Store site selection method and system based on public data, and electronic equipment

Abstract

The embodiment of the invention provides a shop site selection method and system based on public data and electronic equipment. The method is applied to the technical field of data processing, and comprises the following steps: if demand information sent by a client is received, determining a target property conforming to the demand information; determining the shop type of each shop under each target property and the actual index data of each preset influence factor; determining an influence weight and an influence score of each preset influence factor; inputting the influence weight and the influence score into a preset shop evaluation model, and calculating a shop score of each shop; and sending the target property, all shops under the target property and the shop score of each shop to the client. According to the scheme, the efficiency can be improved, the decision accuracy is improved, the user experience is enhanced, and a scientific basis is provided for commercial decision. Through automatic data matching and an evaluation model, personalized and accurate shop selection suggestions can be provided for the user, so that the method and the system take a precedent in the market with fierce competition.

Description

Technical Field

[0001] The present disclosure relates to the technical field of data processing, and particularly to a store location selection method, system, and electronic device based on public data. Background Art

[0002] In traditional physical retail industries such as restaurants, hotels, convenience stores, and barbershops, store location selection plays a crucial role in the success of their operations. Appropriate location selection often means that a solid foundation for success has been laid; otherwise, it may face operational difficulties and be difficult to sustain.

[0003] In the past, store location selection traditionally relied on the personal experience and intuition of operators. They might choose a store opening location through on-site inspections, word-of-mouth consultations, and experience judgments.

[0004] However, these methods lack systematic data analysis and scientific decision-making support, resulting in location selection decisions often being highly subjective and uncertain. Some operators even adopt a trial-and-error method, opening a store first and then adjusting according to the operating conditions. This is not only costly but also inefficient. Therefore, traditional location selection methods are difficult to ensure the accuracy and effectiveness of location selection, and the success or failure of opening a store often has a high degree of contingency. Summary of the Invention

[0005] To address the deficiencies of the prior art, the present disclosure provides a store location selection method, system, and electronic device based on public data. The present disclosure solves the technical problems that existing store location selection methods lack systematic data analysis and scientific decision-making support, resulting in location selection decisions often being highly subjective and uncertain, being difficult to ensure the accuracy and effectiveness of location selection, and the success or failure of opening a store often having a high degree of contingency.

[0006] According to a first aspect of the present disclosure, there is provided a store location selection method based on public data, including: if demand information sent by a client is received, querying property data in a preset property store data structure according to the demand information to determine target properties that meet the demand information; where the target properties are at least one;

[0007] Obtaining store data of all stores under each target property, determining the store types of each store according to the store data, and determining the actual index data of each preset influencing factor according to the store data;

[0008] Determining the influence weights of each preset influencing factor according to the store types, and determining the influence scores of each preset influencing factor according to the actual index data of each preset influencing factor and a preset scoring rule;

[0009] Input the influence weight and the influence score into a preset store evaluation model, and calculate the store scores of each store through the preset store evaluation model according to a preset evaluation formula, the influence score, and the influence weight;

[0010] Send the target property, all stores under the target property, and the store scores of each store to the client.

[0011] According to a second aspect of the present disclosure, there is provided a store location system based on public data for performing the method as described in the first aspect, including: a property store - opening data query module, configured to query property data in a preset property store - opening data structure according to the demand information if demand information sent by the client is received, and determine a target property that meets the demand information; where the target property is at least one;

[0012] A store data acquisition module, configured to acquire store data of all stores under each target property, determine the store types of each store according to the store data, and determine the actual index data of each preset influence factor according to the store data;

[0013] An influence parameter determination module, configured to determine the influence weight of each preset influence factor according to the store type, and determine the influence score of each preset influence factor according to the actual index data of each preset influence factor and a preset scoring rule;

[0014] A store data calculation and management module, configured to input the influence weight and the influence score into a preset store evaluation model, and calculate the store scores of each store through the preset store evaluation model according to a preset evaluation formula, the influence score, and the influence weight;

[0015] A store location front - end application module, configured to send the target property, all stores under the target property, and the store scores of each store to the client.

[0016] According to a third aspect of the present disclosure, there is provided an electronic device, including: a memory and a processor, where a computer program is stored on the memory, and when the processor executes the program, the method as described above is implemented.

[0017] According to a third aspect of the present disclosure, there is provided a machine - readable medium, where a program or instruction is stored on the machine - readable medium, and when the program or instruction is executed by the processor, the method as described above is implemented.

