Site selection method and device based on interest point data, equipment and storage medium

Abstract

The invention provides a site selection method and device based on interest point data, equipment and a storage medium, and the method comprises the steps: collecting nationwide interest point data, obtaining target data, and retrieving corresponding at least one piece of first interest point data and corresponding first related data in the interest point data based on the target data; calculating an attraction score corresponding to the at least one piece of first point-of-interest data based on the business data; setting a first correction coefficient based on the other interest point related data and the first related data; calculating a comprehensive scale weight of the at least one piece of first point-of-interest data based on the attraction score and the first correction coefficient; and retrieving resident data in the first related data based on the position information, calculating community attraction of the at least one piece of first interest point data based on the resident data and the comprehensive scale weight, and obtaining candidate addresses in the region based on the community attraction.

Description

Technical Field

[0001] This disclosure relates to the field of data retrieval, and in particular to a location selection method, apparatus, device, and storage medium based on point-of-interest data. Background Technology

[0002] In site selection recommendation tasks, traditional methods typically consider only single indicators (such as customer traffic, rent, etc.), lacking a comprehensive analysis of competitive and collaborative brand relationships. Existing solutions struggle to effectively model the complex impact of competitive-collaborative relationships on site selection, especially regarding the limited analysis of overlapping impacts from different brands within the same category. They neglect the influence of competing brands and fail to fully consider the synergistic effects of collaborating brands; they do not incorporate competitive and collaborative relationships between brands, resulting in inaccurate calculations of the scope of influence; they can only extrapolate based on existing data, unable to handle dynamic changes in new brands or competitive relationships, lacking flexibility. They lack quantitative modeling of the relationships between competing and collaborating brands. They struggle to comprehensively consider the collaborative and competitive suppression effects in multi-store site selection. The calculation of the scope of influence is rather coarse, ignoring important factors such as brand characteristics, category weights, and distance decay. Summary of the Invention

[0003] This disclosure provides a location selection method, apparatus, device, and storage medium based on point-of-interest data, to at least solve the above-mentioned technical problems existing in the prior art.

[0004] According to a first aspect of this disclosure, a location selection method based on point-of-interest (POI) data is provided, the method comprising:

[0005] The system collects point-of-interest (POI) data from across the country using web crawler technology, retrieves relevant data based on the POI data, and stores the relevant data and the POI data in a database.

[0006] Acquire target data, and based on the target data, retrieve at least one first point of interest data and corresponding first related data from the database;

[0007] Based on the first relevant data, search for the business data corresponding to the at least one first point of interest data, and calculate the attractiveness score corresponding to the at least one first point of interest data based on the business data;

[0008] Obtain the location information of at least one first point of interest data in the first related data, retrieve other point of interest related data in the related data based on the location information, and set a first correction coefficient based on the other point of interest related data and the first related data;

[0009] Calculate the comprehensive scale weight of the at least one first point of interest data based on the attraction score and the first correction coefficient;

[0010] Based on the location information, resident data is retrieved from the first relevant data. Based on the resident data and the comprehensive scale weight, the community attractiveness of the at least one first point of interest data is calculated. The community attractiveness of the at least one first point of interest data is accumulated to obtain the overall attractiveness of the first point of interest data in the area corresponding to the target data. Based on the overall attractiveness and the community attractiveness, candidate addresses in the area are obtained.

[0011] In one possible implementation, calculating the attractiveness score corresponding to the at least one first point of interest data based on the business data includes:

[0012] The business data includes: evaluation data, business scale data, and business category data;

[0013] Assign evaluation weights to the number of comments and star ratings in the evaluation data, and calculate the brand influence score corresponding to the at least one first point of interest data based on the evaluation weights, the number of comments, and the star ratings.

[0014] Based on the business scale data, calculate the brand attractiveness value corresponding to the at least one first point of interest data. Based on the business category data, set at least one category weight. Select the first category weight corresponding to the first point of interest data from the at least one category weight. Calculate the attractiveness score corresponding to the at least one first point of interest data based on the first category weight, the brand attractiveness value, and the brand influence score.

[0015] In one possible implementation, setting the first correction coefficient based on the other points of interest related data and the first related data includes:

[0016] The data related to other points of interest includes the actual customer traffic data and actual consumption data of the customers corresponding to the other points of interest;

[0017] Based on the first relevant data, set the expected customer flow data and expected consumption data corresponding to the first point of interest;

[0018] The competition intensity of points of interest is calculated based on the actual passenger flow data, the actual consumption data, the expected passenger flow data, and the expected consumption data.

[0019] The distance between points of interest is obtained based on the first relevant data and the other relevant data. A competition distance attenuation coefficient is set based on the distance between points of interest. The first correction coefficient is set based on the competition distance attenuation coefficient, the distance between points of interest, and the competition intensity of the points of interest.

[0020] In one possible implementation, calculating the community attractiveness of the at least one first point of interest data based on the resident data and the comprehensive scale weight includes:

[0021] The resident data includes: resident profile feature data and resident traffic data;

[0022] A target group feature vector is constructed based on the at least one first point of interest data and the first related data. A resident feature vector is extracted based on the resident profile features. The similarity between the resident feature vector and the target group feature vector is calculated.

