CN112053116A

CN112053116A - Carpoolable order identification method and device

Info

Publication number: CN112053116A
Application number: CN202010946998.XA
Authority: CN
Inventors: 吴文亮; 叶加文
Original assignee: Guangzhou Huihuiche Information Technology Co ltd
Current assignee: Jiangsu Yunmanman Tongcheng Information Technology Co ltd
Priority date: 2020-09-10
Filing date: 2020-09-10
Publication date: 2020-12-08
Anticipated expiration: 2040-09-10
Also published as: CN112053116B

Abstract

The invention discloses a carpoolable order identification method and a device, comprising the following steps: acquiring a training sample set, wherein the training sample set comprises positive samples and negative samples; training a preset classification model by adopting a positive sample and a negative sample to obtain a target car pooling order identification model; receiving a goods source main order input by a user, and determining a starting point and a destination point of the goods source main order; generating a route space block diagram based on a departure point and a destination point of a goods source main order, and taking an order in the route space block diagram as an order to be carpooled; constructing a sample pair to be carpooled by adopting a goods source main order and a carpooled order; inputting a sample pair to be carpooled into the target carpoolable order identification model, and outputting the carpoolable order. Therefore, the technical problems that the car sharing orders cannot be automatically identified, car owners cannot rapidly and flexibly acquire the car sharing orders, and the effective utilization rate of logistics resources is low in the prior art are solved, and the car owners can rapidly and flexibly acquire the car sharing orders.

Description

Carpoolable order identification method and device

Technical Field

The invention relates to the technical field of order identification, in particular to a carpoolable order identification method and device.

Background

With the rapid development of the logistics industry and the electronic information technology, the logistics transportation industry is increasingly closely associated with electronic commerce, and a large number of owners and owners carry out car sharing transportation on orders through services provided by a logistics transportation transaction electronic commerce platform, so that the problems of difficulty in car searching of owners of small-batch goods sources and difficulty in reasonable goods allocation and carrying of the owners of the goods are solved to a certain extent.

However, in the existing car pooling scheme, car owners generally search the released car pooling information according to their own travel and mainly match the information one by one, so that car pooling orders cannot be automatically identified, the car owners cannot quickly and flexibly acquire the car pooling orders, and the effective utilization rate of logistics resources is low.

Disclosure of Invention

The invention provides a carpoolable order identification method and device, and solves the technical problems that in the prior art, carpoolable orders cannot be automatically identified, car owners cannot rapidly and flexibly acquire the carpoolable orders, and the effective utilization rate of logistics resources is low.

The invention provides a carpoolable order identification method, which comprises the following steps: acquiring a training sample set, wherein the training sample set comprises positive samples and negative samples; training a preset classification model by adopting the positive sample and the negative sample to obtain a target carpoolable order identification model; receiving a goods source main order input by a user, and determining a starting point and a destination point of the goods source main order; generating a route space block diagram based on a departure point and a destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled; the line space block diagram comprises space blocks, and the types of the space blocks comprise geohash blocks or *** S2 blocks; constructing a sample pair to be carpooled by adopting the goods source main order and the order to be carpooled; and inputting the sample pair to be carpooled into the target carpoolable order identification model, and outputting the carpoolable order.

Optionally, the step of obtaining a training sample set includes: determining a planned route from a preset map according to the longitude and latitude of the departure point and the longitude and latitude of the destination point of the preset main order; performing line point sampling operation on the planned line to obtain a main line point set; determining a first target intermediate point corresponding to the longitude and latitude of the starting point of each preset sub-order and a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub-order from the main line point set by adopting a preset intermediate point searching algorithm; adopting a first connecting line between a starting point of the preset sub-order and a destination point of the preset sub-order and a second connecting line between the first target intermediate point and the second target intermediate point corresponding to the preset sub-order as a connecting line sample pair; respectively extracting target features in a plurality of connecting line sample pairs through a feature extraction algorithm; taking a connecting line sample pair corresponding to the target characteristics meeting the preset car sharing condition as the positive sample; and taking the connecting line sample pair corresponding to the target characteristic which does not meet the preset car sharing condition as the negative sample.

Optionally, the step of performing a line point sampling operation on the planned line to obtain a main line point set includes: extracting a plurality of line points from the planned line according to first-level longitude and latitude; the route points comprise a starting point, a destination point and a plurality of intermediate points of the preset main order; converting the longitude and latitude corresponding to the plurality of line points to obtain a plurality of space blocks respectively corresponding to the plurality of line points; and if the space blocks are connected pairwise, constructing a main line point set by adopting the line points.

Optionally, the method further comprises: if the space blocks which are not connected between every two space blocks exist, obtaining line points corresponding to the space blocks which are not connected between every two space blocks as line points to be interpolated; interpolating the area between the line points to be interpolated by adopting an interpolation algorithm to obtain at least one interpolation point; and constructing the main route point set by adopting the plurality of route points and the at least one interpolation point.

Optionally, the step of determining, from the main route point set, a first target intermediate point corresponding to the longitude and latitude of the departure point of each preset sub-order and a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub-order by using a preset intermediate point search algorithm includes: creating a longitude key value query dictionary and a latitude key value query dictionary by adopting the main line point set according to the longitude and the latitude respectively; determining a dictionary with the largest number of key values from the longitude key value dictionary and the latitude key value dictionary as a target query dictionary; determining a first target intermediate point corresponding to the starting point longitude and latitude of each preset sub-order from the main route point set by adopting the target query dictionary; and determining a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub-order from the main line point set by adopting the target query dictionary.

