planned transshipment cargo, and according to the comparison result, selecting to distribute the cargo according to the weight priority of each group of cargo, or selecting the flight according to the available load and available loading volume of the flight and distributing the cargo. The present invention reduces the transportation cost during flight logistics transshipment.

Description

Technical Field

[0001] The present invention relates to the field of logistics and transportation technology, and in particular to an intelligent logistics and transportation system and method based on big data fusion. Background Art

[0002] The aviation logistics industry is a strategic industrial system that uses air transportation and other methods to achieve the physical flow of goods and extend services. It integrates multiple service functions such as transportation, warehousing, distribution, and information, and is an important support for the modern industrial system.

[0003] One of the major advantages of air logistics is its timeliness. Air transport can achieve rapid delivery of goods across cities or countries. However, in actual transportation, due to flight delays, in order to ensure the timeliness of air logistics, it is sometimes necessary to transship the goods loaded on the flight.

[0004] However, the remaining available volume and load capacity of the aircraft used for transfer may not be enough to carry all the goods that need to be transferred. At this time, multiple transfer aircraft are needed to complete the transfer task together. If the best cargo distribution plan cannot be found in time, it will cause economic losses in air logistics. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent logistics transportation system and method based on big data fusion to solve the following technical problems:

[0006] In air logistics transportation, flight delays may occur, and the cargo on the delayed flight needs to be transferred by multiple aircraft. If the best cargo distribution plan cannot be found in time, some important cargo may not be transferred first, and the flight load factor cannot be maximized, resulting in economic losses in air logistics.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An intelligent logistics and transportation system based on big data fusion, including:

[0009] The data collection module is used to obtain information on scheduled transshipment cargo for delayed flights and collect and pre-process various data for all flights at the current airport, including flight scheduled departure time, flight scheduled arrival time, flight route, available loading volume, and available load capacity;

[0010] The flight screening module is used to set a future time window, screen out flights that depart normally from the current airport within the future time window, and secondarily screen out flights whose destination is the same as the delayed flight. The estimated loading time L of each flight is calculated based on the available load. The flight whose departure time is less than the corresponding estimated loading time L is screened tertiary. The number of flights remaining after the third screening is marked as n, and the total time from the scheduled arrival time of n flights is obtained.

[0011] The cargo weight module is used to divide the planned transshipment cargo into m groups according to the cargo order, obtain the value mark and expected delivery time of each group of cargo, and calculate the weight of each group of cargo using the TOPSIS method;

[0012] The transshipment allocation module is used to mark the sum of the available load and the available loading volume of n flights as M1 and V1 respectively, and mark the total weight and total volume of the planned transshipment goods as M2 and V2 respectively. If any of the situations of M1<M2 and V1<V2 exists, the planned transshipment goods are allocated according to the weight priority of each group of goods; if M1≥M2 and V1≥V2 exist at the same time, the n flights are sorted from small to large according to the available load, and flights are selected in sequence according to the order until the available load and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transshipment goods, and the planned transshipment goods are allocated to the selected flight.

[0013] As a further solution of the present invention: in the data collection module, the preprocessing includes data storage format standardization, data compression and data partitioning, wherein:

[0014] The method for standardizing the data storage format is to unify the storage format of all collected data into XML or JSON format;

[0015] The data compression method is to use Huffman coding, LZW coding or RLE coding for lossless compression;

[0016] The data partitioning method is to construct a time zone, a space zone and a cargo zone, wherein the time zone stores the scheduled departure time and the scheduled arrival time of the flight, the space zone stores the flight route, available loading volume and available load, and the cargo zone stores the planned transfer cargo information of the delayed flight, wherein the planned transfer cargo information includes: cargo volume, cargo weight and cargo value.

[0017] As a further solution of the present invention: the specific process of calculating the expected loading time L is:

[0018] Get the average cargo loading rate at the current airport and the available load of each flight selected from the secondary screening. Based on the average loading rate and available load, calculate the time required for each flight to load the available load and mark it as the expected loading time L for the flight.

