CN112884224A - Method and device for selecting address of entity object, computing equipment and computer storage medium - Google Patents
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Abstract
The invention discloses a method and a device for selecting addresses of entity objects, a computing device and a computer storage medium, wherein the method comprises the following steps: carrying out data layering processing on the acquired environmental service index data from multiple dimensions; responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area; determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user; acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object; and presenting the scores and the position information of various types of evaluation index data of each candidate entity object. The invention realizes a general address selection comparison mode.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a method and a device for selecting an address of an entity object, computing equipment and a computer storage medium.
Background
As the times develop, people's daily needs become more and more abundant, and physical stores meeting the needs, such as supermarkets, malls and automobile stores, are generated. Meanwhile, different sites of the physical stores face different coverage groups, surrounding environments and the like, for off-line stores, the physical stores are the most important contacts facing consumers, and the sales volume of the physical stores is directly determined by the quality of the sites, so that the sites have extremely important significance.
In the prior art, site selection strategies are all based on the experience of experts in the field, site selection personnel evaluate and acquire regional data of candidate stores, data quality evaluation is performed through short-time and small-range data acquisition, approximate peripheral passenger flow and competitive pair distribution dimensional data are estimated according to manually acquired data, finally, comprehensive evaluation is performed on the candidate stores by the experts in the field according to the existing experience, and store site selection is confirmed.
However, the inventor finds that the prior art has at least the following defects in the process of implementing the invention: firstly, due to the limitation of factors such as funding and time, site selection personnel need to examine and research on the spot for a long time, and the cost is high and the time consumption is long; secondly, large-scale investigation, investigation and comparative analysis are difficult to achieve; thirdly, the expert experience has regional applicability, the existing experience cannot be directly applied to a new site selection region, cross-regional site selection cannot be performed, and the existing data is difficult to utilize; fourthly, according to the experience of domain experts, the influence of the difference factors on site selection cannot be scientifically and effectively evaluated, and the site selection accuracy is finally influenced.
Disclosure of Invention
In view of the above, the present invention has been made to provide a method, an apparatus, a computing device and a computer storage medium for addressing physical objects that overcome or at least partially solve the above problems.
According to an aspect of the present invention, there is provided a method for addressing an entity object, including:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user;
acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
According to another aspect of the present invention, there is provided an addressing apparatus for a physical object, including:
the hierarchical processing module is suitable for acquiring the environmental service index data and performing data hierarchical processing on the environmental service index data from multiple dimensions;
the positioning module is suitable for responding to an entity object addressing request which is initiated by a user and carries position information, positioning a target area through a GIS system and presenting the target area so that the user can select an addressing area in the target area;
the data analysis module is suitable for determining a candidate site selection area according to the entity object site selection demand information, the site selection area and the environment service index data after hierarchical processing, which are provided by the user;
the data calculation module is suitable for acquiring various types of evaluation index data of each candidate entity object in the candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and the data visualization module is suitable for presenting the scores and the position information of various evaluation index data of each candidate entity object.
According to yet another aspect of the present invention, there is provided a computing device comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is configured to store at least one executable instruction that causes the processor to:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user;
acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
According to yet another aspect of the present invention, there is provided a computer storage medium having at least one executable instruction stored therein, the executable instruction causing a processor to:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user;
acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
The method, the device, the computing equipment and the computer storage medium for addressing the entity object comprise the following steps: acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions; responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area; determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user; acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object; and presenting the scores and the position information of various types of evaluation index data of each candidate entity object. According to the method, the candidate site selection area matching the user site selection requirement and the user site selection range is determined according to the environmental service index data, then each entity store in the candidate site selection area is scored based on the index data, and finally the scoring condition is presented to the user.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
Various other advantages and benefits will become apparent to those of ordinary skill in the art upon reading the following detailed description of the preferred embodiments. The drawings are only for purposes of illustrating the preferred embodiments and are not to be construed as limiting the invention. Also, like reference numerals are used to refer to like parts throughout the drawings. In the drawings:
fig. 1 is a flowchart illustrating an address selecting method for an entity object according to an embodiment of the present invention;
FIG. 2 is a flow chart illustrating a method for addressing an entity object according to another embodiment of the present invention;
FIG. 3 is a schematic diagram illustrating an addressing interface provided by another embodiment of the present invention;
FIG. 4 is an interaction diagram illustrating a method for addressing an entity object according to another embodiment of the present invention;
FIG. 5 illustrates a flow chart of model calculations provided by yet another embodiment of the present invention;
fig. 6 is a schematic structural diagram illustrating an apparatus for addressing an entity object according to another embodiment of the present invention;
fig. 7 is a schematic structural diagram of a computing device provided by an embodiment of the present invention.
