CN113221025A - Interest point recall method, device, equipment and medium - Google Patents
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Abstract
The embodiment of the application discloses a method, a device, equipment and a medium for recalling interest points, which relate to the map data processing technology, wherein the method comprises the following steps: classifying established target interest points on a map, and determining the recall distance of an interest point set obtained by classification; determining a plurality of recall areas of the interest point set according to the coordinates of each target interest point in the interest point set and the determined recall distance; determining a target recall area to which each information interest point belongs according to the coordinates of the information interest points; and in the target recall area, determining a target interest point to be recalled according to the name keyword of the information interest point. According to the method and the device, the number of the recalled interest points can be reduced, the quality of the recalled interest points is improved, and therefore the fusion processing efficiency of the interest points is improved.
Description
Technical Field
The embodiment of the application relates to a computer technology, in particular to a map data processing technology, and particularly relates to a point of interest recall method, device, equipment and medium.
Background
The interest point fusion is to calculate the similarity between the collected interest point information and the constructed interest points of the map and judge whether the two points refer to the same interest point in the real world. The constructed interest points in the recall map are the basis of the interest point fusion processing.
At present, map service providers generally recall all points of interest in a map area set according to coordinates in intelligence data or recall all points of interest having the same index as the points of interest in the intelligence data according to an established point of interest index of "city + word" in map data on the premise of providing more comprehensive point of interest information for map users.
The two above-mentioned point of interest recalling methods have the following defects: the recalled interest points are not only large in quantity, but also the quality of the recalled interest points cannot be guaranteed, so that the calculation processing amount in the subsequent interest point fusion processing process is large.
Disclosure of Invention
The embodiment of the application discloses a method, a device, equipment and a medium for recalling interest points, so that the number of the recalled interest points is reduced, the quality of the recalled interest points is improved, and the fusion processing efficiency of the interest points is improved.
In a first aspect, an embodiment of the present application discloses a method for recalling a point of interest, including:
classifying established target interest points on a map, and determining the recall distance of an interest point set obtained by classification;
determining a plurality of recall areas of the interest point set according to the coordinates of target interest points in the interest point set and the recall distance;
determining a target recall area to which each information interest point belongs according to the coordinates of the information interest points;
and in the target recall area, determining a target interest point to be recalled according to the name keyword of the information interest point.
One embodiment in the above application has the following advantages or benefits: meanwhile, the spatial similarity and semantic similarity of the recalled target interest points and the information interest points are ensured, the number of the recalled interest points is reduced, the quality of the recalled interest points is improved, and the fusion processing efficiency of the interest points is improved.
Optionally, the determining the recall distance of the classified interest point set includes:
calculating the average distance of coordinates between each target interest point in the interest point set and the corresponding historical fused intelligence interest point;
and determining the recall distance of the interest point set according to the coordinate average distance.
One embodiment in the above application has the following advantages or benefits: by carrying out region division on each target interest point on the map according to the interest point category, the effect of preliminarily refining the interest point recall granularity is realized, and the interest point recall processing rationality is improved.
Optionally, the classifying the target interest points established on the map includes:
and classifying the target interest points according to labels of the established target interest points on the map, wherein the labels comprise service categories of the target interest points.
Optionally, in the target recall area, determining a target interest point to be recalled according to the name keyword of the intelligence interest point, including:
respectively carrying out word segmentation processing on the information interest point name and the target interest point name in the target recall area;
removing interfering words in the word segmentation processing result to respectively obtain the residual words of the information interest point name and the target interest point name;
splitting the residual words based on the context of the residual words to obtain at least one name keyword of the information interest point and at least one name keyword of the target interest point;
and determining the target interest points with the overlapped name keywords with the information interest points in the target recall area as the target interest points to be recalled.
One embodiment in the above application has the following advantages or benefits: by respectively carrying out word segmentation processing, interference word removal processing and residual word splitting processing on the information interest points and the target interest points, certain semantic similarity between the recalled target interest points and the information interest points is ensured, the related calculation amount in the processing process is small, and the processing efficiency is high.