[0019] As a further solution of the present invention: during the third screening, if the flight has landed at the current airport, the value of the current time from the scheduled departure time of the flight is calculated and compared with the L corresponding to the flight; if the flight has not landed at the current airport, the value of the time from the expected arrival time of the flight at the current airport to the next scheduled departure time is calculated and compared with the L corresponding to the flight.

[0020] As a further solution of the present invention: the expected delivery time of the goods refers to the time it takes for each group of goods to be transported to the flight terminal airport via the delayed flight and then transported from the terminal airport to the corresponding customer of each group of goods.

[0021] As a further solution of the present invention: the specific steps of calculating the weight of each group of goods by the TOPSIS method are:

[0022] Mark the values ​​of m groups of goods as s1, s2, ..., s m , the expected delivery time of the goods is marked as t1, t2, ..., t m , and mark the maximum value of the expected delivery time of the goods as t max ;

[0023] The expected delivery time of each group of goods is calculated using t max -t j Perform forward operation and reassign the values. After assignment, re-mark the expected delivery time of m groups of goods as e1, e2, ..., e m , j = 1, 2, ..., m;

[0024] The value of each group of goods and the expected delivery time of the goods are standardized. The standardized calculation formula is:

[0025]

[0026]

[0027] s' j With e' j Indicates the normalized value of goods and the expected delivery time of goods;

[0028] will s' j With e' j The maximum and minimum values ​​in are marked as s + , e + and s -, e - , calculate the Euclidean distance between each group of goods and the maximum and minimum values ​​respectively, and mark them as D j + and D j — , calculate the weight of each group of goods according to the Euclidean distance, and the calculation formula is:

[0029] w j =D j — / (D j + +D j — );

[0030] where w j Indicates the weight of each group of goods.

[0031] As a further solution of the present invention: if any of the conditions M1 < M2 and V1 < V2 exists, the specific steps for allocating the goods are:

[0032] S01: Sort the flights in ascending order of total transfer time and mark them as A1.

[0033] A2, ..., A n , sort each group of goods in descending order according to weight and mark it as B1,

[0034] B2, ..., B m ;

[0035] S02: Select a group of goods B one by one starting from B1 j , select B according to the flight arrangement order j Match all flights in turn. If B j The volume and weight do not exceed that of a certain flight A i Available loading volume and available load capacity, then B j Assigned to A i , i and j are index values, i = 1, 2, ..., n, j = 1, 2, ..., m;

[0036] S03: Determine a group of goods B each time j Assigned to a flight A i After that, according to B j Occupied volume and weight, recalculate A i The current available loading volume and the current available load capacity;

[0037] S04: loop through steps S02 and S03 until the volume or weight of each remaining cargo group exceeds the currently available loading volume or currently available load of any flight, then end the loop and output the allocation result.

[0038] As a further solution of the present invention: if M1≥M2 and V1≥V2 exist at the same time, the specific steps of allocating goods are:

[0039] S11: Arrange the flights in ascending order of available load and mark them as a1, a2, ..., a n and mark the available load corresponding to each flight as m1, m2, ..., m n , the available loading spaces are marked as v1, v2, ..., v n ;

[0040] S12: Accumulate the available load weight and available loading volume of the flights one by one in the order of arrangement, and mark the current total load weight and current total loading volume obtained by each accumulation as sumM respectively. k and sumV k , k=2, 3, ..., n;

[0041] S13: After each accumulation, sumM k Compared with M2, sumV k Compared with V2, when sumM is satisfied at the same time k ≥M2 and sumV k When V2 is greater than or equal to V2, stop accumulating, record the k value at this time, and output a1 to a k The flight with the same name will be used as a connecting flight, otherwise the number will continue to be accumulated;

[0042] S14: When determining a1 to a k After the flight is loaded, cargo will be loaded onto the flight with the smallest available load capacity in the order of the flights.