Detailed Description
Exemplary embodiments of the present invention will be described in more detail below with reference to the accompanying drawings. While exemplary embodiments of the invention are shown in the drawings, it should be understood that the invention can be embodied in various forms and should not be limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art.
Fig. 1 shows a flowchart of an addressing method for an entity object according to an embodiment of the present invention, which can be executed by any computing device with data processing capability, as shown in fig. 1, and includes the following steps:
step S110, acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions.
The step realizes the acquisition and layering of big data. The environmental service index data refers to an index of an entity business environment, that is, an environmental index of an entity store, where the entity business environment includes: market environment, brand environment, community environment, office building environment, community and office building surrounding environment.
The business index data of the market environment comprises: market name information, market alias information, local area information, market information point identification (market PoiID), market reference coordinate information (market WKT), market address information, market area information, entrance brand information, stock control company information, and establishment time information.
The business index data of the brand environment includes: brand name information, brand alias information, business status major information, business status minor information, customer order information, business method information, business company information, creation time information, required area information, brand summary information, and property requirement information.
The service index data of the cell environment comprises: the system comprises cell name information, cell information point identification (cell PoiID), cell reference coordinate information (market WKT), cell address information, resident population quantity information, cell house price information, cell grade information and cell longitude and latitude information.
The business index data of the office building environment comprises: office building name information, office building information point identification (office building PoiID), office building reference coordinate information (office building WKT), office building address information, staff number information, office building average wage information, office building grade information and office building longitude and latitude information.
The service index data of the surrounding environment of the community and the office building comprises the following data: peripheral brand information and peripheral market information.
Wherein, the data layering processing refers to: the data collected under the same condition with the same property are summarized together for comparative analysis, thereby obtaining objective and scientific analysis results. The data layering can reduce the influence of factors such as different time, different brands, different addresses and different areas on the analysis result in the environment service index. In this embodiment, data hierarchical processing is performed on the collected environmental service index number through multiple dimensions, specifically, the multiple dimensions include: a time dimension, a brand dimension, an address dimension, and an area dimension. For example, environmental traffic indicator data that matches all of time, brand address, and area is partitioned into a data set.
And step S120, responding to an entity object addressing request which is initiated by a user and carries position information, positioning a target area through a GIS system and presenting the target area so that the user can select an addressing area in the target area.
A user inputs an address on an address selection interface, initiates an entity object address selection request carrying position information, automatically positions a target area based on a visual GIS system, and presents the target area to the user so that the user can select the address area in the inner circle of the target area.
Step S130, determining candidate address selection areas according to the entity object address selection demand information, the address selection areas and the environment service index data after the layering processing provided by the user.
Likewise, the user may input addressing requirements in the addressing interface, for example, the addressing requirements may include: peripheral house price, peripheral office building wages, store rent and commodity number. After the address selection requirement provided by the user and the address selection area selected by the user are obtained, the address selection requirement and the address selection area are matched with the environment service index data after the hierarchical processing, and the candidate address selection area is determined according to the matching result, namely, the candidate address selection area is the area which is in the address selection area selected by the user and matched according with the user address selection requirement according to the big data.
Step S140, obtaining various types of evaluation index data of each candidate entity object in the candidate address region, inputting each type of evaluation index data of each candidate entity object into a corresponding prediction model, and predicting to obtain a score of the type of evaluation index data of the candidate entity object.
Various types of evaluation index data of each candidate entity object (namely, candidate store) in the candidate address selection area are obtained, and the score of each type of evaluation data index of each candidate entity object is predicted by using the constructed data model.
And step S150, presenting scores and position information of various evaluation index data of each candidate entity object.