Optionally, removing the interfering words in the word segmentation processing result includes:
and matching the word segmentation processing result in a preset dictionary, determining the interference words, and removing the interference words, wherein the interference words comprise words used for representing administrative division information, words representing an operation range and words representing a suffix of a name.
Optionally, based on the context of the remaining word, splitting the remaining word, including:
and splitting the residual words based on the context of the residual words by utilizing a preset language analysis model.
Optionally, determining a plurality of recall areas of the interest point set according to the coordinates of the target interest points in the interest point set and the recall distance includes:
and determining a plurality of recall areas of the interest point set by taking the coordinates of each target interest point in the interest point set as a circle center and the recall distance as a radius.
In a second aspect, an embodiment of the present application further discloses a device for recalling an interest point, including:
the recall distance determining module is used for classifying the established target interest points on the map and determining the recall distance of the interest point set obtained by classification;
a recall area determination module, configured to determine, according to the coordinates of each target interest point in the interest point set and the recall distance, a plurality of recall areas of the interest point set;
the target recall area determining module is used for determining a target recall area to which each information interest point belongs according to the coordinates of the information interest points;
and the target interest point recalling module is used for determining a target interest point to be recalled in the target recalling area according to the name keyword of the information interest point.
In a third aspect, an embodiment of the present application further discloses an electronic device, including:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a point of interest recall method according to any of the embodiments of the present application.
In a fourth aspect, embodiments of the present application further disclose a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform a point of interest recall method according to any of the embodiments of the present application.
According to the technical scheme of the embodiment of the application, the recall distance of the target interest point is determined according to the interest point classification, then the target interest point needing to be recalled is determined from the recall area under the interest point classification according to the coordinates and name keywords of the information interest point, the spatial similarity and semantic similarity of the recalled target interest point and the information interest point are ensured, the problems of large number and low quality of the recalled interest points in the existing interest point recall scheme are solved, the number of the recalled interest points is reduced, and the quality of the recalled interest points is improved; when the number of recalled target interest points is reduced and the quality is improved, and each information interest point is subjected to fusion processing, the processing times required for fusion comparison is reduced, so that the daily processed information data volume is improved, and the fusion processing efficiency of the interest points is improved. Other effects of the above-described alternative will be described below with reference to specific embodiments.
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The drawings are included to provide a better understanding of the present solution and are not intended to limit the present application. Wherein:
FIG. 1 is a flow chart of a point of interest recall method disclosed in accordance with an embodiment of the present application;
FIG. 2 is a schematic illustration of a recall area categorized for different points of interest according to an embodiment of the present application;
FIG. 3 is a flow chart of another point of interest recall method disclosed in accordance with an embodiment of the present application;
FIG. 4 is a schematic structural diagram of a point of interest recall apparatus according to an embodiment of the present application;
fig. 5 is a block diagram of an electronic device disclosed according to an embodiment of the present application.
Detailed Description
The following description of the exemplary embodiments of the present application, taken in conjunction with the accompanying drawings, includes various details of the embodiments of the application for the understanding of the same, which are to be considered exemplary only. Accordingly, those of ordinary skill in the art will recognize that various changes and modifications of the embodiments described herein can be made without departing from the scope and spirit of the present application. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a flowchart of an interest point recall method disclosed in an embodiment of the present application, which may be applied to a case where, before an interest point fusion process, an interest point that needs to be recalled is determined according to collected intelligence interest points in points already established on a map, where the intelligence interest points include interest points to be processed acquired from network data by any method, such as interest points uploaded by a map user, interest points collected by a web crawler, and the like. The method of the embodiment may be performed by a point of interest recall apparatus, which may be implemented in software and/or hardware, and may be integrated on any electronic device with computing capability.
As shown in fig. 1, the method for recalling a point of interest disclosed in this embodiment may include:
s101, classifying the established target interest points on the map, and determining the recall distance of the interest point set obtained by classification.