[0023] The resident traffic data is classified based on traffic category, and corresponding traffic mode weights are set based on the classification results. The weighted reachable distance is calculated based on the traffic distance corresponding to the classification results and the traffic mode weights.

[0024] The community attractiveness of the at least one first point of interest data is calculated based on the similarity, the weighted reachability distance, and the comprehensive scale weight.

[0025] In one possible implementation, obtaining candidate addresses within the region based on the overall attractiveness and the community attractiveness includes:

[0026] Obtain the set of optional addresses within the region, and use each optional address as a seed node;

[0027] Obtain the overall attractiveness set corresponding to all points of interest in the target data, sort the values ​​in the overall attractiveness set, and select the seed node corresponding to the point of interest with the largest value as a single candidate address.

[0028] An adjustment coefficient is set according to the type of interest point. Based on the adjustment coefficient, the overall attractiveness set, and the competition intensity of the interest points, the attractiveness of the interest points after removing the interest point with the largest value is calculated. The attractiveness of the remaining interest points is calculated iteratively and repeatedly. After the number of iterations reaches a set value, the node corresponding to the calculation result is selected as multiple candidate addresses.

[0029] According to a second aspect of this disclosure, a location selection device based on point-of-interest data is provided, the device comprising:

[0030] The data collection unit is used to collect point-of-interest (POI) data from across the country based on web crawler technology, retrieve relevant data based on the POI data, and store the relevant data and the POI data in a database.

[0031] A target data retrieval unit is used to acquire target data and retrieve at least one first point of interest data and corresponding first related data from the database based on the target data.

[0032] An attractiveness score calculation unit is used to find the business data corresponding to the at least one first point of interest data based on the first relevant data, and to calculate the attractiveness score corresponding to the at least one first point of interest data based on the business data.

[0033] The first correction coefficient calculation unit is used to obtain the location information of at least one first point of interest data in the first related data, retrieve other point of interest related data in the related data based on the location information, and set a first correction coefficient based on the other point of interest related data and the first related data.

[0034] A comprehensive scale weight calculation unit is used to calculate the comprehensive scale weight of the at least one first point of interest data based on the attractiveness score and the first correction coefficient.

[0035] The attractiveness calculation unit is used to retrieve resident data from the first relevant data based on the location information, calculate the community attractiveness of the at least one first point of interest data based on the resident data and the comprehensive scale weight, and accumulate the community attractiveness of the at least one first point of interest data to obtain the overall attractiveness of the first point of interest data in the area corresponding to the target data.

[0036] A site selection unit is used to obtain candidate addresses within the area based on the overall attractiveness and the community attractiveness.

[0037] In one embodiment, the attractiveness score calculation unit is further configured to: the business data includes: evaluation data, business scale data, and business category data; set evaluation weights for the number of comments and star ratings in the evaluation data; calculate a brand influence score corresponding to the at least one first point of interest data based on the evaluation weights, the number of comments, and the star ratings; calculate a brand attractiveness value corresponding to the at least one first point of interest data based on the business scale data; set at least one category weight based on the business category data; select a first category weight corresponding to the first point of interest data from the at least one category weight; and calculate an attractiveness score corresponding to the at least one first point of interest data based on the first category weight, the brand attractiveness value, and the brand influence score.

[0038] The attractiveness calculation unit is further configured to: ...

[0039] In one embodiment, the first correction coefficient calculation unit is further configured to: include the other point-of-interest related data as including actual customer flow data and actual consumption data of customers corresponding to the other point of interest; set expected customer flow data and expected consumption data corresponding to the first point of interest based on the first related data; calculate the point-of-interest competition intensity based on the actual customer flow data, the actual consumption data, the expected customer flow data, and the expected consumption data; obtain the distance between points of interest based on the first related data and the other point-of-interest related data; set a competition distance attenuation coefficient based on the distance between points of interest; and set the first correction coefficient based on the competition distance attenuation coefficient, the distance between points of interest, and the point-of-interest competition intensity.

[0040] The location selection unit is further configured to: obtain a set of optional addresses within the region, and use each optional address as a seed node; obtain a set of overall attractiveness corresponding to all points of interest in the target data, sort the values ​​in the set of overall attractiveness, and select the seed node corresponding to the point of interest with the largest value as a single candidate address; set an adjustment coefficient according to the type of point of interest, and calculate the attractiveness of the point of interest after removing the point of interest with the largest value based on the adjustment coefficient, the set of overall attractiveness, and the competition intensity of the point of interest, and repeatedly iterate to calculate the attractiveness of the remaining points of interest. After the number of iterations reaches a set value, select the node corresponding to the calculation result as multiple candidate addresses.

[0041] According to a third aspect of this disclosure, an electronic device is provided, comprising:

[0042] At least one processor; and

[0043] A memory communicatively connected to the at least one processor; wherein,

[0044] The memory stores instructions that can be executed by the at least one processor to enable the at least one processor to perform the methods described in this disclosure.

[0045] According to a fourth aspect of this disclosure, a non-transitory computer-readable storage medium is provided storing computer instructions for causing the computer to perform the methods described in this disclosure.

