Logistics intelligent management system and method based on data analysis

Abstract

The invention discloses a logistics intelligent management system and method based on data analysis, and relates to the technical field of logistics intelligent management, and the method comprises the steps: S10, obtaining the logistics demand data of a logistics platform and the logistics transportation data of each logistics company, analyzing the matching condition of the logistics demand data and each logistics transportation data in space and time, and obtaining the logistics demand data of the logistics platform and the logistics transportation data of each logistics company; carrying out preliminary screening on the logistics centers meeting the requirements of the logistics platform; s20, analyzing the execution strength of each logistics center, which is preliminarily screened and reserved, on each logistics order; and S30, carrying out intelligent distribution on the logistics orders of the logistics platform. According to the invention, the distribution decision of the logistics order corresponding to each type of transportation path end point is intelligently generated according to the execution strength of each logistics center on the logistics order, and the process does not need manual docking with a logistics company to obtain the transportation cost and the transportation efficiency provided by the logistics company, thereby improving the system intellectualization effect, and improving the transportation efficiency. And the distribution efficiency of the system on the logistics orders is also improved.

Description

Technical Field

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

[0002] The intelligent logistics system is an advanced logistics management system that integrates informationization, automation, and intelligence. It uses Internet of Things technology, big data analysis, artificial intelligence and other technical means to optimize and upgrade traditional logistics processes to achieve automation, intelligence, and informationization of the logistics process. The intelligent logistics system can help companies improve logistics efficiency and accuracy, reduce logistics costs and risks, and enhance corporate competitiveness.

[0003] Nowadays, when choosing a logistics company, transportation cost and transportation efficiency become important selection criteria, among which transportation cost and transportation efficiency are generally provided by logistics companies. The determination of transportation cost and transportation efficiency is affected by the transportation network and service scope of the logistics company. This influence is generally ignored when selecting a logistics company. Therefore, it takes a certain amount of time and effort to choose a logistics company with high cost performance among many logistics companies. Summary of the invention

[0004] The purpose of the present invention is to provide a logistics intelligent management system and method based on data analysis to solve the problems raised in the prior art.

[0005] To achieve the above object, the present invention provides the following technical solution: a logistics intelligent management method based on data analysis, the method comprising:

[0006] S10: Obtain the logistics demand data of the logistics platform and the logistics transportation data of each logistics company, and analyze the fit between the logistics demand data and the logistics transportation data in space and time.

[0007] Conduct preliminary screening of logistics centers that meet the requirements of the logistics platform;

[0008] S20: According to the logistics order data recorded in each logistics order, the logistics transportation parameters of each logistics center that is initially screened and retained are obtained, and based on the obtained information, the execution strength of each logistics center that is initially screened and retained for each logistics order is analyzed;

[0009] S30: Based on the analysis results of the execution strength of each logistics center on each logistics order, the logistics orders of the logistics platform are intelligently allocated.

[0010] Further, the S10 includes:

[0011] S101: Obtaining logistics order data of the logistics platform and logistics transportation data of each logistics company. The logistics order data includes the time of departure of the transported goods, the coordinates of the location where the transported goods are picked up, the specifications of the transported goods and the destination of the transported goods. The logistics transportation data includes the location coordinates of each logistics center and the volume occupied by the goods to be transported corresponding to each logistics center;

[0012] S102: Randomly select a logistics company and a logistics order, and calculate the spatial fit S between the selected logistics order and the logistics center numbered i in the selected logistics company according to the location coordinates of the logistics centers of the selected logistics company and the pickup range of each logistics center. i Calculate, S i =1-exp(-d i );

[0013] Among them, i = 1, 2, ..., m, represents the number of each logistics center in the selected logistics company, m represents the total number of logistics centers in the selected logistics company, d i It represents the distance between the coordinates of the pickup location of the transported goods recorded in the selected logistics order and the coordinates of the logistics center numbered i in the selected logistics company, and exp represents an exponential function with e as the base;

[0014] S103: According to the real-time storage coefficient of the selected logistics order of the logistics center numbered i in the selected logistics company, in [t1, t2-u i ] time period, the abnormal storage coefficients are eliminated, and based on the elimination results, the matching index Q between the logistics center numbered i in the selected logistics company and the selected logistics order is calculated. i Make predictions;

[0015] S104: If 0.8<Q i ≤1, it is considered that the logistics center numbered i in the selected logistics company meets the requirements of the logistics platform. If 0≤Q i <1, it is considered that the logistics center numbered i in the selected logistics company does not meet the requirements of the logistics platform;

[0016] Repeat the operations of S102 to S104 to determine the logistics center that meets the requirements of the logistics platform.

