Fresh food store site selection method, system and related equipment based on community grid

Abstract

The present invention provides a method, system and related equipment for selecting a fresh food store site based on a community grid. The method includes receiving store demand information input by a user; obtaining business district data that meets preset conditions based on store location information; obtaining community grid data within a first distance range around the business district for each business district in the business district data; matching store level information for each community grid in the community grid data to obtain a target community grid set; calculating store estimated profit for each target community grid in the target community grid set to obtain estimated profit values ​​for each target community grid; and displaying each target community grid in the target community grid set to a user for selection after sorting them from large to small according to the estimated profit values. The method for selecting a fresh food store site of the present invention can automatically select a site by only inputting store demand information, and the site selection process is simpler. Relying on the business district and community grid data, the site selection is more reliable.

Description

Technical Field

[0001] The present invention relates to the field of data analysis technology, and in particular to a method, system and related equipment for selecting a fresh food store site based on a community grid. Background Art

[0002] In the fresh food industry, store location is one of the key factors affecting business success. Compared with stores in other industries, the fresh food industry has some significant characteristics. Fresh products have the characteristics of short shelf life, easy damage and perishability, so the loss rate of stores is high, and the corresponding operating costs will also increase. Fresh products are more dependent on the high frequency of repurchase by the surrounding customer groups, and the product turnover rate is higher, but customers also have high requirements for the shopping experience of fresh products, including product quality, freshness, shopping environment, safety and hygiene, etc. Secondly, there are obvious differences in consumption habits among different customer groups. Older customer groups tend to directly select and purchase offline stores within a 15-minute walking circle, while younger customer groups choose stores that can be delivered within 1 hour. The proportion of online orders will be much higher. They rely more on word of mouth, evaluation, referrals, after-sales experience and other methods to choose online stores. Therefore, store location selection needs to comprehensively consider the online and offline customer flow of the surrounding community, as well as important factors such as customer unit price.

[0003] There are two main methods for selecting store locations. The traditional method is to collect data offline, evaluate and analyze it through industry experts with rich experience. This approach requires a large manpower cost, takes a long time, and the results are highly subjective and rely on personal experience. Another method is to collect consumption data of customer groups around the location through online service providers such as map platforms and food delivery platforms, and then calculate several optimal solutions based on existing algorithm models. This method has high site selection efficiency, but the service provider's paid data usage cost is high, and the model's interpretability is poor. There are large deviations in site selection data in different industries, which may lead to site selection failure.

[0004] Therefore, existing technology requires a site selection method that is targeted at the fresh food industry and can integrate customer group data from multiple online and offline channels to achieve lower cost, higher efficiency and more accurate site selection. Summary of the invention

[0005] The present invention provides a method, system and related equipment for site selection of fresh food stores based on community grids. The main purpose of the present invention is to solve the technical problems mentioned in the background technology of the prior art.

[0006] A first aspect of the present invention provides a method for selecting a fresh food store location based on a community grid, comprising:

[0007] Receiving store demand information input by a user, wherein the store demand information includes store location information and store grade information;

[0008] Acquire business district data that meets preset conditions based on the store location information;

[0009] For each business district in the business district data, obtaining community grid data within a first distance range around the business district;

[0010] Matching the store level information to each community grid in the community grid data to obtain a target community grid set;

[0011] Calculating the estimated store profit for each target community grid in the target community grid set to obtain an estimated profit value for each target community grid in the target community grid set;

[0012] The target community grids in the target community grid set are sorted from large to small according to the estimated profit value and then displayed to the user for selection.

[0013] In an optional implementation of the first aspect of the present invention, the calculating of estimated store profits for each target community grid in the target community grid set to obtain the estimated profit value of each target community grid in the target community grid set includes:

[0014] For each of the target community grids, obtaining the total number of residents in the target community grid;

[0015] Obtain the number of people flow in the target community grid and its surrounding second distance area within a preset time period;

[0016] Obtain the average customer unit price, average customer share, average gross profit margin, and average store operating expenses of all existing stores of the same level;

[0017] Calculate the estimated store sales based on the total number of residents, the number of people flow, the average customer unit price and the average number of visitors;

[0018] An estimated profit value is obtained based on the estimated store sales, the average gross profit margin and the average store operating expenses.