[0018] In a store location selection method, system, and device based on public data provided as above, the embodiments of the present disclosure can improve efficiency, enhance the accuracy of decision-making, enhance the user experience, and provide a scientific basis for business decisions. Through automated data matching and evaluation models, personalized and accurate store selection suggestions can be provided to users, thus gaining an advantage in the highly competitive market. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] To more clearly illustrate the technical solutions in the embodiments of the present disclosure or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of the present disclosure. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0020] Figure 1 FIG. shows a schematic flowchart of a store location selection method based on public data according to an embodiment of the present disclosure;

[0021] Figure 2 FIG. shows a schematic flowchart of a store location selection method based on public data according to an embodiment of the present disclosure;

[0022] Figure 3 FIG. shows a schematic block diagram of a store location selection system based on public data according to an embodiment of the present disclosure;

[0023] Figure 4 FIG. shows a block diagram of an exemplary electronic device according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0024] Now, various exemplary embodiments of the present disclosure will be described in detail with reference to the drawings. It should be noted that: Unless otherwise specifically stated, the relative arrangements, numerical expressions, and values of the components and steps set forth in these embodiments do not limit the scope of the present disclosure.

[0025] Those skilled in the art can understand that terms such as "first" and "second" in the embodiments of the present disclosure are only used to distinguish different steps, devices or modules, etc., and neither represent any specific technical meaning nor indicate an inevitable logical order between them. It should also be understood that in the embodiments of the present disclosure, "a plurality of" may refer to two or more, and "at least one" may refer to one, two or more. It should also be understood that for any component, data or structure mentioned in the embodiments of the present disclosure, in the absence of clear limitations or contrary revelations in the context, it can generally be understood as one or more. In addition, the term "and / or" in the present disclosure is merely a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the character " / " in the present disclosure generally represents an "or" relationship between the associated objects before and after. It should also be understood that the present disclosure emphasizes the differences between various embodiments, and their similarities can be referred to each other. For the sake of brevity, they will not be described one by one.

[0026] At the same time, it should be understood that for the convenience of description, the sizes of the various parts shown in the drawings are not drawn according to the actual proportional relationship. The following description of at least one exemplary embodiment is actually only illustrative and in no way limits the present disclosure and its application or use. Technologies, methods and devices known to those of ordinary skill in the relevant art may not be discussed in detail, but where appropriate, the said technologies, methods and devices should be regarded as part of the specification. It should be noted that: like reference numerals and letters denote like items in the following drawings, and thus, once an item is defined in one drawing, it does not need to be further discussed in subsequent drawings.

[0027] To make the objectives, technical solutions and advantages of the embodiments of the present disclosure clearer, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments in the present disclosure without creative efforts fall within the scope of protection of the present disclosure.

[0028] Figure 1 It is a schematic flowchart of a store location selection method based on public data provided for the embodiments of the present disclosure. As Figure 1 shown, the method includes:

[0029] S101, if demand information sent by a client is received, query property data in a preset property store - opening data structure according to the demand information, and determine target properties that meet the demand information; where the target properties are at least one.

[0030] The control end can usually be a system or platform that manages the entire business process. It is responsible for receiving requests from the client, performing corresponding processing (such as querying the database, calculating scores, etc.), and returning the results to the client. The functions of the control end include data query, logic processing, model application, etc. For example, it can be a backend server, API service, etc.

[0031] The client can be an interface or platform that the user interacts with directly. It can be a web client, a mobile application, or a desktop client. The role of the client is to send demand information to the control end and display the results returned from the control end.

[0032] Demand information can be parameters or conditions provided by the client when initiating a request. This information can be the user's requirements when selecting a property, such as location, area, rental status, traffic conditions, surrounding environment, etc. Demand information is used to filter and select target properties that meet the user's requirements.

[0033] The preset property store data structure can be a structure for storing and organizing all property data, usually a database table, data set or other storage format. This data structure contains various information about the property, such as the property's address, area, house layout, transportation, surrounding community, surrounding population, surrounding parking, surrounding schools, surrounding hospitals, surrounding attractions, surrounding office buildings, store index data, etc., in order to facilitate query and screening according to demand information.

[0034] Property data can refer to detailed information related to each property, usually including the property's address, area, house layout, transportation, surrounding community, surrounding population, surrounding parking, surrounding schools, surrounding hospitals, surrounding attractions, surrounding office buildings, store index data, etc. The data of each property will help evaluate its potential for opening a store.

[0035] The target property may refer to a qualified property selected from the property data by the control end according to the client's demand information. Each target property is a property that meets the user's needs and is considered a potential store location.

[0036] The client can send some parameters containing user requirements to the control end. The control end will query in the preset property store data structure based on the received demand information. The query will return the property records that meet the conditions. These records are the "target properties". The target property can be one or more, depending on the query conditions and data.

[0037] Based on the above technical solution, the optional, preset property store opening data structure formation process includes:

[0038] Obtain the property information, property rental and sales information, store data, initial store ratings, surrounding resident population information, surrounding office worker information, traffic information, walking access information, and surrounding parking space information of each property, and form a preset property store - opening data structure based on the property information, property rental and sales information, store data, initial store ratings, surrounding resident population information, surrounding office worker information, traffic information, walking access information, and surrounding parking space information.

[0039] In this solution, the property information may include a detailed description of the property itself, such as the address, area, housing pattern, property type (commercial, residential, etc.), construction year of the property, number of floors, etc.

