Comprehensive calculation system for accurate data of store site selection based on AI analysis technology

Abstract

The invention relates to the field of business intelligence and artificial intelligence, and discloses an AI analysis technology-based store site selection accurate data comprehensive measurement and calculation system, which comprises a data acquisition module, a data preprocessing module, an AI analysis and optimization module, a financial prediction and risk assessment module and a report generation module. The data acquisition module acquires geographic information, human traffic, rent cost, competitor information and the like of candidate positions from a multi-dimensional data source; the data preprocessing module preprocesses the data; the AI analysis and optimization module generates a recommendation scheme of a candidate position by using multi-objective optimization, linear programming and a machine learning algorithm; the financial prediction and risk assessment module carries out financial income prediction and risk analysis on each position; and the report generation module displays the analysis result through a visual tool and generates a decision support report. According to the method, the optimal store site selection scheme can be dynamically and accurately recommended, and merchants are helped to make scientific decisions and adapt to market changes.

Description

Technical Field

[0001] The present invention relates to the field of business intelligence and artificial intelligence technology, and specifically to a store location accurate data comprehensive calculation system based on AI analysis technology. Background Art

[0002] With the rapid development of the business environment, store site selection has become a key link for enterprises to expand their markets and gain competitive advantages. Traditional store site selection methods usually rely on experience, intuition or simple market research. Although they can provide certain reference value, they often lack systematicity and scientificity. Especially in the context of the continuous expansion of chain enterprises and increasingly fierce competition, relying solely on traditional methods cannot meet the needs of merchants for accurate site selection. The limitations of existing methods are particularly prominent in terms of site selection accuracy, financial return expectations, and potential risk assessment.

[0003] In the existing technology, although some tools based on geographic information systems (GIS) can provide data such as passenger flow and traffic convenience, these tools often lack the ability to comprehensively analyze multi-dimensional data. For example, relying solely on single traffic data cannot fully consider the comprehensive impact of market competition, rental costs, and business district characteristics. In addition, most existing systems are based on static analysis, which makes it difficult to dynamically adapt to changes in the market environment and constantly updated operational data, resulting in lags and inaccuracies in recommended solutions.

[0004] Since existing site selection technologies lack effective integration and analysis of multi-dimensional data, cannot balance conflicts between different goals (such as the contradiction between high traffic and low rent), and do not have a refined assessment of financial benefits and risks, merchants face greater decision-making risks when selecting sites. At the same time, with the dynamic changes in the market, existing technologies cannot be optimized in real time through feedback from operational data, resulting in limited prediction effects of site selection models and difficulty in responding to changes in the actual market. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides a comprehensive calculation system for accurate store site selection data based on AI analysis technology. It uses AI analysis technology to integrate multi-dimensional data for accurate store site selection, and optimizes the site selection model in real time based on actual operation data to improve the scientificity and adaptability of site selection decisions.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: a comprehensive calculation system for accurate data on store location selection based on AI analysis technology, comprising:

[0007] The data collection module is used to obtain multi-dimensional data including geographic information of candidate store locations, traffic data, competitor information, rental costs, and consumer portraits from geographic information platforms, business district databases, financial systems, and market research platforms;

[0008] Data preprocessing module, used to clean, standardize and normalize the collected multi-dimensional data;

[0009] AI analysis and optimization module, which is used to analyze multi-dimensional data based on multi-objective optimization algorithms, linear programming and machine learning algorithms to generate recommended solutions for candidate store locations;

[0010] Financial forecasting and risk assessment module, used to forecast financial returns, calculate investment payback period and conduct risk assessment for recommended candidate store locations;

[0011] The report generation module is used to display the analysis results in a visual form and generate decision support reports including financial forecasts, site selection plans, and risk assessments.

[0012] Preferably, the data acquisition module includes:

[0013] A geographic information collection unit is used to obtain the geographic location, traffic flow and real-time passenger flow data of the candidate store location through a third-party geographic information platform API;

[0014] A business district data collection unit is used to obtain competitor distribution and market share information in the target area from a third-party business district database;

[0015] Financial data collection unit, used to obtain data including rent, decoration costs, and employee wages from the merchant's historical financial system;

[0016] The consumer portrait collection unit is used to obtain information including consumers' age, income, and consumption habits from the market research platform or the merchant's CRM system.

[0017] Preferably, the data preprocessing module preprocesses the collected data through the following steps:

[0018] Fill missing data and use mean filling method or regression interpolation method to repair data;

[0019] The data of different dimensions were normalized and the Min-Max standardization method was used to unify the data dimensions.

[0020] Preferably, the AI ​​analysis and optimization module uses a non-dominated sorting genetic algorithm to optimize multiple objective functions;

[0021] The plurality of objective functions include:

[0022] Maximize the expected turnover, calculated by the following formula:

[0023] R(x)=α1·F(x)+α2·P(x)-α3·C(x)

[0024] Among them, R(x) is the expected turnover of candidate location x, F(x) is the flow of people at location x, P(x) is the average consumption level in the area, C(x) is the influence of competitors, and α1, α2 and α3 are weight coefficients;

[0025] Minimize rental costs, calculated by the following formula:

[0026] L(x)=β1·D center (x)+β2·S(x)

[0027] Where L(x) is the rental cost of candidate location x, D center (x) is the distance between the candidate location and the city center, S(x) is the market value of the area, and β1 and β2 are weight coefficients;

[0028] Maximize market coverage, calculated by the following formula:

[0029] M(x)=γ1·N(x)+γ2·T(x)

[0030] Among them, M(x) is the market coverage of candidate location x, N(x) is the population density of the area, T(x) is the traffic convenience score, and γ1 and γ2 are weight coefficients;

[0031] The optimization process of the non-dominated sorting genetic algorithm comprises the following steps:

[0032] Initialize the population and randomly generate a set of candidate solutions;

[0033] Calculate the fitness value of each candidate solution on the objective function R(x), L(x) and M(x);

[0034] The population is divided into levels according to the non-dominated sorting principle;

[0035] Use crossover and mutation operations to generate new candidate solutions;

[0036] The candidate solutions with higher crowding ranking are selected to enter the next generation until the preset number of iterations or the optimization convergence condition is reached;

[0037] The AI ​​analysis and optimization module generates multiple Pareto optimal solutions based on the above optimization steps, and generates recommended optimal store locations according to user needs.