[0019] In an optional implementation of the first aspect of the present invention, obtaining the number of people flow in the target community grid and its surrounding second distance area within a preset time period includes:

[0020] Obtain offline traffic volume of the target community grid within a preset time period;

[0021] Obtain the number of online traffic in the target community grid and its surrounding second distance area within a preset time period;

[0022] Based on the offline pedestrian flow number and the online pedestrian flow number, the pedestrian flow number of the target community grid and its surrounding second distance area within a preset time period is obtained.

[0023] In an optional implementation of the first aspect of the present invention, obtaining the average customer unit price, average customer ratio, average gross profit margin and average store operating expenses of all existing stores of the same level includes:

[0024] Based on the historical sales data of all existing stores of the same level in the same city, the average customer unit price and average gross profit margin of existing stores of the same level are obtained;

[0025] Based on the historical sales data of all existing stores of the same level in the same business district, the average customer share and average store operating expenses of existing stores of the same level are obtained.

[0026] In an optional implementation of the first aspect of the present invention, the acquiring of business district data meeting preset conditions based on the store location information includes:

[0027] Obtaining a target city where the store is located based on the store location information;

[0028] Obtain all the business districts of the target city from its own database or from a third-party interface;

[0029] The business district data meeting preset conditions are screened from all the business districts in the target city, and the preset conditions include the popularity ranking of the business district and the sales ranking of the business district.

[0030] In an optional implementation of the first aspect of the present invention, matching the store level information for each community grid in the community grid data to obtain a target community grid set includes:

[0031] Based on the store grade information, obtain the regional portrait information and crowd portrait information corresponding to the grade classification;

[0032] Matching the regional portrait information to each community grid in the community grid data to obtain a preliminary screening community grid set;

[0033] The population portrait information is matched with the initially screened community grid set to obtain a target community grid set.

[0034] In an optional implementation of the first aspect of the present invention, the sorting of the target community grids in the target community grid set from large to small according to the estimated profit value and displaying them to the user for selection comprises:

[0035] receiving a designated target community grid selected by a user from among the sorted target community grids;

[0036] Obtain all statistical data of the specified target community grid;

[0037] A comprehensive report of the designated target community grid is generated based on all of the statistical data.

[0038] A second aspect of the present invention provides a fresh food store site selection system based on a community grid, and the fresh food store site selection system based on a community grid includes:

[0039] A demand receiving module, used to receive store demand information input by a user, wherein the store demand information includes store location information and store level information;

[0040] A business district screening module, used to obtain business district data that meets preset conditions based on the store location information;

[0041] A community grid acquisition module, configured to acquire, for each business district in the business district data, community grid data within a first distance range around the business district;

[0042] A community grid screening module, used for matching the store level information with each community grid in the community grid data to obtain a target community grid set;

[0043] An estimated profit calculation module, used to calculate the estimated profit of each store in the target community grid set, and obtain the estimated profit value of each target community grid in the target community grid set;

[0044] The community grid display module is used to sort the target community grids in the target community grid set from large to small according to the estimated profit value and then display them to the user for selection.

[0045] A third aspect of the present invention provides a fresh food store site selection device based on a community grid, the fresh food store site selection device based on a community grid comprising: a memory and at least one processor, the memory storing instructions, the memory and the at least one processor being interconnected via a line;

[0046] The at least one processor calls the instructions in the memory to enable the community grid-based fresh food store site selection device to execute the community grid-based fresh food store site selection method as described in any one of the first aspects above.

[0047] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the community grid-based fresh food store site selection method as described in any one of the first aspects above is implemented.

[0048] Beneficial effects: The present invention provides a method, system and related equipment for selecting a fresh food store site based on a community grid, the method comprising receiving store demand information input by a user; obtaining business district data that meets preset conditions based on store location information; obtaining community grid data within a first distance range around the business district for each business district in the business district data; matching store level information for each community grid in the community grid data to obtain a target community grid set; calculating the estimated store profit for each target community grid in the target community grid set to obtain the estimated profit value of each target community grid; sorting each target community grid in the target community grid set from large to small according to the estimated profit value and displaying it to the user for selection. The method for selecting a fresh food store site of the present invention can automatically select a site by only inputting store demand information, the site selection process is simpler, and the site selection is more reliable by relying on business district and community grid data. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 A schematic diagram of an embodiment of a method for selecting a fresh food store location based on a community grid according to the present invention;

[0050] Figure 2 A schematic diagram of an embodiment of a fresh food store site selection system based on a community grid according to the present invention;