Optionally, the step of training a preset classification model by using the positive sample and the negative sample to obtain a target car pooling order identification model includes: inputting the positive sample and the negative sample into the preset classification model in sequence to obtain a plurality of classification results; the classification result comprises that the preset car sharing condition is met and the preset car sharing condition is not met; judging whether the error rates of the classification results are smaller than a preset threshold value or not; and if so, outputting the target car sharing order identification model.

Optionally, the method further comprises: if not, returning to the step of obtaining the training sample set.

Optionally, the step of generating a route space block diagram based on the departure point and the destination point of the goods source main order, and taking the order in the route space block diagram as the order to be carpooled includes: determining the longitude and latitude of the departure point of the goods source main order and the longitude and latitude of the destination point of the goods source main order; determining a path to be transported based on the longitude and latitude of the departure point of the goods source main order and the longitude and latitude of the destination point of the goods source main order; extracting a plurality of points to be transported from the path to be transported according to the primary longitude and latitude; the to-be-transported point comprises a starting point of the goods source main order, a destination point of the goods source main order and a middle point of the goods source main order; converting the longitude and latitude corresponding to the multiple points to be transported to generate a line space block diagram; the line space block diagram comprises a plurality of space blocks respectively corresponding to the plurality of to-be-transported points; and determining the sub-orders of which the longitude and latitude of the departure point and the longitude and latitude of the destination point are both in the plurality of space blocks as the order to be carpooled.

Optionally, the step of constructing a sample pair to be carpooled by using the goods source main order and the order to be carpooled includes: acquiring a departure point of the order to be carpooled and a destination point of the order to be carpooled; determining a third target intermediate point corresponding to the longitude and latitude of the departure point of the order to be carpooled and a fourth target intermediate point corresponding to the longitude and latitude of the destination point of the order to be carpooled from intermediate points of the goods source main order by adopting a preset intermediate point searching algorithm; and adopting a first connecting line between the starting point of the order to be carpooled and the destination point of the order to be carpooled and a second connecting line between the third target intermediate point and the fourth target intermediate point as a sample pair to be carpooled.

The invention also provides a carpooling order recognition device, which comprises:

the training sample set acquisition module is used for acquiring a training sample set, and the training sample set comprises positive samples and negative samples; the target car pooling order identification model generation module is used for training a preset classification model by adopting the positive sample and the negative sample to obtain a target car pooling order identification model; the goods source main order receiving module is used for receiving a goods source main order input by a user and determining a starting point and a destination point of the goods source main order; the order determining module to be carpooled is used for generating a route space block diagram based on a departure point and a destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled; the line space block diagram comprises space blocks, and the types of the space blocks comprise geohash blocks or *** S2 blocks; the system comprises a to-be-carpooled car sample pair construction module, a to-be-carpooled car sample pair construction module and a to-be-carpooled car sample pair construction module, wherein the to-be-carpooled car sample pair construction module is used for constructing a to-be-carpooled car sample pair by adopting the goods source main; and the carpoolable order output module is used for inputting the sample pairs to be carpooled into the target carpoolable order identification model and outputting the carpoolable orders.

According to the technical scheme, the invention has the following advantages:

in the embodiment of the invention, the preset classification model is trained based on the training data set to obtain the target carpoolable order recognition model, the goods source main order input by the user is received, determining a starting point and a destination point of the order, generating a line space block diagram based on the starting point and the destination point, taking the order in the line space block diagram as a sub-order for carpooling, adopting the goods source main order and the order for carpooling, constructing a sample pair for carpooling, inputting the sample pair for carpooling into a target carpooling identification model, judging whether the order for carpooling can be carpooled or not, if so, displaying the order for carpooling, therefore, the technical problems that the car sharing orders cannot be automatically identified, car owners cannot rapidly and flexibly acquire the car sharing orders, and the effective utilization rate of logistics resources is low in the prior art are solved, and the car owners can rapidly and flexibly acquire the car sharing orders.

Drawings

In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to these drawings without inventive exercise.

Fig. 1 is a flowchart illustrating steps of a method for identifying a car pool order according to an embodiment of the present invention;

FIG. 2 is a flowchart illustrating steps of a method for identifying a pool order according to an alternative embodiment of the present invention;

FIG. 3 illustrates a longitude and latitude schematic of a main route point set of an embodiment of the present invention;

FIG. 4a is a schematic diagram of a route formed by route points according to an embodiment of the present invention;

FIG. 4b is a diagram illustrating an interpolation process of route points according to an embodiment of the present invention;

FIG. 5 is a schematic diagram of a sample pair of wires according to an embodiment of the present invention;

FIG. 6 is a schematic illustration of target features in an embodiment of the invention;

FIG. 7a is a schematic diagram illustrating a comparison between a connected sample pair and a positive sample according to an embodiment of the present invention;

FIG. 7b is a schematic diagram illustrating the alignment of a pair of inline samples with a positive sample according to an alternative embodiment of the present invention;

FIG. 8 is a schematic diagram of a comparison between a pair of connected samples and a negative sample according to another embodiment of the present invention;

FIG. 9 is a flowchart of a preset classification model training process in an embodiment of the present invention;

fig. 10 is a block diagram of a device for identifying a car pool order according to an embodiment of the present invention.