For example, the target interest points may be classified according to tags of the established target interest points on the map to obtain at least one interest point set, and the interest points belonging to the same category may be classified into the same interest point set. Wherein the tags include service categories of the target points of interest, such as restaurants, hotels, attractions, colleges, hospitals, stores, and the like. The specific definition of the service category of the interest points can adopt the classification standard of the interest points in the map industry, and can also be customized and divided based on different map service providers. By classifying the target interest points on the map, the target interest points to be recalled can be determined according to the categories, and the effect of preliminarily refining the granularity of interest point recall is achieved.
After the classification of the target interest points is completed, the distance between the interest points participating in the fusion is statistically analyzed based on the historical fusion processing process of the interest points, and the recall distance of each interest point set is determined, wherein the recall distance determines a map area to be considered when the target interest points needing to be recalled in the corresponding category are determined according to the intelligence interest points. Illustratively, determining the recall distance for each set of points of interest includes: calculating the average distance of coordinates between each target interest point in the set and the corresponding historical fused information interest point aiming at each interest point set, wherein the obtained average distance of each coordinate is equivalent to the recall distance of each target interest point; and determining the recall distance of each interest point set according to the obtained coordinate average distance, for example, averaging the obtained coordinate average distances again to be used as the recall distance of each interest point set. For example, the recall distance of the food category target interest point is 500 meters, and the recall distance of the tourist attraction category target interest point is 2000 meters.
In addition, in the interest point history fusion process, the situation that interest points participating in fusion are artificially determined may exist, so that the average distance between the coordinates of the interest points has a deviation, and further the recall distance under the corresponding category is not accurate enough, and in order to ensure the accuracy of the finally obtained recall distance, the obtained recall distance of the interest point set can be adjusted in a manual verification manner, so that errors in the recall distance calculation are avoided.
In the embodiment, by determining the recall distance of the target interest point according to the category, compared with the situation that the recall distance of each interest point is not distinguished in the prior art, the reasonability of determining the recall area is improved, and the optimization management in the interest point recall process is realized.
S102, determining a plurality of recall areas of the interest point set according to the coordinates of all target interest points in the interest point set and the determined recall distance.
For each interest point set, each recall area is determined by the coordinates of each target interest point and the determined recall distance, the shape of the recall area may be set according to actual requirements, and the embodiment is not particularly limited. For example, for each interest point set, a plurality of recall areas of the interest point set may be determined by taking coordinates of target interest points in the interest point set as a circle center and recall distances as radii. Fig. 2 is a schematic diagram illustrating a recall area classified for different points of interest according to an embodiment of the present application. As shown in fig. 2, points b1, b2, b3 and b4 respectively represent target interest points already established on the map, and point b1 and point b4 belong to the same interest point category and the same interest point set, and therefore correspond to the same recall radius r 1; the points b1, b2 and b3 belong to different interest point classifications and different interest point sets respectively, and the corresponding recall radiuses r1, r2 and r3 are different from one another. The number of the recall areas corresponding to each interest point set is determined by the number of the target interest points included in the interest point set.
S103, determining the target recall area to which each intelligence interest point belongs according to the coordinates of the intelligence interest points.
Continuing with FIG. 2, Point A represents an informative point of interest, with Point A coordinates falling in both circular areas, and therefore the targeted recall area to which Point A belongs includes: a circular region with a radius of r1 centered at point b1, and a circular region with a radius of r2 centered at point b 2. The target recall area is determined according to the coordinates of the information interest points, so that the spatial similarity between the target interest points to be recalled and the information interest points is ensured, namely the information interest points and the corresponding target interest points in the target recall area are preliminarily determined on the spatial distance and possibly point to the same thing in the real world.
And S104, determining the target interest points to be recalled in the target recall area according to the name keywords of the information interest points.
