CN113139765B - 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 device and computing equipment based on a temporal network, wherein the method comprises the following steps: establishing a temporal network of the vehicle according to the historical logistics order data in the preset time; screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index. Through the mode, the vehicle recommendation method and device based on the high-information logistics platform can be suitable for vehicle recommendation of the high-information logistics platform, and is high in flexibility, low in cost, wide in application range and good in 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, device and computing equipment based on a temporal network.
Background
With the rapid development of the logistics industry in recent years, the overall freight traffic tends to increase year by year, wherein the highway freight traffic accounts for 75% of the overall freight traffic. In practice, the road transportation industry still faces inefficiency problems. The traffic department continues to push logistics informatization platform construction, and provides an internet channel for issuing freight demands and selecting potential carrier vehicles from mass logistics vehicles according to the demands to recommend the potential carrier vehicles to a demand party.
Logistics vehicle recommendation is a core problem faced by logistics informatization platforms, and at present, the logistics informatization platforms recommend potential carrier vehicles to a cargo owner in a more traditional mode. The existing vehicle recommendation method comprises the following steps: vehicle representation recommendation, historical vehicle recommendation, and real-time location scheduling. For the vehicle portrait recommendation method, the actual order receiving condition of the driver and the intention of the driver have certain difference, and the accuracy of the reserved information of the driver is low, so that the recommendation result is poor in accuracy, and the requirements are difficult to meet. For the historical vehicle recommendation method, the method is suitable for the condition of having stable freight demands and fixing owners of logistics vehicles, has poor integration capability on zero-dispersion logistics resources and has poor recommendation effect. For the real-time positioning and scheduling method, because a logistics driver is in a shutdown state frequently due to long-time in transit, the real-time positioning has a certain difficulty, and the real-time position of the driver does not represent the intention of the driver in carrying. Therefore, the existing vehicle recommendation method has limited application range and poor recommendation effect.
Disclosure of Invention
In view of the above, embodiments of the present invention provide a temporal network-based logistics recommendation method, apparatus, computing device, and computer storage medium, which overcome or at least partially solve the above problems.
According to an aspect of the embodiment of the present invention, there is provided a temporal network-based logistics recommendation method, including: establishing a temporal network of the vehicle according to the historical logistics order data in the preset time; screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
In an optional manner, the establishing a temporal network of the vehicle according to the historical logistics order data within the preset time includes: acquiring the historical logistics order data in 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 connected with the same node at different moments to form the temporal network in the preset time.
In an optional manner, the constructing a connection edge for 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 departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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; 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 filtering 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 length and the type of vehicles in the logistics demand data; and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners 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 alternative way, the reference node has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, and time is the delay time length of the temporal path p.
In an optional manner, the acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index, includes: acquiring the quantity of the logistics orders completed by the reference vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders; and recommending the vehicles to be recommended in sequence from the large to the small according to the recommendation index.
According to another aspect of the embodiment of the present invention, there is provided a logistic recommendation device based on a temporal network, the device including: the network establishing unit is used for establishing a temporal network of the vehicle according to the historical logistics order data in the 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; a similarity calculation unit for calculating a vehicle similarity 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 cargo owner and recommending the vehicle to the cargo owner according to the vehicle recommending index.
According to another aspect of an embodiment of the present invention, there is provided a computing device including: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other 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 logistic recommendation method based on the temporal network.
According to yet another aspect of the embodiments of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing the processor to perform the steps of the temporal network-based logistics recommendation method described above.
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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
The foregoing description is only an overview of the technical solutions of the embodiments of the present invention, and may be implemented according to the content of the specification, so that the technical means of the embodiments of the present invention can be more clearly understood, and the following specific embodiments of the present invention are given for clarity and understanding.