[0046] This disclosure discloses a site selection method, apparatus, device, and storage medium based on point-of-interest (POI) data. By analyzing the competitive and collaborative relationships among different brands in the target data, it calculates the attractiveness between the brand and the community, and selects candidate addresses within the area based on the calculation results. By integrating competitive and collaborative relationships, it enhances the adaptability of the site selection model to complex scenarios, achieves progressive optimization of multi-store site selection problems, avoids the overall efficiency decline caused by neglecting the influence of previous site selections, and achieves more practical recommendation results by incorporating rental costs and customer flow factors into the site selection recommendation process.

[0047] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of this disclosure, nor is it intended to limit the scope of this disclosure. Other features of this disclosure will become readily apparent from the following description. Attached Figure Description

[0048] The above and other objects, features, and advantages of this disclosure will become readily apparent from the following detailed description of exemplary embodiments, taken in conjunction with the accompanying drawings. Several embodiments of this disclosure are illustrated in the drawings by way of example and not limitation, in which:

[0049] In the accompanying drawings, the same or corresponding reference numerals indicate the same or corresponding parts.

[0050] Figure 1 This illustration shows the implementation flow of a location selection method based on point-of-interest data according to an embodiment of the present disclosure. Figure 1 ;

[0051] Figure 2 This illustration shows the implementation flow of a location selection method based on point-of-interest data according to an embodiment of the present disclosure. Figure 2 ;

[0052] Figure 3 A schematic diagram of a location selection device based on point of interest data according to an embodiment of the present disclosure is shown;

[0053] Figure 4 A schematic diagram of the composition structure of an electronic device according to an embodiment of the present disclosure is shown. Detailed Implementation

[0054] To make the objectives, features, and advantages of this disclosure more apparent and understandable, the technical solutions in the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this disclosure, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0055] Figure 1 This illustration shows the implementation flow of a location selection method based on point-of-interest data according to an embodiment of the present disclosure. Figure 1 ,like Figure 1 As shown, the implementation process of a location selection method based on point-of-interest data in this embodiment includes the following steps:

[0056] Step 101: Collect point-of-interest (POI) data from across the country using web crawler technology, retrieve relevant data based on the POI data, and store the relevant data and the POI data in a database.

[0057] In this embodiment of the disclosure, national Point of Interest (POI) data is collected through web crawling technology, commercial databases, social media and other channels. The relevant data includes: relevant commercial data, including latitude and longitude, address, province, city, district, industry, business district, region and population information.

[0058] In this embodiment of the disclosure, the collected data is cleaned, dirty data is removed, and the data is unified in a standardized format, integrated, and then classified and stored in the database to ensure data quality and facilitate subsequent analysis and query.

[0059] Step 102: Obtain target data, and retrieve at least one first point of interest data and corresponding first related data from the database based on the target data.

[0060] In this embodiment of the disclosure, the target data is the target market and brand, and at least one point of interest data is POI data related to the target brand or market. Correspondingly, the first related data is the relevant business data involved in the POI data related to the target brand or market, including latitude and longitude, address, province, city, district, industry, business district, region, population information, etc.

[0061] Step 103: Based on the first relevant data, find the business data corresponding to the at least one first point of interest data, and calculate the attractiveness score corresponding to the at least one first point of interest data based on the business data.

[0062] In this embodiment, the business data includes: evaluation data, business scale data, and business category data. The evaluation data includes star ratings, number of reviews, market rankings, brand research reports, etc. Evaluation weights are assigned to the number of reviews and star ratings in the evaluation data based on the number of reviews and the star ratings. A brand influence score corresponding to the at least one first point of interest data is calculated based on the evaluation weights, the number of reviews, and the star ratings. The business scale data includes business area, shopping mall area, number of parking spaces, etc. A brand attractiveness value corresponding to the at least one first point of interest data is calculated based on the business scale data. At least one category weight is set based on the business category data. Business categories can be divided into: large shopping malls, supermarkets, restaurants, etc. A first category weight corresponding to the first point of interest data is selected from the at least one category weights. An attractiveness score corresponding to the at least one first point of interest data is calculated based on the first category weight, the brand attractiveness value, and the brand influence score.

[0063] Step 104: Obtain the location information of at least one first point of interest data in the first relevant data; retrieve other point of interest related data in the relevant data based on the location information; set a first correction coefficient based on the other point of interest related data and the first relevant data; calculate the comprehensive scale weight of the at least one first point of interest data based on the attractiveness score and the first correction coefficient.

[0064] In this embodiment, the other points of interest related data refers to the points of interest data of other brands within the same location range, which can be competitors or unrelated brands. The other points of interest related data includes actual customer traffic data and actual consumption data corresponding to the other points of interest; based on the first related data, expected customer traffic data and expected consumption data corresponding to the first point of interest are set; the competition intensity of the points of interest is calculated based on the actual customer traffic data, the actual consumption data, the expected customer traffic data, and the expected consumption data. The relationship between points of interest can be determined based on the magnitude of the competition intensity; for example, a competition intensity greater than zero indicates a complementary relationship, and a competition intensity less than zero indicates a competitive relationship. The distance between points of interest is obtained based on the first related data and the other points of interest related data, wherein the distance between points of interest can be physical distance or temporal distance. A competition distance attenuation coefficient is set based on the distance between points of interest to control the degree to which cooperation or competition intensity weakens with distance. Considering the impact of competition-cooperation, an adjustment coefficient can also be set based on experience to control the magnitude of overall cooperation-competition correction. The first correction coefficient is set based on the competition distance attenuation coefficient, the distance between points of interest, the adjustment coefficient, and the competition intensity of the points of interest.