[0043] The present invention also includes an intelligent logistics and transportation method based on big data fusion, which is applied to the above-mentioned intelligent logistics and transportation system based on big data fusion, comprising:

[0044] Obtain information on scheduled transshipment cargo for delayed flights and collect and pre-process various data for all flights at the current airport, including scheduled departure time, scheduled arrival time, flight route, available loading volume, and available load capacity;

[0045] Set a future time window and filter out flights that depart from the current airport normally within the future time window. Secondarily filter out flights whose destination is the same as the delayed flight. Calculate the estimated loading time L for each flight based on the available payload. Thirdly filter out flights whose departure time is less than the corresponding estimated loading time L. Mark the number of flights remaining after the third screening as n, and obtain the total arrival time of these n flights.

[0046] Divide the planned transshipment cargo into m groups according to the cargo order, obtain the value mark and expected delivery time of each group of cargo, and calculate the weight of each group of cargo using the TOPSIS method;

[0047] Mark the sum of the available load and available loading volume of n flights as M1 and V1 respectively, and mark the total weight and total volume of the planned transshipment cargo as M2 and V2 respectively. If any of M1<M2 and V1<V2 exists, the planned transshipment cargo is allocated according to the weight priority of each group of cargo; if M1≥M2 and V1≥V2 exists at the same time, the n flights are sorted from small to large according to the available load, and flights are selected in sequence until the available load and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transshipment cargo, and the planned transshipment cargo is allocated to the selected flight.

[0048] Beneficial effects of the present invention:

[0049] The present invention utilizes big data technology to collect and store various flight data, which can realize rapid retrieval of large amounts of data and timely screen out all available transfer flights within a future time window. At the same time, the present invention determines whether the total available load weight and the total available loading volume of the screened flights can carry all the planned transfer cargo, and selects different cargo allocation plans according to the situation. When the total available load weight and the available loading volume are insufficient, the cargo is grouped according to the order and the cargo weight is calculated. The cargo with high weight is preferentially allocated to flights with fast transportation time, thereby ensuring the timely delivery of important cargo. When the total available load weight and the available loading volume are sufficient, the cargo is preferentially allocated to flights with small available load weight, thereby improving the flight load factor and reducing the additional transportation cost of the transfer cargo. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention will be further described below with reference to the accompanying drawings.

[0051] Figure 1 This is a module diagram of an intelligent logistics and transportation system based on big data fusion according to the present invention;

[0052] Figure 2 Schematic diagram of the flight screening module of the present invention;

[0053] Figure 3 It is a schematic flow chart of the transfer and distribution module of the present invention. DETAILED DESCRIPTION

[0054] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0055] See also Figure 1-Figure 3 As shown, the present invention is an intelligent logistics and transportation system based on big data fusion, comprising:

[0056] The data collection module is used to obtain information on planned transshipment cargo for delayed flights and collect and pre-process various data of all flights at the current airport, including flight scheduled departure time, flight scheduled arrival time, flight route, available loading volume and available load.

[0057] In the data collection module, the preprocessing includes data storage format standardization, data compression and data partitioning, wherein:

[0058] Since big data technology needs to access various platforms when collecting and processing various types of data, the data storage formats between different platforms may differ. Therefore, in order to improve data compatibility, data readability, and data processing speed, it is necessary to standardize the data storage format and unify the storage format of all collected data into XML or JSON format.

[0059] The amount of aviation logistics-related data is huge. If the data is not properly compressed, the system's operating speed will be affected. Therefore, Huffman coding, LZW coding or RLE coding is used to losslessly compress the collected data, and then the data is stored or transmitted after compression.

[0060] In order to improve the query performance of data and facilitate the management of the data life cycle, time zones, space zones and cargo zones are constructed during data storage. The time zone stores the scheduled departure time and arrival time of flights, the space zone stores flight routes, available loading volume and available load, and the cargo zone stores the planned transshipment cargo information of delayed flights. The planned transshipment cargo information includes: cargo volume, cargo weight and cargo value. Partitioning can help the system expand horizontally more easily. New data can be assigned to new partitions without reorganizing existing data. At the same time, partitioning makes data management more flexible. For example, independent backup and recovery operations can be performed on different partitions without affecting other partitions.