And displaying the scores of various evaluation index data of each candidate entity object and the position information of each candidate entity object on an addressing interface so that a user can decide an addressing result by referring to the displayed information.
Fig. 3 shows a schematic diagram of an addressing interface according to another embodiment of the present invention, as shown in fig. 3, a user may submit an addressing request by clicking an input box or a control in the addressing interface, and click "start model calculation" to start the method according to the embodiment of the present invention to calculate scores of various evaluation indexes of each candidate entity object.
According to the address selection method of the entity object in the embodiment, environment service index data is obtained, and data layering processing is carried out on the environment service index data from multiple dimensions; responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area; determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user; acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object; and presenting the scores and the position information of various types of evaluation index data of each candidate entity object. According to the method, the candidate site selection area matching the user site selection requirement and the user site selection range is determined according to the environmental service index data, then each entity store in the candidate site selection area is scored based on the index data, and finally the scoring condition is presented to the user.
Fig. 2 is a flow chart illustrating a method for addressing an entity object according to another embodiment of the present invention, which can be performed by any computing device having data processing capabilities. As shown in fig. 2, the method comprises the steps of:
step S210, acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions.
The meaning of the environmental service index data refers to the description in the above embodiments, and is not described herein again. Optionally, after the environmental service index data is obtained, the environmental service index data is preprocessed, where the preprocessing includes data cleaning processing, data integration processing, and data conversion processing. The data cleaning process refers to removing invalid data, for example, a regression analysis and a decision tree method are used to infer the maximum possible value of a specific attribute of a record, and the missing value is completed. The data integration processing refers to merging data of a plurality of data sources to form a complete data set. The data conversion process refers to standardizing the data format.
Then, performing data layering processing on the preprocessed environmental service index data, wherein the data layering processing refers to: the data collected under the same condition with the same property are summarized together for comparative analysis, thereby obtaining objective and scientific analysis results. Optionally, the hierarchical processing is performed according to the requirements of the entity objects of different industries or different brands, which is equivalent to summarizing the environmental service index data of each industry together, or summarizing the environmental service index data of each brand together.
Step S220, collecting sample data of various evaluation indexes, scoring the sample data of the evaluation indexes aiming at the sample data of each evaluation index, and constructing a prediction model according to the sample data of the evaluation indexes and corresponding scores.
The evaluation indexes comprise the following various indexes: competition type indexes, geographical type indexes, passenger group type indexes and passenger flow type indexes.
The geo-category metrics include the following sub-category evaluation metrics: the system comprises a store opening budget index, a store opening area index, a store rent index, a store area index and a traffic road condition index;
the guest group index includes the following sub-classification evaluation indexes: residential community population index, office building population index, high consumption capacity index, low consumption capacity index and guest group unit price index;
the passenger flow class index comprises the following subcategory evaluation indexes: population total index, population density index and resident population quantity index.
And obtaining sample data of various evaluation indexes of the sample entity object, scoring the sample data of each evaluation index, and constructing a multiple linear regression model according to the sample data of the evaluation indexes and corresponding scores to obtain a prediction model corresponding to each evaluation index.
In an optional mode, original sample data is collected, a clustering algorithm is used for analyzing similarity and difference among the original sample data, and classification is performed according to an analysis result to obtain sample data of various evaluation indexes. Wherein, the original sample data comprises: macroscopic economic data, project periphery matching data, project passenger flow volume data, project passenger group portrait data and industry key index data.
Step S230, in response to an entity object location request carrying location information initiated by a user, positioning and presenting a target area through a GIS system, so that the user can select a location area in the target area.
The user inputs an address on an address selection interface, initiates an entity object address selection request carrying position information, automatically positions a central point of a target area containing the address corresponding to the position information based on a visual GIS system, and presents the target area to the user so that the user can select the address selection area in the target area, and the user can select the address selection area in a geo-fencing drawing mode.
Step S240, determining a candidate address selection area according to the entity object address selection requirement information, the address selection area and the environment service index data after the hierarchical processing provided by the user.