[0065] In this embodiment of the disclosure, the comprehensive scale weight of the at least one first point of interest data is calculated based on the attractiveness score and the first correction coefficient, wherein the basic brand attractiveness (such as brand influence, business scale, see above for details); the first correction coefficient is used to measure the collaborative and competitive influence of other POIs around the POI.

[0066] Step 105: Retrieve resident data from the first relevant data based on the location information; calculate the community attractiveness of the at least one first point of interest data based on the resident data and the comprehensive scale weight; accumulate the community attractiveness of the at least one first point of interest data to obtain the overall attractiveness of the first point of interest data in the region corresponding to the target data; and obtain candidate addresses in the region based on the overall attractiveness and the community attractiveness.

[0067] In this embodiment, the resident data includes resident profile feature data and resident transportation data. To analyze the matching degree between community resident profiles (age, occupation, income, etc.) and the target group of POI services, a target group feature vector is constructed based on the at least one first point of interest data and the first related data. Resident feature vectors are extracted based on the resident profile features, and the similarity between the resident feature vectors and the target group feature vectors is calculated. To comprehensively consider the accessibility of walking, private cars, and public transportation, the resident transportation data is classified based on transportation categories. Corresponding transportation mode weights are set based on the classification results, and a weighted reachable distance is calculated based on the transportation distance corresponding to the classification results and the transportation mode weights. Since attractiveness is directly proportional to resource scale and inversely proportional to distance, the community attractiveness of the at least one first point of interest data is calculated based on the similarity, the weighted reachable distance, and the comprehensive scale weight. The community attractiveness of the at least one first point of interest data is accumulated to obtain the overall attractiveness of the first point of interest data within the area corresponding to the target data.

[0068] In this embodiment, a set of optional addresses within the region is obtained, and each optional address is used as a seed node. The overall attractiveness of all points of interest within the region is calculated using the above method, resulting in a set of overall attractiveness corresponding to all points of interest in the target data. The values ​​in the set of overall attractiveness are sorted, and the seed node corresponding to the point of interest with the largest value is selected as a single candidate address. If multiple candidate addresses need to be selected, an adjustment coefficient is set according to the type of point of interest, which can be: catering industry, retail industry, logistics industry, etc. Based on the adjustment coefficient, the set of overall attractiveness, and the competition intensity of the points of interest, the attractiveness of the points of interest after removing the point of interest with the largest value is calculated, and the attractiveness of the remaining points of interest is repeatedly calculated iteratively. After the number of iterations reaches a set value, where the number of iterations is the same as the number of candidate addresses, the node corresponding to the calculation result is selected as multiple candidate addresses.

[0069] Figure 2 This illustration shows the implementation flow of a location selection method based on point-of-interest data according to an embodiment of the present disclosure. Figure 2 ,like Figure 2 As shown, the implementation process of a location selection method based on point-of-interest data in this embodiment includes the following steps:

[0070] Step 201, Data Import.

[0071] In this embodiment, nationwide POI (Point of Interest) data and related commercial data are collected through web crawling technology, commercial databases, social media, and other channels. This data includes latitude and longitude, address, province, city, district, industry, business district, region, and population information. The data is then cleaned to remove stale data, standardized in format, integrated, and categorized before being stored in the database to ensure data quality and facilitate subsequent analysis and retrieval.

[0072] Step 202: Calculate the gain of the influence of candidate points.

[0073] In this embodiment of the disclosure, the basic attraction (B) is first performed. i The calculation of basic attractiveness primarily reflects the inherent characteristics of a brand and location, and is typically a static value related to brand influence, business scale, and business type. Calculating basic attractiveness requires separately calculating brand influence, business scale, and business type. Specifically:

[0074] Calculating Brand Influence (B) brand,i A rating score is set based on brand awareness and market evaluation. Data sources include: user reviews (such as star ratings, number of reviews), market rankings, brand research reports, etc. An example formula is:

[0075] B brand,i=w1·StarRating i +w2·log(ReviewCount i )

[0076] Among them: StarRating i The brand's average star rating (e.g., 4.5 stars). ReviewCount i : Number of comments (e.g., 1000). w1, w2: Weights used to balance the influence of star rating and number of comments.

[0077] Calculate the scale of operations (B) size,i ): An attractiveness score is assigned based on the POI's retail space or facility size. Data sources include: mall area, number of seats, number of parking spaces, etc. Example formula:

[0078] B size,i =log(Area) i )

[0079] Among them: Area i The operating area (square meters) of the POI is processed using logarithms to reduce the impact of excessive size.

[0080] Computing business type (B) type,i (): A business format type value is assigned based on the brand's operating category and industry standards or experience. For example, the basic appeal of a large supermarket or department store may be higher than that of a small shop with a single business format. For example: Large department store: B type,i =1.2. Supermarket: B type,i =1. Restaurant: B type,i =0.8.

[0081] The basic attractiveness is calculated using the formula: B i =B brand,i +B size,i +B type,i .

