A store location planning and management system suitable for chain store supervision

Abstract

The present invention belongs to the technical field of chain store supervision, and specifically is a store site selection planning and management system suitable for chain store supervision, comprising a store address proposed acquisition module, a related area demarcation module, a regional consumption potential analysis module, a regional competition analysis module and a planning management terminal; the present invention obtains all pre-planned store addresses through the store address proposed acquisition module, the related area demarcation module demarcates related areas with the corresponding store addresses as the center of the circle, the regional consumption potential analysis module analyzes the consumption potential of all related areas to determine high consumption potential areas and low consumption potential areas, the regional competition analysis module analyzes the competition status of high consumption potential areas and stores related to the chain stores to determine high competition areas and low competition areas, and can narrow down store addresses based on consumption potential performance and competition pressure performance, thereby ensuring the accuracy of the final site selection and reducing the workload of planners, and having a high degree of intelligence.

Description

Technical Field

[0001] The invention relates to the technical field of chain store supervision, and in particular to a store site selection planning and management system suitable for chain store supervision. Background Art

[0002] Chain stores refer to a large number of small-scale, dispersed retail stores of the same brand that sell similar goods and services. Under the organization and leadership of the headquarters, they adopt a common business policy, consistent marketing actions, and implement an organic combination of centralized procurement and decentralized sales. With the popularization of the chain operation model, store location has become one of the key factors that determine the success of a chain enterprise.

[0003] Traditional site selection methods rely on empirical judgment or simple market surveys, which make it difficult to comprehensively consider multi-dimensional factors and gradually narrow the scope of site selection, resulting in low efficiency in site selection decisions, inability to effectively guarantee the accuracy of the final site selection, heavy workload for planners, and low intelligence.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to provide a store site selection planning and management system suitable for chain store supervision, which solves the problem that the prior art is difficult to comprehensively consider multi-dimensional factors and gradually narrow the store site selection scope of the chain store, resulting in low efficiency of site selection decision-making, inability to effectively guarantee the accuracy of the final site selection, large workload for planners, and low degree of intelligence.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A store location planning and management system suitable for chain store supervision, comprising a store address acquisition module, a related area demarcation module, a regional consumption potential analysis module, a regional competition analysis module and a planning management terminal;

[0008] Before the corresponding chain stores open stores, several store addresses are prepared in advance. The store address preparation acquisition module obtains all the store addresses prepared in advance and marks the corresponding store address as the target address i, where i is a natural number greater than 1;

[0009] The associated area demarcation module demarcates a circle with a radius of R1 with the target address i as the center, and marks the demarcated circular area as the associated area of ​​the target address i;

[0010] The regional consumption potential analysis module analyzes the consumption potential of the associated area corresponding to the target address i, marks the corresponding associated area as a high consumption potential area or a low consumption potential area through analysis, and sends the marking information of all associated areas to the planning management terminal, and sends the high consumption potential area to the regional competition analysis module;

[0011] The regional competition analysis module analyzes the competition status of the stores related to the chain stores in the corresponding high consumption potential areas, marks the corresponding high consumption potential areas as high competition areas or low competition areas through analysis, and sends the marking information of all high consumption potential areas to the planning management terminal, and the planning management personnel of the corresponding chain stores select store locations from the low competition areas.

[0012] Furthermore, the specific analysis process of the regional consumption potential analysis module is as follows:

[0013] A monitoring period with the current date as the end date and tracing back with the number of days set to P1, the regional consumption potential coefficient of the associated area corresponding to the target address i is obtained through analysis, and the regional consumption potential coefficient is numerically compared with the preset regional consumption potential coefficient threshold. If the regional consumption potential coefficient is greater than the preset regional consumption potential coefficient threshold, the associated area corresponding to the target address i is marked as a high consumption potential area; if the regional consumption potential coefficient is less than or equal to the preset regional consumption potential coefficient threshold, the associated area corresponding to the target address i is marked as a low consumption potential area.