[0051] Figure 3 A schematic diagram of an embodiment of a fresh food store site selection device based on a community grid according to the present invention. DETAILED DESCRIPTION

[0052] The terms "first", "second", "third", "fourth", etc. (if any) in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "including" or "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0053] For ease of understanding, the specific process of the embodiment of the present invention is described below. Figure 1 In a first aspect, the present invention provides a method for selecting a fresh food store location based on a community grid, comprising:

[0054] S100, receiving store demand information input by a user, wherein the store demand information includes store location information and store grade information. In the present invention, a user can select a specific city based on the city potential ranking or by himself, and determine the grade of the required store. The store location information includes the target city where the store is located. Of course, the store location information can also be more fine-grained location data, and the store grade information can be the grade classification of franchise stores by the franchise enterprise. The grade classification of franchise stores mainly reflects the differences between different stores, so as to provide more targeted services to the corresponding population.

[0055] An exemplary method for classifying stores in the first aspect of the present invention is as follows: stores are divided into different levels based on factors such as the store's geographical location, customer flow, and service quality; based on experience in the fresh food industry, stores are divided into three categories and three grades, totaling 9 types, according to the store's operating data and market positioning.

[0056] High-end community stores: located in high-end residential areas, mainly serving high-income people.

[0057] Mid- to high-end community stores: located in mid- to high-end residential areas, mainly serving middle- and high-income groups.

[0058] Low- to mid-range community stores: located in ordinary residential areas, mainly serving people with ordinary incomes.

[0059] High-end community stores with high traffic volume: located in high-end commercial districts or areas with high traffic volume, mainly serving high-income people.

[0060] Mid- to high-end community stores with high traffic volume: located in mid- to high-end commercial districts or areas with high traffic volume, mainly serving middle- to high-income groups.

[0061] Low- to mid-range community stores: located in ordinary commercial areas or areas with average traffic, mainly serving people with average incomes.

[0062] High-end multi-community traffic stores: located at the intersection of multiple high-end residential and commercial areas, with a wide service range, mainly serving high-income people.

[0063] Mid- to high-end multi-community traffic stores: located at the intersection of multiple mid- to high-end residential and commercial areas, with a wide service scope, mainly serving middle- and high-income groups.

[0064] Low- to mid-range stores with high traffic in multiple communities: located at the intersection of multiple ordinary residential and commercial areas, with a wide service scope, mainly serving people with ordinary incomes.

[0065] S200, based on the store location information, obtain the business district data that meets the preset conditions; taking the target city where the store is located as an example, the business district data that meets the preset conditions based on the store location information includes: obtaining the target city where the store is located based on the store location information; obtaining all the business districts of the target city from the own database or from a third-party interface; screening all the business districts in the target city to obtain the business district data that meets the preset conditions, and the preset conditions include the popularity ranking of the business district and the sales ranking of the business district. An exemplary scenario in step S200 is, for example, after selecting the target city from the system city list, and obtaining the top 20 business district data of the city through a free or paid third-party data platform. The third-party data platform here includes but is not limited to the statistical agencies of the General Administration: providing demographic data, economic data, etc.; local statistical agencies: providing local economic and social data; and real estate websites: such as Lianjia, Anjuke, etc., providing rental and housing price data.

[0066] S300. For each business district in the business district data, obtain the community grid data within the first distance range around the business district; similarly, the community grid data in step S300 of the present invention can also be obtained from the statistical agencies of the General Administration, local statistical agencies, real estate websites and other places. In the present invention, whether it is to obtain the business district data or the community grid data within the first distance range around the business district, after determining the target website for obtaining the data, it is necessary to analyze the web page structure and find the HTML elements containing the grid data. According to the complexity of the web page and the distribution of the data, formulate a reasonable crawling strategy. For example, for data in tabular form, Python's pandas library or BeautifulSoup library can be used for parsing and extraction. For web pages that require login or verification, the requests library can be used to simulate the login process.

[0067] In an optional implementation manner of the present invention, the specific process of writing a crawler program may be as follows:

[0068] Send request: Use the requests library or other HTTP request library to send a GET or POST request to the target website to obtain the source code of the web page.

[0069] Parse the web page: Use the BeautifulSoup library or other HTML parsing library to parse the web page source code and extract the HTML elements containing the grid data.