[0082] In this embodiment of the disclosure, the comprehensive size weight (S) is calculated based on the aforementioned basic attractiveness and the cooperation-competition correction coefficient. i ), where the cooperation-competition correction coefficient (C i The calculation method is as follows:

[0083]

[0084] Among them, POIs from different brands i and POI k The intensity of collaboration-competition is defined by the Extended Customer Satisfaction Index (CSI), where CSI... ik>0: Indicates a cooperative relationship (complementary effect); CSI ik <0: Indicates a competitive relationship (substitution effect). d ik POI i and POI k The physical or temporal distance between them represents the degree to which the intensity of cooperation-competition decreases with distance. γ: Distance attenuation coefficient, controlling the degree to which the intensity of cooperation or competition weakens with distance. A larger γ indicates that the influence of cooperation-competition weakens rapidly over long distances. δ: Adjustment coefficient, used to control the magnitude of the overall cooperation-competition correction: δ>0: indicates that the influence of cooperation and competition is included in the attraction correction. δ=0: indicates that the cooperation-competition relationship is ignored. (Based on POI) i Cooperation-competition correction factor (C) i It can be seen that C i >1: Indicates that the overall collaborative effect is dominant; C i <1: Indicates that the overall competitive effect is dominant. When CSI ik >0 and d ik When it is small, POI k For POI i The impact of collaboration is greater, and the correction factor C i Increased, enhanced attractiveness. When CSI ik <0 and d ik When it is small, POI k For POI i The competitive impact is greater, and the correction coefficient C i As distance decreases, attractiveness declines. The effects of cooperation and competition vary with distance d. ik Increase and gradually decrease, from Control. If CSI ik ≈0 indicates that there is no obvious cooperation or competition between the two, and it does not affect C. i .

[0085] In this embodiment, the attractive force is calculated based on a gravity model, whereby the attractive force is directly proportional to the resource size and inversely proportional to the distance. β is set as the distance attenuation coefficient (an empirical value, typically between 1.0 and 2.0), and POI... i The attractiveness of G to a certain community j ij Expressed as:

[0086]

[0087] Select POI i The attraction gain is:

[0088]

[0089] In this embodiment of the disclosure, R ijTo measure the degree of matching between a Point of Interest (POI) and the needs of the target community, the specific calculation method is as follows: The demand matching degree measures whether the POI's services meet the needs of the target community residents, typically based on characteristics such as community population, income, and consumption habits. By analyzing the matching degree between community resident profiles (age, occupation, income, etc.) and the POI's target group, a resident feature vector is obtained. Then, a target group feature vector is set based on the target POI (e.g., suitable for children / young people / elderly people), using the following formula:

[0090] R ij =Sim(Profile) i ,Target i )

[0091] Among them, Profile i Target: The resident feature vector of community j. i POI i The target group feature vector. Sim: similarity function (e.g., cosine similarity).

[0092] In this embodiment of the disclosure, d ij To calculate the weighted reachability distance, the total distance is defined by comprehensively considering the accessibility of walking, private cars, and public transportation:

[0093]

[0094] in, Distances for the three modes of transportation. w1, w2, w3: Weights of each mode of transportation (determined by the travel habits of community residents).

[0095] Step 203: Sort candidate points.

[0096] In this embodiment of the disclosure, all candidate POIs are: P = {p1, p2, ..., p...} n Treating each node as a node, and based on the method in step 202 above, calculate the initial attraction gain G for each node in the set. i The candidate points are ranked according to the magnitude of the calculation results, and the points with the highest attractiveness, i.e., the TOP K points, are selected as the preferred site selection points.

[0097] Step 204, Candidate point selection.

[0098] In this embodiment of the disclosure, considering the combined impact of potential customer coverage, collaborative and competitive POIs, and the diminishing effect of selected nodes on candidate nodes, the result of step 203 above—the highest ranked value—is added to the selected set S, and then the gravity gain G of the remaining candidate points is updated. iBased on the CSI, the competition suppression effect of the selected nodes is deducted, and the above steps are repeated iteratively until the number of sites or the coverage target is met, wherein the gravity gain G of the remaining candidate points is updated. i The formula is:

[0099]

[0100] Where S: the set of selected POIs, δ F : Adjustment coefficient, controlling the overall magnitude of competition and cooperation: δ F >0: Collaboration effect dominates. δ F <0: Competition effect dominates. When δ F =0: Completely ignores cooperation and competition; the model only considers the basic attractiveness of candidate points. This can be dynamically adjusted in practice: δ F The settings can be adjusted based on business needs and scenarios. For example: For the catering industry: δ > 0.5, emphasizing the importance of collaboration. For the retail industry: δ ∈ [-0.5, 0], balancing collaboration and competition, with a tendency to suppress competition. For the logistics industry: δ ≈ 0, ignoring competition and only considering coverage.

[0101] In this embodiment of the disclosure, based on the algorithm in step 204, a practical example is provided for further illustration: The scenario is that a fast-moving consumer goods (FMCG) brand wants to select three new store locations within a city. The candidate POI set P = {A, B, C, D, E}, along with the potential customer coverage, collaborating POIs, and competing POI distributions for each POI, are input. The initial attractiveness gain G is calculated. i :

[0102] G A =120,G B =110,G C =150,G D =140,G E =100

[0103] In the first iteration, select C (highest attraction gain), update the attraction of other points, and consider G. C The gravitational gain of the remaining candidate points is updated to reflect the impact of its CSI as follows:

[0104] G′ A =G A -δCSI AC

[0105] G′ B =G B -δCSI BC

[0106] In the second iteration, the point with the highest attractiveness after the update is selected, and the final recommended site selection points are C, A, and D.