[0014] Furthermore, the analysis and acquisition method of the regional consumption potential coefficient is as follows:

[0015] Several monitoring periods are set within the monitoring cycle, and the average value of the number of people in the corresponding associated area within the corresponding monitoring period is collected and marked as the flow detection value, and the flow detection value is numerically compared with the preset flow detection threshold. If the flow detection value is greater than the preset flow detection threshold, the corresponding monitoring period is marked as a high-popularity period;

[0016] The percentage of high popularity time periods in the associated area corresponding to the target address i during the monitoring period is obtained and marked as the high popularity detection value, and the flow detection values ​​of all monitoring periods during the monitoring period are averaged to obtain the flow performance value, and the consumption output value of the associated area corresponding to the target address i during the monitoring period is collected; the regional consumption potential coefficient is obtained by numerically calculating the high popularity detection value, flow performance value and consumption output value.

[0017] Furthermore, the specific analysis process of the regional competition analysis module includes:

[0018] Acquire stores of the same type as the corresponding chain store in the corresponding high consumption potential area, and mark the corresponding stores as competitors; collect the number of competitors in the corresponding high consumption potential area and mark it as the competition number detection value, and acquire the locations of all competitors, mark the distance between the location of the corresponding competitor and the store address corresponding to the corresponding high consumption potential area as the competition distance detection value, calculate the average of the competition distance detection values ​​of all competitors to obtain the competition distance value, and compare the competition distance detection value with the preset competition distance detection threshold, count the number of competitors in the corresponding high consumption potential area whose competition distance detection value is less than or equal to the preset competition distance detection threshold and mark them as the competition neighbor detection value;

[0019] The competition initial analysis value is obtained by numerically calculating the competition number check value, competition distance value and competition neighbor check value, and the competition initial analysis value is numerically compared with the preset competition initial analysis threshold. If the competition initial analysis value is greater than the preset competition initial analysis threshold, the corresponding high consumption potential area is marked as a high competition area.

[0020] Furthermore, if the competition initial analysis value is less than or equal to the preset competition initial analysis threshold, the competitiveness value of the corresponding competitor is obtained through comprehensive evaluation and analysis of store performance, and the competitiveness value is numerically compared with the preset competitiveness threshold. If the competitiveness value is greater than the preset competitiveness threshold, the corresponding competitor is marked as a threat object;

[0021] The number of threat objects in the corresponding high consumption potential area is obtained and marked as the competition threat value. The competition comprehensive evaluation value is obtained by numerically calculating the competition initial analysis value, the competition threat value and the regional consumption potential coefficient. The competition comprehensive evaluation value is numerically compared with the preset competition comprehensive evaluation threshold. If the competition comprehensive evaluation value is less than or equal to the preset competition comprehensive evaluation threshold, the corresponding high consumption potential area is marked as a high competition area; if the competition comprehensive evaluation value is greater than the preset competition comprehensive evaluation threshold, the corresponding high consumption potential area is marked as a low competition area.

[0022] Furthermore, the specific analysis process of the comprehensive evaluation and analysis of store performance is as follows:

[0023] The store opening date of the corresponding competitor is collected, and the interval between the current date and the store opening date is marked as the store time value; the store area of ​​the corresponding competitor is collected and marked as the store inspection value, and the total number of complaints received by the competitor during the detection period is marked as the store complaint value. The competitiveness value of the corresponding competitor is obtained by numerically calculating the store time value, the store inspection value and the store complaint value.

[0024] Furthermore, the regional competition analysis module is communicated with the precise site selection recommendation module, which sends all low-competition areas to the precise site selection recommendation module. The precise site selection recommendation module selects preferred areas from all low-competition areas through analysis, marks the store addresses corresponding to the preferred areas as the most suitable store addresses of the corresponding chain stores, and sends the most suitable store addresses to the planning management terminal.

[0025] Furthermore, the specific analysis process of the precise site selection recommendation module is as follows:

[0026] The rent per square meter of the store address corresponding to the corresponding low-competition area is collected and marked as the rent detection value, and the public security risk coefficient of the corresponding low-competition area during the monitoring period is obtained through analysis, and the environmental unsuitability coefficient of the corresponding store address during the monitoring period is obtained through analysis, and the recommended evaluation value is obtained by numerically calculating the comprehensive competition evaluation value, the rent detection value, the public security risk coefficient and the environmental unsuitability coefficient, and all low-competition areas are sorted in descending order according to the numerical value of the recommended evaluation value, and the low-competition area ranked first is marked as the preferred area, and the store address corresponding to the preferred area is marked as the most suitable store address.

