CN113139765A - Logistics recommendation method and device based on temporal network and computing equipment - Google Patents
Logistics recommendation method and device based on temporal network and computing equipment Download PDFInfo
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Abstract
The embodiment of the invention relates to the technical field of Internet application, and discloses a logistics recommendation method, a logistics recommendation device and a computing device based on a temporal network, wherein the method comprises the following steps: establishing a temporal network of the vehicle according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index. Through the mode, the embodiment of the invention can be suitable for vehicle recommendation of a highly information logistics platform, and has the advantages of strong flexibility, low cost, wide application range and good recommendation effect.
Description
Technical Field
The embodiment of the invention relates to the technical field of Internet application, in particular to a logistics recommendation method and device based on a temporal network and a computing device.
Background
With the rapid development of the logistics industry in recent years, the overall freight volume is on the trend of increasing year by year, wherein the highway freight volume accounts for 75 percent of the overall freight volume. In practice, the road transport industry still faces inefficiency. The transportation department continuously promotes the construction of a logistics informatization platform, provides an internet channel for issuing freight transportation requirements and selecting potential carrying vehicles from massive logistics vehicles according to the requirements to recommend the potential carrying vehicles to demanders.
The logistics vehicle recommendation is a core problem faced by a logistics information platform, and the current logistics information platform recommends potential carrier vehicles to a shipper in a more traditional mode. The existing vehicle recommendation method comprises the following steps: vehicle portrait recommendation, historical vehicle recommendation, real-time positioning scheduling. For the vehicle portrait recommendation method, because the actual order taking condition of a driver has certain difference with the intention of the driver, and the accuracy of the reserved information of the driver is low, the accuracy of the recommendation result is poor, and the requirement is difficult to meet. The historical vehicle recommendation method is suitable for the conditions of stable freight requirements and owners of fixed logistics vehicles, and has poor integration capability on scattered logistics resources and poor recommendation effect. For the real-time positioning and scheduling method, because the logistics driver is in transit for a long time, the mobile phone is often in a power-off state, the real-time positioning has certain difficulty, and the real-time position of the driver does not represent the shipping intention. Therefore, the existing vehicle recommendation method has limited application range and poor recommendation effect.
Disclosure of Invention
In view of the foregoing problems, embodiments of the present invention provide a method, an apparatus, a computing device, and a computer storage medium for logistics recommendation based on a temporal network, which overcome or at least partially solve the above problems.
According to an aspect of an embodiment of the present invention, there is provided a logistics recommendation method based on a temporal network, the method including: establishing a temporal network of the vehicle according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
In an optional manner, the establishing a temporal network of vehicles according to historical logistics order data within a preset time includes: acquiring the historical logistics order data within the preset time; abstracting a vehicle as a node in the temporal network; constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles; and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
In an optional manner, the constructing a connection edge connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles includes: respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations; respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles; and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In an optional manner, the screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network includes: screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data; and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
In an optional manner, the calculating the vehicle similarity in the vehicle recommendation network includes: and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
In an optional mode, the vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node i, isi,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
In an optional manner, the obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index includes: acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time; calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders; and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
According to another aspect of the embodiments of the present invention, there is provided a logistics recommendation apparatus based on a temporal network, the apparatus including: the network establishing unit is used for establishing a temporal network of the vehicle according to historical logistics order data in preset time; the vehicle screening unit is used for screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; the similarity calculation unit is used for calculating the similarity of the vehicles in the vehicle recommendation network; and the vehicle recommending unit is used for acquiring a vehicle recommending index according to the vehicle similarity and the vehicle preference of the owner and recommending the vehicle to the owner according to the vehicle recommending index.
According to another aspect of embodiments of the present invention, there is provided a computing device including: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction enables the processor to execute the steps of the logistics recommendation method based on the temporal network.
According to another aspect of the embodiments of the present invention, there is provided a computer storage medium, in which at least one executable instruction is stored, and the executable instruction causes the processor to execute the steps of the logistics recommendation method based on temporal network.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and the embodiments of the present invention can be implemented according to the content of the description in order to make the technical means of the embodiments of the present invention more clearly understood, and the detailed description of the present invention is provided below in order to make the foregoing and other objects, features, and advantages of the embodiments of the present invention more clearly understandable.