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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 designate like parts throughout the figures. In the drawings:
fig. 1 shows a schematic flow chart of a temporal network-based logistics recommendation method according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of establishing a temporal network of the temporal network-based logistics recommendation method 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 shows a schematic structural diagram of a logistic recommendation device based on a temporal network according to an embodiment of the present invention;
FIG. 5 illustrates a schematic 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 present invention are shown in the drawings, it should be understood that the present invention may 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 chart of a temporal network-based logistics recommendation method according to an embodiment of the present invention. As shown in fig. 1, the temporal network-based logistics recommendation method includes:
step S11: and establishing a temporal network of the vehicle according to the historical logistics order data in the preset time.
In the embodiment of the invention, vehicles are abstracted into individuals in a network, and data of a departure place, an arrival place, a departure time and an arrival time in a logistics order are abstracted into links among nodes in a temporal network, so that the logistics order temporal network is formed. Specifically, as shown in fig. 2, 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 set on a logistics platform interface for target demand collection, and a cargo owner is required to pick out the names of the issuing city counties, the names of the destination city counties, the expected departure time, the expected arrival time, the vehicle length and the vehicle type when the demand is added. And acquiring the longitude and latitude of the city county through an open source map service platform API to form a city county longitude and latitude database table, wherein the position data used in the follow-up algorithm are longitude and latitude data.
In step S111, historical logistics order data of the logistics platform registration vehicle in a preset time is collected. The preset time is preferably 1 month. Because of the longer characteristic of the road logistics time period, the time interval of calculating the vehicle recommendation index is used 1 month before the current time, a vehicle list with a transportation order completion record in 1 month is obtained, and fields containing position information and time information in order data of the vehicle completion are collected and stored in the following table 1.
Table 1, historical logistics order data within 1 month
The historical logistics order data includes: vehicle length, vehicle type, vehicle number (ID), departure location latitude and longitude, destination latitude and longitude, departure time, arrival time, etc.
Step S112: abstracting the vehicle as a node in the temporal network.
Vehicles are the subject individuals studied in the embodiments of the present invention, and a vehicle set with a record of completion of a delivery order in approximately 1 month is a basic set for calculating a recommendation index of a vehicle, and abstracts the vehicle to be a node in a temporal network. The set of the vehicle IDs in the acquisition table 1 is defined as set V, which is the 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 degree of the two vehicles.
In the embodiment of the invention, whether edges exist between nodes is defined according to the travel similarity degree of the vehicle for a period of time. When the straight line distance between the travel starting point and the travel ending point of two vehicles in similar time is smaller, one 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 longitude and latitude of the departure places and the longitude and latitude 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; 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, when the linear distance between two positions on a map is smaller than 80 km, the distance is considered to be smaller, and when the time is within the range of 0.5 day, the time is considered to be similar. From the historical logistics order data in table 1, the longitude and latitude are used to calculate the straight line distance of the two locations on the map. For any vehicle a and vehicle B, D (start_a, start_b) represents a first linear distance (distance in kilometers) between the origin in one historical logistics order data of vehicle a and the origin in one historical logistics order data of vehicle B. D (target_a, target_b) represents a second linear 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 ,Start B )≤80∩D(Target A ,Target B ) At 80 or less, this travel of vehicle A is considered to have a higher positional similarity with this travel of vehicle B. Considering the concept of time, within a certain time window,and->Andand meanwhile, the time is larger than 0 or smaller than 0, namely the travel departure time and the arrival time of the vehicle A are both earlier than those of the vehicle B, or the travel departure time and the arrival time of the vehicle A are both later than those of the vehicle B, so that 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 place and the destination of the trip 1 and the trip 2 is calculated to be 8.4 km, the distance between the destinations is 68 km, the departure time and the arrival time of the trip 1 are both earlier than those of the trip 2, and the time difference is not more than 12 hours, then the two nodes corresponding to the vehicle a and the vehicle B are 10 on the 1 st day of 8 months: a connecting edge exists in a time window of 3:00 of 00-8 months and 2 days.
Table 2 travel record sample
The historical logistics order data in table 1 is traversed to form connecting edges in the temporal network.
