CN111797183A - Method and device for mining road attribute of information point and electronic equipment - Google Patents
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Abstract
The disclosure relates to a method and a device for mining road attributes of information points and electronic equipment. The method comprises the following steps: acquiring at least one road in a preset range with a target information point as a center, wherein the road comprises at least one road unit; determining a first association degree score between the target information point and the road unit according to the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit; determining a second association degree score between the target information point and the road according to the first association degree score between the target information point and the road unit; and determining the road attribute of the target information point according to the second relevance grade. The method can reasonably quantify the road attributes and is beneficial to improving the quality of the map search result.
Description
Technical Field
The present disclosure relates to the field of information processing technologies, and in particular, to a method for mining road attributes of information points, an apparatus for mining road attributes of information points, and an electronic device.
Background
POI is an abbreviation of "PointofInformation" and Chinese can be translated into "information points". In the geographic information system, one POI may be one house, one shop, one mailbox, one bus station, and the like. In the map retrieval, after a user inputs a search word, POI corresponding to the search word can be searched and recommended to the user, so that the user can conveniently select the required POI.
In the prior art, the POI related information does not usually contain road attributes, so that under the condition that the search term is a road name, irrelevant POI can be recommended to the user, so that the recommendation result is unreasonable, and the use experience of the user is influenced.
Therefore, it is necessary to provide a new technical solution for mining the road attributes of the information points.
Disclosure of Invention
An object of the present disclosure is to provide a new technical solution for mining road attributes of information points.
According to a first aspect of the present invention, there is provided a method of mining road attributes of information points, comprising:
acquiring at least one road in a preset range with a target information point as a center, wherein the road comprises at least one road unit;
determining a first relevancy score between the target information point and the road unit according to the space similarity feature, the semantic similarity feature and the distance feature between the target information point and the road unit;
determining a second association degree score between the target information point and the road according to a first association degree score between the target information point and the road unit;
and determining the road attribute of the target information point according to the second relevance score.
Optionally, the method further comprises: determining at least one road label according to at least one item of map information point data, map road data and map search log data;
after the determining a second relevancy score between the target information point and the road, the method further comprises:
calculating semantic similarity between the road label and the road name of the road;
and determining a third association score between the target information point and the road label according to the semantic similarity and the second association score.
Optionally, the method further comprises:
acquiring a third association score between the road label and each information point in the plurality of information points;
receiving a search word sent by terminal equipment;
detecting whether the search word is consistent with the road label;
determining at least one recommended information point from a plurality of information points according to a third relevancy score between the road label and each of the plurality of information points under the condition that the search word is consistent with the road label;
and sending the recommendation information point to the terminal equipment.
Optionally, the spatially similar features are obtained by:
determining an information point position set corresponding to the target information point;
determining a road node position set corresponding to the road unit;
and calculating the Hausdorff distance between the information point position set and the road node position set to obtain the space similarity characteristic.
Optionally, the determining the information point position set corresponding to the target information point includes:
and clustering the positions of the plurality of information points to obtain at least one information point position set.
Optionally, the determining a road node set corresponding to the road unit includes:
adding interpolation nodes among the existing nodes of the road unit according to a preset distance interval;
and obtaining the road node set according to the existing nodes and the interpolation nodes.
Optionally, the semantic similar features are obtained by:
calculating a first semantic similarity between the name of the street to which the target information point belongs and the name of the road unit;
calculating a second semantic similarity between the address of the target information point and the name of the road unit;
and determining the semantic similar feature according to at least one of the first semantic similarity and the second semantic similarity.
Optionally, the determining a first relevancy score between the target information point and the road unit according to the spatial similar feature, the semantic similar feature and the distance feature between the target information point and the road unit includes:
and processing the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit based on a pre-trained machine learning model to obtain the first relevancy score.
Optionally, the machine learning model is obtained by:
acquiring training samples, wherein each training sample comprises a space similar feature, a semantic similar feature and a distance feature between an information point and a road unit, and a label which represents whether the information point and the road unit in the training samples have an association relation or not;
and performing machine learning training based on the training samples to obtain the machine learning model.