For example, the entity object addressing requirement information includes: peripheral house price information, peripheral office building wage information, store rent information, the number of commodities and the like. After the address selection requirement provided by the user and the address selection area selected by the user are obtained, the address selection requirement and the address selection area are matched with the environment service index data after the hierarchical processing, and the candidate address selection area is determined according to the matching result, namely, the candidate address selection area is the area which is in the address selection area selected by the user and matched according with the user address selection requirement according to the big data.
In an alternative embodiment, the candidate addressing areas are determined in response to a selection operation by the user. In the method, a user can accurately position an intended addressing area through operation, and the candidate addressing area does not need to be matched in a big data mode.
Step S250, obtaining various types of evaluation index data of each candidate entity object in the candidate address region, inputting each type of evaluation index data of each candidate entity object into a corresponding prediction model, and predicting to obtain a score of the type of evaluation index data of the candidate entity object.
The method comprises the steps of obtaining various types of evaluation index data of various candidate entity objects (namely candidate stores) in a candidate address selection area, inputting the types of evaluation index data of the candidate entity objects into a corresponding multi-linear regression model which is constructed in advance for processing aiming at the various types of evaluation index data of the candidate entity objects, and outputting scores of the types of evaluation index data of the candidate entity objects through the multi-linear regression model.
Specifically, after the candidate addressing area is determined, according to the reference coordinates of the candidate addressing area, each candidate entity object in the candidate area is determined by using the reference coordinates, and various evaluation index data of each candidate entity object are obtained.
Correspondingly, the method comprises the steps of firstly collecting original index data of each candidate entity object, analyzing the similarity and difference between the original index data, and classifying according to the analysis result to obtain various evaluation index data.
Step S260, for each candidate entity object, normalizing the scores of the various types of evaluation index data of the candidate entity object.
Specifically, for each candidate entity object, the scores of the various types of evaluation index data are normalized by dispersion normalization.
Fig. 4 shows a flowchart of model calculation according to another embodiment of the present invention, and as shown in fig. 4, the model calculation mainly includes the following steps:
step S410, classifying original sample data; step S420, a mathematical model is constructed, namely a prediction model is constructed according to sample data; step S430, checking whether the mathematical model meets the significance condition, if so, executing step S440, otherwise, skipping to execute step S420 until the significance condition is met; step S440, if the significance condition is met, the training of the prediction model is finished, and the scores of various evaluation index data of the candidate entity object are calculated by using the prediction model in the subsequent process; step S450, normalize the scores of the various types of evaluation index data.
Step S270, calculating the site selection score of the candidate entity object according to the normalized score of various evaluation index data of the candidate entity object and the weight value of the corresponding evaluation index, and presenting the site selection score and the position information of the candidate entity object.
And calculating the scores of various evaluation index data of the candidate entity object and the weight values corresponding to various evaluation indexes to obtain the final site selection score of the candidate entity object. And presenting the site selection scores and the position information of the candidate entity objects to the user so that the user can decide the site selection result by referring to the presented information.
Optionally, for each type of evaluation index, the weight value of the type of evaluation index is determined according to the average value of the type of evaluation index data. And/or determining the weighted values of various evaluation indexes according to user requirements such as store positioning, the industry to which the evaluation indexes belong, store opening budget, store area, passenger order range and the like, presetting the numerical relationship between each factor and the weighted values, and determining the weighted values of various evaluation index data based on the requirements provided by the user and the preset numerical relationship. For example, a user needs to locate a cosmetic brand, the user needs to stay in a certain market, an average value is calculated according to the unit price information of a guest group of the same-price brand in the cosmetic industry, the average value is calculated according to the guest unit price information of the same-price brand in the cosmetic industry, the weight value of a guest group index is further determined according to the average value, the average value is calculated according to the shop floor rent of the same-class market container cosmetic brand, the average value is calculated according to the area of the same-class market cosmetic brand, and the weight value of a geographic index is further determined according to the average value. In the subsequent process, the weighted values of various evaluation indexes can be adjusted according to the user requirements, so that the site selection score of the candidate entity object is corrected.
In an alternative mode, the address scores of the candidate entity objects and the position information thereof are presented according to the high-low order of the address scores of the candidate entity objects. Namely, the site selection scores and the position information of each candidate entity are presented on the site selection interface according to the height of the site selection scores of the candidate entities.