In the embodiment of the invention, the historical logistics order data of table 1 is traversed, nodes and connecting edges of a logistics order network can be formed first, a static network diagram G of the logistics order within 1 month is obtained, the static network diagram G comprises all nodes in a set V and all connecting edges formed by taking a time window within 1 month as a day, and all connecting edges do not contain directions and weights. And then forming a temporal network according to the static network graph G and the time information.
Step S114: and constructing delay edges connected with the same node at different moments to form the temporal network in the preset time.
The same node is connected through delay edges at different moments, so that a temporal network within preset time is formed. Fig. 3 is a timing network example, where nodes A, B, C, D, E, t1, t2, t3, t4, t5, t6, and t7 are included, and the dotted lines connected between different times of the same node are delay edges, and the solid lines connected between different nodes at the same time are connection 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-Bt 4-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, the logistics demand data issued by a cargo owner on a logistics platform is acquired, and the logistics demand data is stored in a form of a table 3 through a related city county longitude and latitude database table.
Table 3, cargo owner demand data table
| Sequence number | Fields | Type(s) | Field description |
| 1 | Customer_ID | Char(10) | Cargo owner ID |
| 2 | Start_X | Float(53) | Longitude of departure place |
| 3 | Start_Y | Float(53) | Latitude of departure place |
| 4 | Target_X | Float(53) | Destination longitude |
| 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, nodes and connection edges in the temporal network are screened according to the length and the type of vehicles in the logistics demand data; and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners from the temporal network to form the vehicle recommendation network.
Specifically, firstly, data of a vehicle length and a vehicle type which do not meet requirements are simply screened, then nodes in a node set V are screened according to a target requirement P, the data in a table 1 are traversed, when the distance between the departure place of any journey of a vehicle A and the departure place of the target journey is greater than 80 km, the node A is deleted from the set V, and meanwhile, the connecting edge connected with the node A is deleted from a temporal network G to form a new temporal network G P . In a new, undirected temporal network, if there is at least one path between each pair of nodes, the temporal network is connected, and the connected temporal network is called a connected patch. Decomposing the temporal network G according to the concept of connected pieces P Using a time-state network G A Representing the network communication sheet where the vehicle A is located, when the common vehicle set of the cargo owner is V P When= { A, B, C. Cndot. N }, set G f ＝{G A ,G B ···G N And the time network used for recommending the vehicle is calculated. At set G f 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 cargo owner is obtained, the logistics order data completed by the cargo owner in 1 month is collected, a logistics vehicle list used by the cargo owner is obtained, the times of each vehicle for completing the logistics order by the cargo owner in 1 month is calculated, and the common vehicle data of the cargo owner are stored in a table 4.
Table 4, cargo owner's general purpose vehicle data table
| Sequence number | Fields | Type(s) | Field description |
| 1 | Car_ID | Char(10) | Vehicle ID |
| 2 | Customer_ID | Char(10) | Cargo owner ID |
| 3 | Order_times | Float(53) | Number of times the logistic order was completed |
Step S13: and calculating the similarity of the vehicles in the vehicle recommendation network.
In the embodiment of the invention, the set V is calculated sequentially according to the vehicle recommendation network obtained above P The temporal network similarity between the middle node and each node in the network communication sheet where the middle node is located uses the similarity (STDN similarity) of two different nodes at the same time, and the day is the granularity of the time window.
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. Wherein the reference node may be one of the common vehicles of the shipper, the most preferred vehicle to meet the logistical demand data. The saidVehicle similarity s of a reference node to the node j to be recommended, which is different from the reference node i i,j The following relationship is satisfied:
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p D is the time distribution coefficient of the temporal path p p Is the length of the temporal path p. Similarity s between node i and node j i,j Is determined by the length and time distribution of each temporal path p between node i and node j. The temporal distribution coefficient of the temporal path p satisfies the following relation:
wherein t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l And the time corresponding to the last connecting edge passed by the temporal path p. d, d p Time is the number of the temporal paths p passing through the connection edge, time is the delay time length of the temporal paths p, time=t l -t e 。
Time-state network G A The node set excluding the reference node A is { V } 1 ,V 2 ,V 3 ···V n The set of the similarity between other nodes and the reference node A calculated by the method isThe above similarity calculation result is stored in the data table 5 for the subsequent vehicle recommendation to the cargo owner.