The method comprises the steps that name keywords of the information interest points can be determined through semantic analysis, word segmentation processing and the like of the information interest points, wherein the name keywords are core words which are representative except common general words in the information interest point names; and then, associating the target interest points to be recalled in the target recall area according to the name keywords, so that the semantic similarity between the recalled target interest points and the information interest points is ensured, wherein the semantic similarity determines that the information interest points and the corresponding recalled target interest points possibly point to the same thing in the real world from a semantic level. The name keyword of the intelligence interest point may be one or more, and this embodiment is not particularly limited.
According to the technical scheme of the embodiment, the recall distance of the target interest point is determined according to the interest point classification, then the target interest point needing to be recalled is determined from the recall area under the interest point classification according to the coordinates and name keywords of the information interest point, the spatial similarity and semantic similarity of the recalled target interest point and the information interest point are ensured, the problems of large number and low quality of the recalled interest points in the existing interest point recall scheme are solved, the number of the recalled interest points is reduced, and the quality of the recalled interest points is improved; when the number of recalled target interest points is reduced and the quality is improved, and each information interest point is subjected to fusion processing, the processing times required for fusion comparison is reduced, so that the daily processed information data volume is improved, and the fusion processing efficiency of the interest points is improved.
Fig. 3 is a flowchart of another point of interest recall method disclosed in an embodiment of the present application, which is further optimized and expanded based on the above technical solution, and can be combined with the above various optional embodiments. As shown in fig. 3, the method of this embodiment may include:
s201, classifying the established target interest points on the map, and determining the recall distance of the interest point set obtained by classification.
S202, determining a plurality of recall areas of the interest point set according to the coordinates of all target interest points in the interest point set and the determined recall distance.
S203, determining the target recall area to which each intelligence interest point belongs according to the coordinates of the intelligence interest points.
And S204, performing word segmentation processing on the information interest point name and the target interest point name in the target recall area respectively.
S205, removing the interference words in the word segmentation processing result to respectively obtain the residual words of the information interest point name and the target interest point name.
In this embodiment, the intelligence interest points and the target interest points in each target recall area may be processed in the same processing manner, including word segmentation processing, interfering word removal processing, and the like. Regarding the implementation of the word segmentation process, any available word segmentation technology in the prior art can be used, and the embodiment is not particularly limited. In the process of respectively performing word segmentation on the intelligence interest points and the target interest points, all obtained words can be classified according to preset interference word types, and then interference words are removed based on word classification results. Optionally, interfering words in the respective segmentation processing results of the intelligence interest points and the target interest points may be removed in a word matching manner, and the method specifically includes: and matching the word segmentation processing result in a preset dictionary, determining an interference word, and removing the interference word, wherein the preset dictionary is a word database which is counted according to a predetermined interference word type.
The interference words are relative to the name keywords and comprise common words common in the interest point names, and the interference word types may comprise words (place) for representing administrative division information, words (scope) for representing a business scope, and words (suffix) for representing a suffix of the name. Illustratively, the words representing administrative division information: x province, x city, etc., the words indicating the scope of the business: service, wholesale, repair, advertising, restaurants, etc., words representing suffixes of names: in a limited company, a convenience store, a supermarket, etc., the interfering words may be adaptively changed according to actual processing requirements, and the foregoing examples should not be construed as specific limitations to the present embodiment.
S206, based on the context of the residual words, the residual words are split to obtain at least one name keyword of the information interest point and at least one name keyword of the target interest point.
In order to improve the partition granularity of the keywords and avoid missing interest points in the target interest point recall process, the embodiment splits the remaining words in the information interest point name and the target interest point name, thereby determining the corresponding name keywords. Each name keyword may include one word or two words, which are associated with a predetermined keyword partition granularity, and the split name keywords may include overlapping words.
Optionally, based on the context of the remaining words, splitting the remaining words, including: and splitting the residual words based on the contexts of the residual words by using a preset language analysis model, such as a bigram model (bi-gram model) and the like.
S207, determining the target interest points with the overlapped name keywords with the information interest points in the target recall area as target interest points to be recalled.