[0061] The flight screening module is used to set a future time window, screen out flights that take off normally from the current airport within the future time window, and secondarily screen out flights whose destination is the same as the delayed flight. The estimated loading time L of each flight is calculated based on the available load. The specific process of calculating the estimated loading time L is as follows: obtain the average cargo loading rate of the current airport and the available load of each flight screened out twice, and calculate the time required for each flight to load the available load based on the average loading rate and available load, and mark it as the estimated loading time L of the flight.

[0062] The flights are screened three times based on L. If the flight has already landed at the current airport, the distance between the current time and the scheduled departure time of the flight is calculated and compared with the L corresponding to the flight. If the flight has not yet landed at the current airport, the distance between the estimated arrival time at the current airport and the next scheduled departure time is calculated and compared with the L corresponding to the flight. All flights with calculated values ​​less than L are screened out, and the number of flights remaining after three screenings is marked as n. The total duration of the n flights from the scheduled arrival time is obtained.

[0063] The purpose of the third screening is to avoid the situation where the scheduled departure time of the flight selected from the second screening has not yet been completed. Selecting the flight's available load to calculate L means that each flight will be fully loaded with the available load by default, which can ensure sufficient time redundancy.

[0064] The cargo weighting module divides the planned transshipment cargo into m groups based on the cargo order. It then obtains the value tag and expected delivery time for each group, and calculates the weight of each group using the TOPSIS method. The expected delivery time is the time it takes for each group of cargo, originally scheduled to be transported via the delayed flight to the final airport, and then from the final airport to the corresponding customer. This is because air logistics requires the collection of various types of cargo, and these cargoes may ultimately be delivered to different target customers. Even if cargo arrives at the same airport on the same flight, it may still need to be transshipped from the destination airport to different target customers through other means.

[0065] The specific steps of calculating the weight of each group of goods by the TOPSIS method are:

[0066] Step 1: Mark the values ​​of m groups of goods as s1, s2, ..., s m , the expected delivery time of the goods is marked as t1, t2, ..., t m , and mark the maximum value of the expected delivery time of the goods as t max ;

[0067] Step 2: Use t to calculate the expected delivery time for each group of goods. max -tj The reason for performing the forward operation and only forwarding the expected delivery time of the goods is that when the weight is evaluated based on the value of the goods, the higher the value, the higher the weight, but when the weight is evaluated based on the expected delivery time, the shorter the time, the higher the weight. There is a difference in the judgment logic of the two. When the weight of the goods is judged by combining the two, it is necessary to forward-normalize the expected delivery time of the goods to make the judgment logic of the two the same. After the forward operation, the expected delivery time is reassigned and marked as e1, e2, ..., e m , j = 1, 2, ..., m.

[0068] Step 3: Standardize the value and expected delivery time of each group of goods. The standardization calculation formula is:

[0069]

[0070]

[0071] s' j With e' j Indicates the normalized value of goods and the expected delivery time of the goods.

[0072] Step 4: Change s' j With e' j The maximum and minimum values ​​in are marked as s + , e + and s - , e - , calculate the Euclidean distance between each group of goods and the maximum and minimum values ​​respectively, and mark them as D j + and D j — , calculate the weight of each group of goods according to the Euclidean distance, and the calculation formula is:

[0073] w j =D j — / (D j + +D j — );

[0074] where w j Indicates the weight of each group of goods.

[0075] The transshipment allocation module is used to mark the total available load weight and available loading volume of n flights as M1 and V1 respectively, and mark the total weight and total volume of the planned transshipment cargo as M2 and V2 respectively. If either M1 < M2 or V1 < V2 exists, the planned transshipment cargo is allocated according to the weight priority of each group of cargo. The specific steps of allocation are as follows:

[0076] S01: Sort the flights in ascending order of total transfer time and mark them as A1.