[0017] Furthermore, S103 calculates the matching index Q between the logistics center numbered i in the selected logistics company and the selected logistics order. i The specific method for making predictions is:

[0018] According to the order time t1 of the selected logistics order, the delivery time t2 of the transported goods recorded in the selected logistics order, and the specifications of the transported goods, in [t1, t2-u i] time period, calculate the real-time storage coefficient of the selected logistics order for the logistics center numbered i in the selected logistics company, g ti =(R i -k ti ) / f1;

[0019] Where t represents the time value and t1<t<t2-u i ,u i R represents the average outbound time of the collected goods by the logistics center numbered i in the selected logistics company, i represents the inventory volume corresponding to the logistics center numbered i in the selected logistics company, k ti represents the volume of goods to be transported corresponding to the logistics center numbered i in the selected logistics company at time t, f1 represents the total volume of transported goods obtained according to the transported goods specifications recorded in the selected logistics order, and g ti It represents the storage coefficient of the selected logistics order at the logistics center numbered i in the selected logistics company at time t;

[0020] In [t1,t2-u i ] time period, the abnormal storage coefficient is eliminated, and the matching index between the logistics center numbered i in the selected logistics company and the selected logistics order is predicted based on the storage data retained after elimination. The specific prediction formula is:

[0021]

[0022] Among them, a1 and a2 are proportional coefficients and a1+a2=1, b 1i Indicates that in [t1,t2-u i ] The total number of stored data that are removed and retained during the time period, b 2i Indicates that in [t1,t2-u i ]The total number of stored data removed during the time period, Q i It indicates the compatibility index between the logistics center numbered i in the selected logistics company and the selected logistics order.

[0023] Further, the S20 includes:

[0024] S201: When 0.8<Q i When ≤1, the selected logistics order is considered to be the matching logistics order of the logistics center numbered i in the selected logistics company. According to the matching logistics order determination method, the matching logistics orders of each logistics center that has been initially screened and retained are determined. According to the location coordinates of each logistics center that has been initially screened and retained, and the destination of the transported goods recorded in each matching logistics order of each logistics center that has been initially screened and retained, the transportation path existing for each logistics order is determined;

[0025] S202: Obtain the logistics and transportation parameters of the logistics center corresponding to the starting point of each transportation path. The logistics and transportation parameters include the average retention time of the transported goods at each transfer station, the average transportation damage rate of the transported goods, and the transportation method of the transported goods by the logistics center. According to the obtained logistics and transportation parameters, predict the transportation index of each transportation path. The specific prediction formula is:

[0026]

[0027] Where j = 1, 2, ..., n, represents the number corresponding to each transport path, n represents the total number of transport paths, D j represents the travel distance corresponding to the jth transport route on the map, p = 1, 2, ..., Y, represents the number corresponding to each transport mode, Y represents the total number of transport modes, E p It represents the transportation cost required to transport the goods per unit distance when the transport method numbered p is used to transport the goods. p It represents the freight rate index corresponding to the transport mode numbered p. a1, a2, a3, and a4 are all weight coefficients. U 1jp It represents the transportation time of the transported goods on the jth transportation route when the transport mode numbered p is used to transport the transported goods. 2jp It represents the average total detention time of the transported goods at the transfer station included in the jth transport route when the transport mode numbered p is used to transport the transported goods. 3j represents the average delivery time of the goods from the logistics center corresponding to the starting point of the j-th transportation route, U 4j S represents the average delivery time of the logistics center corresponding to the starting point of the j-th transportation route. j represents the transport production index corresponding to the transport route numbered j, L j represents the total volume of transported goods obtained according to the transported goods specifications recorded in the logistics order corresponding to the end point of the j-th transport route, C pj It represents the maximum volume of goods that can be transported in a single trip when the logistics center corresponding to the starting point of the j-th transportation route uses the transportation method numbered p to transport the goods. e is a constant and e>1. W j represents the transport index of the transport path numbered j;

[0028] S203: According to F j =W j *w j The execution strength of the logistics center corresponding to the starting point of the j-th transportation route for the logistics order corresponding to the end point of the j-th transportation route is calculated, where w j It represents the average transportation damage rate of the transported goods by the logistics center corresponding to the starting point of the j-th transportation route.

