CN111274341A - Site selection method and device for network points - Google Patents
Site selection method and device for network points Download PDFInfo
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Abstract
The invention discloses a site selection method and a site selection device for network points, and relates to the technical field of computers. One specific implementation of the method comprises the steps of obtaining a map space index list based on map data; acquiring attribute information, calculating a spatial index code corresponding to the attribute information, and combining the spatial index code into a map spatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information; and obtaining address information of the recommended network points through a preset machine learning model according to the merged map space index list. Therefore, the method and the device can solve the problem that the more appropriate address selection position can be obtained only by comparing each position selected by the user in the prior art, and the address selection position cannot be directly and quickly informed under the condition that the user does not select the address.
Description
Technical Field
The invention relates to the technical field of computers, in particular to a site selection method and a site selection device for network points.
Background
The existing addressing system outputs the predicted future revenue of the position according to the addressing position input by the user. For example: the system obtains the longitude and latitude points selected by the user after the user inputs two positions in the map, and estimates the future revenue of the point as a bank site according to population and economic data around the longitude and latitude points. And then the user is informed of which point is more suitable to be used as a future branch of the bank according to the operation data such as comparison operation and revenue.
In the process of implementing the invention, the inventor finds that at least the following problems exist in the prior art:
data cannot be put into the map index, so range finding can only be done when the user enters the location to find surrounding data to model. Meanwhile, similar positions (positions with similar history data) cannot be compared, and the user performs temporal calculation when inputting the position, which is poor in user experience.
Disclosure of Invention
In view of this, embodiments of the present invention provide a method and an apparatus for site selection, which can solve the problem that the current method can only compare each location selected by a user to obtain a more suitable site selection location, and cannot directly and quickly notify the site selection location without selecting an address by the user.
In order to achieve the above object, according to an aspect of the embodiments of the present invention, there is provided a site selection method, including calculating all spatial index points based on map data to obtain a map spatial index list; wherein each spatial index code in the spatial index list represents a block of map locations; acquiring attribute information, and calculating a spatial index code corresponding to the attribute information according to longitude and latitude data corresponding to the attribute information; merging the spatial index codes corresponding to the attribute information into a map spatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information; and obtaining address information of the recommended network points through a preset machine learning model according to the merged map space index list.
Optionally, obtaining, by a preset machine learning model, address information of a recommended website according to the merged map space index list, where the obtaining includes:
according to the merged map spatial index list, obtaining network point information of a position corresponding to a spatial index code through a preset machine learning model;
and calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
Optionally, after obtaining, according to the merged map spatial index list, mesh point information of a position corresponding to a spatial index code through a preset machine learning model, the method includes:
calculating the correlation coefficient of the dot information of each row and the position corresponding to the space index code according to the merged map space index list;
sorting the correlation coefficients of the spatial index codes based on preset target attribute information;
and acquiring a preset number of spatial index codes, calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
Optionally, comprising:
the spatial index coding is sorted by relevance coefficient based on the number of geographic location interest points.
Optionally, comprising:
and obtaining the dot information of the position corresponding to the spatial index code through a preset gradient lifting algorithm or a lifting tree model according to the merged map spatial index list.
Optionally, calculating the similarity between the target region and the corresponding position of the spatial index code includes:
and calculating the similarity of the target area and the position corresponding to the spatial index code through cosine similarity or a neural network model.
Optionally, comprising:
the attribute information comprises population data, the number of interest points of the geographic position and management data.
In addition, the invention also provides a site selection device, which comprises an acquisition module, a search module and a search module, wherein the acquisition module is used for calculating all spatial index points based on map data so as to obtain a map spatial index list; wherein each spatial index code in the spatial index list represents a block of map locations; acquiring attribute information, and calculating a spatial index code corresponding to the attribute information according to longitude and latitude data corresponding to the attribute information; merging the spatial index codes corresponding to the attribute information into a map spatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information;
and the processing module is used for obtaining the address information of the recommended website through a preset machine learning model according to the merged map space index list.
One embodiment of the above invention has the following advantages or benefits: because the map-based data is adopted, a map space index list is obtained; acquiring attribute information, calculating a spatial index code corresponding to the attribute information, and combining the spatial index code into a map spatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information; according to the merged map space index list, a technical means of obtaining address information of recommended websites through a preset machine learning model is adopted, so that the technical problem that a more appropriate site selection position can be obtained only by comparing each position selected by a user in the prior art and the site selection position cannot be directly and quickly informed under the condition that the user does not select an address is solved.