Detailed Description

The embodiment of the invention provides a carpoolable order identification method and device, which are used for solving the technical problems that in the prior art, carpoolable orders cannot be automatically identified, car owners cannot rapidly and flexibly acquire the carpoolable orders, and the effective utilization rate of logistics resources is low.

In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.

Referring to fig. 1, fig. 1 is a flowchart illustrating steps of a method for identifying a car pool order according to an embodiment of the present invention.

The invention provides a carpoolable order identification method, which comprises the following steps:

101, acquiring a training sample set, wherein the training sample set comprises positive samples and negative samples;

in the embodiment of the invention, in order to train the preset classification model to improve the accuracy of judging whether the order can be carpooled, a training sample set preset by a user needs to be acquired at the moment, and the training sample set comprises a positive sample and a negative sample.

It is worth mentioning that a positive sample indicates that the corresponding order can be carpooled, and a negative sample indicates that the corresponding order cannot be carpooled.

Step 102, training a preset classification model by adopting the positive sample and the negative sample to obtain a target car pooling order identification model;

in specific implementation, the positive sample and the negative sample are sequentially input into a preset classification model for classification training to obtain a classification result, and when the classification result meets the requirement set by a user, a target carpoolable order recognition model can be obtained.

103, receiving a goods source main order input by a user, and determining a starting point and a destination point of the goods source main order;

after the target car pooling order identification model is obtained, a goods source main order input by a user can be received, and then a departure point and a destination point of the goods source main order are determined according to records in the goods source main order, so that the longitude and latitude of the departure point and the longitude and latitude of the destination point of the goods source main order are obtained.

104, generating a route space block diagram based on a starting point and a destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled;

in the embodiment of the present invention, the line space block map includes space blocks, and the types of the space blocks may include, but are not limited to: and a geohash block or a *** S2 block, etc. to search for the order to be carpooled which is in accordance with the order to be input into the target carpooled order identification model, a route space block diagram can be generated according to the departure point and the destination point of the main order of the goods source, if the order to be carpooled exists in the route space block diagram, the order is determined as the order to be carpooled, so that the carpooled order identification can be judged based on the target carpooled identification model in the following.

105, constructing a sample pair to be carpooled by adopting the goods source main order and the order to be carpooled;

in the embodiment of the application, after one or more to-be-carpooled orders are detected, in order to further detect whether the to-be-carpooled orders can be carpooled, a to-be-carpooled sample pair can be constructed by adopting the goods source main order and the to-be-carpooled orders, so that the subsequent judgment process of the target carpooled order identification model is facilitated.

And 106, inputting the sample pair to be carpooled into the target carpoolable order identification model, and outputting the carpoolable order.

In the concrete implementation, after the sample pair to be carpooled is constructed, the sample pair to be carpooled is input into the target carpoolable order identification model, and whether the carpoolable order in the sample pair to be carpooled can be judged, if yes, the order to be carpooled can be displayed, and whether carpooling is carried out or not is determined by a car owner.

Referring to fig. 2, fig. 2 is a flowchart illustrating steps of a method for identifying a car pool order according to an alternative embodiment of the present invention, which includes the following steps 201 and 208:

step 201, obtaining a training sample set, wherein the training sample set comprises positive samples and negative samples;

in the embodiment of the present invention, the step 201 may include the following sub-steps S1-S7:

the substep S1, determining a planned route from a preset map according to the starting point longitude and latitude and the destination point longitude and latitude of the preset main order;

in the specific implementation, the starting point longitude and latitude and the destination point longitude and latitude of the preset main order can be input in the navigation software, and the planning route is generated in the preset map.

Optionally, the planned route may also be determined according to a user-defined rule.

Referring to fig. 3, fig. 3 is a schematic diagram illustrating the longitude and latitude of a main route point set according to an embodiment of the present invention, where the longitude and latitude of some points in the main route point set are included.

A substep S2, executing a line point sampling operation on the planned line to obtain a main line point set;

referring to fig. 4a, fig. 4a shows a path diagram formed by route points according to an embodiment of the present invention, where the path diagram includes a departure point, a destination point and a plurality of main order path intermediate sampling points, and the sub-step S2 may include the following sub-steps:

extracting a plurality of line points from the planned line according to first-level longitude and latitude; the route points comprise a starting point, a destination point and a plurality of intermediate points of the preset main order;

converting the longitude and latitude corresponding to the plurality of line points to obtain a plurality of space blocks respectively corresponding to the plurality of line points;

and if the space blocks are connected pairwise, constructing a main line point set by adopting the line points.

It should be noted that the Geohash block may be obtained by a Geohash algorithm, the Google S2 block may be obtained by a Google S2 algorithm, and both the Geohash algorithm and the Google S2 algorithm are algorithms for encoding longitude and latitude, changing two dimensions into one dimension, and partitioning address locations.

The primary longitude and latitude refers to the longitude and latitude with the longitude or the latitude at a preset interval.

In the embodiment of the invention, after the planned route is obtained, the route point extraction is also needed to be carried out on the planned route, and in order to prevent the calculated amount from being too large in the subsequent sub-order matching process, the route points of a plurality of planned routes can be extracted according to the first-level longitude and latitude; and then, converting the longitude and latitude of the line points through a preset geohash algorithm to obtain a plurality of corresponding space blocks. When the space blocks are connected with each other, a point set is directly formed by adopting a plurality of line points to obtain a main line point set.