[0070] Extract data: Extract the required data based on the structure and attributes of HTML elements. Clean and organize the extracted data to remove redundant and erroneous information. If conditions permit, verify the cleaned data to ensure the accuracy and completeness of the data. Finally, save the extracted data in a big data storage and aggregate it by time range. For example, in the elasticsearch search engine, you can create a real index by year, and then query the data through the index alias, so that you can seamlessly switch between new and old data sources.

[0071] S400, matching the store grade information for each community grid in the community grid data to obtain a target community grid set; in an optional implementation of the first aspect of the present invention, matching the store grade information for each community grid in the community grid data to obtain a target community grid set includes: obtaining regional portrait information and crowd portrait information corresponding to the grade classification based on the store grade information; matching the regional portrait information for each community grid in the community grid data to obtain a preliminary screening community grid set; matching the crowd portrait information for the preliminary screening community grid set to obtain a target community grid set. Taking the store grade classification rule in step S100 as an example, for a high-end community store, the regional portrait information refers to "located in a high-end residential area", and the crowd portrait information refers to "high-income people"; for a mid-to-low-end multi-community traffic store, the regional portrait information refers to "located at the intersection of multiple ordinary residential areas and commercial areas", and the crowd portrait information refers to "ordinary income people".

[0072] S500, performing estimated store profit calculation on each target community grid in the target community grid set to obtain an estimated profit value of each target community grid in the target community grid set;

[0073] In an optional implementation of the first aspect of the present invention, the calculating of estimated store profits for each target community grid in the target community grid set to obtain the estimated profit value of each target community grid in the target community grid set includes:

[0074] For each of the target community grids, the total number of residents in the target community grid is obtained. In the present invention, it is assumed that the store is located at the center point of the target community grid and the range of the target community grid is redefined with a certain distance (e.g., 1 km) as the radius. The number of households recorded within a one-kilometer radius around the store is determined by a big data search engine to obtain the final number of households A (i.e., the total number of residents).

[0075] Obtain the number of pedestrians in the target community grid and the second distance area around it within a preset time period; in this step, assuming that the range of the target community grid itself is still based on a radius of 1 km around the store location, the second distance can be 2 km. In an optional implementation of the first aspect of the present invention, obtaining the number of pedestrians in the target community grid and the second distance area around it within a preset time period includes: obtaining the number of offline pedestrians in the target community grid within a preset time period; obtaining the number of online pedestrians in the target community grid and the second distance area around it within a preset time period; based on the offline pedestrian numbers and the online pedestrian numbers, obtaining the number of pedestrians in the target community grid and the second distance area around it within the preset time period.

[0076] In this step, the preset time period can be the time period of daily store operations or the preset peak time period. Taking the preset time period as the preset peak time period as an example, the number of online traffic can be replaced by the hourly traffic during the peak period. The hourly traffic during the peak period B is data obtained from the online payment platform, accumulating the number of takeout orders within one kilometer of the offline area and three kilometers of the online area. The specific steps are as follows: Obtain offline traffic data within one kilometer of the store from a third-party data platform. At the same time, obtain takeout order data within three kilometers of the online area. Add the above two parts of data to obtain the hourly traffic during the peak period B.

[0077] Obtain the average customer unit price, average customer number percentage, average gross profit margin and average store operating expenses of all existing stores of the same level; in an optional implementation manner of the first aspect of the present invention, obtaining the average customer unit price, average customer number percentage, average gross profit margin and average store operating expenses of all existing stores of the same level includes: obtaining the average customer unit price and average gross profit margin of existing stores of the same level based on historical sales data of all existing stores of the same level in the same city; obtaining the average customer number percentage and average store operating expenses of existing stores of the same level based on historical sales data of all existing stores of the same level in the same business district.

[0078] In this step, the average customer unit price is calculated based on the manually entered store historical data and the store level. The specific steps are as follows: Calculate the average customer unit price of stores of different levels based on the store's historical sales data; Select the average customer unit price that matches the current store level as the value of D. The average customer share is calculated based on the manually entered store historical data. The specific steps are as follows: Calculate the customer share of each store through historical data, that is, According to the level of the store, calculate the average of the proportion of the number of visitors of all stores of this level as the final C value. In order to facilitate data entry, in the present invention, the entry process of store historical data can be achieved by filling out a standardized form questionnaire, which covers the following core parts: Fixed costs: including monthly rent, brand usage fees, employee salaries, etc. These expenses are fixed costs in store operations and need to be entered and updated regularly. Commodity loss rate: record the loss of fresh goods during the operation of the store, including losses caused by improper storage, expiration, etc. Turnover: record the total sales of the store on a daily, weekly, and monthly basis. Average customer spending: record the average amount of each transaction, reflecting the customer's consumption level.