[0038] Preferably, the AI ​​analysis and optimization module uses a linear programming model to optimize site selection. The linear programming model is used to minimize rental costs and meet merchant needs under a given budget and other constraints. The linear programming model includes the following constraints and objective functions:

[0039] The total budget limit meets the following conditions:

[0040]

[0041] Among them, L(x i ) is the candidate position x i The rent of C i For the position x i Other related costs include renovation costs and employee wages, B is the total budget limit provided by the merchant, and n is the number of candidate stores;

[0042] The minimum distance limit meets the following conditions:

[0043]

[0044] Among them, D(x i ,x j ) is the candidate position x i With existing stores x j The distance between min Minimum distance requirements set for merchants;

[0045] Rental cost limit, meeting the following conditions:

[0046]

[0047] Among them, L(x i ) is the candidate position x i The rent, R max a rent cap set for businesses;

[0048] Minimize the total rental cost, defined as:

[0049]

[0050] Where Z is the total rental cost of the candidate store L(x i ) is the candidate position x i The goal is to select candidate locations that satisfy all constraints to minimize the total rental cost Z;

[0051] The linear programming model is solved by the simplex method or the interior point method to generate the optimal site location plan that meets the merchant's budget, distance and rent constraints, and outputs a set of candidate store locations for the merchant to make store opening decisions.

[0052] Preferably, the financial forecasting and risk assessment module is used to perform turnover forecasting, investment payback period calculation and market risk assessment on candidate store locations, and the financial forecasting and risk assessment module includes:

[0053] Sales forecasting unit: Based on factors such as the flow of people at the candidate location, regional consumption level, and competition intensity, a machine learning regression model is used to predict the daily sales of the candidate location. The sales forecasting model is defined as:

[0054] R(x)=θ0+θ1F(x)+θ2P(x)+θ3C(x)

[0055] Among them, R(x) is the predicted daily turnover of candidate location x, F(x) is the expected flow of people at the location, P(x) is the average consumption capacity of the area, C(x) is the competition intensity of the area, θ0, θ1, θ2, θ3 are the coefficients of the regression model, which are obtained by least squares training;

[0056] Investment payback period calculation unit: Calculate the investment payback period of the candidate store location based on the predicted turnover and initial investment cost. The specific calculation formula is:

[0057]

[0058] Among them, ROI is the return on investment, R t (x) is the daily turnover of candidate position x on day t, C t (x) is the operating cost on day t, I0 is the initial investment amount, including rent, renovation and other upfront costs, and T is the total number of days in the payback period.

[0059] Preferably, the financial forecasting and risk assessment module uses the Monte Carlo simulation method to assess the risk of the candidate location under market fluctuations, rent fluctuations and changes in the competitive environment. The risk assessment process includes:

[0060] Risk Factor Modeling: This module models the main factors that affect the financial performance of candidate locations, including:

[0061] Market volatility factors: Based on historical sales data and market trends, we model the fluctuation of future market demand, assuming that market demand follows a normal distribution and set the average value μ m and standard deviation σ m ;

[0062] Rent fluctuation factor: Based on historical rental data and rental market trends, assuming that rental fluctuations follow a normal distribution, the average rental level μ is set r and volatility σ r ;

[0063] Changes in the competitive environment: Considering the changes in the number of competitors and market share, assuming that the fluctuations in the competitive environment follow a normal distribution, we set the average change in the number of competitors μ c and volatility σ c ;

[0064] Simulation process: The Monte Carlo simulation method simulates the financial performance of candidate positions under different market scenarios through multiple random sampling. The specific process is as follows:

[0065] Generate multiple possible market demand values ​​M i :

[0066] M i ~N(μ m ,σ m )

[0067] Among them, M i represents the market demand value in the i-th simulation, N(μ m ,σ m ) is expressed in μ m is the mean, σ m is the normal distribution with standard deviation;

[0068] Generate multiple possible rent levels R i :

[0069] R i ~N(μ r ,σ r )

[0070] Among them, R i represents the rental value in the i-th simulation, N(μ r ,σ r ) is expressed in μ r is the mean, σ r is the normal distribution with standard deviation;

[0071] Generate multiple possible competitive environment impacts C i :

[0072] C i ~N(μ c ,σ c )

[0073] Among them, C i represents the number of competitors or the change in market share in the i-th simulation, N(μ c ,σ c ) is expressed in μ c is the mean, σ c is the normal distribution with standard deviation;

[0074] According to the market demand value, rental level and competitive environment impact value generated by each simulation, the predicted turnover R(x i ):

[0075] R(x i )=f(M i ,R i ,C i )

[0076] Among them, f(M i ,R i ,C i ) is a function of predicted turnover based on market demand, rental fluctuations and changes in the competitive environment;

[0077] Risk Assessment Indicators: The risk indicators of candidate locations are calculated based on the results generated by multiple simulations, including:

[0078] Maximum possible loss: calculated by calculating the probability that the turnover in the simulation results is lower than a certain threshold, defined as:

[0079] VaR=P(R(x i ) <R threshold )

[0080] Among them, R(x i ) is the predicted turnover of the ith simulation, R threshold The minimum turnover threshold set for the merchant, P is the probability that the predicted turnover is lower than the threshold;

[0081] Expected loss: When a loss occurs, evaluate the expected loss value, defined as:

[0082] ES=E[R(x i )|R(x i ) <R threshold ]

[0083] Among them, E[R(x i )] is the expected loss value when the turnover is lower than the threshold;

[0084] Analysis of simulation results: Through multiple Monte Carlo simulations, the turnover distribution of candidate locations under different market scenarios is generated, and based on risk assessment indicators, the financial risks of the location under market fluctuations, rent fluctuations and changes in the competitive environment are judged. The results are used to generate a detailed financial risk assessment report to help merchants make rational decisions.

[0085] Preferably, the report generation module is used to display the analysis results of the candidate locations in a visual manner and generate a site selection decision support report. The data visualization and report generation module includes:

[0086] Interactive map display unit: used to display geographic information and related analysis data of candidate store locations, including heat maps of customer flow, competitor distribution maps, and business district division maps;

[0087] Financial report generation unit: generating detailed financial reports based on the financial forecast data of the candidate positions, the financial reports including turnover forecast reports and investment return reports;

[0088] Risk assessment report generating unit: generating a risk assessment report according to the calculation results of the risk assessment module, wherein the report displays the potential market risks of the candidate location through charts and statistical data;

[0089] Customized site selection plan generation unit: generates a customized site selection plan based on the personalized needs input by the user, wherein the needs include target population, budget constraints, and business district preferences.

[0090] Preferably, the system also includes a feedback optimization module, which optimizes the AI ​​analysis model in real time through a reinforcement learning algorithm, and the reinforcement learning algorithm continuously adjusts the model parameters of the site recommendation based on the actual operation data after the store is opened.