For example, taking an information interest point "green lake and blue osmanthus garden hotel" as an example, the following words can be obtained by performing word segmentation processing on the information interest point: the 'brook' and the 'hotel' are removed, and the remaining words are 'the Pigui garden'; and (3) segmenting the 'Pigui garden' according to a bi-gram mode to generate a name keyword, namely taking two adjacent characters as a word to obtain the name keyword: 'Bigui' and 'Guiyuan'. In a target recall area corresponding to the 'green lake Pigui garden hotel', the same word segmentation processing, interference word removal processing and residual word splitting processing are carried out on each target interest point, and the target interest point which also comprises the 'Pigui' or the 'Guiyuan' in the finally obtained name key words is determined as the target interest point needing to be recalled.
It should be noted that if a plurality of name keywords are determined in the information interest points, the target interest points can be taken as the target interest points to be recalled as long as the target interest points contain at least one same name keyword, so that interest point omission in the interest point recall process is avoided.
According to the technical scheme of the embodiment, the recall distance of the target interest point is determined according to the interest point classification, then the target interest point needing to be recalled is determined from the recall area under the interest point classification according to the coordinates and name keywords of the information interest point, the spatial similarity and semantic similarity of the recalled target interest point and the information interest point are ensured, the problems of large number and low quality of the recalled interest points in the existing interest point recall scheme are solved, the number of the recalled interest points is reduced, the quality of the recalled interest points is improved, and the fusion processing efficiency of the interest points is improved; in addition, in the process of determining the name keywords, the granularity can be divided according to the set words, and the rest words of the information interest points and the target interest points after the interference words are removed are split, so that interest point omission in the interest point recall process is avoided; meanwhile, in the process of recalling the target interest point based on the semantic similarity, the scheme of the embodiment has the advantages that the calculation processing amount is less and the processing efficiency is high through the cooperation of three operation processes of word segmentation processing, interference word removal processing and residual word splitting processing.
Fig. 4 is a schematic structural diagram of an interest point recall apparatus according to an embodiment of the present application, which may be applied to a case where, before an interest point fusion process, an interest point that needs to be recalled is determined according to collected intelligence interest points in points already established on a map, where the intelligence interest points include interest points to be processed acquired from network data by any method, such as interest points uploaded by a map user, interest points collected by a web crawler, and the like. The apparatus disclosed in this embodiment may be implemented in software and/or hardware, and may be integrated on any electronic device with computing capability.
As shown in fig. 4, the point of interest recall apparatus 300 disclosed in this embodiment may include a recall distance determining module 301, a recall area determining module 302, a target recall area determining module 303 and a target point of interest recall module 304, where:
a recall distance determination module 301, configured to classify target interest points established on a map, and determine a recall distance of an interest point set obtained by the classification;
a recall area determination module 302, configured to determine multiple recall areas of the interest point set according to the coordinates of each target interest point in the interest point set and the determined recall distance;
the target recall area determining module 303 is configured to determine a target recall area to which each information interest point belongs according to coordinates of the information interest points;
and the target interest point recalling module 304 is configured to determine a target interest point to be recalled in the target recall area according to the name keyword of the information interest point.
Optionally, the recall distance determining module 301 includes:
the interest point classification unit is used for classifying the established target interest points on the map;
the average distance calculation unit is used for calculating the average distance of coordinates between each target interest point in the classified interest point set and the corresponding historical fused information interest point;
and the recall distance determining unit is used for determining the recall distance of the interest point set according to the coordinate average distance.
Optionally, the interest point classifying unit is specifically configured to: and classifying the target interest points according to labels of the established target interest points on the map, wherein the labels comprise service categories of the target interest points.
Optionally, the target interest point recalling module 304 includes:
the word segmentation processing unit is used for respectively carrying out word segmentation processing on the information interest point name and the target interest point name of the target recall area;
the interference word removing unit is used for removing interference words in the word segmentation processing result to respectively obtain the residual words of the information interest point name and the target interest point name;
the name keyword determining unit is used for splitting the residual words based on the context of the residual words to obtain at least one name keyword of the information interest point and at least one name keyword of the target interest point;
and the target interest point recalling unit is used for determining the target interest points with the overlapped name keywords with the information interest points in the target recalling area as target interest points to be recalled.