Referring to fig. 4b, fig. 4b is a diagram illustrating an interpolation process of route points according to an embodiment of the present invention, wherein a plurality of space blocks converted by the latitude and longitude of the route point of the preset main order are included.

Further, the sub-step S2 may further include the following sub-steps:

if the space blocks which are not connected between every two space blocks exist, obtaining line points corresponding to the space blocks which are not connected between every two space blocks as line points to be interpolated;

interpolating the area between the line points to be interpolated by adopting an interpolation algorithm to obtain at least one interpolation point;

and constructing the main route point set by adopting the plurality of route points and the at least one interpolation point.

In the embodiment of the invention, as the distance between the primary longitude and latitude may be far, the situation that space blocks which are not connected between every two space blocks may occur, at this time, the area between the line points to be interpolated is interpolated by adopting an interpolation algorithm by acquiring the line points corresponding to the space blocks which are not connected between every two space blocks as the line points to be interpolated so as to acquire a plurality of interpolation points; and a point set formed by the plurality of line points and the at least one interpolation point is used as a main line point set, so that the space blocks are kept from being disconnected and consistent, and further, the situation that the starting point and the destination point of the sub-order fall in the area between the line points to be interpolated and the sub-order cannot be detected is prevented.

The interpolation algorithm may adopt an equidistant interpolation algorithm or other non-equidistant interpolation algorithms, which is not limited in this application.

A substep S3 of determining a first target intermediate point corresponding to the longitude and latitude of the starting point of each preset sub order and a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub order from the main line point set by adopting a preset intermediate point searching algorithm;

in one example of the present invention, the sub-step S3 may include the following sub-steps:

creating a longitude key value query dictionary and a latitude key value query dictionary by adopting the main line point set according to the longitude and the latitude respectively;

determining a dictionary with the largest number of key values from the longitude key value dictionary and the latitude key value dictionary as a target query dictionary;

determining a first target intermediate point corresponding to the starting point longitude and latitude of each preset sub-order from the main route point set by adopting the target query dictionary;

and determining a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub-order from the main line point set by adopting the target query dictionary.

In the embodiment of the present invention, after the main route point set is obtained, a corresponding key value query dictionary, that is, a longitude key value query dictionary and a latitude key value query dictionary, may be respectively created according to the longitude and the latitude.

In a specific implementation, the process of constructing a key-value query dictionary may be as follows:

the formula for establishing the latitude and longitude dictionary key is as follows:

lonkey_i＝|lon_key_x|，min_lon<＝x<＝max_lon

latkey_i＝|lat_key_x|，min_lat<＝x<＝max_lat

wherein: min _ lon is the minimum longitude of the current main order line; max _ lon is the maximum longitude of the current main order line; min _ lat is the minimum latitude of the current main order line; max _ lat is the maximum latitude of the current master order line.

The key-value lookup dictionary is as follows:

lonkey_i:

(lon_lonkey_i_x1,lat_latkey_n1_y1)

(lon_lonkey_i_x2,lat_latkey_n2_y2)

(lon_lonkey_i_x3,lat_latkey_n3_y3)

(lon_lonkey_i_x4,lat_latkey_n2_y4)

taking longitude and latitude as (103, 25) and (104, 26) as examples, the obtained longitude key dictionary and latitude key dictionary are as follows:

longitude key value dictionary:

lonkey:103

[103.82381，25.565736]

[103.875265，25.654019]

lonkey:104

[104.355421，25.674809]

[104.907274，25.801758]

[104.988451，25.780516]

latitude key value dictionary:

latkey:25

[103.82381，25.565736]

[103.875265，25.654019]

[104.355421，25.674809]

[104.907274，25.801758]

[104.988451，25.780516]

latkey:26

[105.704427，26.030069]

[105.90691，26.20362]

[106.368016，26.498424]

[106.432995，26.526775]

selecting a dictionary with the most key values from the longitude key value dictionary and the latitude key value dictionary as a target query dictionary, taking the longitude and latitude of the starting place of the preset main order as (103.82381,25.565736), and the longitude and latitude of the destination as: (111.9253,33.03928) for example, the key-value list of the longitude-key-value dictionary and the latitude-key-value dictionary obtained according to the route of the main order is:

longitude key value list:

dict_keys([103,104,105,106,107,108,109,110,111,112,102,113])

latitude key value list:

dict_keys([25,26,27,28,29,30,31,32,33,24,34])

it can be seen that the longitude key dictionary query obtains 12 key values, and the latitude key dictionary query obtains 11 key values, so that the longitude key dictionary is more suitable to be selected.

Determining a first target intermediate point corresponding to the longitude and latitude of the departure point of each preset sub-order from the main route point set by adopting the selected target query dictionary;

In the embodiment of the invention, the preset sub-order line is taken as: (106, 26) to (111, 30) are taken as examples, and the query is performed by using the determined target query dictionary, i.e. the longitude-key value dictionary, and the obtained result is:

the first result inquired according to the starting point of the sub-order is as follows:

lonkey:106

[106.368016，26.498424]

[106.432995，26.526775]

[106.56753，26.585955]

[106.683199，26.694735]

[106.802274，26.699852]

[106.904592，26.832105]

the second result inquired according to the destination point of the sub order is as follows:

lonkey:111

[111.144582，28.672934]

[111.385084，28.804119]

[111.625586，28.935304]

[111.744527，28.906615]

[111.8709225，29.421682]

[111.93412025，29.6792155]

[111.997318，29.936749]

[111.996426，32.771209]

[111.769822，32.988989]

[111.795469，33.096272]

[111.865764，33.08105]

[111.925304，33.039015]

[111.9253，33.03928]

finally, respectively calculating the Euclidean distance between the starting point of the sub-order and each point in the first result, and selecting the point with the minimum Euclidean distance as a first target intermediate point, namely [106.56753,26.585955] in the example;

the euclidean distance between the starting point of the sub-order and each point in the second result is calculated separately and the point with the smallest euclidean distance is selected as the second target intermediate point, i.e., in this example [111.99731829.936749 ].