According to a second aspect of the present invention, there is provided an apparatus for mining road attributes of information points, comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring at least one road in a preset range with a target information point as a center, and the road comprises at least one road unit;
the first processing module is used for determining a first relevance grade between the target information point and the road unit according to the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit;
the second processing module is used for determining a second relevance grade between the target information point and the road according to the first relevance grade between the target information point and the road unit;
and the third processing module is used for determining the road attribute of the target information point according to the second relevance grade.
According to a third aspect of the present invention, there is provided an electronic apparatus comprising:
a memory for storing executable commands;
a processor for implementing the method according to the first aspect of the invention under control of the executable command.
According to the method for mining the road attributes of the information points, the first relevance score between the information points and the road units is determined according to the spatial similar feature, the semantic similar feature and the distance feature, the second relevance score between the information points and the road is further determined, the road attributes of the information points are determined based on the second relevance score, the road attributes of the information points are mined by integrating the spatial similar feature, the semantic similar feature and the distance feature, the influence of information point aggregation factors is considered, the road attributes can be reasonably quantized, the quality of map search results is favorably improved, and the use experience of users is further improved.
Other features of the present disclosure and advantages thereof will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of the specification, illustrate embodiments of the disclosure and together with the description, serve to explain the principles of the disclosure.
FIG. 1 is a schematic diagram of an electronic device that may be used to implement embodiments of the present disclosure.
Fig. 2 is a flowchart of a method of mining road attributes of information points according to an embodiment of the present disclosure.
Fig. 3 is a schematic diagram of clustering results according to an embodiment of the present disclosure.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, the numerical expressions, and numerical values set forth in these embodiments do not limit the scope of the present disclosure unless specifically stated otherwise.
The following description of at least one exemplary embodiment is merely illustrative in nature and is in no way intended to limit the disclosure, its application, or uses.
Techniques, methods, and apparatus known to those of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any particular value should be construed as merely illustrative, and not limiting. Thus, other examples of the exemplary embodiments may have different values.
It should be noted that: like reference numbers and letters refer to like items in the following figures, and thus, once an item is defined in one figure, further discussion thereof is not required in subsequent figures.
< hardware configuration >
Fig. 1 illustrates a hardware configuration of an electronic device that can be used to implement embodiments of the present disclosure.
Referring to fig. 1, an electronic device 1000 includes a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, and an input device 1600. The processor 1100 may be, for example, a central processing unit CPU, a micro control unit MCU, or the like. The memory 1200 includes, for example, a ROM (read only memory), a RAM (random access memory), a nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, and the like. The communication device 1400 is, for example, a wired network card or a wireless network card. The display device 1500 is, for example, a liquid crystal display panel. The input device 1600 includes, for example, a touch screen, a keyboard, a mouse, a microphone, and the like.
In an embodiment applied to this description, the memory 1200 of the electronic device 1000 is used to store instructions for controlling the processor 1100 to operate in support of implementing a method according to any embodiment of this description. Those skilled in the art can design instructions in accordance with the teachings disclosed herein. How the instructions control the operation of the processor is well known in the art and will not be described in detail herein.
It should be understood by those skilled in the art that although a plurality of devices of the electronic apparatus 1000 are shown in fig. 1, the electronic apparatus 1000 of the embodiments of the present specification may refer to only some of the devices, for example, only the processor 1100, the memory 1200 and the communication device 1400.
The electronic apparatus 1000 shown in fig. 1 is, for example, a server for providing a map service.
The hardware configuration shown in fig. 1 is merely illustrative and is in no way intended to limit the present disclosure, its application, or uses.
< method examples >
The present embodiment provides a method for mining road attributes of information points, for example, implemented by the electronic device 1000 shown in fig. 1.
In the present embodiment, the road attribute of an information point is, for example, information of a road having a certain correlation with the information point. The road attributes of an information point may be used to determine a map search result, i.e., in case the search term is the same as or similar to the associated road of the information point, the information point is recommended to the user as a search result.