[0077] A2, ..., A n , sort each group of goods in descending order according to weight and mark it as B1,

[0078] B2, ..., B m ;

[0079] S02: Select a group of goods B one by one starting from B1 j , select B according to the flight arrangement order j Match all flights in sequence. If B j The volume and weight do not exceed that of a certain flight A i Available loading volume and available load capacity, then B j Assigned to A i , i and j are index values, i = 1, 2, ..., n, j = 1, 2, ..., m. The purpose of this step is to ensure that the goods with high weights are allocated to the aircraft with short transportation time first, so that important goods can be delivered in time;

[0080] S03: Determine a group of goods B each time j Assigned to a flight A i After that, according to B j Occupied volume and weight, recalculate A i The current available loading volume and the current available load capacity;

[0081] S04: loop through steps S02 and S03 until the currently available loading volume or the currently available load of any flight is insufficient to load any remaining group of cargoes, then terminate the loop and output the allocation result.

[0082] Through steps S01-S04, high-weight goods can be allocated to flights with short transportation time first, ensuring that important goods can be delivered in time.

[0083] If both M1≥M2 and V1≥V2 exist, then n flights are sorted in ascending order based on available load capacity, and flights are selected in sequence until the available load capacity and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transshipment cargo, and the planned transshipment cargo is allocated to the selected flight. The specific steps of allocation are as follows:

[0084] S11: Arrange the flights in ascending order of available load and mark them as a1, a2, ..., a n and mark the available load corresponding to each flight as m1, m2, ..., m n, the available loading spaces are marked as v1, v2, ..., v n .

[0085] S12: Accumulate the available load weight and available loading volume of the flight item by item in the order of arrangement, and mark the current total load weight and current total loading volume obtained by each accumulation as sumM respectively. k and sumV k , k=2, 3, ..., n.

[0086] S13: After each accumulation, sumM k Compared with M2, sumV k Compared with V2, when sumM is satisfied at the same time k ≥M2 and sumV k When ≥V2, stop accumulating, record the k value at this time, and output a1 to a k The flight that is used is regarded as a connecting flight, otherwise the accumulative amount will continue to be calculated.

[0087] S14: When determining a1 to a k After the flight is loaded, cargo will be loaded onto the flight with the smallest available load capacity in the order of the flights.

[0088] S13 and S14 can ensure maximum utilization of aircraft cabin space and load capacity, improve flight load factors, and give priority to loading cargo on flights with smaller available load capacity, which can maximize the spread of air logistics transportation costs and reduce the additional transfer costs incurred when transferring cargo on delayed flights.

[0089] The present invention also includes an intelligent logistics and transportation method based on big data fusion, which is applied to the above-mentioned intelligent logistics and transportation system based on big data fusion, comprising:

[0090] Obtain information on scheduled transshipment cargo for delayed flights and collect and pre-process various data for all flights at the current airport, including scheduled departure time, scheduled arrival time, flight route, available loading volume, and available load capacity;

[0091] Set a future time window and filter out flights that depart from the current airport normally within the future time window. Secondarily filter out flights whose destination is the same as the delayed flight. Calculate the estimated loading time L for each flight based on the available payload. Thirdly filter out flights whose departure time is less than the corresponding estimated loading time L. Mark the number of flights remaining after the third screening as n, and obtain the total arrival time of these n flights.

[0092] Divide the planned transshipment cargo into m groups according to the cargo order, obtain the value mark and expected delivery time of each group of cargo, and calculate the weight of each group of cargo using the TOPSIS method;

[0093] Mark the sum of the available load and available loading volume of n flights as M1 and V1 respectively, and mark the total weight and total volume of the planned transshipment cargo as M2 and V2 respectively. If any of M1<M2 and V1<V2 exists, the planned transshipment cargo is allocated according to the weight priority of each group of cargo; if M1≥M2 and V1≥V2 exists at the same time, the n flights are sorted from small to large according to the available load, and flights are selected in sequence until the available load and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transshipment cargo, and the planned transshipment cargo is allocated to the selected flight.

[0094] The above formulas are all dimensionless and numerical calculations. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field according to actual conditions.

