CN112966898A - Dispatching method integrating 'taxi taking' requirement and 'tailwind' requirement - Google Patents

Dispatching method integrating 'taxi taking' requirement and 'tailwind' requirement Download PDF

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CN112966898A
CN112966898A CN202110145662.8A CN202110145662A CN112966898A CN 112966898 A CN112966898 A CN 112966898A CN 202110145662 A CN202110145662 A CN 202110145662A CN 112966898 A CN112966898 A CN 112966898A
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邢冠南
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Abstract

The invention discloses a scheduling method integrating 'taxi taking' requirements and 'tailwind' requirements, and relates to the technical field of image processing; comprises a client and a server; an input unit for inputting information is arranged in the client; a recognition unit for parsing the input information: an identification model is arranged in the identification unit, and the existing resources, user requirements and requirement conditions of the user are output after the input information enters the identification model; the server is internally provided with an association unit, the association unit receives information output by the identification model in each client to form a data set and associates the information, and the association rule is as follows: and associating the existing resources of the users in the data set with the user requirements within the confidence interval of the requirement conditions. The invention not only matches the users with obvious supply and demand relationship, but also matches the users with potential supply and demand relationship, and has stronger resource integration and sharing capability and higher flexibility.

Description

Dispatching method integrating 'taxi taking' requirement and 'tailwind' requirement
Technical Field
The invention relates to the technical field of information processing based on computer internet, in particular to a scheduling method integrating 'taxi taking' requirements and 'tailwind' requirements.
Background
In the prior art, some user platforms can issue information, and users issue some reward information to obtain corresponding services; based on the above, various pieces of software with targeted services are developed subsequently, such as dripping car driving, dripping designated driving and the like, for example, a user issues a car driving seeking or designated driving requirement on the software on a server, and a service staff receives orders after acquiring the service requirement, so that the supply of a requirement is completed.
However, the above-described mode is relatively single, so a system according to flexibility is to be developed.
Disclosure of Invention
The invention aims to provide a scheduling method integrating 'taxi taking' requirements and 'tailwind' requirements to solve the problems in the background art. To sum up, the rules in the background art are all established in the supply and demand relationship, and the applicant considers that today of the sharing economy, a thought is not changed to think about the supply and demand problem, and for sharing and matching resources by users who are both in demand, for example, a first user needs to drive on a designated basis, a second user needs to drive (follow the wind mill), the starting point and the ending point of the two users are within a certain acceptable range, then based on the supply and demand relationship, the first user has resources of a vehicle, needs a driving service, and the second user needs resources of a vehicle, further, if the second user is qualified to drive the service, the supply and demand relationship of the two users can be matched, from another perspective, the two users are both resource demanding parties, and after the integration and sharing, the two simultaneously become resource supplying parties.
The technical scheme of the invention is as follows: a dispatching method integrating 'taxi taking' requirements and 'tailwind' requirements comprises a client and a server;
an input unit for inputting information is arranged in the client;
a recognition unit for parsing the input information: an identification model is arranged in the identification unit, and the existing resources, user requirements and requirement conditions of the user are output after the input information enters the identification model;
the server is internally provided with an association unit, the association unit receives information output by the identification model in each client to form a data set and associates the information, and the association rule is as follows: and associating the existing resources of the users in the data set with the user requirements within the confidence interval of the requirement conditions.
In the information processing and pushing system based on the user requirements, the input unit is internally provided with a requirement option for the user to select, when the user selects the requirement option, the client side is immediately switched into the identification mode, a plurality of modularized input fields are provided, and the user inputs in the input fields according to the identification rule of the identification model.
In the information processing and pushing system based on the user requirements, if the user does not select the requirement option, a text input field is provided, the input unit performs semantic recognition through a built-in semantic recognition unit, and the recognition unit performs extraction according to semantics.