In this embodiment, the road refers to an infrastructure for various trackless vehicles and pedestrians to pass through, and includes a highway, an urban road, a rural road, and the like. The road in this embodiment includes at least one road unit. The road unit is, for example, "link" in a navigation system. In a navigation system, a road model usually has "link" as a basic unit. "link" refers to a segment of a road, one actual road being represented in the map data as one or more "links", e.g., two lanes in different directions on the same road being represented in the map as two "links" in different directions. "link" further includes "node", which may be classified as an end point, an intersection point, a shape point, and the like.
As shown in fig. 2, the method includes the following steps S1100-S1400.
In step S1100, at least one road in a preset range centered on a target information point is acquired, wherein the road includes at least one road unit.
In this embodiment, the information about the target information point, such as the unique identifier of the information point, the name of the information point, the longitude and latitude coordinates of the information point, the street to which the information point belongs, the address of the information point, and the like, may be obtained from the information point data in the map.
In this embodiment, the road network data may be used to obtain information about the road, such as a road name, a road unit longitude and latitude coordinate, a road node longitude and latitude coordinate, and the like.
In this embodiment, the distance between the target information point and a certain road is, for example, an average value of distances between the target information point and road units included in the road. The distance between an information point and a road unit may be a distance between the information point and the road unit (considered as a straight line or a curved line), a minimum value of distances between the information point and each node of the road unit, a maximum value of distances between the information point and each node of the road unit, or an average value of distances between the information point and each node of the road unit. The distance between the information points and the road units can be calculated in a unified mode, and the specific calculation mode is not limited. In the case where the information point belongs to a certain accumulation area, the distance from the center point of the accumulation area to the road unit may also be taken as the distance between the information point and the road unit.
In this embodiment, the following processing is performed on the road within the preset range with the target information point as the center, so that the road far away from the information point can be removed, and unnecessary data processing is avoided.
In one example, the preset range is, for example, a circular area with a radius of 2km and centered on the target information point, that is, in a case where a certain link is less than 2km away from the target information point, the relevance score between the link and the target information point is calculated by the subsequent steps.
In step S1200, a first relevance score between the target information point and the road unit is determined according to the spatial similarity feature, the semantic similarity feature, and the distance feature between the target information point and the road unit.
The spatial similarity feature is a similarity degree between an information point position set (denoted as an X set) of an aggregation area where a target information point is located and a road node position set (denoted as a Y set) corresponding to a road unit. The degree of similarity between the above-described X set and Y set can be measured by a Hausdorff (Hausdorff) distance.
The Hausdorff distance is a measure describing the degree of similarity between two sets of points, and is a defining form of the distance between two sets of points. Assuming that there are two sets of a { a1, …, ap }, and B { B1, …, bq }, the Hausdorff distance between these two sets of points is defined as:
h (a, B) ═ max (H (a, B), H (B, a)) formula (1)
h (A, B) ═ max (a ∈ A) min (B ∈ B) | a-B | (2)
h (B, A) ═ max (B ∈ B) min (a ∈ A) | B-a | (3)
Where | is the distance pattern between the set of points a and B. Equation (1) is called the two-way Hausdorff distance, which is the most basic form of Hausdorff distance. H (a, B) in equation (2) and h (B, a) in equation (3) are referred to as the unidirectional Hausdorff distances from the a set to the B set and from the B set to the a set, respectively, i.e., h (a, B) actually first ranks the distance | ai-bj |, between each point ai in the point set a to the point bj in the B set nearest to this point ai, and then takes the maximum value in this distance as the value of h (a, B). h (B, A) can be obtained in the same way. From equation (1), the two-way Hausdorff distance H (A, B) is the greater of the two one-way distances H (A, B) and H (B, A), which measures the maximum degree of mismatch between the two sets of points.
Besides the Hausdorff distance, the similarity between the X set and the Y set may be measured in other ways, which is not limited in this embodiment.
In one example, the spatially similar features are obtained by: determining an information point position set corresponding to a target information point; determining a road node position set corresponding to a road unit; and calculating the Housdov distance between the information point position set and the road node position set to obtain the space similarity characteristic.