[0107] Step 205: Visualize and output the candidate points.

[0108] In this embodiment of the disclosure, visualization output can be achieved in the following ways, such as: map display: marking POI data on a map to intuitively show the distribution of POIs; heat map display: reflecting the density of POIs in a certain area through color depth to help identify hotspot areas; category display: marking different colors or shapes on the map according to different categories of POIs (such as catering, entertainment, shopping, etc.) to facilitate the identification of different types of POI distribution.

[0109] In this embodiment, the hardware architecture employs a distributed architecture for deployment. Specifically, tasks such as data collection, density calculation, brand analysis, and market gap identification are distributed across different servers for processing. Furthermore, to ensure processing speed and stability, load balancing technology can be used to manage and schedule the servers. Additionally, for ease of use by enterprise users, the algorithm can be integrated into the enterprise's internal business systems, or a standalone web application or mobile application can be developed for user access.

[0110] Figure 3 A schematic diagram of a location selection device based on point-of-interest data according to an embodiment of the present disclosure is shown, such as... Figure 3 As shown, an embodiment of this disclosure provides a location selection device based on point-of-interest (POI) data, comprising:

[0111] The data collection unit 301 is used to collect point-of-interest (POI) data from across the country based on web crawler technology, retrieve relevant data based on the POI data, and store the relevant data and the POI data in a database.

[0112] The target data retrieval unit 302 is used to acquire target data and retrieve at least one first point of interest data and corresponding first related data from the database based on the target data.

[0113] The attraction score calculation unit 303 is used to find the business data corresponding to the at least one first point of interest data based on the first relevant data, and to calculate the attraction score corresponding to the at least one first point of interest data based on the business data.

[0114] The attractiveness score calculation unit 303 is further configured to: the business data includes: evaluation data, business scale data, and business category data; set evaluation weights for the number of comments and star ratings in the evaluation data; calculate the brand influence score corresponding to the at least one first point of interest data based on the evaluation weights, the number of comments, and the star ratings; calculate the brand attractiveness value corresponding to the at least one first point of interest data based on the business scale data; set at least one category weight based on the business category data; select the first category weight corresponding to the first point of interest data from the at least one category weight; and calculate the attractiveness score corresponding to the at least one first point of interest data based on the first category weight, the brand attractiveness value, and the brand influence score.

[0115] The first correction coefficient calculation unit 304 is used to obtain the location information of at least one first point of interest data in the first related data, retrieve other point of interest related data in the related data based on the location information, and set a first correction coefficient based on the other point of interest related data and the first related data.

[0116] The first correction coefficient calculation unit 304 is further configured to: include the other point-of-interest related data as including actual customer flow data and actual consumption data of customers corresponding to the other point of interest; set expected customer flow data and expected consumption data corresponding to the first point of interest based on the first related data; calculate the point-of-interest competition intensity based on the actual customer flow data, the actual consumption data, the expected customer flow data, and the expected consumption data; obtain the distance between points of interest based on the first related data and the other point-of-interest related data; set a competition distance attenuation coefficient based on the distance between points of interest; and set the first correction coefficient based on the competition distance attenuation coefficient, the distance between points of interest, and the point-of-interest competition intensity.

[0117] The comprehensive scale weight calculation unit 305 is used to calculate the comprehensive scale weight of the at least one first point of interest data based on the attractiveness score and the first correction coefficient.

[0118] The attractiveness calculation unit 306 is used to retrieve resident data from the first relevant data based on the location information, calculate the community attractiveness of the at least one first point of interest data based on the resident data and the comprehensive scale weight, and accumulate the community attractiveness of the at least one first point of interest data to obtain the overall attractiveness of the first point of interest data in the area corresponding to the target data.

[0119] The attractiveness calculation unit 306 is further configured to: ...

[0120] The site selection unit 307 is used to obtain candidate addresses within the area based on the overall attractiveness and the community attractiveness.

[0121] The location selection unit 307 is further configured to: obtain a set of optional addresses within the region, and use each optional address as a seed node; obtain a set of overall attractiveness corresponding to all points of interest in the target data, sort the values ​​in the set of overall attractiveness, and select the seed node corresponding to the point of interest with the largest value as a single candidate address; set an adjustment coefficient according to the type of point of interest, and calculate the attractiveness of the point of interest after removing the point of interest with the largest value based on the adjustment coefficient, the set of overall attractiveness, and the competition intensity of the point of interest, and repeatedly iterate to calculate the attractiveness of the remaining points of interest. After the number of iterations reaches a set value, select the node corresponding to the calculation result as multiple candidate addresses.

[0122] In an exemplary embodiment, the data collection unit 301, the target data retrieval unit 302, the attractiveness score calculation unit 303, the first correction coefficient calculation unit 304, the comprehensive scale weight calculation unit 305, the attractiveness calculation unit 306, and the addressing unit 307 may be implemented by one or more central processing units (CPUs), graphics processing units (GPUs), application-specific integrated circuits (ASICs), DSPs, programmable logic devices (PLDs), complex programmable logic devices (CPLDs), field-programmable gate arrays (FPGAs), general-purpose processors, controllers, microcontrollers (MCUs), microprocessors, or other electronic components.