It can be seen that the method of the present embodiment can be roughly divided into the following steps: (1) big data acquisition and cleaning; (2) carrying out layered processing on the acquired data from entity requirements of different dimensions; (3) extracting various evaluation index data of the candidate entity object; (4) calculating scores of various evaluation indexes according to various evaluation index data; (5) setting the score weighted values of various evaluation indexes; (6) the ranking of the candidate entity objects is presented.
Fig. 5 is an interaction diagram illustrating an address selecting method for an entity object according to another embodiment of the present invention, and as shown in fig. 5, the main process includes: 1. a user submits an address selection range to screen a store, and when the user newly adds an address, specific position information of the address selection needs to be provided; 2. the environmental service index data is processed in a layered mode; 3. creating a geofence; 4. calling a data rear-end interface to generate various evaluation index data; 5. calculating scores of various evaluation indexes through a prediction model; 6. setting the weight values of various evaluation indexes; 7. calculating an overall score of the candidate region based on the weight values; 8. and returning the overall scores of the candidate regions to the user.
According to the method for selecting the address of the entity object provided by the embodiment, the visual online address selection of the store is realized by using big data in combination with a visual GIS (geographic information system), so that the zero-store address selection cost is reduced; the general equation of site selection is obtained by analyzing a large number of data samples, the general equation has general applicability, can be used for site selection of various stores, and once the site selection equation is obtained, rapid site selection can be carried out; various objective factors influencing stores are fully considered, the weight of the influence of each objective factor is calculated, and the site selection scoring result is more objective.
Fig. 6 is a schematic structural diagram of an apparatus for addressing an entity object according to another embodiment of the present invention, and as shown in fig. 6, the apparatus includes the following modules:
the hierarchical processing module 61 is suitable for acquiring the environmental service index data and performing data hierarchical processing on the environmental service index data from multiple dimensions;
the positioning module 62 is adapted to respond to an entity object addressing request carrying position information initiated by a user, position and present a target area through a GIS system, so that the user can select an addressing area in the target area;
the data analysis module 63 is adapted to determine a candidate address selection area according to the entity object address selection demand information, the address selection area and the environment service index data after hierarchical processing provided by the user;
the data calculation module 64 is adapted to obtain various types of evaluation index data of each candidate entity object in the candidate address region, and input each type of evaluation index data of each candidate entity object into a corresponding prediction model to predict the score of the type of evaluation index data of the candidate entity object;
and a data visualization module 65 adapted to present the scores of the various types of evaluation index data of the respective candidate entity objects and the position information thereof.
Optionally, the data calculation module 64 is further adapted to: calculating the site selection score of the candidate entity object according to the scores of various evaluation index data of the candidate entity object and the weight values of the corresponding evaluation indexes;
the data visualization module 65 is further adapted to: and presenting the address selection scores and the position information of the candidate entity objects.
Optionally, the data visualization module 65 is further adapted to: and presenting the site selection scores and the position information of the candidate entity objects according to the high-low order of the site selection scores of the candidate entity objects.
Optionally, the device further comprises a model calculation module adapted to collect sample data of various evaluation indexes;
and marking the sample data of each type of evaluation index according to the sample data of each type of evaluation index, and constructing a prediction model according to the sample data of each type of evaluation index and the corresponding score.
Optionally, each category of evaluation index includes a plurality of sub-category evaluation indexes, and the prediction model is a multiple linear regression model.
Optionally, the various types of evaluation indicators include: competition indexes, geographical indexes, passenger group indexes and passenger flow indexes;
the competition class index comprises the following subcategory evaluation indexes: an industry number index and an address selection area index;
the geo-category metrics include the following sub-category evaluation metrics: the system comprises a store opening budget index, a store opening area index, a store rent index, a store area index and a traffic road condition index;
the guest group index includes the following sub-classification evaluation indexes: residential community population index, office building population index, high consumption capacity index, low consumption capacity index and guest group unit price index;
the passenger flow class index comprises the following subcategory evaluation indexes: population total index, population density index and resident population quantity index.
Optionally, the model calculation module is further adapted to: and aiming at each candidate entity object, carrying out normalization processing on the scores of various types of evaluation index data of the candidate entity object.