TABLE 5 vehicle similarity and recommendation index data sheet
| Sequence number | Fields | Type(s) | Field description |
| 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 of vehicle N |
Step S14: and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
In step S14, the number of logistic orders of the reference vehicles corresponding to the reference nodes is obtained 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 between the node to be recommended and the reference node and the number of the logistics orders; and recommending the vehicles to be recommended in sequence from the large to the small according to the recommendation index. Specifically, the similarity between other vehicles and the reference vehicle is further weighted according to the number of physical distribution orders completed by the reference vehicle in the near 1 month in the common vehicle list of the cargo owner.
For example, if the number of physical distribution orders completed by the reference node a is m, the function is used to adjust m to obtain the weighting coefficient f= - β m +1，0<β<1, then the set of similarities of other nodes and reference node AThe treatment is changed into->The data in the set is the recommendation Index of the target journey, and the index_N field in the table 5 stores the recommendation Index of the vehicle N obtained after weighting.
And after the recommendation indexes of the vehicles are obtained, recommending the vehicles to the cargo owner through an interface. Specifically, the method comprises the steps of firstly recommending common vehicles of a cargo owner according to the sequence of a vehicle information table, arranging the common vehicles in descending order according to the number of logistic orders completed by the vehicles in the period of nearly 1 month, and then according to a temporal network G f All the nodes in the list are sequentially recommended in descending order to form a recommendation list for the target journey. For example, firstly, a vehicle A with the largest number of times of completing logistics orders in 1 month is recommended to a cargo owner, when the vehicle A can not complete the current cargo of the cargo owner, the node corresponding to the vehicle A is used as a reference node, and the time network G is used as a reference node f And sequentially recommending vehicles to the cargo owner in descending order of all node recommendation indexes.
According to the logistics recommendation method, logistics vehicles meeting target travel demands are reasonably recommended by collecting logistics informatization platform order data and utilizing geographic distance information and time information, data collection facilities are not required to be additionally installed, vehicle recommendation meeting target travel demands can be obtained only by analyzing historical travel data, cost is low, data collection is fast, and calculation efficiency is high; the method is applicable to all types of logistics information platforms and has good universality; the system can cover all types of freight modes, can be adjusted according to actual needs aiming at different freight characteristics, and can acquire recommended results in various scenes, thereby providing data support for promoting logistics transportation development.
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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
Fig. 4 shows a schematic structural diagram of a logistic recommendation device based on a temporal network according to an embodiment of the present invention. As shown in fig. 4, the temporal network-based logistics recommendation apparatus includes: a network establishment unit 401, a vehicle screening unit 402, a similarity calculation unit 403, and a vehicle recommendation unit 404. Wherein:
the network establishing unit 401 is configured to establish a temporal network of the vehicle according to the 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, and obtain a vehicle recommendation network; the similarity calculation unit 403 is configured to calculate a vehicle similarity 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 cargo owner, and recommend the vehicle to the cargo owner according to the vehicle recommendation index.
In an alternative way, the network establishment unit 401 is configured to: acquiring the historical logistics order data in 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 connected with the same node at different moments to form the temporal network in the preset time.
In an alternative way, the network establishment unit 401 is configured to: respectively calculating a first linear distance between departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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; 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 length and the type of vehicles in the logistics demand data; and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners 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 alternative way, the reference node has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, and time is the delay time length of the temporal path p.
In an alternative way, the vehicle recommendation unit 404 is configured to: acquiring the quantity of the logistics orders completed by the reference vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders; and recommending the vehicles to be recommended in sequence from the large to the 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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
The embodiment of the invention provides a non-volatile computer storage medium, which stores at least one executable instruction, and the computer executable instruction can execute the logistic recommendation method based on the temporal network in any of the method embodiments.