[0029] Further, the S30 includes:

[0030] According to the logistics orders corresponding to the end points of each transportation path, the transportation paths are classified and processed. The logistics orders corresponding to the end points of each type of transportation path are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of the μ-th type of transportation path is collected, and the collected data is put into the set M μ In the above equation, the logistics order corresponding to the end point of the μth transport path is assigned to the logistics center corresponding to maxM, where μ = 1, 2, ..., N, indicating the number corresponding to each type of transport path, N indicating the total number, and max indicating the maximum value symbol.

[0031] A logistics intelligent management system based on data analysis, the system includes a logistics data acquisition module, a fit analysis module, a transportation index prediction module, an execution strength analysis module and an intelligent decision generation module;

[0032] The logistics data acquisition module is used to acquire the logistics demand data of the logistics platform and the logistics transportation data of each logistics company;

[0033] The matching analysis module is used to analyze the matching of logistics demand data and various logistics transportation data in space and time, and to preliminarily screen logistics centers that meet the requirements of the logistics platform;

[0034] The transport index prediction module is used to predict the transport index of each transport path;

[0035] The execution strength analysis module is used to analyze the execution strength of each logistics center that has been initially screened and retained for each logistics order;

[0036] The intelligent decision making module is used to intelligently allocate logistics orders of the logistics platform.

[0037] Furthermore, the fit analysis module includes a fitness calculation unit, a fit index prediction unit and a screening unit;

[0038] The fitness calculation unit calculates the spatial fitness between the selected logistics order and each logistics center in the selected logistics company according to the logistics data acquired by the logistics data acquisition module;

[0039] The matching index prediction unit eliminates abnormal storage coefficients according to the real-time storage coefficients of each logistics center in the selected logistics company for the selected logistics order before the transported goods are shipped out of the warehouse, and predicts the matching index of each logistics center in the selected logistics company and the selected logistics order based on the elimination results;

[0040] The screening unit determines the logistics center that meets the requirements of the logistics platform according to the prediction results transmitted by the matching index prediction unit.

[0041] Further, the transport index prediction module includes a transport path determination unit and a transport index prediction unit;

[0042] The transport path determination unit determines the matching logistics orders of each logistics center that are initially screened and retained according to the prediction results transmitted by the matching index prediction unit, and determines the existing transportation paths for each logistics order in combination with the location coordinates of each logistics center and the destination of the transported goods recorded in each logistics order;

[0043] The transport index prediction unit constructs a mathematical model according to the logistics transport parameters of the logistics center corresponding to the starting point of each transport path, and predicts the transport index of each transport path.

[0044] Furthermore, the execution strength analysis module analyzes the execution strength of each logistics center on each logistics order that has been preliminarily selected, based on the average transportation damage rate of the transported goods by the logistics center corresponding to the starting point of each transportation route, and the transportation index of each transportation route.

[0045] Furthermore, the intelligent decision-making generation module classifies the transportation routes according to the logistics orders corresponding to the end points of each transportation route. The logistics orders corresponding to the end points of each type of transportation route are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of each type of transportation route is collected, and the collected data is put into a set. Based on the maximum value in the set, the allocation decision of the logistics order is intelligently generated.

[0046] Compared with the prior art, the present invention has the following beneficial effects:

[0047] 1. The present invention analyzes the spatial compatibility between the logistics order and each logistics center based on the pick-up location of the transported goods recorded in the logistics order, as well as the location coordinates and service scope of each logistics center. Combined with the real-time storage coefficient of the logistics order of each logistics center, the present invention analyzes the temporal and spatial compatibility between each logistics company and the logistics order. Based on the analysis results, it is ensured that after the logistics order is accepted by the logistics company, the transported goods can be smoothly shipped out to avoid backlog, which is conducive to improving transportation efficiency.

[0048] 2. In the process of selecting logistics companies, the present invention takes into account the transportation network of each logistics company, determines the transportation path of each logistics order based on each transportation network point (transportation transit point), and predicts the transportation index of each transportation path in combination with the transportation conditions of the logistics company on the corresponding transportation path. In the prediction process, not only the transportation cost and transportation efficiency are taken into account, but also the traffic operation conditions of the transportation section corresponding to the transportation path, and the single transportation degree of the logistics order are taken into account, so as to ensure that the execution strength of each logistics center on the logistics order obtained by the transportation index is more accurate, thereby further improving the system's analysis effect on logistics data.

