阅读说明：本技术 导航方法、装置、存储介质以及服务器 (Navigation method, navigation device, storage medium and server ) 是由 梁昆 于 2017-06-30 设计创作，主要内容包括：本发明提供一种导航方法,包括：根据导航请求携带的起点的位置信息以及终点的位置信息,生成候选路径集合；基于预设的相似度模型以及当前用户的行为数据,对候选路径进行筛选,生成推荐结果,其中相似度模型用于指示候选路径与当前用户的行为数据之间的相似度。本发明还提供一种导航装置、存储介质以及服务器。(The invention provides a navigation method, which comprises the following steps: generating a candidate path set according to the position information of the starting point and the position information of the end point carried by the navigation request; and screening the candidate paths based on a preset similarity model and the behavior data of the current user to generate a recommendation result, wherein the similarity model is used for indicating the similarity between the candidate paths and the behavior data of the current user. The invention also provides a navigation device, a storage medium and a server.)

A navigation method, comprising:

receiving a navigation request, wherein the navigation request carries position information of a starting point and position information of an end point;

generating a candidate path set according to the position information of the starting point and the position information of the end point, wherein a candidate path in the candidate path set comprises at least one road section;

acquiring behavior data of a current user;

and screening the candidate paths based on a preset similarity model and the behavior data of the current user to generate a recommendation result so that the current user can determine a navigation route from the recommendation result to perform navigation operation, wherein the similarity model is used for indicating the similarity between the candidate paths and the behavior data of the current user.

The navigation method of claim 1, wherein, prior to the receiving the navigation request, further comprising:

collecting behavior data of all access users and attribute information of a road section passed by each access user;

and generating a preset similarity model according to the behavior data of all the access users and the attribute information of the road section passed by each access user based on a preset machine learning algorithm.

The navigation method according to claim 2, wherein the generating a preset similarity model based on a preset machine learning algorithm and according to the behavior data of all the visiting users and the attribute information of the road segment passed by each visiting user comprises:

constructing a sequencing feature according to the behavior data of all the access users and the attribute information of the road section passed by each access user, wherein the sequencing feature comprises an association feature between the access users and the road sections passed by the access users;

learning the data of the sequencing features based on a preset machine learning algorithm to generate feature weights;

and generating a preset similarity model according to the sorting features and the corresponding feature weights.

The navigation method according to claim 1, wherein the filtering the candidate route based on a preset similarity model and the behavior data of the current user to generate a recommendation result includes:

constructing a sorting feature vector according to the behavior data of the current user and the attribute information of the road section included by the candidate path;

and screening the candidate paths based on a preset similarity model and the sorting characteristic vector to generate a recommendation result.

The navigation method according to claim 4, wherein after the filtering the candidate route based on the preset similarity model and the behavior data of the current user to generate a recommendation result, the method further comprises:

and generating a recommended path queue according to the recommendation result, wherein the recommended path queue comprises sorted recommended paths.

The navigation method of claim 5, wherein the generating a recommended path queue according to the recommendation comprises:

according to the similarity model and the sorting feature vector, scoring the candidate paths in the candidate path set to obtain a scoring result;

determining a recommended path according to the scoring result, wherein the recommended path is a candidate path in the candidate path set, and the corresponding scoring result exceeds a preset score threshold;

and sequencing the recommended paths according to the recommended paths and corresponding scoring results to obtain sequenced recommended paths, and determining the sequenced recommended paths as the recommended path queue.

The navigation method according to claim 6, wherein the scoring the candidate paths in the candidate path set according to the similarity model and the ranking feature vector to obtain a scoring result comprises:

and multiplying the similarity model and the sorting feature vector to generate a score vector, and taking the score vector as a scoring result.

The navigation method of claim 1, wherein prior to the obtaining of the behavior data of the current user, further comprising:

acquiring the road condition information of the candidate path;

correcting the candidate paths according to the road condition information to generate a corrected candidate path set;

the screening of the candidate paths in the candidate path set based on the preset similarity model and the behavior data of the current user to generate a recommendation result comprises:

and screening the candidate paths in the corrected candidate path set based on a preset similarity model and the behavior data of the current user to generate a recommendation result.