Further effects of the above-mentioned non-conventional alternatives will be described below in connection with the embodiments.
Drawings
The drawings are included to provide a better understanding of the invention and are not to be construed as unduly limiting the invention. Wherein:
fig. 1 is a schematic diagram of a main flow of a mesh point addressing method according to a first embodiment of the present invention;
fig. 2 is a schematic diagram of a main flow of a mesh point addressing method according to a second embodiment of the present invention;
fig. 3 is a schematic diagram of a main flow of a mesh point addressing method according to a third embodiment of the present invention;
fig. 4 is a schematic diagram of the main modules of a mesh point addressing apparatus according to an embodiment of the present invention;
FIG. 5 is an exemplary system architecture diagram in which embodiments of the present invention may be employed;
fig. 6 is a schematic block diagram of a computer system suitable for use in implementing a terminal device or server of an embodiment of the invention.
Detailed Description
Exemplary embodiments of the present invention are described below with reference to the accompanying drawings, in which various details of embodiments of the invention are included to assist understanding, and which are to be considered as merely exemplary. 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 invention. Also, descriptions of well-known functions and constructions are omitted in the following description for clarity and conciseness.
Fig. 1 is a schematic diagram of a main flow of a mesh point addressing method according to a first embodiment of the present invention, as shown in fig. 1, the mesh point addressing method includes:
step S101, based on the map data, all the spatial index points are calculated to obtain a map spatial index list. And acquiring the attribute information, and calculating the spatial index code corresponding to the attribute information according to the longitude and latitude data corresponding to the attribute information. And merging the spatial index codes corresponding to the attribute information into a map spatial index list. And acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information.
Wherein each spatial index code in the spatial index list represents a block of map locations.
In embodiments, the spatial index may be a geohash, a B-tree based indexing method, an R-tree based indexing method, and so on. The geohash is an address coding method, and can code two-dimensional space longitude and latitude data into a character string.
In some embodiments, the attribute information includes demographic data, POI count, business data, and the like. Further, the geohash code is sorted by the correlation coefficient based on the number of POIs (i.e. the target attribute information may be the number of POIs).
As a specific embodiment, after calculating all the spatial index points based on the map data, a deduplication process is required, and then a map spatial index list is generated. The map data is the existing mesh point data, and the position without the mesh point data is not within the calculation range.
And then, according to longitude and latitude data corresponding to attribute information such as population data, POI number, operation data and the like, calculating a spatial index code corresponding to the attribute information, and further combining the spatial index code corresponding to the attribute information into a data column behind a map spatial index list.
And carrying out spatial index coding of the same level on the existing better-operating network points (with longitude and latitude data), and merging the spatial index coding into a combined map spatial index list to be used as a target value to be predicted by a machine learning model.
And S102, obtaining address information of the recommended website through a preset machine learning model according to the merged map space index list.
In some embodiments, the specific implementation process of step S102 may include: and obtaining the dot information (such as dot quantity) of the position corresponding to the spatial index code through a preset machine learning model according to the merged map spatial index list. And calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
In a further embodiment, after obtaining, according to the merged map spatial index list, mesh point information of a position corresponding to a spatial index code through a preset machine learning model, the method includes:
and calculating the correlation coefficient of the mesh point information of the position corresponding to each row and the space index code according to the merged map space index list. And sequencing the correlation coefficients of the spatial index codes based on preset target attribute information. And acquiring a preset number of spatial index codes, calculating the similarity between a target region (a region without the dots) and the position corresponding to the spatial index code, and recommending the dot information of the position corresponding to the spatial index code with the highest similarity.
Preferably, a correlation coefficient between each column (each column in the geohash list represents different attribute information) and the mesh point information of the corresponding position of the geohash code is calculated by a covariance formula as follows:
wherein Xi is a certain column of attribute information, and Yi is mesh point information (i.e. mesh point number) of a position corresponding to the geohash code.
After the correlation coefficient is calculated, ranking the number attributes of the POIs (geographical location interest points) in the geohash list, wherein the ranking shows that the relevance of different types of geographical location interest points and network point information is high or low: for example:
210207 bath and massage service
200103 mansion/office building
210216 real estate agency service
160100, education
130403, newspapers and periodicals retail
130201 for retailing cake and bread
210205 cosmetic/body-building
150102,ATM
150101, Bank
Therefore, the more important attributes can be selected as the basis for predicting whether the region should be constructed with the network points or not.