[0123] Regarding the apparatus in the above embodiments, the specific manner in which each module and unit performs its operations has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0124] According to embodiments of this disclosure, this disclosure also provides an electronic device and a readable storage medium.

[0125] Figure 4 A schematic block diagram of an example electronic device 800 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0126] like Figure 4As shown, device 800 includes a computing unit 801, which can perform various appropriate actions and processes based on a computer program stored in read-only memory (ROM) 802 or a computer program loaded from storage unit 808 into random access memory (RAM) 803. RAM 803 may also store various programs and data required for the operation of device 800. The computing unit 801, ROM 802, and RAM 803 are interconnected via bus 804. Input / output (I / O) interface 805 is also connected to bus 804.

[0127] Multiple components in device 800 are connected to I / O interface 805, including: input unit 806, such as keyboard, mouse, etc.; output unit 807, such as various types of monitors, speakers, etc.; storage unit 808, such as disk, optical disk, etc.; and communication unit 809, such as network card, modem, wireless transceiver, etc. Communication unit 809 allows device 800 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0128] The computing unit 801 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 801 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 801 performs the various methods and processes described above, such as an addressing method based on point-of-interest (POI) data. For example, in some embodiments, an OPI-based addressing method can be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 808. In some embodiments, part or all of the computer program can be loaded and / or installed on device 800 via ROM 802 and / or communication unit 809. When the computer program is loaded into RAM 803 and executed by the computing unit 801, one or more steps of the OPI-based addressing method described above can be performed. Alternatively, in other embodiments, the computing unit 801 may be configured, by any other suitable means (e.g., by means of firmware), to perform a location method based on point-of-interest data.

[0129] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0131] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0133] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0134] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0135] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0136] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this disclosure, "a plurality of" means two or more, unless otherwise explicitly specified.

[0137] The above description is merely a specific embodiment of this disclosure, but the scope of protection of this disclosure is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this disclosure should be included within the scope of protection of this disclosure. Therefore, the scope of protection of this disclosure should be determined by the scope of the claims.

Claims

1. A site selection method based on point of interest data, characterized in that: The method comprises: Collecting national point of interest data based on web crawler technology, retrieving relevant data based on the point of interest data, and storing the relevant data and the point of interest data in a database; Acquire target data, and retrieve at least one first point of interest data and corresponding first related data corresponding to the database based on the target data; searching for business data corresponding to the at least one first point of interest data based on the first related data, and calculating an attractiveness score corresponding to the at least one first point of interest data based on the business data; Acquire location information of at least one first point of interest data in the first related data, retrieve other point of interest related data in the related data based on the location information, and set a first correction coefficient based on the other point of interest related data and the first related data; Calculating a comprehensive scale weight of the at least one first point of interest data based on the attractiveness score and the first correction coefficient; Based on the location information, the resident data in the first related data is retrieved, the community attraction of the at least one first point of interest data is calculated based on the resident data and the comprehensive scale weight, the community attraction of the at least one first point of interest data is accumulated to obtain the overall attraction of the first point of interest data in the area corresponding to the target data, and the candidate addresses in the area are obtained based on the overall attraction and the community attraction.

2. The method according to claim 1, characterized in that The calculating, based on the business data, an attraction score corresponding to the at least one first point of interest data comprises: The business data includes: evaluation data, business scale data, and business category data; Setting evaluation weights for the number of comments and the rating stars in the evaluation data respectively, and calculating the brand influence score corresponding to the at least one first point of interest data based on the evaluation weights, the number of comments and the rating stars; A brand attraction value corresponding to the at least one first point of interest data is calculated based on the business scale data, at least one category weight is set based on the business category data, a first category weight corresponding to the first point of interest data is selected from the at least one category weight, and an attraction score corresponding to the at least one first point of interest data is calculated based on the first category weight, the brand attraction value and the brand influence score.

3. The method according to claim 1, characterized in that The setting of the first correction coefficient based on the other point of interest related data and the first related data includes: The other points of interest related data include actual customer flow data and actual consumption data of customers corresponding to the other points of interest; Setting expected passenger flow data and expected consumption data corresponding to the first point of interest based on the first relevant data; Calculating the competition intensity of the point of interest based on the actual passenger flow data, the actual consumption data, the expected passenger flow data and the expected consumption data; The distance between points of interest is obtained based on the first relevant data and the other point of interest related data, a competition distance attenuation coefficient is set based on the distance between points of interest, an adjustment coefficient is set based on the point of interest related data and the first point of interest data, and the first correction coefficient is set based on the competition distance attenuation coefficient, the distance between points of interest, the adjustment coefficient and the point of interest competition intensity.

4. The method according to claim 1, characterized in that The calculating the community attractiveness of the at least one first point of interest data based on the resident data and the comprehensive scale weight comprises: The resident data includes: resident portrait feature data and resident traffic data; constructing a target group feature vector based on the at least one first point of interest data and the first related data, extracting a resident feature vector based on the resident portrait feature, and calculating the similarity between the resident feature vector and the target group feature vector; Classifying the resident traffic data based on traffic categories, setting corresponding traffic mode weights based on the classification results, and calculating weighted reachable distances based on the traffic distances corresponding to the classification results and the traffic mode weights; The community attraction of the at least one first point of interest data is calculated based on the similarity, the weighted reachable distance and the comprehensive scale weight.