The navigation method according to claim 8, wherein the obtaining of the traffic information corresponding to the candidate route comprises:

acquiring environment information and image information of the candidate path;

and determining the road condition information of the candidate path according to the environment information and the image information of the candidate path.

A navigation device, comprising:

the first acquisition module is used for receiving a navigation request, wherein the navigation request carries the position information of a starting point and the position information of an end point;

a first generating module, configured to generate a candidate path set according to the position information of the starting point and the position information of the end point, where a candidate path in the candidate path set includes at least one road segment;

the second acquisition module is used for acquiring the behavior data of the current user;

and the second generation module is used for screening the candidate paths based on a preset similarity model and the behavior data of the current user to generate a recommendation result so that the current user can determine a navigation route from the recommendation result to perform navigation operation, wherein the similarity model is used for indicating the similarity between the candidate paths and the behavior data of the current user.

The navigation device of claim 10, wherein the navigation device further comprises a similarity model generation module, wherein the similarity model generation module comprises:

the collection submodule is used for collecting behavior data of all the access users and attribute information of a road section passed by each access user;

and the generating submodule is used for generating a preset similarity model according to the behavior data of all the access users and the attribute information of the road section passed by each access user based on a preset machine learning algorithm.

The navigation device of claim 11, wherein the generation submodule is to:

constructing a sequencing feature according to the behavior data of all the access users and the attribute information of the road section passed by each access user, wherein the sequencing feature comprises the access users and the road sections passed by the access users;

learning the data of the sequencing features based on a preset machine learning algorithm to generate feature weights;

and generating a preset similarity model according to the sorting features and the corresponding feature weights.

The navigation device of claim 10, wherein the second generation module is to:

constructing a sorting feature vector according to the behavior data of the current user and the attribute information of the road section included by the candidate path;

and screening the candidate paths based on a preset similarity model and the sorting characteristic vector to generate a recommendation result.

The navigation device of claim 13, wherein the navigation device further comprises:

and the third generation module is used for generating a recommended path queue according to the recommendation result, wherein the recommended path queue comprises sorted recommended paths.

The navigation device of claim 14, wherein the third generation module comprises:

the scoring submodule is used for scoring the candidate paths in the candidate path set according to the similarity model and the sorting feature vector to obtain a scoring result;

the determining submodule is used for determining a recommended path according to the scoring result, wherein the recommended path is a candidate path in the candidate path set, and the corresponding scoring result exceeds a preset score threshold;

and the sorting submodule is used for sorting the recommended paths according to the recommended paths and corresponding scoring results to obtain sorted recommended paths, and determining the sorted recommended paths as the recommended path queue.

The navigation device of claim 15, wherein the scoring submodule is to: and multiplying the similarity model and the sorting feature vector to generate a score vector, and taking the score vector as a scoring result.

The navigation device of claim 10, wherein the navigation device further comprises a revision module, wherein the revision module comprises:

the obtaining sub-module is used for obtaining the road condition information of the candidate paths;

the correction submodule is used for correcting the candidate paths according to the road condition information to generate a corrected candidate path set;

the second generating module is configured to: and screening the candidate paths in the corrected candidate path set based on a preset similarity model and the behavior data of the current user to generate a recommendation result, and performing navigation operation according to the recommendation result.

The navigation device of claim 17, wherein the acquisition sub-module is to: and acquiring the environment information and the image information of the candidate path, and determining the road condition information of the candidate path according to the environment information and the image information of the candidate path.

A storage medium, wherein the storage medium stores a plurality of instructions adapted to be loaded by a processor to perform the method of any of claims 1 to 9.

A server comprising a processor and a memory, the memory storing a plurality of instructions, the processor loading the instructions in the memory for performing the method of any of claims 1 to 9.