In this embodiment, the information point position set corresponding to the target information point refers to a set formed by positions of all information points in the aggregation area to which the target information point belongs.
In this embodiment, the aggregation area is an information point set formed by relatively aggregating a plurality of information points in spatial distribution. The aggregation area is considered as a whole to analyze the characteristics of the road attributes of the aggregation area.
In the above example, determining the set of information point positions corresponding to the target information point includes: and clustering the positions of the plurality of information points to obtain at least one information point position set.
For example, a DBSCAN clustering algorithm is adopted to perform clustering processing on the POI points by adopting short-distance low-density parameter setting, each cluster in a clustering result is a POI aggregation area, and a set of all information point positions in the POI aggregation area is an information point position set. In addition, noise points, i.e., POI isolated points, are also included in the clustering result.
Fig. 3 shows a schematic diagram of a clustering result according to an embodiment of the present disclosure, where L is one road unit, a1 and a2 are two clusters in the clustering result, i.e., two aggregation areas, each aggregation area includes a plurality of information points, and a3 is a noise point in the clustering result, i.e., an isolated information point. When calculating the spatial similarity features, the aggregation areas a1 and a2 are respectively treated as a whole, and the isolated point a3 is treated separately.
In the above example, the process of acquiring the road node set corresponding to the road unit is, for example: adding interpolation nodes among the existing nodes of the road unit according to a preset distance interval; and obtaining a road node set according to the existing nodes and the interpolation nodes. The existing nodes of the road unit refer to existing road nodes in the road network data, and include end points, shape points and the like. In order to improve the processing accuracy, the present embodiment adds interpolation points between existing nodes. For example, first, traverse each existing node, calculate distance (i, i +1) between the ith node and the (i +1) th node, and calculate n ═ distance (i, i +1)/5, where "5" represents adding an interpolation point every 5 meters. Next, n is rounded to obtain m, and m points are inserted between the ith point and the (i +1) th point at equal intervals. And finally, taking the existing nodes and the interpolation points as elements in the road node set together.
The semantic similarity characteristic between the target information point and the road unit refers to the similarity degree of the information point and the road unit in name semantics.
In one example, obtaining semantically similar features includes: calculating a first semantic similarity between the name of the street to which the information point belongs and the name of the road unit; calculating a second semantic similarity between the name of the address where the information point is located and the name of the road unit; and obtaining semantic similar characteristics according to at least one of the first semantic similarity and the second semantic similarity. The street to which the information point belongs refers to a street area where the information point is located. The address of the information point is, for example, the communication address of the information point.
For example, a first semantic similarity between the name of the street to which the information point belongs and the name of the road unit is denoted as b1, a second semantic similarity between the name of the address to which the information point belongs and the name of the road unit is denoted as b2, and the larger value of the two semantic similarities can be used as the numerical value of the semantic similarity feature.
The semantic similarity is measured by Word Move's Distance (WMD), for example. Word shift distance is a measure developed on the basis of word vectors and used for measuring document similarity, and is a method for calculating the distance between words and sentences, and the smaller the distance is, the higher the similarity is.
The distance characteristic between the target information point and the road unit refers to the spatial distance between the information point and the road unit.
In one example, obtaining distance characteristics between the target information point and the road unit includes: and calculating the closest distance between the information point and the road unit to obtain the distance characteristic. For example, the road unit is expanded in parallel to a certain direction until the road unit just covers the information point, and the expansion distance at this time is the distance characteristic between the information point and the road unit.
In one example, step S1200 further includes: and processing the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit based on a pre-trained machine learning model to obtain a first relevancy score.
The machine learning model is used for acquiring a first relevance score, and the first relevance score is input as each characteristic and output as the first relevance score.
The above-described recognition model is obtained, for example, by: acquiring training samples, wherein each training sample comprises a space similar feature, a semantic similar feature and a distance feature between an information point and a road unit, and a label which represents whether the information point and the road unit in the training samples have an incidence relation or not; and performing machine learning training based on the training samples to obtain a machine learning model. The information point and the road unit have an association relation, which is that: in a certain search log, the search word of the user is the same as or similar to the name of the road unit, and the user clicks the information point as a search result.