[0040] The property rental and sales information may include the rental or sales information of the property, including the current rental price, sales price, vacancy rate, lease term, property rental and sales status (rented, for sale, vacant), etc.

[0041] The initial store rating can be a preliminary rating calculated for the store based on a series of evaluation factors (such as location, traffic, surrounding facilities, etc.), which is used to measure the potential and adaptability of the store.

[0042] The surrounding resident population information may describe the resident population situation within a certain range around the property, including population density, population quantity, age distribution, income level, etc.

[0043] The surrounding office worker information may describe the personnel information in the office areas around the property, including the number of office buildings, total number of office workers, industry distribution, etc.

[0044] The traffic information may include the traffic conditions around the property, such as the location and distance of the main traffic arteries, public transportation (subway, bus, etc.), and the degree of traffic congestion.

[0045] The walking access information may be the walking distance between the property and key surrounding locations, including business districts, commercial centers, stations, etc.

[0046] The surrounding parking space information may describe the parking space information around the property, including the number of public parking lots, availability rate of parking spaces, parking fees, etc.

[0047] Property information and property rental and sales information can be obtained from property management platforms, real estate agents, and property websites (such as Lianjia and Beike). Store data can be obtained from store management systems or information provided by merchants. Peripheral resident population information can be obtained using statistical data released by the government, social survey data, or data provided by third-party market research companies. Peripheral office worker information can be obtained through commercial real estate companies and market research reports. Geographic Information System (GIS) and map APIs (such as Google Maps and Baidu Maps) are used to calculate traffic information and walking access information. Peripheral parking space information can be obtained from urban traffic management platforms, property management systems, or market research reports. Then, the data obtained from various channels is integrated into a unified data structure. This data structure is the "preset property store-opening data structure" and can use database tables, JSON formats, or other data management methods. Attribute fields: Corresponding fields are created based on the above data items. For example, property information can include fields such as "address", "floor area", and "property type"; store data includes fields such as "store area" and "rent". These data items are associated by property ID or store ID to ensure that various types of information under the same property can be matched together. Then, using the preset evaluation model and scoring rules, the initial score of each store is calculated based on the integrated data. Finally, the above data is filled into the preset property store-opening data structure to ensure the accuracy and timeliness of the data.

[0048] In this solution, through this data integration and analysis, merchants and investors can make more accurate decisions in aspects such as site selection, operation, and strategic planning, thereby improving efficiency, reducing costs, increasing market competitiveness, and continuously adjusting to respond to market changes.

[0049] S102, Obtain the store data of all stores under each target property, determine the store type of each store according to the store data, and determine the actual index data of each preset influencing factor according to the store data.

[0050] A store can be a commercial facility opened within a target property, usually referring to an actual business space or business unit.

[0051] Store data can be information related to each store. Specifically, it can include store location (such as specific property unit, floor location, etc.), store area (such as square meters), store rent or price, store type (such as catering, retail, etc.), opening time, business status, daily passenger flow, turnover and other business data.

[0052] The store type can refer to the commercial category to which the store belongs. Different stores can be classified into different types according to their business nature. For example: catering (restaurants, cafes, fast food restaurants), retail (convenience stores, clothing stores, mobile phone stores), service (beauty salons, laundries), hotel (budget hotels, business hotels).

[0053] The preset impact factors can refer to various environmental, geographical, market, transportation and other factors used to evaluate the suitability of a store during store evaluation. Specifically, it can include: residential population density (the number of residents within 1000 meters), the number of office workers (the number of daily office workers within 1000 meters), traffic conditions (such as the passing pedestrian flow at the entrance, traffic congestion, etc.), the surrounding business district environment (the number of stores in the same industry, the surrounding competition situation), road type and traffic conditions (street width, whether it is close to a transportation hub, etc.).

[0054] The actual indicator data can refer to the specific numerical value or category of each factor in the preset impact factors. It is extracted from the actual property and surrounding environment data and reflected in the specific store data under each target property. For example: residential population density: if the number of residents within 1000 meters is 800 people, then the actual indicator data of this factor is 800 people.

[0055] All store data under the target property can be queried from the property database according to the ID or location of the property. The data of each store includes the store type, area, rent, passenger flow, business status, etc. Each store is classified according to its business category (such as restaurants, convenience stores, etc.). The category information of the store can be obtained through the store category management module. Specifically, the store category management module is used to manage store categories, such as barbershops, restaurants, convenience stores, budget hotels, etc. For each store, the control end extracts the actual indicator data of relevant impact factors according to data such as its location and surrounding environment. These data are usually obtained through the property data management module and the public data management module, covering indicators such as residential population density, passing pedestrian flow, and traffic conditions. Specifically, the public data management module is used to manage the obtained public data and provide basic data for the property data management module and the store data calculation management module. The property data management module is used to manage the data of specific properties, including but not limited to the address, area, housing pattern, traffic, surrounding communities, surrounding population, surrounding parking, surrounding schools, surrounding hospitals, surrounding scenic spots, surrounding office buildings, store indicator data, etc. Obtain data from the public data management module and the store data calculation management module, and extract and organize these data according to the "preset property store data structure".