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Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a schematic flow chart illustrating a logistics recommendation method based on a temporal network according to an embodiment of the present invention;
fig. 2 is a schematic flow chart illustrating a temporal network establishment process of a logistics recommendation method based on a temporal network according to an embodiment of the present invention;
fig. 3 is a schematic diagram of a temporal network-based logistics recommendation method according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram illustrating a logistics recommendation device based on a temporal network according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flow diagram of a logistics recommendation method based on a temporal network according to an embodiment of the present invention. As shown in fig. 1, the logistics recommendation method based on the temporal network includes:
step S11: and establishing a temporal network of the vehicle according to historical logistics order data in preset time.
In the embodiment of the invention, vehicles are abstracted into individuals in the network, and data of departure place, arrival place, departure time and arrival time in the logistics order are abstracted into the relation among nodes in the temporal network to form the logistics order temporal network. Specifically, as shown in fig. 2, the method includes:
step S111: and acquiring the historical logistics order data in the preset time.
In the embodiment of the invention, a demand release function is arranged on a logistics platform interface for collecting target demands, and when the demands are added, a goods owner can check the name of a departure place city and a district, the name of a destination city and a district, expected departure time, expected arrival time, vehicle length and vehicle type. And acquiring the latitude and longitude of the city district through the open source map service platform API to form a city district latitude and longitude database table, wherein the position data used in the subsequent algorithm is latitude and longitude data.
In step S111, historical logistics order data of the logistics platform registered vehicle within a preset time is collected. The preset time is preferably 1 month. Due to the characteristic of long road logistics time period, a vehicle list with transportation order completion records in 1 month is obtained by using the time interval calculated by taking 1 month before the current time as a vehicle recommendation index, and fields containing position information and time information in order data completed by the vehicles are collected and stored in the following table 1.
TABLE 1 historical Logistics order data in 1 month
The historical logistics order data comprises: the length of the vehicle, the type of the vehicle, the vehicle number (ID), the latitude and longitude of the departure place, the latitude and longitude of the destination, the departure time, the arrival time and the like.
Step S112: abstracting a vehicle as a node in the temporal network.
The vehicles are the subject individuals studied in the embodiment of the invention, the vehicle set with transportation order completion records in about 1 month is a basic set for calculating vehicle recommendation indexes, and the vehicles are abstracted into nodes in a temporal network. The set of vehicle IDs in table 1 is obtained and defined as a set V, which is an initial node set of the temporal network.
Step S113: and constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles.
In the embodiment of the invention, whether the edges exist between the nodes is defined according to the travel similarity degree of the vehicle in a period of time. When the straight-line distance of the travel starting points and the straight-line distance of the travel ending points of the two vehicles in the similar time are smaller, a connecting edge exists between the two vehicle nodes in the time. Specifically, a first linear distance between departure places and a second linear distance between destinations of two vehicles are respectively calculated according to the latitude and longitude of the departure places and the latitude and longitude of the destinations; respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles; and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In the embodiment of the invention, according to the long-distance road transportation distance, the distance is considered to be smaller when the straight line distance of the two positions on the map is less than 80 kilometers, and the time is considered to be close when the time is within 0.5 day. And calculating the straight line distance of the two positions on the map by using longitude and latitude according to the historical logistics order data in the table 1. For any of the vehicles a and B, D (Start _ a, Start _ B) represents a first straight-line distance (distance in kilometers) between the departure point in one piece of historical logistics order data of the vehicle a and the departure point in one piece of historical logistics order data of the vehicle B. D (Target _ a, Target _ B) represents a second straight-line distance (distance in kilometers) between the destination in the corresponding historical logistics order data of a and the destination in the corresponding historical logistics order data of B. When D (Start)A,StartB)≤80∩D(TargetA,TargetB) At 80, the journey of vehicle A is considered to have a higher position similarity to the journey of vehicle B. Considering the concept of time, in a certain time windowIn the mouth of the mouth, the inner cavity is provided with a plurality of grooves,
and is
And
and meanwhile, the time window is greater than 0 or less than 0, namely, the journey departure time and the arrival time of the vehicle A are both earlier than those of the vehicle B, or the journey departure time and the arrival time of the vehicle A are both later than those of the vehicle B, the similarity of the vehicle A and the vehicle B is established in the time window, and a connecting edge exists between the node A and the node B in the temporal network.