The machine learning training process is, for example, to train a supervised learning model by using a GBDT (gradient boosting decision tree) algorithm. The gradient boosting algorithm is a machine learning technology for regression, classification and sequencing tasks, belongs to a part of a boosting algorithm family, and constructs a final prediction model by integrating a plurality of weak learners (decision trees).
In the above example, a probability value indicating that the information point and the road unit have an association relationship in the machine model is set as the first association degree score.
In step S1300, a second association score between the target information point and the road is determined according to the first association score between the target information point and the road unit.
As an example, a certain road is way (its name is wayname), which includes two road units link1 and link 2. A certain information point is poi. Then, a second relevance score for the poi-way (or pos-wayname) may be calculated from the first relevance score for the poi-link1 and the first relevance score for the poi-link2, e.g., as the second relevance score for the poi-wayname, an average of the first relevance score for the poi-link1 and the first relevance score for the poi-link 2.
In step S1400, the road attribute of the target information point is determined according to the second relevance score.
In this embodiment, if the second association score reaches a certain degree, for example, the second association score is greater than a preset threshold, the name of the road + the second association score may be used as the road attribute of the target information point
According to the method for mining the road attributes of the information points, the first relevance score between the information points and the road units is determined according to the spatial similar feature, the semantic similar feature and the distance feature, the second relevance score between the information points and the road is further determined, the road attributes of the information points are determined based on the second relevance score, the road attributes of the information points are mined by integrating the spatial similar feature, the semantic similar feature and the distance feature, the influence of information point aggregation factors is considered, the road attributes can be reasonably quantized, the quality of map search results is favorably improved, and the use experience of users is further improved.
In one example, after step S1400, the method further includes: determining at least one road label according to at least one item of map information point data, map road data and map search log data; after determining a second relevance score between the target information point and the road, calculating semantic similarity between the road label and the road name of the road; and determining a third association score between the target information point and the road label according to the semantic similarity and the second association score.
The road tag is a tag related to a road, which may be a search term, and may be obtained by screening keywords such as "xx road" and "xx road" from related data.
In the above example, the process of obtaining the third relevancy score is, for example: and multiplying the second association score by the semantic similarity of the road label and the road name to obtain a product which is used as a third association score. The semantic similarity between the road label and the road name can also be measured by the word shift distance.
For example, if the above-described association score of poi-wayname is p1, and the association score of a road tag waytag with the road name wayname is p2, the third association score p3 between the information point and the road tag (poi-wayname) is p1 × p 2.
By the method, the mining result of the road attribute is processed into a more explicit form, namely the third relevancy score, and the method is more suitable for being applied to a search scene.
In one example, applying the third relevance score to the search scenario includes the steps of: acquiring a third association score between the road label and each information point in the plurality of information points; receiving a search word sent by terminal equipment; detecting whether the search word is consistent with the road label; determining at least one recommended information point from the plurality of information points according to a third association score between the road label and each of the plurality of information points under the condition that the search term is consistent with the road label; and sending the recommended information point to the terminal equipment.
As an example, the search word input by the user happens to be an existing road tag waytag1, the road tag waytag1 corresponds to a plurality of interest points, for example, poi1-waytag1, poi2-waytag1 and poi3-waytag1, the third association scores between the interest points and the road tags are p1, p2 and p3 in sequence, and p2> p1> p3, and then the information points in the recommendation list are determined to be poi2, poi1 and poi3 in sequence according to the sequence from high to low of the third association score. The information points are recommended to the user according to the method, the processing speed is high, the recommendation result is accurate, the information points with high association degree with the search terms can be sorted forward, and the use experience of the user is improved.
< apparatus embodiment >
The embodiment provides a device for mining road attributes of information points, which comprises an acquisition module, a first processing module, a second processing module and a third processing module.