The executable instructions may be particularly useful for causing a processor to:
Establishing a temporal network of the vehicle according to the historical logistics order data in the preset time;
screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating the similarity of vehicles in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
In one alternative, the executable instructions cause the processor to:
acquiring the historical logistics order data in 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 connected with the same node at different moments to form the temporal network in the preset time.
In one alternative, the executable instructions cause the processor to:
respectively calculating a first linear distance between departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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;
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 one alternative, the executable instructions cause the processor to:
screening nodes and connecting edges in the temporal network according to the length and the type of vehicles in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners from the temporal network to form the vehicle recommendation network.
In one 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 alternative way, the reference node has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, and time is the delay time length of the temporal path p.
In one alternative, the executable instructions cause the processor to:
acquiring the quantity of the logistics orders completed by the reference vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders;
and recommending the vehicles to be recommended in sequence from the large to the 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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
An embodiment of the present invention provides a computer program product comprising a computer program stored on a computer storage medium, the computer program comprising program instructions which, when executed by a computer, cause the computer to perform the temporal network-based logistics recommendation method of any of the method embodiments described above.
The executable instructions may be particularly useful for causing a processor to:
establishing a temporal network of the vehicle according to the historical logistics order data in the preset time;
screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating the similarity of vehicles in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
In one alternative, the executable instructions cause the processor to:
acquiring the historical logistics order data in 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 connected with the same node at different moments to form the temporal network in the preset time.
In one alternative, the executable instructions cause the processor to:
respectively calculating a first linear distance between departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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;
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 one alternative, the executable instructions cause the processor to:
screening nodes and connecting edges in the temporal network according to the length and the type of vehicles in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners from the temporal network to form the vehicle recommendation network.
In one 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 alternative way, the reference node has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, and time is the delay time length of the temporal path p.
In one alternative, the executable instructions cause the processor to:
acquiring the quantity of the logistics orders completed by the reference vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders;
And recommending the vehicles to be recommended in sequence from the large to the 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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
FIG. 5 illustrates a schematic diagram of a computing device according to an embodiment of the present invention, and the embodiment of the present invention is not limited to the specific implementation of the device.
As shown in fig. 5, the computing device may include: a processor 502, a communication interface (Communications Interface) 504, a memory 506, and a communication bus 508.
Wherein: processor 502, communication interface 504, and memory 506 communicate with each other via 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 perform the relevant steps in the embodiment of the temporal network-based logistics recommendation method described above.
In particular, program 510 may include program code including computer-operating instructions.
The processor 502 may be a central processing unit CPU, or a specific integrated circuit ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention. The device includes one or each processor, which may be the same type of processor, such as one or each CPU; but may also be different types of processors such as one or each CPU and one or each ASIC.
A memory 506 for storing a program 510. Memory 506 may comprise high-speed RAM memory or may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 510 may be specifically operable to cause the processor 502 to:
Establishing a temporal network of the vehicle according to the historical logistics order data in the preset time;
screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating the similarity of vehicles in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
In an alternative, the program 510 causes the processor to:
acquiring the historical logistics order data in 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 connected with the same node at different moments to form the temporal network in the preset time.
In an alternative, the program 510 causes the processor to:
respectively calculating a first linear distance between departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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;
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 length and the type of vehicles in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners 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 alternative way, the reference node has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, 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 vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders;
and recommending the vehicles to be recommended in sequence from the large to the 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 the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network; calculating the similarity of vehicles in the vehicle recommendation network; according to the vehicle similarity and the vehicle preference of the cargo owner, the vehicle recommendation index is obtained, the vehicle is recommended to the cargo owner according to the vehicle recommendation index, the vehicle recommendation method can be suitable for vehicle recommendation of a highly-informationized logistics platform, no additional data acquisition facilities are needed, the vehicle recommendation meeting the target travel requirement can be obtained only through analysis of historical travel data, the flexibility is high, the cost is low, the vehicle recommendation method is suitable for all types of logistics information platforms, the application range is wide, and the recommendation effect is good.