[0091] The present invention also provides a method for comprehensive calculation of store location accurate data based on AI analysis technology, comprising the following steps:

[0092] Obtain multi-dimensional data from geographic information platforms, business district databases, financial systems and market research platforms;

[0093] Cleaning, normalizing and standardizing the multi-dimensional data to obtain an analyzable data set;

[0094] Analyze data based on multi-objective optimization algorithms and linear programming models to generate recommendations for candidate store locations;

[0095] Based on the predicted turnover, rental costs, and market coverage of the candidate store locations, we conduct financial profit forecasts and calculate the payback period, and generate a comprehensive evaluation report in combination with the risk assessment model;

[0096] Monitor the actual store operation data and optimize the AI ​​analysis model based on the actual operating data.

[0097] The present invention provides a comprehensive calculation system for accurate store location data based on AI analysis technology.

[0098] It has the following beneficial effects:

[0099] 1. The present invention conducts comprehensive analysis of multi-dimensional data through multi-objective optimization, linear programming and machine learning algorithms, and can balance factors such as turnover, rental costs and market coverage among multiple candidate locations, generate accurate site selection plans, and ensure that merchants can maximize the use of each site selection opportunity and improve the success rate.

[0100] 2. Through the feedback optimization module and reinforcement learning algorithm, the system of the present invention can continuously update the site selection model based on the actual operation data after the store is opened, so that the site selection plan recommended in the future is more in line with market changes and actual operating conditions, and has the ability of dynamic adjustment and self-learning.

[0101] 3. The present invention uses a financial forecasting and risk assessment module to calculate the turnover and investment payback period of each candidate location in detail, and evaluates potential risks such as market fluctuations and rental changes through Monte Carlo simulation, helping merchants to foresee and control financial risks when making decisions.

[0102] 4. The report generation module of the present invention presents complex data and analysis results in a clear and intuitive form through interactive maps, financial statements, risk assessment reports and other visualization tools, helping merchants to quickly understand and compare the advantages and disadvantages of different candidate locations, so as to make efficient decisions.

[0103] 5. The system of the present invention integrates dynamic data such as real-time traffic, rent, competitor information, etc., which can timely reflect market changes and adjust site selection strategies to ensure that merchants can always maintain competitive advantages in a changing market environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0104] Figure 1 It is a schematic diagram of the system architecture of the present invention;

[0105] Figure 2 It is a schematic diagram of the method flow of the present invention. DETAILED DESCRIPTION

[0106] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0107] Please see attached Figure 1, an embodiment of the present invention provides a comprehensive calculation system for accurate store location data based on AI analysis technology, which aims to help merchants accurately select store locations in a complex market environment through analysis, processing and optimization of multi-dimensional data, and provide comprehensive financial forecasting and risk assessment. The system combines geographic information, traffic flow, competitor analysis, rental costs, consumer portraits and other information, and uses multi-objective optimization, linear programming and machine learning techniques to generate the best site selection recommendation plan, and provides decision support for merchants through visual display and report generation. The following is a description of the specific implementation methods of each module of the system of the present invention.

[0108] Data acquisition module

[0109] This module mainly collects geographic information of candidate store locations, traffic data, competitor information, rental costs, consumer portraits and other data from multiple channels such as geographic information platforms, business district databases, financial systems and market research platforms, providing reliable data input for subsequent data processing, analysis and optimization.

[0110] In this embodiment, the data collection module integrates with multiple data source interfaces to obtain multi-dimensional data related to store location selection. The specific data types collected include the following:

[0111] 1. Geographic Information Collection

[0112] In this embodiment, the data collection module obtains the geographic coordinates (latitude and longitude) of the candidate store location, the surrounding transportation network, the road layout and the traffic flow data through geographic information platforms such as Amap API or Baidu Map API.

[0113] The geographic coordinates of the candidate location return specific latitude and longitude information through the API interface, and combined with the accessibility of surrounding roads, parking facilities and the location of public transportation stations, the system can preliminarily evaluate the transportation convenience of the location.

[0114] The traffic flow data T(x) around the candidate location is calculated using the historical and real-time traffic information provided by the traffic platform. The formula is as follows:

[0115]

[0116] Among them, T(x) is the traffic convenience of candidate location x, D trans (x,t i ) is the location x to the transportation node t i The distance is , and m is the number of accessible transportation nodes in the area.

[0117] 2. Collection of pedestrian flow data

[0118] In this embodiment, the data collection module obtains real-time and historical traffic data of candidate locations through navigation platforms such as Amap and Baidu Map. Traffic flow F(x) is an important factor in site selection analysis. The system dynamically analyzes candidate locations based on traffic flow trends in different time periods. The formula is as follows:

[0119]

[0120] Among them, F(x) is the average flow of people at candidate location x, f t (x) is the real-time flow of people at the location at time t, and T is the statistical time period.

[0121] 3. Information collection of business districts and competitors

[0122] In this embodiment, the data collection module obtains the distribution of business districts around the candidate location, competitor information and market share through the business district database and the market research platform.

[0123] The business district information includes the business type, business district coverage, main consumer types, etc. of the candidate location. Through the business district data, the business environment of the candidate location can be determined, such as whether it is suitable for retail, catering or other formats.

[0124] Competitor information includes the number, distribution, and business scope of competitors around the candidate location. The competition intensity C(x) is calculated using the following formula:

[0125]

[0126] Among them, C(x) is the competitive impact on candidate position x, and D(x,x i ) is the candidate position x and the competitor position x i The distance is , and n is the number of competitors near the candidate location.

[0127] 4. Rental cost data collection

[0128] In this embodiment, the data collection module obtains the rental cost and rental fluctuation history of the candidate location through the merchant's historical financial system and a third-party rental database.

[0129] The system obtains the current rental level L(x) based on the location and commercial value of the candidate location and combines it with historical rental fluctuation data to predict future rental trends. The rental cost L(x) is calculated using the following formula:

[0130] L(x)=β1·D center (x)+β2·S(x)

[0131] Where L(x) is the rental cost of the candidate location, D ecnter(x) is the distance from the location to the city center, S(x) is the market value of the area, and β1 and β2 are the weight coefficients in the rental model.

[0132] 5. Consumer portrait data collection

[0133] In this embodiment, the data collection module obtains consumer portrait information in the candidate area through a market research platform or a merchant's CRM system, including age, income, occupation, consumption preferences, etc.

[0134] The system uses data modeling to analyze whether candidate locations match the target customer group based on these consumer characteristics. For example, if the target customers are mainly high-income people, the system will prioritize recommending commercial areas that match the income level of these people.

[0135] Consumer profile data can be used to predict the potential market coverage M(x) of candidate locations, and the formula is as follows:

[0136] M(x)=γ1·N(x)+γ2·T(x)

[0137] Among them, M(x) is the market coverage of the candidate location, N(x) is the population density of the area, T(x) is the traffic convenience score, and γ1 and γ2 are the weight coefficients of the model.