Optionally, the interfering word removing unit is specifically configured to:
and matching the word segmentation processing result in a preset dictionary, determining interference words, removing the interference words, and respectively obtaining the remaining words of the information interest point name and the target interest point name, wherein the interference words comprise words used for representing administrative division information, words representing the management range and words representing the suffix of the name.
Optionally, the name keyword determination unit is specifically configured to:
and splitting the residual words by using a preset language analysis model based on the context of the residual words to obtain at least one name keyword of the information interest point and at least one name keyword of the target interest point.
Optionally, the recall area determining module 302 is specifically configured to:
and determining a plurality of recall areas under the interest point set by taking the coordinates of each target interest point in the interest point set as a circle center and the recall distance as a radius.
The interest point recall apparatus 300 disclosed in the embodiment of the present application can execute the interest point recall method disclosed in the embodiment of the present application, and has functional modules and beneficial effects corresponding to the execution method. Reference may be made to the description of any method embodiment of the present application for details not explicitly described in this embodiment.
According to an embodiment of the present application, an electronic device and a readable storage medium are also provided.
Fig. 5 is a block diagram of an electronic device for implementing the point of interest recall method in the embodiment of the present application, as shown in fig. 5. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device may also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not meant to limit implementations of embodiments of the present application described and/or claimed herein.
As shown in fig. 5, the electronic apparatus includes: one or more processors 401, memory 402, and interfaces for connecting the various components, including high-speed interfaces and low-speed interfaces. The various components are interconnected using different buses and may be mounted on a common motherboard or in other manners as desired. The processor may process instructions for execution within the electronic device, including instructions stored in or on the memory to display Graphical information for a Graphical User Interface (GUI) on an external input/output device, such as a display device coupled to the Interface. In other embodiments, multiple processors and/or multiple buses may be used, along with multiple memories and multiple memories, as desired. Also, multiple electronic devices may be connected, with each device providing portions of the necessary operations, e.g., as a server array, a group of blade servers, or a multi-processor system. In fig. 5, one processor 401 is taken as an example.
The memory 402 is a non-transitory computer readable storage medium provided by the embodiments of the present application. Wherein the memory stores instructions executable by the at least one processor to cause the at least one processor to perform the point of interest recall method provided by the embodiments of the present application. The non-transitory computer-readable storage medium of the embodiments of the present application stores computer instructions for causing a computer to perform the point of interest recall method provided by the embodiments of the present application.
The memory 402 may include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program required for at least one function; the storage data area may store data created according to use of the electronic device of the point of interest recall method, and the like. Further, the memory 402 may include high speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory 402 may optionally include memory located remotely from the processor 401, which may be connected via a network to an electronic device for implementing the point of interest recall method of the present embodiments. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The electronic device for implementing the point of interest recall method in the embodiment may further include: an input device 403 and an output device 404. The processor 401, the memory 402, the input device 403 and the output device 404 may be connected by a bus or other means, and fig. 5 illustrates an example of a connection by a bus.
The input device 403 may receive input numeric or character information and generate key signal inputs related to user settings and function control of the electronic apparatus for implementing the point of interest recall method in the present embodiment, such as an input device of a touch screen, a keypad, a mouse, a track pad, a touch pad, a pointing stick, one or more mouse buttons, a track ball, a joystick, or the like. The output device 404 may include a display device, an auxiliary lighting device such as a Light Emitting Diode (LED), a tactile feedback device, and the like; the tactile feedback device is, for example, a vibration motor or the like. The Display device may include, but is not limited to, a Liquid Crystal Display (LCD), an LED Display, and a plasma Display. In some implementations, the display device can be a touch screen.