[0056] Based on the above technical solutions, optionally, determining the store type of each store according to the store data includes:

[0057] Query the store type in the store category management system according to the said store data.

[0058] In this solution, the store category management system can be a database or module for managing and maintaining different types of stores. It is responsible for defining, classifying, and storing various categories of stores, such as catering, retail, entertainment, service industries, etc. The system helps determine the characteristics, requirements, and evaluation criteria of stores through category management, and then provides important support in aspects such as site selection, evaluation, and operation.

[0059] A query can be initiated to the store category management system based on the store data to identify which category the store belongs to. Usually, this query is based on the following fields:

[0060] Store name: Can be directly mapped to the category.

[0061] Business type: Such as "catering", "clothing", "bookstore", etc., as the basis for classification.

[0062] Other attributes: Such as store scale, target customer group, etc., as auxiliary information to help determine a more accurate category.

[0063] Then the query result will return the category type of the store (such as catering, retail, medical, etc.).

[0064] In this solution, the store category management system enables enterprises to accurately identify and optimize store types and site selection environments through standardized management, personalized evaluation, and data-driven decision-making. In this way, it can improve the accuracy of decision-making, the optimization degree of resource allocation, and provide strong data support, thereby helping enterprises achieve greater success and long-term sustainable development in the highly competitive market.

[0065] S103. Determine the influence weights of each preset influence factor according to the said store type, and determine the influence scores of each preset influence factor according to the actual index data of each preset influence factor and the pre-designed scoring rules.

[0066] The influence weight can refer to the relative importance of different influence factors on the final evaluation score in the evaluation model. Different store types (such as restaurants, convenience stores, retail stores, etc.) may have different degrees of attention to certain factors, so the weights of influence factors will vary among different store types. For example, there are three influence factors in the evaluation model: traffic convenience, population density, and density of same-industry stores. And for a restaurant store: Traffic convenience weight: 0.5

[0067] Population density weight: 0.3

[0068] Density of same-industry stores weight: 0.2

[0069] The pre-designed scoring rules refer to how to score the actual indicator data values of different influencing factors (such as population quantity, traffic flow, etc.). Each influencing factor has a scoring criterion corresponding to its indicator data. These rules determine how to allocate scores according to specific numerical ranges or enumerated values. For example, for the influencing factor of the resident population quantity within 500 meters, the pre-designed scoring rules may be as follows:

[0070] 0 - 500 people: Get 1 point

[0071] 501 - 1000 people: Get 3 points

[0072] 1001 - 1500 people: Get 5 points

[0073] Over 1501 people: Get 7 points

[0074] The influencing score can refer to the score calculated based on the actual indicator data of the influencing factor and the pre-designed scoring rules. The score of each influencing factor is calculated based on the actual indicator data value of this factor and its corresponding score range or rules. For example, assuming that traffic convenience is an influencing factor, its score depends on the road type or traffic flow in this area, and a score is generated according to the pre-set rules.

[0075] According to the different types of stores, the weights of some influencing factors may be different. For example, for a restaurant, the surrounding pedestrian flow and traffic convenience may be more important, while for a clothing store, the surrounding shopping environment and the density of similar stores may be more important. Therefore, different types of stores will have different weights of influencing factors, and these weights can be set through the store evaluation model management module. Then, the influencing scores can be queried according to the actual indicator data of each pre-set influencing factor in the pre-designed scoring rules. Specifically, the store evaluation model management module is used to manage the evaluation models of various types of stores, set up the evaluation models according to the store categories, and the settings include evaluation factors, weights of evaluation factors, scoring rules of evaluation factors, etc. Then, according to the actual indicator data of each influencing factor and the pre-set scoring rules, calculate the scores of each store on each factor. For example: If the pedestrian-reachable population within 500 meters is in the range of (0, 500], get 1 point; in the range of (500, 1000], get 3 points; and so on. If the adjacent road is an "inner passage of the mall", then the score of this factor is 3 points.

[0076] Based on the above technical solution, optionally, the formation process of the pre-designed scoring rules includes:

[0077] Obtain the historical indicator data of each pre-set influencing factor, determine the sub-scoring rules of each pre-set influencing factor according to the interval-based scoring algorithm and the historical indicator data, and determine the pre-designed scoring rules according to the scoring rules of each pre-set influencing factor.

[0078] In this solution, historical indicator data can refer to data related to preset influencing factors collected in the past, and this data can include various variables that affect store location selection and evaluation.

[0079] The interval-based scoring algorithm can be an algorithm that scores by dividing the numerical range of certain indicators into different intervals and then assigning a score to each interval. For example, assume that an indicator is "the number of people reachable on foot", and the different intervals can be divided as follows:

[0080] The number of people reachable on foot (0, 500] → 1 point

[0081] The number of people reachable on foot (500, 1000] → 3 points

[0082] The number of people reachable on foot (1000, 1500] → 5 points

[0083] The scoring intervals can be defined according to actual needs, and the specific rules are adjusted according to the business and actual scenarios to evaluate the impact degree of different intervals on the store. For example, a higher number of people reachable on foot usually means that this location is more suitable for opening a store, and the score will also be higher.