A substep S4, using a first connecting line between the starting point of the preset sub-order and the destination point of the preset sub-order, and a second connecting line between the first target intermediate point and the second target intermediate point corresponding to the preset sub-order as a connecting line sample pair;

referring to fig. 5, fig. 5 shows a schematic diagram of a pair of link samples in an embodiment of the present invention.

In a specific implementation, the first target intermediate point and the second target intermediate point are intermediate points corresponding to a departure place and a destination of the sub-order, the departure place and the destination of the sub-order are connected to obtain a connection line of the departure place and the destination of the sub-order, namely a first connection line, the first target intermediate point and the second target intermediate point are connected to obtain an intermediate point connection line closest to the sub-order, namely a second connection line, and the connection line sample pair is obtained by combining the first connection line and the second connection line.

A substep S5 of respectively extracting target features in the plurality of connecting line sample pairs through a feature extraction algorithm;

the feature extraction algorithm refers to an algorithm that calculates the correlation between the first connection line and the second connection line in the connection line sample pair.

Referring to fig. 6, fig. 6 is a schematic diagram illustrating target characteristics in an embodiment of the present invention, where a main order is a main order and an order assembly is a sub-order, and the target characteristics may include, but are not limited to, the following characteristics:

the method is characterized in that: shortest distance sf _ mfs _ dist from departure place of car sharing order to sampling point on main order line

And (2) feature: the shortest distance from the departure place of the car pooling order to the sampling point on the main order line is compared with the line of the main order interval

sf_mfs_dist/mfs_mts_dist

And (3) feature: the shortest distance from the departure place of the car pooling order to the sampling point on the line of the main order is compared with the line of the car pooling order

sf_mfs_dist/sub_dist

And (4) feature: shortest distance st _ mts _ dist from car pooling order destination to sampling point on main order line

And (5) feature: the shortest distance from the car pooling order destination to the sampling point on the main order line is compared with the line of the main order interval

st_mts_dist/mfs_mts_dist

And (6) feature: the shortest distance from the destination of the car pooling order to the sampling point on the main order line is compared with the car pooling order line

st_mts_dist/sub_dist

And (7) feature: an angle theta1 formed by a sampling point close to the destination of the main order, a sampling point close to the departure of the main order, and a carpooling destination

And (2) characteristic 8: an angle theta2 formed by a sampling point close to the departure place of the main order, a sampling point close to the destination of the main order, and a carpooling departure place

And (2) characteristic 9: linear distance of car pool order, linear distance ratio to main order

sub_dist/main_dist

The characteristics are as follows: the ratio of the total detour distance of the carpool order to the linear distance of the main order section

(sf_mfs_dist+sub_dist+st_mts_dist)/mfs_mts_dist

And (2) characteristic 11: the ratio of the extra detour distance of the car pooling order to the straight distance of the car pooling order

(sf_mfs_dist+st_mts_dist)/sub_dist

Wherein, the abbreviations of the target characteristics are as follows:

(1)main_dist:main_from---->main_to

(2)sub_dist:sub_from---->sub_to

(3)sf_mfs_dist:sub_from---->main_from_sample

(4)sf_mts_dist:sub_from---->main_to_sample

(5)st_mts_dist:sub_to---->main_to_sample

(6)st_mfs_dist:sub_to---->main_from_sample

(7)mfs_mts_dist:main_from_sample---->main_to_sample

a substep S6, taking a connecting line sample pair corresponding to the target characteristics meeting the preset car sharing condition as the positive sample;

for comparison, please refer to fig. 7a and 7b, fig. 7a and 7b respectively show a comparison diagram of a pair of connected samples and a positive sample according to two alternative embodiments of the present invention, wherein the positive sample may be labeled as 1 at the end of the data display, the unlabeled sample is the unlabeled sample, and the labeled sample is the labeled sample.

In the embodiment of the invention, after the target characteristics are obtained, the target characteristics are judged according to the preset carpooling conditions set by the user so as to determine the preset sub-orders belonging to the positive sample.

For example: the target characteristic is the ratio of the linear distance of the car pooling order to the linear distance of the main order, and the preset car pooling condition can be set to be that the ratio of the linear distance of the car pooling order to the linear distance of the main order is smaller than a certain threshold value.

When a plurality of target features are provided, the corresponding connection sample pair is determined to be a positive sample under the condition that all the target features meet the preset car sharing condition, otherwise, the corresponding connection sample pair is determined to be a negative sample.

And a substep S7, taking the connecting line sample pair corresponding to the target characteristic not meeting the preset car sharing condition as the negative sample.

Referring to fig. 8, fig. 8 is a diagram illustrating a comparison between a pair of connection samples and a negative sample according to another embodiment of the present invention, wherein the negative sample can be labeled as 0 at the end of the data display.