For example, in the two trip information in table 2, the distance between the departure location and the destination is calculated to be 8.4 km for trip 1 and trip 2, the distance between the destinations is 68 km, the departure time and the arrival time of trip 1 are both earlier than those of trip 2, and the time difference is not more than 12 hours, then two nodes corresponding to vehicle a and vehicle B are 10 on 8 month 1: there is a connecting edge in the time window of 00-8 months, 2 days, 3: 00.
TABLE 2 example of journey records
And traversing the historical logistics order data in the table 1 to form a connecting edge in the temporal network.
In the embodiment of the present invention, by traversing the historical logistics order data in table 1, a node and a connection edge of the logistics order network may be first formed, and a static network graph G of the logistics order in 1 month is obtained, where the static network graph G includes all nodes in the set V and all connection edges formed by taking a time window in approximately 1 month as a day, and all connection edges do not include a direction and a weight. And then forming a temporal network according to the static network graph G and the time information.
Step S114: and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
The same node is connected between different moments through the delay edges, and a temporal network within the preset time is formed in this way. Fig. 3 is an example of a time-state network, which includes nodes A, B, C, D, E, where t1, t2, t3, t4, t5, t6, and t7 represent different time instants, dashed lines connecting different time instants of the same node are delay edges, and solid lines connecting different nodes at the same time are connecting edges. The temporal path is a path connecting different temporal vehicle nodes in the temporal network, and may include a connection edge and a delay edge. For example, At1-Bt1-Bt4-Ct4 includes two connecting edges and two delay edges.
Step S12: and screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network.
In the embodiment of the invention, logistics demand data issued by a shipper on a logistics platform is acquired, and the logistics demand data is stored in a form of a table 3 through a related city district latitude and longitude database table.
TABLE 3 owner demand data sheet
| Serial number | Field(s) | Type (B) | Description of field |
| 1 | Customer_ID | Char(10) | Owner ID |
| 2 | Start_X | Float(53) | Longitude of origin |
| 3 | Start_Y | Float(53) | Latitude of departure place |
| 4 | Target_X | Float(53) | |
| 5 | Target_Y | Float(53) | Destination latitude |
| 6 | Start_time | Date | Expected departure time |
| 7 | End_time | Date | Expected arrival time |
| 8 | Car_length | Float(53) | Vehicle length |
| 9 | Car_type | Varchar(15) | Vehicle model |
In step S12, screening nodes and connecting edges in the temporal network according to the vehicle length and vehicle type in the logistics demand data; and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
Specifically, the data of the vehicle length and the vehicle type which do not meet the requirements are simply screened firstly, then the nodes in the node set V are screened according to the target requirements P, the data in the table 1 are traversed, when the distance between the departure place of any trip of the vehicle A and the departure place of the target trip is more than 80 kilometers, the node A is deleted from the set V, meanwhile, the connecting edge connected with the node A is deleted from the temporal network G, and a new temporal network G is formedP. In an undirected new temporal network, if at least one path exists between each pair of nodes, the temporal network is connected, and the connected temporal network is called a connection slice. Decomposing the temporal network G according to the concept of a slice of connectivityPUsing a temporal network GAThe network communication sheet shows the vehicle A, and when the general vehicles of the cargo owner are collected as VPWhen { A, B, C · N }, set Gf＝{GA,GB···GNThe temporal network for calculating the recommended vehicle usage is calculated. In the set GfAnd adding corresponding time information to obtain a vehicle recommendation network for calculating a vehicle recommendation index.
In the embodiment of the invention, historical cooperative vehicle information of a shipper is obtained, logistics order data completed by the shipper in about 1 month is collected specifically, a logistics vehicle list used by the shipper is obtained, the times of the completion of the logistics order of each vehicle for the shipper in about 1 month is calculated, and the data of the shipper general vehicles are stored as shown in a table 4.
TABLE 4 data sheet of the owner's common vehicle
| Serial number | Field(s) | Type (B) | Description of field |
| 1 | Car_ID | Char(10) | Vehicle ID |
| 2 | Customer_ID | Char(10) | Owner ID |
| 3 | Order_times | Float(53) | Number of times of completing logistic order |
Step S13: and calculating the vehicle similarity in the vehicle recommendation network.