Various implementations of the systems and techniques described here can be realized in digital electronic circuitry, Integrated circuitry, Application Specific Integrated Circuits (ASICs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs that are executable and/or interpretable on a programmable system including at least one programmable processor, which may be special or general purpose, receiving data and instructions from, and transmitting data and instructions to, a storage system, at least one input device, and at least one output device.
These computer programs, also known as programs, software applications, or code, include machine instructions for a programmable processor, and may be implemented using high-level procedural and/or object-oriented programming languages, and/or assembly/machine languages. As used herein, the terms "machine-readable medium" and "computer-readable medium" refer to any computer program product, apparatus, and/or Device for providing machine instructions and/or data to a Programmable processor, such as a magnetic disk, optical disk, memory, Programmable Logic Device (PLD), including a machine-readable medium that receives machine instructions as a machine-readable signal. The term "machine-readable signal" refers to any signal used to provide machine instructions and/or data to a programmable processor.
To provide for interaction with a user, the systems and techniques described here can be implemented on a computer having: a display device for displaying information to a user, for example, a Cathode Ray Tube (CRT) or an LCD monitor; and a keyboard and a pointing device, such as a mouse or a trackball, by which a user can provide input to the computer. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user can be any form of sensory feedback, e.g., visual feedback, auditory feedback, or tactile feedback; and input from the user may be received in any form, including acoustic, speech, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a back-end component, e.g., as a data server, or that includes a middleware component, e.g., an application server, or that includes a front-end component, e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here, or any combination of such back-end, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication, e.g., a communication network. Examples of communication networks include: local Area Networks (LANs), Wide Area Networks (WANs), and the internet.
The computer system may include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other.
According to the technical scheme of the embodiment of the application, the recall distance of the target interest point is determined according to the interest point classification, then the target interest point needing to be recalled is determined from the recall area under the interest point classification according to the coordinates and name keywords of the information interest point, the spatial similarity and semantic similarity of the recalled target interest point and the information interest point are ensured, the problems of large number and low quality of the recalled interest points in the existing interest point recall scheme are solved, the number of the recalled interest points is reduced, and the quality of the recalled interest points is improved; when the number of recalled target interest points is reduced and the quality is improved, and each information interest point is subjected to fusion processing, the processing times required for fusion comparison is reduced, so that the daily processed information data volume is improved, and the fusion processing efficiency of the interest points is improved.
It should be understood that various forms of the flows shown above may be used, with steps reordered, added, or deleted. For example, the steps described in the present application may be executed in parallel, sequentially, or in different orders, as long as the desired results of the technical solutions disclosed in the present application can be achieved, and the present invention is not limited herein.
The above-described embodiments should not be construed as limiting the scope of the present application. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions may be made in accordance with design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present application shall be included in the protection scope of the present application.
Claims (10)
1. A point of interest recall method, comprising:
classifying established target interest points on a map, and determining the recall distance of an interest point set obtained by classification;
determining a plurality of recall areas of the interest point set according to the coordinates of target interest points in the interest point set and the recall distance;
determining a target recall area to which each information interest point belongs according to the coordinates of the information interest points;
and in the target recall area, determining a target interest point to be recalled according to the name keyword of the information interest point.
2. The method of claim 1, wherein determining recall distances for the categorized set of points of interest comprises:
calculating the average distance of coordinates between each target interest point in the interest point set and the corresponding historical fused intelligence interest point;
and determining the recall distance of the interest point set according to the coordinate average distance.
3. The method of claim 1, wherein classifying the established target points of interest on the map comprises:
and classifying the target interest points according to labels of the established target interest points on the map, wherein the labels comprise service categories of the target interest points.
4. The method of claim 1, wherein determining a target point of interest to recall in the target recall area based on a name keyword of the informative point of interest comprises:
respectively carrying out word segmentation processing on the information interest point name and the target interest point name in the target recall area;
removing interfering words in the word segmentation processing result to respectively obtain the residual words of the information interest point name and the target interest point name;
splitting the residual words based on the context of the residual words to obtain at least one name keyword of the information interest point and at least one name keyword of the target interest point;
and determining the target interest points with the overlapped name keywords with the information interest points in the target recall area as the target interest points to be recalled.