[0138] Through the design of the above data collection module, the system can provide accurate and comprehensive basic data for subsequent data analysis and optimization, and ensure the calculation accuracy of the subsequent AI analysis and optimization module. The multi-dimensional and multi-channel data input mechanism of the data collection module enables the system to evaluate the location potential of each candidate store from all aspects and angles.

[0139] Data preprocessing module

[0140] Since data from different sources may have problems such as incomplete data and inconsistent dimensions, the data preprocessing module ensures the consistency and accuracy of the data, providing a good data foundation for subsequent AI analysis and optimization.

[0141] In this embodiment, the data preprocessing module processes the collected multi-dimensional data such as geographic information, traffic, rent, competitor information, etc. to eliminate outliers, process missing data, and ensure that the dimensions of different types of data are consistent. The data preprocessing steps mainly include the following:

[0142] 1. Data cleaning

[0143] In this embodiment, data cleaning is used to process incomplete or abnormal data obtained from different data sources. The system detects missing values ​​and abnormal values ​​in the collected data and uses appropriate cleaning methods to repair them.

[0144] Missing value processing: For missing values ​​in candidate locations, the system uses mean filling or regression interpolation. For example, if a candidate location is missing some historical rental data, you can use the regression model to predict and fill in the missing values ​​using data from other locations in the area.

[0145] Outlier processing: For outliers in the data, the system first detects obviously abnormal data points (such as extremely high or low rents, number of competitors) based on the data distribution, and then uses the median or neighborhood interpolation method to replace them to ensure the rationality of the data.

[0146] 2. Standardization

[0147] In this embodiment, the data preprocessing module performs standardization processing on data of different dimensions so that subsequent algorithms can perform analysis on the same scale. The system uses Z-score standardization or Min-Max normalization method to make each data dimension have a unified dimension.

[0148] 3. Data consistency check

[0149] In this embodiment, after completing the standardization and normalization processing, the system performs a consistency check on the pre-processed data to ensure the integrity and correctness of the data.

[0150] The system first checks the correlation between different data dimensions to ensure that geographic information, number of competitors, foot traffic and other data are consistent at the corresponding candidate locations. For example, it checks whether the foot traffic data matches the competitor data at that location to avoid biased analysis results due to data misalignment.

[0151] Through association analysis, check whether each data dimension meets the established logical relationship to avoid unreasonable data combinations.

[0152] 4. Time Series Data Processing

[0153] In this embodiment, for data with time series characteristics such as passenger flow and rental data, the system will perform specific time dimension processing.

[0154] The system smoothes time series data through the moving average method to eliminate the impact of short-term fluctuations and make trends clearer.

[0155] For data with significant seasonal or cyclical fluctuations, the system will further use the difference method to eliminate the seasonal impact, ensure the stability of the data, and facilitate subsequent model processing.

[0156] Through the design of the above-mentioned data preprocessing module, this system ensures that data from multiple different sources and different dimensions can be uniformly formatted into structured data that can be used for subsequent AI analysis after standardization, cleaning and consistency checking.

[0157] AI analysis and optimization module

[0158] This module analyzes the multi-dimensional data passed from the data collection and data preprocessing modules, and uses algorithms such as multi-objective optimization, linear programming, and machine learning to generate recommended solutions for candidate store locations. The AI ​​analysis and optimization module balances multiple goals, such as maximizing expected turnover, minimizing rental costs, and maximizing market coverage, to ensure the comprehensive optimization of the site selection plan. In addition, the module can also predict financial performance based on historical data and market trends, supporting merchants in making scientific site selection decisions.

[0159] In this embodiment, the AI ​​analysis and optimization module processes multi-dimensional data through a series of algorithms to generate a site selection plan that meets the needs of merchants. It mainly includes the following analysis and optimization steps:

[0160] Multi-objective optimization analysis

[0161] In this embodiment, the AI ​​analysis and optimization module uses the non-dominated sorting genetic algorithm (NSGA-II) for multi-objective optimization. The system simultaneously considers multiple conflicting objectives, such as maximizing turnover, minimizing rental costs, and maximizing market coverage, and generates an optimal solution set (Pareto frontier) for candidate store locations.

[0162] Goal 1: Maximize the expected turnover. The system calculates the expected turnover R(x) of the candidate location by analyzing the flow of people, spending power, and competitor information:

[0163] R(x)=α1·F(x)+α2·P(x)-α3·C(x)

[0164] Among them, R(x) is the expected turnover of candidate location x, F(x) is the flow of people at location x, P(x) is the average consumption level in the area, C(x) is the influence of competitors, and α1, α2 and α3 are weight coefficients.

[0165] Goal 2: Minimize rental costs. Rental costs L(x) are an important consideration for merchants when selecting locations. The rent is related to the geographical characteristics and market value of the candidate location. The system calculates it using the following formula:

[0166] L(x)=β1·D center (x)+β2·S(x)

[0167] Where L(x) is the rental cost of candidate location x, D center (x) is the distance between the candidate location and the city center, S(x) is the market value of the area, and β1 and β2 are weight coefficients.

[0168] Goal 3: Maximize market coverage. The system evaluates market potential by calculating the market coverage M(x) of the location for the target consumer group and combining the population density and transportation convenience of the area:

[0169] M(x)=γ1·N(x)+γ2·T(x)

[0170] Among them, M(x) is the market coverage of candidate location x, N(x) is the population density of the area, T(x) is the traffic convenience score, and γ1 and γ2 are weight coefficients.

[0171] In the optimization process of NSGA-II, a set of candidate solutions is first randomly generated, the fitness value of each candidate solution under the above objective function is calculated, and non-dominated sorting is performed, crossover mutation is used to generate new solutions, and finally a set of Pareto optimal solutions is output for merchants to choose. The specific steps are as follows:

[0172] 1) Initialize the population and randomly generate a set of candidate solutions;

[0173] 2) Calculate the fitness value of each candidate solution on the objective function R(x), L(x) and M(x);

[0174] 3) Divide the population into levels according to the non-dominated sorting principle;

[0175] 4) Use crossover and mutation operations to generate new candidate solutions;

[0176] 5) Select candidate solutions with higher congestion ranking to enter the next generation until the preset number of iterations or optimization convergence condition is reached.

[0177] 2. Linear programming model

[0178] In this embodiment, the AI ​​analysis and optimization module uses a linear programming model for optimization to meet the merchant's budget, distance and rental restrictions. The goal of the linear programming model is to minimize the total rental cost and optimize the site selection plan based on this. The formula is as follows:

[0179]

[0180] Among them, Z is the total rental cost, L(x i ) is the candidate position x i The rent of n is the number of candidate locations.

[0181] Constraint 1: Budget limit. The system ensures that the total rent and other related costs do not exceed the budget B provided by the merchant. The constraints are:

[0182]

[0183] Among them, L(xi ) is the candidate position x i The rent of C i For the position x i Other related costs include decoration costs and employee wages, B is the total budget limit provided by the merchant, and n is the number of candidate stores.