[0027] Furthermore, the specific analysis and acquisition method of the public security risk coefficient is as follows:

[0028] Collect all public security risk events that occurred in the corresponding low-competition area during the monitoring period, classify all public security risk events, and mark the number of occurrences of the corresponding type of public security risk events as the public security risk count value;

[0029] A set of preset public security impact weight values ​​is set in advance for each type of public security risk event, and the product of the public security risk value of the corresponding type of public security risk event and the corresponding preset public security impact weight value is marked as the public security risk value. The public security risk coefficient is obtained by summing up the public security risk values ​​of all types of public security risk events occurring in the corresponding low-competition area during the monitoring period.

[0030] Furthermore, the analysis and acquisition method of the environmental unsuitability coefficient is as follows:

[0031] The real-time temperature and real-time humidity of the area where the store address corresponding to the corresponding low-competition area is located are collected, and the real-time temperature is calculated by the difference between the median of the preset suitable temperature range and the absolute value is taken to obtain the target temperature value, and the real-time humidity is calculated by the difference between the median of the preset suitable humidity range and the absolute value is taken to obtain the target humidity value; and the dust concentration and noise decibel value of the area where the corresponding store address is located are collected and marked as the target pollution value and the target noise value respectively, and the actual environment value is obtained by numerically calculating the target temperature value, the target humidity value, the target gray value and the target noise value;

[0032] Compare the actual environment value with the preset actual environment threshold. If the actual environment value is greater than the preset actual environment threshold, determine that the corresponding store address is in an environmentally unsuitable state at the corresponding time; obtain the total duration of the corresponding store address in the environmentally unsuitable state on the corresponding date and mark it as the environmentally unsuitable detection value, compare the environmentally unsuitable detection value with the preset environmentally unsuitable detection threshold, and if the environmentally unsuitable detection value is greater than the preset environmentally unsuitable detection threshold, mark the corresponding date as an unsuitable date for the corresponding store address;

[0033] The number percentage of unsuitable dates corresponding to the corresponding store address within the monitoring period is obtained and marked as the unsuitable day percentage value, and the average of all environmental unsuitability detection values ​​corresponding to the target address i within the monitoring period is calculated to obtain the unsuitability performance value. The environmental unsuitability coefficient of the corresponding store address is obtained by numerically calculating the unsuitable day percentage value and the unsuitability performance value.

[0034] Compared with the prior art, the present invention has the following beneficial effects:

[0035] 1. In the present invention, the consumption potential of the associated areas of all the pre-planned store addresses is analyzed through the regional consumption potential analysis module to determine the high consumption potential area and the low consumption potential area. The regional competition analysis module analyzes the competition status of the stores related to the chain stores in all the high consumption potential areas to determine the high competition area and the low competition area. The site selection range can be narrowed down based on the consumption potential performance and the competition pressure performance, which can not only ensure the accuracy of the final site selection, but also help to reduce the workload of planners;

[0036] 2. In the present invention, all low-competition areas are sent to the precise site selection recommendation module through the regional competition analysis module. The precise site selection recommendation module selects preferred areas from all low-competition areas through analysis, and marks the store addresses corresponding to the preferred areas as the most suitable store addresses of the corresponding chain stores. The most suitable store addresses can be accurately pushed to the planners of the chain stores, further reducing the workload of the planners and improving the accuracy of store site selection, with a high degree of intelligence. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to facilitate understanding by those skilled in the art, the present invention is further described below in conjunction with the accompanying drawings;

[0038] Figure 1 This is a system block diagram of Embodiment 1 of the present invention;

[0039] Figure 2 This is a system block diagram of Embodiment 2 of the present invention. DETAILED DESCRIPTION

[0040] The following will be combined with the drawings in the embodiments 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.

[0041] Embodiment 1: Figure 1 As shown, the store location planning and management system proposed by the present invention is suitable for chain store supervision, including a store address acquisition module, a related area demarcation module, a regional consumption potential analysis module, a regional competition analysis module and a planning management terminal;

[0042] Before the corresponding chain store opens a store, a number of store addresses are planned in advance, preferably, the planned store addresses are not less than ten; the store address planning acquisition module obtains all the store addresses planned in advance, and marks the corresponding store address as the target address i, where i is a natural number greater than 1; the associated area demarcation module draws a circle with a radius of R1 with the target address i as the center, preferably, R1 is five kilometers; and the demarcated circular area is marked as the associated area of ​​the target address i.