[0095] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

[0096] The above is a detailed description of an embodiment of the present invention. However, the content described is only a preferred embodiment of the present invention and should not be considered to limit the scope of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An intelligent logistics and transportation system based on big data fusion, characterized by: include: The data collection module is used to obtain information on scheduled transshipment cargo for delayed flights and collect and pre-process various data for all flights at the current airport, including flight scheduled departure time, flight scheduled arrival time, flight route, available loading volume, and available load capacity; The flight screening module is used to set a future time window, screen out flights that depart normally from the current airport within the future time window, and secondarily screen out flights whose destination is the same as the delayed flight. The estimated loading time L of each flight is calculated based on the available load. The flight whose departure time is less than the corresponding estimated loading time L is screened tertiary. The number of flights remaining after the third screening is marked as n, and the total time from the scheduled arrival time of n flights is obtained. The cargo weight module is used to divide the planned transshipment cargo into m groups according to the cargo order, obtain the value mark and expected delivery time of each group of cargo, and calculate the weight of each group of cargo using the TOPSIS method; The transshipment allocation module is used to mark the sum of the available load and the available loading volume of n flights as M1 and V1 respectively, and mark the total weight and total volume of the planned transshipment goods as M2 and V2 respectively. If any of the situations of M1<M2 and V1<V2 exists, the planned transshipment goods are allocated according to the weight priority of each group of goods; if M1≥M2 and V1≥V2 exist at the same time, the n flights are sorted from small to large according to the available load, and flights are selected in sequence according to the order until the available load and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transshipment goods, and the planned transshipment goods are allocated to the selected flight.

2. The intelligent logistics and transportation system based on big data fusion according to claim 1 is characterized in that: In the data collection module, the preprocessing includes data storage format standardization, data compression and data partitioning, wherein: The method for standardizing the data storage format is to unify the storage format of all collected data into XML or JSON format; The data compression method is to use Huffman coding, LZW coding or RLE coding for lossless compression; The data partitioning method is to construct a time zone, a space zone and a cargo zone, wherein the time zone stores the scheduled departure time and the scheduled arrival time of the flight, the space zone stores the flight route, available loading volume and available load, and the cargo zone stores the planned transfer cargo information of the delayed flight, wherein the planned transfer cargo information includes: cargo volume, cargo weight and cargo value.

3. The intelligent logistics and transportation system based on big data fusion according to claim 1 is characterized in that: The specific process of calculating the expected loading time L is as follows: Get the average cargo loading rate at the current airport and the available load of each flight selected from the secondary screening. Based on the average loading rate and available load, calculate the time required for each flight to load the available load and mark it as the expected loading time L for the flight.

4. The intelligent logistics and transportation system based on big data fusion according to claim 3 is characterized in that: During the third screening, if the flight has landed at the current airport, the distance between the current time and the scheduled departure time of the flight is calculated and compared with the L corresponding to the flight; if the flight has not landed at the current airport, the distance between the estimated arrival time of the flight at the current airport and the next scheduled departure time is calculated and compared with the L corresponding to the flight.

5. The intelligent logistics and transportation system based on big data fusion according to claim 1 is characterized in that: The expected delivery time of the goods refers to the time it takes for each group of goods to be transported to the flight's final airport via the delayed flight and then transported from the final airport to the corresponding customer of each group of goods.

6. The intelligent logistics and transportation system based on big data fusion according to claim 5 is characterized in that: The specific steps of calculating the weight of each group of goods by the TOPSIS method are: Mark the values ​​of m groups of goods as s1, s2, ..., s m , the expected delivery time of the goods is marked as t1, t2, ..., t m , and mark the maximum value of the expected delivery time of the goods as t max ; The expected delivery time of each group of goods is calculated using t max -t j Perform forward operation and reassign the values. After assignment, re-mark the expected delivery time of m groups of goods as e1, e2, ..., e m , j = 1, 2，...，m； The value of each group of goods and the expected delivery time of the goods are standardized. The standardized calculation formula is: s' j With e' j Indicates the normalized value of goods and the expected delivery time of goods; will s' j With e' j The maximum and minimum values ​​in are marked as s + , e + and s - , e - , calculate the Euclidean distance between each group of goods and the maximum and minimum values ​​respectively, and mark them as D j + and D j — , calculate the weight of each group of goods according to the Euclidean distance, and the calculation formula is: w j =D j — / (D j + +D j — ); where w j Indicates the weight of each group of goods.