It is further worth to be noted that, according to the merged map space index list, mesh point information of a position corresponding to the geohash code is obtained through a preset gradient boosting algorithm (e.g., catboost) or a boosted tree (e.g., XBGoost) model.
The catboost algorithm library is a gradient boosting algorithm library capable of processing class-type features. XBGoos is a lifting tree model.
In addition, the similarity of the target area and the position corresponding to the geohash code can be calculated through cosine similarity or a neural network model. Preferably, the neural network may be a deep fm model, FNN, PNN, or the like. Preferably, the present invention may employ the deep fm model. The deep FM algorithm combines the advantages of a factorization machine and a neural network in feature learning, and simultaneously extracts low-order combined features and high-order combined features.
In summary, the site selection method of the invention creatively uses the method of selecting the site at the bank site by combining the geographical position index and the model, and meanwhile, the site selection can be well calculated by batch processing in the background, the temporary calculation is not needed when the user inputs the site, the user experience is improved, and the output result is clear and easy to understand. In addition, the invention uses a recommendation system algorithm, so that the system can directly calculate the position of the website to be built according to the existing data without the need of selecting the position by a user and repeatedly comparing, and the output result is very visual.
Fig. 2 is a schematic diagram of a main flow of a mesh point addressing method according to a second embodiment of the present invention, as shown in fig. 2, the mesh point addressing method includes:
step S201, based on the map data, calculating all the geohash points to obtain a map geohash list.
Wherein each geohash code in the geohash list represents a block of map locations.
Step S202, acquiring the attribute information, and calculating a geohash code corresponding to the attribute information according to the longitude and latitude data corresponding to the attribute information.
Step S203, the geohash code corresponding to the attribute information is merged into the map geohash list.
Step S204, acquiring longitude and latitude data of the existing target network points, and performing geohash coding to merge the data into a map geohash list merged with the geohash codes corresponding to the attribute information.
And S205, obtaining the network point information of the position corresponding to the geohash code through a preset machine learning model according to the merged map geohash list.
And S206, calculating the similarity between the target area and the position corresponding to the geohash code, and recommending the mesh point information of the position corresponding to the geohash code with the highest similarity.
Fig. 3 is a schematic diagram of a main flow of a mesh point addressing method according to a third embodiment of the present invention, as shown in fig. 3, the mesh point addressing method includes:
step S301, calculating all the geohash points based on the map data to obtain a map geohash list.
Wherein each geohash code in the geohash list represents a block of map locations.
Step S302, acquiring the attribute information, and calculating a geohash code corresponding to the attribute information according to the longitude and latitude data corresponding to the attribute information.
Wherein the attribute information comprises population data, POI number and business data.
Step S303, merging the geohash code corresponding to the attribute information into the map geohash list.
Step S304, acquiring longitude and latitude data of the existing target network points, and performing geohash coding to merge the data into a map geohash list merged with the geohash codes corresponding to the attribute information.
And step S305, obtaining the mesh point information of the position corresponding to the geohash code through a preset catboost or XBoost model according to the merged map geohash list.
Step S306, calculating the correlation coefficient of the network point information of each column and the position corresponding to the geohash code according to the merged map geohash list.
Step S307, based on the preset target attribute information, sorting the geohash codes according to the correlation coefficient.
Wherein the geohash encoding is sorted by correlation coefficient based on the number of POIs.
Step S308, acquiring a preset number of geohash codes, and calculating the similarity between the target area and the position corresponding to the geohash codes through cosine similarity or a DeepFM model.
And step S309, recommending the network point information of the position corresponding to the geohash code with the highest similarity.
Fig. 4 is a schematic diagram of main modules of a mesh point addressing device according to an embodiment of the present invention, and as shown in fig. 4, the mesh point addressing device 400 includes an obtaining module 401 and a processing module 402. The obtaining module 401 calculates all spatial index points based on the map data to obtain a map spatial index list; wherein each spatial index code in the spatial index list represents a block of map locations; acquiring attribute information, and calculating a spatial index code corresponding to the attribute information according to longitude and latitude data corresponding to the attribute information; merging the spatial index codes corresponding to the attribute information into a map spatial index list; and acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information. The processing module 402 obtains address information of recommended websites through a preset machine learning model according to the merged map space index list.
Preferably, the attribute information includes demographic data, geographic location interest point number and business data.