[0084] The sub-scoring rules can refer to the scoring rules corresponding to each preset influencing factor. It calculates scores based on the actual data of the influencing factor and the preset interval-based scoring algorithm. That is to say, it is the rule definition of how to obtain scores from historical data for each preset influencing factor. For example, the sub-scoring rules include the input data range: that is, how the historical data of a certain influencing factor will be mapped to the scoring interval. The score interval: for example, it is judged whether the store is suitable to open according to whether the value of the indicator "the number of people within 500 meters reachable on foot" belongs to a certain interval. Standardization: Some indicators may have different dimensions, so they need to be standardized before unified scoring.

[0085] Relevant historical indicator data can be extracted from historical data sources. For example, obtain data such as the number of surrounding resident population and the number of people reachable on foot in the past 6 months or 12 months. This data may need to be extracted from different data systems or data management modules. The interval-based scoring algorithm will classify the historical indicator data according to the preset intervals. For example, the historical data of the number of people reachable on foot may have a value of 1 point within the interval (0, 500], 3 points within the interval (500, 1000], and so on. At this time, it is necessary to traverse the historical data of each influencing factor, classify each data value according to the preset interval, and assign the corresponding score to it. Then, based on the previously obtained historical data and the interval-based scoring results, the sub-scoring rules for each preset influencing factor can be obtained. Summarize the sub-scoring rules to obtain the preset scoring rules.

[0086] In this solution, by setting up a pre-designed scoring rule, the store evaluation can be made more scientific, flexible, and accurate, which helps improve the merchant's site selection decision-making level, reduce business risks, and at the same time enhance the resource allocation efficiency and return on investment, ensuring continuous business growth and market competitiveness.

[0087] S104. Input the influence weight and the influence score into a pre-set store evaluation model, and calculate the store scores of each store according to a pre-set evaluation formula, the influence score, and the influence weight through the pre-set store evaluation model.

[0088] The pre-set store evaluation model can be a pre-defined algorithm or mathematical model used to comprehensively evaluate different types of stores according to different evaluation factors (such as traffic convenience, population density, the number of stores in the same industry, etc.) and their weights. Such an evaluation model usually includes multiple factors, each with a corresponding weight, used to evaluate the influence of the factor on the store suitability.

[0089] The pre-set evaluation formula is a mathematical expression used to calculate the final score of the store. Usually, it is a weighted sum formula that calculates a total score based on the scores and weights of each influence factor as the evaluation score of the store.

[0090] The store score is the output result of the store evaluation model, which represents the suitability or potential of a certain store on the current property. The store score is obtained by calculating the scores of each influence factor and performing weighted aggregation according to the weights of each factor. A high score indicates that the store is more suitable to be opened on this property, may attract more customers, or has a better business prospect. A low score indicates that the suitability of the store to be opened on this property is poor, and it may face fewer customers or poor business performance.

[0091] The influence weight and the influence score can be input into a pre-set store evaluation model, which needs to receive the scores and weights of each influence factor and calculate the store score according to the evaluation formula of the model.

[0092] Based on the above technical solution, optionally, the pre-set evaluation formula is:

[0093]

[0094] Wherein, S c is the store score; n is the store index; a k is the influence weight of the kth influence factor; xk is the influence score of the kth factor.

[0095] In this solution, for example, there are the following three influence factors and related scores and weights:

[0096] Influence factor:

[0097] The number of people reachable on foot within 500 meters around (Impact factor 1)

[0098] The type of roads around (Impact factor 2)

[0099] The number of stores in the same industry (Impact factor 3)

[0100] The corresponding scores and weights:

[0101] The number of people reachable on foot within 500 meters around:

[0102] Score rating 1 = 5

[0103] Weight weight 1 = 0.4

[0104] The type of roads around:

[0105] Score rating 2 = 3

[0106] Weight weight 2 = 0.3

[0107] The number of stores in the same industry:

[0108] Score rating 3 = 4

[0109] Weight weight 3 = 0.3

[0110] Calculation steps:

[0111] Perform calculations according to the formula:

[0112] Store rating = (0.4 × 5) + (0.3 × 3) + (0.3 × 4) = 4.1

[0113] Then the final rating of the store is 4.1.

[0114] On the basis of the above technical solution, optionally, the training process of the preset store evaluation model includes:

[0115] Construct a store evaluation model, obtain the historical influence weights and historical influence scores of the preset impact factors, and obtain the historical store ratings, and train the store evaluation model according to the historical influence weights, historical influence scores, preset impact factors, historical store ratings, and preset evaluation formula.

[0116] In this solution, the historical influence weight can refer to determining the influence degree of each impact factor on the store evaluation model according to historical data and market performance. It reflects the actual influence of each impact factor on store operations over a certain period of time in the past.