[0079] The estimated store sales are calculated based on the total number of residents, the number of people flow, the average customer unit price and the average number of visitors. In this step, the estimated store sales are calculated by the following formula: store sales = (A + B) × C × D;

[0080] The estimated profit value is obtained based on the estimated store sales, the average gross profit margin and the average store operating expenses. In the present invention, the average gross profit margin (i.e., the estimated gross profit margin) is calculated by the following formula: In addition to the cost of purchasing goods, store operating expenses mainly include the following categories: rental costs: calculated based on the housing price, rental-to-sale ratio and store size of the community grid; employee salaries: calculated based on the average wage data of the city and the number of employees corresponding to the store level; monthly operating expenses: including brand licensing fees, equipment and system maintenance fees, transportation fees and other miscellaneous expenses. This part of the cost can be used as a constant and obtained by manually entering data. The estimated profit value can be calculated by the following formula: estimated profit = estimated sales × estimated gross profit margin - ∑ store operating expenses. In the process of calculating the estimated profit value, the present invention proposes a site selection calculation method suitable for the fresh food industry by integrating data sources from multiple channels such as online public data, customer group data of payment platforms, and manually entered store historical data, which can more accurately estimate the sales and profits of fresh food stores. This method not only improves the accuracy of the prediction, but also provides strong data support for the store's business decisions.

[0081] In the process of calculating the estimated profit value of the present invention, since there are many intermediate data that need to be obtained from third-party data platforms, in the current commercial site selection system, it is crucial to obtain accurate passenger flow data. At present, many maps and food delivery platforms can provide such data, but since these data are usually paid and have restrictions on the authorization time and number of calls, long-term use of these paid data will significantly increase the operating costs of the site selection system. In addition, frequent calls to external data interfaces will also increase the complexity of the system and potential failure points.

[0082] In order to solve the above-mentioned problems existing in the existing commercial site selection system in obtaining data from a third-party data platform, the present invention can also effectively manage and utilize external paid data sources by introducing an interface proxy module, thereby reducing the use cost of the system and improving the efficiency of data acquisition. The technical solution includes the following steps:

[0083] Setting of interface proxy module: An interface proxy module is set up in the commercial site selection system, which is responsible for calling external paid data sources. The interface proxy module is connected to the big data storage system to store and retrieve historical data.

[0084] Processing of query requests: When the system receives a query request, the interface proxy module first queries the latest data from the big data storage. If the required data is found in the big data storage, the data is directly returned to the caller to avoid calling the external payment interface.

[0085] Data acquisition and processing: If the required data is not found in the big data storage, the interface proxy module will query the address and coordinate information of the community grid. The interface proxy module calls the external payment interface to obtain the required passenger flow data. The acquired data is formatted and standardized to ensure data consistency and readability.

[0086] Data storage and return: The standardized data is saved in the big data storage according to the time dimension for subsequent query. The processed data is returned to the caller to complete the entire query process.

[0087] The method of effectively managing and utilizing an external paid data source through the interface proxy module of the present invention can have the following advantages: Reduce costs: Through the caching mechanism, the number of calls to the external paid data source is reduced, which significantly reduces the cost of using the system. Improve efficiency: The interface proxy module can quickly respond to query requests, improving the overall efficiency of the system. Data consistency: Through format standardization processing, the consistency and readability of the data are ensured, which facilitates subsequent data analysis and application. The present invention manages and uses external paid data sources through an interface proxy module, and uses a caching mechanism to reduce the number of calls to the external paid data source, which significantly reduces the cost of using the addressing system.

[0088] In addition, it should be noted that in the method of calculating the estimated profit value exemplified in the above step S500 of the present invention, physical store data of stores of the same level are used as examples. In other optional implementations of the present invention, for new brands that do not yet have physical stores, store data of competitors with similar brand positioning to their own can be used as a substitute.