[0049] 3. The present invention intelligently generates allocation decisions for logistics orders corresponding to the end points of various transportation routes based on the execution strength of logistics centers on logistics orders. This process does not require manual connection with logistics companies to obtain transportation costs and transportation efficiency provided by logistics companies. While improving the intelligent effect of the system, it also improves the system's efficiency in allocating logistics orders. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 A schematic diagram of the workflow of a logistics intelligent management system and method based on data analysis of the present invention;

[0051] Figure 2 The present invention is a schematic diagram of the working principle of a logistics intelligent management system and method based on data analysis. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0053] Example: Figure 1 and Figure 2 As shown, the present invention provides a logistics intelligent management system and method technical solution based on data analysis, a logistics intelligent management method based on data analysis, the method comprising:

[0054] S10: Obtain the logistics demand data of the logistics platform and the logistics transportation data of each logistics company, and analyze the fit between the logistics demand data and the logistics transportation data in space and time.

[0055] Conduct preliminary screening of logistics centers that meet the requirements of the logistics platform;

[0056] S10 includes:

[0057] S101: Obtaining logistics order data of the logistics platform and logistics transportation data of each logistics company. The logistics order data includes the time of departure of the transported goods, the coordinates of the location where the transported goods are picked up, the specifications of the transported goods and the destination of the transported goods. The logistics transportation data includes the location coordinates of each logistics center and the volume occupied by the goods to be transported corresponding to each logistics center. The location coordinates are determined based on the world coordinate system.

[0058] S102: Randomly select a logistics company and a logistics order, and calculate the spatial fit S between the selected logistics order and the logistics center numbered i in the selected logistics company according to the location coordinates of the logistics centers of the selected logistics company and the pickup range of each logistics center. i Calculate, S i =1-exp(-d i );

[0059] Among them, i = 1, 2, ..., m, represents the number of each logistics center in the selected logistics company, m represents the total number of logistics centers in the selected logistics company, d i Indicates the distance between the coordinates of the pickup location of the transported goods recorded in the selected logistics order and the coordinates of the logistics center numbered i in the selected logistics company, d i The unit is kilometers, and exp represents an exponential function with e as the base;

[0060] S103: According to the real-time storage coefficient of the selected logistics order of the logistics center numbered i in the selected logistics company, in [t1, t2-u i ] time period, the abnormal storage coefficients are eliminated, and based on the elimination results, the matching index Q between the logistics center numbered i in the selected logistics company and the selected logistics order is calculated. i To make a prediction, the specific method is:

[0061] According to the order time t1 of the selected logistics order, the delivery time t2 of the transported goods recorded in the selected logistics order, and the specifications of the transported goods, in [t1, t2-u i ] time period, calculate the real-time storage coefficient of the selected logistics order for the logistics center numbered i in the selected logistics company, g ti =(R i -k ti ) / f1;

[0062] Where t represents the time value and t1<t<t2-u i ,u i R represents the average outbound time of the collected goods by the logistics center numbered i in the selected logistics company, i represents the inventory volume corresponding to the logistics center numbered i in the selected logistics company, k tirepresents the volume of goods to be transported corresponding to the logistics center numbered i in the selected logistics company at time t, f1 represents the total volume of transported goods obtained according to the transported goods specifications recorded in the selected logistics order, and g ti It represents the storage coefficient of the selected logistics order at the logistics center numbered i in the selected logistics company at time t;

[0063] In [t1,t2-u i ] time period, the abnormal storage coefficient is eliminated, if g ti >g (t+c)i , then it is considered that the storage coefficient g of the selected logistics order at the selected logistics center numbered i in the selected logistics company at time t+c (t+c)i is the abnormal storage coefficient, c represents k ti The collection interval is based on the data that is retained after the data is removed. The matching index between the logistics center numbered i in the selected logistics company and the selected logistics order is predicted. The specific prediction formula is:

[0064]

[0065] Among them, a1 and a2 are proportional coefficients and a1+a2=1, b 1i Indicates that in [t1,t2-u i ] The total number of stored data that are removed and retained during the time period, b 2i Indicates that in [t1,t2-u i ]The total number of stored data removed during the time period, Q i It represents the compatibility index between the logistics center numbered i in the selected logistics company and the selected logistics order;

[0066] S104: If 0.8<Q i ≤1, it is considered that the logistics center numbered i in the selected logistics company meets the requirements of the logistics platform. If 0≤Q i <1, it is considered that the logistics center numbered i in the selected logistics company does not meet the requirements of the logistics platform;

[0067] Repeat the operations of S102 to S104 to determine the logistics center that meets the requirements of the logistics platform;

[0068] S20: According to the logistics order data recorded in each logistics order, the logistics transportation parameters of each logistics center that is initially screened and retained are obtained, and based on the obtained information, the execution strength of each logistics center that is initially screened and retained for each logistics order is analyzed;