[0043] The regional consumption potential analysis module analyzes the consumption potential of the associated area corresponding to the target address i, marks the corresponding associated area as a high consumption potential area or a low consumption potential area through analysis, and sends the marking information of all associated areas to the planning management terminal, which can reasonably analyze and accurately judge the consumption potential performance of the area near each store address, provide important data reference for the planners of the corresponding chain stores when selecting store locations, and realize the preliminary elimination of addresses to be selected, which is conducive to ensuring the rationality and accuracy of the site selection of planners and significantly reducing the workload of planners; the specific analysis process of the regional consumption potential analysis module is as follows:

[0044] A monitoring period with the current date as the end date and the number of days set to P1 is traced back, preferably, P1 is one hundred and eighty days; a number of monitoring periods are set within the monitoring period, the average value of the number of people in the corresponding associated area within the corresponding monitoring period is collected and marked as a flow detection value, and the flow detection value is numerically compared with a preset flow detection threshold value. If the flow detection value is greater than the preset flow detection threshold value, it indicates that the flow of people in the corresponding associated area of ​​the corresponding monitoring period is large, and the corresponding monitoring period is marked as a high popularity period;

[0045] The number of high popularity time periods in the associated area corresponding to the target address i in the monitoring period is obtained and marked as a high popularity detection value, and the flow detection values ​​of all monitoring periods in the monitoring period are averaged to obtain a flow performance value, and the consumption output value of the associated area corresponding to the target address i in the monitoring period is collected, where the consumption output value is a data value indicating the total amount of consumption generated in the associated area corresponding to the target address i in the monitoring period;

[0046] The regional consumption potential coefficient XPi is obtained by numerically calculating the high popularity detection value XFi, the traffic performance value XLi and the consumption output value XNi through the formula XPi=ry1*XFi+(ry2*XLi+ry3*XNi) / ry1; wherein ry1, ry2 and ry3 are preset proportional coefficients, ry1>ry2>ry3>0; and the larger the value of the regional consumption potential coefficient XPi is, the greater the consumption potential of the associated area corresponding to the target address i in the monitoring period is;

[0047] The regional consumption potential coefficient XPi is numerically compared with the preset regional consumption potential coefficient threshold. If the regional consumption potential coefficient XPi is greater than the preset regional consumption potential coefficient threshold, it indicates that the consumption potential of the associated area corresponding to the target address i during the monitoring period is relatively large, and the associated area corresponding to the target address i is marked as a high consumption potential area; if the regional consumption potential coefficient XPi is less than or equal to the preset regional consumption potential coefficient threshold, it indicates that the consumption potential of the associated area corresponding to the target address i during the monitoring period is relatively small, and the associated area corresponding to the target address i is marked as a low consumption potential area.

[0048] The regional consumption potential analysis module sends the high consumption potential area to the regional competition analysis module. The regional competition analysis module analyzes the competition status of the corresponding high consumption potential area and the stores related to the chain stores, and marks the corresponding high consumption potential area as a high competition area or a low competition area through analysis, and sends the marking information of all high consumption potential areas to the planning management terminal. The planning management personnel of the corresponding chain stores select store locations from the low competition area, and realize reasonable analysis and feedback of the competition pressure of all high consumption potential areas. Store addresses can be further narrowed on the basis of high consumption potential areas, further reducing the site selection scope of planners, ensuring the accuracy of the final site selection while reducing the workload of planners; the specific analysis process of the regional competition analysis module is as follows:

[0049] Acquire stores of the same type as the corresponding chain store in the corresponding high consumption potential area (for example, if the corresponding chain store sells clothing, then the clothing store is a store of the same type), and mark the corresponding store as a competitor;

[0050] The number of competitors in the corresponding high consumption potential area is collected and marked as the competition number detection value, and the positions of all competitors are obtained, and the distance between the position of the corresponding competitor and the store address corresponding to the corresponding high consumption potential area is marked as the competition distance detection value, the competition distance detection values ​​of all competitors are averaged to obtain the competition distance value, and the competition distance detection value is numerically compared with the preset competition distance detection threshold, and the number of competitors in the corresponding high consumption potential area whose competition distance detection value is less than or equal to the preset competition distance detection threshold is counted and marked as the competition neighbor detection value;