Therefore, in the mode, the visual online site selection of the stores is realized by combining big data with a visual GIS system, so that the site selection cost of the zero stores is reduced; the general equation of site selection is obtained by analyzing a large number of data samples, the general equation has general applicability, can be used for site selection of various stores, and once the site selection equation is obtained, rapid site selection can be carried out; various objective factors influencing stores are fully considered, the weight of the influence of each objective factor is calculated, and the site selection scoring result is more objective.
The embodiment of the invention provides a nonvolatile computer storage medium, wherein the computer storage medium stores at least one executable instruction, and the computer executable instruction can execute the address selection method of the entity object in any method embodiment.
The executable instructions may be specifically configured to cause the processor to:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user;
acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
In an alternative, the executable instructions cause the processor to:
and calculating the site selection score of the candidate entity object according to the scores of various evaluation index data of the candidate entity object and the weight values of the corresponding evaluation indexes, and presenting the site selection score and the position information of the candidate entity object.
In an alternative, the executable instructions cause the processor to:
and presenting the site selection scores and the position information of the candidate entity objects according to the high-low order of the site selection scores of the candidate entity objects.
In an alternative, the executable instructions cause the processor to:
collecting sample data of various evaluation indexes;
and marking the sample data of each type of evaluation index according to the sample data of each type of evaluation index, and constructing a prediction model according to the sample data of each type of evaluation index and the corresponding score.
In an alternative mode, each type of evaluation index includes a plurality of sub-classification evaluation indexes, and the prediction model is a multiple linear regression model.
In an alternative manner, the various types of evaluation metrics include: competition indexes, geographical indexes, passenger group indexes and passenger flow indexes;
the competition class index comprises the following subcategory evaluation indexes: an industry number index and an address selection area index;
the geo-category metrics include the following sub-category evaluation metrics: the system comprises a store opening budget index, a store opening area index, a store rent index, a store area index and a traffic road condition index;
the guest group index includes the following sub-classification evaluation indexes: residential community population index, office building population index, high consumption capacity index, low consumption capacity index and guest group unit price index;
the passenger flow class index comprises the following subcategory evaluation indexes: population total index, population density index and resident population quantity index.
In an alternative, the executable instructions cause the processor to:
and aiming at each candidate entity object, carrying out normalization processing on the scores of various types of evaluation index data of the candidate entity object.
Therefore, in the mode, the visual online site selection of the stores is realized by combining big data with a visual GIS system, so that the site selection cost of the zero stores is reduced; the general equation of site selection is obtained by analyzing a large number of data samples, the general equation has general applicability, can be used for site selection of various stores, and once the site selection equation is obtained, rapid site selection can be carried out; various objective factors influencing stores are fully considered, the weight of the influence of each objective factor is calculated, and the site selection scoring result is more objective.
Fig. 7 is a schematic structural diagram of an embodiment of a computing device according to the present invention, and a specific embodiment of the present invention does not limit a specific implementation of the computing device.
As shown in fig. 7, the computing device may include: a processor (processor)702, a Communications Interface 704, a memory 706, and a communication bus 708.
Wherein: the processor 702, communication interface 704, and memory 706 communicate with each other via a communication bus 708. A communication interface 704 for communicating with network elements of other devices, such as clients or other servers. The processor 702, configured to execute the program 710, may specifically perform relevant steps in the above embodiments of the method for addressing an entity object of a computing device.
In particular, the program 710 may include program code that includes computer operating instructions.
The processor 702 may be a central processing unit CPU, or an Application Specific Integrated Circuit (ASIC), or one or more Integrated circuits configured to implement an embodiment of the present invention. The computing device includes one or more processors, which may be the same type of processor, such as one or more CPUs; or may be different types of processors such as one or more CPUs and one or more ASICs.
The memory 706 stores a program 710. The memory 706 may comprise high-speed RAM memory, and may also include non-volatile memory (non-volatile memory), such as at least one disk memory.