[0117] Historical impact scores can refer to the scores of various impact factors calculated based on past data or historical records. They reflect the actual performance of a certain factor in the historical environment, usually obtained through the analysis of past situations, data statistics, and the application of scoring rules. These scores play a very important role in model training because they provide the model with previous actual evaluation data, which helps to train an accurate store evaluation model.

[0118] Historical store ratings can be store evaluation scores based on past data and actual performance, which reflect the comprehensive evaluation results of a certain store within a specific time period. These ratings are calculated according to a series of known evaluation criteria, historical data, and relevant impact factors.

[0119] Historical impact weights, historical impact scores, and historical store ratings can be read from the database. Then, data with different dimensions are processed so that each impact factor is at the same magnitude during model training. Fill in the missing impact factor data to ensure the integrity of model training. Then select an appropriate algorithm: Since the scoring task is a weighted calculation task, a simple linear regression model or a weighted summation algorithm may be suitable for handling such problems. More complex machine learning algorithms (such as decision trees, support vector machines, neural networks, etc.) can also be used to automatically adjust the weights of impact factors. Ensure that the model can receive multiple inputs such as impact factors and impact scores, and can calculate the final score according to the preset evaluation formula.

[0120] Then, the historical data of each impact factor is input into the model. The historical scores (historical impact scores) and corresponding weights (historical impact weights) of each impact factor will become the input features of the model. Calculate the score of each store according to the preset evaluation formula and use the historical store ratings for verification. Then use machine learning algorithms (such as regression analysis, neural networks, etc.) to adjust the model parameters so that the store ratings output by the model are closer to the historical store ratings. After the initial training, it is necessary to use the validation set for evaluation to verify the accuracy of the model. If the scoring results of the model can better predict the actual performance of the store, it indicates that the model training is successful. Specifically, the model can be applied to a new data set to see if the model can accurately predict the actual ratings of the store.

[0121] In this solution, building a store evaluation model can not only help merchants make more scientific and accurate decisions but also gain an advantage in the market competition. By providing data-driven and personalized optimization suggestions, merchants can improve resource allocation efficiency, reduce costs, increase profits, and continuously optimize their operation strategies in a dynamically changing market environment, ultimately achieving sustainable business growth.

[0122] S105, send the target property, all the stores under the target property, and the store ratings of each store to the client.

[0123] The data can be organized into a data format that meets the needs of the client, such as JSON or XML. This data can include target property information, information on all relevant stores, and the ratings of each store. Data transmission is usually completed through a network request. Specifically, common network transmission methods can be used, such as HTTP API calls, WebSocket, etc.

[0124] In the embodiments of this application, if demand information sent by the client is received, query property data in a preset property store - opening data structure according to the demand information to determine target properties that meet the demand information; where the target properties are at least one; obtain the store data of all the stores under each target property, determine the store types of each store according to the store data, and determine the actual index data of each preset influencing factor according to the store data; determine the influence weights of each preset influencing factor according to the store types, and determine the influence scores of each preset influencing factor according to the actual index data of each preset influencing factor and a preset scoring rule; input the influence weights and the influence scores into a preset store evaluation model, and calculate the store ratings of each store through the preset store evaluation model according to a preset evaluation formula, influence scores, and influence weights; send the target property, all the stores under the target property, and the store ratings of each store to the client. Through the above - mentioned store location selection method based on public data, the efficiency can be improved, the accuracy of decision - making can be increased, the user experience can be enhanced, and a scientific basis can be provided for business decisions. Through automated data matching and evaluation models, personalized and accurate store selection suggestions can be provided for users, so as to gain an advantage in the highly competitive market.

[0125] Figure 2 It is a schematic flowchart of a store location selection method based on public data provided by an embodiment of the present disclosure. As Figure 2 shown, the method includes:

[0126] S201, if demand information sent by the client is received, query property data in a preset property store - opening data structure according to the demand information to determine target properties that meet the demand information; where the target properties are at least one.

[0127] S202, obtain the store data of all the stores under each target property, determine the store types of each store according to the store data, and determine the actual index data of each preset influencing factor according to the store data.

[0128] S203. Determine the influence weights of each preset influence factor according to the store type, and determine the influence scores of each preset influence factor according to the actual index data of each preset influence factor and the preset scoring rules.

[0129] S204. Input the influence weights and the influence scores into a preset store evaluation model, and calculate the store scores of each store through the preset store evaluation model according to a preset evaluation formula, the influence scores, and the influence weights.

[0130] S205. Send the target property, all the stores under the target property, and the store scores of each store to the client.

[0131] S206. If a preset update time point is reached, re-collect the store data of each store under each property, and re-calculate the store scores of each store under each property according to the re-collected store data, and re-update the preset property store-opening data structure according to the re-collected store data and the re-calculated store scores.