In the information processing and pushing system based on the user requirements, the associated user requirements include designated driving and tailgating, and existing resources corresponding to the designated driving requirements are 'available driving' and 'driving requirements'; the windward vehicle corresponds to 'no vehicle' and 'vehicle using requirement', and whether a user has a requirement condition of 'having driving qualification' needs to be further determined at the moment; if so, the data are correlated.
In the information processing and pushing system based on the user requirement, the confidence is calculated according to the following algorithm:
Figure RE-GDA0003067260750000031
wherein R is 1 or 0, namely, whether the driver's license is possessed or not is represented; i corresponds to a certain demand condition, and e is a weight factor occupied by the demand condition; during calculation, a confidence pool is established according to the requirement of a user, a confidence threshold value is established in the confidence pool, all users with the confidence degrees larger than the threshold value in the data set are included, and priority levels are established according to the threshold value for sequencing.
In the information processing and pushing system based on the user requirements, address parameters are also calculated in the confidence degree calculation, when a relevant user is matched according to a certain requirement, the address information of the departure place and the destination address information of the user requiring the requirement are firstly captured, path matching is carried out, meanwhile, the user requirements on the matched path are captured in advance, and the user requirements are input into a confidence pool according to the confidence degree.
In the information processing and pushing system based on the user requirements, after the user starts, the user capable of being associated is captured in a certain area on the traveling route, and bidirectional pushing is performed.
In the information processing and pushing system based on user requirements, the travel route is formulated in advance according to the requirement users meeting the conditions on the map:
firstly, map preprocessing is divided into palace lattices, nodes are defined, the palace lattices take roads and rivers as the criticality,
marking map position points Q where users meeting the requirements are located, and clustering all the points Q to form a plurality of clustering centroids;
layering a map; the same node necessarily exists between the lower-layer map and the upper-layer map, and the formed centroid position is also used as the same node between the upper-layer map and the lower-layer map and is named as a reference point;
partitioning a map, namely firstly, connecting line segments of a starting point and an end point of travel, offsetting the line segments in two directions, setting an offset threshold, forming two line segments after offsetting, partitioning an area at the position enclosed between the two line segments, wherein the partitioning can be carried out according to area parameters, and partitioning on each layer of map;
firstly, searching a starting point, an end point and a clustering mass center on the lowest map, if the three points are all positioned in the same block or adjacent blocks, directly adopting an A-star algorithm to carry out path searching, and shifting a route to the clustering mass center;
if the position of the upper map is not in the same block or adjacent blocks, all reference points in the blocks are searched, if the centroid exists as the reference point, the upper map connected with the centroid reference point is preferentially selected, searching is conducted on the upper map, and if the centroid does not exist as the centroid reference point, the upper map connected with the point with the lowest cost in other reference points is used as a searching object, and the steps are repeated until a proper path is found.
In the information processing and pushing system based on user requirements, the traveling route is passively adjusted according to the requirement user Q meeting the conditions on the map:
constructing a path set: rc＝Rj∪Rs，RcIs a set of paths of an initial phase, RjFor automatic formation of a set of paths based on navigation, RsRecommending a set of paths to form for the demand user distribution based on the compliance;
exhaustively exhausting all Q positions in the range of the path set, clustering Q by using a hierarchical clustering algorithm, customizing the number and degree of clustering points M by using the hierarchical clustering algorithm, and setting a threshold value of the clustering number according to the distribution of the path set;
setting an adjusting point P on all the paths, wherein the adjusting point P is positioned at the intersection of different paths and is positioned on the path closest to the clustering point M;
a dynamic positioning point O exists in the advancing process, a regular hexagon is arranged by taking the positioning point O as the center, when the regular hexagon advances, after the hexagon enters an adjusting point P, the path intersection point is expanded, the range is expanded to the next point P, and the selected path is determined by referring to the following formula:
Figure RE-GDA0003067260750000051
g is the selection weight of the selected path, XeThe weight of the distance d between the positioning point O and the projection point O' on the selected path is calculated by the formula
Figure RE-GDA0003067260750000052
The smaller the value, the higher is desired; y iseThe weight of the included angle between the positioning point O and the selected path is calculated by the formula
Figure RE-GDA0003067260750000053
Figure RE-GDA0003067260750000054
Wherein VOBeing the speed in travel, α1For the orientation of the vehicle on the map, α2For the orientation of the selected path, the smaller the value, the higher the expectation, ZqThe weight of the shortest distance S between the P point and the selected path is calculated according to the formula:
Figure RE-GDA0003067260750000055
the smaller the value, the higher is desired;
l is a coefficient representing the contradiction between X, Y, Z, the value lying between 1 and 0, the more towards 0 the higher is desired.