The program 710 may specifically be used to cause the processor 702 to perform the following operations:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information, an address selection area and environment service index data after hierarchical processing, which are provided by a user;
acquiring various types of evaluation index data of each candidate entity object in a candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
In an alternative, the program 710 causes the processor 702 to:
and calculating the site selection score of the candidate entity object according to the scores of various evaluation index data of the candidate entity object and the weight values of the corresponding evaluation indexes, and presenting the site selection score and the position information of the candidate entity object.
In an alternative, the program 710 causes the processor 702 to:
and presenting the site selection scores and the position information of the candidate entity objects according to the high-low order of the site selection scores of the candidate entity objects.
In an alternative, the program 710 causes the processor 702 to:
collecting sample data of various evaluation indexes;
and marking the sample data of each type of evaluation index according to the sample data of each type of evaluation index, and constructing a prediction model according to the sample data of each type of evaluation index and the corresponding score.
In an alternative mode, each type of evaluation index includes a plurality of sub-classification evaluation indexes, and the prediction model is a multiple linear regression model.
In an alternative manner, the various types of evaluation metrics include: competition indexes, geographical indexes, passenger group indexes and passenger flow indexes;
the competition class index comprises the following subcategory evaluation indexes: an industry number index and an address selection area index;
the geo-category metrics include the following sub-category evaluation metrics: the system comprises a store opening budget index, a store opening area index, a store rent index, a store area index and a traffic road condition index;
the guest group index includes the following sub-classification evaluation indexes: residential community population index, office building population index, high consumption capacity index, low consumption capacity index and guest group unit price index;
the passenger flow class index comprises the following subcategory evaluation indexes: population total index, population density index and resident population quantity index.
In an alternative, the program 710 causes the processor 702 to: and aiming at each candidate entity object, carrying out normalization processing on the scores of various types of evaluation index data of the candidate entity object.
Therefore, in the mode, the visual online site selection of the stores is realized by combining big data with a visual GIS system, so that the site selection cost of the zero stores is reduced; the general equation of site selection is obtained by analyzing a large number of data samples, the general equation has general applicability, can be used for site selection of various stores, and once the site selection equation is obtained, rapid site selection can be carried out; various objective factors influencing stores are fully considered, the weight of the influence of each objective factor is calculated, and the site selection scoring result is more objective.
The algorithms or displays presented herein are not inherently related to any particular computer, virtual system, or other apparatus. Various general purpose systems may also be used with the teachings herein. The required structure for constructing such a system will be apparent from the description above. In addition, embodiments of the present invention are not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the embodiments of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. Those skilled in the art will appreciate that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functionality of some or all of the components according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. The word "comprising" does not exclude the presence of elements or steps not listed in a claim. The word "a" or "an" preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the unit claims enumerating several means, several of these means may be embodied by one and the same item of hardware. The usage of the words first, second and third, etcetera do not indicate any ordering. These words may be interpreted as names. The steps in the above embodiments should not be construed as limiting the order of execution unless specified otherwise.
Claims (10)
1. A method of addressing an entity object, comprising:
acquiring environmental service index data, and performing data layering processing on the environmental service index data from multiple dimensions;
responding to an entity object addressing request carrying position information initiated by a user, positioning and presenting a target area through a GIS (geographic information system) so that the user can select an addressing area in the target area;
determining a candidate address selection area according to entity object address selection demand information provided by a user, the address selection area and environment service index data after hierarchical processing;
acquiring various types of evaluation index data of each candidate entity object in the candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and presenting the scores and the position information of various types of evaluation index data of each candidate entity object.
2. The method of claim 1, wherein the method further comprises:
and calculating the site selection score of the candidate entity object according to the scores of various evaluation index data of the candidate entity object and the weight values of the corresponding evaluation indexes, and presenting the site selection score and the position information of the candidate entity object.
3. The method of claim 2, wherein the presenting the addressing score and the location information of the candidate entity object further comprises:
and presenting the site selection scores and the position information of the candidate entity objects according to the high-low order of the site selection scores of the candidate entity objects.
4. The method of claim 1, wherein the method further comprises:
collecting sample data of various evaluation indexes;
and marking the sample data of each type of evaluation index according to the sample data of each type of evaluation index, and constructing a prediction model according to the sample data of each type of evaluation index and the corresponding score.
5. The method of claim 4, wherein each class of evaluation metric comprises a plurality of sub-class evaluation metrics, and the predictive model is a multiple linear regression model.