[0184] Constraint 2: Minimum distance limit. The system ensures that the location of new stores maintains a certain distance from existing stores to prevent the store density from being too high. The distance constraint is:

[0185]

[0186] Among them, D(x i ,x j ) is the candidate position x i With existing stores x j The distance between min The minimum distance requirement set for merchants.

[0187] Constraint 3: Rent limit. The system ensures that the rent of the candidate location does not exceed the upper limit R set by the merchant. max :

[0188]

[0189] Among them, L(x i ) is the candidate position x i The rent, R max A rent cap set for businesses.

[0190] Through linear programming solving algorithms (such as the simplex method or the interior point method), the system generates the optimal store location plan that meets budget and distance requirements.

[0191] Through the design of the above-mentioned AI analysis and optimization module, this system can generate scientific and reasonable site selection recommendation plans based on the multi-dimensional needs and actual limitations of merchants.

[0192] Financial Forecasting and Risk Assessment Module

[0193] This module calculates the financial performance of merchants at specific locations by combining factors such as turnover forecasts, rental costs, and operating costs of candidate locations. It also provides merchants with a comprehensive basis for investment decision-making through risk assessments of market fluctuations, competitive environment, etc. The financial forecasting and risk assessment module can help merchants predict future profits and quantify potential risks.

[0194] In this embodiment, the financial forecasting and risk assessment module performs financial benefit forecasting and risk assessment on candidate store locations, which is specifically divided into the following parts:

[0195] 1. Sales forecast

[0196] In this embodiment, the financial forecasting module first predicts the daily turnover of each candidate location, and calculates the expected daily turnover R(x) of the location based on the multivariate linear regression model and historical data of the AI ​​analysis and optimization module, taking into account factors such as traffic flow, consumption capacity, and the influence of competitors. The prediction formula is as follows:

[0197] R(x)=θ0+θ1F(x)+θ2P(x)+θ3C(x)

[0198] Among them, R(x) is the predicted daily turnover of candidate location x, F(x) is the expected flow of people at this location, P(x) is the average consumption capacity of the area, C(x) is the competition intensity of the area, θ0, θ1, θ2, θ3 are the coefficients of the regression model, which are obtained by least squares training.

[0199] The system analyzes historical store data, trains a regression model, and predicts relevant data for new locations to obtain a preliminary sales estimate.

[0200] The forecasting model can also be dynamically adjusted based on real-time data, using algorithms such as random forests to improve forecast accuracy and ensure that forecast results can be updated in a timely manner as the market changes.

[0201] 2. Calculation of Return on Investment (ROI)

[0202] In this embodiment, the financial forecasting module calculates the investment return period ROI of the candidate location through the turnover R(x) and the initial investment cost I0 (including rent, decoration costs, employee wages, etc.) of the candidate location. The calculation formula for the return period is:

[0203]

[0204] Among them, ROI is the return on investment, R t (x) is the daily turnover of candidate position x on day t, C t (x) is the operating cost on day t, I0 is the initial investment amount, including rent, renovation and other upfront costs, and T is the total number of days in the payback period.

[0205] Through the above formula, the system can calculate the merchant's return on investment in a specific location, that is, how long it will take to recover the initial investment.

[0206] The system can simulate the payback period under different scenarios by adjusting various initial investment costs (such as decoration fees, labor costs, etc.), helping merchants make more rational investment decisions.

[0207] 3. Risk Assessment

[0208] In this embodiment, the risk assessment module uses the Monte Carlo simulation method to assess the risks under market fluctuations, rent fluctuations and changes in the competitive environment. By setting the probability distribution of parameters such as market demand, rent costs and the number of competitors, the system performs multiple simulations to analyze the financial performance under different market scenarios. The main risk assessment steps are as follows:

[0209] Risk factor modeling: The system first models market demand M(x), rental fluctuation R(x) and competitive environment changes C(x), assuming that these factors follow a normal distribution. The formula is as follows:

[0210] M i ~N(μ m ,σ m ),R i ~N(μ r ,σ r ),C i ~N(μ c ,σ c )

[0211] Among them, M i represents the simulated value of market demand, R i represents the simulated value of rent, C i Represents the simulated value of the competitive environment, μ m , μ r , μ c are the mean, σ m , σ r , σ c are the standard deviations, as follows:

[0212] Market volatility factors: Based on historical sales data and market trends, we model the fluctuation of future market demand, assuming that market demand follows a normal distribution and set the average value μ m and standard deviation σ m ;

[0213] Rent fluctuation factor: Based on historical rental data and rental market trends, assuming that rental fluctuations follow a normal distribution, the average rental level μ is set r and volatility σ r ;

[0214] Changes in the competitive environment: Considering the changes in the number of competitors and market share, assuming that the fluctuations in the competitive environment follow a normal distribution, we set the average change in the number of competitors μ c and volatility σ c .

[0215] Monte Carlo simulation: The system performs multiple random sampling (the number of simulations can be set, such as 1,000 or 10,000 times) to generate financial performance forecasts under multiple market scenarios. In each simulation, the system calculates the turnover R(x) under each scenario and records the final financial performance.

[0216] Risk Indicator Calculation: The system uses the following risk indicators to quantify the financial risk of each candidate location:

[0217] Maximum possible loss (Value at Risk, VaR): VaR is defined as the maximum possible loss value that may occur within a certain period of time under a certain confidence level. Its calculation formula is:

[0218] VaR=P(R(x i ) <R threshold )

[0219] Among them, R(x i ) is the predicted turnover of the ith simulation, R threshold The minimum turnover threshold set for the merchant, and P is the probability that the predicted turnover is lower than the threshold.

[0220] Expected Shortfall (ES): ES measures the expected loss when a loss occurs. The formula is as follows:

[0221] ES=E[R(x i )|R(x i ) <R threshold ]

[0222] Among them, E[R(x i )] is the expected loss value when the turnover is lower than the threshold.

[0223] Through these indicators, the system can help merchants evaluate the potential financial risks of different candidate locations, identify possible high-risk locations and provide response recommendations.

[0224] 4. Risk countermeasure simulation

[0225] In this embodiment, the system not only assesses risks, but also simulates response strategies under different market scenarios. By adjusting key factors such as rent negotiation and promotion intensity, the system can simulate the impact of these countermeasures on financial performance and help merchants develop effective response plans when facing potential risks.

[0226] Through the design of the above-mentioned financial forecasting and risk assessment modules, this system can provide merchants with a comprehensive financial benefit analysis and quantify the risks in a market fluctuation and competitive environment.