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 a construction of such a system is apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It will be appreciated that the teachings of the present invention described herein may be implemented in a variety of programming languages, and the above description of specific languages is provided for disclosure of enablement and best mode of the present invention.
In the description provided herein, numerous specific details are set forth. However, it is understood 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 above 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 disclosure and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be construed as reflecting the intention that: i.e., the claimed invention 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 apparatus of the embodiments may be adaptively changed and disposed in one or more apparatuses different from the embodiments. 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. Any combination of all features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or units of any method or apparatus so disclosed, may be used in combination, except insofar as at least some of such features and/or processes or units 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 but not others included in other embodiments, 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 can 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 use of the words first, second, third, etc. do not denote any order. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specifically stated.
Claims (9)
1. A temporal network-based logistics recommendation method, the method comprising:
establishing a temporal network of the vehicle according to the historical logistics order data in the preset time, wherein the temporal network comprises the following steps: acquiring the historical logistics order data in 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; constructing delay edges connected with the same node at different moments to form the temporal network in the preset time;
Screening the temporal network according to the logistics demand data and the historical cooperative vehicle information to obtain a vehicle recommendation network;
calculating the similarity of vehicles in the vehicle recommendation network;
and acquiring a vehicle recommendation index according to the vehicle similarity and the vehicle preference of the cargo owner, and recommending the vehicle to the cargo owner according to the vehicle recommendation index.
2. The method according to claim 1, wherein 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 departure places and a second linear distance between destinations of the two vehicles according to the longitude and latitude of the departure places and the longitude and latitude 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;
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.
3. The method of claim 1, wherein the filtering the temporal network according to the logistic demand data and the historical collaborative vehicle information to obtain a vehicle recommendation network comprises:
Screening nodes and connecting edges in a temporal network according to the length and the type of vehicles in the logistics demand data;
and screening out a network structure formed by nodes corresponding to the common vehicles of the cargo owners from the temporal network to form the vehicle recommendation network.
4. The method of claim 1, wherein the calculating the vehicle similarity 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.
5. The method according to claim 4, characterized in that the reference node i has a vehicle similarity s with the node j to be recommended that is different from the reference node i i,j The following relationship is satisfied:
d p ＝hops*time，
time＝t l -t e ，
wherein p is a temporal path existing between the nodes j to be recommended of the reference node i, t p The time distribution coefficient of the temporal path p is t is the preset time, t e For the moment t corresponding to the first connecting edge passed by the temporal path p l D, corresponding to the moment d of the last connecting edge passed by the temporal path p p Is the length of the temporal path p, hops is the number of the temporal path p passing through the connecting edges, and time is the delay time length of the temporal path p.
6. The method of claim 4, wherein the obtaining a vehicle recommendation index based on the vehicle similarity and the owner's vehicle preference and recommending the vehicle to the owner based on the vehicle recommendation index comprises:
acquiring the quantity of the logistics orders completed by the reference vehicles corresponding to the reference nodes 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 between the node to be recommended and the reference node and the number of the logistics orders;
and recommending the vehicles to be recommended in sequence from the large to the small according to the recommendation index.
7. A temporal network-based logistics recommendation apparatus, the apparatus comprising:
the network establishment unit is used for establishing a temporal network of the vehicle according to the historical logistics order data in the preset time, and comprises the following steps: acquiring the historical logistics order data in 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; constructing delay edges connected with the same node at different moments to form the temporal network in the 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;
a similarity calculation unit for calculating a vehicle similarity 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 cargo owner and recommending the vehicle to the cargo owner according to the vehicle recommending index.
8. A computing device, comprising: the device comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete communication with each other through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to perform the steps of the temporal network-based logistics recommendation method in accordance with any one of claims 1-6.
9. 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 1-6.
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