7. The intelligent logistics and transportation system based on big data fusion according to claim 6 is characterized in that: If any of the following conditions exists: M1<M2 and V1<V2, the specific steps for allocating goods are as follows: S01: Sort the flights in ascending order of total transfer time and mark them as A1. A2, ..., A n , sort each group of goods in descending order according to weight and mark it as B1, B2,...,B m ; S02: Select a group of goods B one by one starting from B1 j , select B according to the flight arrangement order j Match all flights in turn. If B j The volume and weight do not exceed that of a certain flight A i Available loading volume and available load capacity, then B j Assigned to A i , i and j are index values, i = 1, 2, ..., n, j = 1, 2, ..., m; S03: Determine a group of goods B each time j Assigned to a flight A i After that, according to B j Occupied volume and weight, recalculate A i The current available loading volume and the current available load capacity; S04: loop through steps S02 and S03 until the volume or weight of each remaining cargo group exceeds the currently available loading volume or currently available load of any flight, then end the loop and output the allocation result.

8. The intelligent logistics and transportation system based on big data fusion according to claim 1 is characterized in that: If M1≥M2 and V1≥V2 exist at the same time, the specific steps for allocating goods are: S11: Arrange the flights in ascending order of available load and mark them as a1, a2, ..., a n and mark the available load corresponding to each flight as m1, m2, ..., m n , the available loading spaces are marked as v1, v2, ..., v n ; S12: Accumulate the available load weight and available loading volume of the flights one by one in the order of arrangement, and mark the current total load weight and current total loading volume obtained by each accumulation as sumM respectively. k and sumV k , k=2, 3, ..., n; S13: After each accumulation, sumM k Compared with M2, sumV k Compared with V2, when sumM is satisfied at the same time k ≥M2 and sumV k When V2 is greater than or equal to V2, stop accumulating, record the k value at this time, and output a1 to a k The flight with the same name will be used as a connecting flight, otherwise the number will continue to be accumulated; S14: When determining a1 to a k After the flight is loaded, cargo will be loaded onto the flight with the smallest available load capacity in the order of the flights.

9. An intelligent logistics and transportation method based on big data fusion, applied to an intelligent logistics and transportation system based on big data fusion according to any one of claims 1 to 8, characterized in that: include: Obtain information on scheduled transshipment cargo for delayed flights and collect and pre-process various data for all flights at the current airport, including scheduled departure time, scheduled arrival time, flight route, available loading volume, and available load capacity; Set a future time window and filter out flights that depart from the current airport normally within the future time window. Secondarily filter out flights whose destination is the same as the delayed flight. Calculate the estimated loading time L for each flight based on the available payload. Thirdly filter out flights whose departure time is less than the corresponding estimated loading time L. Mark the number of flights remaining after the third screening as n, and obtain the total arrival time of these n flights. Divide the planned transshipment cargo into m groups according to the cargo order, obtain the value mark and expected delivery time of each group of cargo, and calculate the weight of each group of cargo using the TOPSIS method; The total available load weight and available loading volume of n flights are marked as M1 and V1 respectively, and the total weight and total volume of the planned transshipment cargo are marked as M2 and V2 respectively. If either M1 < M2 or V1 < V2 exists, the planned transshipment cargo is allocated according to the weight priority of each group of cargo; If M1≥M2 and V1≥V2 exist at the same time, n flights will be sorted from small to large according to the available load capacity, and flights will be selected in sequence until the available load capacity and available loading volume of the selected flight are both greater than the total weight and total volume of the planned transfer cargo, and the planned transfer cargo will be allocated to the selected flight.

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