5. The method according to claim 3, characterized in that: The obtaining of candidate addresses within the area based on the overall attractiveness and the community attractiveness comprises: Obtain a set of optional addresses in the area, and use each of the optional addresses as a seed node; Obtaining the overall attraction sets corresponding to all the points of interest in the target data, sorting the values ​​in the overall attraction sets, and selecting the seed node corresponding to the point of interest with the largest value as a single candidate address; An adjustment coefficient is set according to the type of the point of interest, and based on the adjustment coefficient, the overall attraction set and the competition intensity of the points of interest, the attraction of the points of interest after removing the point of interest with the largest value is calculated, and the attraction of the remaining points of interest is repeatedly calculated. After the number of repeated iterations reaches the set value, the nodes corresponding to the calculation results are selected as multiple candidate addresses.

6. A site selection device based on point of interest data, characterized in that: The device comprises: A data collection unit, used to collect national point of interest data based on a web crawler technology, retrieve relevant data based on the point of interest data, and store the relevant data and the point of interest data in a database; a target data retrieval unit, configured to obtain target data, and retrieve corresponding at least one first point of interest data and corresponding first related data in the database based on the target data; an attraction score calculation unit, configured to search for business data corresponding to the at least one first point of interest data based on the first related data, and calculate an attraction score corresponding to the at least one first point of interest data based on the business data; a first correction coefficient calculation unit, configured to obtain location information of at least one first point of interest data in the first related data, retrieve other point of interest related data in the related data based on the location information, and set a first correction coefficient based on the other point of interest related data and the first related data; a comprehensive scale weight calculation unit, configured to calculate a comprehensive scale weight of the at least one first point of interest data based on the attractiveness score and the first correction coefficient; an attraction calculation unit, configured to retrieve resident data in the first related data based on the location information, calculate the community attraction of the at least one first point of interest data based on the resident data and the comprehensive scale weight, and accumulate the community attraction of the at least one first point of interest data to obtain the overall attraction of the first point of interest data in the area corresponding to the target data; A site selection unit is used to obtain candidate addresses within the area based on the overall attractiveness and the community attractiveness.

7. The device according to claim 6, characterized in that The attraction score calculation unit is also used for, wherein the business data includes: evaluation data, business scale data, and business category data; setting evaluation weights for the number of comments and the rating star in the evaluation data, respectively, and calculating the brand influence score corresponding to the at least one first point of interest data based on the evaluation weights, the number of comments, and the rating star; calculating the brand attraction value corresponding to the at least one first point of interest data based on the business scale data, setting at least one category weight based on the business category data, selecting the first category weight corresponding to the first point of interest data from the at least one category weight, and calculating the attraction score corresponding to the at least one first point of interest data based on the first category weight, the brand attraction value, and the brand influence score; The attraction calculation unit is also used for, wherein the resident data includes: resident portrait feature data and resident traffic data; constructing a target group feature vector based on the at least one first point of interest data and the first related data, extracting a resident feature vector based on the resident portrait feature, and calculating the similarity between the resident feature vector and the target group feature vector; classifying the resident traffic data based on traffic categories, setting corresponding traffic mode weights based on the classification results, and calculating a weighted reachable distance based on the traffic distance corresponding to the classification results and the traffic mode weight; calculating the community attraction of the at least one first point of interest data based on the similarity, the weighted reachable distance and the comprehensive scale weight.

8. The device according to claim 6, characterized in that The first correction coefficient calculation unit is further used for: the other interest point related data include actual passenger flow data and actual consumption data of customers corresponding to the other interest points; setting expected passenger flow data and expected consumption data corresponding to the first interest point based on the first related data; calculating the interest point competition intensity based on the actual passenger flow data, the actual consumption data, the expected passenger flow data and the expected consumption data; obtaining the distance between interest points based on the first related data and the other interest point related data, setting the competition distance attenuation coefficient based on the distance between interest points, setting the adjustment coefficient based on the interest point related data and the first interest point data, and setting the first correction coefficient based on the competition distance attenuation coefficient, the distance between interest points, the adjustment coefficient and the interest point competition intensity; The site selection unit is also used to obtain a set of optional addresses in the area, and take each of the optional addresses as a seed node; obtain a set of overall attractions corresponding to all the points of interest in the target data, sort the values ​​in the overall attraction set, and select the seed node corresponding to the point of interest with the largest value as a single candidate address; set an adjustment coefficient according to the type of the point of interest, calculate the attraction of the point of interest after removing the point of interest with the largest value based on the adjustment coefficient, the overall attraction set and the competition intensity of the point of interest, and repeatedly iterate the calculation of the attraction of the remaining points of interest, and after the number of repeated iterations reaches the set value, select the nodes corresponding to the calculation results as multiple candidate addresses.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method according to any one of claims 1 to 5.

10. A non-transitory computer-readable storage medium storing computer instructions, characterized in that: The computer instructions are used to make a computer execute the method according to any one of claims 1-5.

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