[0051] By formula The competition number check value LSi, the competition distance value LPi and the competition neighbor check value LYi are numerically calculated to obtain the competition preliminary analysis value LXi; wherein sp1, sp2, sp3 are preset proportional coefficients, sp3>sp1>sp2>0; and the larger the value of the competition preliminary analysis value LXi is, the greater the competition pressure of the corresponding high consumption potential area is in a preliminary sense;

[0052] The preliminary competition analysis value LXi is numerically compared with the preset preliminary competition analysis threshold. If the preliminary competition analysis value LXi is greater than the preset preliminary competition analysis threshold, it indicates that the competition pressure of the corresponding high consumption potential area is relatively large, and the high consumption potential area is marked as a high competition area.

[0053] Furthermore, if the competition initial analysis value LXi is less than or equal to the preset competition initial analysis threshold, the competitiveness value of the corresponding competitor is obtained through comprehensive evaluation and analysis of store performance, specifically: the store opening date of the corresponding competitor is collected, and the interval between the current date and the store opening date of the corresponding competitor is marked as the store storage value; and the store area of ​​the corresponding competitor is collected and marked as the store inspection value, and the total number of complaints received by the competitor during the detection period (preferably, the detection period is thirty days) is marked as the store complaint value;

[0054] By formula The store time value NYi, the store inspection value NXi and the store complaint value NWi are numerically calculated to obtain the competitiveness value NPi of the corresponding competitor; wherein b1, b2 and b3 are preset proportional coefficients with values ​​greater than zero; wherein the larger the value of the competitiveness value NPi is, the stronger the competitiveness of the corresponding competitor is in general;

[0055] Compare the competitive capability value NPi with the preset competitive capability threshold. If the competitive capability value NPi is greater than the preset competitive capability threshold, it indicates that the competitive capability of the corresponding competitor is relatively strong, and the corresponding competitor is marked as a threat object. Obtain the number of threat objects in the corresponding associated area and mark it as a competitive threat value.

[0056] The competition preliminary analysis value LXi, the competition threat value HFi and the regional consumption potential coefficient XPi are numerically calculated by the formula WXi=sw3*XPi / (sw1*LXi+sw2*HFi) to obtain the competition comprehensive evaluation value WXi; wherein sw1, sw2, sw3 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the competition comprehensive evaluation value WXi is, the smaller the competition pressure of the corresponding high consumption potential region is in general;

[0057] The comprehensive competition evaluation value WXi is numerically compared with the preset comprehensive competition evaluation threshold. If the comprehensive competition evaluation value WXi is less than or equal to the preset comprehensive competition evaluation threshold, it indicates that the competition pressure in the corresponding high consumption potential area is relatively large on the whole, and it is not suitable to open a store here. The corresponding high consumption potential area is marked as a high competition area; if the comprehensive competition evaluation value WXi is greater than the preset comprehensive competition evaluation threshold, it indicates that the competition pressure in the corresponding high consumption potential area is relatively small on the whole, and the corresponding high consumption potential area is marked as a low competition area.

[0058] Embodiment 2: Figure 2 As shown, the difference between this embodiment and the first embodiment is that the regional competition analysis module is connected to the precise location recommendation module in communication, and the regional competition analysis module sends all low-competition areas to the precise location recommendation module. The precise location recommendation module selects preferred areas from all low-competition areas through analysis, marks the store addresses corresponding to the preferred areas as the most suitable store addresses for the corresponding chain stores, and sends the most suitable store addresses to the planning management terminal, which can accurately push the most suitable store addresses to the planners of the chain stores, further reduce the workload of the planners and improve the accuracy of store location selection, and has a high degree of intelligence; the specific analysis process of the precise location recommendation module is as follows:

[0059] The public security risk coefficient of the corresponding low-competition area during the monitoring period is obtained through analysis, specifically: all public security risk events that occurred in the associated area corresponding to the corresponding low-competition area during the monitoring period are collected, all public security risk events are classified (such as theft, fighting, fire, etc.), and the number of occurrences of the corresponding type of public security risk events is marked as the public security risk value;

[0060] A set of preset public security impact weight values ​​corresponding to each type of public security risk event is set in advance, wherein the values ​​of the preset public security impact weight values ​​are all positive numbers, and the greater the adverse impact of the corresponding type of public security risk event on nearby business activities, the greater the value of the preset public security impact weight value that matches it;