In some embodiments, the processing module 402 obtains, according to the merged map space index list, address information of a recommended website through a preset machine learning model, including:
according to the merged map spatial index list, obtaining network point information of a position corresponding to a spatial index code through a preset machine learning model; and calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
In a further embodiment, after the processing module 402 obtains, according to the merged map spatial index list, mesh point information of a position corresponding to a spatial index code through a preset machine learning model, the method includes:
calculating the correlation coefficient of the dot information of each row and the position corresponding to the space index code according to the merged map space index list; sorting the correlation coefficients of the spatial index codes based on preset target attribute information; and acquiring a preset number of spatial index codes, calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
In a preferred embodiment, the spatial index coding is sorted by correlation coefficient based on the number of POIs.
As another embodiment, the processing module 402 obtains, according to the merged map spatial index list, mesh point information of a position corresponding to the spatial index code by using a preset gradient lifting algorithm or a lifting tree model.
In still other embodiments, the processing module 402 calculates the similarity between the target region and the corresponding position of the spatial index code through cosine similarity or a neural network model.
It should be noted that the site selection method and the site selection device of the present invention have a corresponding relationship in the specific implementation content, so the repeated content is not described again.
Fig. 5 illustrates an exemplary system architecture 500 of a mesh point addressing method or device to which embodiments of the invention may be applied.
As shown in fig. 5, the system architecture 500 may include terminal devices 501, 502, 503, a network 504, and a server 505. The network 504 serves to provide a medium for communication links between the terminal devices 501, 502, 503 and the server 505. Network 504 may include various connection types, such as wired, wireless communication links, or fiber optic cables, to name a few.
The user may use the terminal devices 501, 502, 503 to interact with a server 505 over a network 504 to receive or send messages or the like. The terminal devices 501, 502, 503 may have installed thereon various communication client applications, such as shopping-like applications, web browser applications, search-like applications, instant messaging tools, mailbox clients, social platform software, etc. (by way of example only).
The terminal devices 501, 502, 503 may be various electronic devices having a web site screen and supporting web browsing, including but not limited to smart phones, tablet computers, laptop portable computers, desktop computers, and the like.
The server 505 may be a server providing various services, such as a background management server (for example only) providing support for shopping websites browsed by users using the terminal devices 501, 502, 503. The backend management server may analyze and perform other processing on the received data such as the product information query request, and feed back a processing result (for example, target push information, product information — just an example) to the terminal device.
It should be noted that the site selection method provided by the embodiment of the present invention is generally executed by the server 505, and accordingly, the computing device is generally disposed in the server 505.
It should be understood that the number of terminal devices, networks, and servers in fig. 5 is merely illustrative. There may be any number of terminal devices, networks, and servers, as desired for implementation.
Referring now to FIG. 6, a block diagram of a computer system 600 suitable for use with a terminal device implementing an embodiment of the invention is shown. The terminal device shown in fig. 6 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 6, the computer system 600 includes a Central Processing Unit (CPU)601 that can perform various appropriate actions and processes according to a program stored in a Read Only Memory (ROM)602 or a program loaded from a storage section 608 into a Random Access Memory (RAM) 603. In the RAM603, various programs and data necessary for the operation of the computer system 600 are also stored. The CPU601, ROM602, and RAM603 are connected to each other via a bus 604. An input/output (I/O) interface 605 is also connected to bus 604.
The following components are connected to the I/O interface 605: an input portion 606 including a keyboard, a mouse, and the like; an output section 607 including a display such as a Cathode Ray Tube (CRT), a liquid crystal dot site selector (LCD), and the like, and a speaker and the like; a storage section 608 including a hard disk and the like; and a communication section 609 including a network interface card such as a LAN card, a modem, or the like. The communication section 609 performs communication processing via a network such as the internet. The driver 610 is also connected to the I/O interface 605 as needed. A removable medium 611 such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, or the like is mounted on the drive 610 as necessary, so that a computer program read out therefrom is mounted in the storage section 608 as necessary.
In particular, according to the embodiments of the present disclosure, the processes described above with reference to the flowcharts may be implemented as computer software programs. For example, embodiments of the present disclosure include a computer program product comprising a computer program embodied on a computer readable medium, the computer program comprising program code for performing the method illustrated in the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication section 609, and/or installed from the removable medium 611. The computer program performs the above-described functions defined in the system of the present invention when executed by the Central Processing Unit (CPU) 601.
It should be noted that the computer readable medium shown in the present invention can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, 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), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present invention, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In the present invention, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: wireless, wire, fiber optic cable, RF, etc., or any suitable combination of the foregoing.
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 invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, 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 or flowchart illustration, and combinations of blocks in the block diagrams 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.