In the embodiment of the invention, the set V is sequentially calculated according to the vehicle recommendation network obtained in the previous stepPThe temporal network similarity of the middle node and each node in the network communication slice where the middle node is located uses the similarity (STDN similarity) of two different nodes at the same time, and takes days as the granularity of a time window.
Specifically, vehicle similarity between a reference node and a node to be recommended, which is different from the reference node, is calculated according to the vehicle recommendation network. Wherein the reference node can be a goods owner common vehicleOne of the vehicles is the most preferred vehicle to meet the logistics demand data. The vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node ii,jThe following relation is satisfied:
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of said temporal path p, dpIs the length of the temporal path p. Similarity s of node i and node ji,jDetermined by the length and time distribution of each temporal path p between node i and node j. The time distribution coefficient of the temporal path p satisfies the following relation:
wherein t is the preset time teThe time t corresponding to the first connecting edge passed by the temporal path plAnd the time corresponding to the last connecting edge passed by the temporal path p. dpHops is the number of the temporal path p passing through the connecting edge, time is the delay time length of the temporal path p, and time tl-te。
When the state network GAThe set of nodes excluding the reference node A is { V }1,V2,V3···VnThe set of the similarity between the other nodes and the reference node A calculated according to the method is
The above similarity calculation results are stored in the data table 5 for subsequent vehicle recommendation to the shipper.
TABLE 5 vehicle similarity and recommendation index data sheet
| Serial number | Field(s) | Type (B) | Description of field |
| 1 | Car_ID_A | Char(10) | Vehicle ID |
| 2 | Car_ID_N | Char(10) | Vehicle ID of the same communication sheet as the vehicle |
| 3 | Similarity | Float(53) | Temporal network similarity of two vehicles |
| 4 | Index_N | Float(53) | Recommendation index for vehicle N |
Step S14: and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
In step S14, acquiring the number of completed logistics orders of the reference vehicle corresponding to the reference node within the preset time; calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders; and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index. Specifically, the similarity between other vehicles and the reference vehicle is further weighted according to the number of times of logistics orders completed by the reference vehicle in the last 1 month in the cargo owner common vehicle list.
For example, if the number of times the reference node a completes the logistics order is m, m is adjusted by a function to obtain a weighting coefficient f ═ βm+1，0<β<1, set of similarity of other nodes and reference node A
Process conversion to
The data in the set is the recommended Index of the target trip, and the recommended Index of the vehicle N obtained after weighting is stored in the Index _ N field in table 5.
And after the recommendation indexes of all vehicles are obtained, recommending the vehicles to the owner through an interface. Specifically, the goods owner common vehicles are recommended firstly according to the sequence of the vehicle information table, the goods owner common vehicles are arranged in a descending order according to the logistics order number completed by the vehicles in about 1 month, and then the goods owner common vehicles are arranged according to the temporal network GfAnd sequentially recommending all the node recommendation indexes in a descending order to form a recommendation list of the target trip. For example, a vehicle a with the largest number of times of finishing logistics orders within 1 month is recommended to a cargo owner, and when the vehicle a cannot finish the current cargo transportation of the cargo owner, a node corresponding to the vehicle a is taken as a reference node to follow a temporal network GfAnd recommending the vehicles to the owner in sequence by all the nodes in the system in descending order.
According to the logistics recommendation method, the logistics vehicle capable of meeting the target travel requirement is reasonably recommended by collecting the order data of the logistics information platform and utilizing the geographic distance information and the time information, a data collection facility does not need to be additionally installed, and the vehicle recommendation meeting the target travel requirement can be obtained only by analyzing historical travel data, so that the logistics recommendation method is low in cost, fast in data collection and high in calculation efficiency; the method can be suitable for all types of logistics information platforms and has good universality; the method can cover all types of freight modes, can adjust according to actual needs and different freight characteristics, obtains recommendation results in various scenes, and provides data support for promoting the development of the logistics transportation industry.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
Fig. 4 shows a schematic structural diagram of a logistics recommendation device based on a temporal network according to an embodiment of the invention. As shown in fig. 4, the logistics recommendation device based on the temporal network includes: a network establishing unit 401, a vehicle screening unit 402, a similarity calculating unit 403, and a vehicle recommending unit 404. Wherein:
the network establishing unit 401 is configured to establish a temporal network of the vehicle according to historical logistics order data within a preset time; the vehicle screening unit 402 is configured to screen the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; the similarity calculation unit 403 is used for calculating vehicle similarities in the vehicle recommendation network; the vehicle recommendation unit 404 is configured to obtain a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommend a vehicle to the owner according to the vehicle recommendation index.