[0089] S600. Sort each of the target community grids in the target community grid set from large to small according to the estimated profit value and display them to the user for selection. In summary, the overall process of user site selection in the present invention is as follows: City and store level selection: Users can rank cities based on their potential or select specific cities on their own, and determine the level of the required stores. Community grid screening: Within the selected city, retrieve the recorded community grids, and screen out community grids with income levels that match the store level. Profit estimation and recommendation: Apply the store profit estimation method, calculate the estimated profits of the screened community grids, and sort them according to the calculation results, and finally recommend the most optimal site selection plan to the user.

[0090] In general, the community grid-based fresh food store site selection method of the present invention has at least the following characteristics: Data fusion: Combine offline field visits and online big data analysis to comprehensively evaluate the feasibility of store site selection. Algorithm optimization: Develop a site selection algorithm suitable for the fresh food industry to improve the accuracy and interpretability of the model. Cost control: Reduce the cost of data collection and analysis and improve the economic benefits of site selection. Through these measures, the special needs of store site selection in the fresh food industry can be better met and the business success rate can be improved.

[0091] The main points to be protected by the technical solution of the present invention are: (1) Data fusion and site selection algorithm optimization: protect a data fusion method that combines online public data, paid platform customer group data, and manually entered store historical data, as well as an optimized fresh food store site selection algorithm. (2) Interface proxy and cache mechanism to reduce site selection costs: protect a technical solution that effectively manages and utilizes external paid data sources through an interface proxy module. The cache mechanism reduces the number of calls to external paid data sources and reduces the cost of using the addressing system. (3) Acquisition and utilization of community grid data: protect a method for collecting and processing community grid data through crawler technology. And use the community grid dimension to summarize various public and paid data to estimate store sales, profits and other addressing indicators. These key points and protection points outline the core innovation and technical advantages of the invention, which aims to improve the accuracy and economic benefits of fresh food store site selection by integrating multi-channel data and optimization algorithms.

[0092] In an optional implementation of the first aspect of the present invention, the step of sorting each of the target community grids in the target community grid set from large to small according to the estimated profit value and presenting them to the user for selection includes: receiving a designated target community grid selected by the user from the sorted target community grids; obtaining all statistical data of the designated target community grid, the statistical data including location-related data, population-related data, economic-related data, and estimated profit-related data of the designated target community grid; and generating a comprehensive report on the designated target community grid based on all the statistical data.

[0093] In an optional implementation of the first aspect of the present invention, using the data sources and store profit prediction method mentioned in the community grid-based fresh food store site selection method of the present invention, the present invention can also be used to regularly calculate the site selection potential of a specific city through an automated batch processing task. The evaluation process specifically includes the following steps:

[0094] Business district data acquisition: Select the target city from the city list and obtain the top 20 business district data of the city through a paid platform.

[0095] Community grid analysis: For each business district, query the community grids within 1 km of its surrounding area, and estimate the potential profit of each community grid based on the aforementioned algorithm.

[0096] Site potential assessment: The estimated profits of these community grids are averaged to obtain the site potential index of the city.

[0097] See also Figure 2 In a second aspect, the present invention provides a fresh food store site selection system based on a community grid, and the fresh food store site selection system based on a community grid includes:

[0098] The demand receiving module 10 is used to receive the store demand information input by the user, wherein the store demand information includes the store location information and the store level information;

[0099] A business district screening module 20, configured to obtain business district data meeting preset conditions based on the store location information;

[0100] The community grid acquisition module 30 is used to acquire, for each business district in the business district data, community grid data within a first distance range around the business district;

[0101] A community grid screening module 40 is used to match the store level information to each community grid in the community grid data to obtain a target community grid set;

[0102] An estimated profit calculation module 50 is used to calculate the estimated store profit of each target community grid in the target community grid set to obtain the estimated profit value of each target community grid in the target community grid set;

[0103] The community grid display module 60 is used to sort the target community grids in the target community grid set from large to small according to the estimated profit value and then display them to the user for selection.

[0104] In an optional implementation of the second aspect of the present invention, the estimated profit calculation module includes:

[0105] A total number of residents obtaining unit, used for obtaining the total number of residents in each target community grid;

[0106] A pedestrian flow acquisition unit, used to acquire the pedestrian flow in the target community grid and its surrounding second distance area within a preset time period;

[0107] The same-level store operation data acquisition unit is used to obtain the average customer unit price, average customer number ratio, average gross profit margin and average store operation cost of all existing stores of the same level;

[0108] An estimated sales calculation unit, used to calculate the estimated store sales based on the total number of residents, the number of people flow, the average customer unit price and the average number of visitors;

[0109] The estimated profit calculation unit obtains an estimated profit value based on the estimated store sales, the average gross profit margin and the average store operating expenses.