[0069] The S20 includes:

[0070] S201: When 0.8<Q iWhen ≤1, the selected logistics order is considered to be the matching logistics order of the logistics center numbered i in the selected logistics company. According to the matching logistics order determination method, the matching logistics orders of each logistics center that is initially screened and retained are determined. According to the location coordinates of each logistics center that is initially screened and retained, and the destination of the transported goods recorded in each matching logistics order of each logistics center that is initially screened and retained, the transportation path existing in each logistics order is determined. The starting point of the transportation path is the location coordinates of the logistics center, and the end point of the transportation path is the destination of the transported goods recorded in the logistics order. The middle point of the transportation path is determined by the transfer station of the logistics company corresponding to the logistics center;

[0071] S202: Obtain the logistics and transportation parameters of the logistics center corresponding to the starting point of each transportation path. The logistics and transportation parameters include the average retention time of the transported goods at each transfer station, the average transportation damage rate of the transported goods, and the transportation method of the transported goods by the logistics center. According to the obtained logistics and transportation parameters, predict the transportation index of each transportation path. The specific prediction formula is:

[0072]

[0073] Where j = 1, 2, ..., n, represents the number corresponding to each transport path, n represents the total number of transport paths, D j represents the travel distance corresponding to the jth transport route on the map, p = 1, 2, ..., Y, represents the number corresponding to each transport mode, Y represents the total number of transport modes, E p It represents the transportation cost required to transport the goods per unit distance when the transport method numbered p is used to transport the goods. Unit distance = 1 meter, y p It indicates the freight rate index corresponding to the transport mode numbered p. The freight rate index is a dynamic relative number that reflects the trend and degree of freight rate changes in different periods. The calculation method of the freight rate index is the existing technology. a1, a2, a3, and a4 are all weight coefficients. U 1jp It represents the transportation time of the transported goods on the jth transportation route when the transport mode numbered p is used to transport the transported goods. 2jp It indicates the average total detention time of the transported goods at the transfer station included in the jth transport route when the transport mode numbered p is used to transport the transported goods. The average total detention time = the sum of the average detention time of the transported goods at each transfer station included in the jth transport route. U 3j U represents the average delivery time of the goods at the logistics center corresponding to the starting point of the j-th transportation route. Delivery time = the time of shipping goods - the time of receiving orders for the goods. The average delivery time is calculated by averaging the delivery time. Shipping time = the time when the goods are collected. The order receiving time is the time when the logistics company receives the logistics order and starts processing it.4j S represents the average delivery time of the goods at the logistics center corresponding to the starting point of the j-th transportation route. Delivery time = the time when the goods are signed for - the time when the goods arrive at the destination. j It represents the transport production index corresponding to the transport path numbered j. The transport production index is a comprehensive index based on the passenger and freight volume of railways, highways, waterways, civil aviation and other transport modes from the perspective of comprehensive transportation. It reflects the overall operation status of the transportation industry through weighted synthesis. The calculation method of the transport production index is the existing technology. L j represents the total volume of the transported goods obtained according to the transported goods specifications recorded in the logistics order corresponding to the end point of the j-th transport route (the transported goods specifications refer to the length, width and height of the transported goods), C pj It represents the maximum volume of goods that can be transported in a single trip when the logistics center corresponding to the starting point of the j-th transportation route uses the transportation method numbered p to transport the goods. e is a constant and e>1. W j represents the transport index of the transport path numbered j;

[0074] S203: According to F j =W j *w j The execution strength of the logistics center corresponding to the starting point of the j-th transportation route for the logistics order corresponding to the end point of the j-th transportation route is calculated, where w j It represents the average transportation damage rate of the transported goods by the logistics center corresponding to the starting point of the j-th transportation route;

[0075] S30: Intelligently allocate logistics orders of the logistics platform according to the analysis results of the execution strength of each logistics center on each logistics order;

[0076] S30 includes:

[0077] According to the logistics orders corresponding to the end points of each transportation path, the transportation paths are classified and processed. The logistics orders corresponding to the end points of each type of transportation path are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of the μ-th type of transportation path is collected, and the collected data is put into the set M μ In the above equation, the logistics order corresponding to the end point of the μth transport path is assigned to the logistics center corresponding to maxM, where μ = 1, 2, ..., N, indicating the number corresponding to each type of transport path, N indicating the total number, and max indicating the maximum value symbol.