In an alternative manner, the network establishing unit 401 is configured to: acquiring the historical logistics order data within the preset time; abstracting a vehicle as a node in the temporal network; constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles; and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
In an alternative manner, the network establishing unit 401 is configured to: respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations; respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles; and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In an alternative approach, the vehicle screening unit 402 is configured to: screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data; and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
In an alternative manner, the similarity calculation unit 403 is configured to: and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
In an optional mode, the vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node i, isi,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
In an alternative manner, the vehicle recommendation unit 404 is configured to: acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time; calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders; and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
The embodiment of the invention provides a nonvolatile computer storage medium, wherein at least one executable instruction is stored in the computer storage medium, and the computer executable instruction can execute the logistics recommendation method based on the temporal network in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
establishing a temporal network of the vehicle according to historical logistics order data in preset time;
screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating vehicle similarity in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
In an alternative, the executable instructions cause the processor to:
acquiring the historical logistics order data within the preset time;
abstracting a vehicle as a node in the temporal network;
constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles;
and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
In an alternative, the executable instructions cause the processor to:
respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations;
respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles;
and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In an alternative, the executable instructions cause the processor to:
screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
In an alternative, the executable instructions cause the processor to:
and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
In an optional mode, the vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node i, isi,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
In an alternative, the executable instructions cause the processor to:
acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time;
calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders;
and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
An embodiment of the present invention provides a computer program product, where the computer program product includes a computer program stored on a computer storage medium, where the computer program includes program instructions, and when the program instructions are executed by a computer, the computer is caused to execute the method for recommending logistics based on a temporal network in any of the above method embodiments.
The executable instructions may be specifically configured to cause the processor to:
establishing a temporal network of the vehicle according to historical logistics order data in preset time;
screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating vehicle similarity in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
In an alternative, the executable instructions cause the processor to:
acquiring the historical logistics order data within the preset time;
abstracting a vehicle as a node in the temporal network;
constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles;
and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
In an alternative, the executable instructions cause the processor to:
respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations;
respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles;
and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In an alternative, the executable instructions cause the processor to:
screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
In an alternative, the executable instructions cause the processor to:
and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
In an optional mode, the vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node i, isi,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
In an alternative, the executable instructions cause the processor to:
acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time;
calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders;
and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
Fig. 5 is a schematic structural diagram of a computing device according to an embodiment of the present invention, and the specific embodiment of the present invention does not limit the specific implementation of the device.
As shown in fig. 5, the computing device may include: a processor (processor)502, a Communications Interface 504, a memory 506, and a communication bus 508.
Wherein: the processor 502, communication interface 504, and memory 506 communicate with one another via a communication bus 508. A communication interface 504 for communicating with network elements of other devices, such as clients or other servers. The processor 502 is configured to execute the program 510, and may specifically execute relevant steps in the above-described logistics recommendation method based on a temporal network.
In particular, program 510 may include program code that includes computer operating instructions.
The processor 502 may be a central processing unit CPU or an application Specific Integrated circuit asic or an Integrated circuit or Integrated circuits configured to implement embodiments of the present invention. The one or each processor included in the device may be the same type of processor, such as one or each CPU; or may be different types of processors such as one or each CPU and one or each ASIC.