[0110] In an optional implementation of the second aspect of the present invention, the pedestrian flow number acquisition unit includes:

[0111] An offline pedestrian flow acquisition subunit is used to acquire the offline pedestrian flow within a preset time period of the target community grid;

[0112] An online pedestrian flow acquisition subunit is used to acquire the online pedestrian flow within a preset time period of the target community grid and a second distance area around it;

[0113] The total subunit is used to obtain the number of people flowing in the target community grid and its surrounding second distance area within a preset time period based on the offline number of people flowing and the online number of people flowing.

[0114] In an optional implementation of the second aspect of the present invention, the same-level store operation data acquisition unit includes:

[0115] The average customer unit price and gross profit margin acquisition subunit is used to obtain the average customer unit price and average gross profit margin of existing stores of the same level based on the historical sales data of all existing stores of the same level in the same city;

[0116] The average number of visitors and operating expenses acquisition subunit is used to obtain the average number of visitors and the average store operating expenses of existing stores of the same level based on the historical sales data of all existing stores of the same level in the same business district.

[0117] In an optional implementation of the second aspect of the present invention, the business district screening module includes:

[0118] A target city acquisition unit, configured to acquire a target city where a store is located based on the store location information;

[0119] A business district acquisition unit, used to acquire all business districts of the target city from its own database or from a third-party interface;

[0120] The business district screening unit is used to screen all the business districts in the target city to obtain business district data that meets preset conditions, wherein the preset conditions include the popularity ranking of the business district and the sales ranking of the business district.

[0121] In an optional implementation of the second aspect of the present invention, the community grid screening module includes:

[0122] A portrait information acquisition unit, used for acquiring regional portrait information and crowd portrait information corresponding to the grade classification based on the store grade information;

[0123] A regional condition matching unit, used for matching the regional portrait information with each community grid in the community grid data to obtain a preliminary screening community grid set;

[0124] The crowd matching unit is used to match the crowd portrait information of the initial screening community grid set to obtain a target community grid set.

[0125] In an optional implementation of the second aspect of the present invention, the community grid-based fresh food store site selection system further includes:

[0126] A community grid selection module, configured to receive a designated target community grid selected by a user from among the sorted target community grids;

[0127] A community grid statistical data acquisition module, used to acquire all statistical data of the designated target community grid;

[0128] The community grid report generation module is used to generate a comprehensive report of the designated target community grid based on all the statistical data.

[0129] Figure 3It is a structural schematic diagram of a fresh food store site selection device based on a community grid provided by an embodiment of the present invention. The fresh food store site selection device based on the community grid may have relatively large differences due to different configurations or performances, and may include one or more processors 70 (central processing units, CPU) (for example, one or more processors) and a memory 80, and one or more storage media 90 for storing application programs or data (for example, one or more massive storage devices). Among them, the memory and the storage medium may be short-term storage or persistent storage. The program stored in the storage medium may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the fresh food store site selection device based on the community grid. Furthermore, the processor may be configured to communicate with the storage medium to execute a series of instruction operations in the storage medium on the fresh food store site selection device based on the community grid.

[0130] The community grid-based fresh food store location selection device of the present invention may also include one or more power supplies 100, one or more wired or wireless network interfaces 110, one or more input and output interfaces 120, and / or one or more operating systems, such as Windows Server, Mac OS X, Unix, Linux, FreeBSD, etc. Those skilled in the art will appreciate that Figure 3 The illustrated structure of the community grid-based fresh food store site selection device does not constitute a limitation on the community grid-based fresh food store site selection device, and may include more or fewer components than illustrated, or a combination of certain components, or a different arrangement of components.

[0131] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the community grid-based fresh food store location selection method.

[0132] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the system or system or unit described above can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0133] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including a number of instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program codes.

[0134] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for selecting a fresh food store location based on a community grid, characterized in that: include: Receiving store demand information input by a user, wherein the store demand information includes store location information and store grade information; Acquire business district data that meets preset conditions based on the store location information; For each business district in the business district data, obtaining community grid data within a first distance range around the business district; Matching the store level information to each community grid in the community grid data to obtain a target community grid set; Calculating the estimated store profit for each target community grid in the target community grid set to obtain an estimated profit value for each target community grid in the target community grid set; The target community grids in the target community grid set are sorted from large to small according to the estimated profit value and then displayed to the user for selection.