In another example of the present invention, the step 102 can be replaced by the following step 202 and 205:

referring to fig. 9, fig. 9 is a flowchart illustrating a training process of a preset classification model in an embodiment of the present invention.

Step 202, inputting the positive sample and the negative sample to the preset classification model in sequence to obtain a plurality of classification results;

in the embodiment of the invention, after a positive sample and a negative sample are sequentially input into the preset classification model, the preset classification model is operated once every time one positive sample or negative sample is input, and a classification result is obtained; the classification result comprises that the preset car sharing condition is met and the preset car sharing condition is not met.

Step 203, judging whether the error rates of the classification results are smaller than a preset threshold value;

and step 204, if yes, outputting the target car sharing order identification model.

When the iteration times of the preset classification model reach a threshold value, namely all the positive samples and the negative samples are input, the output classification result is compared with the correct classification of the positive samples and the negative samples, and when the error is smaller than the error threshold value, the preset classification model is trained and the target car-pooling order recognition model can be output.

It should be noted that the error threshold may be set by a user according to an actual situation, which is not limited in this embodiment of the present invention.

And step 205, if not, returning to the step of obtaining the training sample set.

In a specific implementation, a training sample set may appear to train a preset classification model, and still a target car pool order recognition model cannot be obtained, at this time, the step of obtaining the training sample set may be returned, and a new training sample set is obtained again to continue the training process of the model.

Optionally, after the structure of the preset classification model is adjusted, the step of inputting the positive samples and the negative samples of the training sample set into the preset classification model may be returned, which is not limited in this embodiment of the present invention.

Step 206, receiving a goods source main order input by a user, and determining a starting point and a destination point of the goods source main order;

in the embodiment of the present invention, the specific implementation process of step 206 is similar to that of step 103, and is not described herein again.

Step 207, generating a route space block diagram based on the departure point and the destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled;

in an embodiment of the present invention, the step 207 may include the following sub-steps:

determining the longitude and latitude of the departure point of the goods source main order and the longitude and latitude of the destination point of the goods source main order;

determining a path to be transported based on the longitude and latitude of the departure point of the goods source main order and the longitude and latitude of the destination point of the goods source main order;

extracting a plurality of points to be transported from the path to be transported according to the primary longitude and latitude; the to-be-transported point comprises a starting point of the goods source main order, a destination point of the goods source main order and a middle point of the goods source main order;

converting the longitude and latitude corresponding to the multiple points to be transported to generate a line space block diagram; the line space block diagram comprises a plurality of space blocks respectively corresponding to the plurality of to-be-transported points;

and determining the sub-orders of which the longitude and latitude of the departure point and the longitude and latitude of the destination point are both in the plurality of space blocks as the order to be carpooled.

In the specific implementation, a to-be-transported path is determined based on the longitude and latitude of the departure point and the longitude and latitude of the destination point of the goods source main order in combination with preset navigation software or a navigation map; and judging whether the longitude and latitude of the departure point and the longitude and latitude of the destination point of the sub-order are both in the space block or not based on the space block converted from each to-be-transported point in the to-be-transported path, and if so, determining the sub-order to be the order to be carpooled.

Step 208, adopting the goods source main order and the order to be carpooled to construct a sample pair to be carpooled;

optionally, the step 208 may comprise the following sub-steps:

acquiring a departure point of the order to be carpooled and a destination point of the order to be carpooled;

determining a third target intermediate point corresponding to the longitude and latitude of the departure point of the order to be carpooled and a fourth target intermediate point corresponding to the longitude and latitude of the destination point of the order to be carpooled from intermediate points of the goods source main order by adopting a preset intermediate point searching algorithm;

and adopting a first connecting line between the starting point of the order to be carpooled and the destination point of the order to be carpooled and a second connecting line between the third target intermediate point and the fourth target intermediate point as a sample pair to be carpooled.

In the embodiment of the application, the target car pool order obtained by training is used for identifying the sample pairs of the input required by the model. The method comprises the steps that after a to-be-carpooled order is identified based on a goods source main order, a departure point of the to-be-carpooled order and a destination point of the to-be-carpooled order are obtained, and a third target intermediate point corresponding to the longitude and latitude of the departure point of the to-be-carpooled order and a fourth target intermediate point corresponding to the longitude and latitude of the destination point of the to-be-carpooled order are determined from the intermediate points of the goods source main order based on the preset intermediate point searching algorithm; and connecting the departure point of the order to be carpooled and the destination point of the order to be carpooled as a third connecting line, connecting the third target intermediate point and the fourth target intermediate point as a fourth connecting line, and using the third connecting line and the fourth connecting line as a sample pair to be carpooled.

Step 209, inputting the sample pair to be carpooled into the target carpoolable order identification model, and outputting the carpoolable order.

In another example of the present invention, after a to-be-carpooled order is obtained, the to-be-carpooled sample pair is input into the target carpooled order recognition model, the target carpooled order recognition model classifies according to characteristics of the to-be-carpooled sample pair to determine whether the to-be-carpooled order satisfies a preset carpooled condition, the to-be-carpooled order satisfying the preset cocoa carpooled condition is output as a carpooled order, and the order can be displayed on a screen for a car owner to check in an actual operation, so that the car owner can further confirm whether to carpool.

Referring to fig. 10, fig. 10 is a block diagram illustrating a car pool order recognition apparatus according to an alternative embodiment of the present invention.