[0132] The preset update time point can be a time node set in the system or application, used to indicate that at this time point, it is necessary to re-collect the store data of each property and re-calculate the store scores based on the new data. Specifically, this update time point can be regular, such as a fixed time every day, week, or month. Or triggered, such as automatically triggered when certain conditions are met (such as when certain key data changes). Or periodic, according to business requirements, it may be within a fixed period (such as every once in a while) to re-collect data.

[0133] One or more update time points can be predefined in the system. It can be a specific time (such as 0:00 every day) or a fixed period (such as the 1st of each month, Monday, etc.). If it is a triggered update, it can be set to certain specific conditions (such as data changes, an event occurs, etc.). The system will regularly check the current time to compare whether the preset update time point has been reached. This detection can be achieved through a scheduler (such as a Cron job), which regularly judges the matching of the current system time and the preset update time point. When the preset update time point is reached, the system will automatically trigger the data collection process to re-obtain the latest data of all stores under each property from the database or other data sources. Then, re-calculate the store scores according to the updated store data. Usually, this means that it is necessary to calculate the scores of each preset influence factor again (such as the population within 500 meters, traffic conditions, etc.), and re-calculate the store scores in the preset evaluation formula based on these scores and their weights. After re-calculating the scores of each store, the system will update the new store scores and store data to the property store-opening data structure.

[0134] In this embodiment, regularly updating the property and store data, recalculating the store scores, and updating the data structure can improve the flexibility, accuracy, and adaptability of the system, and enhance the effectiveness of business decisions. It not only enables merchants to remain competitive in a dynamically changing market but also can improve operational efficiency through automated means, reduce manual intervention, increase user satisfaction and trust, thereby promoting long-term business development.

[0135] Figure 3 FIG. is a schematic block diagram of a store location system based on public data provided by an embodiment of the present disclosure.

[0136] It is characterized in that the system includes:

[0137] A property store-opening data query module 301, configured to query property data in a preset property store-opening data structure according to the demand information if receiving the demand information sent by the client, and determine a target property that meets the demand information; wherein, the target property is at least one;

[0138] A store data acquisition module 302, configured to acquire store data of all stores under each target property, determine the store types of each store according to the store data, and determine the actual index data of each preset influencing factor according to the store data;

[0139] An influence parameter determination module 303, configured to determine the influence weights of each preset influencing factor according to the store type, and determine the influence scores of each preset influencing factor according to the actual index data of each preset influencing factor and a preset scoring rule;

[0140] A store data calculation and management module 304, configured to input the influence weights and the influence scores into a preset store evaluation model, and calculate the store scores of each store through the preset store evaluation model according to a preset evaluation formula, influence scores, and influence weights;

[0141] A store location front-end application module 305, configured to send the target property, all stores under the target property, and the store scores of each store to the client.

[0142] Figure 4FIG. 0 shows a schematic block diagram of an electronic device 400 that can be used to implement embodiments of the present disclosure. The electronic device is intended to represent various forms of digital computers, such as, for example, 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, for example, personal digital processors, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely exemplary and are not intended to limit the implementations of the present disclosure described and / or claimed herein.

[0143] The electronic device 400 includes a computing unit 401 that can perform various appropriate actions and processes in accordance with a computer program stored in the ROM 402 or a computer program loaded from the storage unit 408 into the RAM 404. In the RAM 404, various programs and data required for the operation of the electronic device 400 can also be stored. The computing unit 401, the ROM 402, and the RAM 404 are connected to each other via a bus 404. The I / O interface 405 is also connected to the bus 404.

[0144] A plurality of components in the electronic device 400 are connected to the I / O interface 405, including: an input unit 406, such as a keyboard, a mouse, etc.; an output unit 407, such as various types of displays, speakers, etc.; a storage unit 408, such as a magnetic disk, an optical disk, etc.; and a communication unit 409, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 409 allows the electronic device 400 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0145] The computing unit 401 can be various general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 401 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various dedicated 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 401 executes the various methods and processes described above, such as the method for store location selection based on common data. For example, in some embodiments, the method for store location selection based on common data can be implemented as a computer software program, which is tangibly included in a machine-readable medium, such as the storage unit 408. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 400 via the ROM 402 and / or the communication unit 409. When the computer program is loaded into the RAM 404 and executed by the computing unit 401, one or more steps of the method for store location selection based on common data described above can be executed. Alternatively, in other embodiments, the computing unit 401 can be configured to execute the method for store location selection based on common data in any other suitable manner (e.g., by means of firmware).

[0146] The various embodiments of the systems and techniques described above in this document 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), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include: being implemented in one or more computer programs, which can be executed and / or interpreted on a programmable system including at least one programmable processor, the programmable processor can be a special or general programmable processor, and can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit the data and instructions to the storage system, the at least one input device, and the at least one output device.

[0147] The program code for implementing the methods of the present disclosure can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer, or other programmable data processing devices, such that when the program codes are executed by the processor or controller, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The program codes can be executed entirely on the machine, partially on the machine, executed partially on the machine and partially on a remote machine as an independent software package, or executed entirely on a remote machine or server.