[0078] A logistics intelligent management system based on data analysis, the system includes a logistics data acquisition module, a fit analysis module, a transportation index prediction module, an execution strength analysis module and an intelligent decision generation module;

[0079] The logistics data acquisition module is used to acquire the logistics demand data of the logistics platform and the logistics transportation data of each logistics company;

[0080] The matching analysis module is used to analyze the matching of logistics demand data and various logistics transportation data in space and time, and to preliminarily screen the logistics centers that meet the requirements of the logistics platform;

[0081] The fit analysis module includes a fit calculation unit, a fit index prediction unit and a screening unit;

[0082] The fitness calculation unit calculates the spatial fitness between the selected logistics order and each logistics center in the selected logistics company according to the logistics data acquired by the logistics data acquisition module;

[0083] The matching index prediction unit eliminates abnormal storage coefficients according to the real-time storage coefficients of each logistics center in the selected logistics company for the selected logistics order before the transported goods are shipped out of the warehouse, and predicts the matching index of each logistics center in the selected logistics company and the selected logistics order based on the elimination results;

[0084] The screening unit determines the logistics center that meets the requirements of the logistics platform according to the prediction results transmitted by the matching index prediction unit;

[0085] The transportation index prediction module is used to predict the transportation index of each transportation route;

[0086] The transport index prediction module includes a transport path determination unit and a transport index prediction unit;

[0087] The transport path determination unit determines the matching logistics orders of each logistics center that has been initially screened and retained according to the prediction results transmitted by the matching index prediction unit, and determines the existing transport paths for each logistics order in combination with the location coordinates of each logistics center and the destination of the transported goods recorded in each logistics order;

[0088] The transportation index prediction unit constructs a mathematical model based on the logistics transportation parameters of the logistics center corresponding to the starting point of each transportation path to predict the transportation index of each transportation path;

[0089] The execution strength analysis module analyzes the execution strength of each logistics center for each logistics order based on the average transportation damage rate of the transported goods at the logistics center corresponding to the starting point of each transportation route and the transportation index of each transportation route;

[0090] The intelligent decision-making generation module classifies the transportation routes according to the logistics orders corresponding to the end points of each transportation route. The logistics orders corresponding to the end points of each type of transportation route are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of each type of transportation route is collected, and the collected data is put into a set. Based on the maximum value in the set, the allocation decision of the logistics order is intelligently generated.

[0091] Example 1: Assume i=1, a1=a2=0.5, and remove the total number of stored data b retained in the time period [t1, t2-u1] 11 =7, the total number of stored data removed in the time period [t1, t2-u1] b 21 =3, the spatial fit between the selected logistics order and the logistics center No. 1 in the selected logistics company S1 = 0.86, the storage coefficient of the logistics center No. 1 in the selected logistics company for the selected logistics order at time t2-u1 The storage coefficient of the logistics center numbered 1 in the selected logistics company for the selected logistics order at time t1 Then the matching index between the logistics center numbered 1 in the selected logistics company and the selected logistics order is:

[0092]

[0093] The matching index between the logistics center numbered 1 in the selected logistics company and the selected logistics order is 0.46.

[0094] Example 2: Assume that the logistics order corresponding to the end point of the first transport path is the same as the logistics order corresponding to the end point of the third transport path, then the first transport path and the third transport path belong to the first category of transport paths, and the execution strength of each logistics center on the logistics order corresponding to the end point of the first transport path is put into set M1, and the maximum value in set M1 is searched. The system assigns the logistics order corresponding to the end point of the first transport path to the logistics center corresponding to the found maximum value.

[0095] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A logistics intelligent management method based on data analysis, characterized by: The method comprises: S10: Obtain the logistics demand data of the logistics platform and the logistics transportation data of each logistics company, analyze the spatial and temporal fit between the logistics demand data and each logistics transportation data, and conduct a preliminary screening of logistics centers that meet the requirements of the logistics platform; S20: According to the logistics order data recorded in each logistics order, the logistics transportation parameters of each logistics center that is initially screened and retained are obtained, and based on the obtained information, the execution strength of each logistics center that is initially screened and retained for each logistics order is analyzed; S30: Based on the analysis results of the execution strength of each logistics center on each logistics order, the logistics orders of the logistics platform are intelligently allocated.