The modules described in the embodiments of the present invention may be implemented by software or hardware. The described modules may also be provided in a processor, which may be described as: a processor includes an acquisition module and a processing module. Wherein the names of the modules do not in some cases constitute a limitation of the module itself.
As another aspect, the present invention also provides a computer-readable medium that may be contained in the apparatus described in the above embodiments; or may be separate and not incorporated into the device. The computer readable medium carries one or more programs which, when executed by a device, cause the device to include obtaining a map space index list based on map data; acquiring attribute information, calculating a spatial index code corresponding to the attribute information, and combining the spatial index code into a geospatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information; and obtaining address information of the recommended network points through a preset machine learning model according to the merged map space index list.
According to the technical scheme of the embodiment of the invention, the problem that the more appropriate address selection position can be obtained only by comparing each position selected by the user in the prior art and the address selection position cannot be directly and quickly informed under the condition that the user does not select the address can be solved.
The above-described embodiments should not be construed as limiting the scope of the invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions can occur, depending on design requirements and other factors. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (10)
1. A method for site selection of a mesh point, comprising:
calculating all spatial index points based on the map data to obtain a map spatial index list; wherein each spatial index code in the spatial index list represents a block of map locations;
acquiring attribute information, and calculating a spatial index code corresponding to the attribute information according to longitude and latitude data corresponding to the attribute information; merging the spatial index codes corresponding to the attribute information into a map spatial index list;
acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information;
and obtaining the address information of the recommended website through a preset machine learning model according to the merged map space index table.
2. The method according to claim 1, wherein obtaining recommended website address information through a preset machine learning model according to the merged map space index list comprises:
according to the merged map spatial index list, obtaining network point information of a position corresponding to a spatial index code through a preset machine learning model;
and calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
3. The method as claimed in claim 2, wherein after obtaining the website information of the position corresponding to the spatial index code through a preset machine learning model according to the merged map spatial index list, the method includes:
calculating the correlation coefficient of the dot information of each row and the position corresponding to the space index code according to the merged map space index list;
sorting the correlation coefficients of the spatial index codes based on preset target attribute information;
and acquiring a preset number of spatial index codes, calculating the similarity between the target area and the position corresponding to the spatial index code, and recommending the website information of the position corresponding to the spatial index code with the highest similarity.
4. The method of claim 3, comprising:
the spatial index coding is sorted by relevance coefficient based on the number of geographic location interest points.
5. The method of claim 2, comprising:
and obtaining the dot information of the position corresponding to the spatial index code through a preset gradient lifting algorithm or a lifting tree model according to the merged map spatial index list.
6. The method of claim 2, wherein calculating the similarity between the target region and the corresponding position of the spatial index code comprises:
and calculating the similarity of the target area and the position corresponding to the spatial index code through cosine similarity or a neural network model.
7. The method according to any one of claims 1-6, comprising:
the attribute information comprises population data, the number of interest points of the geographic position and management data.
8. A mesh point addressing apparatus, comprising:
the acquisition module is used for calculating all the spatial index points based on the map data to obtain a map spatial index list; wherein each spatial index code in the spatial index list represents a block of map locations; acquiring attribute information, and calculating a spatial index code corresponding to the attribute information according to longitude and latitude data corresponding to the attribute information; merging the spatial index codes corresponding to the attribute information into a map spatial index list; acquiring longitude and latitude data of the existing target network points, and performing spatial index coding to merge the data into a map spatial index list combined with the spatial index coding corresponding to the attribute information;
and the processing module is used for obtaining the address information of the recommended website through a preset machine learning model according to the merged map space index list.
9. An electronic device, comprising:
one or more processors;
a storage device for storing one or more programs,
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-7.
10. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-7.
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Cited By (7)
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| CN113128773A (en) * | 2021-04-23 | 2021-07-16 | 中国工商银行股份有限公司 | Training method of address prediction model, address prediction method and device |
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| CN113723882A (en) * | 2021-08-26 | 2021-11-30 | 拉扎斯网络科技（上海）有限公司 | Relay cabinet site selection method, order distribution method and device and electronic equipment |
| CN113792107A (en) * | 2021-09-15 | 2021-12-14 | 北京沃东天骏信息技术有限公司 | Region identification method and device |
| CN114911787A (en) * | 2022-05-31 | 2022-08-16 | 南京大学 | Multi-source POI data cleaning method fusing position and semantic constraints |
| WO2023174095A1 (en) * | 2022-03-14 | 2023-09-21 | 青岛海尔空调器有限总公司 | Control method and apparatus for parking air conditioner, parking air conditioner and storage medium |
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