A carpoolable order identification apparatus, comprising:

a training sample set obtaining module 901, configured to obtain a training sample set, where the training sample set includes positive samples and negative samples;

a target car pooling order identification model generating module 902, configured to train a preset classification model by using the positive sample and the negative sample to obtain a target car pooling order identification model;

a source primary order receiving module 903, configured to receive a source primary order input by a user, and determine a departure point and a destination point of the source primary order;

a to-be-carpooled order determining module 904, configured to generate a route space block diagram based on a departure point and a destination point of the goods source main order, and use an order in the route space block diagram as a to-be-carpooled order;

a sample pair to be carpooled constructing module 905, configured to construct a sample pair to be carpooled by using the goods source main order and the order to be carpooled;

a car pooling order output module 906 for inputting the sample pair to be car pooling into the target car pooling order identification model and outputting a car pooling order.

Optionally, the training sample set obtaining module 901 includes:

the planning route determining submodule is used for determining a planning route from a preset map according to the starting point longitude and latitude and the destination point longitude and latitude of the preset main order;

the main line point set determining submodule is used for executing line point sampling operation on the planned line to obtain a main line point set;

the target intermediate point searching submodule is used for determining a first target intermediate point corresponding to the longitude and latitude of the starting point of each preset sub order and a second target intermediate point corresponding to the longitude and latitude of the target point of each preset sub order from the main line point set by adopting a preset intermediate point searching algorithm;

the connecting line sample pair generation submodule is used for adopting a first connecting line between a starting point of the preset sub-order and a destination point of the preset sub-order and a second connecting line between the first target intermediate point and the second target intermediate point corresponding to the preset sub-order as a connecting line sample pair;

the target feature extraction submodule is used for respectively extracting target features in a plurality of connecting line sample pairs through a feature extraction algorithm;

the positive sample determining submodule is used for taking a connecting line sample pair corresponding to the target characteristic meeting the preset car sharing condition as the positive sample;

and the negative sample sub-module is used for taking the connecting line sample pair corresponding to the target characteristic which does not meet the preset car sharing condition as the negative sample.

Optionally, the main line point set determining submodule includes:

the route point extraction unit is used for extracting a plurality of route points from the planned route according to primary longitude and latitude; the route points comprise a starting point, a destination point and a plurality of intermediate points of the preset main order;

a space block determination unit, configured to convert the longitude and latitude corresponding to the multiple line points to obtain multiple space blocks corresponding to the multiple line points, respectively; the line space block diagram comprises space blocks, and the types of the space blocks comprise geohash blocks or *** S2 blocks;

and the first main line point set constructing unit is used for constructing a main line point set by adopting the plurality of line points if the plurality of space blocks are connected pairwise.

Optionally, the main line point set determining submodule further includes:

the device comprises a to-be-interpolated line point determining unit, a to-be-interpolated line point determining unit and a to-be-interpolated line point determining unit, wherein the to-be-interpolated line point determining unit is used for acquiring a line point corresponding to a non-connected space block between every two space blocks as a to-be-interpolated line point if the non-connected space block exists between every two space blocks;

the interpolation point determining unit is used for interpolating the area between the line points to be interpolated by adopting an interpolation algorithm to obtain at least one interpolation point;

a second main route point set constructing unit, configured to construct the main route point set by using the plurality of route points and the at least one interpolation point.

Optionally, the target intermediate point searching sub-module includes:

a dictionary creating unit, configured to create a longitude key value query dictionary and a latitude key value query dictionary by using the main route point set according to the longitude and the latitude respectively;

a target query dictionary determining unit, configured to determine, from the longitude-key dictionary and the latitude-key dictionary, a dictionary with the largest number of key values as a target query dictionary;

the first target intermediate point determining unit is used for determining a first target intermediate point corresponding to the starting point longitude and latitude of each preset sub order from the main route point set by adopting the target query dictionary;

and the second target intermediate point determining unit is used for determining a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub order from the main line point set by adopting the target query dictionary.

Optionally, the target car pool order identification model generation module 902 includes:

the classification result determining submodule is used for sequentially inputting the positive sample and the negative sample into the preset classification model to obtain a plurality of classification results; the classification result comprises that the preset car sharing condition is met and the preset car sharing condition is not met;

the error rate judgment submodule is used for judging whether the error rates of the classification results are smaller than a preset threshold value or not;

and the target car pooling order identification model generation submodule is used for outputting the target car pooling order identification model if the target car pooling order identification model is generated.

Optionally, the target car pool order identification model generation module 902 further includes:

and the returning submodule is used for returning to the step of acquiring the training sample set if the training sample set is not acquired.

Optionally, the to-be-carpooled order determining module 904 includes:

the goods source main order longitude and latitude determining submodule is used for determining the longitude and latitude of a starting point of the goods source main order and the longitude and latitude of a destination point of the goods source main order;

the to-be-transported path determining submodule is used for determining a to-be-transported path based on the longitude and latitude of the departure point of the goods source main order and the longitude and latitude of the destination point of the goods source main order;

the to-be-transported point extraction sub-module is used for extracting a plurality of to-be-transported points from the to-be-transported path according to the primary longitude and latitude; the to-be-transported point comprises a starting point of the goods source main order, a destination point of the goods source main order and a middle point of the goods source main order;

the line space block diagram generating submodule is used for converting the longitude and latitude corresponding to the multiple to-be-transported points to generate a line space block diagram; the line space block diagram comprises a plurality of space blocks respectively corresponding to the plurality of to-be-transported points;

and the order to be carpooled determining submodule is used for determining the sub-orders of which the longitude and latitude of the departure point and the longitude and latitude of the destination point are both in the plurality of space blocks as the orders to be carpooled.