2. The method for intelligent logistics management based on data analysis according to claim 1, characterized in that: The S10 includes: S101: Obtaining logistics order data of the logistics platform and logistics transportation data of each logistics company. The logistics order data includes the time of departure of the transported goods, the coordinates of the location where the transported goods are picked up, the specifications of the transported goods and the destination of the transported goods. The logistics transportation data includes the location coordinates of each logistics center and the volume occupied by the goods to be transported corresponding to each logistics center; S102: Randomly select a logistics company and a logistics order, and calculate the spatial fit S between the selected logistics order and the logistics center numbered i in the selected logistics company according to the location coordinates of the logistics centers of the selected logistics company and the pickup range of each logistics center. i Calculate, S i =1-exp(-d i ); Among them, i = 1, 2, ..., m, represents the number of each logistics center in the selected logistics company, m represents the total number of logistics centers in the selected logistics company, d i It represents the distance between the coordinates of the pickup location of the transported goods recorded in the selected logistics order and the coordinates of the logistics center numbered i in the selected logistics company, and exp represents an exponential function with e as the base; S103: According to the real-time storage coefficient of the selected logistics order of the logistics center numbered i in the selected logistics company, in [t1, t2-u i ] time period, the abnormal storage coefficients are eliminated, and based on the elimination results, the matching index Q between the logistics center numbered i in the selected logistics company and the selected logistics order is calculated. i Make predictions; S104: If 0.8<Q i ≤1, it is considered that the logistics center numbered i in the selected logistics company meets the requirements of the logistics platform. If 0≤Q i <1, it is considered that the logistics center numbered i in the selected logistics company does not meet the requirements of the logistics platform; Repeat the operations of S102 to S104 to determine the logistics center that meets the requirements of the logistics platform.

3. The method for intelligent logistics management based on data analysis according to claim 2 is characterized by: S103 calculates the matching index Q between the logistics center numbered i in the selected logistics company and the selected logistics order. i The specific method for making predictions is: According to the order time t1 of the selected logistics order, the delivery time t2 of the transported goods recorded in the selected logistics order, and the specifications of the transported goods, in [t1, t2-u i ] Within the time period, the real-time storage coefficient of the selected logistics order for the logistics center numbered i in the selected logistics company is calculated. g ti =(R i -k ti ) / f1; Where t represents the time value and t1<t<t2-u i ,u i R represents the average outbound time of the logistics center numbered i in the selected logistics company for the collected goods. i represents the inventory volume corresponding to the logistics center numbered i in the selected logistics company, k ti represents the volume of goods to be transported corresponding to the logistics center numbered i in the selected logistics company at time t, f1 represents the total volume of transported goods obtained according to the transported goods specifications recorded in the selected logistics order, and g ti It represents the storage coefficient of the selected logistics order at the logistics center numbered i in the selected logistics company at time t; In [t1,t2-u i ] time period, the abnormal storage coefficient is eliminated, and the matching index between the logistics center numbered i in the selected logistics company and the selected logistics order is predicted based on the storage data retained after elimination. The specific prediction formula is: Among them, a1 and a2 are proportional coefficients and a1+a2=1, b 1i Indicates that in [t1,t2-u i ] The total number of stored data that are removed and retained during the time period, b 2i Indicates that in [t1,t2-u i ]The total number of stored data removed during the time period, Q i It indicates the compatibility index between the logistics center numbered i in the selected logistics company and the selected logistics order.

4. The method for intelligent logistics management based on data analysis according to claim 3 is characterized by: The S20 includes: S201: When 0.8<Q i When ≤1, the selected logistics order is considered to be the matching logistics order of the logistics center numbered i in the selected logistics company. According to the matching logistics order determination method, the matching logistics orders of each logistics center that has been initially screened and retained are determined. According to the location coordinates of each logistics center that has been initially screened and retained, and the destination of the transported goods recorded in each matching logistics order of each logistics center that has been initially screened and retained, the transportation path existing for each logistics order is determined; S202: Obtain the logistics and transportation parameters of the logistics center corresponding to the starting point of each transportation path. The logistics and transportation parameters include the average retention time of the transported goods at each transfer station, the average transportation damage rate of the transported goods, and the transportation method of the transported goods by the logistics center. According to the obtained logistics and transportation parameters, predict the transportation index of each transportation path. The specific prediction formula is: Where j = 1, 2, ..., n, represents the number corresponding to each transport path, n represents the total number of transport paths, D j represents the travel distance corresponding to the jth transport route on the map, p = 1, 2, ..., Y, represents the number corresponding to each transport mode, Y represents the total number of transport modes, E p It represents the transportation cost required to transport the goods per unit distance when the transport method numbered p is used to transport the goods. p It represents the freight rate index corresponding to the transport mode numbered p. a1, a2, a3, and a4 are all weight coefficients. U 1jp It represents the transportation time of the transported goods on the jth transportation route when the transport mode numbered p is used to transport the transported goods. 2jp It represents the average total detention time of the transported goods at the transfer station included in the jth transport route when the transport mode numbered p is used to transport the transported goods. 3j represents the average delivery time of the goods from the logistics center corresponding to the starting point of the j-th transportation route, U 4j S represents the average delivery time of the goods from the logistics center corresponding to the starting point of the j-th transportation route. j represents the transport production index corresponding to the transport route numbered j, L j represents the total volume of transported goods obtained according to the transported goods specifications recorded in the logistics order corresponding to the end point of the j-th transport route, C pj It represents the maximum volume of goods that can be transported in a single trip when the logistics center corresponding to the starting point of the j-th transportation route uses the transportation method numbered p to transport the goods. e is a constant and e>1. W j represents the transport index of the transport path numbered j; S203: According to F j =W j *w j The execution strength of the logistics center corresponding to the starting point of the j-th transportation route for the logistics order corresponding to the end point of the j-th transportation route is calculated, where w j It represents the average transportation damage rate of the transported goods by the logistics center corresponding to the starting point of the j-th transportation route.