[0061] The product of the security risk value of the corresponding type of security risk event and the corresponding preset security impact weight value is marked as the security risk value, and the security risk value of all types of security risk events occurring in the corresponding low-competition area during the monitoring period is summed up to obtain the security risk coefficient; wherein, the larger the value of the security risk coefficient is, the more unfavorable it is for the normal operation of the corresponding store address;

[0062] And through analysis, the environmental unsuitability coefficient of the store address corresponding to the corresponding low-competition area within the monitoring period is obtained, specifically: the real-time temperature and real-time humidity of the area where the corresponding store address is located are collected, the real-time temperature is calculated with the median of the preset suitable temperature range and the absolute value is taken to obtain the target temperature value, the real-time humidity is calculated with the median of the preset suitable humidity range and the absolute value is taken to obtain the target humidity value; and the dust concentration and noise decibel value of the area where the corresponding store address is located are collected and marked as the target pollution value and the target noise value respectively;

[0063] The target temperature value HNi, the target humidity value HPi, the target gray value HSi and the target noise value HYi are numerically calculated by the formula HXi=(g1*HNi+g2*HPi+g3*HSi+g4*HYi) / 4 to obtain the actual environment value HXi; wherein, g1, g2, g3 and g4 are preset proportional coefficients with values ​​greater than zero, and the larger the value of the actual environment value HXi is, the worse the real-time environment condition of the corresponding store address is, and the greater the adverse impact on the normal operation of the store is;

[0064] Compare the actual environment value HXi with the preset actual environment threshold. If the actual environment value HXi is greater than the preset actual environment threshold, it indicates that the real-time environment condition of the corresponding store address is poor, which has a great adverse impact on the normal operation of the store. Then, it is judged that the corresponding store address is in an unsuitable environment at the corresponding time.

[0065] The total duration of the store address corresponding to the corresponding date being in an unsuitable environment state is obtained and marked as an unsuitable environment detection value, and the unsuitable environment detection value is numerically compared with a preset unsuitable environment detection threshold. If the unsuitable environment detection value is greater than the preset unsuitable environment detection threshold, indicating that the environmental condition of the corresponding date of the corresponding store address is relatively bad, the corresponding date is marked as an unsuitable date for the corresponding store address;

[0066] The number ratio of unsuitable dates corresponding to the corresponding store addresses within the monitoring period is obtained and marked as the unsuitable date ratio, and all environmental unsuitable detection values ​​corresponding to the corresponding store addresses within the monitoring period are averaged to obtain the unsuitable performance value;

[0067] The environmental unsuitability coefficient FYi of the corresponding store address is obtained by numerically calculating the unsuitable day value FKi and the unsuitable performance value FLi through the formula FYi=ew1*FKi+ew2*FLi / ew1; wherein ew1 and ew2 are preset proportional coefficients, ew1>ew2>0; and the larger the value of the environmental unsuitability coefficient FYi, the worse the environmental condition of the environment of the corresponding store address during the monitoring period is, and the greater the adverse impact on the normal operation of the store;

[0068] The rent per square meter of the corresponding store address is collected and marked as the rent detection value. The competition comprehensive evaluation value WXi, the rental detection value ZFi, the public security risk coefficient ZYi and the environmental unsuitability coefficient FYi are numerically calculated to obtain the recommended evaluation value TLi; wherein k1, k2, k3, k4 are preset proportional coefficients with values ​​greater than zero;

[0069] Moreover, the larger the value of the recommended evaluation value TLi is, the more suitable it is for the chain store to open a store in the corresponding low-competition area. All low-competition areas are sorted in descending order according to the values ​​of the recommended evaluation values, and the low-competition area ranked first is marked as the preferred area, and the store address corresponding to the preferred area is marked as the most suitable store address.

[0070] The working principle of the present invention is as follows: when in use, all pre-planned store addresses are acquired through the store address planning and acquisition module; the associated area demarcation module demarcates associated areas with the corresponding store addresses as the center of the circle; the regional consumption potential analysis module analyzes the consumption potential of all associated areas to determine high consumption potential areas and low consumption potential areas; the regional competition analysis module analyzes the competition status of stores related to chain stores in all high consumption potential areas to determine high competition areas and low competition areas; the planning and management personnel of the corresponding chain stores select store sites from the low competition areas, and can limit store addresses based on consumption potential performance and competition pressure performance, thereby reducing the site selection scope of planners, ensuring the accuracy of the final site selection while reducing the workload of planners, and having a high degree of intelligence.