The system comprises an acquisition module, a display module and a display module, wherein the acquisition module is used for acquiring at least one road in a preset range with a target information point as a center, and the road comprises at least one road unit;
the first processing module is used for determining a first relevancy score between the target information point and the road unit according to the space similarity feature, the semantic similarity feature and the distance feature between the target information point and the road unit;
the second processing module is used for determining a second association degree score between the target information point and the road according to the first association degree score between the target information point and the road unit;
and the third processing module is used for determining the road attribute of the target information point according to the second relevance grade.
In one example, the apparatus further comprises a road label obtaining module and a fourth processing module. The road label acquisition module is used for: and determining at least one road label according to at least one item of map information point data, map road data and map search log data. The fourth processing module is used for: after determining a second relevance score between the target information point and the road, calculating semantic similarity between the road label and the road name of the road; and determining a third association score between the target information point and the road label according to the semantic similarity and the second association score.
In one example, the apparatus further comprises a recommendation module to: acquiring a third association score between the road label and each information point in the plurality of information points; receiving a search word sent by terminal equipment; detecting whether the search word is consistent with the road label; determining at least one recommended information point from the plurality of information points according to a third association score between the road label and each of the plurality of information points under the condition that the search term is consistent with the road label; and sending the recommended information point to the terminal equipment.
In one example, the apparatus further comprises a spatially similar feature acquisition module to: determining an information point position set corresponding to a target information point; determining a road node position set corresponding to a road unit; and calculating the Housdov distance between the information point position set and the road node position set to obtain the space similarity characteristic.
In one example, the spatially similar feature obtaining module is further configured to: and clustering the positions of the plurality of information points to obtain at least one information point position set.
In one example, the spatially similar feature obtaining module is further configured to: adding interpolation nodes among the existing nodes of the road unit according to a preset distance interval; and obtaining a road node set according to the existing nodes and the interpolation nodes.
In one example, the apparatus further comprises a semantic similar feature acquisition module configured to: calculating a first semantic similarity between the name of the street to which the target information point belongs and the name of the road unit; calculating a second semantic similarity between the address of the target information point and the name of the road unit; and determining semantic similar characteristics according to at least one of the first semantic similarity and the second semantic similarity.
In one example, the first processing module is further to: and processing the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit based on a pre-trained machine learning model to obtain a first relevancy score.
In one example, the apparatus further comprises a model training module to: acquiring training samples, wherein each training sample comprises a space similar feature, a semantic similar feature and a distance feature between an information point and a road unit, and a label which represents whether the information point and the road unit in the training samples have an incidence relation or not; and performing machine learning training based on the training samples to obtain a machine learning model.
< electronic device embodiment >
The embodiment is an electronic device comprising a memory and a processor. A memory for storing executable commands. A processor for implementing the method as described in the method embodiments of the present invention under the control of executable commands.
The present disclosure may be systems, methods, and/or computer program products. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement various aspects of the present disclosure.
The computer readable storage medium may be a tangible device that can hold and store the instructions for use by the instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic memory device, a magnetic memory device, an optical memory device, an electromagnetic memory device, a semiconductor memory device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer readable storage medium would include the following: a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), a Static Random Access Memory (SRAM), a portable compact disc read-only memory (CD-ROM), a Digital Versatile Disc (DVD), a memory stick, a floppy disk, a mechanical coding device, such as punch cards or in-groove projection structures having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media as used herein is not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission medium (e.g., optical pulses through a fiber optic cable), or electrical signals transmitted through electrical wires.
The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to a respective computing/processing device, or to an external computer or external storage device via a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmission, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. The network adapter card or network interface in each computing/processing device receives computer-readable program instructions from the network and forwards the computer-readable program instructions for storage in a computer-readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of the present disclosure may be assembler instructions, Instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer-readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, the electronic circuitry that can execute the computer-readable program instructions implements aspects of the present disclosure by utilizing the state information of the computer-readable program instructions to personalize the electronic circuitry, such as a programmable logic circuit, a Field Programmable Gate Array (FPGA), or a Programmable Logic Array (PLA).