5. The method for intelligent logistics management based on data analysis according to claim 4 is characterized in that: The S30 includes: According to the logistics orders corresponding to the end points of each transportation path, the transportation paths are classified and processed. The logistics orders corresponding to the end points of each type of transportation path are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of the μ-th type of transportation path is collected, and the collected data is put into the set M μ In the above equation, the logistics order corresponding to the end point of the μth transport path is assigned to the logistics center corresponding to maxM, where μ = 1, 2, ..., N, indicating the number corresponding to each type of transport path, N indicating the total number, and max indicating the maximum value symbol.

6. A logistics intelligent management system based on data analysis applied to the logistics intelligent management method based on data analysis according to any one of claims 1 to 5, characterized in that: The system includes a logistics data acquisition module, a fit analysis module, a transportation index prediction module, an execution strength analysis module and an intelligent decision generation module; The logistics data acquisition module is used to acquire the logistics demand data of the logistics platform and the logistics transportation data of each logistics company; The matching analysis module is used to analyze the matching of logistics demand data and various logistics transportation data in space and time, and to preliminarily screen logistics centers that meet the requirements of the logistics platform; The transport index prediction module is used to predict the transport index of each transport path; The execution strength analysis module is used to analyze the execution strength of each logistics center that has been initially screened and retained for each logistics order; The intelligent decision making module is used to intelligently allocate logistics orders on the logistics platform.

7. The logistics intelligent management system based on data analysis according to claim 6 is characterized by: The fit analysis module includes a fit degree calculation unit, a fit index prediction unit and a screening unit; The fitness calculation unit calculates the spatial fitness between the selected logistics order and each logistics center in the selected logistics company according to the logistics data acquired by the logistics data acquisition module; The matching index prediction unit eliminates abnormal storage coefficients according to the real-time storage coefficients of each logistics center in the selected logistics company for the selected logistics order before the transported goods are shipped out of the warehouse, and predicts the matching index of each logistics center in the selected logistics company and the selected logistics order based on the elimination results; The screening unit determines the logistics center that meets the requirements of the logistics platform according to the prediction results transmitted by the matching index prediction unit.

8. The logistics intelligent management system based on data analysis according to claim 7 is characterized by: The transport index prediction module includes a transport path determination unit and a transport index prediction unit; The transport path determination unit determines the matching logistics orders of each logistics center that are initially screened and retained according to the prediction results transmitted by the matching index prediction unit, and determines the existing transportation paths for each logistics order in combination with the location coordinates of each logistics center and the destination of the transported goods recorded in each logistics order; The transport index prediction unit constructs a mathematical model according to the logistics transport parameters of the logistics center corresponding to the starting point of each transport path, and predicts the transport index of each transport path.

9. The logistics intelligent management system based on data analysis according to claim 8 is characterized by: The execution strength analysis module analyzes the execution strength of each logistics center for each logistics order that has been preliminarily selected based on the average transportation damage rate of the transported goods by the logistics center corresponding to the starting point of each transportation route and the transportation index of each transportation route.

10. The logistics intelligent management system based on data analysis according to claim 9 is characterized by: The intelligent decision-making generation module classifies the transportation routes according to the logistics orders corresponding to the end points of each transportation route. The logistics orders corresponding to the end points of each type of transportation route are the same. The execution strength of each logistics center on the logistics orders corresponding to the end points of each type of transportation route is collected, and the collected data is put into a set. Based on the maximum value in the set, the allocation decision of the logistics order is intelligently generated.

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