[0148] Embodiments of the present application also provide a machine-readable medium. The machine-readable medium can be a tangible medium that can contain or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. The machine-readable medium can include, 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 the machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0149] 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; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which the user can provide input to the computer. Other kinds of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0150] The systems and techniques described herein can be implemented in a computing system including backend components (e.g., as a data server), or a computing system including middleware components (e.g., an application server), or a computing system including frontend components (e.g., a user computer having a graphical user interface or a web browser through which the user can interact with an implementation of the systems and techniques described herein), or a computing system including any combination of such backend components, middleware components, or frontend components. The components of the system can be interconnected to each other by any form or medium of digital data communication (e.g., a communication network). Examples of the communication network include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0151] The computer system can include a client and a server. The client and the server are generally far from each other and typically interact through a communication network. The client-server relationship is generated by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, or a server of a distributed system, or a server incorporating a blockchain.

[0152] It should be understood that the various forms of processes shown above can be used, with steps reordered, added or deleted. For example, the steps described in this disclosure can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and no limitations are imposed herein.

[0153] The above specific embodiments do not constitute a limitation on the protection scope of this disclosure. Those skilled in the art should understand that various modifications, combinations, sub - combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of this disclosure shall be included within the protection scope of this disclosure.

Claims

1. A store location selection method based on public data, characterized in that: The method is executed by a control end, and the method includes: If the demand information sent by the client is received, the property data is searched in the preset property store opening data structure according to the demand information to determine the target property that meets the demand information; wherein the target property is at least one; Acquire store data of all stores under each target property, determine the store type of each store according to the store data, and determine actual indicator data of each preset influencing factor according to the store data; Determine the influence weight of each preset influence factor according to the store type, and determine the influence score of each preset influence factor according to the actual indicator data of each preset influence factor and the pre-designed scoring rules; The influence weight and the influence score are input into a preset store evaluation model, and the store score of each store is calculated by the preset store evaluation model according to a preset evaluation formula, the influence score and the influence weight; The target property, all stores under the target property, and the store rating of each store are sent to the client.

2. The method according to claim 1, characterized in that in, The default evaluation formula is: Among them, S c is the store rating; n is the store index; a k is the influence weight of the kth influencing factor; xk is the influence score of the kth factor.

3. The method according to claim 1, characterized in that in, After sending the target property, all stores under the target property, and store ratings of each store to the client, the method further includes: If the preset update time point is reached, the store data of each store under each property is re-collected, and the store score of each store under each property is recalculated based on the re-collected store data; the preset property store opening data structure is re-updated based on the re-collected store data and the recalculated store score.

4. The method according to claim 1, characterized in that: in, The formation process of the preset property store opening data structure includes: Acquire the property information, property rental and sale information, store data, store initial score, surrounding residential population information, surrounding office staff information, traffic information, walking reach information and surrounding parking space information of each property, and form a preset property store opening data structure based on the property information, property rental and sale information, store data, store initial score, surrounding residential population information, surrounding office staff information, traffic information, walking reach information and surrounding parking space information.

5. The method according to claim 1, characterized in that in, The process of forming pre-design sub-rules includes: The historical indicator data of each preset influencing factor is obtained, the scoring sub-rules of each preset influencing factor are determined according to the interval-based scoring algorithm and the historical indicator data, and the pre-designed scoring rules are determined according to the scoring rules of each preset influencing factor.

6. The method according to claim 1, characterized in that in, The training process of the preset store evaluation model includes: Construct a store evaluation model, obtain the historical impact weights and historical impact scores of preset impact factors, and obtain historical store scores, and train the store evaluation model based on the historical impact weights, historical impact scores, preset impact factors, historical store scores and preset evaluation formulas.

7. The method according to claim 1, characterized in that in, Determining the store type of each store according to the store data includes: The store type is queried in the store category management system according to the store data.

8. A store location selection system based on public data, used to execute the method according to any one of claims 1 to 7, characterized in that: The system is configured on a control terminal, and the system includes: The property store opening data query module is used to query the property data in the preset property store opening data structure according to the demand information when receiving the demand information sent by the client, and determine the target property that meets the demand information; wherein the target property is at least one; A store data acquisition module, used to acquire store data of all stores under each target property, determine the store type of each store according to the store data, and determine actual indicator data of each preset influencing factor according to the store data; An influence parameter determination module, used to determine the influence weight of each preset influence factor according to the store type, and to determine the influence score of each preset influence factor according to the actual indicator data of each preset influence factor and the pre-designed scoring rules; A store data calculation management module, used to input the influence weight and the influence score into a preset store evaluation model, and calculate the store score of each store according to a preset evaluation formula, the influence score and the influence weight through the preset store evaluation model; The store location selection front-end application module is used to send the target property, all stores under the target property and the store rating of each store to the client.

9. An electronic device, comprising: 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 of any one of claims 1 to 7.

10. A machine-readable medium, characterized in that The machine-readable medium stores a program or an instruction, and when the program or the instruction is executed by a processor, the method according to any one of claims 1 to 7 is implemented.

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