[0071] The above formulas are all dimensionless and numerical calculations. The formula is a formula obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters in the formula are set by technicians in this field according to actual conditions. The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the contents of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that technicians in the relevant technical field can understand and use the present invention well. The present invention is only limited by the claims and their full scope and equivalents.

Claims

1. A store location planning and management system suitable for chain store supervision, characterized in that: It includes a store address acquisition module, a related area demarcation module, a regional consumption potential analysis module, a regional competition analysis module and a planning management terminal; Before the corresponding chain stores open stores, several store addresses are prepared in advance. The store address preparation acquisition module obtains all the store addresses prepared in advance and marks the corresponding store address as the target address i, where i is a natural number greater than 1; The associated area demarcation module demarcates a circle with a radius of R1 with the target address i as the center, and marks the demarcated circular area as the associated area of ​​the target address i; The regional consumption potential analysis module analyzes the consumption potential of the associated area corresponding to the target address i, marks the corresponding associated area as a high consumption potential area or a low consumption potential area through analysis, and sends the marking information of all associated areas to the planning management terminal, and sends the high consumption potential area to the regional competition analysis module; The regional competition analysis module analyzes the competition status of stores related to the chain stores in the corresponding high consumption potential areas, marks the corresponding high consumption potential areas as high competition areas or low competition areas through analysis, and sends the marking information of all high consumption potential areas to the planning management terminal, so that the planning management personnel of the corresponding chain stores select store locations from the low competition areas; The specific analysis process of the regional consumption potential analysis module is as follows: The monitoring period is set to P1 with the current date as the end date and traced back to the previous date. The regional consumption potential coefficient of the associated area corresponding to the target address i is obtained through analysis. If the regional consumption potential coefficient is greater than the preset regional consumption potential coefficient threshold, the associated area corresponding to the target address i is marked as a high consumption potential area; if the regional consumption potential coefficient is less than or equal to the preset regional consumption potential coefficient threshold, the associated area corresponding to the target address i is marked as a low consumption potential area; The specific method for analyzing and obtaining the regional consumption potential coefficient is as follows: Several monitoring periods are set within the monitoring cycle, and the average value of the number of people in the corresponding associated area within the corresponding monitoring period is collected and marked as the flow detection value, and the flow detection value is numerically compared with the preset flow detection threshold. If the flow detection value is greater than the preset flow detection threshold, the corresponding monitoring period is marked as a high-popularity period; The percentage of high popularity time periods in the associated area corresponding to the target address i in the monitoring period is obtained and marked as the high popularity detection value, and the flow detection values ​​of all monitoring periods in the monitoring period are averaged to obtain the flow performance value, and the consumption output value of the associated area corresponding to the target address i in the monitoring period is collected; the regional consumption potential coefficient is obtained by numerically calculating the high popularity detection value, flow performance value and consumption output value; The specific analysis process of the regional competition analysis module includes: Obtain stores of the same type as the corresponding chain store in the associated area corresponding to the target address i, and mark the corresponding stores as competitors; collect the number of competitors in the corresponding high consumption potential area and mark them as the competition number check value, and obtain the locations of all competitors, mark the distance between the location of the corresponding competitor and the store address corresponding to the corresponding high consumption potential area as the competition distance check value, calculate the average of the competition distance check values ​​of all competitors to obtain the competition distance value, and count the number of competitors in the corresponding high consumption potential area whose competition distance check value is less than or equal to the preset competition distance check threshold and mark them as the competition neighbor check value; The competition initial analysis value is obtained by numerically calculating the competition number check value, the competition distance value and the competition neighbor check value. If the competition initial analysis value is greater than the preset competition initial analysis threshold, the corresponding high consumption potential area is marked as a high competition area. If the competition initial analysis value is less than or equal to the preset competition initial analysis threshold, the competitive ability value of the corresponding competitor is obtained through comprehensive evaluation and analysis of store performance. If the competitive ability value is greater than the preset competitive ability threshold, the corresponding competitor is marked as a threat object; The number of threat objects in the corresponding high consumption potential area is obtained and marked as the competition threat value. The competition comprehensive evaluation value is obtained by numerically calculating the competition preliminary analysis value, the competition threat value and the regional consumption potential coefficient. If the competition comprehensive evaluation value is less than or equal to the preset competition comprehensive evaluation threshold, the corresponding high consumption potential area is marked as a high competition area; if the competition comprehensive evaluation value is greater than the preset competition comprehensive evaluation threshold, the corresponding high consumption potential area is marked as a low competition area; The specific analysis process of the comprehensive evaluation and analysis of store performance is as follows: The store opening date of the corresponding competitor is collected, and the interval between the current date and the store opening date is marked as the store storage time value; the store area of ​​the corresponding competitor is collected and marked as the store inspection value, and the total number of complaints received by the competitor during the detection period is marked as the store complaint value, and the competitiveness value of the corresponding competitor is obtained by numerically calculating the store storage time value, the store inspection value and the store complaint value; The regional competition analysis module is connected to the precise location recommendation module in communication. The regional competition analysis module sends all low-competition areas to the precise location recommendation module. The precise location recommendation module selects preferred areas from all low-competition areas through analysis, marks the store addresses corresponding to the preferred areas as the most suitable store addresses for the corresponding chain stores, and sends the most suitable store addresses to the planning management terminal; The specific analysis process of the site selection precision recommendation module is as follows: The rent per square meter of the store address corresponding to the corresponding low-competition area is collected and marked as the rent detection value, and the public security risk coefficient of the corresponding low-competition area during the monitoring period is obtained through analysis, and the environmental unsuitability coefficient of the corresponding store address during the monitoring period is obtained through analysis, and the recommended evaluation value is obtained by numerically calculating the comprehensive competition evaluation value, the rent detection value, the public security risk coefficient and the environmental unsuitability coefficient, and all low-competition areas are sorted in descending order according to the numerical value of the recommended evaluation value, and the low-competition area ranked first is marked as the preferred area, and the store address corresponding to the preferred area is marked as the most suitable store address.