[0227] Report generation module

[0228] The main function of this module is to display the previous data analysis results, financial forecasts and risk assessment results in a visual way, and generate a site selection decision support report. The report generation module provides merchants with detailed financial statements, site selection plan comparisons and risk analysis, etc., to help merchants evaluate and select candidate store locations from different dimensions. The reports generated by this module enable merchants to quickly understand complex data results and make scientific and reasonable site selection decisions.

[0229] In this embodiment, the report generation module generates an intuitive and clear decision report based on the output results of the AI ​​analysis and optimization module and the financial forecasting and risk assessment module, and displays it through data visualization tools. The report generation module specifically includes the following parts:

[0230] 1. Interactive map display

[0231] In this embodiment, the report generation module displays the geographic information, business district distribution, passenger flow, competitor information and other data of the candidate store locations in the form of an interactive map.

[0232] Crowd flow heat map: The system generates a heat map based on the collected crowd flow data of candidate locations. The heat map calculates the average crowd flow density D(x) in the area, and the formula is as follows:

[0233]

[0234] Where D(x) is the crowd density at candidate location x, f t (x) is the real-time flow of people at time t, and T is the statistical time period.

[0235] Business district distribution map: The business district types and competitor distribution around the candidate location are displayed through map annotations. The influence of competitors is calculated through the formula to calculate the competition intensity C(x):

[0236]

[0237] Among them, D(x,x i ) is the candidate position x and the competitor position x i The distance between them, n is the number of competitors.

[0238] Traffic convenience display: Based on geographic information and traffic data, the report displays the traffic convenience of the location by calculating the accessibility score T(x) of the traffic node:

[0239]

[0240] Among them, D trans (x,t i ) is the location x to the transportation node ti The distance is , and m is the number of accessible transportation nodes in the area.

[0241] 2. Financial statement generation

[0242] In this embodiment, the report generation module generates a detailed financial report based on the calculation results of the financial forecast and risk assessment module. The financial report includes information such as the expected turnover, cost analysis, and investment payback period of the candidate store location.

[0243] Turnover report: Based on the turnover forecast model, generate daily, monthly and annual turnover expectations for each candidate location and display its historical fluctuations and trends.

[0244] Return on Investment Report: Based on the ROI formula, the report shows the return on investment period for each candidate location and compares different locations in the form of a bar chart or line graph.

[0245] Cost analysis: The system generates a detailed cost analysis report based on the rental and operating costs of each candidate location, and provides cost comparisons of different candidate locations.

[0246] 3. Risk assessment report generation

[0247] In this embodiment, the report generation module generates a detailed risk assessment report based on the results of the risk assessment module, and displays the potential risks in different market scenarios through visualization tools.

[0248] Maximum Possible Loss (VaR) Display: The system calculates the maximum possible loss (VaR) of each candidate position under different market scenarios and displays the possible loss range at its confidence level.

[0249] Expected loss (ES) display: The system will also generate a bar chart or line chart based on the expected loss (ES) calculation results to display the expected loss situation when a loss occurs.

[0250] 4. Comprehensive comparison report of site selection scheme

[0251] In this embodiment, the report generation module also provides a comprehensive comparison report of multiple candidate locations to help merchants intuitively understand the advantages and disadvantages of each candidate location.

[0252] The system generates a comprehensive scoring report of candidate locations based on different site selection requirements (such as budget, target customer groups, etc.) entered by the user. The comprehensive scoring formula is:

[0253] S(x)=w1·R(x)+w2·M(x)-w3·C(x)

[0254] Among them, S(x) is the comprehensive score of the candidate location, R(x) is the predicted turnover, M(x) is the market coverage, C(x) is the competitive impact, and w1, w2, and w3 are weights set according to user needs.

[0255] The report uses radar charts, bar charts, and other forms to intuitively display the scoring performance of each candidate location in different dimensions, helping merchants quickly compare and select the best location.

[0256] Through the design of the above report generation module, this system can provide merchants with clear and intuitive decision-making reports and display complex analysis results through a variety of data visualization tools.

[0257] In some embodiments, the system also includes a feedback optimization module, which optimizes the AI ​​analysis model in real time through a reinforcement learning algorithm. The reinforcement learning algorithm continuously adjusts the model parameters of the site recommendation based on the actual operation data after the store is opened.

[0258] This module uses the actual data accumulated during store operations, such as sales, traffic, costs, and market changes, to dynamically adjust and optimize the site selection model, making the subsequent recommended site selection plan more accurate. The introduction of the feedback optimization module enables the system to have the ability of self-learning and iterative optimization, adapt to the ever-changing market environment, and provide more reliable site selection decisions.

[0259] In this embodiment, the feedback optimization module continuously optimizes the AI ​​analysis model through a reinforcement learning algorithm. The specific steps are as follows:

[0260] 1. Initialization of reinforcement learning model

[0261] In this embodiment, the system uses the pre-trained AI analysis model to generate a recommendation plan for the candidate store location when making the initial site selection recommendation. As the actual store operation data accumulates, the feedback optimization module uses this data to continuously update the model parameters and optimize the future site selection recommendation results.

[0262] The reinforcement learning model consists of state S, action A and reward R. State S represents the operating environment of the store (such as traffic, turnover, rental cost, etc.), action A represents the adjustment of the location decision, and reward R represents the actual benefit of the location plan under a specific state.

[0263] 2. Reward Function Design

[0264] In this embodiment, the system constructs the reward function R based on the actual business data of the store. t , which is used to measure the benefits of adopting a specific location strategy in each time period t. The reward function is constructed based on turnover R(x), cost C(x) and other key operating indicators, and the formula is as follows:

[0265] R t =R(x)-C(x)

[0266] Among them, R(x) is the actual turnover of store x in time period t, and C(x) is the operating cost. The size of the reward value directly reflects the effectiveness of the location selection decision.

[0267] 3. Application of Q-Learning Algorithm

[0268] In this embodiment, the system uses the Q-Learning reinforcement learning algorithm to optimize the site selection model. After each store is actually operated, the system updates the Q value table based on the new operation data. The formula is as follows:

[0269] Q(s,a)=Q(s,a)+α[r+γmax a′ Q(s′,a′)-Q(s,a)]

[0270] in:

[0271] Q(s,a) is the value of taking action a in state s;

[0272] α is the learning rate, which indicates the influence of new data on the model at each update;

[0273] γ is the discount factor, which measures the impact of future rewards;

[0274] r is the instant reward in the current state, that is, the actual revenue of the store in this period;

[0275] max a′ Q(s′,a′) is the most optimal addressing action that can be taken in the next state S′.

[0276] Every time the store operation data is updated, the system adjusts the model according to the new data. Through repeated updates, the reinforcement learning algorithm can find the best site selection decision strategy and optimize future recommendation plans.