Optionally, the to-be-carpooled sample pair construction module 905 includes:

the data acquisition submodule of the order to share the car is used for acquiring a departure point of the order to share the car and a destination point of the order to share the car;

the target intermediate point determining submodule is used for determining a third target intermediate point corresponding to the longitude and latitude of the departure point of the order to be carpooled and a fourth target intermediate point corresponding to the longitude and latitude of the destination point of the order to be carpooled from the intermediate points of the goods source main order by adopting a preset intermediate point searching algorithm;

and the sample pair generation submodule for the car pooling is used for adopting a third connecting line between the starting point of the order to be car pooled and the destination point of the order to be car pooled and a fourth connecting line between the third target intermediate point and the fourth target intermediate point as a sample pair for the car pooling.

It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.

In the embodiments provided in the present invention, it should be understood that the disclosed apparatus and method may be implemented in other ways. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.

The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.

In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.

The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.

The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.

Claims

1. A carpoolable order identification method is characterized by comprising the following steps:

acquiring a training sample set, wherein the training sample set comprises positive samples and negative samples;

training a preset classification model by adopting the positive sample and the negative sample to obtain a target carpoolable order identification model;

receiving a goods source main order input by a user, and determining a starting point and a destination point of the goods source main order;

generating a route space block diagram based on a departure point and a destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled; the line space block diagram comprises space blocks, and the types of the space blocks comprise geohash blocks or *** S2 blocks;

constructing a sample pair to be carpooled by adopting the goods source main order and the order to be carpooled;

and inputting the sample pair to be carpooled into the target carpoolable order identification model, and outputting the carpoolable order.

2. The method of claim 1, wherein the step of obtaining a training sample set comprises:

determining a planned route from a preset map according to the longitude and latitude of the departure point and the longitude and latitude of the destination point of the preset main order;

performing line point sampling operation on the planned line to obtain a main line point set;

determining a first target intermediate point corresponding to the longitude and latitude of the starting point of each preset sub-order and a second target intermediate point corresponding to the longitude and latitude of the destination point of each preset sub-order from the main line point set by adopting a preset intermediate point searching algorithm;

adopting a first connecting line between a starting point of the preset sub-order and a destination point of the preset sub-order and a second connecting line between the first target intermediate point and the second target intermediate point corresponding to the preset sub-order as a connecting line sample pair;

respectively extracting target features in a plurality of connecting line sample pairs through a feature extraction algorithm;

taking a connecting line sample pair corresponding to the target characteristics meeting the preset car sharing condition as the positive sample;

and taking the connecting line sample pair corresponding to the target characteristic which does not meet the preset car sharing condition as the negative sample.

3. The method of claim 2, wherein the step of performing a route point sampling operation on the planned route to obtain a main route point set comprises:

4. The method of claim 3, further comprising:

5. The method of claim 2, wherein said step of determining from said set of main route points, using a preset intermediate point search algorithm, a first target intermediate point corresponding to a start point latitude and longitude of each preset sub-order, and a second target intermediate point corresponding to a destination point latitude and longitude of each preset sub-order comprises:

6. The method according to claim 1, wherein the step of training a preset classification model using the positive samples and the negative samples to obtain a target car pool order recognition model comprises:

inputting the positive sample and the negative sample into the preset classification model in sequence to obtain a plurality of classification results; the classification result comprises that the preset car sharing condition is met and the preset car sharing condition is not met;

judging whether the error rates of the classification results are smaller than a preset threshold value or not;

and if so, outputting the target car sharing order identification model.

7. The method of claim 6, further comprising:

if not, returning to the step of obtaining the training sample set.

8. The method according to claim 1, wherein the step of generating a route space block diagram based on the departure point and the destination point of the goods source main order, and taking the order in the route space block diagram as the order to be carpooled comprises:

9. The method according to claim 8, wherein the step of constructing a sample pair to be carpooled using the source main order and the order to be carpooled comprises:

and adopting a third connecting line between the departure point of the order to be carpooled and the destination point of the order to be carpooled and a fourth connecting line between the third target intermediate point and the fourth target intermediate point as a sample pair to be carpooled.

10. A carpoolable order identification device, comprising:

the training sample set acquisition module is used for acquiring a training sample set, and the training sample set comprises positive samples and negative samples;

the target car pooling order identification model generation module is used for training a preset classification model by adopting the positive sample and the negative sample to obtain a target car pooling order identification model;

the goods source main order receiving module is used for receiving a goods source main order input by a user and determining a starting point and a destination point of the goods source main order;

the order determining module to be carpooled is used for generating a route space block diagram based on a departure point and a destination point of the goods source main order, and taking the order in the route space block diagram as an order to be carpooled; the line space block diagram comprises space blocks, and the types of the space blocks comprise geohash blocks or *** S2 blocks;

the system comprises a to-be-carpooled car sample pair construction module, a to-be-carpooled car sample pair construction module and a to-be-carpooled car sample pair construction module, wherein the to-be-carpooled car sample pair construction module is used for constructing a to-be-carpooled car sample pair by adopting the goods source main;

and the carpoolable order output module is used for inputting the sample pairs to be carpooled into the target carpoolable order identification model and outputting the carpoolable orders.