And a memory 506 for storing a program 510. The memory 506 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may specifically be used to cause the processor 502 to perform the following operations:
establishing a temporal network of the vehicle according to historical logistics order data in preset time;
screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating vehicle similarity in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
In an alternative, the program 510 causes the processor to:
acquiring the historical logistics order data within the preset time;
abstracting a vehicle as a node in the temporal network;
constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles;
and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
In an alternative, the program 510 causes the processor to:
respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations;
respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles;
and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
In an alternative, the program 510 causes the processor to:
screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
In an alternative, the program 510 causes the processor to:
and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
In an optional mode, the vehicle similarity s of the reference node and the node j to be recommended, which is different from the reference node i, isi,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
In an alternative, the program 510 causes the processor to:
acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time;
calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders;
and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
According to the embodiment of the invention, a temporal network of the vehicle is established according to historical logistics order data in preset time; screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network; calculating vehicle similarity in the vehicle recommendation network; the method comprises the steps of obtaining a vehicle recommendation index according to the vehicle similarity and the vehicle preference of a goods owner, recommending vehicles to the goods owner according to the vehicle recommendation index, being suitable for vehicle recommendation of a highly information logistics platform, not needing to additionally install a data acquisition facility, only needing to obtain vehicle recommendation meeting target travel requirements through historical travel data analysis, being strong in flexibility, low in cost, suitable for logistics information platforms of all types, wide in application range and good in recommendation effect.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A logistics recommendation method based on a temporal network is characterized by comprising the following steps:
establishing a temporal network of the vehicle according to historical logistics order data in preset time;
screening the temporal network according to logistics demand data and historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating vehicle similarity in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the owner, and recommending the vehicle to the owner according to the vehicle recommendation index.
2. The method according to claim 1, wherein the establishing a temporal network of vehicles according to historical logistics order data within a preset time comprises:
acquiring the historical logistics order data within the preset time;
abstracting a vehicle as a node in the temporal network;
constructing a connecting edge for connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity of the two vehicles;
and constructing delay edges connecting the same node at different moments to form the temporal network within the preset time.
3. The method according to claim 2, wherein the constructing the connecting edges connecting the nodes corresponding to the two vehicles at the same time according to the travel similarity degree of the two vehicles comprises:
respectively calculating a first linear distance between the departure places and a second linear distance between the destinations of the two vehicles according to the latitude and longitude of the departure places and the latitude and longitude of the destinations;
respectively calculating the departure time difference and the arrival time difference of the two vehicles according to the departure time and the arrival time of the two vehicles;
and if the first linear distance and the second linear distance meet a first threshold value and the first time difference and the second time difference meet a second threshold value, a connecting edge exists between the nodes corresponding to the two vehicles at the moment corresponding to the travel.
4. The method of claim 1, wherein the screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network comprises:
screening nodes and connecting edges in the temporal network according to the vehicle length and the vehicle type in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the goods owner common vehicles from the temporal network to form the vehicle recommendation network.
5. The method of claim 1, wherein the calculating vehicle similarities in the vehicle recommendation network comprises:
and calculating the vehicle similarity between the reference node and the node to be recommended, which is different from the reference node, according to the vehicle recommendation network.
6. The method according to claim 5, characterized in that the reference node has a vehicle similarity s to the node to be recommended j different from the reference node ii,jThe following relation is satisfied:
dp＝hops*time，
time＝tl-te，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, tpIs the time distribution coefficient of the temporal path p, t is the preset time, teThe time t corresponding to the first connecting edge passed by the temporal path plThe time corresponding to the last connecting edge passed by the temporal path p, dpIs the length of the temporal path p, hos is the number of the temporal path p passing through the connecting edge, and time is the delay time length of the temporal path p.
7. The method of claim 5, wherein obtaining a vehicle recommendation index based on the vehicle similarity and the owner's vehicle preferences and recommending a vehicle to the owner based on the vehicle recommendation index comprises:
acquiring the quantity of the logistics orders completed by the reference vehicle corresponding to the reference node within the preset time;
calculating a recommendation index of a vehicle to be recommended corresponding to the node to be recommended according to the vehicle similarity of the node to be recommended and the reference node and the quantity of the logistics orders;
and recommending the vehicles to be recommended in sequence from large to small according to the recommendation index.
8. A logistics recommendation device based on a temporal network is characterized in that the device comprises:
the network establishing unit is used for establishing a temporal network of the vehicle according to historical logistics order data in preset time;
the vehicle screening unit is used for screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network;
the similarity calculation unit is used for calculating the similarity of the vehicles in the vehicle recommendation network;
and the vehicle recommending unit is used for acquiring a vehicle recommending index according to the vehicle similarity and the vehicle preference of the owner and recommending the vehicle to the owner according to the vehicle recommending index.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the steps of the logistics recommendation method based on the temporal network according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform the steps of the temporal network based logistics recommendation method of any one of claims 1-7.
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