2. A store location planning and management system suitable for chain store supervision according to claim 1, characterized in that: The specific analysis and acquisition method of the public security risk coefficient is as follows: Collect all public security risk events that occurred in the corresponding low-competition area during the monitoring period, classify all public security risk events, and mark the number of occurrences of the corresponding type of public security risk events as the public security risk count value; A set of preset public security impact weight values ​​is set in advance for each type of public security risk event, and the product of the public security risk value of the corresponding type of public security risk event and the corresponding preset public security impact weight value is marked as the public security risk value. The public security risk coefficient is obtained by summing up the public security risk values ​​of all types of public security risk events occurring in the corresponding low-competition area during the monitoring period.

3. A store location planning and management system suitable for chain store supervision according to claim 1, characterized in that: The analysis and acquisition method of the environmental unsuitability coefficient is as follows: The real-time temperature and real-time humidity of the area where the store address corresponding to the corresponding low-competition area is located are collected, and the real-time temperature is calculated by the median of the preset suitable temperature range and the absolute value is taken to obtain the target temperature value, and the real-time humidity is calculated by the median of the preset suitable humidity range and the absolute value is taken to obtain the target humidity value; and the dust concentration and noise decibel value of the area where the corresponding store address is located are collected and marked as the target pollution value and the target noise value respectively, and the actual environment value is obtained by numerically calculating the target temperature value, the target humidity value, the target gray value and the target noise value; If the actual environment value is greater than the preset actual environment threshold, it is determined that the target address i is in an unsuitable environment state at the corresponding time; the total time that the corresponding store address is in an unsuitable environment state on the corresponding date is obtained and marked as the unsuitable environment detection value; if the unsuitable environment detection value is greater than the preset unsuitable environment detection threshold, the corresponding date is marked as an unsuitable date for the corresponding store address; The number percentage of unsuitable dates corresponding to the corresponding store addresses within the monitoring period is obtained and marked as the unsuitable day percentage value, and the average of all environmental unsuitability detection values ​​corresponding to the corresponding store addresses within the monitoring period is calculated to obtain the unsuitability performance value, and the environmental unsuitability coefficient of the corresponding store address is obtained by numerically calculating the unsuitable day percentage value and the unsuitability performance value.

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