2. The method for selecting a fresh food store location based on a community grid according to claim 1, characterized in that: The calculating of the estimated store profit for each target community grid in the target community grid set to obtain the estimated profit value of each target community grid in the target community grid set includes: For each of the target community grids, obtaining the total number of residents in the target community grid; Obtain the number of people flow in the target community grid and its surrounding second distance area within a preset time period; Obtain the average customer unit price, average customer share, average gross profit margin, and average store operating expenses of all existing stores of the same level; Calculate the estimated store sales based on the total number of residents, the number of people flow, the average customer unit price and the average number of visitors; An estimated profit value is obtained based on the estimated store sales, the average gross profit margin and the average store operating expenses.

3. The method for selecting a fresh food store location based on a community grid according to claim 2, characterized in that: The step of obtaining the number of people flow in the target community grid and its surrounding second distance area within a preset time period includes: Obtain offline traffic volume of the target community grid within a preset time period; Obtain the number of online traffic in the target community grid and its surrounding second distance area within a preset time period; Based on the offline pedestrian flow number and the online pedestrian flow number, the pedestrian flow number of the target community grid and its surrounding second distance area within a preset time period is obtained.

4. The method for selecting a fresh food store location based on a community grid according to claim 2, characterized in that: The acquisition of the average customer unit price, average customer ratio, average gross profit margin and average store operating expenses of all existing stores of the same level includes: Based on the historical sales data of all existing stores of the same level in the same city, the average customer unit price and average gross profit margin of existing stores of the same level are obtained; Based on the historical sales data of all existing stores of the same level in the same business district, the average customer share and average store operating expenses of existing stores of the same level are obtained.

5. The method for selecting a fresh food store location based on a community grid according to claim 1, characterized in that: The business district data meeting preset conditions obtained based on the store location information includes: Obtaining a target city where the store is located based on the store location information; Obtain all the business districts of the target city from its own database or from a third-party interface; The business district data meeting preset conditions are screened from all the business districts in the target city, and the preset conditions include the popularity ranking of the business district and the sales ranking of the business district.

6. The method for selecting a fresh food store location based on a community grid according to claim 1, characterized in that: The matching of the store level information for each community grid in the community grid data to obtain a target community grid set includes: Based on the store grade information, obtain the regional portrait information and crowd portrait information corresponding to the grade classification; Matching the regional portrait information to each community grid in the community grid data to obtain a preliminary screening community grid set; The population portrait information is matched with the initially screened community grid set to obtain a target community grid set.

7. The method for selecting a fresh food store location based on a community grid according to claim 1, characterized in that: The step of sorting the target community grids in the target community grid set from large to small according to the estimated profit value and displaying them to the user for selection comprises: receiving a designated target community grid selected by a user from among the sorted target community grids; Obtain all statistical data of the specified target community grid; A comprehensive report of the designated target community grid is generated based on all of the statistical data.

8. A fresh food store location selection system based on community grid, characterized in that: The fresh food store location selection system based on community grid includes: A demand receiving module, used to receive store demand information input by a user, wherein the store demand information includes store location information and store level information; A business district screening module, used to obtain business district data that meets preset conditions based on the store location information; A community grid acquisition module, configured to acquire, for each business district in the business district data, community grid data within a first distance range around the business district; A community grid screening module, used for matching the store level information with each community grid in the community grid data to obtain a target community grid set; An estimated profit calculation module, used to calculate the estimated profit of each store in the target community grid set, and obtain the estimated profit value of each target community grid in the target community grid set; The community grid display module is used to sort the target community grids in the target community grid set from large to small according to the estimated profit value and then display them to the user for selection.

9. A fresh food store location selection device based on community grid, characterized in that: The community grid-based fresh food store location selection device comprises: a memory and at least one processor, the memory stores instructions, and the memory and the at least one processor are interconnected via a line; The at least one processor calls the instructions in the memory to enable the community grid-based fresh food store site selection device to execute the community grid-based fresh food store site selection method as described in any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the community grid-based fresh food store location selection method as described in any one of claims 1 to 7 is implemented.

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