The invention has the advantages that: the method not only matches users with obvious supply and demand relations, but also matches users with potential supply and demand relations, and has stronger resource integration and sharing capability and higher flexibility.
Detailed Description
The present invention is further illustrated by the following examples, which are not to be construed as limiting the invention.
Example (b): comprises a client and a server;
an input unit for inputting information is arranged in the client; the input unit is internally provided with a demand option for a user to select, when the user selects the option, the client is immediately switched into an identification mode, a plurality of modularized input fields are provided, and the user inputs in the input fields according to the identification rule of the identification model. If the user does not select the requirement option, providing a text input field, carrying out semantic recognition in the input unit through a built-in semantic recognition unit, and extracting according to the semantics by the recognition unit.
A recognition unit for parsing the input information: an identification model is arranged in the identification unit, and the existing resources, user requirements and requirement conditions of the user are output after the input information enters the identification model;
the server is internally provided with an association unit, the association unit receives information output by the identification model in each client to form a data set and associates the information, and the association rule is as follows: and associating the existing resources of the users in the data set with the user requirements within the confidence interval of the requirement conditions.
For example, the following steps are carried out: the associated user requirements comprise designated driving and tailgating, and existing resources corresponding to the designated driving requirements are 'driving requirement'; the windward vehicle corresponds to 'no vehicle' and 'vehicle using requirement', and whether a user has a requirement condition of 'having driving qualification' needs to be further determined at the moment; if yes, the two are correlated; after mutual association, the contact ways of the two parties can be provided for direct communication.
The confidence is calculated according to the following algorithm:
Figure RE-GDA0003067260750000061
wherein R is 1 or 0, namely, whether the driver's license is possessed or not is represented; the driving license is a hard condition, and of course, the condition is specifically whether or not driving can be engaged. As it is also avoided here that the user for some reason is not able to drive (has drunk) at this time.
i corresponds to a certain demand condition, and e is a weight factor occupied by the demand condition; establishing a confidence pool aiming at a user requirement during calculation, establishing a confidence threshold in the confidence pool, bringing all users with confidence degrees larger than the threshold in a data set, and establishing priority levels for sequencing according to the threshold;
address parameters are also calculated in the confidence coefficient calculation, when a relevant user is matched according to a certain requirement, the address information of the departure place and the destination address information of the user with the requirement are firstly captured, path matching is carried out, meanwhile, the user requirement on a matched path is captured in advance, and the user requirement is input into a confidence pool according to the confidence coefficient;
after departure, capturing users capable of being associated in a certain area on a traveling route, and performing bidirectional pushing:
the traveling route is preset according to the requirements of users meeting the conditions on the map:
firstly, map preprocessing is divided into palace lattices, nodes are defined, the palace lattices take roads and rivers as the criticality,
marking map position points Q where users meeting the requirements are located, and clustering all the points Q to form a plurality of clustering centroids;
layering a map; the same node necessarily exists between the lower-layer map and the upper-layer map, and the formed centroid position is also used as the same node between the upper-layer map and the lower-layer map and is named as a reference point;
partitioning a map, namely firstly, connecting line segments of a starting point and an end point of travel, offsetting the line segments in two directions, setting an offset threshold, forming two line segments after offsetting, partitioning an area at the position enclosed between the two line segments, wherein the partitioning can be carried out according to area parameters, and partitioning on each layer of map;
firstly, searching a starting point, an end point and a clustering mass center on the lowest map, if the three points are all positioned in the same block or adjacent blocks, directly adopting an A-star algorithm to carry out path searching, and shifting a route to the clustering mass center;
if the position of the upper map is not in the same block or adjacent blocks, all reference points in the blocks are searched, if the centroid exists as the reference point, the upper map connected with the centroid reference point is preferentially selected, searching is conducted on the upper map, and if the centroid does not exist as the centroid reference point, the upper map connected with the point with the lowest cost in other reference points is used as a searching object, and the steps are repeated until a proper path is found.