[0277] 4. Dynamically adjust model parameters

[0278] In this embodiment, the feedback optimization module not only updates the site selection model, but also dynamically adjusts the model parameters. For example, as actual operation data accumulates, the system can adjust the weight coefficient according to the actual performance of the store, so that the accuracy of the prediction model is continuously improved.

[0279] The updated weight coefficients through reinforcement learning enable the model to more accurately predict the turnover of future candidate locations and adjust the optimization direction according to new market dynamics.

[0280] 5. Adaptive Optimization

[0281] In this embodiment, the feedback optimization module has adaptive capabilities and can adjust the site selection strategy when market conditions or consumption trends change. For example, when the market environment changes significantly (such as competitors entering, rent increases, etc.), the system can respond quickly and adjust the strategy through reinforcement learning to generate new site selection suggestions.

[0282] The system continuously monitors actual operational data and external market information, detects changes in key indicators (such as large fluctuations in traffic flow, rental fluctuations, etc.), and automatically adjusts parameters in the model to ensure that the recommendation results remain dynamically updated and accurate.

[0283] Through the design of the above-mentioned feedback optimization module, the system can use the reinforcement learning algorithm to continuously optimize the site selection model according to the actual performance of the store after operation, so that the subsequent site selection plan can more accurately reflect the dynamic changes of the market environment.

[0284] In general, the present invention uses modules such as data collection, data preprocessing, AI analysis and optimization, financial forecasting and risk assessment, and report generation. The system can generate the optimal store location plan based on multi-dimensional data, combined with multi-objective optimization, linear programming, and machine learning algorithms, and optimize the model in real time through reinforcement learning. The system can dynamically analyze the potential market value, financial benefits, and risks of candidate locations, helping merchants make scientific and reasonable site selection decisions and adapt to the ever-changing market environment.

[0285] Please see attached Figure 2 The present invention also provides a method for comprehensive calculation of store location accurate data based on AI analysis technology, comprising the following steps:

[0286] S1. Obtain multi-dimensional data from geographic information platforms, business district databases, financial systems and market research platforms;

[0287] S2. Cleaning, normalizing and standardizing the multi-dimensional data to obtain an analyzable data set;

[0288] S3, analyze the data based on multi-objective optimization algorithm and linear programming model to generate recommended solutions for candidate store locations;

[0289] S4. Based on the predicted turnover, rental costs, and market coverage of the candidate store locations, financial benefit forecasts and investment payback period calculations are performed, and a comprehensive evaluation report is generated in combination with the risk assessment model;

[0290] S5. Monitor the actual store operation data and optimize the AI ​​analysis model based on the actual operation data.

[0291] Each step of the method of the present invention is implemented based on the functions of the aforementioned system. The technical details and key algorithms involved have been fully described in the system embodiment and will not be repeated here.

[0292] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A comprehensive calculation system for accurate store location data based on AI analysis technology, characterized in that: include: The data collection module is used to obtain multi-dimensional data including geographic information of candidate store locations, traffic data, competitor information, rental costs, and consumer portraits from geographic information platforms, business district databases, financial systems, and market research platforms; Data preprocessing module, used to clean, standardize and normalize the collected multi-dimensional data; AI analysis and optimization module, which is used to analyze multi-dimensional data based on multi-objective optimization algorithms, linear programming and machine learning algorithms to generate recommended solutions for candidate store locations; Financial forecasting and risk assessment module, used to forecast financial returns, calculate investment payback period and conduct risk assessment for recommended candidate store locations; The report generation module is used to display the analysis results in a visual form and generate decision support reports including financial forecasts, site selection plans, and risk assessments.

2. According to claim 1, a store location accurate data comprehensive calculation system based on AI analysis technology is characterized in that: The data acquisition module comprises: A geographic information collection unit is used to obtain the geographic location, traffic flow and real-time passenger flow data of the candidate store location through a third-party geographic information platform API; A business district data collection unit is used to obtain competitor distribution and market share information in the target area from a third-party business district database; Financial data collection unit, used to obtain data including rent, decoration costs, and employee wages from the merchant's historical financial system; The consumer portrait collection unit is used to obtain information including consumers' age, income, and consumption habits from the market research platform or the merchant's CRM system.

3. According to claim 1, a comprehensive calculation system for accurate store location data based on AI analysis technology is characterized in that: The data preprocessing module preprocesses the collected data through the following steps: Fill missing data and use mean filling method or regression interpolation method to repair data; The data of different dimensions were normalized and the Min-Max standardization method was used to unify the data dimensions.

4. According to claim 1, a comprehensive calculation system for accurate store location data based on AI analysis technology is characterized in that: The AI ​​analysis and optimization module uses a non-dominated sorting genetic algorithm to optimize multiple objective functions; The plurality of objective functions include: Maximize the expected turnover, calculated by the following formula: R(x)=α1·F(x)+α2·P(x)-α3·C(x) Among them, R(x) is the expected turnover of candidate location x, F(x) is the flow of people at location x, P(x) is the average consumption level in the area, C(x) is the influence of competitors, and α1, α2 and α3 are weight coefficients; Minimize rental costs, calculated by the following formula: L(x)=β1·D center (x)+β2·S(x) Where L(x) is the rental cost of candidate location x, D center (x) is the distance between the candidate location and the city center, S(x) is the market value of the area, and β1 and β2 are weight coefficients; Maximize market coverage, calculated by the following formula: M(x)=γ1·N(x)+γ2·T(x) Among them, M(x) is the market coverage of candidate location x, N(x) is the population density of the area, T(x) is the traffic convenience score, and γ1 and γ2 are weight coefficients; The optimization process of the non-dominated sorting genetic algorithm comprises the following steps: Initialize the population and randomly generate a set of candidate solutions; Calculate the fitness value of each candidate solution on the objective function R(x), L(x) and M(x); The population is divided into levels according to the non-dominated sorting principle; Use crossover and mutation operations to generate new candidate solutions; The candidate solutions with higher crowding ranking are selected to enter the next generation until the preset number of iterations or the optimization convergence condition is reached; The AI ​​analysis and optimization module generates multiple Pareto optimal solutions based on the above optimization steps, and generates recommended optimal store locations according to user needs.

5. According to claim 4, a comprehensive calculation system for accurate store location data based on AI analysis technology is characterized in that: The AI ​​analysis and optimization module uses a linear programming model to optimize site selection. The linear programming model is used to minimize rental costs and meet merchant needs under a given budget and other constraints. The linear programming model includes the following constraints and objective functions: The total budget limit meets the following conditions: Among them, L(x i ) is the candidate position x i The rent of C i For the position x i Other related costs include renovation costs and employee wages, B is the total budget limit provided by the merchant, and n is the number of candidate stores; The minimum distance limit meets the following conditions: Among them, D(x i ,x j ) is the candidate position x i With existing stores x j The distance between min Minimum distance requirements set for merchants; Rental cost limit, meeting the following conditions: Among them, L(x i ) is the candidate position x i The rent, R max a rent cap set for businesses; Minimize the total rental cost, defined as: Where Z is the total rental cost of the candidate store L(x i ) is the candidate position x i The goal is to select candidate locations that satisfy all constraints to minimize the total rental cost Z; The linear programming model is solved by the simplex method or the interior point method to generate the optimal site location plan that meets the merchant's budget, distance and rent constraints, and outputs a set of candidate store locations for the merchant to make store opening decisions.