6. The method of claim 5, wherein the types of assessment indicators comprise: competition indexes, geographical indexes, passenger group indexes and passenger flow indexes;
the competition class index comprises the following sub-class evaluation indexes: an industry number index and an address selection area index;
the geo-category indicator includes the following sub-category evaluation indicators: the system comprises a store opening budget index, a store opening area index, a store rent index, a store area index and a traffic road condition index;
the object group index comprises the following sub-classification evaluation indexes: residential community population index, office building population index, high consumption capacity index, low consumption capacity index and guest group unit price index;
the passenger flow class index comprises the following subcategory evaluation indexes: population total index, population density index and resident population quantity index.
7. The method of claim 1, wherein the method further comprises:
and aiming at each candidate entity object, carrying out normalization processing on the scores of various types of evaluation index data of the candidate entity object.
8. An apparatus for addressing a physical object, comprising:
the hierarchical processing module is suitable for acquiring environmental service index data and performing data hierarchical processing on the environmental service index data from multiple dimensions;
the positioning module is suitable for responding to an entity object addressing request which is initiated by a user and carries position information, positioning a target area through a GIS system and presenting the target area so that the user can select an addressing area in the target area;
the data analysis module is suitable for determining a candidate site selection area according to the entity object site selection demand information provided by a user, the site selection area and the environment service index data after hierarchical processing;
the data calculation module is suitable for acquiring various types of evaluation index data of each candidate entity object in the candidate address selection area, inputting the evaluation index data of each candidate entity object into a corresponding prediction model aiming at each type of evaluation index data of each candidate entity object, and predicting to obtain the score of the evaluation index data of each candidate entity object;
and the data visualization module is suitable for presenting the scores and the position information of various evaluation index data of each candidate entity object.
9. A computing device, comprising: the system comprises a processor, a memory, a communication interface and a communication bus, wherein the processor, the memory and the communication interface complete mutual communication through the communication bus;
the memory is used for storing at least one executable instruction, and the executable instruction causes the processor to execute the operation corresponding to the addressing method of the entity object according to any one of claims 1-7.
10. A computer storage medium having stored therein at least one executable instruction for causing a processor to perform operations corresponding to the method of addressing a physical object as claimed in any one of claims 1 to 7.
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| CN113256341A (en) * | 2021-06-08 | 2021-08-13 | 北京众荟信息技术股份有限公司 | Site selection method and device for business place, electronic equipment and storage medium |
| CN113256144A (en) * | 2021-06-07 | 2021-08-13 | 联仁健康医疗大数据科技股份有限公司 | Target object determination method and device, electronic equipment and storage medium |
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| CN113379464B (en) * | 2021-06-29 | 2023-12-05 | 北京百度网讯科技有限公司 | Block chain-based site selection method, device, equipment and storage medium |
| CN113379462A (en) * | 2021-06-29 | 2021-09-10 | 北京百度网讯科技有限公司 | Site selection method, device, equipment and storage medium |
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| CN113689226A (en) * | 2021-07-08 | 2021-11-23 | 深圳市维度数据科技股份有限公司 | Method and device for selecting address of commercial complex, electronic equipment and storage medium |
| CN113837799A (en) * | 2021-09-22 | 2021-12-24 | 和元达信息科技有限公司 | Intelligent business site selection method, system, equipment and readable storage medium |
| CN114331206A (en) * | 2022-01-06 | 2022-04-12 | 重庆紫光华山智安科技有限公司 | Point location addressing method and device, electronic equipment and readable storage medium |
| CN114331206B (en) * | 2022-01-06 | 2022-11-01 | 重庆紫光华山智安科技有限公司 | Point location addressing method and device, electronic equipment and readable storage medium |
| TWI831113B (en) * | 2022-01-13 | 2024-02-01 | 東方線上股份有限公司 | Method and system for selecting selling location |
| CN114399202A (en) * | 2022-01-17 | 2022-04-26 | 青岛文达通科技股份有限公司 | Big data visualization system for urban community |
| CN117435824A (en) * | 2023-12-22 | 2024-01-23 | 山东艾琳智能科技有限公司 | Site selection screening system based on Internet |
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