The traveling route is passively adjusted according to the requirement user Q meeting the conditions on the map:
constructing a path set: rc＝Rj∪Rs，RcIs a set of paths of an initial phase, RjFor automatic formation of a set of paths based on navigation, RsRecommending a set of paths to form for the demand user distribution based on the compliance;
exhaustively exhausting all Q positions in the range of the path set, clustering Q by using a hierarchical clustering algorithm, customizing the number and degree of clustering points M by using the hierarchical clustering algorithm, and setting a threshold value of the clustering number according to the distribution of the path set;
setting an adjusting point P on all the paths, wherein the adjusting point P is positioned at the intersection of different paths and is positioned on the path closest to the clustering point M;
a dynamic positioning point O exists in the advancing process, a regular hexagon is arranged by taking the positioning point O as the center, when the regular hexagon advances, after the hexagon enters an adjusting point P, the path intersection point is expanded, the range is expanded to the next point P, and the selected path is determined by referring to the following formula:
Figure RE-GDA0003067260750000081
g is the selection weight of the selected path, XeThe weight of the distance d between the positioning point O and the projection point O' on the selected path is calculated by the formula
Figure RE-GDA0003067260750000091
The smaller the value, the higher is desired; y iseThe weight of the included angle between the positioning point O and the selected path is calculated by the formula
Figure RE-GDA0003067260750000092
Figure RE-GDA0003067260750000093
Wherein VOBeing the speed in travel, α1For the orientation of the vehicle on the map, α2For the orientation of the selected path, the smaller the value, the higher the expectation, ZqThe weight of the shortest distance S between the P point and the selected path is calculated according to the formula:
Figure RE-GDA0003067260750000094
the smaller the value, the higher is desired;
l is a coefficient representing the contradiction between X, Y, Z, the value lying between 1 and 0, the more towards 0 the higher is desired.

Claims (4)

1. A dispatching method integrating 'taxi taking' requirements and 'tailwind' requirements is characterized in that: comprises a client and a server;
an input unit for inputting information is arranged in the client;
a recognition unit for parsing the input information: an identification model is arranged in the identification unit, and the existing resources, user requirements and requirement conditions of the user are output after the input information enters the identification model;
the server is internally provided with an association unit, the association unit receives information output by the identification model in each client to form a data set and associates the information, and the association rule is as follows: associating existing resources of the users in the data set with user requirements within a confidence interval of the requirement conditions;
the associated user requirements comprise designated driving and tailgating, and the existing resources corresponding to the designated driving requirements are 'driving requirement'; the windward vehicle corresponds to 'no vehicle' and 'vehicle using requirement', and whether a user has a requirement condition of 'having driving qualification' needs to be further determined at the moment; if yes, the two are correlated;
the confidence is calculated according to the following algorithm:
Figure FDA0002930147000000011
wherein R is 1 or 0, namely, whether the driver's license is possessed or not is represented; i corresponds to a certain demand condition, and e is a weight factor occupied by the demand condition; establishing a confidence pool aiming at a user requirement during calculation, establishing a confidence threshold in the confidence pool, bringing all users with confidence degrees larger than the threshold in a data set, and establishing priority levels for sequencing according to the threshold;
address parameters are also calculated in the confidence coefficient calculation, when a relevant user is matched according to a certain requirement, the address information of the departure place and the destination address information of the user with the requirement are firstly captured, path matching is carried out, meanwhile, the user requirement on a matched path is captured in advance, and the user requirement is input into a confidence pool according to the confidence coefficient;
after starting, capturing users capable of being associated in a certain area on a traveling route, and performing bidirectional pushing;
the traveling route is passively adjusted according to the requirement user Q meeting the conditions on the map:
constructing a path set: rc＝Rj∪Rs，RcIs a set of paths of an initial phase, RjFor automatic formation of a set of paths based on navigation, RsRecommending a set of paths to form for the demand user distribution based on the compliance;
exhaustively exhausting all Q positions in the range of the path set, clustering Q by using a hierarchical clustering algorithm, customizing the number and degree of clustering points M by using the hierarchical clustering algorithm, and setting a threshold value of the clustering number according to the distribution of the path set;
setting an adjusting point P on all the paths, wherein the adjusting point P is positioned at the intersection of different paths and is positioned on the path closest to the clustering point M;
a dynamic positioning point O exists in the advancing process, a regular hexagon is arranged by taking the positioning point O as the center, when the regular hexagon advances, after the hexagon enters an adjusting point P, the path intersection point is expanded, the range is expanded to the next point P, and the selected path is determined by referring to the following formula:
Figure FDA0002930147000000021
g is the selection weight of the selected path, XeThe weight of the distance d between the positioning point O and the projection point O' on the selected path is calculated by the formula
Figure FDA0002930147000000022
The smaller the value, the higher is desired; y iseThe weight of the included angle between the positioning point O and the selected path is calculated by the formula
Figure FDA0002930147000000031
Wherein VOBeing the speed in travel, α1For the orientation of the vehicle on the map, α2For the orientation of the selected path, the smaller the value, the higher the expectation, ZqThe weight of the shortest distance S between the P point and the selected path is calculated according to the formula:
Figure FDA0002930147000000032
the smaller the value, the higher is desired;
l is a coefficient representing the contradiction between X, Y, Z, the value lying between 1 and 0, the more towards 0 the higher is desired.
2. The dispatching method integrating the 'taxi taking' requirement and the 'tailwind' requirement as claimed in claim 1, wherein: the input unit is internally provided with a demand option for a user to select, when the user selects the option, the client is immediately switched into an identification mode, a plurality of modularized input fields are provided, and the user inputs in the input fields according to the identification rule of the identification model.
3. The dispatching method integrating the 'taxi taking' requirement and the 'tailwind' requirement as claimed in claim 2, wherein: if the user does not select the requirement option, providing a text input field, carrying out semantic recognition in the input unit through a built-in semantic recognition unit, and extracting according to the semantics by the recognition unit.
4. The dispatching method integrating the 'taxi taking' requirement and the 'tailwind' requirement as claimed in claim 3, wherein: the traveling route is preset according to the requirements of users meeting the conditions on the map:
firstly, map preprocessing is divided into palace lattices, nodes are defined, the palace lattices take roads and rivers as the criticality,
marking map position points Q where users meeting the requirements are located, and clustering all the points Q to form a plurality of clustering centroids;
layering a map; the same node necessarily exists between the lower-layer map and the upper-layer map, and the formed centroid position is also used as the same node between the upper-layer map and the lower-layer map and is named as a reference point;
partitioning a map; firstly, connecting a starting point and an end point of travel by line segments, carrying out deviation in two directions on the line segments, setting a deviation threshold value, forming two line segments after deviation, and partitioning an area at the surrounding position between the two line segments, wherein the partitioning can be carried out according to an area parameter, and the partitioning is carried out on each layer of map;
firstly, searching a starting point, an end point and a clustering mass center on the lowest map, if the three points are all positioned in the same block or adjacent blocks, directly adopting an A-star algorithm to carry out path searching, and shifting a route to the clustering mass center;
if the position of the upper map is not in the same block or adjacent blocks, all reference points in the blocks are searched, if the centroid exists as the reference point, the upper map connected with the centroid reference point is preferentially selected, searching is conducted on the upper map, and if the centroid does not exist as the centroid reference point, the upper map connected with the point with the lowest cost in other reference points is used as a searching object, and the steps are repeated until a proper path is found.
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