6. According to claim 1, a store location accurate data comprehensive calculation system based on AI analysis technology is characterized in that: The financial forecasting and risk assessment module is used to perform turnover forecasting, investment payback period calculation and market risk assessment on candidate store locations. The financial forecasting and risk assessment module includes: Sales forecasting unit: Based on factors such as the flow of people at the candidate location, regional consumption level, and competition intensity, a machine learning regression model is used to predict the daily sales of the candidate location. The sales forecasting model is defined as: R(x)=θ0+θ1F(x)+θ2p(x)+θ3C(x) Among them, R(x) is the predicted daily turnover of candidate location x, F(x) is the expected flow of people at the location, P(x) is the average consumption capacity of the area, C(x) is the competition intensity of the area, θ0, θ1, θ2, θ3 are the coefficients of the regression model, which are obtained by least squares training; Investment payback period calculation unit: Calculate the investment payback period of the candidate store location based on the predicted turnover and initial investment cost. The specific calculation formula is: Among them, ROI is the return on investment, R t (x) is the daily turnover of candidate position x on day t, C t (x) is the operating cost on day t, I0 is the initial investment amount, including rent, renovation and other upfront costs, and T is the total number of days in the payback period.

7. According to claim 1, a store location accurate data comprehensive calculation system based on AI analysis technology is characterized in that: The financial forecasting and risk assessment module uses the Monte Carlo simulation method to assess the risk of candidate locations under market fluctuations, rent fluctuations and changes in the competitive environment. The risk assessment process includes: Risk Factor Modeling: This module models the main factors that affect the financial performance of candidate locations, including: Market volatility factors: Based on historical sales data and market trends, we model the fluctuation of future market demand, assuming that market demand follows a normal distribution and set the average value μ m and standard deviation σ m ; Rent fluctuation factor: Based on historical rental data and rental market trends, assuming that rental fluctuations follow a normal distribution, the average rental level μ is set r and volatility σ r ; Changes in the competitive environment: Considering the changes in the number of competitors and market share, assuming that the fluctuations in the competitive environment follow a normal distribution, we set the average change in the number of competitors μ c and volatility σ c ; Simulation process: The Monte Carlo simulation method simulates the financial performance of candidate positions under different market scenarios through multiple random sampling. The specific process is as follows: Generate multiple possible market demand values ​​M i : M i ~N(μ m ,s m ) Among them, M i represents the market demand value in the i-th simulation, N(μ m ,σ m ) is expressed in μ m is the mean, σ m is the normal distribution with standard deviation; Generate multiple possible rent levels R i : R i ~N(μ r ,s r ) Among them, R i represents the rental value in the i-th simulation, N(μ r ,σ r ) is expressed in μ r is the mean, σ r is the normal distribution with standard deviation; Generate multiple possible competitive environment impacts C i : C i ~N(μ c ,s c ) Among them, C i represents the number of competitors or the change in market share in the i-th simulation, N(μ c ,σ c ) is expressed in μ c is the mean, σ c is the normal distribution with standard deviation; According to the market demand value, rental level and competitive environment impact value generated by each simulation, the predicted turnover R(x i ): R(x i )=f(M i ,R i ,C i ) Among them, f(M i ,R i ,C i ) is a function of predicted turnover based on market demand, rental fluctuations and changes in the competitive environment; Risk Assessment Indicators: The risk indicators of candidate locations are calculated based on the results generated by multiple simulations, including: Maximum possible loss: calculated by calculating the probability that the turnover in the simulation results is lower than a certain threshold, defined as: VaR=P(R(x i ) <R threshold ) Among them, R(x i ) is the predicted turnover of the ith simulation, R threshold The minimum turnover threshold set for the merchant, P is the probability that the predicted turnover is lower than the threshold; Expected loss: When a loss occurs, evaluate the expected loss value, defined as: IS=E[R(x i )|R(x i ) <R threshold ] Among them, E[R(x i )] is the expected loss value when the turnover is lower than the threshold; Analysis of simulation results: Through multiple Monte Carlo simulations, the turnover distribution of candidate locations under different market scenarios is generated, and based on risk assessment indicators, the financial risks of the location under market fluctuations, rent fluctuations and changes in the competitive environment are judged. The results are used to generate a detailed financial risk assessment report to help merchants make rational decisions.

8. According to claim 1, a store location accurate data comprehensive calculation system based on AI analysis technology is characterized in that: The report generation module is used to display the analysis results of the candidate locations in a visual manner and generate a site selection decision support report. The data visualization and report generation module includes: Interactive map display unit: used to display geographic information and related analysis data of candidate store locations, including heat maps of customer flow, competitor distribution maps, and business district division maps; Financial report generation unit: generating detailed financial reports based on the financial forecast data of the candidate positions, the financial reports including turnover forecast reports and investment return reports; Risk assessment report generating unit: generating a risk assessment report according to the calculation results of the risk assessment module, wherein the report displays the potential market risks of the candidate location through charts and statistical data; Customized site selection plan generation unit: generates a customized site selection plan based on the personalized needs input by the user, wherein the needs include target population, budget constraints, and business district preferences.

9. The store location accurate data comprehensive calculation system based on AI analysis technology according to claim 1 is characterized in that: The system also includes a feedback optimization module, which optimizes the AI ​​analysis model in real time through a reinforcement learning algorithm. The reinforcement learning algorithm continuously adjusts the model parameters of the site recommendation based on the actual operation data after the store is opened.

10. A method for comprehensive calculation of accurate store location data based on AI analysis technology, applied to the system according to any one of claims 1 to 9, characterized in that: The following steps are involved: Obtain multi-dimensional data from geographic information platforms, business district databases, financial systems and market research platforms; Cleaning, normalizing and standardizing the multi-dimensional data to obtain an analyzable data set; Analyze data based on multi-objective optimization algorithms and linear programming models to generate recommendations for candidate store locations; Based on the predicted turnover, rental costs, and market coverage of the candidate store locations, we conduct financial profit forecasts and calculate the payback period, and generate a comprehensive evaluation report in combination with the risk assessment model; Monitor the actual store operation data and optimize the AI ​​analysis model based on the actual operating data.

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