5. The method of claim 4, wherein removing the interfering words from the segmentation processing result comprises:
and matching the word segmentation processing result in a preset dictionary, determining the interference words, and removing the interference words, wherein the interference words comprise words used for representing administrative division information, words representing an operation range and words representing a suffix of a name.
6. The method of claim 4, wherein splitting the remaining term based on the context of the remaining term comprises:
and splitting the residual words based on the context of the residual words by utilizing a preset language analysis model.
7. The method of claim 1, wherein determining a plurality of recall regions for the set of points of interest based on the target point of interest coordinates and the recall distance in the set of points of interest comprises:
and determining a plurality of recall areas of the interest point set by taking the coordinates of each target interest point in the interest point set as a circle center and the recall distance as a radius.
8. A point of interest recall apparatus, comprising:
the recall distance determining module is used for classifying the established target interest points on the map and determining the recall distance of the interest point set obtained by classification;
a recall area determination module, configured to determine, according to the coordinates of each target interest point in the interest point set and the recall distance, a plurality of recall areas of the interest point set;
the target recall area determining module is used for determining a target recall area to which each information interest point belongs according to the coordinates of the information interest points;
and the target interest point recalling module is used for determining a target interest point to be recalled in the target recalling area according to the name keyword of the information interest point.
9. An electronic device, comprising:
at least one processor; and
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores instructions executable by the at least one processor to enable the at least one processor to perform the point of interest recall method of any of claims 1-7.
10. A non-transitory computer readable storage medium having stored thereon computer instructions for causing a computer to perform the point of interest recall method of any one of claims 1-7.
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Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106919641A (en) * | 2017-01-12 | 2017-07-04 | 北京三快在线科技有限公司 | A kind of interest point search method and device, electronic equipment |
| WO2018046959A1 (en) * | 2016-09-12 | 2018-03-15 | University Of Leicester | Image storage and retrieval |
| CN109634983A (en) * | 2018-12-13 | 2019-04-16 | 百度在线网络技术（北京）有限公司 | Recall determination method, apparatus, equipment and the medium of interest point information |
| CN109710709A (en) * | 2018-12-13 | 2019-05-03 | 北京百度网讯科技有限公司 | Interest point data processing method, device, electronic equipment and storage medium |
| CN110390054A (en) * | 2019-07-25 | 2019-10-29 | 北京百度网讯科技有限公司 | Point of interest recalls method, apparatus, server and storage medium |
| CN110555432A (en) * | 2018-06-04 | 2019-12-10 | 百度在线网络技术（北京）有限公司 | Method, device, equipment and medium for processing interest points |
-
2020
- 2020-01-21 CN CN202010072428.2A patent/CN113221025B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2018046959A1 (en) * | 2016-09-12 | 2018-03-15 | University Of Leicester | Image storage and retrieval |
| CN106919641A (en) * | 2017-01-12 | 2017-07-04 | 北京三快在线科技有限公司 | A kind of interest point search method and device, electronic equipment |
| CN110555432A (en) * | 2018-06-04 | 2019-12-10 | 百度在线网络技术（北京）有限公司 | Method, device, equipment and medium for processing interest points |
| CN109634983A (en) * | 2018-12-13 | 2019-04-16 | 百度在线网络技术（北京）有限公司 | Recall determination method, apparatus, equipment and the medium of interest point information |
| CN109710709A (en) * | 2018-12-13 | 2019-05-03 | 北京百度网讯科技有限公司 | Interest point data processing method, device, electronic equipment and storage medium |
| CN110390054A (en) * | 2019-07-25 | 2019-10-29 | 北京百度网讯科技有限公司 | Point of interest recalls method, apparatus, server and storage medium |
Non-Patent Citations (1)
| Title |
|---|
| 徐爽;张谦;李琰;刘嘉勇;: "基于距离类别的多源兴趣点融合算法", 计算机应用, no. 05 * |
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