Various aspects of the present disclosure are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the disclosure. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer-readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer-readable program instructions may also be stored in a computer-readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer-readable medium storing the instructions comprises an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions. It is well known to those skilled in the art that implementation by hardware, by software, and by a combination of software and hardware are equivalent.
Having described embodiments of the present disclosure, the foregoing description is intended to be exemplary, not exhaustive, and not limited to the disclosed embodiments. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen in order to best explain the principles of the embodiments, the practical application, or improvements made to the technology in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the present disclosure is defined by the appended claims.
Claims (11)
1. A method of mining road attributes of an information point, comprising:
acquiring at least one road in a preset range with a target information point as a center, wherein the road comprises at least one road unit;
determining a first relevancy score between the target information point and the road unit according to the space similarity feature, the semantic similarity feature and the distance feature between the target information point and the road unit;
determining a second association degree score between the target information point and the road according to a first association degree score between the target information point and the road unit;
and determining the road attribute of the target information point according to the second relevance score.
2. The method of claim 1, wherein the method further comprises: determining at least one road label according to at least one item of map information point data, map road data and map search log data;
after the determining a second relevancy score between the target information point and the road, the method further comprises:
calculating semantic similarity between the road label and the road name of the road;
and determining a third association score between the target information point and the road label according to the semantic similarity and the second association score.
3. The method of claim 2, wherein the method further comprises:
acquiring a third association score between the road label and each information point in the plurality of information points;
receiving a search word sent by terminal equipment;
detecting whether the search word is consistent with the road label;
determining at least one recommended information point from a plurality of information points according to a third relevancy score between the road label and each of the plurality of information points under the condition that the search word is consistent with the road label;
and sending the recommendation information point to the terminal equipment.
4. The method of claim 1, wherein the spatially similar features are obtained by:
determining an information point position set corresponding to the target information point;
determining a road node position set corresponding to the road unit;
and calculating the Hausdorff distance between the information point position set and the road node position set to obtain the space similarity characteristic.
5. The method of claim 4, wherein the determining the set of information point locations corresponding to the target information point comprises:
and clustering the positions of the plurality of information points to obtain at least one information point position set.
6. The method of claim 4, wherein the determining the set of road nodes to which the road unit corresponds comprises:
adding interpolation nodes among the existing nodes of the road unit according to a preset distance interval;
and obtaining the road node set according to the existing nodes and the interpolation nodes.
7. The method of claim 1, wherein the semantically similar features are obtained by:
calculating a first semantic similarity between the name of the street to which the target information point belongs and the name of the road unit;
calculating a second semantic similarity between the address of the target information point and the name of the road unit;
and determining the semantic similar feature according to at least one of the first semantic similarity and the second semantic similarity.
8. The method of claim 1, wherein the determining a first relevance score between the target information point and the road unit according to the spatial similarity feature, the semantic similarity feature, and the distance feature between the target information point and the road unit comprises:
and processing the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit based on a pre-trained machine learning model to obtain the first relevancy score.
9. The method of claim 8, wherein the machine learning model is obtained by:
acquiring training samples, wherein each training sample comprises a space similar feature, a semantic similar feature and a distance feature between an information point and a road unit, and a label which represents whether the information point and the road unit in the training samples have an association relation or not;
and performing machine learning training based on the training samples to obtain the machine learning model.
10. An apparatus for mining road attributes of information points, comprising:
the system comprises an acquisition module, a display module and a control module, wherein the acquisition module is used for acquiring at least one road in a preset range with a target information point as a center, and the road comprises at least one road unit;
the first processing module is used for determining a first relevance grade between the target information point and the road unit according to the space similar feature, the semantic similar feature and the distance feature between the target information point and the road unit;
the second processing module is used for determining a second relevance grade between the target information point and the road according to the first relevance grade between the target information point and the road unit;
and the third processing module is used for determining the road attribute of the target information point according to the second relevance grade.
11. An electronic device, comprising:
a memory for storing executable commands;
a processor for implementing the method of any one of claims 1-9 under control of the executable command.
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